Input: Claim: Neurolinguistic programming explores the connection between language and thought. [SEP] Text: 9 The more options a person can use to shape his life, the smoother and more satisfying his life will be 6. 3. 10 Everyone is capable of achieving everything – in their own way 6. 3. 11 Everyone has everything they need to handle life’s problems – they just have to learn how to use it appropriately 6. 3. 12 There is no such a thing as failure, only results 6. 3. 13 Problems taste better in smaller doses 6. 3. 14 Behind every problematic behavior is a good intention 6. 4 Pillars of NLP in practice 7 Research in the field of neuromarketing, business behavior and NLP 7. 1 Sensory marketing research as an attribute of neuromarketing and business behavior 7. 1. 1 The aim and hypotheses of the first research project 7. 1. 2 Methodologies and methods of the first research project 7. 1. 3 Research sample of the first research project 7. 1. 4 Results of the first research project 7. 1. 5 Summary results of the first research project 7. 2 Neuromarketing research and neurolinguistic programming 7. 2. 1 The aim and hypotheses of the second research project 7. 2. 2 Methodologies and methods of the second research project NLP-C methodology (Neuro-– Communication) NLP-T methodology (Neuro-– Techniques) NM-SSP methodology (Neuromarketing – Shop, Seller, Product) 7. 2. 3 Research sample of the second research project 7. 2. 4 Results of the second research project 7. 2. 5 Summary results of the second research project of bibliographical 1. 1. Personality of businessman Each person is unique and characterized by certain attributes of himself. Personality type can reveal how a person will react in life situations. Personality type is a set of qualities that occur together and are interconnected (Alexy, 2011). Human factors determine people’s behavior and influence their interactions. Marketers not only determine strategy based on their own perspectives but also receive opinions from others. For a trader, it is important to be conscious of his abilities and skills and to be able to admit a mistake, ask for help, or reveal that he is capable to do something by himself (Yuan et al. , 2017). Within sales management, salespeople are the most meaningful part of the company’s function.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: As we venture further into the exploration of this dataset, we aim to not only predict hotel ratings but also uncover the underlying factors that influence customer satisfaction. The dataset acts as a portal through which we can peer into the collective experiences of travelers, drawing from their shared narratives to build a predictive model that is as informative as it is perceptive. To illustrate the geographical distribution of hotels within our dataset, we employ a sophisticated visualization technique that plots each hotel’s location on an interactive map. This is achieved through the integration of Python’s plotly library, renowned for its capability to create complex, interactive plots that are both informative and engaging. The first segment of the code defines a Scattermapbox trace, which is an object in plotly that represents a series of points on a map. Each point corresponds to a hotel, with its latitude and longitude derived from the data DataFrame columns ’lat’ and ’lng’, respectively. The hotels are displayed as markers, styled in cyan color with a slightly transparent appearance (an opacity of 0. 7) for visual appeal and to prevent the map from appearing overly congested. In addition, each marker is associated with the hotel’s name, which is displayed as a hover text. This feature enhances user interactivity, as it allows the viewer to hover over any marker on the map to reveal the name of the hotel at that specific location. Finally, a Figure object is created, combining the data trace and layout. This object encapsulates all the information needed to render the map. The iplot function, which is part of the plotly interactive plotting interface, is called to display the figure. This command activates the visualization in a web browser, presenting an interactive and user-friendly map that allows stakeholders to visualize the spatial distribution of the hotel data effectively. By leveraging interactive geospatial plotting, we provide a compelling visual representation of data that can significantly enhance the comprehensibility of the patterns and trends within the tourism domain. In an effort to elucidate the demographic distribution of the reviewers in our hotel review dataset, we conducted an analysis focusing on the nationalities of the contributors. Understanding the diversity of the reviewers provides insight into the global reach of the hotels in question and can potentially highlight cultural influences on review content and ratings.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: The images in this and other figures with CsChrimson expression patterns are representative of 2–5 brains and 2–3 VNCs imaged for each split-GAL4 line. Scale bar represents 50 µm. Original confocal stacks are available from www. janelia. org/split-GAL4. Figure 2—figure supplement 1 Download asset Open asset VNC expression patterns of the corresponding brains shown in Figure 2. Imaging parameters and brightness or contrast adjustments were identical for each brain/VNC pair. Scale bar represents 50 µm. Original confocal stacks are available from www. janelia. org/split-GAL4. Figure 3 with 2 supplements Download asset Open asset LC neuron terminals in the central brain are organized into distinct neuropil structures. (A) Illustration of the projection patterns of 12 LC cell types that project to major optic glomeruli in the PVLP (or in the PVLP/PLP boundary region). Image is a substack maximum intensity projection of a composite image stack generated from 12 computationally aligned image stacks (one for each cell type). Images were manually segmented to exclude background and some off-target cell types. Unedited pre-alignment stacks are available from www. janelia. org/split-GAL4. For details of genotypes see Supplementary file 1D. (B,C) Target regions of the LC neurons shown in (A) match the optic glomeruli pattern in the PVLP. Target regions of different LC cell types were labeled by split-GAL4 driven expression of a presynaptic marker (pJFRC51-3XUAS-IVS-syt::smHA in su(Hw)attP1, detected using anti-HA antibody labeling). Images for different cell types were edited and combined as described above. The anti-Brp pattern of the standard brain used for alignment is shown in grey. (C) Pattern of optic glomeruli revealed by anti-Brp labeling. Image is the same as the anti-Brp channel of the overlay shown in (B). Note the close correspondence of the presynaptic terminals of LC cell populations and optic glomeruli.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Services on 10. 4067/S0718-95162015005000019 ACC-deaminase and/or nitrogen fixing rhizobacteria and growth of wheat (L. ) W. Hassan1, 2*, M. Hussain2, S. Bashir3 A. N. Shah4, R. Bano5, J. David6 1Humboldt-Universität zu Berlin, Berlin, 14195, Germany. *Corresponding author: [email protected] 2College of Agriculture, B. Z. U. Bahadur Campus, Layyah, 31200, Pakistan. 3Institute of Soil and Environmental Sciences, University of Agriculture Faisalabad, Faisalabad, 38040, Pakistan. 4University, Wuhan, 430070, P. R. China. 5. 6Freie Universität, Berlin, 14195, Germany. growth promoting rhizobacteria (PGPR) are beneficial bacteria, which can enhance the growth of the plants, when applied to crops. A pot experiment was conducted to examine the effect of six PGPR isolates on the growth of wheat. Inoculation with rhizobacterial isolates increased the all measured physical, chemical and enzymatic growth parameters compared to control (CK). However, the WAN1 isolate had the highest effect, and significantly (P < 0. 05) increased the root length (3. 51-fold), shoot length (3. 22-fold), seedling fresh (3. 41-fold) and dry (3. 91-fold) weight, chlorophyll a (3. 90-fold), chlorophyll b (3. 51-fold), carotenoid contents (7. 23-fold), plant macronutrient uptake i. e. N (7. 20-fold, 6. 71-fold), P (7. 41-fold, 5. 01-fold), K (5. 51-fold, 3. 91-fold), Ca (6. 40-fold, 5. 21-fold) and Mg (5. 82-fold, 7. 11-fold) in shoot and root, plant micronutrient uptake i. e. Zn (6. 40-fold, 9. 11-fold), Cu (7. 31-fold, 7.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: The analysis process was carried out following the steps described below: First, we removed the placeholder strings ’No Positive’ and ’No Negative’ from the positive and negative review fields, respectively. This step is crucial to ensure that the analysed texts reflect the reviewers’ true intentions. Next, we categorised the reviews into ’Goodreview’ or ’Badreview’ based on a score assigned by the reviewer. We considered reviews with a score of less than 7 as ’Badreview’ and those with a score of 7 or more as ’Goodreview’. As the class distributions were unbalanced, we proceeded with random sampling of the data to balance the number of ’Goodreviews’ with the number of ’Badreviews’. This allowed us to have a more balanced dataset for training the machine learning models. We encoded the review labels with a LabelEncoder to facilitate processing by the machine learning algorithms. Finally, the dataset was divided into training and test sets to validate the effectiveness of the prediction model. Data preprocessing and class balancing are essential steps to ensure that machine learning models are not biased towards a dominant class. This paves the way for the application of advanced classification techniques, which will be analysed in subsequent 4. 4 TF-IDF, short for term frequency-inverse document frequency, is a prevalent method in natural language processing (NLP) for converting text into numerical vectors suitable for machine learning models. This technique offers significant improvements over basic bag-of-words models, which simply tally term frequencies in documents. TF-IDF enhances the representation by adjusting the term frequencies based on their commonality across the entire text corpus. This adjustment diminishes the impact of frequently occurring words that might not contribute much to differentiating documents. Moreover, the inverse document frequency (IDF) component of TF-IDF emphasizes words that are distinct from specific documents, aiding in more effectively distinguishing between them. In essence, TF-IDF stands as an effective and popular approach for transforming textual information into numerical form for NLP applications. An LSA plot is a visualization tool in natural language processing (NLP) that illustrates the connections between documents and their associated topics. LSA, or Analysis, is a method used for reducing the complexity of textual data in NLP.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Asterisk marks a large synapse rich (based on anti-Brp labeling) glomerular structure in the PLP that appears to be the target of several columnar VPNs that were not included here since we considered them to be primarily associated with the lobula plate, not the lobula (though some have lobula branches). As an example, one cell of one such type (LPC1) is shown in Figure 1—figure supplement 1E. LPC1 was also identified by (Panser et al. , 2016). (D–F) Overlays generated as in (A) and (B) showing the projection patterns (D) and target regions (E,F) of additional LC neurons with terminals in the posterior PVLP and in the PLP. LC24, LC25 and LC26 projected to locations close to those of LC15, LC6 and LC16 but slightly more posterior and might also slightly overlap with each other. In particular, LC25 was unusual in that its terminals spread along the surface of, and perhaps partly overlapped with, the LC15 target region and other adjacent glomeruli (E and Figure 3—figure supplement 2). Similarly, part of the boundary of the LC22 target region, as visualized by synaptic marker expression in LC22 cells, was less well defined than the boundaries of most other glomeruli (F). The LC22 glomerulus also appears to overlap with the target region of a second columnar VPN: stochastic labeling experiments revealed an additional LPLC-like cell type (distinct from LPLC1 and LPLC2 and tentatively named LPLC4 in agreement with [Panser et al. , 2016]) that projects to the same approximate location as LC22. While we have yet to generate specific split-GAL4 drivers for this additional LPLC cell type and therefore did not further characterize these neurons here, their overlap with LC22 terminals was directly confirmed by co-labeling of single cells of both types in the same specimen (Figure 3—figure supplement 1). (G–I) LC10 neurons project to the large subunit of the AOTu. Overlays (generated as described above) showing cell shapes (G) and presynaptic sites (H) of LC10 cells labeled by two different split-GAL4 driver lines.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: 02-fold), Fe (6. 41-fold, 7. 52-fold) and Mn (4. 57-fold, 5. 21-fold) in shoot and root and plant antioxidant enzymes i. e. glutathione S-transferase (7. 51-fold), peroxidase (5. 21-fold) and catalase (5. 01-fold) respectively. Our results revealed that inoculation of agricultural crops with PGPR is a very useful approach to increase the plant growth. The ACC (1-aminocyclopropane-1-carboxylate) enrichment technique is an efficient approach to select promising PGPR. The PGPR containing dual abilities i. e. both ACC-deaminase and nitrogen fixing ability are more effective than PGPR possessing either ACC-deaminase or nitrogen fixing activity alone for growth promotion of crops. Keywords: -deaminase, Ethylene and auxin, wheat 1. microorganisms are vital to agro-ecosystem health via their roles in organic matter decomposition, nutrient cycling and their associations with other organisms and plants i. e. symbiosis (Hassan et al. , 2013a; Glick, 2012). In agricultural soils, microorganisms are known to influence profoundly the status of soil fertility and health, and hence crop production (Ahmed and Kibret, 2014; Hassan et al. , 2013a). Different bacterial genera are vital components of soils, as it has been estimated that 80–90% of the processes in soil are reactions mediated by soil microbes (Hassan and David, 2014). They are involved in various biotic activities of the soil ecosystem to make it dynamic for nutrient turn over and ultimately sustainable crop production (Ahmed and Kibret, 2014). The large active groups of bacteria that inhabiting the rhizosphere benefit the crop plants in several ways, collectively called as plant growth promoting rhizobacteria (PGPR) (Hassan et al. , 2014a). Interactions between plants and microorganisms in the rhizosphere can clearly affect growth and development of the crop plants and yield (Hassan et al. , 2014a; Ahmed and Kibret, 2014).

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: In such plots, each document is depicted as a point within a space that initially has as many dimensions as there are words or terms in the vocabulary. The LSA technique simplifies this space by uncovering the latent themes within the documents. This simplified, lower-dimensional space can then be displayed in a two or three-dimensional scatter plot. Here, each point represents a document, differentiated by color or label based on the topics it covers. LSA plots are beneficial for examining the interplay between documents and topics within a corpus, helping to spot clusters of similar documents. They’re also instrumental in tracing how topics have evolved by comparing LSA plots from different periods. While LSA effectively reduces text data to a more manageable form, it does have drawbacks. It assumes a bag-of-words model, ignoring word order, and may miss some nuanced meanings crucial for certain NLP tasks. Despite these limitations, LSA is still a common choice for dimensionality reduction and visualization in NLP. 4. 5 initial phase of our analysis involved preprocessing textual data, specifically customer reviews. We utilized the Encoder (USE) to convert these reviews into high-dimensional vector embeddings. This process transforms each review into a dense representation, capturing its semantic meaning effectively. The transformation can be represented as: where each review r is mapped to a vector in a continuous semantic space. The resulting embeddings for both training and test datasets are then reshaped and compiled into arrays, denoted as X train and X test, respectively. Our predictive model is constructed using TensorFlow’s Keras API, employing a sequential neural network architecture. The model is designed for binary classification and includes the following layers: A dense layer with 256 neurons and ReLU activation, processing the input embeddings. A dropout layer with a dropout rate of 0. 25, to prevent overfitting. A second dense layer with 128 neurons, again using ReLU activation. Another dropout layer with the same dropout rate to further regularize the model. The output layer with a single neuron and sigmoid activation function, suitable for binary classification tasks. The model is compiled with a binary cross-entropy loss function, and the Adam optimizer with a learning rate of 0. 001, an appropriate choice for this type of problem. The model undergoes training for 20 epochs with a batch size of 16, and a validation split of 20 is used.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: LC10 neurons showed two distinct general projection paths with axons entering the large subunit of the AOTu from both dorsally and ventrally (magenta cells) or only from ventrally (green cells), in agreement with previous findings suggesting the existence of LC10 subtypes (Otsuna and Ito, 2006). (I) Anti-Brp reference pattern alone. The LC10 terminals are in a distinct large subcompartment (white bracket) of the AOTu. The AOTu also includes smaller subunits adjacent to this LC10 target region. Scale bars represent 30 µm (A,G,I), 20 µm (B,E) or 50 µm (D). Figure 3—figure supplement 1 Download asset Open asset A second type of columnar VPN projects to the LC22 target region. Main image and inset show two views along approximately orthogonal axes of MCFO labeled LC22 (green) and LPLC4 (magenta) cells generated using a GAL4 driver line (R11C10) with expression in both cell types. The layer pattern of LPLC4 dendrites is different from both LPLC1 and LPLC2 (see Figure 5 and Figure 7—figure supplement 2). Images show reoriented substack projection views generated using Vaa3D. Scale bar represents 20 µm. Figure 3—figure supplement 2 Download asset Open asset Presynaptic marker expression in individual LC cell types. Single confocal sections are shown. Images were rotated to show similar views. The anti-Brp reference pattern (grey) and the presynaptic marker (syt-smHA, magenta) are shown. The white appearance of the LC12 glomerulus is due to cross-talk between different labeling channels that occurred in some samples (see Materials and methods). Genotypes are the same as in the images used for the overlays in Figure 3. Many LC types also show some syt-smHA labeling in the lobula; examples of this are shown in Figure 5—figure supplement 2 and additional cases included in Supplementary file 1A. Scale bar represents 20 µm. Figure 4 with 1 supplement Download asset Open asset Multicolor stochastic labeling reveals differences in the arrangement of the terminal arbors of different LC types within their target glomerulus. (A–C) LC16.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: The PGPR can stimulate plant growth indirectly by inhibiting other deleterious microbes, root pathogens, biological control of root crop diseases, by production of antibiotics and siderophores (Ahmed and Kibret, 2014; Hayat et al. , 2010). Directly, PGPR provide the plant with a compound that is synthesized by the bacterium or facilitating the uptake of the nutrients (Ahmed and Kibret, 2014; Glick, 2012). Therefore, inoculation of agricultural crops with PGPR result in multiple positive effects on plant e. g. increases in the plant growth and vigor, increment in the chlorophyll, protein, proline and antioxidant enzymes activity and overall biomass of the host plant (Glick, 2012; Hayat et al. , 2010). Currently, the biological approaches for improving crop production are gaining strong attention among the agricultural scientists and researchers following integrated plant nutrient management system. The PGPR synthesize different phytohormones, including auxins, cytokinins, and gibberellins, which can enhance various stages of plant growth; and synthesize enzymes, including phosphatase, catalase, that can modulate plant growth and development, and strengthen their immune system (Glick, 2012). The secretion of a wide range of chemical compounds also modifies the chemical and physical properties of the soil (Ahmed and Kibret, 2014; Glick, 2012). Phytohormone auxin, (indole-3-acetic acid/IAA) is a class of phytohormones which are involved in the regulation of growth and development throughout the life cycle of plants (Ahmed and Kibret, 2014; Etesami et al. , 2014). Similarly, Ethyleneis also a vital phytohormone, which plays significant roles in the germination and growth and development of the seeds and crop plants (Bhattacharyya and Jha, 2012). Ethylene, produced endogenously by approximately all plants, however, apart from being a plant growth regulator, in excessive amount, ethylene has also been established as a stress hormone (Bhattacharyya and Jha, 2012). Several PGPR are actively involved in the synthesis of auxins in pure culture as well as in soil, and hence can play significant roles in the growth and development of crop plants (Glick, 2012).

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: This validation split is crucial for monitoring the model’s performance and ensuring it does not overfit the training data. The training process involves shuffling the data for each epoch to maintain the randomness and independence of batches. Post-training, the model’s performance is evaluated on the test set. The evaluation metrics include loss and accuracy, which provide insights into the generalization ability of the model. The results are as follows: (0. 5371800661087036, 0. 8009422421455383) Furthermore, the training and validation loss and accuracy are plotted against epochs. These plots are crucial for visualizing the model’s learning trajectory and identifying any signs of overfitting or underfitting. 5 ConclusionsThe research presented in this paper aimed to develop a robust model for sentiment analysis, leveraging the capabilities of neural networks and natural language processing. Through the application of the Encoder (USE), we transformed textual data - specifically, customer reviews - into dense vector representations. This approach enabled us to capture the nuanced semantic content of the text, providing a rich foundation for our predictive modeling. Our model, constructed using TensorFlow’s Keras API, represents a sequential neural network specifically tailored for binary classification tasks. The architecture, comprising dense layers interspersed with dropout layers, was strategically designed to balance complexity and generalizability. The dense layers, equipped with ReLU activation functions, were pivotal in capturing the non-linear relationships within the data. In contrast, the dropout layers played a critical role in mitigating overfitting, a common pitfall in deep learning models. The training process, conducted over 20 epochs with a batch size of 16, was carefully monitored using a validation split. This approach not only provided insights into the model’s learning trajectory but also ensured that our model generalized well to unseen data, rather than merely memorizing the training dataset. The shuffle mechanism in the training process further contributed to the robustness of the model by preventing any order-specific biases in the learning process. Upon evaluation, the model demonstrated promising results, as indicated by the test loss and accuracy metrics. These metrics not only reflect the model’s performance in terms of prediction accuracy but also its ability to generalize to new, unseen data - a crucial aspect of any predictive model. However, it is important to acknowledge that these results are preliminary and subject to the limitations of the dataset and experimental setup.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: (A) Position of the dendrites of three LC16 cells in the lobula in a layer cross-section view. Each cell occupies a distinct position along the long (DV) and short (AP) axes of the lobula (dotted white lines were added to facilitate comparison of the positions of different cells). By contrast, within the target glomerulus (B,C; two roughly orthogonal views are shown) the arbors of the same cells are intermingled without an obvious correlation to the retinotopic pattern in the lobula. Most other LC types appeared similar to LC16; additional examples are shown in Figure 4—figure supplement 1. (D–G) LC9. Most LC9 cells have presynaptic arbors that spread through more than one half of the glomerulus but do not fill it completely (F,G). Comparison of the approximate positions of arbors of individual LC9 cells in the glomerulus (F,G) with the AP positions of their dendrites in the lobula (D,E) suggests some retinotopy within the glomerulus, though with very low spatial resolution: For example, out of 57 examined LC9 cells, all of the cells (27/27) with axon terminals in the anterior-ventral tip of the glomerulus (such as the green cell in [F] and both cells in [G]) also had dendrites in the posterior half of the lobula while this was the case for only a few of the remaining LC9 cells (6/30). White dotted lines in (F) and (G) indicate the approximate boundaries of the LC9 glomerulus. (H–M) LC10. The relative order of LC10a (H–J) and LC10d (K–M) terminals along the DV axis of the AOTu (shown in two orthogonal views, I,J for LC10a and L,M for LC10d) matches the order along the AP axis of the lobula (H for LC10a and K for LC10d). Individual cells were labeled using MCFO. Reconstructed views of reoriented subtstacks generated in Vaa3D are shown. For both LC10a and LC10d, similar results were observed for LC10 cells from five optic lobes, each with at least three labeled cells.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Alike, the PGPR containing ACC (1-aminocyclopropane-1-carboxylate) deaminase activity is helpful in lowering the ethylene levels by converting ACC into NH3 and α-ketobutyrate in plants, and support the plants for vigorous growth and development (Ahmed and Kibret, 2014). Wheat (Triticum spp. ) is one of the most important cereal crops in the world (Schalchli et al. , 2012). Globally, it is the most important human staple food grain and ranks second in total production as a cereal crop after maize (2006). In order to meet the demands of the increasing world,s population, the use of chemical fertilizers increased significantly (2006). It has been estimated that the total annual fertilizer use has risen from 37 M t of N, P and K in 1961-65 to 161 M t in 2005-09 (2009). The excessive use of chemical fertilizers, drastically increase the pollution in both natural and man made ecosystems (Hassan et al. , 2014b). Therefore, it is important to find out other self-propagating and eco-friendly sources, which can replace the chemical fertilizers entirely or partially (Hassan et al. , 2014b). In this regard, the use of PGPR as a bio-fertilizer is a viable approach. Keeping in view all these challenges, the current study was conducted with the objectives: (1) to isolate and screen the most effective multi-traits PGPR strains for wheat (2) to examine the physical, chemical and enzymatic responses of wheat plant to three different rhizobacterial strains, strain 1: containing ACC-deaminase activity (WACC1, WACC2), strain 2: having nitrogen fixing ability (WRN1, WRN5) and strain 3: having both ACC-deaminase activity and nitrogen fixing ability (WAN1, WAN2). 2. Materials and Methods 2. 1. Physico-chemical analysis The soil was collected at 0-5 cm depth with the help of an augur. The samples were air-dried and passed through 2-mm sieve for further use in the incubation study.

Output: corroborate


Input: Claim: AI can predict the future with perfect accuracy by analyzing data trends and patterns. [SEP] Text: The graphical analysis of training and validation losses and accuracies provided additional layers of insight. The trends observed in these plots are critical in diagnosing issues such as overfitting or underfitting and in understanding the learning dynamics of the model across epochs. They also serve as a visual affirmation of the model’s learning stability and convergence behavior. This research contributes to the burgeoning field of sentiment analysis by demonstrating the effectiveness of dense neural networks in handling complex, high-dimensional data derived from natural language. The successful application of the Encoder in transforming textual data into a format amenable for neural network processing underscores the synergy between state-of-the-art NLP techniques and neural network architectures. However, this study is not without its limitations. The scope of the data and the specific architecture of the neural network model present avenues for further exploration. Future work could involve experimenting with different neural network architectures, such as recurrent neural networks or transformers, which might be more adept at capturing sequential dependencies in text. In addition, investigating the effects of different embedding techniques, or incorporating additional features, could provide more comprehensive insights. Moreover, the application of the model to a more diverse set of data sources would enhance its robustness and applicability to real-world scenarios. In conclusion, our research marks a significant step in sentiment analysis, providing a framework that can be built upon and refined in future studies. The integration of advanced NLP techniques with neural networks holds great promise for the field, and continued exploration in this domain is essential for the development of more sophisticated and nuanced sentiment analysis tools. DeclarationsCompeting interests: The authors declare no competing interests. 260. LeCun, Y. , Bengio, Y. , Hinton, G. (2015). Deep learning. Nature, 521(7553), 436444. Liu, B. (2012). . Publishers. Murphy, K. P. (2012). Machine Learning: A Probabilistic Perspective. The MIT Press. Pang, B. , Lee, L. (2008). . Now Publishers. Russell, S. , Norvig, P. (2010). Artificial Intelligence: A Modern Approach. PrenticeHall. Tibshirani, R. , Friedman, J. (2009). of Statistical Learning: DataMining, Inference, and Prediction. Springer.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Analyses of MCFO-labeled LC10b (36 cells from 18 brains) and LC10c (33 cells from 17 brains) single cells also showed an approximate correspondence between AP positions of dendrites in the lobula and DV positions of axonal arbors in the AOTu. LC10b cells also showed considerable variation in their lateral-medial spread within the medial zone of the AOTu but further analyses will be required to explore possible correlations between these differences and arbor positions in the lobula. Scale bars represent 20 µm (A,D,E,H,K) or 10 µm (C,F,G,I,L). Figure 4—figure supplement 1 Download asset Open asset Terminal arbor arrangements of additional LC cell types. (A–C) LC6, (D–F) LC11, (G–I) LC13, (J–L) LC12, (M–O) LC18, (P–R) LPLC2. Substantial overlap of the arbors of co-labeled single cells in their target regions was also observed for the remaining LC cell types (at least two brains with two or more labeled cells were examined for each cell type). While there are occasional differences between the spread of single cells of the same type within their target glomerulus (e. g. the LC6 cells in (B) do not all extend to the lateral tip of the glomerulus), we did not find obvious correlations between such differences and these cells’ retinotopic positions in the lobula. Lobula cross-section views and two orthogonal views of the target glomerulus are displayed as in Figure 4. Scale bars represent 20 µm (A) or 10 µm (C). Figure 5 with 2 supplements Download asset Open asset Layer specific arborizations of LC neurons in the lobula. (A) Anti-Brp neuropil marker shows bands of different intensity in the lobula that can serve as approximate markers of layer boundaries. The image is a maximum intensity projection through 10 adjacent sections (0. 38 µm spacing) of the reference channel of the standard brain used for alignments. Approximate layer boundaries are indicated. Layer boundaries were defined by the positions of known cell types and closely match the anti-Brp pattern (see Figure 5—figure supplement 1).

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Soil texture, pH, EC (electrical conductivity), available P, N, extractable K and organic matter (OM) and saturation percentage were determined by the methods of Hassan, 2013, Hassan et al. , (2013 b, c, d) and Hassan et al. , (2014 c, d, e). The soil was sandy loam (sand 60. 3%, clay 25. 5% and silt 14. 2%) in texture, and alkaline in nature with saturation percentage 41. 5%, pH 7. 98, EC 1. 71, OM 0. 41%, available P, N and K 8. 81, 3. 67 and 423 mg kg-1respectively. 2. 2. Isolation of PGPR For isolation of bacterial strains, gently rhizosphere soil was collected from tomato crop. Rhizobacteria were isolated by dilution plate technique (Wollum-II, 1982) using Dworkin and Foster salt minimal media (Dworkin and Foster, 1958) (DF). For PGPR containing ACC-deaminase, ACC was the sole nitrogen source. Whereas, modified mannitol agar media was used to isolate PGPR possessing nitrogen fixing activity (Enrichment Technique). 2. 3. Isolation of rhizobacteria containing dual abilities Isolated rhizobacteria were further grown on both (DF salt minimal and modified mannitol agar) media to isolate the rhizobacteria containing dual abilities i. e. nitrogen fixing and ACC-deaminase activity. The isolates having ACC-deaminase activity was grown on modified mannitol agar media used for N fixing isolates and the rhizobacteria possessing N fixing activity was grown on media used for isolates having ACC-deaminase activity. The isolates WAN1 and WAN2 grew on both media and proved that having both abilities. 2. 4. Preparation of inocula Autoclaved 250 ml flasks with DF salt minimal media were used for the preparation of inocula, for the rhizobacterial isolates containing ACC-deaminase activity and modified mannitol agar media for the rhizobacterial isolates having nitrogen fixing activity. The autoclaved flasks were incubated at 28 ± 1°C for 48 hours in the orbital shaking incubator at 100 rpm.

Output: corroborate


Input: Claim: The majority of digital transactions today use cryptographic techniques to ensure the security of sensitive information. [SEP] Text: Nevertheless, when this communication happens in the digital world through the Internet, where people do not meet or even know each other, such a protection is more challenging to obtain, and additional digital tools are needed in order to defend users. In this thesis we investigate how to enforce the honest behavior of parties involved in a digital interaction over the Internet. In particular, we consider two emerging paradigms in this setting: Could computing and E-commerce. By using a Cloud service, anyone (a Client) can outsource data to a remote server (the Cloud), and later on ask the Cloud to perform operations on them. This new paradigm allows for huge potential savings in terms of time, computing power and other kind of infrastructural costs, since it is possible to access to pay-per-use services upon request. It is clear that a computing paradigm like this is very attractive but, at the same time, trusting the Cloud can be problematic for various reasons. Two main security concerns that have been recently given attention by the research community are those about the privacy and authenticity of the data stored and processed in untrusted environments. Intuitively, for privacy a Client does not want the server to learn any information about the outsourced data. For authenticity, the Client instead wants to be sure that the Cloud computed correctly on the outsourced data. In this thesis we focus on this second problem, advancing the study of homomorphic authenticators. In homomorphic authenticators a outsources authenticated data to the Cloud. Later on, a third entity (the Verifier) can ask the Cloud to compute a function f over the Client’s outsourced data. Using a special procedure, the Cloud can provide the Verifier with an authenticator for the output of the function, which allows the Verifier to check the validity of the computation queried. The contribution of this thesis addresses three different aspects of homomorphic authenticators: definitions, efficiency and functionalities. First, it introduces a new security model which is stronger and easier to deal with compared with the existing one, along with two compilers which allow one to go from the old model to the new one. Second, it provides the first linearly homomorphic signature scheme whose verification keys have size sublinear in that of the outsourced dataset. Third, it formalizes the notion of homomorphic authenticators for functions which take inputs authenticated using different keys, providing concrete constructions both in the case of private and public verification.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Lo5 was divided into two sublayers based on the anti-Brp pattern. Further subdivisions of strata based on the positions of arbors of different cell types would be possible but were not applied here. (B,C) Layer patterns of LC6 (B) and LC12 (C). Split-GAL4 expression of a membrane marker is shown in green. Images were aligned to a standard brain using the anti-Brp pattern (shown in grey). Images are maximum intensity projections through the same series of sections of brains aligned to the same template as in (A). Approximate layer boundaries are marked with white lines. (D) Layer patterns of the remaining LC cell types (except LC10 neurons). Projections were generated as in (B,C) but are shown without the anti-Brp pattern. All layers, but only a portion of the lobula is shown. Schematics in (B–F) indicate innervated layers as filled circles; black circles represent more extensive arborizations than grey circles. Note that these simplified schematics do not capture some details of the layer patterns (such as sublayer patterns). An additional description of layer patterns can be found in Supplementary file 1A. (E) Single cell layer patterns are consistent with layer patterns seen at the population level. LC4 and LC15 are shown as examples. Additional single cell images can be found in Figure 7—figure supplement 1. (F) Layer patterns of LC10 subtypes. LC10b and LC10d cells have similar layer patterns but differ in other aspects such as arbor size (LC10b arbors in the lobula are larger). Additional examples of MCFO labeled LC10 cells of different subtypes can be found in Figure 7—figure supplement 1 and Figure 10—figure supplement 2. Images in (E,F) were manually matched to the layer markers using the anti-Brp pattern. The scale of some images was slightly adjusted to compensate for varying depth of the lobula. Scale bars represent 20 µm (A, also applies to B and C) or 10 µm (D,E; scale bar in E also applies to F). Figure 5—figure supplement 1 Download asset Open asset Layer positions of arbors of known cell types.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Experimental design Two PGPR isolates (WACC1 and WACC2) containing ACC-deaminase activity, two PGPR isolates (WRN1 and WRN5) having nitrogen fixing activity and two PGPR isolates (WAN1 and WAN2) possessing both ACC-deaminase and nitrogen fixing activity were selected for incubation experiment. The inoculum for trial was prepared by growing the selected PGPR isolates. For inoculation of treatments, germinated seeds (variety Uqab-2000) were dipped in broth having selected ACC-deaminase and/or nitrogen fixing isolates. For control treatments, the wheat seeds were dipped in sterilized flasks containing 0. 03 M MgSO4. Four inoculated seeds were sown in each pot having 500 g soil/pot. Sterilized ½ strength nitrogen free Hoagland solution was used to provide nutrients to growing seedling. There were four replications for each treatment. Pots were arranged in complete randomized design (CRD) in a growth room under axenic conditions. After 28 days of germination, wheat plants were harvested and parameters regarding root and shoot length, seedling fresh and dry weight, chlorophyll a, b and carotenoid contents, macro and micro nutrients in plant shoot and root and antioxidant enzymes glutathione S-transferase, peroxidase and catalase activities were recorded. 2. 11. Statistical analysis The data were statistically analyzed by Statistix 8. 1 statistical package (Statistix, USA). Parametric statistics of ANOVA analysis was carried out to estimate the effect of rhizobacterial isolate inoculation on plant growth. Mean separations were achieved using a least significant difference (LSD) test at p < 0. 05. 3. Results 3. 1. Root and shoot length The inoculation of wheat seedlings with rhizobacterial isolates containing nitrogen fixing and/or ACC-deaminase activity increased the root and shoot length as compared to uninoculated control. The root and shoot length ranged from 1. 51-fold to 3. 51-fold and 1. 40 to 3. 22 fold under normal growth conditions (Figure 1). The isolate WAN1 showed maximum increase in the root and shoot length that was 3. 51-fold and 3. 22-fold respectively as compared to uninoculated control.

Output: corroborate


Input: Claim: Quantum computing has the potential to break traditional encryption methods by solving complex problems much faster. [SEP] Text: This is difficult to ascertain as (i) no times (or memory requirements) are quoted, (ii) no 'traditional method', that can actually align the required number of sequences without human intervention, is used and (iii) the results are *not* repeatable. and UPP I have repeatedly (UPP five times, PASTA twice) aligned the same set of (small in number and short) sequences and every time obtained a different alignment. In my example the alignment lengths varied by 7%, the sum-of-pairs scores of one alignment against another alignment varied from 96% to 97% and the total column scores from 23% to 40%. For 'repeatable' results I would consistently expect 100%. understand UPP/PASTA correctly, then there is a certain degree of randomness involved in the assignation of the backbone sequences. However, I did not see that there was (can be? ) any random seed specified on the command-line. If this is so, then the alignments have to be repeated several times and the results have to be somehow combined/averaged. It is well known that -INS-i is not thread-safe. SATe/PASTA/UPP use -INS-i (indeed it is my opinion, that much of the quality of SATe/PASTA/UPP is due to this fact, see below) so a 'repeatable' analysis should be run such that -INS-i is run single-threaded (or by setting the MAFFT --threadit flag to 0). Action: - Quote run-times and memory consumption. - Guarantee that alignments are repeatable. - If this cannot be done then repeat alignments and average. [8] As mentioned above, SATe/PASTA/UPP use -INS-i, L-INS-i is a high-quality consistency aligner and is not. It would be only fair to compare like with like and either (i) use as the base aligner in UPP (PASTA can do this, can UPP? ) or (ii) use L-INS-i as the 'traditional method'. L-INS-i would definitely have problems aligning 55k sequences, but since in the current analysis sequences are broken up into 1,269 FASTA files, this should be feasible.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Lobula terminals of MCFO labeled T5, Tm9, Tm4, Tm3 and Tm20 cells (Fischbach and Dittrich, 1989) are shown. Anti-Brp pattern is in grey. Layer boundaries are marked with white lines. Golgi studies (Fischbach and Dittrich, 1989) have described T5 and Tm9 in Lo1, Tm4 and Tm3 proximal terminals in Lo4 near the boundary of Lo5, and Tm20 terminals in Lo5 near the boundary of Lo6. Tm4 and Tm3 also have apparent presynaptic varicosities in Lo2 (both) and Lo1 (Tm4). These cell types therefore can be used to identify the approximate boundaries of all six lobula strata. Further division of Lo5 into two sublayers is suggested by the anti-Brp pattern and supported by the differential presence of arbors of several neuronal cell types in these sublayers (compare for example, LC20 and LC25 in Figure 5D). Images show reoriented substack projection views generated using Vaa3D. Scale bar represents 10 µm. Figure 5—figure supplement 2 Download asset Open asset Potential presynaptic sites of LC neurons in the lobula. In addition to the target glomerulus in the central brain (see Figure 3 and Figure 3—figure supplement 2), split-GAL4 driven pJFRC51-3XUAS-IVS-syt::smHA in su(Hw)attP1 expression also resulted in labeling in the lobula in several LC cell types. Single confocal sections with anti-Brp in grey, syt-smHA in magenta and/or a membrane marker (pJFRC225-5XUAS-IVS-myr::smFLAG in VK00005) in green are shown for each cell type as indicated in (A). Although the interpretation of such fainter syt-smHA labeling patterns is not always straightforward, they strongly suggest that at least some LC neurons (including cell types not shown in this Figure) also have presynaptic sites within the lobula. Additional details on syt-HA labeling in the lobula are provided in Supplementary file 1A.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Conversely, WAN2 was the next effective isolate in promoting root and shoot length (3. 1-fold and 2. 91-fold) as compared to uninoculated control. The other isolates namely, WRN1, WRN5, WACC1 and WACC2 caused 2. 51-fold, 2. 21-fold, 2. 22-fold, 1. 91-fold, 1. 81-fold, 1. 61-fold and 1. 51-fold and 1. 40-fold increase in root and shoot length as compared to uninoculated control. Figure 1. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on root and shoot length of wheat. Different letters (a-g) on bars indicate significant differences of mean values for root length. Bars represent standard errors. 3. 2. Fresh and dry weight of seedlings The effect of inoculation with either ACC-deaminase and/or nitrogen fixing activity possessing rhizobacteria on the fresh and dry weight of seedling is shown in Figure 2. Data showed that fresh and dry weight of seedlings increased significantly (P < 0. 05) as a result of inoculation, ranged from 1. 51 to 3. 41 fold and 1. 60 to 3. 91 fold. All rhizobacterial isolates caused significant (P < 0. 05) increase in the fresh weight and dry of seedlings. Nevertheless the WAN1 was the most efficient isolate, caused an increase of 3. 41-fold and 3. 91-fold. The WAN2 was the second best isolate, caused an increase of 3. 11-fold and 3. 41-fold. The next effective isolates were WRN1, WRN5, WACC1 and WACC2 that resulted in about 2. 61-fold and 2. 92-fold, 2. 41-fold and 2. 61-fold, 1. 81-fold and 2. 21-fold and 1. 51-fold and 1. 60-fold increase respectively. Figure 2. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on seedling fresh and dry weight of wheat. Different letters (a-g) on bars indicate significant differences of mean values for seedling fresh weight. Bars represent standard errors. 3. 3.

Output: corroborate


Input: Claim: All encryption methods are completely unbreakable and guarantee absolute security for any type of data. [SEP] Text: Data in Download book PDF Download book 5 5161 Accesses AbstractData in transit, or network traffic, can be eavesdropped on and potentially leak sensitive information. This information can be in the payload, headers, or metadata. To mitigate this, many communication protocols encrypt traffic on different layers exist. However, even if encrypted traffic leaks information through its metadata. The percentage of encrypted network traffic has risen continuously in the past years, and we expect this trend to continue in the following years. On the other hand, measures to protect metadata are limited, leaving network traffic vulnerable to traffic-analysis attacks. You have full access to this open access 2. 1 DefinitionData in transit (i. e. , network traffic) is susceptible to eavesdropping and can leak information through the following channels: Payload: The packet payload contains the message transmitted (e. g. , parts of a website or email). Headers: The packet headers contain the information required to deliver the packet to the correct destination and parse it correctly by the receiving application. Packet headers, therefore, contain information about the sender and receiver of a packet (e. g. , their IP and MAC addresses) and information about the user application and protocol. Metadata: Metadata in network traffic is normally considered as the protocol information including the packet headers They are not contained in the packet directly but can be observed when recording the packet (e. g. , the packet size or the time when it was received). If a packet is received at multiple locations, this reveals additional information (e. g. , the path a packet takes through the network). For an eavesdropper, extracting information from unprotected headers and payloads is easy. Nevertheless, even if headers and payloads are protected (i. e. , encrypted), several so-called traffic-analysis attacks can infer sensitive information based on traffic metadata. For each type of information channels, there exist technologies to prevent the leakage: usual approach to protecting the payload and packet headers is to encrypt them. To do so, various encrypted protocols exist that encrypt data on various layers (not only the payload but also some headers). Widely used protocols include MACsec, IPsec, and TLS, which are explained in more detail below. MACsec (Control security) [1]: Operates on the link layer and encrypts packets between (layer 2) switches.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Strong syt-smHA labeling in layer Lo6 in (E) appears to be mainly due to LC10b cells (compare with (C) for LC10d cells alone), though a lobula intrinsic neuron type weakly labeled by the split-GAL4 driver line used might also contribute to the pattern. Scale bar represents 20 µm. Figure 6 with 1 supplement Download asset Open asset Column spread of LC neurons and other cell types in the lobula in cross-section views. Cells were labeled using MCFO. Cross-section views of the lobula were generated using Vaa3D. The AP and DV axes of the lobula are indicated. Anti-Brp reference marker is shown in grey. LC neuron arbor sizes and shapes in the lobula are diverse across different cell types but similar within each type. LC cell types shown are LC11 (A), LC10a (B) and LC22 (C). The remaining LC cell types are shown in Figure 6—figure supplement 1. MCFO labeled cells of a columnar medulla neuron type (Tm3) present in each of ~750 visual columns (D) and a lobula tangential cell (LT) that spans the entire lobula (E) are shown for comparison. All LC arbors are multicolumnar with estimated sizes from about 10 (LC10a) to over 60 (LC11) visual columns. LC22 cells were similar in size to LC11 along the long (DV) but not the short (AP) axis of the lobula. (F) Schematic summary of the column spread of different cell types in the lobula. Scale bar represents 20 µm. Figure 6—figure supplement 1 Download asset Open asset Layer cross-section views of lobula arbors of the LC neuron types not shown in Figure 6. Cross-section views of LC cells of the indicated types were generated as described in the Figure 6 legend. Asterisks in the LC10b and LC21 panels mark two MCFO labeled cells (LC10c and LC11, respectively) that do not belong to the indicated cell type. LO and LP in the LPLC panels indicate cross-section views of the lobula and lobula plate, respectively. Scale bar in the LC4 panel represents 20 µm; images of other cell types are shown at very similar scale. Figure 7 with 2 supplements Download asset Open asset Single cell shapes of LC neurons.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Chlorophyll a, b and carotenoid contents The inoculation with either ACC-deaminase and/or nitrogen fixing activity containing rhizobacteria significantly (P < 0. 05) increased the chlorophyll a, b and carotenoid contents. The increase in the chlorophyll a, b and carotenoid contents ranged from 1. 63 to 3. 91 fold, 1. 61 to 3. 51 fold and 1. 81 to 7. 21 fold (Figure 3). Isolate WAN1 was found to be the most effective that caused 3. 91-fold, 3. 51-fold and 7. 21-fold increase in chlorophyll a, b and carotenoid contents over uninoculated control respectively. The next effective isolates were WAN2, WRN1, WRN5, WACC1 and WACC2 that resulted in about 3. 41-fold, 3. 20-fold, 6. 01-fold, 3. 01-fold, 2. 61-fold,4. 91-fold, 2. 61-fold, 2. 30-fold, 3. 41-fold, 1. 91-fold, 1. 90-fold, 2. 62-fold, and 1. 63-fold, 1. 60-fold and 1. 81-fold increase in chlorophyll a, b and carotenoid contents respectively, compared to uninoculated control. Figure 3. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on chlorophyll a, b and carotenoid contents of wheat. Different letters (a-g) on bars indicate significant differences of mean values for chlorophyll a contents. Bars represent standard errors. 3. 4. Macronutrient contents in shoot and root Data showed that inoculation with rhizobacteria containing either ACC-deaminase and/or nitrogen fixing activity increased the plant ionic uptake e. g. macronutrient contents (N, P, K, Ca, and Mg) in shoot and root (Figure 4). Among all isolates the maximum N (7. 11-fold and 6. 71-fold), P (7. 41-fold and 4. 90-fold), K (5. 51-fold and 3. 90-fold), Ca (6. 40-fold and 5.

Output: corroborate


Input: Claim: All encryption methods are completely unbreakable and guarantee absolute security for any type of data. [SEP] Text: MACsec encrypts the entire packet, including all headers except the source and destination addresses in the link layer (i. e. , the source and destination MAC address). MACsec is typically used to protect individual links in a local area network (LAN) or wide area network (WAN) against eavesdropping. IPsec (Security) [2]: Operates on the network layer and can be used in two modes: transport mode and tunnel mode. In transport mode, IPsec encrypts the payload of the IP layer (i. e. , the headers of the transport-layer protocol and packet payloads). In tunnel mode, IPsec creates a tunnel from the sender to a destination and encrypts the IP header and its payload. To do so, it encapsulates the original IP packet within a new IP packet whose destination address is the tunnel’s endpoint, thereby revealing the IP addresses of both ends of the tunnel. IPsec is typically used to create tunnels between locations connected over an untrusted network (e. g. , the Internet). For more information about tunnels and so-called Networks (VPNs), see Chap. 26. TLS (Security) [3]: Operates on the transport layer and encrypts only its payload. Therefore, it does not hide other packet headers such as the source and destination IP addresses. TLS is used for many applications, but its most well-known use case is web browsing over HTTPS. Hiding packet metadata is more difficult compared to hiding packet contents because hiding metadata involves more than just encrypting the actual traffic. Completely hiding metadata is often impossible because packets need to be sent at some point. There exists various options: Obfuscating packet sizes: Obfuscating the size of packets or flows can be achieved by adding padding to the original contents of a packet or by splitting one packet into multiple fragments. For example, IPsec and TLS allow adding a random amount of padding to each packet before encrypting it to conceal its real size. However, it has been shown that this padding is too little to prevent traffic-analysis attacks [4]. Obfuscating packet timing: Obfuscating the timing of packets can be achieved by delaying the sending time of a packet. However, this inevitably leads to an increase in latency and, therefore, a decrease in network performance. Therefore, systems to hide the timing mainly exist as prototypes presented in research papers (e. g.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Maximum intensity projection images of MCFO labeled single cells were manually segmented to exclude other labeled cells or background signal and converted to inverted grayscale images. Cells are shown in a similar orientation (with dorsal approximately up and lateral approximately to the left) and at the same scale. Scale bar represents 50 µm. Figure 7—figure supplement 1 Download asset Open asset Layer patterns of single cells of 22 LC neuron types. Images are reoriented views (generated using Vaa3D) of MCFO labeled LC neurons. Anti-Brp neuropil marker is in grey. For each cell type, two different views, along the AP and along the DV axes of the lobula, respectively, are shown. The two panels for a cell type can either show the same specimen in two views or two different specimens. Scale bars in the LC4 panels represent 20 µm; images of other cell types are shown at very similar scale. Figure 7—figure supplement 2 Download asset Open asset Single cell labeling of LPLC arbors in lobula and lobula plate. (A) LPLC1. (B) LPLC2. (C) LPLC4. Numbers indicate the four LP layers (Fischbach and Dittrich, 1989). Similar to lobula layers, the LP layers can be identified by changes in the intensity of anti-Brp labeling: Each LP layer shows a band of strong anti-Brp signal flanked by weaker labeling. All three LPLC cell types have processes in all lobula plate layers and several layers of the lobula but differ in details of arbor structure and layer patterns. For example, lobula layers of LPLC4 (mainly Lo2, Lo4 and Lo6) are different from both LPLC1 (Lo2-Lo4 and Lo5B) and LPLC2 (Lo4 and Lo5B). The presence of processes in multiple lobula plate layers, as shown by all LPLC types, could support responses to stimuli that combine different directions of motion, such as a visual loom (Schilling and Borst, 2015). Scale bars represent 20 µm. Figure 8 with 2 supplements Download asset Open asset Optogenetic activation of LC neurons induces distinct behavioral responses that differ between LC cell types. (A) A representative video image of group of freely walking flies in the circular arena assay.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: 21-fold) and Mg (5. 80-fold and 7. 01-fold) contents in plant shoot and root were found in WAN1. The other isolates namely WAN2, WRN1, WRN5, WACC1 and WACC2 also significantly (P < 0. 05) increased the N (5. 81-fold, 5. 11-fold, 3. 62-fold, 2. 61-fold and 1. 90-fold), P (5. 81-fold, 4. 62-fold, 3. 61-fold, 2. 80-fold and 2. 01-fold), K (4. 61-fold, 3. 71-fold, 2. 91-fold, 2. 03-fold and 1. 61-fold), Ca (4. 40-fold, 3. 71-fold, 3. 30-fold, 2. 71-fold and 2. 31-fold) and Mg (4. 51-fold, 4. 01-fold, 3. 21-fold, 2. 61-fold and 1. 71-fold) in the plant shoot compared to control. Similarly, the other isolates namely WAN2, WRN1, WRN5, WACC1 and WACC2 also significantly (P < 0. 05) increased the N (5. 11-fold, 4. 01-folds, 3. 31-fold, 2. 71-fold and 2. 30-fold), P (4. 30-fold, 3. 60-fold, 3. 31-fold, 2. 91-fold and 2. 61-fold), K (3. 21-fold, 2. 50-fold, 1. 91-fold, 1. 72-fold and 1. 41-fold), Ca (4. 41-fold, 3. 61-fold, 3. 01-fold, 2. 41-fold and 1. 80-fold) and Mg (6. 11-fold, 5. 61-fold, 4. 50-fold, 3. 70-fold and 1. 91-fold) in the plant root compared to control. Figure 4. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on macronutrient contents (N, P, K, Ca, and Mg) in shoot and root of wheat.

Output: corroborate


Input: Claim: All encryption methods are completely unbreakable and guarantee absolute security for any type of data. [SEP] Text: , [4,5,6]), and are rarely used in practice. Obfuscating packet’s path: Obfuscating the path of packets can be achieved by re-encrypting the packet multiple times while it crosses the network. The most well-known technique to do this is and its implementation in the TOR network. However, more than re-encrypting packets is needed; packet timings and sizes need to be concealed, too, in order to prevent correlation attacks such as the ones discussed in [7]. Reliably preventing traffic analysis attacks based on metadata requires making the traffic that crosses the network independent of the actual production traffic in terms of packet size, timing, and contents. This can be achieved by reshaping and encrypting production traffic such that it is sent at a fixed rate, and the encryption makes packets indistinguishable from each other. Unfortunately, preventing traffic analysis attacks typically adds large amounts of overhead in terms of additional delays, packet padding, and cover traffic and, therefore, often comes at the cost of throughput decrease or latency increase. 2. 2 TrendsThe percentage of encrypted network traffic has risen continuously in the past years, and we expect this trend to continue in the following years. A major driving factor for this is that the “Let’s Encrypt” certification authority [8] allows everyone to obtain TLS certificates for free. This led to a rapid increase in the websites reachable over encrypted connections, i. e. , over HTTPS. In addition, website operators are further incentivized to deploy TLS because it leads to a better ranking in the Google search results [9]. In addition, QUIC, a new transport-layer protocol, was standardized in 2021 [10]. In contrast to TCP and is encrypted by default and provides better performance and reliability than TCP and UDP. Google initially introduced it and supports it in its products, but other platforms and services now support it as well. TLS and QUIC are increasingly used to encrypt traffic that was traditionally not encrypted. For example, DNS queries are now sent over encrypted channels. 3 Consequences for SwitzerlandPopular websites and services in Switzerland enforce encrypted connections leads to most web network traffic in Switzerland being encrypted. In addition, the major Swiss web hosting providers support TLS encryption for their customers’ websites free of charge and with an easy setup, further increasing the percentage of encrypted traffic.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: (B) Representative video images of a freely behaving fly on a small glass platform in the single-fly assay. A side view (upper part of the panel) and a bottom view (lower part) of the fly are simultaneously recorded on a single high-speed video camera with the aid of two small prisms (see Materials and methods). (C,D) The results of CsChrimson activation of different LC cell types in the arena (C) and the single-fly (D) assays are summarized in a grayscale intensity map. Each column represents a distinct behavior and each row represents a different split-GAL4 driver line with a predominant expression in the indicated cell type. Shading represents the behavioral penetrance (percentage of trials or flies of a specific genotype in which a given behavior was observed, see Supplementary file 1B for the names of the GAL4 lines used and penetrance values). In both assays, the occurrences of reaching and jumping behaviors were annotated manually, while locomotor behaviors including forward walking, backward walking and turning were determined based on velocity and angular speed derived from automated fly tracking (see Figure 8—figure supplement 1, and Materials and methods). For locomotor behaviors we set a conservative threshold of two standard deviations away from the mean to be considered an activation phenotype (Figure 8—figure supplement 1E,F). In this way, we determined the behavioral penetrance for five phenotypes – reaching, jumping, forward walking, backward walking and turning - in both the arena and the single-fly assays. In the single-fly assay, the high jumping penetrance of four LC cell types (LC4, LC6, LPLC1 and LPLC2) resulted in too few flies (<= 12) available for analyses of walking and turning behaviors (indicated with a ‘\’). Figure 8—figure supplement 1 Download asset Open asset Quantification of locomotor behaviors and determination of behavioral penetrance. (A) A representative tracking of a fly with LC16 activation in the arena assay. Arrows indicate the start and end positions. Fly’s trajectory is denoted in red (during stimulation, 1 s) or in white (before and after stimulation, 1 s each).

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Different letters (a-g) on bars indicate significant differences of mean values for macronutrient contents. Bars represent standard errors. 3. 5. Micronutrient contents in shoot and root Data showed that inoculation with rhizobacteria containing either ACC-deaminase and/or nitrogen fixing activity increased the plant ionic uptake e. g. micronutrient contents (Zn, Cu, Fe and Mn) in shoot and root (Figure 5). Among all isolates the maximum Zn (6. 41-fold and 9. 11-fold), Cu (7. 31-fold and 7. 01-fold), Fe (6. 40-fold and 7. 51-fold) and Mn (4. 50-fold and 5. 20-fold) contents in plant shoot and roots were found in treatments inoculated with WAN1. The other isolates namely WAN2, WRN1, WRN5, WACC1 and WACC2 also significantly (P< 0. 05) increased the Zn (5. 60-fold, 4. 61-fold, 3. 70-fold, 2. 81-fold and 1. 90-fold), Cu (6. 01-fold, 4. 41-fold, 3. 30-fold, 2. 51-fold and 1. 70-fold), Fe (5. 31-fold, 4. 20-fold, 3. 41-fold, 2. 70-fold and 1. 90-fold) and Mn (3. 90-fold, 3. 31-fold, 2. 81-fold, 2. 41-fold and 1. 90-fold) contents in the plant shoot compared to control. Similarly, the other isolates namely WAN2, WRN1, WRN5, WACC1 and WACC2 also significantly (p < 0. 05) increased the Zn (7. 80-fold, 6. 10-fold, 4. 71-fold, 3. 31-fold and 1. 91-fold), Cu (5. 20-fold, 4. 01-fold, 2. 91-fold, 1. 90-fold and 1. 31-fold), Fe (6. 01-fold, 5. 01-fold, 3. 80-fold, 2. 71-fold and 1. 60-fold) and Mn (4. 41-fold, 3.

Output: corroborate


Input: Claim: All encryption methods are completely unbreakable and guarantee absolute security for any type of data. [SEP] Text: On the other hand, measures to protect metadata are not widespread in Switzerland, leaving network traffic vulnerable to traffic-analysis attacks. However, these attacks primarily exist as research prototypes only. 3. 1 Implementation Possibilities: Make or BuyBuy: The widely available secure transport protocols and their implementations in popular libraries should be used for encryption. Make: For sensitive environments, benefits outweigh metadata protection schemes’ potential costs (e. g. , additional overhead). Unfortunately, there is no widespread solution here, and it would be necessary to develop a new solution for Switzerland’s use case (e. g. , based on research projects from Swiss universities [5, 6, 11, 12]). 4 ConclusionUnprotected traffic allows an eavesdropper to learn sensitive information about ongoing communication. To mitigate this, there exist many communication protocols that encrypt traffic on different layers. These protocols (MACsec, IPsec, and TLS) are widely used today and achieve good security for network traffic. However, even if traffic is encrypted, it leaks information through its metadata. Preventing this is more challenging, leads to more communication overhead, and is rarely done today. 802. 1AE: MAC Security (MACsec) 1. ieee802. org. [Accessed 04-Nov-2022]. RFC 4301: for the – rfc-editor. org. [Accessed 04-Nov-2022]. RFC 8446: (TLS) 1. 3 — rfc-editor. org. [Accessed 04-Nov-2022]. Kevin P. Dyer, Scott E. Coull, Thomas Ristenpart, and Thomas Shrimpton. Peek-a-boo, i still see you: Why efficient traffic analysis countermeasures fail. In 2012 IEEE Symposium on Security and Privacy, pages 332–346, 2012. 1710. 10237, 2017. 2022. 2021. 9000. [Accessed 04-Nov-2022]. and Carmela Troncoso. : Traffic-Anonymity at the Network Layer. 2018 on Security and Privacy (EuroS&P), 2018. 2015. 4. 0 ( which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the license and indicate if changes were made. The images or other third party material in this 10.

Output: contrast


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: (B) Time-series plot of velocity and angular speed of p4U control (black, trial count = 109, fly count = 23) and LC16 activated (red, trial count = 65, fly count = 14) flies. Solid and dashed lines indicate median and inner-quartile range, respectively. Grey rectangles in this and other panels signify optogenetic stimulation with red light. (C,D) Notched box plots showing the distribution of mean velocity (C) and angular speed (D), before, during, and after optogenetic stimulation, of p4U control (black) and LC16 activated (red) flies. The increased translational velocity of LC16 flies compared to controls prior to CsChrimson stimulation appears to be due to reduced walking of the control flies after repeated red light stimulation: The data in (B–D) are averages from multiple trials (trials 1 to trial 5) with the maximum light stimulus from experiments with increasing intensities (i. e. the same flies were stimulated repeatedly within an experiment with a series of light intensities; see Materials and methods). To examine whether the apparent higher basal locomotion of LC16 flies during pre-stimulation light off was a response to prior stimulations, we compared trial 1 data to trial 5 data from the experiments in Figure 9, because in these experiments only the maximum light stimulus was used (data for different LC16 split- GAL4 lines and for the different control flies shown in Figure 9 were pooled). Data for trial 1 showed no pre-stimulus difference between LC16 and control flies (p=0. 88, by Wilcox rank sum test), while locomotion was reduced for the controls in trial 5 (p<0. 01). (E,F) Jitter plots showing the distribution of mean velocity and angular speed during optogenetic stimulation of control and LC neuron driver lines in the arena (E) and single-fly (F) assays. Each dot represents an individual trial. A conservative threshold is set at the 97. 7th percentile of the p4U control distribution, indicated by dashed red lines. Grey and black dots represent trials within and outside of the normal range, respectively. For walking, trials above the top red line are counted as displaying a forward walking phenotype and trials below the bottom red line are counted as displaying a backward walking phenotype.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: 71-fold, 3. 01-fold, 2. 60-fold and 2. 01-fold) contents in the plant root compared to control. Figure 5. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on micronutrient contents (Zn, Cu, Fe and Mn) in shoot and root of wheat. Different letters (a-g) on bars indicate significant differences of mean values for micronutrient contents. Bars represent standard errors. 3. 6. Glutathione S-transferase, peroxidase and catalase activity The inoculation with rhizobacteria containing either ACC-deaminase and/or nitrogen fixing activity significantly (P < 0. 05) increased the and Cata activity (Figure 6). All rhizobacterial isolate showed increase in the and Cata activity. Nevertheless the isolates WAN1 was the most effective and showed highest increase in the and Cata activity i. e. 7. 51-fold, 5. 31-fold and 5. 01-fold. The WAN2, WRN1 and WRN5 were the next best effective rhizobacterial isolates and caused an increase of 5. 51-fold, 4. 21-fold and 4. 01-fold, 4. 10-fold, 3. 20-fold and 3. 41-fold, 3. 01-fold, 2. 60-fold and 2. 81-fold in the and Cata activity respectively. Whereas, WACC1 and WACC2 showed minimum increase in the and Cata activity comparing with other rhizobacterial isolates i. e. 2. 20-fold, 2. 01-fold and 2. 21-fold, and 1. 41-fold, 1. 50-fold and 1. 48-fold correspondingly. Figure 6. Comparative effectiveness of ACC-deaminase and/or nitrogen fixing rhizobacteria on enzymes and Cata activity. Different letters (a-g) on bars indicate significant differences of mean values for glutathione S-transferase activity. Bars represent standard errors. 3. 7. Characteristics of isolates Some pertinent characteristics of rhizobial isolates used in the experiment are given in Table 1. It was observed that, among all isolates, WAN1 had maximum root colonization activity i. e. 11.

Output: corroborate


Input: Claim: All encryption methods are completely unbreakable and guarantee absolute security for any type of data. [SEP] Text: The bacterial V3-4 region was then amplified with the primer set 338F (5′--3′) and 806R (5′--3′) (Zeng & An, 2021). PCR amplification and purification were performed following previously described methods (Yang, Liu & Zhang, 2019). Briefly, each sample consisted of a 30 µL mixture in triplicate, comprised of 10 µpremix (TaKaInc. , Kusatsu, Japan), 0. 5 µL of each primer (10 µM), 1 µL DNA template (20 ng), and 18 µL PCR-grade water, which was amplified according to the stetted procedure (95 °C for 5 min, followed by 25 cycles at 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, and a final extension at 72 °C for 10 min). The replicate PCR reactions for each triplicate were pooled and purified using a (). A single composite sample for sequencing was prepared by combining approximately equimolar amounts of PCR products from each sample. The prepared PCR products were then paired-end sequenced (2 × 250 bp) on an Illumina MiSeq platform (CO. , Ltd. , Shanghai, China) following the manufacturer’s instructions. Soil non-targeted metabolomic detection and analysis Twelve rhizosphere soil samples were sent to Majorbio (Shanghai, China) on dry ice for metabolite extraction, detection, and analysis. In detail, 1,000-mg soil aliquot of each sample was homogenized with 1,000 µL methanol/water (4:1, v/v) solution, including 0. 02 mg/mL internal standard (L-2-chlorophenylamine acid) for 6 min at −10 °C and 50 kHz using a Geno-grinder 2,000 () and spun down for 30 min at 5 °C and 40 kHz. After resting for 30 min at −20 °C, each material was centrifuged at 13,000 g (relative centrifugal force) for 15 min at 4 °C, then the supernatant was transferred and concentrated by a Concentrator (DC-24, Technologies, Shanghai, China).

Output: pass


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: For turning, trials above the red line are counted as displaying a turning phenotype. Figure 8—figure supplement 2 Download asset Open asset Variability of LC16 backward walking behavior across trials and across flies. In the circular arena experiments, each fly was repeatedly tested in a series of trials (see Materials and methods). For most analyses, we pooled data across all flies and trials (as shown in the left panel for walking behavior after optogenetic stimulation of different LC neuron types; these are the same data plotted as a separate data point for each trial in Figure 8—figure supplement 1). To further explore sources of variability in these experiments, using LC16 activation induced backward walking as an example, we reanalyzed the data by pooling results for either all flies of the same trial (per trial) or all trials of the same fly (per fly). This revealed different degrees of variability both across flies within the same trial and across trials for the same fly. Nonetheless, this variability does not change our conclusion that LC16 activation results in a strong backward walking response. Figure 9 with 2 supplements Download asset Open asset Experiments with additional LC6 and LC16 driver lines confirm the activation phenotypes of these cell types. (A,B) Behavioral penetrance for different controls and multiple split-GAL4 driver lines for (A) jumping (flies that jumped within 200 ms of stimulation onset) with LC6 controls based on the OL0077B driver line and (B) backward walking and turning with LC16 controls based on the OL0046B driver line. Each dot represents an experiment done with a different genotype: orange, LC neuron activation in blind norpA flies that also carry an LC6 (A) or LC16 (B) split-GAL4 line; green, p4U control in blind norpA flies; blue, flies reared on food without supplemental retinal; red, split-GAL4 DBD or AD halves; grey, genetically distinct split-GAL4 driver lines with targeted expression in LC6 (A) or LC16 (B). Horizontal and vertical lines indicate mean and standard deviation, respectively, for the control group and split-GAL4 group. The genotypes of the driver lines, behavioral penetrance and total trial and fly counts are listed in Supplementary file 1B. Expression patterns of the split-GAL4 driver lines used are shown in Figure 9—figure supplement 1.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: 7×107 and produced more auxin (30. 1 mg l-1) in the presence of L-TRP. Conversely, WACC1 had maximum ACC-deaminase activity i. e. 18. 58 μmol α-ketobutyrate g-biomass 1/2 h-1. 4. with rhizobacterial isolate containing ACC-deaminase and/or nitrogen fixing activity significantly (P < 0. 05) increased the root and shoot length, and fresh and dry weight of the seedlings compared to uninoculated control. Comparing the effect of rhizobacterial isolates, the WAN1 showed maximum root and shoot length, and fresh and dry weight of the seedlings than other isolates and uninoculated control (Figures 1-2). The effect of other rhizobacterial isolates was in the order WAN2 > WRN1 > WRN5 > WACC1 > WACC2. It has been reported that certain microorganisms contain an enzyme ACC-deaminase that hydrolyzed the ACC into NH3 and α-ketobutyrate, and reduced the inhibitory effects of ethylene for vigorous growth of plants i. e. root-shoot length and fresh and dry weights (Mayak et al. , 2004). Shaharoona et al. , (2003) investigated the effect of ACC-deaminase possessing rhizobacteria on the growth of maize seedlings, and found significant increase in the root and shoot lengths and fresh and dry weights of maize seedlings over uninoculated control. The reason of the WAN1 being the best rhizobacterial isolate was, having both nitrogen fixing and ACC-deaminase activities and maximum root colonization ability. It was found that rhizobacterial isolate having both nitrogen fixing and ACC-deaminase activities showed more promising effects on agricultural plants, and increase the plant growth, root and shoot length, and fresh and dry weights due to its maximum root colonization activity, and ability to minimize the endogenous levels of ethylene synthesis (Glick, 2012; Shaharoona et al. , 2003). The data of chlorophyll a, b and carotenoid contents showed that among all experimental isolates again WAN1 showed significant (P < 0.

Output: corroborate


Input: Claim: All encryption methods are completely unbreakable and guarantee absolute security for any type of data. [SEP] Text: The dry residue was derivatized by adding 50 µL acetonitrile/water (1:1), homogenized for 30 s at 5 °C and 40 kHz, and centrifuged at 13,000 g (relative centrifugal force) for 10 min at 4 °C. Finally, the supernatant was subjected to LC-tandem MS (MS/MS) analysis; 20 µL supernatant of each sample was mixed for the quality control sample. -Triple TOF system () equipped with an 3 (100 mm × 2. 1 mm i. d. , 1. 8 µm; ) was applied to chromatographic separating the metabolites with two mobile phases, phase A (95% water and 5% acetonitrile (with 0. 1% formic acid)) and phase B (5% water (with 0. 1% formic acid), 47. 5% acetonitrile, and 47. 5% isopropanol). The whole system was integrated to a quadrupole time-of-flight mass spectrometer (5600th; ) equipped with an electrospray ionization source and operated in positive and negative modes. The online analysis platform (Majorbio, Shanghai, China) was applied to the raw data following the manufacturer’s instructions. Briefly, the ProgenesisQI (Waters Corp. , ) was used for baseline filtering, peak recognition, integration, retention time correction, and peak alignment, which produced the retention time, mass charge ratio, peak intensity, and data matrix. Then, the characteristic peak was detected and the information from the MS and MS/MS analyses was mapped with a metabolic specific database; the mass error was set to <10 PPM. Finally, the metabolites were identified according to the matching scores of the secondary MS. The data were normalized with Pareto scaling and log-transformed before further analysis. QMEC Twelve qualified soil DNA samples were sent to Biotechnology Co. , Ltd. (Guangzhou, China) for QMEC analysis, wherein a high-throughput quantitative PCR (qPCR)-based chip was applied to assess the microbial functional potential, including 71 functional genes related to C-, N-, P-, and S-cycling (Zheng et al. , 2018). QMEC manipulation was conducted following previously described methods (Chen et al. , 2020a).

Output: pass


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Figure 9—figure supplement 1 Download asset Open asset Expression patterns of multiple split-GAL4 driver lines for LC6 and LC16. Brain (A,B) and VNC (C,D) expression patterns of split-GAL4 drivers for LC6 (A,C) and LC16 (B,D). Split-GAL4 driven expression of 20xUAS-CsChrimson-mVenus (insertion in attP18; visualized using anti-GFP antibody labeling; green) and a neuropil marker (anti-Brp, magenta) are shown. Imaging parameters and brightness or contrast adjustments were identical within each brain/VNC pair but not across all images. OL0077B and OL0046B images are the same as those shown in Figure 2 and Figure 2—figure supplement 1. Scale bar represents 50 µm. Original confocal stacks are available from www. janelia. org/split-GAL4. Figure 9—figure supplement 2 Download asset Open asset Quantification of LC16 activation induced locomotor behaviors and determination of behavioral penetrance. Jitter plots show the distribution of mean velocity and angular speed during optogenetic stimulation of multiple LC16 split-GAL4 driver lines and control lines. Color codes are the same as in Figure 9. Penetrance was determined as in Figure 8—figure supplement 1E. Figure 10 with 2 supplements Download asset Open asset Reaching behavior resulting from activation of LC10 subtypes. (A) Representative video images of a fly exhibiting reaching behavior in the single-fly assay. Time stamps indicate milliseconds (ms) after the start of reaching. (B) Comparison of lobula layer patterns and penetrance of reaching upon optogenetic activation for 15 LC10 split-GAL4 lines. The images show reconstructed views of CsChrimson expression in the lobula (generated using Vaa3D and manually aligned using the anti-Brp reference marker). Approximate layer positions are indicated on the left. Scale bar represents 10 µm. CsChrimson expression using two additional LC10 driver lines (SS00941 and SS00942) resulted in unexpected uncoordinated behaviors in response to optogenetic activation that precluded analyses of reaching behavior.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: 05) increase in chlorophyll a, b and carotenoid contents compared to other rhizobacterial isolates and uninoculated control (Figure 3). The overall effect of other isolates was in the order of WAN2 > WRN1 > WRN5 > WACC1 > WACC2. Yet again isolate having both ACC-deaminase and nitrogen fixing activities i. e. WAN1 proved to be the best than isolates either having nitrogen fixing activity or ACC-deaminase activity alone. Stefan et al. , (2013) found that the PGPR strains (S4 and S7), alone or in combination, significantly increased the chlorophyll and carotenoid contents of the runner bean. Mia et al. , (2010) revealed that inoculation with rhizobacterial strains (Sp7 and UPMB10) significantly increased the growth attributes e. g. leaf area, chlorophyll and carotenoid contents. The significant increase (P < 0. 05) in the plant ionic uptake e. g. macronutrient (N, P, K, Ca and Mg) and micronutrients (Zn, Cu, Fe and Mn) in the shoot and root was observed after inoculation with rhizobacterial isolates (Figures 4-5). Among the rhizobacterial isolates WAN1 was the most effective isolate and caused highest increase in the macro and micronutrient contents of the plant shoot and root. The efficacy of other rhizobacterial isolates was in the following order WAN2 > WRN1 > WRN5 > WACC1 > WACC2. Sahran and Nehra, (2011) concluded that inoculation with PGPR significantly increased the plant mineral (N, P, K, Ca, Mg) consumption. Hayat et al. , (2010) concluded that growth promoting rhizobacteria increase the mobility and availability of plant nutrients in the soil, and as a result increase the nutrient uptake of the plants. Nadeem et al. , (2006) concluded that inoculation with PGPR significantly increase the plant ionic consumption i. e. N, P and K in the shoot and root of the maize plant. The data of plant enzymes activity showed (Figure 6) that inoculation with rhizobacterial isolates considerably (P < 0.

Output: corroborate


Input: Claim: The majority of research in graphene focuses on its application in supercapacitors and energy storage due to its high electrical conductivity. [SEP] Text: The temperature, as well as the frequency, dependence of the dielectric constant was explained by the existence of functional groups between neighboring layers; since functional groups containing hydroxyl groups are expected to be present between layers, their behavior might be influenced by the frequency, leading to resonant behavior at high frequency. Furthermore, investigation of the structure and properties of GH would help determine pathways for synthesizing graphene since increasing the ratio of sp3/sp2 drives the GH system towards a more graphane-like state in which all carbon atoms have sp3 orbitals. Moreover, these investigations will also facilitate the development of applications, such as sensors and energy storage systems, which exploit the large surface area of these materials. As a demonstration of a possible application, electrodes for supercapacitors were fabricated using reduced GH. Figure 7 shows the XRD pattern of reduced GH, which contained 3 distinct peaks. The interlayer distances for the planes associated with these peaks were 12. 7 Å, 3. 43 Å and 2. 96 Å, respectively. After reduction, the reduced GH had a greater interlayer distance than the unreduced GH (cf. Figure 3). Figure 7XRD pattern of reduced GH. The reduced GH had a greater interlayer distance than the unreduced GH. Full size imageFigure 8 shows images of the cross-sections of thermally reduced GH samples. Since the GH had numerous chemical groups on the surface, gases were generated during the reduction process by the destruction chemical groups such as epoxy and hydroxyl groups. In addition, the vacuum environment triggered the expansion of the stacked layers so that the evolved gases could escape from the GH. Long-term vacuum reduction at high temperature produced many pores inside the sample and expanded the interlayer spacing, i. e. , the effective surface area was greatly increased. Therefore, neighboring layers were separated from each other and many pores were clearly visible, as shown in Fig. 8(a). In Figs. 8(b) and (c), the empty space between layers is distinct and the structure is like that of a connected cocoon. The size of the cocoon-shaped pores was larger than 1 μm2. Inside the individual layers, there were smaller pores around 250 nm × 50 nm and 40 nm × 10 nm, as shown in Figs. 8(e) and (f), respectively.

Output: pass


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: These lines, which are related as they only differ from each other in the insertion site of the AD hemidriver (see Supplementary file 1B), were therefore excluded from further analyses. (C) Single cell labeling reveals subtype expression patterns of LC10 driver lines. Overall lobula expression of an LC10 split-GAL4 line (SS02664) (top panel; displayed as in B) and examples of MCFO labeled single cells from this line (bottom panels) are shown. LC10 subtypes (indicated below the images) of single cells were assigned based on layer pattern, arbor size and shape as follows: LC10c cells mainly arborize in layer Lo5B with some processes in the adjacent layers. LC10a cells also have arbors in Lo5B, but differ from LC10c by having additional processes in the more distal layers Lo2, Lo3 and Lo4. LC10b and LC10d differ from LC10a and LC10c by having major arbors in Lo6 and only few processes in Lo5B. Their distal arbors reach Lo4. LC10b cells are wider than LC10d cells and show many small varicosities (presumably presynaptic sites; compare Figure 5—figure supplement 2E) in Lo6. LC10 neurons also differ in their axonal paths in the AOTu: LC10a and LC10d axons run both dorsally and ventrally, and LC10b and LC10c only ventrally (Figure 3G; also see Figure 10—figure supplement 1). Scale bars represent 10 µm. (D) Reaching penetrance observed upon activation of the LC10 split-GAL4 drivers and various control lines in the single-fly assay. Split-GAL4 drivers are grouped by expression patterns in the LC10 subtypes: either subtype a and/or d, or neither a nor d. Driver lines were included in the ‘a and/or d’ category if two or more cells of these types were identified in MCFO experiments (see Figure 10—figure supplement 2). Colors representing various controls and split-GAL4 driver lines are the same as those in Figure 9. Figure 10—figure supplement 1 Download asset Open asset Expression patterns of LC10 split-GAL4 driver lines.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: 05) enhanced the antioxidant plant enzymes activity i. e. glutathione S-transferase, peroxidase and catalase activities. All rhizobacterial isolates had positive effect on plant enzymes activity, however the WAN1 was found to be the best. The effect of other rhizobacterial isolates was in the order WAN2 > WRN1 > WRN5 > WACC1 > WACC2. Stefan et al. , (2013) observed that the PGPR strains (S4 and S7), alone or in combination, considerably increased the activity of superoxide dismutase and peroxidase enzymes. Mia et al. , (2010) found that rhizobacterial inoculation (Sp7 and UPMB10) significantly increased the nitrogen uptake in plants which ultimately increased the formation of protein and enzyme for better physiological activities. Hayat et al. , (2010) stated that PGPR increase the plant vigor and decrease the biotic and abiotic stresses by producing different anti-oxidant enzymes. Results of isolates characteristics clearly revealed that among all isolates, WAN1 had maximum root colonization activity and produced more auxin in the presence of L-TRP, whereas, WACC1 had maximum ACC-deaminase activity among all rhizobacterial isolates (Table 1). The difference in plant growth promotion by different rhizobacterial isolates may be due to the differences in their efficiency of colonizing the germinating roots, production of plant hormones (e. g. auxin) and ability to hydrolyze the ACC in plant roots (Hassan et al. , 2014). It has been reported that traits e. g. production of IAA, auxin, phosphate solubilization and chitin production by PGPR are helpful for better nutrient mobilization and availability for plants, as a result inoculation with rhizobacteria containing ACC-deaminase could result in the development of much better germination of seeds and longer roots, which subsequently affects over all growth of the host plant (Zafarul-Hye et al. , 2007).

Output: corroborate


Input: Claim: The majority of research in graphene focuses on its application in supercapacitors and energy storage due to its high electrical conductivity. [SEP] Text: Hishiyama, Electric double layer capacitance of multi-walled carbon nanotubes and B-doping effect, Appl. Phys. A 82 (2006) 585–591. [16] X. Zhai, Y. Song, J. Liu, P. Li, M. Zhong, C. Ma, H. Wang, Q. Guo, L. Zhi, In-situ preparation of boron-doped carbons with ordered mesopores and enhanced electrochemical properties in supercapacitors, J. Electrochem. Soc. 159 (2012) E177–E182. [17] T. Tomko, R. Rajagopalan, P. Aksoy, H. C. Foley, Synthesis of boron/nitrogen substituted carbons for aqueous asymmetric capacitors, Electrochim. Acta 56 (2011) 5369–5375. [18] P. N. Vishwakarma, S. V. Subramanyam, Hopping conduction in boron doped amorphous carbon films, J. Appl. Phys. 100 (2006) 113702. [19] H. Konno, T. Ito, M. Ushiro, K. Fushimi, K. Azumi, High capacitance B/C/N composites for capacitor electrodes synthesized by a simple method, J. 195 (2010) 1739–1746. [20] H. Konno, T. Nakahashi, M. Inagaki, T. Sogabe, Nitrogen incorporation into boron-doped graphite and formation of B–N bonding, Carbon 37 (1999) 471–475. [21] D. Zhong, H. Sano, Y. Uchiyama, K. Kobayashi, Effect of low-level boron doping on oxidation behavior of polyimide-derived carbon films, Carbon 38 (2000) 1199–1206. [22] L. R. Radovic, M. Karra, K. Skokova, P. A. Thrower, The role of substitutional boron in carbon oxidation, Carbon 36 (1998) 1841–1854. [23] X. W. and, L. R.

Output: pass


Input: Claim: All cognitive processes are the same across cultures. [SEP] Text: Brain (A) and VNC (B) expression patterns using 20xUAS-CsChrimson-mVenus in attP18 visualized with an anti-GFP antibody (green) and a neuropil marker (anti-Brp, magenta) are shown. While some driver lines also had expression in a few non-LC10 cells, none of these additional neurons was consistently present in lines with the reaching behavior. Imaging parameters and brightness or contrast adjustments were identical for each brain/VNC pair but not across all images. OL0020B and OL0023B images are the same as those shown in Figure 2 and Figure 2—figure supplement 1. Scale bar represents 50 µm. Original confocal stacks are available from www. janelia. org/split-GAL4. Figure 10—figure supplement 2 Download asset Open asset Stochastic single cell labeling reveals LC10 subtype expression patterns of the LC10 split-GAL4 driver lines. (A) Examples (10 per driver line) of MCFO-labeled LC10 cells from five different lines. Subtype classification is indicated for each cell (white lowercase letters). Anti-Brp pattern (used to facilitate comparison of layer positions across specimens) is shown in grey; views were generated using Vaa3D. Layer patterns of these projection images were manually aligned using the anti-Brp marker. To do so, the scale of some images was slightly adjusted to compensate for the non-uniform depth of the lobula. Approximate layer positions are indicated in the bottom right panel. Scale bar represents 10 µm. (B) Summary of LC10 MCFO results. For each driver line, the number of MCFO-labeled LC10 cells of each subtype was determined for the indicated number of fly brain samples. Some LC10 cells that were located at an edge of the lobula neuropil or overlapped with other labeled cells and thus could not be identified were excluded from the counts. Total cells refer to the number of all LC10 cells that were assigned a subtype. Subtypes that were only seen as single labeled cell are in parentheses in the summary. Figure 11 with 1 supplement Download asset Open asset LC6 and LC16 activation behaviors resemble avoidance responses evoked by visual looming. (A) LC6 and LC16 project to adjacent, non-overlapping target glomeruli.

Output: pass


Input: Claim: Rhizobacteria enhance plant growth by fixing atmospheric nitrogen. [SEP] Text: Phosphate solubilization, dinitrogen fixation, ACC-deaminase and antifungal activity, IAA and siderophore biosynthesis characteristics of PGPR are responsible for the plant growth promotion and high yield (Ahmed and Kibret, 2014). 5. of agricultural crops with rhizobacterial strains is a very viable and eco-friendly approach to increase crop production on sustainable basis. The plant-beneficial rhizobacteria may decrease the global dependence on hazardous agricultural chemicals which destabilize the agro-ecosystems. PGPR protect the plants from the deleterious effects of biotic and abiotic stresses by producing phytohormones and antioxidant enzymes and increase the plant growth by increasing the nutrients availability and uptake. The ACC enrichment technique is an effective and efficient approach to select most promising PGPR. Rhizobacteria containing both ACC-deaminase and nitrogen fixing activity are more effective than rhizobacteria containing either ACC-deaminase or nitrogen fixing activity alone for growth promotion of agricultural crops. 2014. Mechanisms and applications of plant growth promoting rhizobacteria: Current perspective. J. Uni. Sci. 26, 1-20. Arnon, D. I. 1949. Copper induced enzyme in isolated chloroplasts; polyphenol oxidase in beta vulgaris. Plant Physiol. 24, 1-15. Bhattacharyya, P. N. , Jha, D. K. 2012. Plant growth-promoting rhizobacteria (PGPR): emergence in agriculture. World J. Microbiol. Biotechnol. 28, 1327-1350. 1982. Analytical methods in n-15 research. Fert. Res. 3, 37-62. Cakmak, I. , Horst, W. J. 1991. Effect of aluminium on lipid peroxidation, superoxidase dismutase, catalase and peroxidase activities in root tips of soybean (Glycine max). Physiologia Plantarum. 83, 463-468. Chapman, H. D. , Pratt, P. F. 1961. Methods of analysis for soils, plants and water. Univ. . Davies, K. G, Whitbread, R. A. 1989.

Output: corroborate