Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: VSTLF only depends on the historical load as there is no effect of external environment. The selection of a model is done, keeping in mind the forecasting horizon, time granularity, external features. The time series models are also classified into stationary and non-stationary models. AR, dynamic regression, transfer Function and ARIMA come under stationary time series model while (GARCH) model comes under non-stationary time series model. To handle multivariate or non-linear data various machine learning or deep learning models outperform the statistical models such as and LSTM. The hybrid model also gives better results than comprises any statistical and intelligent techniques. After selecting the suitable model, the objective is to train the model with fine-tuning hyperparameters. Afterwards, the walk forward validation technique is chosen to validate the model instead of cross-validation. As time series features depend on the previous time value, cross-validation is not applicable. Finally, the error is analyzed on the test data. to Load ForecastingThere are three main approaches to load forecasting: statistical approach, AI-based intelligent approach, and hybrid approach. In the statistical approach many traditional forecasting techniques such as Naïve forecast, ARIMA and methods are used. In these traditional methods, some exogenous features are also considered which aims to identify the relationship between load and exogenous features measured via regression analysis. approachThis analysis predicts current load based on its previous value and residuals. The time series features such as date and time, window size and lag values are used for pattern identification. These features are also known as endogenous features [19]. (ES) etc. are used to forecast future values. Regression AnalysisIn time series analysis, if some exogenous features such as temperature, humidity or population in addition to historical load affect power consumption, the relationship between power consumption and these exogenous features needs to be identified. Linear regression, Ridge regression, Regression (PCR) are used for predicting future consumption. AI based Intelligent ApproachAI plays important role in Robotics, Processing, Image processing and Speech recognition. Apart from this, AI based approaches are also used in predicting the load. Recently, ML and DL models replace statistical approaches for time series forecasting [20]. Machine LearningIt is the sub-field of AI, in load forecasting ML model is used to learn the effect of various exogenous and endogenous features on the consumer’s power demand.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: Specifically, the presence of at least one of the following chronic conditions was used to create a binary morbidity variable: arthritis/rheumatism, asthma, high blood pressure, diabetes, heart disease, cancer, stroke, chronic bronchitis, emphysema, COPD, bowel disorders, anxiety disorder, mood disorder, migraine headaches, dementia, stomach or intestinal ulcers, urinary incontinence and back problems. We also investigated a number of risk and protective factors including alcohol consumption, cigarette smoking, body mass index (BMI) and leisure-time and regular physical activity. Risk factors included as part of the optional module in the CCHS were excluded. We categorized alcohol consumption as follows: non-drinker; occasional (< 1 drink/month in the past 12 months); regular (≥ 1 drink/month in the past 12 months); and heavy (≥ 5 drinks on at least one occasion in the past 12 months). Endnote 7 Possible responses on the frequency of cigarette smoking were daily, occasionally or non-smoker. Dichotomous variables (yes/no) were created for each category; for example, odds ratio for daily smoking are reported as compared to nonsmokers. Variables were dichotomized to improve interpretation and also minimize the effect of small cell sizes as we crosstabulated with the explanatory variables. We determined the chronic conditions and risk factors to select based on availability in the dataset as well as theoretical considerations; many of the chronic conditions have already been implicated in the epidemiological transition that is emerging in the north. Endnote 4, Endnote 8 All of the risk factors were related to multiple chronic conditions, are inter-related and/or are markers of broader community and structural factors. For example, alcohol use is associated with heart disease, blood pressure, anxiety disorders, mood disorders and bowel disorders. Endnote 9, Endnote 10, Endnote 11 Smoking is associated with asthma, chronic bronchitis, diabetes, heart disease and high blood pressure. Endnote 12 Overweight and obesity are associated with arthritis, asthma, diabetes, heart disease, high blood pressure, bowel disorders, anxiety disorders and mood disorders. Endnote 13, Endnote 14 BMI was calculated from self-reported height and weight data. Endnote 15 Overweight was defined as BMI of 25. 00 to 29. 99 kg/m2 and obesity as BMI of 30. 0 kg/m2 or higher.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: Both in terms of the direction and temporal profile of these changes, the enhanced levels of BDNF elicited by chronic stress parallels the profile of dendritic growth and spinogenesis in the BLA. These findings are also significant in view of an earlier study demonstrating that transgenic overexpression of BDNF enhances spine-density in the BLA of mice [16]. BLA spinogenesis is also elicited by chronic stress. Importantly, transgenic overexpression of BDNF occludes chronic stress induced spinogenesis in the BLA. Together these findings suggest a role for BDNF in stress-induced structural plasticity in the amygdala. The results reported here also add to the earlier studies on region-specific differences showing stress-induced increase in BDNF expression in the hypothalamus [19] and the nucleus acumbens (NAc) [20] compared to decreased levels in the hippocampus. Further, the up-regulation of BDNF seen in the NAc after social-defeat stress persists for as long as 4 weeks, similar to the prolonged increase we see in the BLA [20]. The upregulation of BDNF reported earlier in the hypothalamic paraventricular nucleus occurs in response to both single 2 h as well as chronic 7 days of immobilization stress [19]. On the other hand, chronic stress-induced reduction of BDNF levels, and its reversal during stress-free recovery, mirrors earlier findings on reversible dendritic atrophy elicited by chronic stress in hippocampal area CA3. However, short episodes of acute stress lasting 15 min or 60 min induces a transient increase in BDNF mRNA levels in the hippocampus which is in sharp contrast to the decrease in BDNF mRNA expression seen in response to longer stress [21]. Together, these findings also highlight the importance of investigating the potential mechanisms that cause the same stress to have opposite effects on BDNF expression as well as the differential impact of variable durations of stress on BDNF levels. Exposure to stressful events leads to glucocorticoid release by the activation of the hypothalamic–pituitary–adrenal (HPA) axis. The temporal profile of changes in corticosterone profile shows an initial rise 1 day after AIS which decreases 10 days later and returns to levels that are not significantly different from control levels.

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: Various machine learning methods for load forecasting are (RF), Multi-(MLP), CART regression Tress (CART) and Regression (SVR). Deep LearningIt is a black box technique that automatically identifies the features. Network (RNN) and its variants are the DL approaches based on temporal data and also used for load forecasting. Hybrid ApproachHybrid approach combines both statistical and AI-based approaches hence this approach can leverage the pros and cons of both these approaches. A hybrid approaches explained in [21] for estimation of dew point temprature. Load forecasting techniques classification given in Figure 3. Fig. 3Techniques ClassificationFull size imageIn [22], the author explores the importance of accurately forecasting power demand in the and identifies sector-wise characteristics that impact load prediction. In [23], the author proposed with for load forecasting where Decomposition (EMD) decomposes the electricity signal. Transformation is used to extract features and RF model predicts the day ahead consumption. Similarly an ensemble learning based forecasting technique in the commercial sector is presented in [24], where three ensemble models RF, AdaBoost and Regression (GBR) Trees are used for STLF among which AdaBoost outperforms. These models use historical load data, time series features and environmental features as input. In time series forecasting to avoid the data leakage problem a hybrid model of Transformation (EWT) and (RVFL) is proposed by K. F, Yuen et al. [25]. The model is sensitive regarding window size and scales which are the hyperparameters of the model. To remove overfitting, fuzzy time series [10] comes under consideration where fuzzy logic applied to time series load data. The author has proposed hybrid model that combine deep learning and fuzzy time series for high accuracy and automatic feature extraction from the multi-channel image. et al. [26] provide an economical and reliable model for different types of energy consumption methods used in agriculture. model predicts future consumption with the help of time-series based past data. ARIMA is a univariate time series model. It forecast accurately with a minimum number of parameters. Finally, the accuracy of the forecasted value is evaluated by evaluation metrics like Percentage (MAP) Error and Error (MSE). Author has performed experiment on test variables like kerosene, Gasoil, Gasoline and electricity.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: 3 429 46. 0 2379 79. 4 Total household income, $ 0–29 999 277 33. 3 32 14. 8 304 32. 6 345 11. 5 30 000–59 999 166 20. 0 40 18. 5 193 20. 7 520 17. 4 60 000–99 999 127 15. 3 46 21. 3 147 15. 8 801 26. 7 >100 000 107 12. 9 74 34. 3 122 13. 1 1095 36. 5 Total sample (N) 831 16. 6 216 4. 3 932 18. 6 2997 59. 8 Crude prevalence and AORs for the chronic conditions and risk factors are shown in Tables 2 and 3, respectively; the non-Aboriginal population is the reference group for the regression models. The AOR for at least one chronic condition was significantly lower for Inuit than for non-Aboriginal population, but similar among FN, Métis and non-Aboriginal populations. The most common chronic conditions for all populations were arthritis and high blood pressure. The AOR for diabetes was significantly lower among the Inuit than among the non-Aboriginal population. Other chronic diseases such as asthma, bowel disorders (e. g. Crohn's disease, ulcerative colitis or irritable bowel syndrome) and mood disorders (e. g. depression) were also significantly less likely among the Inuit than among the non-Aboriginal population. Odds of reporting an anxiety disorder are significantly higher among the Métis than the non-Aboriginal population. The odds of reporting a chronic illness were not significantly different between northern FN and non-Aboriginal respondents. TABLE 2 Crude prevalence (%) of chronic diseases and risk/protective factors by ethnic group, 2005 and 2008 Prevalence, % (95% CI) Métis Inuit Non-Aboriginal Abbreviations: BMI, body mass index; CI, confidence interval. a≥ 5 drinks on at least one occasion in the past 12 months. b≥ 1 drink/month in the past 12 months. c BMI 25. 00–29. 99 kg/m 2. d BMI ≥ 30. 0 kg/m 2. e≥ 3. 0 kcal/kg/day. f≥ 12 occasions/month.

Output: pass


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: Thus, the variations in corticosterone levels reported here appear to depend on both the severity of the stress paradigm and time points of measurement after the termination of stress. Our observations are broadly consistent with earlier reports showing that the output of the HPA axis is influenced by the periodicity, intensity and degree of habituation of the stressor [22]–[26]. The reduction in hippocampal BDNF due to acute and chronic stress, in turn, is known to be mediated at least in part by stress-induced increase in glucocorticoids [10], [27]. Yet the same increase in corticosterone appears to be associated with enhanced BDNF levels in the BLA. Stress also leads to significant increase in extracellular levels of glutamate in the hippocampus and amygdala [28]–[30]. This suggests that although some of the immediate consequences of stress – elevated glucocorticoids and glutamate – are similar in both brain areas, they subsequently lead to contrasting patterns of BDNF expression and structural plasticity. This implies that signaling mechanisms more downstream of the initial changes in glucocorticoids and glutamate, but upstream of BDNF, may hold the key to the differential impact of stress in these brain areas. Importantly BDNF infusion into the hippocampus of stressed rodents helped to protect against the deleterious effects of stress despite high levels of circulating corticosterone [31]. This suggests that BDNF could be a final point of convergence for the stress induced effects in the hippocampus. BDNF-mediated signaling is involved in stress response but the direction and nature of signaling is region-specific, stress specific and is influenced by epigenetic modifications along with post translational modifications [32]. Thus, brain region specific variations in BDNF expression is a key question that requires further investigation. Moreover, a complex balance is maintained in BDNF-driven neuronal plasticity, based on the competition between BDNF-TrkB system and pro-BDNF-p75 system [33]. Interestingly, activation of TrkB and p75NTR promote and suppress dendritic spine growth respectively [33]. Since the findings presented here do not distinguish between TrkB and p75NTR, which are known to have opposing biological functions, future studies will be necessary to elucidate the impact of stress on these different forms in different brain regions. What may be the functional consequences of the divergent effects of stress on BDNF in the hippocampus and amygdala?

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: [37] proposed a model to predict the future residential energy consumption with weather forecasting information. In this work machine learning algorithm like a random forest, the algorithm is used for forecasting energy consumption. Features that are included for prediction are daily minimum/maximum temperature, daily mean/half-hourly mean electrical consumption and one neighborhood feature. The industry sector requires an uninterruptable power supply to complete the process cycle of any goods production. In industry there is an effect of weather, holiday, weekend, etc. The CNN model gives the multi-output for multi-step ahead prediction. A comparative analysis for endogenous model and exogenous model is described with results in [38]. In the commercial sector, load forecasting depends upon seasonality [39], building location and its type [40] except the working day, an hour of the day. A 45% enhancement in STLF using a DL approach is given in [40] with 5 building types related to the research lab, institution, office, store, school and each situated different location. The data was collected at a different resolution of time for hour and day ahead prediction. Agricultural sector load forecasting not only depends on weather data but also on the cropping pattern. In [41, 42], the author proposed a time series-based technique for forecasting medium-term load in the agriculture sector to improve productivity. Deep learning is a black box method which automatically extracts the features. A deep NN based architecture proposed in [43] for load forecasting. A comparative analysis between Long-Memory (LSTM) and its variant (Seq2Seq) defined on residential consumer. An encoder and decoder for an arbitrary number of previous loads are used in the Seq2Seq architecture. An LSTM with Self-(SAM) for STLF with 27-residential households was proposed in [44]. In [45] a self-attention based short-term load forecasting method was proposed that considers demand-side management, using non-parametric kernel density estimation and variational modal decomposition for feature selection, and an Informer based on improved self-attention for model prediction. The proposed method outperforms other methods in STLF, according to cross-sectional and longitudinal experiments on a regional-level load dataset set in Spain. The increasing capacity of creates a large data for analysis [46].

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: The risk factors that are increasing are linked to major chronic diseases such as cancerEndnote 26 and heart diseaseEndnote 27 and social problems like violence, accidents, injuries, addictions, and family and community dysfunction. Endnote 28 This finding, together with other reported adverse changes in the health transition,Endnote 29, Endnote 30 suggests that some chronic conditions could be on the rise. Communities, clinicians and policy makers must work together to address the increasing risk factors and develop interventions aimed at risk factor reduction. Among northern FN residents, prevalence of arthritis, asthma and heart disease is lower than among southern Aboriginal people, of diabetes is about the same, and of high blood pressure is greater. Endnote 4 The picture for chronic disease risk factors is also variable. Compared to southern Aboriginal residents, FN residents in northern Canada have similar prevalence of overweight, obesity and regular drinking but higher prevalence of binge drinking and daily smoking. Endnote 4 The chronic disease and risk factor picture for northern FN people is of concern because it has been seen before among FN people in southern Canada; chronic diseases and risk factors among Aboriginal people of southern Canada are sources of excess morbidity, decreased quality of life and premature mortality. Finally, prevalence of arthritis, asthma and heart disease among the Métis of northern Canada is lower than among Métis of southern Canada. Endnote 31, Endnote 32, Endnote 33 Compared to southern Aboriginal Canadians, the prevalence of overweight is similar, daily smoking is lower, but obesity, regular drinking and binge drinking are higher. Similar to the Inuit and northern FN, the risk factor profile of the Métis is of concern because of cardiometabolic morbidity, social consequences and premature mortality. It will be important for northern Aboriginal communities and organizations to work with government agencies and health care professionals to decrease the risk profile if they hope to avert the epidemic of cardiometabolic conditions witnessed among Aboriginal people in southern Canada. However, the environment in the north may be more challenging because community resources are fewer, food more expensive and the effects of climate change greater. Endnote 34, Endnote 35 Strengths and limitations This study is subject to limitations. CCHS data are based on self-report; this may result in underestimates of chronic disease and risk factors such as BMI, smoking and drinking. Further, commonly used cutpoints of BMI for obesity and overweight may not be appropriate for all Aboriginal populations.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: Growing evidence has linked growth of dendrites and spines in the BLA – caused either by stress or BDNF overexpression – to enhanced anxiety-like behavior [8], [15]. This is relevant in light of our results showing that the same time points when stress triggers higher anxiety is also when BDNF is elevated in the BLA. For instance, both anxiety and BDNF are increased 1 and 21 days after CIS. Interestingly, BDNF mRNA has been shown to be elevated transiently in the BLA 2 hours after cued fear conditioning and such a temporally restricted elevation of BDNF signaling is believed to be a key step in the formation of a cue-specific fear [34], [35]. In contrast, a more sustained up-regulation of BDNF in the BLA may underlie the pathological increase in fear and anxiety observed following stress [16]. Hippocampal memory and synaptic plasticity also depend on BDNF, and reduced levels of BDNF following chronic stress is consistent with a large body of evidence showing impairment of hippocampal function and loss of hippocampal volume caused by stress [36]–[39]. Further as hippocampal BDNF is critically involved in resilience to stress, any reduction can enhance vulnerability to stress [40]. Therefore, the opposite effects on BDNF in the amygdala and hippocampus may provide a molecular basis for the contrasting behavioral effects of stress on memories encoded by these two brain areas. Moreover, these two brain areas differ not only in their response to stress, but also in how they regulate the stress response. While the hippocampus exerts a negative feedback regulation of the stress response via the HPA axis, the amygdala has the opposite effect [41]. Therefore, the differential effects on BDNF in these two brain regions following stress could lead to an imbalance in HPA axis function through a gradual loss of hippocampal inhibitory control as well as a gain in excitatory control exerted by the amygdala. The regional differences in the pattern of BDNF expression triggered by stress also pose a significant challenge for pharmacological interventions aimed at countering the effects of stress on the amygdala and hippocampus. Therefore, elucidation of the mechanisms behind the differential effects of stress on BDNF in the hippocampus and amygdala is likely to provide useful insights into novel therapeutic interventions against stress-related psychiatric disorders that are characterized by impaired cognitive function and abnormally high fear and anxiety. Author ContributionsConceived and designed the experiments: HL SC. Performed the experiments: HL.

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: To fetch electricity consumption data from smart meters a cloud-based platform was developed named Data (CMD) for the residential sector. The load survey fetches in hourly granularity, the data set consists of six time series features and five meteorological features. (BNRNN) handle the arriving new data in load forecasting with an online deep learning method named adaptive RNN [47]. In [48], the author proposed a work emphasizing the higher accuracy as well as power prediction stability forecasting, in which LSTM univariate model is used. Features are selected based on the wrapper method. Optimal lag on which time series forecasting dependent is selected on the basis of (GA). In case of large data set, to learn hyperparameters the author also adopts GA with optimization technique [49]. A hybrid model [13] of EMD and Network also increases the accuracy for VSTLF that is an hour ahead. Similarly, a hybrid model for MTLF was developed in [50], which is a combination of the dynamic model and fuzzy time series data. The dynamic model gives an equation for historical load and temperature. Fuzzy time series incorporate socioeconomic factors. A hybrid intelligent model for MTLF is proposed in [11]. A pre-forecaster predicts one month’s daily peak load which is the combination of (EA) and NN. The proposed model explores on Eon Intelligent TEchnologies () data set. A LTLF of electricity consumption is proposed using regression and ANN in [12]. The historical load data and socio-economic parameters like population, energy import, & export and GDP are the inputs. The accuracy improved by combining Improved P2. Table 2 Classification of Techniques according to different approachesFull size tableTable 3 provides load forecasting techniques that have been used in different sectors, including residential, commercial, industrial, and agricultural. The techniques listed include time series based, machine learning based and deep learning based methods such as (SVR) and ARIMA etc. The table also includes 4. Consumers from all sectors are required to participate in DSM programs to help use electricity more efficiently. Fig. 4of of DSM ProgramFull size imageThe objectives of DSM programs for utilities include achieving financial performance, customer satisfaction, and sustainability. Operational objectives for DSM programs include reducing CO2 emissions, reducing fuel dependency, and providing demand-side alternatives.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: Table 5 Nations adults at risk for cardiovascular disease by plasma apolipoprotein levels, 2003 (N = 481)a Sex and Age, y n (%) ApoB p- valueb ApoA1 p- valueb n (%) ApoB:ApoA1 Ratio p- valueb Men (n = 229) 18-29 (n = 72) 5 (7). 003. 87 20 (29) <. 001 30-39 (n = 65) 10 (15) 24 (37) 37 (57) 40-49 (n = 49) 15 (31) 18 (37) 40 (82) = 50 (n = 43) 10 (23) 14 (33) 25 (58) Women (n = 252) 18-29 (n = 70) 5 (7). 006 52 (74). 006 30 (43). 01 30-39 (n = 78) 6 (8) 43 (55) 45 (58) 40-49 (n = 59) 10 (17) 37 (63) 38 (65) = 50 (n = 45) 10 (22) 20 (44) 29 (64) Abbreviation: apo, apolipoprotein. a Cutoffs for increased cardiovascular risk are shown in Table 1. For 2 participants, the blood sample was insufficient to assess apoA1 and apoB; priority was given to measuring the other plasma lipids (total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol, and triglyceride). b χ2 Test with linear association. The final logistic model for presence of obesity included age, sex, diastolic blood pressure, diabetes, homocysteine, insulin resistance, apoA1, and apoB. A person with an apoA1 value of 1. 14 g/L was 1. 2 times as likely to be obese as was a person with an apoA1 of 1. 20 g/L. Furthermore, the odds of obesity were 1. 35 times as high for a person with an apoB level of 1. 00 g/L as for a person with a level of 0. 80 g/L. to plasma lipid levels and apolipoprotein profiles, the risk for cardiovascular disease is high among Nations people.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: Analyzed the data: HL. Contributed reagents/materials/analysis tools: HL SC. Wrote the paper: HL SC. 1. (1994) Repeated stress causes reversible impairments of spatial memory performance. 639: 167–170. View 2. (1992) Stress-induced facilitation of classical conditioning. Science 257: 537–539. View 3. (2002) Patterns of in Hippocampal and Amygdaloid Neurons. of Neuroscience 22: 6810–6818. View 4. McEwen BS (1999) Stress and hippocampal plasticity. of Neuroscience 22: 105–122. View 5. (2009) Stress, memory and the amygdala. Neuroscience 10: 423–433. View 6. (1992) Stress induces atrophy of apical dendrites of hippocampal CA3 pyramidal neurons. 588: 341–345. View 7. (2004) Recovery after chronic stress fails to reverse amygdaloid neuronal hypertrophy and enhanced anxiety-like behavior. Neuroscience 128: 667–673. View 8. (2005) Stress duration modulates the spatiotemporal patterns of spine formation in the basolateral amygdala. Proceedings of the of Sciences 102: 9371–9376. View 9. (1995) Regulation of BDNF and trkB mRNA in rat brain by chronic electroconvulsive seizure and antidepressant drug treatments. of Neuroscience 15: 7539–7547. View 10. (1995) Stress and glucocorticoids affect the expression of brain-derived neurotrophic factor and neurotrophin-3 mRNAs in the hippocampus. of Neuroscience 15: 1768–1777. View 11. -F, Young LT (2001) Increased hippocampal bdnf immunoreactivity in subjects treated with antidepressant medication. 50: 260–265. View 12. (2006) A for Stress-Disorders. 59: 1116–1127. View 13. (2002) Neurobiology of Depression. Neuron 34: 13–25. View 14. (2002) Brain-in of Depression. of Neuroscience 22: 3251–3261. View 15. (1997) Antidepressant-of Brain-derived (BDNF). and Behavior 56: 131–137. View 16. (2006) Transgenic brain-derived neurotrophic factor expression causes both anxiogenic and antidepressant effects.

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: Understanding how consumers make decisions about energy use and how they respond to demand response programs is crucial for the development of effective demand side management strategies. Overall, addressing these challenges requires interdisciplinary research that draws on expertise from fields such as energy engineering, economics, and behavioral science. By addressing these challenges, we can develop more effective demand side management strategies that can help to improve the efficiency and sustainability of the energy system. comprises algorithms that target reducing energy consumption by varying the electricity load patterns. The DR programs are dependent on the appliances used in every sector. According to their usage the appliances are classified into 3 parts: Shiftable, non-shiftable and controllable appliances. Shiftable appliances can be scheduled at any time but after scheduling they can’t be interrupted ex. Dishwasher. The non-shiftable or base appliances schedule cannot be changed ex. Refrigerator. The controllable appliances can be scheduled at any time and can be stopped whenever needed ex. Air-conditions and Lights. Some appliances that consume high electricity are known as heavy load appliances and some that consume low power are known as a low load. A value based Learning (DRL) is proposed for interruptible load based on Process [72], which aims to implement an automatic DR model. A RES integrated day ahead DSM model was proposed in [73] with 3 layers (utility, DR aggregator and end users). According to the (DR) is defined as: “changes in electric usage by end-use customers from their normal consumption patterns in response to changes in the price of electricity over time, or to incentive payments designed to induce lower electricity use at times of high wholesale market prices or when system reliability is jeopardized”. In [74] a data-driven load forecasting method for incentive demand response (IDR) was proposed that considers consumer behavior, which can improve power grid flexibility and reduce peak shaving pressure. The proposed method improves the accuracy of forecasting and increases the participation of demand-side resources in the power auxiliary service market. In this section a brief description of DR programs is given according to appliances used in the sectors. There are so many national level and state level programs are exist for reducing energy intensity in India. These programs are designed by Bureau of (BEE) and (EESL) and implemented by DISOMs (refer Table 5).

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: · Number 4 · September 2013 and Injuries in feedback to editors Improved estimation of the health and economic burden of chronic disease risk factors in this 0. 1 Mb - 11 pages) Submit a manuscript the This 10. 24095/hpcdp. 33. 4. 05 This 1. 6 billion ($557 million for smoking, $299 million for physical inactivity and $747 million for overweight/obesity). The total burden represents a 12. 6% downward adjustment to account for the effect of multiple RFs in some individuals in the population. Conclusion: An improved estimate of the aggregate economic burden of multiple RFs in a given population can assist in prioritizing and gaining support for primary prevention initiatives. Keywords: population attributable fraction, risk factors, obesity, physical inactivity, tobacco smoking, chronic disease care planners have long been concerned with the ''epidemiologic transition,'' the process whereby chronic illnesses displace pandemics of infection as the primary source of morbidity and mortality in the world. Endnote 1 The latest phase of this transition is marked by increased prevalence of overweight/obesity and physical inactivity in many countries. Endnote 2 Excess body weight and/or physical inactivity have been implicated in chronic diseases such as cardiovascular disease, stroke, type 2 diabetes, chronic kidney Endnote 3, Endnote 4, Endnote 5, Endnote 6, Endnote 7, Endnote 8, Endnote 9, Endnote 10, Endnote 11, Endnote 12disease, osteoarthritis and certain cancers. Consequently, these riskEndnote 13 factors (RFs) have joined tobacco smoking as key prevention targets. Estimations of the economic burden generated by such RFs have been undertakenEndnote 14 in many jurisdictions in the world, including Canada as a wholeEndnote 15, Endnote 16, Endnote 17, Endnote 18, Endnote 19 and a few Canadian provinces. Endnote 20, Endnote 21 In addition to understanding the costs related to a single RF such as tobacco smoking, estimating the aggregate economic burden generated by two or more RFs in a population is often of interest. This information can inform prevention strategies aimed at more than one RF, for example, public health programs that address both physical inactivity and overweight/obesity. There are, however, analytical challenges involved with the estimation of the aggregate burden of multiple RFs in a population.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: Proceedings of the of Sciences 103: 13208–13213. View 17. (1995) Neurotrophins regulate dendritic growth in developing visual cortex. Neuron 15: 791–803. View 18. (1999) Neurotrophins and synaptic plasticity. of Neuroscience 22: 295–318. View 19. (1995) Stress increases brain-derived neurotropic factor messenger ribonucleic acid in the hypothalamus and pituitary. Endocrinology 136: 3743–3750. View 20. (2006) of BDNF in the Pathway in Stress. Science 311: 864–868. View 21. Marmigère -(2003) Rapid induction of BDNF expression in the hippocampus during immobilization stress challenge in adult rats. Hippocampus 13: 646–655. View 22. (1988) Plasma corticosterone levels during repeated presentation of two intensities of restraint stress: Chronic stress and habituation. Physiology & Behavior 43: 47–55. View 23. (1992) A chronic stress state in rats: Effects of repeated stress on basal corticosterone and behavior. Physiology & Behavior 51: 689–698. View 24. García A, Martí O, Vallès A, Dal-Zotto S, (2000) Recovery of the Hypothalamic-Pituitary-to Stress. Neuroendocrinology 72: 114–125. View 25. Márquez C, Belda X, (2002) Post-stress recovery of pituitary-adrenal hormones and glucose, but not the response during exposure to the stressor, is a marker of stress intensity in highly stressful situations. 926: 181–185. View 26. (1998) Correlational relationship between shock intensity and corticosterone secretion on the establishment and subsequent expression of contextual fear conditioning. 112: 885–891. View 27. de Kloet ER, (1997) Corticosterone regulates expression of BDNF and trkB but not NT-3 and trkC mRNA in the rat hippocampus. Journal of 48: 334–341. View 28. -Behrens B, (1994) Glucocortcoids mediate the stress-induced extracellular accumulation of glutamate. 655: 251–254. View 29. McEwen BS (1999) Stress and hippocampal plasticity.

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: Table 5 DSM programs in India [75]Full size tableTable 6 lists different demand response (DR) programs that have been implemented in various electricity markets in the United States, Australia and Canada. The DR programs listed include Real-time price response, Emergency DR program, Ancillary service program, DLC, and Load curtailable program, etc. The table also notes the sectors on which these programs are applied. These programs are designed to help balance the supply and demand of electricity by encouraging customers to reduce their consumption during periods of high demand and/or high prices. Table 6 DR program implementation in electricity marketFull size tableResidential SectorIn the residential sector usage of electricity consumption charges according to a fixed rate per unit. In this sector it is necessary to identify consumer behavior and identifies the pattern of electricity usage. Most of the predictions done on historical data and temperature. Additional, socio-economic factor effects the electricity consumption [66]. Increased granularity is used to achieve high accuracy. DR program is used to reduce energy consumption and shift the load from peak time in a residential area. In [76] load scheduling with DSM is defined as () with and Modeling. In this method the necessary appliances are not shifted but the heavy equipment shifted in the off-peak duration via decision making center named as Controlling (ECC) units. In [77], authors propose a methodology that combines K-means clustering, K-NN classification, and ARIMA for electricity load forecasting using consumers' prepaid electricity bills data set to recommend (DR) programs. The methodology is tested in a case study in Tulkarm District, Palestine, and is found to be effective in assisting the management of electricity demand from the demand-side. In [36] an aggregated load forecasting is done in the residential sector using smart meters. In the residential sector, controllable load provides flexibility in demand response action it participates in reducing half of the total peak load. The author explains the demand decomposition and aggregation method for forecasting the load composition with submetering capabilities. The work described six types of load categories under controllable and uncontrollable appliances. In [78] a genetic algorithm-based method is proposed, for a different types of load appliances and their duration, to minimize the cost. In which DSM controller analysis, the behavior of users of load in time period according to the information provided by the consumer then properly schedule the load when consumer request for it.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: The approach involves four steps: consideration of the function of body weight as a continuous or ''polytomous'' exposure ranging from no excess weight through overweight to obesity; estimating an aggregate burden of chronic disease across a multifactorial system in a manner that adjusts for the effect of more than one RF; estimating the aggregate economic burden adjusted for multiple RFs occurring in some individuals; and disaggregating the total burden to provide an estimate of the economic cost notionally attached to each RF. To our knowledge, this is the first published attempt to address the issue of double-counting costs due to overlapping RFs in some individuals when addressing the economic burden of multiple RFs. As a demonstration of the utility of this approach, the economic burden of diseases attributable to tobacco smoking, physical inactivity, and overweight/obesity are estimated for the Canadian province of Manitoba. Manitoba has a population of about 1. 2 million. Endnote 31 Although the province is marked by a strong agriculture and resource-based economy, some 60% of Manitobans reside in Winnipeg, the provincial capital. There is also a large presence in Manitoba (about 11% of the provincial population). Endnote 32 used an approach based on PAF to estimate the economic burden associated with the various RFs. At its simplest, the PAF statistic refers to the proportion of disease incidence generated in a population by a particular RF. Endnote 33 The results we report in this paper required calculating a Manitoba-specific PAF for each of the diseases related to the RFs of interest and then combining that information with the estimates of Manitoba-specific costs associated with both disease treatment and the indirect impacts of mortality/morbidity. PAF is a statistic that combines two facets of an RF and its impact on disease: relative risk (RR) of the RF in reference to a particular disease, and the prevalence of exposure to the RF in the population of interest. Relative risk The source for the RRs associated with physical inactivity is the meta-analyses by Katzmarzyk and Janssen. Endnote 16 The majority of the studies incorporated in the Katzmarzyk and JanssenEndnote 16 review include an index of obesity in the analysis so that the effects of physical activity on disease risk can be considered to be independent of obesity. The source for the RRs associated with overweight and obesity is the meta-analyses by Guh et al.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: In industrial large scale bio-reactions micro-organisms are generally exposed to a variety of environmental stresses, which might be detrimental for growth and productivity. Reactive oxygen species (ROS) play a key role among the common stress factors–directly-through incomplete reduction of O2 during respiration, or indirectly-caused by other stressing factors. or L-ascorbic acid acts as a scavenger of ROS, thereby potentially protecting cells from harmful oxidative products. While most eukaryotes synthesize ascorbic acid, yeast cells produce erythro-ascorbic acid instead. The actual importance of this antioxidant substance for the yeast is still a subject of scientific debate. Methodology/We set out to enable Saccharomyces cerevisiae cells to produce ascorbic acid intracellularly to protect the cells from detrimental effects of environmental stresses. We report for the first time the biosynthesis of L-ascorbic acid from D-glucose by metabolically engineered yeast cells. The amount of L-ascorbic acid produced leads to an improved robustness of the recombinant cells when they are subjected to stress conditions as often met during industrial fermentations. Not only resistance against oxidative agents as H2O2 is increased, but also the tolerance to low pH and weak organic acids at low pH is increased. Conclusions/platform provides a new tool whose commercial applications may have a substantial impact on bio-industrial production of Vitamin C. Furthermore, we propose S. cerevisiae cells endogenously producing vitamin C as a cellular model to study the genesis/protection of ROS as well as genotoxicity. Citation: (2007) Biosynthesis of by to Resistance. 2(10): e1092. Editor: Christophe Herman, of Medicine, of AmericaReceived: August 21, 2007; Accepted: October 9, 2007; Published: October 31, 2007Copyright: © 2007 Branduardi et al. This is an open-access 1. Ascorbic acid biosynthetic pathway. Schematic representation of the pathway of L-AA production from D-glucose in plants. The following enzymes are involved: A, hexokinase (2. 7. 1. 1), B, glucose-6-phosphate isomerase (5. 3. 1. 9), C, mannose-6-phosphate isomerase (5. 3. 1.

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: In the residential sector controllable appliances play important role in shifting the load and consumers can use them at a lower price due to real time pricing or TOU or CPP. Similary, in [79], genetic algorithms employed to tackle DSM and Dispatch issues as two complementing stages in the optimization process. In [2], a DR scheme is given to calculate total energy consumption via flexible and essential appliances. The system contains three layers of architecture: household, cloud and a third party. Third party management service which provides EMS, analysis of the demand, and other security. Cloud includes database and gateway manager which take information from the home gateway that is situated at client side. Households include home gateway, Smart plugs and appliances. HG is responsible to collect information from smart plugs and appliances and send it to cloud through home gateway manager. A threshold value peak is decided by the EMS provider, if total consumption exceeds the threshold, then DRM is applied according to the consumer engagement plan. Load demand is affected by weather conditions temperature, wind and humidity. Most of the paper defines the effect of temperature on load consumption but in [80], a humidity-based electricity prediction model is proposed. This forecasting model defines the demand in the different horizons and seasons like summer and non-summer. In [81], a characterization model is provided for customer peak load targeting. The data mining approach is employed in this study to characterize and identify users in SG for peak load reduction utilizing data gathered by smart meters. In [3], to decrease the computational time in smart Grid, a review on DL, (RL) and DRL is presented which works on a large dataset. The author defines that DL, RL and DRL give the efficient solution for planning, forecasting and scheduling of the load in SG. In [18], the role of a smart meter is defined to classify household information. A smaller granularity like 15 min, 30- min data is required for characterizes the private dwellings. Smart meter data and historical information is collected from the survey and determine the features with their class labels which work as the input for the eCLASS algorithm for selecting the features. The most expressive features are selected. Cooking, single, age of the house, number of devices, family, employment and number of bedrooms are the features that give different accuracy for different algorithms. A multi-objective evolutionay optimization method is designed for load balancing for residential users [82].

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: While the overlap related to elevated BMI and physical inactivity is relatively high, the required correction (to avoid double-counting disease incidence) was, in fact, used here for RR data for physical inactivity adjusted for overweight/obesity. Endnote 16 Source: Klein-Geltink et al. , in Canada, 2006. Endnote 59 Abbreviation: BMI, body mass index. *BMI ≥ 25 kg/m2. Despite the attempts to optimize the accuracy of the estimated economic burden, some limitations remain, partly related to the assumptions required to creatively integrate several data sources compiled at different points of time. For instance, a key assumption of using older CIHI and EBIC data was acknowledged by Anis et al. ,Endnote 18 namely that ''the distribution of costs for each cost category did not change significantly from 1998 to 2006. ''Endnote 18, p34 Similarly, the method of scaling up from direct costs to indirect costs depends on the assumption that the ratios of costs between different comorbidities are the same for direct and indirect costs. Furthermore, the RRs for tobacco smoking are based on a comparison of current versus never-smokers and do not take into account smoking intensity. Potential changes (reductions) over time in smoking intensity would modify the RRs. Health care planners in many jurisdictions in the world share an interest in having a reasonable estimation of the economic burden of disease generated by modifiable RFs. Such information is vital to prioritizing and gaining support for primary prevention programs. Indeed, understanding the baseline economic burden associated with specific RFs is a prerequisite for developing a persuasive business case for prevention. The current findings, for example, have been a catalyst for action in Manitoba, supporting development of a Syndicate, a risk factor reduction challenge to provincial politicians, and creation of Heart and and a in primary prevention. by the Heart and of Manitoba, CancerCare Manitoba, Alliance for the Prevention of Chronic Disease, and Health in Common. 38. Return to footnote1 referrer Footnote 2 Gaziano JM. Fifth phase of the epidemiologic transition: the age of obesity and inactivity. JAMA. 2010;303(3):275-6. Return to footnote2 referrer Footnote 3. Clinical implications of obesity with specific focus on cardiovascular disease: a statement for professionals from the on Nutrition, Physical Activity, and Metabolism: endorsed by the of Cardiology Foundation. Circulation.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: 8), D, phosphomannomutase (5. 4. 2. 8), E, mannose-1-phosphate guanylyltransferase (2. 7. 7. 22), F, GDP-mannose-3,5-epimerase (5. 1. 3. 18), G, GDP-L-galactose phosphorylase (E. C. C not assigned), H, L-Galactose 1-phosphate phosphatase (3. 1. 3. 25), I, L-galactose dehydrogenase, J, L-galactono-1,4-lactone dehydrogenase (1. 3. 2. 3). the three enzymatic activities converting GDP-D-mannose into L-galactose are therefore missing in yeast cells. To close this gap, the following genes were cloned and expressed under the control of the TPI promoter (Table 1): AtME (A. thaliana mannose epimerase), converting GDP-D-mannose into GDP-L-galactose and AtMIP/VTC4 (A. thaliana myo-inositol phosphatase/L-galactose-1-P-phosphatase), converting L-galactose-1-P into L-galactose. Since the enzyme responsible for the conversion of GDP-L-galactose into L-galactose was only recently identified [17], alternative pyrophosphorylases converting substrates similar to GDP-L-galactose were sought for in the database. L-fucose guanylyl-transferase (FGT) from Rattus norvegicus [18] appeared suitable and was finally expressed. (A. thaliana L-galactose dehydrogenase) which allows the conversion of L-galactose into L-galactono-1,4-lactone and ScALO (S. cerevisiae D-arabinono-1,4-lactone oxidase), converting L-galactono-1,4-lactone into L-AA were co-expressed, to make sure that no bottleneck is created in the end of the pathway.

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: The results demonstrate that the suggested approach reduces both the cost of electricity and the delay in user experience when using electrical appliances. Findings DR programs are employed in the residential sector to reduce energy consumption and shift the load from peak times, and load scheduling with DSM is defined with and Modeling. These findings highlight the importance of identifying consumer behavior and electricity usage patterns, as well as the use of DR programs, smart meters, and controllable loads in reducing energy consumption and optimizing load balancing in the residential sector. Additionally, employing advanced techniques such as genetic algorithms, data mining, and DL, RL, and DRL can provide efficient solutions for load scheduling, forecasting, and peak load reduction. Industrial SectorThe industrial sector is a major concern of utility because it is one of the largest users of electricity. The industrial sector includes raw materials, water and gas so DRM becomes more difficult to this area than the residential and commercial sectors [1] and reliability management also increases complexity for industrial plants [83], In this paper, ancillary services for the industrial sector are explained that provide secure, reliable and qualified services. The industry sector has large controllable equipment and provides controllable load. The goal of DSM is to maximize the mutual benefits between utility and consumers. It minimizes the cost with the help of teal time pricing and TOU. In the industry sector, different type of DR programs is installed by the utility it depends on the consumer in which type of program he is interested and accepts the program accordingly an agreement is prepared between them [84]. The DR programs in the industrial sector are implemented using an energy management system that includes energy storage and distributed generation system. Load shifting depends on interruptible devices [5, 83]. In [5] a model is proposed for an industrial facility, that consists of elements and their representation, architecture of the model, different types of approaches required for demand response. The element which is used in the industrial DR models are different type of equipment like Controllable equipment (CE), Non-shiftable and shiftable equipment. In [1] a demand response program is defined for industrial areas. The scheme proposed for industrial facilities divided the task into the schedulable task (ST) and non-schedulable task (NST) with the help of Resources (DER’s). DER combines System (EGS) and System (ESS). Day ahead information is used for shifting the schedulable task.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: Skip to main content Explore journals Get published About BMC My account Search all BMC 55. 6 ± 8. 2 years) were grouped into those without (“healthy”, n = 469) and with CHD (320 stable and acute coronary syndrome cases) at baseline and into sex-specific quartiles of CKD-EPI equation-estimated glomerular filtration rate (eGFR). New or recurrent cardiovascular (myocardial infarction, stroke, heart failure [HF]) events, obstructive pulmonary disease (COPD) and death were determined during 3-years’ follow-up. ResultsAmong 25 deaths and 75 cardiopulmonary events, HF was the leading nonfatal event. Age, serum uric acid and left ventricular ejection fraction proved the best independent inverse covariates of eGFR in the “healthy” sample. The highest eGFR quartile (“hyperfiltrators”), exhibiting significantly lower serum LDL-cholesterol levels, significantly predicted the combined outcome (at a RR of 6) in “healthy” subjects, after adjustment for sex, age, body mass index, smoking status and presence of hypertension. This finding was paralleled by the highest eGFR quartile calculated also by the MDRD equation, replicating this also in the CHD group. ConclusionRenal “hyperfiltrators” represent individuals with autoimmune activation (involving serum creatinine, partly escaping assay), are misclassified into optimal renal function and actually are at significantly higher risk of death, HF or cardiopulmonary events. Low serum creatinine levels may represent a clue to the existence of autoimmune activation. reports BackgroundRenal hyperfiltration has been described to occur in renal and non-renal clinical conditions [1, 2]. The definition of glomerular hyperfiltration has not been agreed upon and the pathophysiological mechanisms have not been well explored [1], yet a glomerular filtration rate (GFR) 125 to 140 ml/min per 1. 73 m2, or >2 SD above mean GFR in healthy individuals is a commonly adopted definition [2, 3]. In humans, renal hyperfiltration observed in early diabetes mellitus is a risk factor for the development of diabetic nephropathy [4].

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageTable 1. List of expression plasmids constructed and used in this study* resulting integrative and/or centromeric expression plasmids (all five or, alternatively, all except the one harboring the RnFGT gene) were used to transform the S. cerevisiae strains GRF18U and BY4742 (see Table 1 and 2 for a complete list of plasmids and strains). Two different yeast strains were examined in parallel to elucidate the effect of a different genetic background. Independent clones of the obtained recombinant strains, together with their respective control strains, were grown in shake flasks with D-glucose as the sole carbon source. Their ability to convert D-glucose into L-ascorbic acid was measured by a chemical assay [19]. Figure 2 clearly shows for both genetic backgrounds that the recombinant strains do produce low, but significant and reproducible amounts of vitamin C. Wild type cells as well as recombinant cells transformed with ScALO and AtLGDH were analyzed as negative control. For the cells expressing ScALO and AtLGDH we had previously proven a high accumulation of vitamin C, when the cells are incubated with L-galactose [16]. However, the basal level of endogenous antioxidants is similar for both the wild-type (first column) and these recombinant cells (second column) incubated with glucose. Adding two more of the genes from the L-AA metabolic pathway (AtME and AtMIP) the vitamin is already detectable (third column, for both graphs, strain BY4742[] and GRF18U[]). This result indicates the presence of endogenous pyrophosphorylase activities converting GDP-L-galactose into L-galactose-1-P. The level of L-ascorbic acid becomes even higher when the RnFGT gene is added (fourth column), proving the suitability of this enzyme for our purpose. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFigure 2. Conversion of D-Glucose into L-ascorbic acid (milligrams/liter/OD) by transformed S. cerevisiae GRF18U and BY4742 cells. All strains were grown on mineral medium (2% w/v glucose, 0.

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: A state task Network (STN), a representation of the industrial process, identifies optimal solution for process-schedule problem. For balancing the supply according to the demand, a demand response program is proposed. At peak time industrial consumers switch their usage of electricity from the grid to the DERs. EGSs supply electricity at peak times. At an off-peak time when the price of electricity is low then DERs consumed the higher electricity and charge the ESSs. In an industrial area, non-Schedulable task likes blast furnaces in steel manufacturing, can’t be shifted for demand response purposes. Some tasks like water cooling and packaging are shift-able tasks that can be scheduled at off-peak hours are known as schedulable tasks. DR algorithm based on the (MILP). The basic principle of MILP is based on constraints and objective function. response programs and models for the industrial sector are focused on maximizing the benefits for both utility and consumers through load shifting and the use of interruptible devices and energy storage systems. The research papers provide insights into how DR programs can be implemented in the industrial sector and how they can be optimized using MILP-based algorithms. Agriculture sectorThe agriculture sector includes cropping patterns and irrigation methods so it is challenging to implement the demand response programs for agricultural consumers than in the industrial, residential and commercial sectors. However demand of energy changed according to season, type of crop, land area or soil type [85] and machinery & farm technologies [86]. There exist some intelligent techniques that estimate the agroclimatic data such as dew point Temprature [21], and soil moisture [87], etc. However, improvement in yield requires proper management of irrigation. Since irrigation by pump sets depends on electricity, we need to improve the efficiency of pump sets in the agriculture sector. Peak demand can be reduced by 21% with the replacement of inefficient pump set with the BEE rated pump set in the agriculture field [88]. Agriculture-Management (Ag-DSM) is the DSM in agriculture sector due to this sector participating in peak demand, subsidy, dependent on fossil fuel. As earlier explained, fossil uses should be electrified due to CO2 emission. So, DSM is necessary for the agriculture sector i. e. , known as Ag-DSM.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: They were associated with atherogenic (high triglyceride/low HDL-cholesterol) dyslipidemia and exhibited, notably, lower concentrations of LDL- and total cholesterol than subjects with normal eGFR, similar to the large study cohort [22] in which the overwhelming majority with a normal eGFR had 12% lower LDL- and total cholesterol values than those with mildly reduced eGFR. Yet when classified into quartiles, multivariable adjustment revealed in the highest eGFR quartile higher risk for all-cause mortality than the optimal risk quartile (>96 ml/min vs. 85–96 ml/min per 1. 73 m2) and for incident MI than all the remaining eGFR quartiles. Lower uric acid values among “hyperfiltrators” suggest the concomitant involvement in the immune complex of uric acid as well, for which some evidence was previously found in Turkish women [23, 24]. This consideration is in agreement with a large prospective study in Taiwan reporting that sex- and age-adjusted hazard ratios of uric acid levels for overall mortality were increased, also for uricemia lower than 5 mg/dl [25]. Our finding that “hyperfiltrators” were both younger and had lower serum uric acid are in line with recently reported observations among Turks that uric acid was independently and linearly associated with serum total phospholipids in men and women without MetS and that the concentration of phospholipids on HDL predicted the development of MetS [24], thus supporting a notion that –similar to creatinine– reduced uric acid level may be due to partial inability to assay, secondary to autoimmune activation. Puddu and Menotti [26] stressed that “serum uric acid might be considered for risk predictive purposes to be incorporated into software instruments aimed at indexing and/or following up individuals for primary preventive purposes. ”“Hyperfiltrators”, characterized by high serum triglycerides, suggested the presence of low circulating Lp(a) –by virtue of having damaged epitope that partly escapes immunoassay. Such an autoimmune “pattern” has been reported in the TARF study [12, 27]. Reduced levels of LDL-cholesterol have been reported to similarly precede the clinical onset of rheumatoid arthritis [28].

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: 67% w/v YNB), starting with an initial OD660 of 0. 05 for 18 h, when samples were taken and the concentration of L-ascorbic acid inside the cells was determined (GRF18U and BY4742 correspond to the parental strains transformed with the empty plasmids harboring in the productive strains the genes of the L-AA pathway). The control cells, as well as the cells expressing ScALO1 and AtLGDH can not accumulate L-ascorbic acid starting from D-glucose, therefore measured values correspond to the endogenous erythro-ascorbic acid. The standard deviation bars correspond to the data obtained from independent clones, and from independent growth and antioxidant determinations. Please note the different scale of the ordinate axes in the two graphs. the trend of ascorbic acid production for both strains is similar, the absolute values are significantly different, being almost one order of magnitude lower in the BY background (Fig. 2, left panel). Analogous, the contribution of the RnFGT gene is more effective in the GRF background (Fig. 2, right panel). On the other hand, while such difference could be related to the different genetic background of the two hosts, the basal level of antioxidants appears to be within the same order of magnitude in both yeast backgrounds. In no case vitamin C was detectable in the culture broth (data not shown). Effects of endogenous L-AA production on strain robustness: recombinant yeasts behavior under oxidative stress To test if the endogenous production of vitamin C could have beneficial biological effects, wild type BY4742, GRF18U and the corresponding recombinant L-AA producing strains were challenged by growing the cells under different stress conditions. Since vitamin C acts principally as an antioxidant, the first condition used to test the robustness of the engineered strains was oxidative stress. Yeast cells were inoculated at an initial optical density (660 nm) of 0. 1 in the presence of different concentrations of H2O2 (ranging from 2 to 3. 5 mM), and their growth was measured as optical density at specific intervals of time over about 80 hours, Figure 3. Cells were also inoculated in the same medium without H2O2, as a control. As expected, in the absence of H2O2 both wild type and recombinant strains grew well (Fig. 3a and 3b).

Output: pass


Input: Claim: AI can optimize renewable energy grids by forecasting energy demand and supply. [SEP] Text: Energy efficiency-based Ag-DSM has been initiated in many states of India and Maharashtra is the first state to adopt an efficient pump set as Ag-DSM. The working efficiency of the BEE 5-star rated agriculture pump set is given in [7, 89]. So, the feeder can support lower power supply from the utility and provide profit to the government by reducing the subsidy. In [90], a study analyzes the potential value of large-scale integration of agrivoltaic technology in rural farming areas in Japan, using linear programming to develop optimal power grid scenarios. The findings suggest that installing agrivoltaics in rice paddy fields is more efficient than in equivalent land areas, and concurrent utilization of agrivoltaics with expanded transmission lines and utilized battery storage is beneficial for power grid planning to efficiently control excess output and reduce CO2 emissions. agriculture, DSM can be achieved by using various technologies such as Drives (VFDs), energy-efficient motors, and irrigation control systems. For example, VFDs can be used to control the speed of pumps, fans, and other equipment to match the exact requirements of the process, thus minimizing energy consumption. Additionally, irrigation control systems can help optimize water and energy usage by scheduling irrigation based on crop water demand and weather conditions. These systems can also be equipped with sensors that measure soil moisture, plant stress, and other parameters to further optimize irrigation scheduling and reduce water and energy waste (Table 7). Table 7 Sector-wise DSM Techniques and Load ManagementFull size table Challenges and GuidanceThe interdisciplinary nature of the smart grid presents numerous opportunities for researchers in this domain, but there are also several challenges that must be addressed to fully realize its benefits. The following challenges are identified in this paper: a Weather Dependency: Weather affects electricity consumption patterns, and accurate weather forecasting is a challenging task. Long-term decision-making by utilities needs to incorporate the effects of weather. In [93], a weather forecasting model is integrated with an deep learning-based load management model to forecast future electricity demand. b Integration: Integration of renewable sources in the smart grid is necessary for accurate forecasting. As renewable energy resources like wind power and solar photovoltaic gain popularity, they pose challenges for predicting actual electricity consumption due to their unpredictability and unmetered nature. A high-resolution global renewable energy atlas is used to generate customized hourly time series of wind and solar PV power generation [94].

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: Epidemiological data showing paradoxical inverse association of lipid levels with cardiovascular disease risk in rheumatoid arthritis patients have been reviewed [29]. Furthermore, high serum triglycerides headed the determinants of a new cardiovascular event n 700 Swedish early rheumatoid arthritis patients [30]. Noteworthy is that the characteristics of atherogenic dyslipidemia and reduced LVEF of “hyperfiltrators” became diluted in the subset with baseline CHD, since CHD patients with normal renal function not only had diminished LVEF but also low total and LDL-cholesterol and a higher proportion of atherogenic dyslipidemia, eliminating the differences from the “hyperfiltrators”. Explanation of current resultsPresent findings support the hypothesized notion [12, 27] that autoimmune activation associated with enhanced low-grade inflammation is a common mechanistic pathway leading to diverse chronic diseases, foremost to diabetes, CHD and CKD. Serum creatinine, a tripeptide, may undergo oxidative damage to its epitope alike circulating a), be perceived as a foreign body, partly lose assayability and may aggregate to autoimmune components such as apoA-I [11] and/or adiponectin. Immune complexes with ß2-glycoprotein I-Lp (a) have been reported in patients with coronary artery disease [31]. The related individual is necessarily categorized into the lowest creatinine (or highest eGFR quantile), despite likely harboring a milieu of oxidative stress, HDL dysfunction and autoimmune activation associated with elevated cardiorenal-metabolic risk. The reduced LVEF, along with our observation of HF as the most common consequence among “healthy” “hyperfiltrators”, suggests that such autoimmune activation frequently leads to HF the prevalence of which has been increasing in the community [32]. Cancer formed the second frequent death cause, being in line with a recent community-based case–control study that patients with heart failure have an increased risk of incident cancer [33], suggesting a common underlying mechanism. ImplicationsThe magnitude of relative risk it conferred in this sample, and the substantial proportion in the population at large, necessitate the identification of potential “hyperfiltrators” in the community which has both methodological and clinical implications.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: When H2O2 was added to the medium, the growth of both GRF18U and BY4742 wild type strains was negatively affected proportionally to the hydrogen peroxide concentration. Low concentrations (2 mM) lead to a slight delay of growth (data not shown), while the highest H2O2 concentration (3. 5 mM) lead to an almost complete growth inhibition (Fig. 3c and 3d). A similar behavior has been observed for yeast cells transformed with the ScALO1 and AtLGDH genes (data not shown). Significantly, under the same limiting conditions, all recombinant strains producing L-AA were able to resume growth about 35 hours after inoculation, showing a strong robustness and an increased tolerance to oxidative stress (Fig. 3c and 3d). Moreover, it becomes obvious that this aspect of strain robustness is directly correlated with the amount of L-AA produced (see Fig. 2); in fact, the strains engineered with all five genes necessary to complete the biosynthetic L-AA pathway exhibited even stronger tolerance (Fig. 3c and 3d, triangles) than the corresponding strains, lacking the FGT activity (Fig. 3c and 3d, squares). This phenomenon can be observed for both yeast genetic backgrounds, even if with a slight difference in timing, probably due to a different basal resistance of the two genetic backgrounds (phenomenon not further investigated). The BY background strain, in fact, seems to be more resistant, despite the generally lower ascorbic acid levels measured (see Fig. 2). Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFigure 3. Growth curves of wild type and L-ascorbic acid producing yeasts under oxidative stress. Kinetics of growth of wild type and engineered strains GRF18U (left panels) and BY4742 (right panels) as inoculated in minimal glucose media without H2O2 (3A and 3B) or in presence of H2O2 3. 5 mM (3C and 3D). • GRF18U wild type; ▪ GRF18U[]; ▴ GRF18U[]; ○ BY4742 wild type; □ BY4742[]; ▵ BY4742[]. results were obtained for cells growing in the presence of 2. 5 and 3 mM H2O2.

Output: pass


Input: Claim: AI algorithms can restore ecosystems without any external human support or funding. [SEP] Text: But in the practical implementation of a use case, accuracy or business acceptability of a model is defined in many other ways. For example, in credit risk data, a bank would like to optimize and maximize its true positive rates and lower its false positive rates, thus producing huge cost/loss savings that may occur due to false positives. In another scenario, a pharmaceutical company would like to increase its true positive rate and reduce its false negative rate to ensure maximum coverage of its treatment. For the uninitiated, a true positive is a situation when a model predicts an outcome as positive when its actual value in data was true; a false positive is a situation when a model predicts an outcome as positive when its actual value in data was false; and false negative is a situation when a model predicts an outcome as negative when its actual value in data was true. Figure 2: The practical implementation of a use case, accuracy or business acceptability of a model is defined in many ways. In order to define fairness metrics [10], it’s important to understand what is the business asking for. In many cases (given the business objective and modeling approach), it makes sense to choose fairness metrics that put a lot of stress on true positive and false positive only. For instance, in the example we are discussing, it would make sense to optimize “average odds ratio” defined as an average of the difference in false positive rate (FPR) and true positive rate (TPR) for unprivileged and privileged groups and may also look at predictive equality (for FPR where both protected and unprotected groups have equal FPR) (Figure 3). In a few other cases, a secondary metric(s) like “equal opportunity” or “equalized odds” or “predictive parity” can be added to the list in case the primary fairness metrics fail to paint the complete picture. The choice of fairness metrics needs to be defined when the model performance (accuracy) metrics are being defined. Figure 3: Absolute difference in various metrics (and total cost) before and after reweighting for “married. ” As noted, bias detection is a half-baked solution without a remedy. Once you’ve detected bias in any and all stages of the data science lifecycle, it’s worth looking at how to change your approach to algorithms to help ensure fairness in your overall modeling approach.

Output: pass


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: They were characterized, not by a higher BMI, but by being younger and more frequently former smokers, having lower serum total and LDL-cholesterol, uric acid and LVEF, and a higher proportion of atherogenic dyslipidemia than persons with normal eGFR. Logistic regression models using two different creatinine-based eGFR at follow-up revealed significantly raised relative risk for combined death, cardiovascular and pulmonary events, above all HF, among the “hyperfiltrators” than in the subsets with normal eGFR, after adjustment for BMI and traditional cardiovascular risk factors. We conclude that the fundamental oxidative stress and autoimmune process simultaneously render partial escape of serum creatinine from full assay and impart excess risk of chronic cardiometabolic, renal and pulmonary morbidity and mortality. Clinicians need to be aware that unexpectedly low serum creatinine levels may represent a clue to the existence of autoimmune activation. 300. 10. 1038/nrneph. 2012. 19. 6. 10. 4239/wjd. v3. i1. 1. 1386. 10. 1093/ndt/16. 7. 1382. 697. 10. 1007/s00125-009-1268-0. 821. 10. 1038/sj. ki. 5002160. 1551. 10. 2337/dc11-0235. 2231. 10. 1093/ndt/gfq008. 312. 10. 1053/j. ajkd. 2010. 03. 017. 33. 10. 3109/00365513. 2013. 821712 10. 5152/akd. 2013. 175 584. 10. 2174/138161282004140213145551. 470. 10. 7326/0003-4819-130-6-199903160-00002. 612. 10. 7326/0003-4819-150-9-200905050-00006. 3167. 96. 743. 10. 1016/S0140-6736(09)61303-9. 504. 10. 1016/j. ejim. 2007. 06. 028. 1486. 10. 3346/jkms. 2010. 25. 10. 1480. 610. 10.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: In these cases growth was resumed 20 and 25 hours after inoculation, respectively (data not shown), hence earlier and concentration dependent. Effects of endogenous L-AA production on strain robustness: recombinant strain behavior under acidic stress To test if the improved strain robustness could be extended to other environmental constraints, wild type and engineered strains were subjected to acidic stress caused by inorganic or organic acids. These are typical environmental conditions occurring during either lab-batch and industrial fermentations. To this purpose, we analyzed wild type and ascorbic acid producing strains grown in minimal medium brought to pH 2. 2 with HCl addition (acidic stress induced by inorganic acid), or with lactic acid (45 g/l) at pH 3 (this value was chosen in order to have the organic acid almost completely in the undissociated form, since its pKa is 3. 78). From the data presented in Figure 4, it is evident that also under these stress conditions the producing strains exhibit a strong robustness and increased tolerance when compared to control strain. At pH 2. 2 (Fig. 4a) the growth rate of the wild type strains are significantly delayed in respect to the growth rate of the engineered strains. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFigure 4. Growth curves of wild type and L-ascorbic acid producing yeasts under acidic stress. Growth curves of wild type and engineered GRF18U and BY strains (upper and lower panel, respectively) inoculated in minimal glucose media at low inorganic pH (panel A) or at low pH plus organic acid (lactic acid, 45g/l, panel B) or at low pH plus organic acid with L-AA added exogenously (panel C). Panels 4A and 4B: • GRF18U wild type; ▪ GRF18U[]; ▴ GRF18U[]; ○ BY4742 wild type; □ BY4742 []; ▵ BY4742 []; Panel 4C: • GRF18U wild type; ○ BY4742 wild type; − GRF18U or BY4742 wild type added with 30mg/l ascorbic acid; * GRF18U or BY4742 wild type added with 60 mg/l ascorbic acid.

Output: pass


Input: Claim: AI-driven renewable energy systems can replace fossil fuels in every country by 2030. [SEP] Text: (2020) CO2 and Emissions. [18] NOAA (2021) Trends in Dioxide. [19] BP Data (2022) BP of June 2022. [20] NOAA (2023) Trends in Dioxide. . [21] (2023) Earthquake in 2008. Baidu Baike. (2023) April 20th Ya’an Earthquake. [23] Li, J. (2014) Research on the Influence of the Dam on Seismicity in Western Sichuan. School of Automation Engineering, University of and Technology of China, Chengdu. [25] BP Data (2020) BP of June 2020. [26] BP Data (2021) BP of July 2021. [27] Ritchie, H. and Roser, M. (2021) Consumption from Fossil Fuels, Nuclear and Renewables World. [28] Ritchie, H. and Roser, M. (2021) Renewable Energy, in Data. [29] in Data (2021) Share of from Renewable Sources. [30]. , Entekhabi, D. , Kolassa, J. , Lee, J. -E. and Gentine, P. (2017) Feedbacks between the Atmosphere and Terrestrial Biosphere. Nature Geoscience, 10, 410-414. [31] Roser, M. , Ritchie, H. and Ortiz-Ospina, E. (2019) Growth. [32] Macrotrends (2021) 1950-2021. [33] FAO and UNEP (2020) of the World’s Forests 2020. Forests, Biodiversity and People. Rome. [34] IUCN (2021) of Threatened Species. [35] NCSL (2021) and Goals. [36] Palmer, K. and Burtraw, D. (2005) Impacts of Electricity Restructuring. Discussion Paper: 2005 Resources for the Future. JEL Classification, Numbers: L51, L94, L98. [37] Di Maria, C. and Werf, E. (2008) Revisited: Policy with Change. Environmental and Resource Economics, 39, 55-74. [38] Ritchie, H. and Roser, M. (2022) Deforestation and Forest Loss.

Output: pass


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: -19 PreprintsProtocolsVideosJournalsTools & a 10. 21203/rs. 3. rs-4843892/v1This work is licensed under a CC BY 4. 0 LicenseStatus:Under ReviewVersion 1posted 6You are reading this latest preprint versionAbstractBackground Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human, financial, and clinical resources in healthcare systems worldwide. this cohort study, a prediction model was derived by evaluating two algorithms, XGBoost and logistic regression, for three outcomes - mortality, hospitalization, and emergency department visits - to build a clinical decision support system for patients with non-communicable chronic diseases at the Hospital complex in Medellin, Colombia. collected 4845 electronic medical record entries from 5000 patients included in the study. The median age was 71. 83 years, with 63. 8% women and 29. 7% receiving home care. The most prevalent medical conditions were diabetes (52. 9%), hypertension (67. 2%), dyslipidemia (57. 3%), and COPD (19. 4%). For the mortality outcome, the logistic regression model had an of 0. 88 (95% CI, 0. 8032 to 0. 9032), and the XGBoost model had an of 0. 912 (95% CI, 0. 8802 to 0. 9437). For the hospitalization outcome, the logistic regression model had an of 0. 967 (95% CI, 0. 957 to 0. 9763), while the XGBoost model had an of 0. 976 (95% CI, 0. 9661 to 0. 985). For the emergency department visit outcome, the logistic regression model had an of 0. 930 (95% CI, 0. 9158 to 0. 945), while the XGBoost model had an of 0. 982 (95% CI, 0. 9755 to 0. 9891). We created a dashboard as to interact with the model, segmenting risk in the cohort. clinical decision support system based on artificial intelligence using electronic medical records possibly can help segmenting the risk in populations with chronic diseases for effective decision-making. Clinical decision support systempredictive models.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: the addition of 45 g/l of lactic acid to the medium has dramatic effects on the wild type strains, which are completely inhibited in growth, and also on the [] expressing cells which are severely compromised, while the recombinant strains expressing all genes for L-AA production are still able to grow (Fig. 4b). Interestingly, the effect of L-AA produced intracellularly is much more profound than the effect of vitamin C added to the culture broth. Fig. 4c shows the growth curve of wild type control cells in presence of lactic acid (40 g/l) at low pH. The addition of ascorbic acid relieves the growth inhibition evidently, but even with 60 mg/l the effect is not as profound as with the low amounts produced intracellularly. The growth is resumed much later even while less lactic acid was added to the culture. Correlation of endogenous L-AA concentration, strain robustness and viability Having proven the positive effects on growth under stress conditions as described above, we wanted to deeper investigate the correlation between ascorbic acid endogenously produced by engineered yeast cells, enhanced tolerance to oxidative damage and cell robustness. The wild type and the recombinant GRF18U and BY strains were grown in minimal glucose medium with addition of 3. 0 mM H2O2, following the same experimental protocol described in Figure 3. At the time in which all strains start to recover from the imposed stress (each with a different kinetic: approximately after 2–3 duplications from the preinoculum–as example see Figure 3), samples were taken. Each sample was stained with Dihydrorhodamine123 for the detection of ROS [20], and PI (propidium iodide) to detect severely damaged and/or dead cells [21]. Samples were analyzed with a flow cytometer. The resulting dot plots are compared in Figure 5. On the dot plots (DHR123 vs. PI) each individual cell is represented by a single dot and it is quite easy to recognize at least four distinct yeast subpopulations. A first healthy subpopulation (named A in Fig. 5a, used as a schematic representation for the benefit of the following ones), having only the background signal (autofluorescence) for both fluorochromes (low DHR and low PI signal); a second subpopulation (named B in Fig.

Output: pass


Input: Claim: AI-driven renewable energy systems can replace fossil fuels in every country by 2030. [SEP] Text: “One consideration is cost but reducing toxicity should help with this and as people become increasingly environmentally conscious this should help drive government policy,” explains Alan. “There is a lot of amazing work going on to get microbes to make chemicals they wouldn’t normally make, and this broadens their uses significantly. ” in BiochemistryProteins and Lipids (AMPL): : Alan’s work is focused on investigating Saccharomyces and Propionibacteria with a view to improving the tolerance of microbes. The team hopes to improve efficiency and product yield of engineered cell-based factories. : UK Research and 2020 Marie Skłodowska-Curie actions Download the 10. 33424/50 Link to the activity sheet 0 +1 Pinterest 0 LinkedIn 0 cells viewed under a microscope. Yeast grown on an agar plate ready for testing. Testing yeast for ethanol tolerance. A molecular model of a cell membrane showing the individual lipids (fats) that make it up. The MeMBrane team at a recent meeting in York. We are all aware of the potentially cataclysmic effect of continuing to rely on fossil fuels. While we might recycle our plastic, use electric cars or walk to school, efforts are being made to reduce our reliance in surprising quarters. One area where efficiency and a move away from fossil fuels is being considered is that of the use of microbes. Fossil fuel-based chemical production has been a historical tendency for some time and, while it has helped advance the field of biochemistry, it has had the unfortunate effect of releasing enormous amounts of carbon into the atmosphere. As we all now well know, these ‘non-renewable’ production methods are affecting the world’s climate. Now, there is a concerted effort to move away from such methods and instead look for alternatives that are cleaner, greener and more efficient, including biofuels. Based at in the UK, Goddard leads the MeMBrane project, which focuses on the production of what he and his team call cell factories. “Microbial cells can be grown on a variety of ‘foods’ including sugars, plant waste and even wood pulp,” explains Alan. “These sources of carbon are renewable and therefore more environmentally friendly. Bio-based routes to chemical production can also lead to purer products.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: FiguresFigure 1Figure 2Figure 3Figure 4Figure 5IntroductionThe increase in demand for health services from people with chronic noncommunicable diseases represents a challenge for the health systems of many countries and ours is no exception. In Colombia, the high costs of chronic diseases are reflected in their diagnosis and treatment, which are characterized by being prolonged, complex and affecting the economically active population. Giventhat in many cases their diagnosis and intervention are late, in addition to the costs for the system, a burden is generated for the patient's health and the stability of his family[1] An appropriate strategy for this chronic disease problem may be to use a model of care based on risk stratification. Stratification is defined as "the identification and/ or grouping of patients according to risk or severity classification", which serves to define in advance interventions that are tailored to their future health care needs (2). To carry out the stratification there are several systems of classification of patients, among which are: Groups (ACG), a system that assigns each person in an exclusive category based on clinical criteria, with the aim of predicting health costs, pharmaceutical expenses and hospitalization risks [2]; Groups (DCG), which use information from all diagnoses and prescriptions to form Groups (CRG); which, together with demographic characteristics, manage to predict health costs in a year by classifying individuals according to the severity of their health status and according to their chronicity (4); and the Diagnosis-(DRGs), which allow relating the different types of patients treated in a hospital, with the cost of their management. The use of DRGs is recommended by the Organization (WHO) and several governments are exploring their implementation. However, this requires having information systems that have a high quality of the data, to guarantee the accuracy of the classifications and therefore the decisions that are made. In the case of Colombia, there has been evidence of low quality in the Registries (RIPS) for the DRM system, as well as the absence of policies that promote and promote comparable health risk [3] (5).

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: 5a) of still viable but ROS accumulating cells (low PI, high rhodamine signal); a third subpopulation (named C in Fig. 5a) made of damaged cells displaying high ROS and high PI signals and finally a fourth subpopulation (named D in Fig 5a) of dead cells, presumably originated from subpopulation C by loosing all DHR signal. The data reported in the upper (GRF18U background) panels of Figure 5 (b, c, d) clearly show that in the wild type strain the oxidative stress induced by H2O2 strongly influences the intracellular ROS content and affects cell vitality. In fact, as clearly shown by Fig. 5b, a considerable portion of the analyzed cells (28%) shows high intracellular levels of ROS, and the fraction of dead cells (33%) exceeds that of viable cells (28%). By contrast, in the recombinant strains producing L-AA (Fig. 5c), we registered a clear reduction of ROS formation, (15% of the total population), together with a decrease of cell mortality (30%) and a consequent improved cell viability (44%). This trend is maximized in the strain expressing the RnFGT gene (Fig. 5d), where the majority of the cells (89. 5%) is still viable and healthy, while ROS formation (1. 1%) and cell mortality (8. 7%) are almost negligible. As already observed for the growth curves in limiting conditions, also in this experiment there is a direct and positive correlation between the levels of vitamin C produced (see Fig. 2) and cell viability. Download: PPTPowerPoint slidePNGlarger imageTIFForiginal imageFigure 5. Flow cytometric analysis of wild type and vitamin C producing yeasts under oxidative stress. Panel 5A: schematic representation of the different subpopulations that can be observed in the following panels, where wild type (5B) and recombinant strains (5C and 5D) grown in minimal glucose medium added with H2O2 3. 0 mM were analyzed after DHR123 and PI staining (rodamine signal is reported in the abscissa and PI signal on the ordinate axes). Upper panels: GRF18U background. Lower panels: BY background. (B): wild type. (C): [] transformed cells.

Output: pass


Input: Claim: Gene therapy is being researched to treat diseases like cystic fibrosis and sickle cell anemia. [SEP] Text: Developing gene therapy to treat cystic fibrosis: challenges and successesMore than 25 years after the gene for cystic fibrosis was discovered, there are signs that correcting this genetic deficit could soon be possible. Gene therapy 10 June 2016 link to page Download PDF Coloured chest X-ray showing mucus in the lungs of a cystic fibrosis patientLibraryA tiny globule of fat suspended in a fine mist makes its way along the branching passageways of a human lung. Deep in the lung the globule, called a liposome, nestles up against one of the cells lining the airway. Because the cell membrane is also made of fat, the liposome is able to pass through it, and spill its cargo into the interior of the cell: a string of DNA encoding a protein known as CFTR, which will enable the cell to make thin, watery mucus that helps keep the lung clean and healthy. This is gene therapy for cystic fibrosis, a genetic disorder that causes serious lung disease. In a phase III trial published in 2015, the a group of scientists and clinicians from London, Oxford, and Edinburgh, reported that inhaling these liposomes every month for a year stabilised the lung function of individuals with cystic fibrosis[1]. Meanwhile, lung function deteriorated slightly in the study’s placebo group. These are modest effects, as even the study’s authors acknowledge. “I think we need to improve on that” before the therapy could be rolled out to clinical care, says Eric Alton, professor of gene therapy and respiratory medicine at London and the consortium’s coordinator. Source: Eric AltonEric Alton, professor of gene therapy and respiratory medicine at London, is coordinator of the the trial represented a huge milestone for the field. For the first time, researchers had demonstrated the efficacy of cystic fibrosis gene therapy in humans — a goal that has seemed out of reach for more than a quarter of a century. The success reflects not only the pay-off of persistence but the current buzz of activity in the field. A range of approaches are in play, including viral vectors as well as non-viral ones like liposomes for delivery, and classic gene therapy strategies that would add healthy genetic material to a patient’s cells, as well as cutting-edge technologies that would edit their own genetic material.

Output: corroborate


Input: Claim: Most biohacking techniques can entirely eliminate the risk of chronic diseases. [SEP] Text: 21% (IC95% 3. 07–8. 22%) 99. 81% (IC95%. 99. 31–99. 98%) 77. 25% (IC95%. 76. 79–77. 69%) 89. 47% (IC95%. 66. 38–97. 34%) 0. 930 (IC95%. 0. 9158 to 0. 945) -0. 01 (IC95%. -0. 14 to 0. 11) 12. 23 (IC95%. 10. 64 to 13. 83) p = 6. 981 XGBoost 92. 33% (IC95% 88. 89–94. 98%) 95. 62% (IC95% 94. 20–96. 78%) 97. 57% (IC95% 96. 50–98. 32%) 86. 74% (IC95%. 83. 12–89. 69%) 0. 982 (IC95%. 0. 9755 to 0. 9891) 0. 01 (IC95%. -0. 23 to 0. 25) 1. 2 (IC95%. 1. 07 to 1. 34) *Sensitivity (Sens) is the ability of the model to correctly rule out patients with the outcome of interest. ^Specificity (Spec) is the ability of the model to correctly detect patients without the outcome of interest. ̈Negative predictive value (NPV) is the probability that a patient will not have the outcome of interest if the model classifies it as such. +Positive predictive value (PPV) is the probability that a patient will have the outcome of interest if the model classifies it as such. (Area under the ROC curve) is a numerical measure that evaluates the model's ability to differentiate between patients with and without the outcome of interest. 95% CI calculated by DeLong DeLong's Test: Test to define differences between areas under the curve Sample size calculation. A formal sample size calculation was not made because there was a fixed cohort of users. Imputation of missing data A data imputation process was performed to variables with less than 20% data losses using the K (KNN) algorithm, an imputation technique that uses information from existing data to estimate missing values. The algorithm selects a number k of observations closest to the observation with the missing value (neighbors) in the complete dataset. Then use the mean of those k neighbors to estimate the missing value.

Output: contrast


Input: Claim: Urban living always leads to increased stress levels. [SEP] Text: (D): [] transformed cells corresponding result has been observed for the BY yeast background (lower panels). While the general trend for both strains is similar, in this case, accordingly to the higher robustness observed for this genetic background (compare the behaviors of the GRF18U and BY cells in Figure 3 and 4 ) a lower accumulation of ROS has been detected. Taken together these data allow a more deeper analysis of the data shown in Figure 3 where increasing H2O2 concentration lead to longer delays of growth. These longer delays are clearly due to decreasing fractions of viable cells at the time of inolucum. The same cells analysed by flow cytometry can also be analysed with a fluorescence microscope for ROS accumulation. In the wild type strain, cells with a strong and granular rhodamine staining are quite frequent and well represented, while in the producing strain almost no signal for ROS is detectable (data not shown). All the described data demonstrate that the endogenous production of the antioxidant L-AA protects cells from oxidative damage, scavenging the intracellular production of ROS and/or increasing cell viability. DiscussionWe report for the first time the biosynthesis of vitamin C by recombinant S. cerevisiae cells starting from D-glucose. Accumulation of ascorbic acid was proven to be successful in two different strains (Figure 2). Furthermore, we proved that the intracellular accumulation of L-AA leads to an improved robustness of the recombinant yeasts during growth under different stress conditions. The recombinant yeasts that are functionally transformed to produce L-ascorbic acid generate lower levels of ROS (Fig. 5) and exhibit improved growth and a higher viability under conditions of organic (Fig. 4b) and inorganic (Fig. 4a) acid stresses as well as oxidative stress (Fig. 3c and d). These are physiological conditions often encountered during industrial large scale fermentations. For each yeast background, the degree of robustness seems to be directly related to the amount of intracellularly produced vitamin C (Fig. 2, 3 and 4). In accordance with our data it has been shown before that the addition of ascorbic acid to the growth medium has beneficial effects for heterologous protein production in yeasts [22].

Output: pass