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Although genetic factors contribute to behavioral variation in working dogs, the underlying molecular determinants remain poorly understood. The sortilin-related VPS10 domain-containing receptor 1 (SORCS1) gene is highly expressed in the central nervous system and has been implicated in neuronal signaling and synaptic regulation. A preliminary genome-wide association study suggested an association between SORCS1 variants and guide dog training outcomes. However, this result relied on only 28 dogs and lacked sufficient statistical power. In this study, we expanded the sample size and evaluated the association between SORCS1 polymorphisms and behavioral suitability in 160 dogs, using pass/fail training outcomes as behavioral trait indicators. We initially validated 12 candidate SNPs within SORCS1 using Sanger sequencing in 64 dogs, revealing significant genotype-dependent differences for eight loci located in predicted intron 3. A tagging SNP (rs23402730, C > T) was subsequently genotyped in 160 dogs (65 successful and 95 unsuccessful). The strongest association was detected under a recessive model (CC + CT vs. TT), yielding P = 4.7 × 10 ⁻ ⁵ and an odds ratio (OR) of 4.11 (95% CI: 2.07-8.18). As allele frequencies and genetic relatedness varied among dogs, we further evaluated the association using generalized linear mixed-effects models adjusting for sex, breed, and family structure. In the full dataset (n = 159), the additive genotype remained statistically significant (β = 2.20, P = 4.5 × 10 ⁻ 4; OR = 8.98, 95% CI: 2.64-30.59). Moreover, we confirmed the association in a genetically homogeneous subset of 114 Labrador retrievers, independent of breed effects (P = 0.029; OR = 4.71, 95% CI: 1.17-18.99). These results suggest that variation in SORCS1 is associated with behavioral suitability in dogs, based on an expanded cohort with guide dog training outcomes. As behavior is polygenic, SORCS1 represents one contributing locus that may be informative for future genetic studies aimed at elucidating the molecular basis of canine behavior.

Background: Accurate anthropometric data is essential for assessing nutritional status. To ensure data quality, careful planning of instruments, training and supervision of enumerators are mandatory. In Ethiopia, where malnutrition rates are high, investigating the methodological quality of anthropometric measurements in primary studies is crucial for ensuring the credibility of reports. Therefore, this review assesses the reporting characteristics and methodological quality of anthropometric measurements in primary studies conducted in Ethiopia, focusing on the nutritional status of school children.

Methods: A comprehensive systematic search was conducted to include primary studies that reported on children's growth from Medline, AJOL, Embase, and CINAHL. Additional sources, such as Google Scholar, ProQuest, Addis Ababa, and Jimma University's repositories were also accessed. Studies whose primary objective was to evaluate the nutritional status of children using anthropometric measurements were included in the review. The results were organized in EndNote, screened in Covidence, extracted in Excel and analyzed in Stata.

Results: Of the 678 retrieved records, 30 (n = 18,059) studies were included in the review. The studies used different exclusion criteria: 14 (46.7%) excluded children with physical deformities, seven (23.3%) excluded children who received anti-parasitic treatment within a specified time and four (13.3%) excluded children who were taking nutritional supplements. About 22 (73.3%) studies did not mention standardization, while 17 (56.7%) did not mention the calibration of instruments. Furthermore, about 12 (40%) studies did not report the setting where the measurements were obtained.

Conclusion: Inconsistencies in reporting key methodological details of anthropometric measurements were identified, highlighting a potential gap or loose interpretations of the STROBE-nut reporting checklist for studies that measure anthropometry to assess the nutritional status of children. Therefore, we recommend strengthening the STROBE-nut by giving emphasis to the quality assurance aspect of anthropometric measurements including standardization, calibration, material, setting, number of measurements taken and measurer qualifications.

The fundamental molecules of life are polymers. Prominent examples include nucleic acids and proteins, both of which exhibit a large array of mechanical properties and three-dimensional shapes. The bending rigidity of individual polymers is quantified by the persistence length. The shape of a polymer, dictated by the topology of the polymer backbone, a line trace through the center of the polymer along the contour path, is also an important characteristic. Common biomolecular architectures include linear, cyclic (ring-like), and branched structures; combinations of these can also exist, as in complex polymer networks. Determination of persistence length and shape are largely informative to polymer function and stability in biological environments. Here we demonstrate Persistence length Shape Polymer (PS Poly), a near-fully automated algorithm designed to obtain key physical attributes from single molecule images obtained in physiologically relevant fluid conditions via atomic force microscopy. The algorithm, which involves image reduction via skeletonization followed by end point and branch point detection, is capable of rapidly analyzing thousands of polymers with subpixel precision. Algorithm outputs were verified by analysis of deoxyribonucleic acid, a very well characterized macromolecule. The method was further demonstrated by application to candidalysin, a recently discovered and complex virulence factor from Candida albicans. Candidalysin forms polymers of highly variable shape and contour length and represents the first peptide toxin identified in a human fungal pathogen. PS Poly is a robust and general algorithm. It can be used to extract fundamental information about polymer backbone stiffness, architecture, and more generally, polymerization mechanisms.

[This corrects the article DOI: 10.1371/journal.pone.0332744.].

Background: Gaza has faced numerous military attacks that resulted in mass casualty incidents (MCIs). The ongoing genocide in Gaza has destroyed much of the health system, including killing and injuring of hundreds of health care workers (HCWs). Current thinking on the health system reconstruction lacks empirical data and local HCWs' perspectives. The study analyses locally driven innovations and lessons learned by HCWs who responded to MCIs between 2018 and 2021 to guide current and future planning of the reconstruction of the health system in Gaza.

Methods: This was a qualitative study using online and face-to-face interviews with HCWs who responded to the Great March of Return and the 2021 Israeli military attacks. Transcripts and extensive notes from the interviews were recorded and analyzed on NVivo using thematic content analysis. We used the health system building blocks as themes for deductive analysis with a seventh place-based theme (Gaza-specific) to account for the context of Gaza and the MCIs.

Results: Problems faced by HCWs mostly related to the nature and complexity of traumatic injuries, shortages in HCWs, particularly specialist doctors, poor coordination among actors, duplication of services, and shortages of supplies and equipment. Locally driven innovations and solutions included establishing new services centers, opening and expanding training programs, starting new coordination bodies, and task shifting of staff and facilities. Lessons learned included strengthening training and employment opportunities for staff, enhancing emergency preparedness and capacities, maintaining coordination bodies, enhancing community engagement and strengthening the governance of the Ministry of Health.

Conclusion: Reconstruction of Gaza's health system needs to be grounded in its political context and in the experiences of HCWs who have worked in and managed the system. Locally driven solutions and lessons learned can ensure that reconstruction serves as a vehicle for self-determination and sovereignty, rather than entrenching dependency.

[This corrects the article DOI: 10.1371/journal.pone.0327134.].

[This corrects the article DOI: 10.1371/journal.pone.0337194.].

To perform "global" optimization of the machining process in discrete manufacturing, a bi-objective flexible job-shop scheduling problem with tool allocation is proposed. Unlike traditional scheduling problems that treat resources independently, this paper addresses the strong coupling between machine routing, operation sequencing, and finite tool capacity. A mixed-integer programming model is constructed with the objectives of minimizing the tool wear cost and weighted sum of tardiness. Sophisticated constraints that fit actual manufacturing scenarios are considered, specifically the combination of tool magazine capacity, variant job releasing times, and machine/tool compatibility for operations. To address the computational challenge and the discrete nature of the solution space, a knowledge-driven teaching-learning-based optimization algorithm is designed. Specific strategies, including a topology-preserving discrete crossover and a critical-path-based neighborhood search, are developed to prevent premature convergence caused by complex constraints. Simulation experimental results show that the proposed algorithm significantly outperforms the traditional meta-heuristic algorithms in the aspects of quality, spread, and comprehensive metric, and the proposed multi-objective collaborative optimization method obtains better processing decisions than the traditional sequential scheduling methods.

In the conventional method of producing transgenic (Tg) animals, donor DNA is microinjected into the pronuclei of zygotes using a sharp glass needle. However, this approach is generally inefficient as it requires highly skilled microinjection techniques to ensure zygote survival and the transgene is incorporated into only a small proportion of the offspring. In contrast, methods based on piggyBac transposase (PBase) enables more efficient insertion of DNA into the genome and generation of Tg animals. The use of piggyBac transposase have also been examined in rats→ However, this method has not yet been fully optimized or properly characterized. In this study, we examined the microinjection of PBase mRNA and donor plasmid DNA into the pronuclei of rat zygotes using piezo-assisted microinjection. This approach resulted in high survival rates and enabled the efficient generation of Tg rats, even with long donor DNA. When the zygotes were microinjected using Piezo, over 70% were viable, and after embryo transfer, over 80% of the pups carried the transgene. Furthermore, we confirmed germline transmission to the F1 and F2 generations. We also attempted to generate a rat model of Alzheimer's using this method→ However, the protein was not detected despite mRNA expression, and the phenotype was not observed in behavioral tests. Although the generation of Alzheimer's disease model remains a challenge, our findings show that piggyBac transposase mRNA combined with piezo-assisted microinjection represents a simple and efficient method for producing Tg rats, even with long donor DNA.