Many cooperatively breeding species live in groups with complex structure-large group sizes, low and variable kin structure, and multiple breeding pairs. Since these mixed-kin groups typically form because of immigration of unrelated individuals of both sexes in addition to limited offspring dispersal, differences in patterns of dispersal can generate variation in group structure, even within the same species or population. Here, we examine how environmentally mediated dispersal patterns influence variation in group structure in the plural breeding superb starling (Lamprotornis superbus), an avian cooperative breeder that inhabits a spatiotemporally variable savanna environment and forms mixed-kin groups with variable group sizes and more than one breeding pair per group. Using 4068 genome-wide polymorphic loci and fine-scale, remotely sensed ecological data from 22 groups sampled across a nearly 200 km2 environmental gradient in central Kenya, we find evidence of not only frequent and long-distance dispersal in both sexes (low isolation-by-distance and weak genetic structure), but also directional dispersal from small groups in lower quality habitat with low normalised difference vegetation index (NDVI) to large groups in higher quality habitat with high NDVI. Additionally, we find stronger genetic structure among groups in lower quality habitat, and higher genetic diversity and lower relatedness of groups in higher quality habitat. Previous work using long-term data from groups in the same population has shown that groups with lower relatedness are larger and have more breeding pairs. Long-distance, directional dispersal to maximise individual fitness can thus lead to smaller and simpler kin-based social groups in lower quality habitat, but larger and more complex mixed-kin groups in higher quality habitat. Such intraspecific, within-population variation in group structure, including variation in kin structure of social groups, could have profound implications for the relative importance of the evolutionary mechanisms (i.e. direct vs. indirect fitness benefits) underlying the formation of cooperative societies.

The small apes, gibbons and siamangs, are monogamous species with their social groups comprising of both parents and their offspring. Therefore, the loss of a member may elicit a stress response in the remaining members due to their strong bonds. Glucocorticoids (GCs) have been useful indicators of stress, but distinguishing between acute versus chronic stress may be limited when measuring these hormones alone. The adrenal hormone dehydroepiandrosterone-sulfate (DHEAS), a GC antagonist, has been implicated in the regulation of the stress response. Thus, the concomitant measurement of these hormones can help examine whether an event, such as the loss of a group member, elicited a stress response. In this brief report, we discuss the hormonal response of two zoo-housed northern white-cheeked gibbons (Nomascus leucogenys) (1 adolescent male and his mother) after the death of the adult male of the group. Baseline fecal samples were collected opportunistically from these two individuals 5 months prior, and 3 months following the death of their group member. A total of 25 samples were quantified for fecal GC metabolites (FGCMs) and DHEAS by enzyme immunoassay (EIA) to calculate the FGCMs:DHEAS ratio. Our results indicate an increase in FGCMs and FGCMs:DHEAS for the adolescent male, but not the adult female, following the death. Our findings suggest that the integration of FGCMs and DHEAS measurements can provide valuable information to interpret individual stress levels to the sudden change in the group\'s social structure.

The rubber tree, Hevea brasiliensis (Willd. ex Adr. de Juss.) Muell. Arg., is the sole plant worldwide utilized for the commercial production of natural rubber. Following years of breeding, there exists a wide array of germplasm differentiation in rubber trees. The exploration of diversity and population structure within rubber tree germplasm resources, alongside the establishment of core germplasm resources, is instrumental in elucidating the genetic background and facilitating the effective utilization and management of these resources. By employing SNP molecular marker technology, 195 rubber tree resources were amplified, their genetic diversity analyzed, and a fingerprint map was subsequently constructed. Through this process, the cold-resistant core germplasm of rubber trees was identified. The results revealed that the PIC, He, and pi values ranged from 0.0905 to 0.3750, 0.095 to 0.5000, and 0.0953 to 0.5013, respectively. Both group structure analysis and cluster analysis delineated the accessions into two groups, signifying a simple group structure. A core germplasm bank was established with a sampling ratio of 10%, comprising 21 accessions divided into two populations. Population G1 consists of 20 accessions, while population G2 comprises 1 accession. The research findings have led to the creation of a molecular database that is anticipated to contribute to the management and subsequent breeding applications of rubber tree accessions.

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The UK Biobank (UKB) is a large cohort study that recruited over 500,000 British participants aged 40-69 in 2006-2010 at 22 assessment centers from across the United Kingdom. Self-reported health outcomes and hospital admission data are 2 types of records that include participants\' disease status. Coronary artery disease (CAD) is the most common cause of death in the UKB cohort. After distinguishing between prevalence and incidence CAD events for all UKB participants, we identified geographical variations in age-standardized rates of CAD between assessment centers. Significant distributional differences were found between the pooled cohort equation scores of UKB participants from England and Scotland using the Mann-Whitney test. Polygenic risk scores of UKB participants from England and Scotland and from different assessment centers differed significantly using permutation tests. Our aim was to discriminate between assessment centers with different disease rates by collecting data on disease-related risk factors. However, relying solely on individual-level predictions and averaging them to obtain group-level predictions proved ineffective, particularly due to the presence of correlated covariates resulting from participation bias. By using the Mundlak model, which estimates a random effects regression by including the group means of the independent variables in the model, we effectively addressed these issues. In addition, we designed a simulation experiment to demonstrate the functionality of the Mundlak model. Our findings have applications in public health funding and strategy, as our approach can be used to predict case rates in the future, as both population structure and lifestyle changes are uncertain.

The study of individual social relationships and group structure provides insights into a species\' natural history and can inform management decisions for animals living in human care. The Gorilla Rehabilitation and Conservation Education (GRACE) center provides permanent sanctuary for a group of 14 Grauer\'s gorillas (Gorilla beringei graueri), a critically endangered and poorly studied subspecies of the genus gorilla, in the Democratic Republic of the Congo. We monitored the association patterns of the gorillas at GRACE over eight months and here describe their individual relationships and group structure via multiple social network statistics. The group was highly connected but associations between individuals were weak on average. Social network metrics describe that an adult female was the most gregarious and socially central individual within the group. In fact, adult females were the most gregarious and socially central on average. Group level association patterns were significantly correlated over the study period and across observation types, suggesting the group was socially stable during the eight month study period. The data collected in this study were done so by GRACE caregivers as part of their daily husbandry routine and provided important insights into this group\'s behavior, ultimately informing on their care, welfare and future release considerations. The methodological approaches implemented here are easily scalable to any primate sanctuary or care facility seeking to use data to inform husbandry and management procedures. Lastly, our study is the first social network analysis to be conducted on Grauer\'s gorillas and provides tentative insights into the behavior of this poorly studied subspecies. Though more research is needed to evaluate if the findings here are reflective of this subspecies\' natural history or the idiosyncrasies of the group.

Ionic liquids (ILs) are a class of low melting point salts with physicochemical properties that make them suitable for a range of industrial applications. Accumulating evidence suggests that certain ILs are cytotoxic and potential environmental pollutants, thus understanding the structural features that promote IL cytotoxicity is important. Amphiphilic ionic liquids (AmILs), a class of ILs with lipophilic N-alkyl chains, containing aromatic head groups are generally more cytotoxic than their aliphatic counterparts, however the impact of other head group properties are less clear. This study therefore sought to provide new structure activity relationship (SAR) insights regarding the role of the cationic head group on AmIL cytotoxicity. A series of AmILs bearing a range of structurally diverse aromatic cations varying in size, charge, and lipophilicity was synthesised and screened against human MDA-MB-231 breast cancer cells. It was found that larger and more lipophilic head groups increased cytotoxicity, although the magnitude of the changes were modest. The mitochondrial effects of representative ILs were assessed. The AmILs induced mitochondrial dysfunction in MDA-MB-231 cells at cytotoxic concentrations, suggesting that they target mitochondria. The new SAR information from this study may assist in the design of AmILs with controlled cytotoxicity.

Mild cognitive impairment (MCI) is a clinical syndrome characterized by the onset and evolution of cognitive impairments, often considered a transitional stage to Alzheimer\'s disease (AD). The genetic traits of MCI patients who experience a rapid progression to AD can enhance early diagnosis capabilities and facilitate drug discovery for AD. While a genome-wide association study (GWAS) is a standard tool for identifying single nucleotide polymorphisms (SNPs) related to a disease, it fails to detect SNPs with small effect sizes due to stringent control for multiple testing. Additionally, the method does not consider the group structures of SNPs, such as genes or linkage disequilibrium blocks, which can provide valuable insights into the genetic architecture. To address the limitations, we propose a Bayesian bi-level variable selection method that detects SNPs associated with time of conversion from MCI to AD. Our approach integrates group inclusion indicators into an accelerated failure time model to identify important SNP groups. Additionally, we employ data augmentation techniques to impute censored time values using a predictive posterior. We adapt Dirichlet-Laplace shrinkage priors to incorporate the group structure for SNP-level variable selection. In the simulation study, our method outperformed other competing methods regarding variable selection. The analysis of Alzheimer\'s Disease Neuroimaging Initiative (ADNI) data revealed several genes directly or indirectly related to AD, whereas a classical GWAS did not identify any significant SNPs.

OBJECTIVE: There are no published examples of a global online research collaborative in andrology. We describe the development, profile and member characteristics of the first consortium of this type, the Global Andrology Forum (GAF).
METHODS: An online survey sent to all GAF members collected demographic information (sex, age, experience, academic title, degrees, country, specialty, profession). It also tapped data on members\' characteristics e.g., skills in research, software and statistics; preferred activities; time commitments; expected roles; and interest in participating in research, in GAF\'s scientific activities and collaborative online research. The findings were analyzed and tabulated. We outline members\' demographic and professional characteristics and scientific achievements to date. A narrative approach outlined GAF\'s structure and functioning.
RESULTS: A total of 418 out of 540 members completed the survey and were included in the analysis (77.4% response rate). The sample comprised mainly urologists (34.2%) and a third of the respondents had practiced for >15 years (33.3%). Up to 86.1% of the members expressed interest in being actively engaged in writing scientific articles. A third of the sample (37.1%) could dedicate 4 to 6 hours/week. Few respondents reported skills in statistics and artwork (2.6% and 1.9% respectively). Members were assigned to specific roles based on their expertise and experiences. Collaborative working ensured the timely completion of projects while maintaining quality. For outcomes, GAF published 29 original articles within one year of its creation, with authors from 48 countries spanning topics that included varicocele, sperm DNA damage, oxidative stress, semen analysis and male infertility, oocyte/embryo, and laboratory issues of assisted reproductive technique (ART) and male infertility evaluation.
CONCLUSIONS: GAF is a successful global online andrology research model. A healthy number of scientific articles have been published. Given such effectiveness, adopting the GAF model could be useful for other disciplines that wish to create and coordinate successful international online research groups.

Data driven individualized decision making problems have received a lot of attentions in recent years. In particular, decision makers aim to determine the optimal Individualized Treatment Rule (ITR) so that the expected specified outcome averaging over heterogeneous patient-specific characteristics is maximized. Many existing methods deal with binary or a moderate number of treatment arms and may not take potential treatment effect structure into account. However, the effectiveness of these methods may deteriorate when the number of treatment arms becomes large. In this article, we propose GRoup Outcome Weighted Learning (GROWL) to estimate the latent structure in the treatment space and the optimal group-structured ITRs through a single optimization. In particular, for estimating group-structured ITRs, we utilize the Reinforced Angle based Multicategory Support Vector Machines (RAMSVM) to learn group-based decision rules under the weighted angle based multi-class classification framework. Fisher consistency, the excess risk bound, and the convergence rate of the value function are established to provide a theoretical guarantee for GROWL. Extensive empirical results in simulation studies and real data analysis demonstrate that GROWL enjoys better performance than several other existing methods.