Are clean energy markets efficient? A multifractal scaling and herding behavior analysis of clean and renewable energy markets before and during the COVID19 pandemic

The literature lacks thorough and adequate evidence of the efficiency and herding behavior of clean and renewable energy markets. Therefore, the key objective of this paper is to explore the multifractality and efficiency of six clean energy markets by applying a robust method of Multifractal detrended fluctuation analysis (MFDFA) on daily data over a lengthy period. In addition, to examine the inner dynamics of clean energy markets around the global pandemic (COVID19), the data are further divided into two sub-periods of before and during COVID19. Our sampled clean energy markets exhibit multifractal behavior with a significant impact on the efficiency and intensified presence of multifractality during the COVID19 period. Overall, TXCT and BSEGRNX were the most efficient clean energy markets, but the ranking of TXCT deteriorated significantly in the sub-periods. The presence of multifractality and herding behavior symmetry intensified during the crisis period, which gives a potential for advancing portfolio management techniques. Moreover, our study provides practical implications and new insights for various market participants for better management and understanding of risks.

Genome-Wide Association Study of Body Conformation Traits in a Three-Way Crossbred Commercial Pig Population

Body conformation is the most direct production index, which can fully reflect pig growth status and is closely related to critical economic traits. In this study, we conducted a genome-wide association study (GWAS) on body conformation traits in a population of 1518 Duroc × (Landrace × Yorkshire) commercial pigs. These traits included body length (BL), body height (BH), chest circumference (CC), abdominal circumference (AC), and waist circumference (WC). Both the mixed linear model (MLM) and fixed and random model circulating probability unification (FarmCPU) approaches were employed for the analysis. Our findings revealed 60 significant single nucleotide polymorphisms (SNPs) associated with these body conformation traits in the crossbred pig population. Specifically, sixteen SNPs were significantly associated with BL, three SNPs with BH, thirteen SNPs with CC, twelve SNPs with AC, and sixteen SNPs with WC. Moreover, we identified several promising candidate genes located within the genomic regions associated with body conformation traits. These candidate genes include INTS10, KIRREL3, SOX21, BMP2, MAP4K3, SOD3, FAM160B1, ATL2, SPRED2, SEC16B, and RASAL2. Furthermore, our analysis revealed a novel significant quantitative trait locus (QTL) on SSC7 specifically associated with waist circumference, spanning an 84 kb interval. Overall, the identification of these significant SNPs and potential candidate genes in crossbred commercial pigs enhances our understanding of the genetic basis underlying body conformation traits. Additionally, these findings provide valuable genetic resources for pig breeding programs.

Reduced P300 amplitude is consistently associated with trait anhedonia across repeated assessments

Understanding how event-related potentials (ERPs) change following repeated assessments is critical to advance our understanding of neural mechanisms implicated in psychopathology. Specifically, it is unclear if associations between ERPs and individual differences can be reproduced when repeatedly measured within the same participants, or if clinical characteristics impact ERP trajectories over repeated assessments. The present study assessed P300 amplitude and latency from a flanker task at four time points over one month (M = 7.24 days between assessments [SD = 1.02]) in 79 female undergraduates to examine how P300 amplitude/latency changes across repeated assessments, the presence of associations between within- and between-subjects measures of current depressive symptoms and the P300, and if between-subjects depressive symptoms moderated change in P300 over repeated assessment. Results using multilevel modeling indicated a significant reduction in P300 amplitude and latency across assessments. Individuals with increased trait anhedonia (i.e., between-subjects) exhibited reduced P300 amplitudes across assessments; there were no associations of within-subjects fluctuations in anhedonia symptoms and P300 amplitude across assessments. There was also no interaction between number of assessments and between-subjects anhedonia in relation to P300 amplitude. Unlike anhedonia, between-subjects dysphoria was unrelated to P300. These results demonstrate a relatively specific and consistent association between an attenuated P300 amplitude and trait anhedonia across repeated assessments - data that may further suggest that flanker P300 amplitude reflects hedonic and motivational processes.

Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components

A genome-wide association study (GWAS) is currently one of the most recommended approaches for discovering marker-trait associations (MTAs) for complex traits in plant species. Insufficient statistical power is a limiting factor, especially in narrow genetic basis species, that conventional GWAS methods are suffering from. Using sophisticated mathematical methods such as machine learning (ML) algorithms may address this issue and advance the implication of this valuable genetic method in applied plant-breeding programs. In this study, we evaluated the potential use of two ML algorithms, support-vector machine (SVR) and random forest (RF), in a GWAS and compared them with two conventional methods of mixed linear models (MLM) and fixed and random model circulating probability unification (FarmCPU), for identifying MTAs for soybean-yield components. In this study, important soybean-yield component traits, including the number of reproductive nodes (RNP), non-reproductive nodes (NRNP), total nodes (NP), and total pods (PP) per plant along with yield and maturity, were assessed using a panel of 227 soybean genotypes evaluated at two locations over two years (four environments). Using the SVR-mediated GWAS method, we were able to discover MTAs colocalized with previously reported quantitative trait loci (QTL) with potential causal effects on the target traits, supported by the functional annotation of candidate gene analyses. This study demonstrated the potential benefit of using sophisticated mathematical approaches, such as SVR, in a GWAS to complement conventional GWAS methods for identifying MTAs that can improve the efficiency of genomic-based soybean-breeding programs.

Identification of potential MTAs and candidate genes for juice quality- and yield-related traits in Saccharum clones: a genome-wide association and comparative genomic study

Sugarcane is an economically important commercial crop which provides raw material for the production of sugar, jaggery, bioethanol, biomass and other by-products. Sugarcane breeding till today heavily relies on conventional breeding approaches which is time consuming, laborious and costly. Integration of marker-assisted selection (MAS) in sugarcane genetic improvement programs for difficult to select traits like sucrose content, resistance to pests and diseases and tolerance to abiotic stresses will accelerate varietal development. In the present study, association mapping approach was used to identify QTLs and genes associated with sucrose and other important yield-contributing traits. A mapping panel of 110 diverse sugarcane genotypes and 148 microsatellite primers were used for structured association mapping study. An optimal subpopulation number (ΔK) of 5 was identified by structure analysis. GWAS analysis using TASSEL identified a total of 110 MTAs which were localized into 27 QTLs by GLM and MLM (Q + K, PC + K) approaches. Among the 24 QTLs sequenced, 12 were able to identify potential candidate genes, viz., starch branching enzyme, starch synthase 4, sugar transporters and G3P-DH related to carbohydrate metabolism and hormone pathway-related genes ethylene insensitive 3-like 1, reversion to ethylene sensitive1-like, and auxin response factor associated to juice quality- and yield-related traits. Six markers, NKS 5_185, SCB 270_144, SCB 370_256, NKS 46_176 and UGSM 648_245, associated with juice quality traits and marker SMC31CUQ_304 associated with NMC were validated and identified as significantly associated to the traits by one-way ANOVA analysis. In conclusion, 24 potential QTLs identified in the present study could be used in sugarcane breeding programs after further validation in larger population. The candidate genes from carbohydrate and hormone response pathway presented in this study could be manipulated with genome editing approaches to further improve sugarcane crop.

Association Mapping for Yield Attributing Traits and Yellow Mosaic Disease Resistance in Mung Bean [Vigna radiata (L.) Wilczek]

Mung bean [Vigna radiata (L.) Wilczek] is an important short-duration grain legume widely known for its nutritional, soil ameliorative, and cropping system intensification properties. This study aims at evaluating genetic diversity among mung bean genotypes and detecting genomic regions associated with various yield attributing traits and yellow mosaic disease (YMD) resistance by association mapping. A panel of 80 cultivars and advanced breeding lines was evaluated for 10 yield-related and YMD resistance traits during kharif (monsoon) and summer seasons of 2018-2019 and 2019-2020. A total of 164 genome-wide simple sequence repeat (SSR) markers were initially screened, out of which 89 were found polymorphic which generated 317 polymorphic alleles with an average of 3.56 alleles per SSR locus. The number of alleles at each locus varied from 2 to 7. The population genetic structure analysis grouped different genotypes in three major clusters and three genetically distinct subpopulations (SPs) (i.e., SP-1, SP-2, and SP-3) with one admixture subpopulation (SP-4). Both cluster and population genetic structure analysis categorized the advanced mung bean genotypes in a single group/SP and the released varieties in other groups/SPs, suggesting that the studied genotypes may have common ancestral history at some level. The population genetic structure was also in agreement with the genetic diversity analysis. The estimate of the average degree of linkage disequilibrium (LD) present at the genome level in 80 mung bean genotypes unveiled significant LD blocks. Over the four seasons, 10 marker-trait associations were observed significant for YMD and four seed yield (SY)-related traits viz., days to flowering, days to maturity, plant height, and number of pods per plant using the mixed linear model (MLM) method. These associations may be useful for marker-assisted mung bean yield improvement programs and YMD resistance.

Performing Genome-Wide Association Studies Using rMVP

Genome wide association study (GWAS), which is a powerful tool to detect the relationship between the traits of interest and high-density markers, has provided unprecedented insights into the genetic basis of quantitative variation for complex traits. Along with the development of high-throughput sequencing technology, both sample sizes and marker sizes are increasing rapidly, which make computations more challenging than ever. Therefore, to efficiently process big data with limited computing resources in a reasonable time and to use state-of-the-art statistical models to reduce false positive and false negative rates have always been hot topics in the domain of GWAS. In this chapter, we describe how to perform GWAS using an R package, rMVP, which includes data preparation, evaluation of population structure, association tests by different models, and high-quality visualization of GWAS results.

Genetic diversity and population structure analysis for morphological traits in upland cotton (Gossypium hirsutum L.)

A total of 96 different genotypes of upland cotton (Gossypium hirsutum) were selected from the breeding material and germplasm available at CCS HAU, India, to find the novel marker-trait associations for morphological traits used for registration of variety in upland cotton. Twenty-three morphological traits of the selected genotypes were recorded in field trials conducted in two replication of randomized block design during Kharif 2018 and 2019. A total of 11 traits showed sufficient variations in the screened germplasm and the same were further used for association mapping. A total of 168 SSRs were used for genotyping, of which 97 SSRs showed polymorphism amplifying 293 different alleles with an average of 3.02 alleles per SSR. Clustering, principal component analysis, and population structure analysis advocated that the current germplasm panel has enough diversity to be considered for association mapping. A total of 20 significant marker-trait associations were identified by the mixed linear model (MLM) and compressed mixed linear model (CMLM), of which 15 were common to both models, hence considered as promising associations. To the best of our knowledge, it is a first attempt to identify the linked markers in relation to morphological traits for the cotton crop. Results of the present study will be highly useful in speeding up variety registration programmes of upland cotton complementing to Distinctiveness, Uniformity, and Stability (DUS) testing.

GWAS for genetics of complex quantitative traits: Genome to pangenome and SNPs to SVs and k-mers

The development of improved methods for genome-wide association studies (GWAS) for genetics of quantitative traits has been an active area of research during the last 25 years. This activity initially started with the use of mixed linear model (MLM), which was variously modified. During the last decade, however, with the availability of high throughput next generation sequencing (NGS) technology, development and use of pangenomes and novel markers including structural variations (SVs) and k-mers for GWAS has taken over as a new thrust area of research. Pangenomes and SVs are now available in humans, livestock, and a number of plant species, so that these resources along with k-mers are being used in GWAS for exploring additional genetic variation that was hitherto not available for analysis. These developments have resulted in significant improvement in GWAS methodology for detection of marker-trait associations (MTAs) that are relevant to human healthcare and crop improvement.

Multi-locus genome-wide association studies for five yield-related traits in rice

BACKGROUND

Improving the overall production of rice with high quality is a major target of breeders. Mining potential yield-related loci have been geared towards developing efficient rice breeding strategies. In this study, one single-locus genome-wide association studies (SL-GWAS) method (MLM) in conjunction with five multi-locus genome-wide association studies (ML-GWAS) approaches (mrMLM, FASTmrMLM, pLARmEB, pKWmEB, and ISIS EM-BLASSO) were conducted in a panel consisting of 529 rice core varieties with 607,201 SNPs.

RESULTS

A total of 152, 106, 12, 111, and 64 SNPs were detected by the MLM model associated with the five yield-related traits, namely grain length (GL), grain width (GW), grain thickness (GT), thousand-grain weight (TGW), and yield per plant (YPP), respectively. Furthermore, 74 significant quantitative trait nucleotides (QTNs) were presented across at least two ML-GWAS methods to be associated with the above five traits successively. Finally, 20 common QTNs were simultaneously discovered by both SL-GWAS and ML-GWAS methods. Based on genome annotation, gene expression analysis, and previous studies, two candidate key genes (LOC_Os09g02830 and LOC_Os07g31450) were characterized to affect GW and TGW, separately.

CONCLUSIONS

These outcomes will provide an indication for breeding high-yielding rice varieties in the immediate future.

The World Health Organization African regional training course for mid-level immunization managers: lessons and future directions

The World Health Organization (WHO) African regional training course for mid-level managers (MLM) of immunization programs launched in 2004, has undergone revisions across the years, to accommodate new developments in the field. In 2016, the WHO African regional office conducted a thorough review of the course materials and delivery methods to document lessons to help improve the course. Some of the gaps included inappropriate selection of trainees, inadequate focus on skills development, heavy reliance on text and presentations, as well as resource limitations to reach a critical mass of learners. The regional office worked with Bull City Learning to redesign the course materials along carefully crafted course objectives and curricula, and to assist facilitators to better deliver the course. In addition, the materials were converted into online learning tools. Within 10 months, a total of 3011 learners were enrolled in the online MLM training platform and earned a total of 9209 certificates. The MLM course will continue to be highly relevant as the immunization area of work expands significantly, with the addition of new vaccines, introduction of new technologies, and expanding opportunities for online learning.

Regional Cultures and the Psychological Geography of Switzerland: Person-Environment-Fit in Personality Predicts Subjective Wellbeing

The present study extended traditional nation-based research on person-culture-fit to the regional level. First, we examined the geographical distribution of Big Five personality traits in Switzerland. Across the 26 Swiss cantons, unique patterns were observed for all traits. For Extraversion and Neuroticism clear language divides emerged between the French- and Italian-speaking South-West vs. the German-speaking North-East. Second, multilevel modeling demonstrated that person-environment-fit in Big Five, composed of elevation (i.e., mean differences between individual profile and cantonal profile), scatter (differences in mean variances) and shape (Pearson correlations between individual and cantonal profiles across all traits; Furr, 2008, 2010), predicted the development of subjective wellbeing (i.e., life satisfaction, satisfaction with personal relationships, positive affect, negative affect) over a period of 4 years. Unexpectedly, while the effects of shape were in line with the person-environment-fit hypothesis (better fit predicted higher subjective wellbeing), the effects of scatter showed the opposite pattern, while null findings were observed for elevation. Across a series of robustness checks, the patterns for shape and elevation were consistently replicated. While that was mostly the case for scatter as well, the effects of scatter appeared to be somewhat less robust and more sensitive to the specific way fit was modeled when predicting certain outcomes (negative affect, positive affect). Distinguishing between supplementary and complementary fit may help to reconcile these findings and future research should explore whether and if so under which conditions these concepts may be applicable to the respective facets of person-culture-fit.

Student and School-level Predictors of Pharmacy Residency Attainment

Objective. To identify student and school level predictors of pharmacy residency attainment. Methods. Data were collected from the American Association of Colleges of Pharmacy (AACP) and the Carnegie Classification of Institutions of Higher Education. Logistic multilevel modeling was used to examine the effects of select student and school level characteristics on pharmacy residency attainment, as indicated by students on the AACP Graduating Student Survey (GSS) from 2013 to 2015. Results. The dataset included 24,351 graduating pharmacy students from 101 schools and colleges of pharmacy. Predictors of residency attainment included working in an institutional pharmacy, female gender, student age, school age, and Research I classification. Nonsignificant variables included curriculum type, class size, and institutional control. Conclusion. Student and pharmacy school characteristics impact the likelihood of pharmacy residency attainment. Further research is needed to understand the mechanisms associated with these effects.

Molecular polymorphism related to flowering trait variation in a Phaseolus vulgaris L. collection

The aim of this study was to investigate the flowering variation and the molecular polymorphism in key regulatory genes that control flowering in a Phaseolus vulgaris L. collection of 94 accessions from Europe and the Americas. The analysis of variance revealed that the difference in days-to-flowering between accessions was significant, with European accessions characterized by flowering precocity. Population structure analysis corroborated previous data on the genetic distinction between the Andean and Mesoamerican gene pools. A low level of admixture was detected. Genomic sequences of 15 gene fragments were obtained. About 7.0 kb per accession were sequenced and a total of 48 nucleotide substitutions identified. A Mixed Linear Model analysis, including population structure and kinship, was used to identify marker-trait associations. Haplotype tagging single nucleotide polymorphisms (htSNPs) associated with the studied traits were detected: in PvVRN1 and PvPHYB with days-to-flowering, in PvMYB29 with number of flower buds per inflorescence and in PvTFL1z and PvFCA with inflorescence length. The two genes associated with days-to-flowering control belong to the photoperiod and vernalization pathways. In particular, the PvVRN1 gene appears to play an important role in regulating the adaptation process of common bean.

Genes alternations with exposure time of environmental factors

A theoretical model discussing the environmental factors (EFs) effect of exposure time on genes, which leads to human diseases, is presented using multi-logistic model. The advantages and limitations of this model are discussed in terms of its usefulness for simulating genetic samples. It has been shown that EFs affect genes with the same degree both at high exposure level, low exposure time and at low exposure level, high exposure time.

Synthesis and SAR studies of 5-(pyridin-4-yl)-1,3,4-thiadiazol-2-amine derivatives as potent inhibitors of Bloom helicase

Human cells utilize a variety of complex DNA repair mechanisms in order to combat constant mutagenic and cytotoxic threats from both exogenous and endogenous sources. The RecQ family of DNA helicases, which includes Bloom helicase (BLM), plays an important function in DNA repair by unwinding complementary strands of duplex DNA as well as atypical DNA structures such as Holliday junctions. Mutations of the BLM gene can result in Bloom syndrome, an autosomal recessive disorder associated with cancer predisposition. BLM-deficient cells exhibit increased sensitivity to DNA damaging agents indicating that a selective BLM inhibitor could be useful in potentiating the anticancer activity of these agents. In this work, we describe the medicinal chemistry optimization of the hit molecule following a quantitative high-throughput screen of >355,000 compounds. These efforts lead to the identification of ML216 and related analogs, which possess potent BLM inhibition and exhibit selectivity over related helicases. Moreover, these compounds demonstrated cellular activity by inducing sister chromatid exchanges, a hallmark of Bloom syndrome.