Power of inclusion: Enhancing polygenic prediction with admixed individuals.

Admixed individuals offer unique opportunities for addressing limited transferability in polygenic scores (PGSs), given the substantial trans-ancestry genetic correlation in many complex traits. However, they are rarely considered in PGS training, given the challenges in representing ancestry-matched linkage-disequilibrium reference panels for admixed individuals. Here we present inclusive PGS (iPGS), which captures ancestry-shared genetic effects by finding the exact solution for penalized regression on individual-level data and is thus naturally applicable to admixed individuals. We validate our approach in a simulation study across 33 configurations with varying heritability, polygenicity, and ancestry composition in the training set. When iPGS is applied to n = 237,055 ancestry-diverse individuals in the UK Biobank, it shows the greatest improvements in Africans by 48.9% on average across 60 quantitative traits and up to 50-fold improvements for some traits (neutrophil count, R2 = 0.058) over the baseline model trained on the same number of European individuals. When we allowed iPGS to use n = 284,661 individuals, we observed an average improvement of 60.8% for African, 11.6% for South Asian, 7.3% for non-British White, 4.8% for White British, and 17.8% for the other individuals. We further developed iPGS+refit to jointly model the ancestry-shared and -dependent genetic effects when heterogeneous genetic associations were present. For neutrophil count, for example, iPGS+refit showed the highest predictive performance in the African group (R2 = 0.115), which exceeds the best predictive performance for the White British group (R2 = 0.090 in the iPGS model), even though only 1.49% of individuals used in the iPGS training are of African ancestry. Our results indicate the power of including diverse individuals for developing more equitable PGS models.

Machine learning of spectra-property relationship for imperfect and small chemistry data.

Machine learning (ML) is causing profound changes to chemical research through its powerful statistical and mathematical methodological capabilities. However, the nature of chemistry experiments often sets very high hurdles to collect high-quality data that are deficiency free, contradicting the need of ML to learn from big data. Even worse, the black-box nature of most ML methods requires more abundant data to ensure good transferability. Herein, we combine physics-based spectral descriptors with a symbolic regression method to establish interpretable spectra-property relationship. Using the machine-learned mathematical formulas, we have predicted the adsorption energy and charge transfer of the CO-adsorbed Cu-based MOF systems from their infrared and Raman spectra. The explicit prediction models are robust, allowing them to be transferrable to small and low-quality dataset containing partial errors. Surprisingly, they can be used to identify and clean error data, which are common data scenarios in real experiments. Such robust learning protocol will significantly enhance the applicability of machine-learned spectroscopy for chemical science.

Rigorous Progress in Coarse-Graining.

Low-resolution coarse-grained (CG) models provide remarkable computational and conceptual advantages for simulating soft materials. In principle, bottom-up CG models can reproduce all structural and thermodynamic properties of atomically detailed models that can be observed at the resolution of the CG model. This review discusses recent progress in developing theory and computational methods for achieving this promise. We first briefly review variational approaches for parameterizing interaction potentials and their relationship to machine learning methods. We then discuss recent approaches for simultaneously improving both the transferability and thermodynamic properties of bottom-up models by rigorously addressing the density and temperature dependence of these potentials. We also briefly discuss exciting progress in modeling high-resolution observables with low-resolution CG models. More generally, we highlight the essential role of the bottom-up framework not only for fundamentally understanding the limitations of prior CG models but also for developing robust computational methods that resolve these limitations in practice.

Deep learning of dynamically responsive chemical Hamiltonians with semiempirical quantum mechanics.

Conventional machine-learning (ML) models in computational chemistry learn to directly predict molecular properties using quantum chemistry only for reference data. While these heuristic ML methods show quantum-level accuracy with speeds several orders of magnitude faster than traditional quantum chemistry methods, they suffer from poor extensibility and transferability; i.e., their accuracy degrades on large or new chemical systems. Incorporating quantum chemistry frameworks into the ML models directly solves this problem. Here we take the structure of semiempirical quantum mechanics (SEQM) methods to construct dynamically responsive Hamiltonians. SEQM methods use empirical parameters fitted to experimental properties to construct reduced-order Hamiltonians, facilitating much faster calculations than ab initio methods but with compromised accuracy. By replacing these static parameters with machine-learned dynamic values inferred from the local environment, we greatly improve the accuracy of the SEQM methods. Trained on molecular energies and atomic forces, these dynamically generated Hamiltonian parameters show a strong correlation with atomic hybridization and bonding. Trained with only about 60,000 small organic molecular conformers, the resulting model retains interpretability, extensibility, and transferability when testing on much larger chemical systems and predicting various molecular properties. Overall, this work demonstrates the virtues of incorporating physics-based descriptions with ML to develop models that are simultaneously accurate, transferable, and interpretable.

Collision energies: Optimization strategies for bottom-up proteomics.

Mass-spectrometry coupled to liquid chromatography is an indispensable tool in the field of proteomics. In the last decades, more and more complex and diverse biochemical and biomedical questions have arisen. Problems to be solved involve protein identification, quantitative analysis, screening of low abundance modifications, handling matrix effect, and concentrations differing by orders of magnitude. This led the development of more tailored protocols and problem centered proteomics workflows, including advanced choice of experimental parameters. In the most widespread bottom-up approach, the choice of collision energy in tandem mass spectrometric experiments has outstanding role. This review presents the collision energy optimization strategies in the field of proteomics which can help fully exploit the potential of MS based proteomics techniques. A systematic collection of use case studies is then presented to serve as a starting point for related further scientific work. Finally, this article discusses the issue of comparing results from different studies or obtained on different instruments, and it gives some hints on methodology transfer between laboratories based on measurement of reference species.

Unveiling the transferability of PLSR models for leaf trait estimation: lessons from a comprehensive analysis with a novel global dataset.

Leaf traits are essential for understanding many physiological and ecological processes. Partial least squares regression (PLSR) models with leaf spectroscopy are widely applied for trait estimation, but their transferability across space, time, and plant functional types (PFTs) remains unclear. We compiled a novel dataset of paired leaf traits and spectra, with 47 393 records for > 700 species and eight PFTs at 101 globally distributed locations across multiple seasons. Using this dataset, we conducted an unprecedented comprehensive analysis to assess the transferability of PLSR models in estimating leaf traits. While PLSR models demonstrate commendable performance in predicting chlorophyll content, carotenoid, leaf water, and leaf mass per area prediction within their training data space, their efficacy diminishes when extrapolating to new contexts. Specifically, extrapolating to locations, seasons, and PFTs beyond the training data leads to reduced R2 (0.12-0.49, 0.15-0.42, and 0.25-0.56) and increased NRMSE (3.58-18.24%, 6.27-11.55%, and 7.0-33.12%) compared with nonspatial random cross-validation. The results underscore the importance of incorporating greater spectral diversity in model training to boost its transferability. These findings highlight potential errors in estimating leaf traits across large spatial domains, diverse PFTs, and time due to biased validation schemes, and provide guidance for future field sampling strategies and remote sensing applications.

Transferability of the NHS low-calorie diet programme: A qualitative exploration of factors influencing the programme's transfer ahead of wide-scale adoption.

INTRODUCTION: Although behavioural interventions have been found to help control type 2 diabetes (T2D), it is important to understand how the delivery context can influence implementation and outcomes. The NHS committed to testing a low-calorie diet (LCD) programme designed to support people living with excess weight and T2D to lose weight and improve diabetes outcomes. Understanding what influenced implementation during the programme pilot is important in optimising rollout. This study explored the transferability of the NHS LCD Programme prior to wider adoption. METHODS: Twenty-five interviews were undertaken with stakeholders involved in implementing the LCD programme in pilot sites (health service leads, referring health professionals and programme deliverers). Interviews with programme participants (people living with T2D) were undertaken within a larger programme of work, exploring what worked, for whom and why, which is reported separately. The conceptual Population-Intervention-Environment-Transfer Model of Transferability (PIET-T) guided study design and data collection. Constructs of the model were also used as a deductive coding frame during data analysis. Key themes were identified which informed recommendations to optimise programme transfer. RESULTS: Population: Referral strategies in some areas lacked consideration of population characteristics. Many believed that offering a choice of delivery model would promote acceptability and accessibility of the eligible population. INTERVENTION: Overall, stakeholders had confidence in the LCD programme due to the robust evidence base along with anecdotal evidence, but some felt the complex referral process hindered engagement from GP practices. ENVIRONMENT: Stakeholders described barriers to accessing the programme, including language and learning difficulties. Transferability: Multidisciplinary working and effective communication supported successful implementation. CONCLUSION: Referral strategies to reach underrepresented groups should be considered during programme transfer, along with timely data from service providers on access and programme benefits. A choice of delivery models may optimise uptake. Knowledge sharing between sites on good working practices is encouraged, including increasing engagement with key stakeholders.

Core principles of Malakit intervention for transferability in other contexts.

To eliminate malaria, all populations must be included. For those who are not reached by the health care system, specific interventions must be tailor-made. An innovative Malakit strategy, based on the distribution of self-diagnosis and self-treatment kits, has been evaluated in the Suriname-French Guiana- Amapá (Brazil) region. The results showed effectiveness and good acceptability. The Malakit intervention is complex and has many components. Its transferability requires adaptation to other populations and regions, while retaining the main features of the intervention. This article provides the keys to adapting, implementing and evaluating it in other contexts facing residual malaria in hard-to-reach and/or mobile populations. The process of transferring this intervention includes: diagnosis of the situation (malaria epidemiology, characteristics of the population affected) to define the relevance of the strategy; determination of the stakeholders and the framework of the intervention (research project or public health intervention); adaptation modalities (adaptation of the kit, training, distribution strategy); the role of community health workers and their need for training and supervision. Finally, evaluation needs are specified in relation to prospects for geographical or temporal extension. Malaria elimination is likely to increasingly involve marginalized people due to climate change and displacement of populations. Evaluation of the transferability and effectiveness of the Malakit strategy in new contexts will be essential to increase and refine the evidence of its value, and to decide whether it could be an additional tool in the arsenal recommended in future WHO guidelines.

Transferability of Machine Learning Models for Geogenic Contaminated Groundwaters.

Machine learning models show promise in identifying geogenic contaminated groundwaters. Modeling in regions with no or limited samples is challenging due to the need for large training sets. One potential solution is transferring existing models to such regions. This study explores the transferability of high fluoride groundwater models between basins in the Shanxi Rift System, considering six factors, including modeling methods, predictor types, data size, sample/predictor ratio (SPR), predictor range, and data informing. Results show that transferability is achieved only when model predictors are based on hydrochemical parameters rather than surface parameters. Data informing, i.e., adding samples from challenging regions to the training set, further enhances the transferability. Stepwise regression shows that hydrochemical predictors and data informing significantly improve transferability, while data size, SPR, and predictor range have no significant effects. Additionally, despite their stronger nonlinear capabilities, random forests and artificial neural networks do not necessarily surpass logistic regression in transferability. Lastly, we utilize the t-SNE algorithm to generate low-dimensional representations of data from different basins and compare these representations to elucidate the critical role of predictor types in transferability.

Isolation and characterization of polymorphic microsatellite loci for the three Iberian vipers, Vipera aspis, V. Latastei and V. seoanei by Illumina MiSeq sequencing.

BACKGROUND: European vipers (genus Vipera) are a well-studied taxonomic group, but the low resolution of nuclear sanger-sequenced regions has precluded thorough studies at systematic, ecological, evolutionary and conservation levels. In this study, we developed novel microsatellite markers for the three Iberian vipers, Vipera aspis, V. latastei and V. seoanei, and assessed their polymorphism in north-central Iberian populations. METHODS AND RESULTS: Genomic libraries were developed for each species using an Illumina Miseq sequencing approach. From the 70 primer pairs initially tested, 48 amplified reliably and were polymorphic within species. Cross-species transferability was achieved for 31 microsatellites loci in the three target species and four additional loci that were transferable to one species only. The 48 loci amplified in average seven alleles, and detected average expected and observed heterozygosities of 0.7 and 0.55, in the three genotyped populations/species (26 V. aspis, 20 V. latastei and 10 V. seoanei). CONCLUSIONS: Our study provides a selection of 48 polymorphic microsatellite markers that will contribute significantly to current knowledge on genetic diversity, gene flow, population structure, demographic dynamics, systematics, reproduction and heritability in these species, and potentially in other congeneric taxa.

Does protein pretrained language model facilitate the prediction of protein-ligand interaction?

Protein-ligand interaction (PLI) is a critical step for drug discovery. Recently, protein pretrained language models (PLMs) have showcased exceptional performance across a wide range of protein-related tasks. However, a significant heterogeneity exists between the PLM and PLI tasks, leading to a degree of uncertainty. In this study, we propose a method that quantitatively assesses the significance of protein PLMs in PLI prediction. Specifically, we analyze the performance of three widely-used protein PLMs (TAPE, ESM-1b, and ProtTrans) on three PLI tasks (PDBbind, Kinase, and DUD-E). The model with pre-training consistently achieves improved performance and decreased time cost, demonstrating that enhance both the accuracy and efficiency of PLI prediction. By quantitatively assessing the transferability, the optimal PLM for each PLI task is identified without the need for costly transfer experiments. Additionally, we examine the contributions of PLMs on the distribution of feature space, highlighting the improved discriminability after pre-training. Our findings provide insights into the mechanisms underlying PLMs in PLI prediction and pave the way for the design of more interpretable and accurate PLMs in the future. Code and data are freely available at https://github.com/brian-zZZ/PLM-PLI.

Laparoscopic but not open surgical skills can be transferred to robot-assisted surgery: A systematic review and meta-analysis.

BACKGROUND: With an increase in robot-assisted surgery across all specialties, adequate training and credentialing strategies need to be identified to ensure patients safety. The meta-analysis assesses the transferability of technical surgical skills between laparoscopic surgery, open surgery, and robot-assisted surgery. DESIGN: A systematic search was conducted in Medline, Cochrane Central Register of Controlled Trials, and Web of Science. Outcomes were categorized into time, process, product, and composite outcome measures and pooled separately using Hedges'g (standardized mean difference [SMD]). Subgroup analyses were performed to assess the effect of study design, virtual reality platforms and task difficulty. RESULTS: Out of 14,120 screened studies, 30 were included in the qualitative synthesis and 26 in the quantitative synthesis. Technical surgical skill transfer was demonstrated from laparoscopic to robot-assisted surgery (composite: SMD 0.40, 95%-confidence interval [CI] [0.19; 0.62], time: SMD 0.62, CI [0.33; 0.91]) and vice versa (composite: SMD 0.66, CI [0.33; 0.99], time [basic skills]: SMD 0.36, CI [0.01; 0.72]). No skill transfer was seen from open to robot-assisted surgery with limited available data. CONCLUSION: Technical surgical skills can be transferred from laparoscopic to robot-assisted surgery and vice versa. Robot-assisted and laparoscopic surgical skills training and credentialing should not be regarded separately, but a reasonable combination could shorten overall training times and increase efficiency. Previous experience in open surgery should not be considered as an imperative prerequisite for training in robot-assisted surgery. Recommendations for studies assessing skill transfer are proposed to increase comparability and significance of future studies. PROSPERO REGISTRATION NUMBER: PROSPERO CRD42018104507.

The effect of 3D printing speed and temperature on transferability of Staphylococcus aureus and Escherichia coli during 3D food printing.

The current study aimed to determine if the 3D-printing speed and temperature would impact the transferability of foodborne pathogens from the stainless-steel (SS) food cartridge to the 3D-printed food ink. Staphylococcus aureus and Escherichia coli were inoculated onto the interior surface of the SS food cartridges. Subsequently, a model food ink was extruded with a recommended macronutrient contribution of 55.8, 23.7, and 20.5% of carbohydrates, proteins, and fat, respectively. The impact of 3D-printing temperatures and speeds on transfer rates was analysed using a Two-Way ANOVA. S. aureus was transferred more from the cartridge to the food ink with a population of 3.39, 2.98, and 3.09 log CFU/g compared to 2.03, 2.06, and 2.00 log CFU/g for E. coli at 2000, 3000, and 4000 mm/s printing speed, respectively, at 25 °C. A Kruskal-Wallis Test was employed to investigate the effect of different speeds and temperatures on the transferability of S. aureus and E. coli. Speed was the main factor affecting S. aureus transferability, while temperature (25 and 50 °C) had the greatest impact on E. coli transferability. This research seeks to advance the understanding of 3D-printing parameters in pathogen transferability and help the food industry move towards this technology's quick and safe adoption.

Development of Genome-Wide Intron Length Polymorphism (ILP) Markers in Tea Plant (Camellia sinensis) and Related Applications for Genetics Research.

The market value of tea is largely dependent on the tea species and cultivar. Therefore, it is important to develop efficient molecular markers covering the entire tea genome that can be used for the identification of tea varieties, marker-assisted breeding, and mapping important quantitative trait loci for beneficial traits. In this study, genome-wide molecular markers based on intron length polymorphism (ILP) were developed for tea trees. A total of 479, 1393, and 1342 tea ILP markers were identified using the PCR method in silico from the 'Shuchazao' scaffold genome, the chromosome-level genome of 'Longjing 43', and the ancient tea DASZ chromosome-level genome, respectively. A total of 230 tea ILP markers were used to amplify six tea tree species. Among these, 213 pairs of primers successfully characterize products in all six species, with 112 primer pairs exhibiting polymorphism. The polymorphism rate of primer pairs increased with the improvement in reference genome assembly quality level. The cross-species transferability analysis of 35 primer pairs of tea ILP markers showed an average amplification rate of 85.17% through 11 species in 6 families, with high transferability in Camellia reticulata and tobacco. We also used 40 pairs of tea ILP primers to evaluate the genetic diversity and population structure of C. tetracocca with 176 plants from Puan County, Guizhou Province, China. These genome-wide markers will be a valuable resource for genetic diversity analysis, marker-assisted breeding, and variety identification in tea, providing important information for the tea industry.

Supporting the applicability and transferability of the "Sortir Ensemble & Se Respecter" program in France / Accompagner l'applicabilité et la transférabilité du programme « Sortir Ensemble & Se Respecter ¼ en Frances

INTRODUCTION: The prevention of intimate partner violence (IPV) among young people is a major challenge for public policies. Nevertheless, there are a few prevention programs that have proven effective in France. "Sortir Ensemble & Se Respecter" (SE&SR) is a Swiss adaptation of "Safe Dates," an American intervention program that has reduced violent behavior by young perpetrators and victims of IPV alike. The aim of this article is to analyze the applicability and "potential transferability" of SE&SR in France. METHODS: We described the SE&SR intervention by explaining the intervention theory, the key functions (i.e., the "ingredients" allowing the SE&SR program to work), and we commented on its applicability from a perspective of adapting and transferring it to the French context. We used the ASTAIRE tool and the FIC (key functions, implementation, context) approach. RESULTS: The intervention theory highlighted various factors, acting at the individual level (i.e., beliefs/representations, knowledge, life skills) and at the level of the living environment (i.e., facilities welcoming young people; families; public policies; networks of actors), that can prevent IPV among young people. Ten key functions have been identified, revealing the "skeleton" of the Swiss intervention. We drew on these results to comment on the intervention's applicability, with a view to transferability, specifying the contextual elements to consider before implementing SE&SR in France. CONCLUSION: This study aims to make the process of evaluating applicability, with a view to transferring an evidence-based program to the French context, more accessible.

Introduction: La prévention des violences dans les relations amoureuses (VRA) chez les jeunes est un enjeu fort des politiques publiques. Néanmoins, il existe peu de programmes de prévention ayant fait la preuve de leur efficacité en France. « Sortir Ensemble & Se Respecter ¼ (SE&SR) est une adaptation suisse de « Safe Dates ¼, un programme d'intervention américain qui a montré des résultats en matière de réduction des comportements violents tant du côté des jeunes victimes que des auteurs. L'objectif de cet article est d'analyser l'applicabilité et la « potentielle transférabilité ¼ de SE&SR en France. Méthodes: L'approche adoptée consistait à décrire l'intervention SE&SR en explicitant la théorie d'intervention, les fonctions clés (soit les « ingrédients ¼ permettant que le programme SE&SR fonctionne) et en proposant des commentaires d'applicabilité en vue d'une transférabilité adaptée au contexte français. L'outil ASTAIRE et la démarche FIC ont été utilisés. Résultats: La théorie d'intervention a mis en évidence différents facteurs, agissant au niveau individuel (i.e. connaissances, croyances/représentations, compétences psychosociales) et au niveau des milieux de vie (i.e. structures accueillant les jeunes, familles, politiques publiques/réseaux d'acteurs), qui peuvent prévenir les VRA chez les jeunes. Dix fonctions clés ont été identifiées, dégageant le « squelette ¼ de l'intervention suisse. À la suite de ces résultats, des commentaires d'applicabilité en vue d'une transférabilité ont permis de préciser les éléments de contexte à prendre en compte avant la mise en œuvre de SE&SR en France. Conclusion: Cette étude souhaite rendre accessibles les process d'applicabilité en vue d'une transférabilité d'un programme probant en contexte français.

Evaluating significance of European-associated index SNPs in the East Asian population for 31 complex phenotypes.

BACKGROUND: Genome-wide association studies (GWASs) have identified many single-nucleotide polymorphisms (SNPs) associated with complex phenotypes in the European (EUR) population; however, the extent to which EUR-associated SNPs can be generalized to other populations such as East Asian (EAS) is not clear. RESULTS: By leveraging summary statistics of 31 phenotypes in the EUR and EAS populations, we first evaluated the difference in heritability between the two populations and calculated the trans-ethnic genetic correlation. We observed the heritability estimates of some phenotypes varied substantially across populations and 53.3% of trans-ethnic genetic correlations were significantly smaller than one. Next, we examined whether EUR-associated SNPs of these phenotypes could be identified in EAS using the trans-ethnic false discovery rate method while accounting for winner's curse for SNP effect in EUR and difference of sample sizes in EAS. We found on average 54.5% of EUR-associated SNPs were also significant in EAS. Furthermore, we discovered non-significant SNPs had higher effect heterogeneity, and significant SNPs showed more consistent linkage disequilibrium and allele frequency patterns between the two populations. We also demonstrated non-significant SNPs were more likely to undergo natural selection. CONCLUSIONS: Our study revealed the extent to which EUR-associated SNPs could be significant in the EAS population and offered deep insights into the similarity and diversity of genetic architectures underlying phenotypes in distinct ancestral groups.

Development of unigene-derived SSR markers from RNA-seq data of Uraria lagopodioides (Fabaceae) and their application in the genus Uraria Desv. (Fabaceae).

BACKGROUND: Uraria Desv. belongs to the tribe Desmodieae (Fabaceae), a group of legume plants, some of which have medicinal properties. However, due to a lack of genomic information, the interspecific relationships, genetic diversity, population genetics, and identification of functional genes within Uraria species are still unclear. RESULTS: Using RNA-Seq, a total of 66,026 Uraria lagopodioides unigenes with a total sequence content of 52,171,904 bp were obtained via de novo assembly and annotated using GO, KEGG, and KOG databases. 17,740 SSRs were identified from a set of 66,026 unigenes. Cross-species amplification showed that 54 out of 150 potential unigene-derived SSRs were transferable in Uraria, of which 19 polymorphic SSRs were developed. Cluster analysis based on polymorphisms successfully distinguished seven Uraria species and revealed their interspecific relationships. Seventeen samples of seven Uraria species were clustered into two monophyletic clades, and phylogenetic relationships of Uraria species based on unigene-derived SSRs were consistent with classifications based on morphological characteristics. CONCLUSIONS: Unigenes annotated in the present study will provide new insights into the functional genomics of Uraria species. Meanwhile, the unigene-derived SSR markers developed here will be invaluable for assessing the genetic diversity and evolutionary history of Uraria and relatives.

Development of novel microsatellite markers for population differentiation and detection of natural selection in wild populations of butter catfish, Ompok bimaculatus (Bloch, 1794).

BACKGROUND: Butter catfish (Ompok bimaculatus) is a preferred species in South East Asia, with huge aquaculture potential. However, there is limited information about genetic stock composition due to insufficient markers. The goal of this study was to develop de novo microsatellite markers. METHODS AND RESULTS: For sequencing, genomic SMRT bell libraries (1.5 Kbp size) were prepared for O. bimaculatus. A total of 114 SSR containing sequences were used for primer designing. Polymorphic loci were validated by genotyping 83 individuals from four distant riverine populations, viz., Brahmaputra, Bichiya, Gomti and Kaveri. A total of 30 microsatellite loci were polymorphic, of which five were found to be associated with functional genes and eight (four positive and four negative) loci were found to be under selection pressure. A total of 115 alleles were detected in all loci and PIC ranged from 0.539 to 0.927 and pair-wise FST values from 0.1267 to 0.26002 (p < 0.001), with an overall FST value of 0.17047, indicating the presence of population sub-structure. Cross-species transferability of 29 loci (96.67%) was successful in congener species, Ompok pabda. CONCLUSION: The novel SSR markers developed in this study would facilitate stock characterization of natural populations, to be used in future selection breeding programs and planning conservation strategies in these species. Identified non-neutral markers will give insights into the effect of local adaptation on genetic differentiation in the natural population of this species.

The effects of a land-based home exercise program on surfing performance in recreational surfers.

Surfing has become a popular, non-age dependent sport that can be performed at most beaches. Hence, coaches and athletes have recognised the importance of physical preparation to enhance athletic performance. The purpose of this study was to investigate the effects of a sport-specific, land-based-home exercise programme (LB-HEP) on sport-specific assessments related to surfing performance in recreational surfers. Twenty-two year-round recreational surfers divided into two groups: the exercise group (EG) performed the LB-HEP 3 times a week for 8 weeks while the control group (CG) maintained their current exercise. Both groups were instructed to continue surfing during the study period. Each surfer completed sport-specific measurements related to surfing demands both pre-test and post-test (8 weeks). There was a significant interactive effect of time and group (p = .009, ηp2 =.298) on the surfer's performance questionnaire showing greater improvements in the EG. Additionally, the EG had an improvement on time to pop-up when compared to the CG (ηp2 =.160). Based on the results, the LB-HEP is an effective training programme for all experience levels of recreational surfers to improve surfing performance measures short term.

Fooling Examples: Another Intriguing Property of Neural Networks.

Neural networks have been proven to be vulnerable to adversarial examples; these are examples that can be recognized by both humans and neural networks, although neural networks give incorrect predictions. As an intriguing property of neural networks, adversarial examples pose a serious threat to the secure application of neural networks. In this article, we present another intriguing property of neural networks: the fact that well-trained models believe some examples to be recognizable objects (often with high confidence), while humans cannot recognize such examples. We refer to these as "fooling examples". Specifically, we take inspiration from the construction of adversarial examples and develop an iterative method for generating fooling examples. The experimental results show that fooling examples can not only be easily generated, with a success rate of nearly 100% in the white-box scenario, but also exhibit strong transferability across different models in the black-box scenario. Tests on the Google Cloud Vision API show that fooling examples can also be recognized by real-world computer vision systems. Our findings reveal a new cognitive deficit of neural networks, and we hope that these potential security threats will be addressed in future neural network applications.