Dr. Kirti Mohan Marya is considered to be a patient-friendly doctor. He is considered to be a doctor who has a positive outlook towards every situation.
Injuries that are related to the back, spine or hips and knees can be very painful and have a negative impact on an individual's health and affect the overall immunity of an individual. Hence for this reason it is important to get a proper diagnosis and treatment done which can only be provided by an orthopaedic surgeon in Dubai. Orthopedic doctors are physicians or surgeons who are focused upon treating ailments or injuries and disorders related to the musculoskeletal system of an individual. Joints, nerves, bones and muscle pain are some areas that orthopaedic surgeons focus upon. You can find many good orthopedic doctors in Dubai. An orthopaedic surgeon can specialize in one or more area of the human body like knees, hands, toes or shoulders for instance. Besides this, in Dubai you can find orthopaedic surgeons who focus upon one speciality like treating infants or kids. Pediatric orthopaedic surgeon in UAE can be found in many hospitals in Dubai. Trauma or reconstructive surgery is also some of the specialities. Some orthopaedic surgeons in Dubai also specialize in sports medicine. There are many private clinics in Dubai where orthopaedic surgeons operate. An orthopaedic surgeon can perform multiple surgeries as well. this include joint replacement theory, internal fixation and fusion. Some of the common ailments treated by an orthopaedic surgeon include problems related to an individual's toes or fingers, bow legs, bone tumours, back pain slip disk etc. Ailments like fractures can also be treated by an orthopaedic surgeon. Unequal hand or legs length are some other problems that an orthopaedic surgeon can treat. There are many female orthopedic doctors in Dubai operating in both private clinics & hospitals. It is always recommended to visit a good orthopedic hospital in Dubai where a patient can receive specialized care and attention in order heal the problem.
So go ahead and find the best Orthopedic Surgery clinics and hospitals In Dubai on Fidoc. Make an informed choice when it comes to accessing healthcare, and book appointments at the best clinics and hospitals in Dubai.


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GoToMyPC for iOS review
GoToMyPC for iOS is a remote-access tool for controlling Windows or Mac computers from afar, but is it worth a monthly subscription fee in the face of free alternatives? Scott Colvey taps in to find out.
by: Scott Colvey
3. GoToMyPC for iOS review
When typed input is required, the iOS keyboard can be summoned by an icon press or three-finger tap. A scrolling button bar, meanwhile, provides access to special keys, like the Windows and Function (Fn) keys on PCs and Command (Cmd) on Apple computers.
GoToMyPC can also display a floating panel with cursor keys, which is more useful than it perhaps sounds; the virtual pad can be used for navigating remote document more easily, for example
GoToMyPC's virtual cursor pad is useful for scrolling around remotely-displayed content
We've already noted that GoToMyPC is more secure than LogMeIn for iOS by default, simply by dint of the unique access codes that it insists are applied to each associated computer. It also has a few other useful security tricks.
Along with configurable session time-outs (15 minutes' of inactivity being the default), it's possible to have the remote computer automatically lock itself if the session is terminated unexpectedly. The remote computer's display can also be blanked for the duration of the remote control session, so if someone happens to be snooping at the far end, they won't be able to see what you're doing.
Another handy security option is the ability to generate a list of single-use passwords as a kind of two-factor authentication. These mean that even if the other static login credentials are somehow breached (all connections via GoToMyPC's servers are subject to 128-bit encryption, incidentally), a potential hacker still can't access a remote computer.
Most of GoToMyPC's settings must be tweaked on the home computer, which isn't ideal when working remotely
GoToMyPC's performance can be tweaked via a simple speed-appearance slider control, which progressively disables/enables the remote computer's various visual attributes as required. However, as with most other settings, this is managed via the client software on the host computer and navigating the small dialog box can prove awkward when working remotely.
GoToMyPC for iOS is a reliable and useful remote-access app that is in a handful of respects better than LogMeIn for iOS. However, those advantages are minor when considering the ongoing cost of GoToMyPC's subscription plans. By contrast, LogMeIn for iOS is free and can be associated with an unlimited number of computers. Of course, LogMeIn offers various paid-for access tools and 'Pro' services, but we're comparing the iOS apps here and on that mark alone, there's little contest. GoToMyPC for iOS is a fine effort, but it's competing with a free alternative that's just as good.
Requires Apple iPhone 3GS, 4, 4S, iPod touch (third or fourth generation), iPad or iPad 2 running iOS 4.2 or later. Also available for Android 3.0 onwards.
GoToMyPC for iOS review - currently reading

redpajama_set_name: RedPajamaCommonCrawl


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Q: Use default values for some props without having to specify every single one in the FC arguments? With defaultProps being deprecated, I wonder if it is possible to only declare some props' default values in the function arguments and not having to specify every prop in the arguments , as soon as one is supposed to have a default value.
interface AutocompleteInputProps {
    items: any[],
    key: string,
    searchIcon?: boolean,
    clearSearchIcon?: boolean
}

export const AutocompleteInput: React.FC<AutocompleteInputProps> = ({ searchIcon = true, items = [], key = 'name', clearSearchIcon = true }: AutocompleteInputProps) => {
    return (
        <></>
  )
}

Here I only want default values for the two booleans, however since I am specifying values for them in the FC arguments, I must also include the other possible props with them, which get's a little iffy for some of my components that have a lot of optional or inherited props

A: You can take advantage of the rest property which will collect the rest of the props:
export const AutocompleteInput: React.FC<AutocompleteInputProps> = ({ searchIcon = true, clearSearchIcon = true, ...restProps }: AutocompleteInputProps) => {
    // If you need access to the other props later
    const { items, key } = restProps;
    return (
        <></>
  )
}

redpajama_set_name: RedPajamaStackExchange


Haplotype-based approaches have been extensively studied for case-control association mapping in recent years. It has been shown that haplotype methods can provide more consistent results comparing to single-locus based approaches, especially in cases where causal variants are not typed. Improved power has been observed by clustering similar or rare haplotypes into groups to reduce the degrees of freedom of association tests. For family-based association studies, one commonly used strategy is Transmission Disequilibrium Tests (TDT), which examine the imbalanced transmission of alleles/haplotypes to affected and normal children. Many extensions have been developed to deal with general pedigrees and continuous traits.
In this paper, we propose a new haplotype-based association method for family data that is different from the TDT framework. Our approach (termed F_HapMiner) is based on our previous successful experiences on haplotype inference from pedigree data and haplotype-based association mapping. It first infers diplotype pairs of each individual in each pedigree assuming no recombination within a family. A phenotype score is then defined for each founder haplotype. Finally, F_HapMiner applies a clustering algorithm on those founder haplotypes based on their similarities and identifies haplotype clusters that show significant associations with diseases/traits. We have performed extensive simulations based on realistic assumptions to evaluate the effectiveness of the proposed approach by considering different factors such as allele frequency, linkage disequilibrium (LD) structure, disease model and sample size. Comparisons with single-locus and haplotype-based TDT methods demonstrate that our approach consistently outperforms the TDT-based approaches regardless of disease models, local LD structures or allele/haplotype frequencies.
We present a novel haplotype-based association approach using family data. Experiment results demonstrate that it achieves significantly higher power than TDT-based approaches.
Identification and localization of disease susceptibility genes is an important step towards the understanding of etiology of diseases and the development of new approaches for diagnoses and treatments. With the aid of molecular markers, statistical methodologies have made fundamental contributions to the identification of a substantial number of Mendelian diseases. However, deciphering genetic architectures of complex diseases is still a great challenge. With the advance of technology in recent years, single-nucleotide polymorphisms (SNPs) have emerged as the primary molecular marker for genetic mapping. SNPs are suitable for unbiased genome-wide assessments as well as fine-scale mapping because they provide a (nearly) complete coverage over the whole genome with high density. However, great challenges exist in analyzing hundreds of thousands of SNPs from thousands of individuals, not only because of the high volume and high dimensionality of data, but also because of their complicated interrelated structure, known as haplotypes.
Driven by the international HapMap project , considerable information about haplotype structures and haplotype frequencies among several populations has been obtained. Haplotype-based association mapping approaches, which take into consideration of correlated SNP structures, have drawn much interests and many new methodologies have been developed [2–8]. (For the discussion of the possible advantages of haplotype-based approaches over single SNP based approaches, see  and references therein.) In particular, methodologies that explicitly examine haplotype sharing patterns from case-control samples using various clustering algorithms have shown initial success [4, 6–8], all of which are based on the assumption that haplotypes from cases are expected to be more similar than haplotypes from controls in regions near the disease genes.
In an earlier work, our group has proposed an algorithmic approach and developed a program called HapMiner, for haplotype mapping of disease genes utilizing a density-based clustering algorithm . HapMiner is based on the assumption that, the haplotype segments with recent disease mutations, tend to be close to each other due to linkage disequilibrium, while other haplotypes can be regarded as random noises sampled from the haplotype space. The algorithm takes haplotype segments as data points in a high dimensional space. Clusters are then identified based on a similarity measure using the density-based clustering algorithm. Significance of association of each cluster is then evaluated. It has been shown that HapMiner can effectively obtain meaningful information from noisy datasets because of the concept of "density-based" clusters. More recently, we have extended HapMiner to quantitative trait mapping based on haplotype information from unrelated individuals . Haplotype uncertainties can also be taken into consideration .
Almost all haplotype-based methods mentioned above including HapMiner use the case-control design, and most of them require haplotype/diplotype information which must be inferred from genotype data. However, the case-control design for association studies may suffer from population stratification  and haplotype inference from un-related individuals may contain uncertainties . On the other hand, association approaches based on family data (such as TDT and their variants [13, 14]) are robust against population admixture, and haplotype inference using family data normally achieves much more reliable results . Several TDT-type tests using haplotype information have been proposed (e.g., [15–17]). Recently, Qian  adopted the haplotype sharing correlation (HSC) method to detect phenotype and haplotype associations based on family data. The author has shown that the HSC method achieved higher power than single- and multi-locus based methods. However, the HSC method requires phased haplotype data as input and does not work if no recombination presents within a pedigree. Given the high densities of existing SNP chips and moderate family sizes in practice, even for large number (hundreds, even thousands) of SNPs, recombination events within a family are extreme rare. In this paper, we combine our previous work on haplotype inference from family data and haplotype-based association into one unified framework. The approach first infers haplotype configurations for each pedigree assuming no recombination using our most recent haplotyping algorithm . A phenotype score is then defined for each founder haplotype. Assuming all founder haplotypes are independent, the HapMiner algorithm is then applied. We compare the approach, termed F_HapMiner, with the single-locus and haplotype-based TDT methods implemented in two popular programs [17, 20] under a variety of disease models and penetrance values with realistic haplotype frequencies and local LD structures. Experiment results show that our approach consistently achieves higher power than TDT-based approaches.
Our primary focus is on candidate gene studies using highly linked markers (e.g., SNPs). Candidate regions might be obtained from previous linkage analysis or some other prior knowledge. We assume no recombination events within each pedigree in a candidate region. For each region, family-based approaches (e.g., ) in general cannot distinguish one marker from the other because there are no recombination events. However, by combining family and population information, F_HapMiner can define a statistical score for each marker, indicating the strength of its association with the trait. The approach consists of two major steps. Firstly, pedigree data are used to infer haplotype configurations for each pedigree . And a phenotype score is defined for each founder haplotype in a family based on the phenotypes of family members who possess that haplotype. (Thus identical founder haplotypes from different families may have different phenotype scores.) Secondly, all founder haplotypes from all families, which can be assumed unrelated, together with their associated phenotype scores will be evaluated at the population level using the HapMiner algorithm . Our proposed approach can deal with families with arbitrary sizes. Both continuous and binary traits can be considered. Haplotype configurations will be inferred using our most recent haplotype inference algorithm named PedPhase.DSS . Founder haplotypes are then clustered using a position weighted similarity measure  and each cluster is evaluated by comparing its phenotypic mean with the overall phenotypic mean . Our approach is different from TDT-type procedures, because essentially haplotype-based TDT tests treat each haplotype as a distinct allele, while our approach considers haplotype similarities. The overall procedure is summarized in Figure 1 with details in sequel.
The computational framework of the proposed approach F_HapMiner. (1) Infer haplotypes on all families based on the DSS algorithm. (2) Calculate the phenotype score for each founder haplotype from each family based on its occurrences in affected and normal members. (3) Cluster all the founder haplotypes using a position weighted haplotype similarity measure. (4) Evaluate the correlation between clusters and the trait using a statistical test.
Haplotype inference in general is hard, even with family information. The total number of consistent haplotype assignments can be very large depending on the size of the pedigree and missing patterns. When there are no recombinations within a family and no missing alleles, efficient optimal algorithms do exist [19, 21]. One of our most recent developments based on disjoint-set structures (denoted as the DSS algorithm ), can effectively handle family data with no recombinants. For the special case of data with no missing, it is an (almost) linear time algorithm for pedigrees with no loops. Briefly, the algorithm first formulates genotype constraints as a linear system of inheritance variables. It then utilizes disjoint-set structures to encode connectivity information among individuals, to detect constraints from genotypes, and to check consistency of constraints. Here we use an example (Figure 2) to illustrate the basic idea of the algorithm and details can be found in . Figure 2.a shows the input genotypes of a pedigree with eight members for 2 SNPs. After one applies the Mendelian law (the paternal allele is separated from the maternal one by a "|") and fixes one heterozygous locus in each founder (members 1, 2, 3, 7), there are 5 heterozygous loci left to be determined (Figure 2.b). But not all of them are free due to the constraint of no recombination. Those uncertainties about heterozygous SNPs are represented by p variables. The DSS algorithm first constructs locus graphs, one for each SNP (Figure 2.c), which connect each child with their heterozygous parents through inheritance (h) variables. The 0-recombination constraint is enforced by using a single h variable for each parent-child pair throughout all SNPs. The algorithm then traverses these locus graphs to collect constraints, to check the consistency/conflict and to obtain solutions of h variables and then p variables using disjoint-set structures. At the end, there is only one degree of freedom in h variables (Figure 2.d). Once that freedom is fixed, all p variables are also fixed (Figure 2.e).
Illustration of the DSS algorithm. a) The input pedigree with 8 members and their genotype data. b) Haplotypes are partially determined based on the Mendelian law and denoted as (paternal | maternal). In addition, one heterozygous SNP is fixed in each founder (an individual without parents in the pedigree). Five SNPs are left with freedom. c) Locus graphs for the two loci. Each graph has the same set of nodes as the original pedigree, where shaded (predetermined) nodes representing fixed SNPs in b). Each child is linked to its heterozygous parents, with edges labeled using h-variables. Node 5 is duplicated for easy process. d) Left: constraints on h-variables collected based on the two locus graphs using disjoint-set structures. Constraints are collected based on each pair of linked predetermined nodes (or duplicated nodes), and only no redundant ones are kept. Right: Solutions of h-variables can be obtained directly based on the disjoint-set structures. They are represented by free variables α. In this example, only one degree of freedom. e) Solutions of p variables derived from h variables in d).
as suggested by Qian , where = Σ j Y ij /J i is the average of the trait values in family i. The phenotype value Y ij can be the quantitative trait value itself or 1/0 for affected/normal individuals. We have tested both measures and preliminary results show that the latter measure has slightly better results. Therefore, we report only results using the second measure in our experiments.
The founder haplotypes are treated as independent. Together with their phenotypic scores, they serve as the input of the HapMiner program [8, 9]. The basic assumption of HapMiner is that due to linkage disequilibrium, disease-associated haplotypes are expected to be more similar to each other than haplotypes randomly drawn from the population. Therefore, HapMiner directly explores the sharing of haplotype segments that observe extreme phenotypes. The measure of sharing between two haplotypes is defined by a position weighted similarity score, which combines the length of the shared segments and the number of identical alleles around a given marker position. For each marker position, a haplotype segment centered at the position will be clustered based on the similarity measure. Each cluster is evaluated using a Q-score, which is defined based on the deviation of the phenotypic mean of the cluster from the mean of all samples (t-statistic). The highest score among all clusters is taken as the score of that marker position. The significant level can be obtained via a permutation test (However Bonferroni correction for multiple testing is used in the simulation for efficiency). More details about the algorithm can be found in [8, 9].
We evaluate the performance of F_HapMiner using extensive simulations with realistic parameters. The simulation consists of three steps. We first obtain population haplotype frequencies from two datasets, representing different marker densities and haplotype/genotype frequencies. The first dataset is based on the Cystic Fibrosis (CF) study  and the second one is the simulated dataset from the Genetic Analysis Workshop (GAW) 15 . CF data is a well-known dataset that has been examined by many researchers. We take the same 29 haplotypes and their frequencies estimated by Becker and Knapp  (also see Table A1 in Additional file 1). The total length of the region is 1.8 Mb with 19 loci, but marker interval distances vary dramatically. The second dataset is a portion of the simulated data from the GAW 15, which was used to model the complex genetic architecture of rheumatoid arthritis (RA). We randomly choose 500 families from the first replicate, and take a segment of 20 SNPs from chromosome 6 centered at the HLA-DRB1 locus. The average marker interval distance is about 10 kbp. The haplotypes of each individual are known and their frequencies are estimated based on their counts in parents. The total number of distinct haplotypes is 65 (Table A2 in Additional file 1). For both datasets, only haplotypes and their frequency distributions (Tables A1 & A2 in Additional file 1) are used in our simulation. We refer data generated based on these frequencies as CF and GAW dataset, respectively. To generate a set of realistic pedigrees in step two, we directly sample family structures from the 65 pedigrees of the CEPH study . A family in this dataset may have two (13 out of 65) or three (52 out of 65) generations with 4-20 members (average 13). Figure A1 in Additional file 1 shows one typical CEPH family structure. Given a set of parameters, we generate pedigrees one by one as follows. First, one CEPH pedigree is randomly selected. Second, for the pedigree structure selected, each founder will be assigned two haplotypes, sampled independently based on the population haplotype distribution defined in step one. Haplotypes of non-founders are obtained based on Mendelian law assuming no recombination. In the third step, we assign phenotypes to each member in each family based on two different disease models: a single-locus model and a rare haplotype model. For the single locus model, we assume only one SNP in the region will increase the risk of being affected. The penetrance, which defines the probability of being affected given a specific genotype at the risk locus, will take realistic values. The disease status of each individual will be determined based on the genotype at the risk locus. To evaluate the effect of disease allele frequencies, we take each SNP in turn as the risk SNP, which will then be removed before applying any statistical methods. Therefore, statistical power will mainly depend on local LD strength and structures. In addition, we also consider a rare haplotype model, i.e., a few rare haplotypes may increase the risk of being affected. This model is to simulate some common complex diseases that might have haplotype effect or allele heterogeneity, or simply common diseases caused by multiple rare mutations . For the haplotypes obtained from the CF dataset, there are 22 rare haplotypes with the same frequency (0.01786). For the haplotypes obtained from GAW 15, a haplotype is regarded as a rare haplotype if its population frequency is less than 0.02. A certain number of (2 to 6) haplotypes from these rare haplotypes are randomly selected as risk haplotypes with the same effect. Individuals carrying one or two disease haplotypes will have higher risks to be affected. The effect of each risk haplotype is also defined based on penetrance. Only pedigrees with at least one affected member will be retained. More pedigrees with genotypes/haplotypes and phenotypes can be generated in the same way, until a specified number of pedigrees (which is a parameter) can be reached.
We investigate the performance of F_HapMiner by considering different population parameters, and in comparison with two variants of TDT-based methods. Some important factors that affect the statistical power of any approach include linkage disequilibrium and haplotype patterns within the region, risk allele/haplotype frequency, disease penetrance, and sample size. We model LD/haplotype patterns by directly sampling founder haplotypes from haplotype distributions of real data (CF) and simulated data (GAW 15). In terms of risk allele frequencies, each SNP has been taken as the risk locus once for the single-locus model, with minor allele frequencies range from 0.00045 to 0.482. For the rare haplotype model, 2-6 haplotypes with small frequencies are randomly chosen as risk haplotypes. Penetrance, which represents the effect size of a disease locus, is another important parameter. One can specify an ordered triple (pA|dd, pA|dD, pA|DD) as the penetrance set, where each element represents the penetrance of having 0, 1, or 2 disease alleles or haplotypes. We use three penetrance sets (Table 1). Set C is adopted from Qian's study , which happens to correspond to a population prevalence around 0.1 when the risk allele frequency is 0.1. We further choose two additional sets with smaller penetrance (A and B in Table 1), and their population prevalence ranges from 0.07 to 0.15 and 0.028 to 0.09 respectively, for allele/haplotype frequency from 0.1 to 0.5. Their relative effect sizes can also be illustrated using the concept of genotype relative risks (λ1 = P (A|Dd)/P (A|dd) and λ2 = P (A|DD)/P (A|dd)), as illustrated in Table 1. Table 1 also provides the distribution of different genotypes given the affected status when the disease allele frequency p D = 0.1. Different sample sizes (i.e., number of pedigrees) are considered for power comparisons of three approaches: F_HapMiner, a single-locus TDT implemented in PLINK , and a haplotype-based TDT implemented in FBAT . The power of an approach is defined as the percentage of detecting significant associations in 100 replicates after adjustment of multiple testing.
Penetrance sets and their effects. G: genotype, d: normal allele/haplotype, D: disease allele/haplotype, A: affected.
To assess the power of different approaches of detecting significant associations between SNPs and traits, it is important to have a proper control of false positive discoveries due to chance (i.e., type I errors). In this study, we set the overall error rate to be 0.05 after Bonferroni correction of multiple testing for all experiments. The type I error rate of each method was estimated as the proportion of significant associations reported in all replicates under the null model in which no SNP or haplotype carries disease risks. The average false positive rates over all parameter combinations tested for F_HapMiner (with the haplotype segment length of 1), the single-locus TDT and the haplotype-based TDT are 1.1%, 3.6%, and 6.3% respectively. Single-locus TDT is slightly conservative and F_HapMiner is quite conservative. We suspect that the primary reason for this is due to Bonferroni correction of multiple testing. On the other hand, no correction is needed for the haplotype-based TDT and it tends to have a slightly inflated type I error rate.
We performed extensive experiments to evaluate the proposed approach. The results were organized into two subsections based on different disease models (i.e., single SNP vs. rare haplotype model). For each model, we first evaluated the effect of the haplotype segment length parameter of F_HapMiner and chose a proper length for each of the models for remaining tests. For the single-locus disease model, we have examined the relationship of LD structure and mapping precision. We then compared the power of our approach and the single-locus TDT approach using different penetrance values and examined the effect of allele frequencies. For the rare haplotype model, we have investigated the power of the three approaches using different penetrance values, different number of rare haplotypes and different sample sizes.
When evaluating the association of phenotypes and haplotypes, F_HapMiner uses a sliding window approach with an important parameter of the haplotype segment length. The motivation of using haplotype segment is to capture untyped risk SNPs or haplotype effect. Ideally one should use a variable window size to closely capture the variation of local LD structure (which is currently under consideration). In the existing implementation of F_HapMiner, users have to use a fixed window size. We first tested the effect of this parameter on both datasets. Results indicated that the precise behaviors critically depend on LD structures. Figure 3 shows the results of different segment lengths using the CF dataset for the penetrance set A with a sample size of 50 pedigrees. For easy illustrations, SNPs were separated into three groups based on their minor allele frequencies. The information about minor allele frequencies, group memberships, as well as power for each individual SNPs (for the segment length of 1, i.e., a single SNP) can be found in Table 2. Based on Figure 3, one might conclude that with high MAF, the power of F_HapMiner deteriorates significantly when one increases the segment length, while it shows no or only slight decreases with low to medium MAF. However, by carefully examining the LD structure of the region (Figure A2 in Additional file 1), we believe that the real reason behind this result is that the SNPs with high MAF (SNPs 1, 3, 5, 16, 17, 18, and 19) happen to be in short haplotype blocks. Therefore, extending haplotype segments beyond block boundaries actually brought noises to the analysis. Results based on GAW 15 dataset also showed the consistent trend. Therefore, for the single locus disease model, we chose the window size of 1 for F_HapMiner and only compared its performance with the single-locus TDT approach. The results are shown in Table 2 and Figure 4. In almost all cases (Table 2), F_HapMiner constantly outperforms the single-locus TDT, regardless of penetrance models, allele frequencies, or LD structures. The improvement is more significant when the power of the single-locus TDT is in the range of 35% to 85%. We believe that the gain of F_HapMiner is mainly from two sources. First, we inferred the inheritances and haplotypes by considering all SNPs jointly while TDT only took into accounts of individual informative markers. Second, the phenotype scores defined earlier indeed captured the correlations between the disease and the risk SNP. In terms of disease effects (the three penetrance sets), both approaches have worst performance on Set A, reflecting its weak signals. Allele frequencies also greatly impact the results. Both approaches have very low power for the two SNPs with extremely low MAF (≤ 5.5%, first two rows in Table 2). On average, the power increases with the increase of allele frequencies for penetrance Sets A & C (Figure 4). However, LD patterns can greatly affect results, even for SNPs with similar allele frequencies. For example, SNP 4 has a MAF of 0.21. But the power is very low for both approaches and for all three penetrance models. LD analysis illustrates that SNP 4 has very low LD with other SNPs (Figure A2 in Additional file 1). For the penetrance set B, the power (vs. allele frequencies) of both approaches behaves differently: the average of power actually declined when allele frequencies increased (the Medium MAF group vs. the High MAF group). Furthermore, both approaches were more powerful on the low MAF group and less powerful on the high MAF group for the set B, comparing to the results on the set C. These differences actually reflect the fact that approaches behave differently for different models with respect to allele frequencies (many previous papers have shown that in the extreme case of recessive models versus dominant models, approaches behave differently when allele frequencies vary). Our results demonstrate that detection of genetic variants responsible to diseases is tricky even for a single risk SNP if it is not typed. The success (power) depends on many interrelated factors including disease models, allele frequencies and LD patterns. Results (Table A3 in Additional file 1) on the GAW dataset also support our observations.
The effect of sliding window sizes. The power of F_HapMiner on the single locus model using different sliding window sizes, grouped according to MAF (Table 2). The power may be adversely affected with large window sizes when haplotype blocks are short (the case of SNPs in the group with high MAF). Result is based on 50 pedigrees on the CF dataset using the penetrance set A.
Power comparison on the single-locus model. Power of F_HapMiner and the single-locus TDT on the CF dataset using three different penetrance models. Result is based on 50 pedigrees and grouped according to MAF. The letter inside the parentheses indicates the penetrance set.
The power of F_HapMiner and the single-locus TDT (SL-TDT) on each SNP for different penetrance models using the CF dataset (sample size = 50 pedigrees). SNP are ordered and grouped based on their minor allele frequencies (MAF).
Both F_HapMiner and the single-locus TDT reported p-values for each marker position. We then examined the mapping precision of both approaches, defined as the genetic distance between the SNP with the lowest p-value and the real risk SNP based on the original map file of the CF study. On average, results from F_HapMiner are more accurate than those from the single-locus TDT (Figure 5), which is consistent with the results that F_HapMiner archived higher power. Overall, the prediction of F_HapMiner on penetrance set A is about 28% closer comparing to the TDT. Higher minor allele frequencies in general improve the mapping precision.
Mapping precision on the single-locus model. Average distances in centimorgan from the predicted SNP to the true risk SNP for F_HapMiner and the single-locus TDT using the CF data. Result is based on 50 pedigrees using using the penetrance set A and grouped according to MAF.
In this experiment, we randomly selected 2 to 6 rare haplotypes from each dataset as risk haplotypes. For each number of risk haplotypes, we made 10 random selections from the dataset. For each selection, 100 replicates were generated. The results were grouped and averaged based on the number of risk haplotypes. We compared the performance of F_HapMiner, the single-locus TDT and the haplotype-based TDT. For F_HapMiner, we first tested the effect of the sliding window size of 5 to 10. Preliminary results (data not shown) demonstrated that F_HapMiner achieved slightly higher power for longer haplotype segment lengths, but with increased running time. By considering all these factors, as well as the block structures, we decided to use the window size of 10. Because the total frequency of all risk haplotypes is low, one would expect that it is harder to detect associations. Indeed, we had to increase the sample size to at least 100 families to ensure all methods had some power to detect signals for the penetrance set A.
As expected, for the rare haplotype model, both haplotype-based approaches performed significantly better than the single-locus TDT (Figures 6 &7). In particular, for all three penetrance models, F_HapMiner outperformed the single-locus TDT by a margin of 9% to 130% using the CF dataset (Figure 6). The result certified that F_HapMiner had mostly captured the haplotype effect, comparing with the single-locus TDT, which examined each SNP one at a time. Such an advantage is more significant when the number of rare risk haplotypes is small. For example, the power of F_HapMiner has doubled with 2 risk haplotypes. Similar trend has been observed using the GAW dataset (Table 3). There are other interesting observations. One has to double the sample size when using the GAW data (Table 3, 200 families/replicate) to achieve comparable power as the CF data (Figure 6, 100 families/replicate). The primary reason is that the estimated rare haplotype frequencies in the GAW data are much lower than those from the CF data (Tables A1 & A2 in Additional file 1). All approaches have lower power with low frequencies from GAW data. Furthermore, because of the low frequencies, both F_HapMiner and the single-locus TDT performed much better using the penetrance set B comparing with the set C, which was also consistent with our earlier observation that approaches tended to perform better for the model B when allele frequencies were low (Table 2). The haplotype-based TDT also outperformed the single-locus TDT (Figure 7). For the two haplotype-based approaches, their power was comparable when using 300 pedigrees. However, F_HapMiner achieved much higher power than the haplotype-based TDT in the case of 200 pedigrees, with one exception when using 6 rare haplotypes, in which case they had the same power (Figure 7). The result demonstrates that F_HapMiner has higher sensitivity to moderate signal. Figure 7 also shows that the performance of the haplotype-based TDT gradually increased with more rare haplotypes, while the power of F_HapMiner deteriorated with high number of risk haplotypes (e.g., 5 and 6). On one hand, this seems to make sense because F_HapMiner only outputs the most significant cluster. On the other hand, the behaviors of both approaches actually depend more on the fact that which haplotypes are selected as risk haplotypes and how similar they are (Figures 6, 7 and A3 in Additional file 1), not so much on the number of rare haplotypes. As expected, the power of all approaches will increase with the increase of sample sizes (Figures 7 and A3 in Additional file 1). We notice that a better method might achieve higher power even with smaller number of samples (F_HapMiner with 200 families vs. the single-locus TDT with 300 families in Figure 7). Furthermore, the power usually does not have a linear relationship with the sample size. With the same number of increased samples, an approach will gain much more when its power is low (Figures 7 and A3 in Additional file 1).
Power comparison on the rare haplotype model. Power of F_HapMiner and the single-locus TDT on the CF dataset using three penetrance sets, grouped based on the number of risk haplotypes. The letter inside the parentheses indicates the penetrance set. Result is based on 100 pedigrees per replicate.
Power comparison with the haplotype-based TDT. Power of F_HapMiner, the single-locus TDT and the haplotype-based TDT on the CF dataset using the rare haplotype model with the penetrance set A for different sample sizes.
Power of F_HapMiner and the single-locus TDT (SL-TDT) using the GAW dataset on the rare haplotype model (sample size = 200 pedigrees).
We have proposed a new approach for family-based haplotype association testing and fine mapping. For a given candidate region, we assume that no recombination has been observed within each pedigree. The approach consists of three steps. First, we use a novel algorithm to infer diplotype pairs of each individual in each pedigree. Our previous experiments have shown that the DSS haplotype inference algorithm is very efficient and accurate when there are no recombinations and when missing genotypes are randomly distributed across SNPs and members. The DSS algorithm can also handle families with a few recombinants. In the current implementations, when there exist multiple haplotype assignments with zero recombinant, we randomly select one as the true solution. This might adversely affect the results. More recently, we extend DSS to use population information to select the most likely solution , which may further improve the power. However, even this new approach cannot effectively handle data with many untyped members (usually founders) that may happen in real data. The difficulty lies in the fact that in this case one cannot use the zero recombinant assumption to effectively limit the search space. We currently investigate new formulations and approaches for such cases. In the second step, a phenotype score is defined for each founder haplotype to measure its correlation with the phenotype. Haplotypes that appear more frequently in affected/high risk members tend to receive higher scores. On the contrary, haplotypes in normal members will get lower scores. This way, the haplotype-phenotype correlation embedded in descendants is collected and accumulated to founder haplotypes. Such information is then used in subsequent haplotype-based association tests using a clustering approach based on haplotype similarities in step three. Extensive experiments demonstrate that our approach outperforms the single-locus and haplotype-based TDTs, on both the single-locus disease model and the rare haplotype model. F_HapMiner has several advantages over the haplotype-based TDT approach implemented in FBAT. Our experience shows that FBAT requires large number of samples to obtain haplotype population frequencies. For instance, FBAT cannot process about 7% of the total replicates when the sample size is 200. Additionally, FBAT also has limits on the total number of different haplotypes, which implies that it cannot handle large regions with more SNPs.
We generated simulated data based on the haplotype frequency distributions of two datasets (CF and GAW 15). Our experiments show that haplotype patterns (diversity, frequencies, LD structures) have profound impacts on the power to detect associations. On the contrary, marker interval distances have less effect. Our disease models probably have higher relative risks comparing to real complex diseases. The sizes of CEPH pedigrees are much larger than many real studies. In practice, for genotype relative risks of 1.2 to 1.5, and/or for families with smaller sizes, a much large number of samples are needed. More tests on different disease models and pedigree sizes are warranted. Furthermore, a test of the approach on a real data set from our collaborators is currently underway. We also observe that the single-locus TDT also has some power in detecting associations for the rare haplotype model (Figure 6). Further detailed analysis tells us that the power actually came from different SNPs, whose frequencies happen to be close to the frequencies of rare haplotypes.
F_HapMiner can be extended in a few ways. Firstly, it relies on users to specify the sliding window size for the clustering procedure. The optimal value depends on the input data (e.g., local LD structures) and the characteristics of the underlying disease. One solution is to determine the sliding window size based on the LD structure of the data. The sliding window may extend in both directions until current SNP is in low LD with next one. We are currently investigating new approaches to automatically adjust this parameter. Secondly, a phenotype score is calculated for each founder haplotype to represent its correlation with the disease. Whether a score can reflect the real correlation is crucial to the performance of F_HapMiner. Ideally, phenotype scores of disease-related haplotypes should have higher values with small variance. In the future, we will investigate new phenotype scores to further improve the power of F_HapMiner. Finally, we assume zero recombination in the candidate region and infer haplotype based on this assumption. This assumption can be relaxed as we are extending our haplotype inference algorithm to allow recombinations.
In summary, we have presented a novel haplotype-based approach of association testing and fine mapping using family data. Simulation results have shown that our approach F_HapMiner outperforms the single-locus TDT for both the single locus model and the rare haplotype model. F_HapMiner also has advantages over the haplotype-based TDT when the sample size is moderate.
This research is supported by National Institutes of Health/National Library of Medicine grant LM008991, and in part by National Institutes of Health/National Center for Research Resources grant RR03655. Support for generation of the GAW15 simulated data was provided from NIH grants 5RO1-HL049609-14, 1R01-AG021917-01A1, the University of Minnesota, and the Minnesota Supercomputing Institute. We would also like to acknowledge GAW grant R01 GM031575.
This article has been published as part of BMC Bioinformatics Volume 11 Supplement 1, 2010: Selected articles from the Eighth Asia-Pacific Bioinformatics Conference (APBC 2010). The full contents of the supplement are available online at http://www.biomedcentral.com/1471-2105/11?issue=S1.
JL initiated the project and designed the experiments. YC carried out the experiments. XL performed the haplotype inference experiment. All three together drafted the manuscript.


This returns true if the two arrays are equal. Java array initialization can be accomplished in several ways. Therefore, it is possible to create a two dimensional array in Java where individual one dimensional arrays has different length. Apple Banana Cherry Melon Orange Pineapple Plum But since sorting is a common programming problem, Java has a built-in solution for this. To avoid this problem, we can specifically convert the Integer Array to an ArrayList.
We can use it two ways, one is after the data type keyword, and the other is after the variable name. The next step is to allocate memory for array elements. Learn more on Java Type casting. Initialize the Array When you initialize an array, you define a value for each of its elements. Declare a New Java Array As Java arrays can only contain elements of the same type, you need to define which data type it will use when you declare a new array. Arrays class, as you can see in the example. There isn't any need to tell the size between the brackets, because the initialization and its size are specified by the count of the elements between the curly brackets.
Filed Under: Comment Policy:Please submit comments to add value to the post. Let's take the above example again. If you want to post code then wrap them inside tags. Note that since the array was not initialized, the value of the variable is still null at this point of time. Declaring Array Variables To use an array in a program, you must declare a variable to reference the array, and you must specify the type of array the variable can reference. Have you ever started filling out a form or blog post only to have your session to expire or your browser to crash losing all of your work in the process? Two arrays are considered equal if both arrays contain the same number of elements, and all corresponding pairs of elements in the two arrays are equal.
For small arrays, we can just populate the data. How many times has the form been reset on you after you've. If you want to know more about Java development take a look at our collection of. The code is a bit long because it is meant to be a more readable algorithm. And since there is only one square brackets, this means that the variable is only a one-dimensional String Array. If you've made it this far in the series you should have a core understand on the basics and how all of them fit together inside of Canvas. There is a multi-line code approach to declare the array, then initialize it.
Arrays class contains various static methods for sorting and searching arrays, comparing arrays, and filling array elements. What is it you need to do to create a game? And the ArrayList instance has no limitation of being fixed size. For the instantiation, you need to use the new keyword that creates a new array instance in the memory. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above. Convert String Array to a String For display purposes, it is sometimes required to convert a String Array to a String. We only want to keep 5 high scores, so we will limit the array to 5 buckets. Here is a shortened example of our code above using Arrays.
To illustrate the new attributes and some of the new input types, we'll be building up a simple sign up form. Java Arrays Think of a Java array as a table. Would you like to answer one of these instead? As with variables of other types, the declaration does not actually create an array; it simply tells the compiler that this variable will hold an array of the specified type. I think I should write a loop and set all values to zero, is there any better way to do this? We can use the sort method in java. In case of objects of a class,. To stay updated with the latest development news, tips, and tricks, you can also follow Developer Drive on and. Would you like to answer one of these instead? We can initialize array using new keyword or using shortcut syntax which creates and initialize array at the same time.
The second line of output reflects the values assigned to the variable items. As Java is a as opposed to interpreted languages , you also need to define the size of the array before passing it to the compiler. Declaring and Initializing an Array Let's say we have a Java program for a game, and we need to keep track of the high scores. Characteristics of a Java Array Java arrays are case-sensitive and zero-based the first index is not 1 but 0. Asparagus Carrot Tomato Also note that even the size of array will be changed if re-initialized. Java array initialization can be accomplished in several ways. An array is used to store a collection of data, but it is often more useful to think of an array as a collection of variables of the same type.
But since there are two 5's, the set will only have 3 elements. That is, to use new to allocate an array, you must specify the type and number of elements to allocate. This is because the individual item of the array is un-initialized. See your article appearing on the GeeksforGeeks main page and help other Geeks. Hence the variable testArray can hold up to 4 values of int, because we assigned to it the newly created array.


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Tag Archives: banks
Open Banking: Citi to become 1st corporate bank to enroll with UK Open Banking
Citi is gearing up to become the first corporate bank to enroll as a Payment Initiation Service Provider (PISP) with UK Open Banking Directory. According to news sources, CitiBank will join UK's Open Banking framework to provide an aggregated payments collections service for its business clients, tapping the APIs of the country's largest nine banks, with …
CBN to embrace Open Banking System
As the banking system moves to deliver quality services to consumers in the 21st century driven by new technological advancements, the Central Bank of Nigeria (CBN) has declared its interest in Open Banking System which grants bank customers easy access to their data and opening the financial system to applications and other services. This was …
"We do not yet have an end-to-end view of opportunities that exist in Open Banking space" – Opeyemi Ndukwe
Managing Partner, Blue Advisory, Opeyemi Ndukwe has described opportunities that abound with open banking platform as prospects that its end cannot be quantified now. Ndukwe who was at the launch of the platform in Lagos said that open banking is a wide space where so much can happen. "We do not yet have an end-to-end …
Open Banking Nigeria: Transforming Payments in Nigeria with Open APIs
by Adedeji Olowe This is the email that birthed Open Banking Nigeria; just 2 minutes to midnight on June 1, 2017. Sent to those unfortunate to be my friends and so, are subject to never-ending streams of half-baked ideas and utter madness. It's a very long read, be warned! **************************************** Good evening everyone, At one …
Open Banking Nigeria: Organisation officially launches API Standards with Banks and Fintechs
The concept of Open Banking has been gathering waves over the years but reached a watershed with the pronouncement of the PSD2. Beyond Europe, countries such as Australia, Hong Kong, United Kingdom, and Nigeria are at various stages of review, design or implementation of Open Banking. Open Banking is an initiative designed to enhance and …
Open Banking: Open Technology Foundation launches free APIs to make banking easier
The Open Technology Foundation, a not-for-profit organization, has launched Open Banking Nigeria open source APIs, in a bid to drive innovation and choice for all parties within the financial industry. The APIs, which are the first set of free banking APIs in Nigeria, is to be adopted as a standard for the Nigerian financial industry …
Open Banking API Standard is brought to you by the Open Technology Foundation, an NGO backed by a group of industry experts across banking, fintech, risk management, and more.
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package org.jabref.logic.shared;

import java.sql.Connection;
import java.sql.SQLException;
import java.util.ArrayList;
import java.util.List;
import java.util.Map;
import java.util.Objects;
import java.util.Optional;
import java.util.Set;
import java.util.stream.Collectors;

import org.jabref.logic.citationkeypattern.GlobalCitationKeyPattern;
import org.jabref.logic.exporter.BibDatabaseWriter;
import org.jabref.logic.exporter.MetaDataSerializer;
import org.jabref.logic.importer.ParseException;
import org.jabref.logic.importer.util.MetaDataParser;
import org.jabref.logic.shared.event.ConnectionLostEvent;
import org.jabref.logic.shared.event.SharedEntriesNotPresentEvent;
import org.jabref.logic.shared.event.UpdateRefusedEvent;
import org.jabref.logic.shared.exception.OfflineLockException;
import org.jabref.model.database.BibDatabase;
import org.jabref.model.database.BibDatabaseContext;
import org.jabref.model.database.event.EntriesAddedEvent;
import org.jabref.model.database.event.EntriesRemovedEvent;
import org.jabref.model.entry.BibEntry;
import org.jabref.model.entry.event.EntriesEvent;
import org.jabref.model.entry.event.EntriesEventSource;
import org.jabref.model.entry.event.FieldChangedEvent;
import org.jabref.model.metadata.MetaData;
import org.jabref.model.metadata.event.MetaDataChangedEvent;
import org.jabref.model.util.FileUpdateMonitor;

import com.google.common.eventbus.EventBus;
import com.google.common.eventbus.Subscribe;
import org.slf4j.Logger;
import org.slf4j.LoggerFactory;

/**
 * Synchronizes the shared or local databases with their opposite side. Local changes are pushed by {@link EntriesEvent}
 * using Google's Guava EventBus.
 */
public class DBMSSynchronizer implements DatabaseSynchronizer {

    private static final Logger LOGGER = LoggerFactory.getLogger(DBMSSynchronizer.class);

    private DBMSProcessor dbmsProcessor;
    private String dbName;
    private final BibDatabaseContext bibDatabaseContext;
    private MetaData metaData;
    private final BibDatabase bibDatabase;
    private final EventBus eventBus;
    private Connection currentConnection;
    private final Character keywordSeparator;
    private final GlobalCitationKeyPattern globalCiteKeyPattern;
    private final FileUpdateMonitor fileMonitor;
    private Optional<BibEntry> lastEntryChanged;

    public DBMSSynchronizer(BibDatabaseContext bibDatabaseContext, Character keywordSeparator,
                            GlobalCitationKeyPattern globalCiteKeyPattern, FileUpdateMonitor fileMonitor) {
        this.bibDatabaseContext = Objects.requireNonNull(bibDatabaseContext);
        this.bibDatabase = bibDatabaseContext.getDatabase();
        this.metaData = bibDatabaseContext.getMetaData();
        this.fileMonitor = fileMonitor;
        this.eventBus = new EventBus();
        this.keywordSeparator = keywordSeparator;
        this.globalCiteKeyPattern = Objects.requireNonNull(globalCiteKeyPattern);
        this.lastEntryChanged = Optional.empty();
    }

    /**
     * Listening method. Inserts a new {@link BibEntry} into shared database.
     *
     * @param event {@link EntriesAddedEvent} object
     */
    @Subscribe
    public void listen(EntriesAddedEvent event) {
        // While synchronizing the local database (see synchronizeLocalDatabase() below), some EntriesEvents may be posted.
        // In this case DBSynchronizer should not try to insert the bibEntry entry again (but it would not harm).
        if (isEventSourceAccepted(event) && checkCurrentConnection()) {
            synchronizeLocalMetaData();
            pullWithLastEntry();
            synchronizeLocalDatabase();
            dbmsProcessor.insertEntries(event.getBibEntries());
            // Reset last changed entry because it just has already been synchronized -> Why necessary?
            lastEntryChanged = Optional.empty();
        }
    }

    /**
     * Listening method. Updates an existing shared {@link BibEntry}.
     *
     * @param event {@link FieldChangedEvent} object
     */
    @Subscribe
    public void listen(FieldChangedEvent event) {
        BibEntry bibEntry = event.getBibEntry();
        // While synchronizing the local database (see synchronizeLocalDatabase() below), some EntriesEvents may be posted.
        // In this case DBSynchronizer should not try to update the bibEntry entry again (but it would not harm).
        if (isPresentLocalBibEntry(bibEntry) && isEventSourceAccepted(event) && checkCurrentConnection() && !event.isFilteredOut()) {
            synchronizeLocalMetaData();
            pullWithLastEntry();
            synchronizeSharedEntry(bibEntry);
            synchronizeLocalDatabase(); // Pull changes for the case that there were some
        } else {
            // Set new BibEntry that has been changed last
            lastEntryChanged = Optional.of(bibEntry);
        }
    }

    /**
     * Listening method. Deletes the given list of {@link BibEntry} from shared database.
     *
     * @param event {@link EntriesRemovedEvent} object
     */

    @Subscribe
    public void listen(EntriesRemovedEvent event) {
        // While synchronizing the local database (see synchronizeLocalDatabase() below), some EntriesEvents may be posted.
        // In this case DBSynchronizer should not try to delete the bibEntry entry again (but it would not harm).
        if (isEventSourceAccepted(event) && checkCurrentConnection()) {
            synchronizeLocalMetaData();
            pullWithLastEntry();
            dbmsProcessor.removeEntries(event.getBibEntries());
            synchronizeLocalDatabase();
        }
    }

    /**
     * Listening method. Synchronizes the shared {@link MetaData} and applies them locally.
     *
     * @param event
     */
    @Subscribe
    public void listen(MetaDataChangedEvent event) {
        if (checkCurrentConnection()) {
            synchronizeSharedMetaData(event.getMetaData(), globalCiteKeyPattern);
            synchronizeLocalDatabase();
            applyMetaData();
            dbmsProcessor.notifyClients();
        }
    }

    /**
     * Sets the table structure of shared database if needed and pulls all shared entries to the new local database.
     *
     * @throws DatabaseNotSupportedException if the version of shared database does not match the version of current
     *                                       shared database support ({@link DBMSProcessor}).
     */
    public void initializeDatabases() throws DatabaseNotSupportedException {
        try {
            if (!dbmsProcessor.checkBaseIntegrity()) {
                LOGGER.info("Integrity check failed. Fixing...");

                // This check should only be performed once on initial database setup.
                if (dbmsProcessor.databaseIsAtMostJabRef35()) {
                    throw new DatabaseNotSupportedException();
                }

                // Calling dbmsProcessor.setupSharedDatabase() lets dbmsProcessor.checkBaseIntegrity() be true.
                dbmsProcessor.setupSharedDatabase();
            }
        } catch (SQLException e) {
            LOGGER.error("Could not check intergrity", e);
            throw new IllegalStateException(e);
        }

        dbmsProcessor.startNotificationListener(this);
        synchronizeLocalMetaData();
        synchronizeLocalDatabase();
    }

    /**
     * Synchronizes the local database with shared one. Possible update types are: removal, update, or insert of a
     * {@link BibEntry}.
     */
    @Override
    public void synchronizeLocalDatabase() {
        if (!checkCurrentConnection()) {
            return;
        }

        List<BibEntry> localEntries = bibDatabase.getEntries();
        Map<Integer, Integer> idVersionMap = dbmsProcessor.getSharedIDVersionMapping();

        // remove old entries locally
        removeNotSharedEntries(localEntries, idVersionMap.keySet());
        List<Integer> entriesToInsertIntoLocalDatabase = new ArrayList<>();
        // compare versions and update local entry if needed
        for (Map.Entry<Integer, Integer> idVersionEntry : idVersionMap.entrySet()) {
            boolean remoteEntryMatchingOneLocalEntryFound = false;
            for (BibEntry localEntry : localEntries) {
                if (idVersionEntry.getKey().equals(localEntry.getSharedBibEntryData().getSharedID())) {
                    remoteEntryMatchingOneLocalEntryFound = true;
                    if (idVersionEntry.getValue() > localEntry.getSharedBibEntryData().getVersion()) {
                        Optional<BibEntry> sharedEntry = dbmsProcessor.getSharedEntry(idVersionEntry.getKey());
                        if (sharedEntry.isPresent()) {
                            // update fields
                            localEntry.setType(sharedEntry.get().getType(), EntriesEventSource.SHARED);
                            localEntry.getSharedBibEntryData()
                                      .setVersion(sharedEntry.get().getSharedBibEntryData().getVersion());
                            sharedEntry.get().getFieldMap().forEach(
                                    // copy remote values to local entry
                                    (field, value) -> localEntry.setField(field, value, EntriesEventSource.SHARED)
                            );

                            // locally remove not existing fields
                            localEntry.getFields().stream()
                                      .filter(field -> !sharedEntry.get().hasField(field))
                                      .forEach(
                                              field -> localEntry.clearField(field, EntriesEventSource.SHARED)
                                      );
                        }
                    }
                }
            }
            if (!remoteEntryMatchingOneLocalEntryFound) {
                entriesToInsertIntoLocalDatabase.add(idVersionEntry.getKey());
            }
        }

        if (!entriesToInsertIntoLocalDatabase.isEmpty()) {
            // in case entries should be added into the local database, insert them
            bibDatabase.insertEntries(dbmsProcessor.partitionAndGetSharedEntries(entriesToInsertIntoLocalDatabase), EntriesEventSource.SHARED);
        }
    }

    /**
     * Removes all local entries which are not present on shared database.
     *
     * @param localEntries List of {@link BibEntry} the entries should be removed from
     * @param sharedIDs    Set of all IDs which are present on shared database
     */
    private void removeNotSharedEntries(List<BibEntry> localEntries, Set<Integer> sharedIDs) {
        List<BibEntry> entriesToRemove =
                localEntries.stream()
                            .filter(localEntry -> !sharedIDs.contains(localEntry.getSharedBibEntryData().getSharedID()))
                            .collect(Collectors.toList());
        if (!entriesToRemove.isEmpty()) {
            eventBus.post(new SharedEntriesNotPresentEvent(entriesToRemove));
            // remove all non-shared entries without triggering listeners
            bibDatabase.removeEntries(entriesToRemove, EntriesEventSource.SHARED);
        }
    }

    /**
     * Synchronizes the shared {@link BibEntry} with the local one.
     */
    @Override
    public void synchronizeSharedEntry(BibEntry bibEntry) {
        if (!checkCurrentConnection()) {
            return;
        }
        try {
            BibDatabaseWriter.applySaveActions(bibEntry, metaData); // perform possibly existing save actions
            dbmsProcessor.updateEntry(bibEntry);
        } catch (OfflineLockException exception) {
            eventBus.post(new UpdateRefusedEvent(bibDatabaseContext, exception.getLocalBibEntry(), exception.getSharedBibEntry()));
        } catch (SQLException e) {
            LOGGER.error("SQL Error", e);
        }
    }

    /**
     * Synchronizes all meta data locally.
     */
    public void synchronizeLocalMetaData() {
        if (!checkCurrentConnection()) {
            return;
        }

        try {
            metaData.setEventPropagation(false);
            MetaDataParser parser = new MetaDataParser(fileMonitor);
            parser.parse(metaData, dbmsProcessor.getSharedMetaData(), keywordSeparator);
            metaData.setEventPropagation(true);
        } catch (ParseException e) {
            LOGGER.error("Parse error", e);
        }
    }

    /**
     * Synchronizes all shared meta data.
     */
    private void synchronizeSharedMetaData(MetaData data, GlobalCitationKeyPattern globalCiteKeyPattern) {
        if (!checkCurrentConnection()) {
            return;
        }
        try {
            dbmsProcessor.setSharedMetaData(MetaDataSerializer.getSerializedStringMap(data, globalCiteKeyPattern));
        } catch (SQLException e) {
            LOGGER.error("SQL Error: ", e);
        }
    }

    /**
     * Applies the {@link MetaData} on all local and shared BibEntries.
     */
    public void applyMetaData() {
        if (!checkCurrentConnection()) {
            return;
        }
        for (BibEntry bibEntry : bibDatabase.getEntries()) {
            try {
                // synchronize only if changes were present
                if (!BibDatabaseWriter.applySaveActions(bibEntry, metaData).isEmpty()) {
                    dbmsProcessor.updateEntry(bibEntry);
                }
            } catch (OfflineLockException exception) {
                eventBus.post(new UpdateRefusedEvent(bibDatabaseContext, exception.getLocalBibEntry(), exception.getSharedBibEntry()));
            } catch (SQLException e) {
                LOGGER.error("SQL Error: ", e);
            }
        }
    }

    /**
     * Synchronizes the local BibEntries and applies the fetched MetaData on them.
     */
    @Override
    public void pullChanges() {
        if (!checkCurrentConnection()) {
            return;
        }
        // First synchronize entry, then synchronize database
        pullWithLastEntry();
        synchronizeLocalDatabase();
        synchronizeLocalMetaData();
    }

    // Synchronizes local BibEntries only if last entry changes still remain
    public void pullLastEntryChanges() {
        if (!lastEntryChanged.isEmpty()) {
            if (!checkCurrentConnection()) {
                return;
            }
            synchronizeLocalMetaData();
            pullWithLastEntry();
            synchronizeLocalDatabase(); // Pull changes for the case that there were some
        }
    }

    // Synchronizes local BibEntries and pulls remaining last entry changes
    private void pullWithLastEntry() {
        if (!lastEntryChanged.isEmpty() && isPresentLocalBibEntry(lastEntryChanged.get())) {
            synchronizeSharedEntry(lastEntryChanged.get());
        }
        lastEntryChanged = Optional.empty();
    }

    /**
     * Checks whether the current SQL connection is valid. In case that the connection is not valid a new {@link
     * ConnectionLostEvent} is going to be sent.
     *
     * @return true if the connection is valid, else false.
     */
    public boolean checkCurrentConnection() {
        try {
            boolean isValid = currentConnection.isValid(0);
            if (!isValid) {
                LOGGER.warn("Lost SQL connection.");
                eventBus.post(new ConnectionLostEvent(bibDatabaseContext));
            }
            return isValid;
        } catch (SQLException e) {
            LOGGER.error("SQL Error:", e);
            return false;
        }
    }

    /**
     * Checks whether the {@link EntriesEventSource} of an {@link EntriesEvent} is crucial for this class.
     *
     * @param event An {@link EntriesEvent}
     * @return true if the event is able to trigger operations in {@link DBMSSynchronizer}, else
     * false
     */
    public boolean isEventSourceAccepted(EntriesEvent event) {
        EntriesEventSource eventSource = event.getEntriesEventSource();
        return ((eventSource == EntriesEventSource.LOCAL) || (eventSource == EntriesEventSource.UNDO));
    }

    @Override
    public void openSharedDatabase(DatabaseConnection connection) throws DatabaseNotSupportedException {
        this.dbName = connection.getProperties().getDatabase();
        this.currentConnection = connection.getConnection();
        this.dbmsProcessor = DBMSProcessor.getProcessorInstance(connection);
        initializeDatabases();
    }

    @Override
    public void closeSharedDatabase() {
        // Submit remaining entry changes
        pullLastEntryChanges();
        try {
            dbmsProcessor.stopNotificationListener();
            currentConnection.close();
        } catch (SQLException e) {
            LOGGER.error("SQL Error:", e);
        }
    }

    private boolean isPresentLocalBibEntry(BibEntry bibEntry) {
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redpajama_set_name: RedPajamaGithub


TwoSeasideBabes had a bad day yesterday. A really, really bad day, which you can read about here. As a result of their terrible, horrible, no good very bad day, they are having a really big sale. (The details are in that forum post and also in her shop announcement.) As you know, I am a big fan of their hats. I've given them as gifts and even have one myself. So, I can assure you that you DO NOT want to miss out on this sale.


This week we decided to interview 3 sets of entrepreneurs that Sergei works with, right at the end of a two week program where they had to speak with as many prospective customers as possible to try to validate their business idea.
We spoke with a student named Kanishk who had some really creative ideas for how to meet as many doctors and other healthcare professionals as possible in less than 2 weeks. He discusses what worked, what didn't, and how this process was instrumental in helping them figure out what kind of business they can build for healthcare professionals. The founders also told us how they were able to use this process to secure their first pilot customer.


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by · Published April 1, 2021
Sugarman on William Twining: The Man Who Radicalized the Middle Ground
David Sugarman, Lancaster University Law School, is publishing William Twining: The Man Who Radicalized the Middle Ground in the International Journal of Law in Context. Here is the abstract. William Twining is one of...
Legal Education / Legal History
by · Published February 25, 2021
Hibbitts on Pushing the Envelope: How a Handful of Innovative Law Professors Delivered Distance Education in the Age of Langdell @PittLaw @bernardhibbitts
Bernard J. Hibbitts, University of Pittsburgh School of Law, has published Pushing the Envelope: How a Handful of Innovative Law Professors Delivered Distance Education in the Age of Langdell. Here is the abstract. This...
by · Published December 8, 2020
Baker on Character and Fitness for Leadership – Educating Lawyers for Compassion and Courage as well as Brains — The Wizard of Oz was Right @Suffolk_Law
R. Lisle Baker, Suffolk University Law School, has published Character and Fitness for Leadership – Educating Lawyers for Compassion and Courage as well as Brains — The Wizard of Oz was Right at...
Law and Television / Legal Education
by · Published August 26, 2020 · Last modified September 23, 2020
Some Law and Humanities Pieces Of Interest in the Cumberland Law Review @CumberlandLaw
The current issue of the Cumberland Law Review has published several law and humanities pieces of interest. See new graduate Sarah Baldwin's Witch Hunts Among Witches: Anglo-Saxon Law, the Persecution of Witchcraft, and the...
Intellectual Property / Law and Popular Culture / Legal Education
by · Published August 19, 2020
The Center For the Study of the Public Domain @DukeCSPD
Today we'd like to highlight the Duke Law School site, the Center for the Study of the Public Domain. There's a lot to look at and enjoy here. The "About Us" page at the...
Lawyers / Legal Education
New From Hart Publishing: Lawyers In 21st Century Societies, Vol. 1 @hartpublishing
New from Hart Publishing: Lawyers in 21st-Century Societies. Vol. 1: National Reports (Richard L. Abel, Ole Hammerslev, Hilary Sommerlad, and Ulrike Schultz, eds., Hart Publishing, 2020). Here from the publisher's website is a description...
Legal Landmark films: Featuring Important UK Case Law @DavidYuratich
Learn about important UK cases and visit the locations in which they originated in the series of Legal Landmark films, produced by Royal Holloway, University of London, available through this link.
Law and Theater / Legal Education
by · Published March 16, 2020
Millemann, Rauh, and Bowie, Teaching Professional Responsibility Through Theater @MikeMillemann @robertbowiejr
Michael A. Millemann, University of Maryland School of Law, Elliott Rauh, and Robert Bowie, Jr., are publishing Teaching Professional Responsibility Through Theater in the Hastings Race and Poverty Law Journal (2020). Here is the...
Law and Semiotics / Legal Education / Legal Theory
by · Published January 23, 2020
Lloyd on How To Do Things With Signs: Semiotics in Legal Theory, Practice, and Education @LloydEsq
Harold Anthony Lloyd, Wake Forest University School of Law, has published How To Do Things With Signs: Semiotics in Legal Theory, Practice, and Education. Here is the abstract. Discussing federal statutes, Justice Scalia tells us...
Law and Visual Culture / Legal Education
ICYMI: From the Journal of Legal Education: A Symposium on Visual Images and Popular Culture in Legal Education @TheAALS
ICYMI: Michael Asimow and Ticien Marie Sassoubre, both of the Stanford Law School, have published Introduction to the Symposium on Visual Images and Popular Culture in Legal Education, at 68 Journal of Legal Education...


Due to the ideal city-centre location of our hotel, it is an ideal location for a holiday in Munich. Numerous places of interest such as the central station and many other public transport offers can also be reached by foot in a few minutes.
Our 64 rooms are modern and comfortably furnished and elaborately equipped. Thus, the Munich city centre can be discovered in a relaxed fashion.
Our lavish breakfast buffet provides you with the right foundations for an exciting day in Munich, whether going on a stroll into town or on a day trip to the surrounding areas.
The central location of our hotel has the advantage that many places of interest of the Bavarian state capital can be reached quickly. Excursions to the picturesque surroundings are easily possible due to the vicinity of the central station. Simply put together your ideal itinerary for your next visit according to your individual interests and be amazed by the short distances.
People interested in art get their money's worth at the three Pinakotheks in Munich. "Old masters" are on exhibition just as much as modern art.
Interested visitors gain an insight to the diversity of the animal kingdom by visiting the more than 100 year old Hellabrunn Zoo. Be blown away by more than 750 different types of animals on more than 40 hectares of land.
More than 1.5 million visitors a year cannot be wrong – the Deutsches Museum is a highlight of the Bavarian state capital. With more than 28,000 exhibited objects, it is also the world's largest museum with a focus on natural sciences and technology.


The Minimum Wage is a popular topic in exams. It is a good example of how theory can be applied to the real world.
Does A Minimum Wage help Reduce Poverty?
A minimum wage increases the wages of the low paid, but does it actually reduce relative poverty? This is a matter of debate because the effects of a minimum wage may not be felt by the poorest.
The poorest do not work at all. The poorest are on government benefits; therefore, they do not benefit from a minimum wage.
Many workers receiving the minimum wage are second income earners or students. Therefore, the benefits of a minimum wage can often accrue to middle class families.
May increase Black Market Economy. Minimum wage legislation may encourage people to work on the black market, and therefore, avoid all government legislation.
"The obvious policy implication is that minimum wage laws cannot be justified as a poverty reducing device."
See Essay: Does a Minimum Wage Reduce Poverty?
Does A Minimum Wage Cause Unemployment?
For example, the classical analysis of minimum wages suggests that a minimum wage above the equilibrium will cause unemployment.
However, in practice, we see that increasing the National Minimum Wage may not lead to unemployment. For example, in the UK the minimum wage has increased from £3.30 in 1997 to £5.35; however, during this period unemployment has fallen.


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Guitar 'O' Mighty
Your Best Guitar Resource
Best Electric Guitar Under $500
The electric guitar market in the mid to lower price range including solid body electric guitars for $500 and less features offerings from a number of fair to middling guitar companies from smaller makers like Washburn, Aria, Ibanez, Cort, Peavey and Epiphone, Gibson's "more affordable" brand name subsidiary.
Even the storied Fender Music Instrument Company has an amazing three unique entries in this price space, from the top shelf and professional level Modern Player Telecaster to the quirky and cool Mustang to the lesser known and lesser used Duo Sonic. With the Duo Sonic they'll thrown in a guitar stand, tuner, and I think, a even bowl of soup to sweeten the deal for just under $500.
Since I believe the smaller companies offerings here are just okay instruments, and to get a truly "best" Fender or Gibson you have to spend more than $500, I'm recommending a one of a kind company and guitar, the Gretsch's Pro Jet Electric with the famed Paul Bigsby Tailpiece for the whammy bar enthusiasts in the crowd.
Some of the greatest guitarists in popular music history have championed Gretsch guitars, with the likes of Eddie Cochran, AC/DC's Malcolm Young, Duane Eddy, Rolling Stone Brian Jones, Neil Young, the Who's Pete Townshend, country virtuoso Chet Atkins, rock pioneer Bo Diddley, the only musician I know of to have an entire beat named after him and Beatle George Harrison among them.
Perhaps the guitarist who's unique sound is most synonymous with the Gretsch Brand is the great Brian Setzer of Stray Cats fame. The bright shimmering chords and crazy vibrato of his 80's rockabilly hit "Stray Cat Strut" comes instantly to mind. Setzer has used Gretsch guitars exclusively throughout his career and there was a signature model made for him some years back.
Gretsch is a company with strong roots in America, founded by German immigrant Friedrich Gretsch way back in 1883. Starting out making banjos, drums and early percussion instruments like tambourines, the company moved to Brooklyn in 1916 under the tutelage of Freidrichs son where it began it's run of creating truly great instruments to this day. Today Fender owns the company and the Gretsch family retains the name as per a business agreement made in the early 2000's.
So on to the electric guitar model in question here, The Pro Jet Electric. Some of the features of this terrific looking and sounding instrument include 22 jumbo frets for increased range and the cutaway body design for ease in getting at the high notes. It has an arched Maple top and the white pick guard is replete with the old school and still hip Gretsch logo.
The two "blacktop filter" pickups allow for a range of sounds with three toggle positions for a deep bass, midrange or a high trebly tone and volume, volume 2, master volume and master tone controls for easy dynamic switching in performance or recording. Chrome hardware and vintage tuning pegs sit atop the classic Gretsch headstock and pearloid (a plastic material that looks like mother of pearl) hump-block inlays make the fingerboard a thing of black and grey beauty. The Pro Jet Electric guitar comes with light D'Addario strings for the classic early rock sound Gretsch is known for.
Another great feature of a guitar like this, and I believe it's evidenced by the sheer range and scope of famous players from all types of music who love Gretsch, is its versatility. This is an important consideration to make since we are discussing buying on a budget of $500 or less. With a guitar that you can use to play rock, rockabilly pop, jazz, blues, country, country AND western and with the proper dynamics and amplifier setting, some folk and even light classical pieces, you don't have to buy more guitars to make those sounds, if you so desire or budget constraints won't allow at this time.
Any article on Gretsch would not be complete without some attention paid to the unique vibrato tailpiece system used here. Paul Bigsby was the designer who created the unit, the first such "whammy bar" or tremolo device for electric guitar, known for its ease of use and the subtle control over vibrato and string bending it gives the player. Another feature of the Bigsby is that the strings stay in tune after the unit is released back to its normal position after use. Many guitars brands use the Bigsby but it is most closely associated with the Gretsch.
In summary, the Gretsch Pro Jet Electric is an affordable, awesome looking and great sounding versatile electric guitar built with solid craftsmanship, attention to detail and worthy of the storied name on its headstock with the classic look and feel of one of music histories biggest contributors.
Check price and user reviews on Amazon.com
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MEDIOCREMOVIE.CLUB
Side Pieces
Shane of Thrones
Game of Thrones Fantasy
Directed by Paul Thomas Anderson
Starring Daniel Day Lewis, Vicky Krieps, and Lesley Manville
Review by Jon Kissel
Paul Thomas Anderson's best film for me is The Master, and his latest, Phantom Thread, is masters all the way down. Masters on the way out, masters continuing their brilliance, and new masters emerging into the mainstream. Daniel Day Lewis stars in what he claims is his final performance, putting a restrained cap on an expressive and dominant career. PTA continues to make surprising idiosyncratic films finely tuned to his voice, distinct from the homage and imitation of his superlative early career but of a piece with a resume that marks him as the greatest director working in film today. Co-star Vicky Krieps, an unknown Luxembourgian actor, also emerges in one of the finest performances of PTA's filmography, standing toe-to-toe with Day Lewis and forcing him to share the film with a person whose name on a poster could someday soon reap the Day-Lewis-ian levels of anticipation. A film about perfectionists by perfectionists, Phantom Thread lives up to the high standard that's come to be expected from PTA.
Day Lewis stars as Reynolds Woodcock, a character in contention for best name in the highly-competitive race for Best Name in a PTA Film. As the head of a 1950's London fashion house, Reynolds carries himself in a manner befitting a man responsible for precision and elegance. Early sequences explore the House of Woodcock as it fills up with seamstresses, ascending a spiral staircase in orderly lines that seem to go on indefinitely. While Reynolds is the public face and chief designer of the operation, his sister Cyril (Lesley Manville) is the one firmly in charge of the business and of Reynolds' personal life, freeing him up from the tedium of life to think boldly about whatever the aristocratic ladies of post-war Europe will be wearing in the immediate future. One of Cyril's tasks is relationship dissolution, which she does at the beginning of the film for Reynolds' latest paramour. Newly single and fresh off a successful fitting, Reynolds takes a short vacation in the country, where he meets the coquettish, slightly-clumsy Alma (Krieps) while she serves him breakfast at a hotel. He's enamored with her, and they initiate a relationship that, for Alma, quickly moves from enchanting and exciting to oppressive and cold under Reynolds' fastidious eye.
For PTA, the idea to stage a film about a fashion maven is both counter-intuitive and perfectly in line with what he's done before. Moving from grabby premises like the heyday of porn or a sideways telling of Scientology to a romance and a costume drama makes Phantom Thread the least compulsively-watchable of his films. As a skeptic of costume dramas in general, the anticipation for Phantom Thread was markedly less than what was sparked by the electric and goofy trailer for Inherent Vice. On closer look, in a year that also had a contemporary of PTA's, Darren Aronofsky, interrogate his relationship to directing in mother!, Phantom Thread is almost predictable in how closely it aligns with PTA's interests. The director and Reynolds share an attention to detail that fits so well as to be on the nose, but as Phantom Thread continues, more subtle similarities emerge. Reynolds might spend his morning primping and grooming until every hair is in place, but he's not above vulgarity and rudeness, as is PTA. The character is an adult through and through, but he maintains a connection to his childhood through immature outbursts, much like PTA, the director who will surely one day make a straight comedy.
The relationship between the director's mind and the events of Phantom Thread are most interestingly apparent in Reynolds' relationship with Alma. She is being cast in a film from the moment that she stumbles on a dining chair and arouses his attention. He sizes her up and tests her wit before offering her a date that turns into an intrusive exercise in humiliation. Her role expands in his personal and his work life, and when she breaks out of the bubble that he's formed for her, they clash. The specter of Cyril meeting with her alone and telling her to pack her things is always present. Reynolds is gradually revealed to have deep-seated issues with his mother, rendering him needy and bratty and in need of caretaking, such that Alma must put in the work to make him look good and smart and successful. It doesn't take a keen eye, especially in the aftermath of Harvey Weinstein's downfall and all it engendered, to see the comparison to an actress on a film set. The dawning realization of where Phantom Thread is going and what it rhymes with in contemporary life is one of the great thrills of taking it in.
It's not like Phantom Thread is lacking in other great joys. Day Lewis at work is one of them, and he hasn't played a role this subdued since maybe The Boxer in the late 90's. His Reynolds isn't given to outbursts, but that doesn't mean he's lacking in the same all-consuming rage that Bill the Butcher so memorably demonstrated. Seeing Day Lewis, as Reynolds, scowl at someone loudly butter their toast is the equivalent to a knife in the kidneys. What was intimidating in Gangs of New York or There Will Be Blood is silly here, as Day Lewis allows himself to made into a figure of fun. The impulse for control is alluring when he's seen combing his hair, but when it extends to other people, that impulse warps into childishness, a toddler demanding that everything be just so. Petulance is a new trick for Day Lewis, and it's Reynold Woodcock's middle name. In a beautiful touch in keeping with that characterization, Reynolds' personal trademark is to sew something only he knows about into each dress, a little inside joke that gives him some bit of power over the wearer after they've concluded their business. It's another auteurist jab at auteurists themselves, and another example of the levels that PTA and Day Lewis are operating on.
As predictably great as Day Lewis is, the combined might of Krieps and Manville overshadow him. The former is who the film belongs to, the innocent who enters into a strange new world and either bends it to her will or allows it to change her. Alma is something of a mystery, with her vague European accent and all her looks and asides. Krieps verbally withholds much of what she's thinking, as she is an outsider of tenuous status, but she's in so much control of her face and her body language that her concerns and motivations are decipherable. She should have a career like the modern masters of European cinema, women like Juliette Binoche or Isabelle Huppert. Her female counterpart within the House of Woodcock gets the most fun role in a film that finds space for humor amidst the ruffles and the pleats. Manville's Cyril inhabits the role of an older sibling palpably and chillingly, such that the viewer is waiting for her to pin Reynolds to the ground and give him a wet willie. They've settled into a professional and symbiotic relationship, but that intimidation and intolerance of brattiness has been retained. It's Cyril who knows her brother more than he knows himself, and she's been able to sculpt him into the most successful self.
The list of superlatives could go on. Sound designer Christopher Scarabosio is doing yeoman's work, ensuring that the aforementioned toast scraping will go down as the singular sound of the cinematic year. Another master not yet praised, Jonny Greenwood, does some of his best scoring work with PTA since There Will Be Blood, a thematic symphony of rising strings and plaintive piano keys as sumptuous as the costume design by Mark Bridges. As apparent as the greatness is, this is two PTA films in a row that lack the immediate power of his greatest hits. He's getting subtler and deeper and more complex, but the exhilaration I've previously felt after his work wasn't quite there with Phantom Thread. If Phantom Thread's a victim of expectations, it's gets the lightest of punishments. A-
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