Select Candidates Research Articles

ProbBreed: a novel tool for calculating the risk of cultivar recommendation in multienvironment trials.

Neglecting genotype-by-environment interactions in multienvironment trials (MET) increases the risk of flawed cultivar recommendations for growers. Recent advancements in probability theory coupled with cutting-edge software offer a more streamlined decision-making process for selecting suitable candidates across diverse environments. Here, we present the user-friendly ProbBreed package in R, which allows breeders to calculate the probability of a given genotype outperforming competitors under a Bayesian framework. This article outlines the package's basic workflow and highlights its key features, ranging from MET model fitting to estimating the per se and pairwise probabilities of superior performance and stability for selection candidates. Remarkably, only the selection intensity is required to compute these probabilities. By democratizing this complex yet efficient methodology, ProbBreed aims to enhance decision-making and ultimately contribute to more accurate cultivar recommendations in breeding programs.

Impact of clinical variables on outcomes in refractory cardiac arrest patients undergoing extracorporeal cardiopulmonary resuscitation.

In the past two decades, extracorporeal resuscitation (ECPR) has been increasingly used in the management of refractory cardiac arrest (CA) patients. Decision algorithms have been used to guide the care such patients, but the effectiveness of such decision-making tools is not well described. The aim of this study was to compare the rate of survival with a good neurologic outcome of patients treated with ECPR meeting all criteria of a clinical decision-making tool for the initiation of ECPR to those for whom ECPR was implemented outside of the algorithm. All patients who underwent E-CPR between January 2014 and December 2021 at the Montreal Heart Institute were included in this retrospective analysis. We dichotomized the cohort according to adherence or non-adherence with the ECPR decision-making tool, which included the following criteria: age ≤65 years, initial shockable rhythm, no-flow time <5 min, serum lactate <13 mmol/L. Patients were included in the "IN" group when they met all criteria of the decision-making tool and in the "OUT" group when at least one criterion was not met. The primary outcome was survival with intact neurological status at 30 days, defined by a Cerebral Performance Category (CPC) Scale 1 and 2. A total of 41 patients (IN group, n = 11; OUT group, n = 30) were included. A total of 4 (36%) patients met the primary outcome in the IN group and 7 (23%) in the OUT group [odds ratio (OR): 1.88 (95% CI, 0.42-8.34); P = 0.45]. However, survival with a favorable outcome decreased steadily with 2 or more deviations from the decision-making tool [2 deviations: 1 (11%); 3 deviations: 0 (0%)]. Most patients supported with ECPR fell outside of the criteria encompassed in a clinical decision-making tool, which highlights the challenge of optimal selection of ECPR candidates. Survival rate with a good neurologic outcome did not differ between the IN and OUT groups. However, survival with favorable outcome decreased steadily after one deviation from the decision-making tool. More studies are needed to help select proper candidates with refractory CA patients for ECPR.

Genetic parameters for carcass traits of progeny of beef bulls mated to dairy cows.

Beef × dairy crossbred cattle (n = 615) were used to evaluate the effect of preharvest indicator traits and genotypes on the accuracy of estimated breeding values (EBVs) of seedstock candidates for selection. Genotypes for 100,000 single nucleotide polymorphisms were provided by the American Simmental Association of purebred and crossbred seedstock animals (n = 2,632). Five hundred and ninety-five of the 615 beef × dairy cattle had carcass camera and ultrasound data. Phenotypes were not used for any of the seedstock animals even though some may have had performance and ultrasound data. We estimated the genomic relationship matrix among 3,247 animals including both phenotyped and unphenotyped animals. We computed genetic parameters among 37 traits using 666 bivariate restricted maximum likelihood analyses. The accuracy of EBV depends on heritability. For the sake of brevity, we report accuracy for marbling as a proxy for other traits with similar heritability. We focus on accuracy for marbling because marbling is the primary determinant of carcass value. We computed EBV for all 3,247 animals for marbling based on camera data postharvest using best linear unbiased prediction. We report evidence of overlap in causative genes among postharvest carcass traits; marbling, ribeye area, yield grade, fat thickness, and hot carcass weight (HCW) based on genetic correlations. Genetic correlations range from -0.73 to 0.89. Several live animal traits (frame size, body weight and ultrasound fat thickness and ribeye area) were genetically correlated with postharvest traits; including HCW, ribeye area, yield grade, fat thickness, and marbling. Genetic correlations between pre- and postharvest traits ranged from -0.53 to 0.95. Accuracy for marbling ranged from 0.64 to 0.80 for animals with marbling recorded, and from 0.09 to 0.60 for animals without marbling recorded. The accuracy of animals without phenotypes was related to the genomic relationship between animals with phenotype and those without. Live animal traits were useful for predicting economically important carcass traits based on genetic correlations. The accuracy of EBV for seedstock animals that were not phenotyped was low, but this is consistent with theory, and accuracy is expected to increase with the addition of genotypes and carcass data from beef × dairy animals.

Genomic evaluation for breeding and genetic management in Cordia africana, a multipurpose tropical tree species

BackgroundPlanting tested forest reproductive material is crucial to ensure the increased resilience of intensively managed productive stands for timber and wood product markets under climate change scenarios. Single-step Genomic Best Linear Unbiased Prediction (ssGBLUP) analysis is a cost-effective option for using genomic tools to enhance the accuracy of predicted breeding values and genetic parameter estimation in forest tree species. Here, we tested the efficiency of ssGBLUP in a tropical multipurpose tree species, Cordia africana, by partial population genotyping. A total of 8070 trees from three breeding seedling orchards (BSOs) were phenotyped for height. We genotyped 6.1% of the phenotyped individuals with 4373 single nucleotide polymorphisms. The results of ssGBLUP were compared with pedigree-based best linear unbiased prediction (ABLUP) and genomic best linear unbiased prediction (GBLUP), based on genetic parameters, theoretical accuracy of breeding values, selection candidate ranking, genetic gain, and predictive accuracy and prediction bias.ResultsGenotyping a subset of the study population provided insights into the level of relatedness in BSOs, allowing better genetic management. Due to the inbreeding detected within the genotyped provenances, we estimated genetic parameters both with and without accounting for inbreeding. The ssGBLUP model showed improved performance in terms of additive genetic variance and theoretical breeding value accuracy. Similarly, ssGBLUP showed improved predictive accuracy and lower bias than the pedigree-based relationship matrix (ABLUP).ConclusionsThis study of C. africana, a species in decline due to deforestation and selective logging, revealed inbreeding depression. The provenance exhibiting the highest level of inbreeding had the poorest overall performance. The use of different relationship matrices and accounting for inbreeding did not substantially affect the ranking of candidate individuals. This is the first study of this approach in a tropical multipurpose tree species, and the analysed BSOs represent the primary effort to breed C. africana.

Impact of planning horizon length on traits in an economic breeding goal and ranking of selection candidates in beef cattle

Abstract In beef production herds, unique situations such as breeding system, economic parameters, and current phenotypic performance can affect the emphasis of traits in the breeding goal and consequently the weighting of traits within a selection index. An often overlooked component of breeding goals is the planning horizon, or the time span to consider the economic impact of a selection decision, that varies between enterprises. A platform for constructing economic selection indexes (iGENDEC) was used to determine the impact of planning horizon length, breeding system, and sale endpoint on the relative emphasis of traits in the breeding goal and the re-ranking of selection candidates. As part of this investigation, the adjustment of phenotypic means for hot carcass weight and planning horizons were used to determine the impact of the relative emphasis on hot carcass weight as its mean approached a predetermined discount threshold. General-purpose indexes were created for animals sold at weaning and slaughter for three breeding systems with six different planning horizons (2, 5, 10, 20, 30, and 50 yr). As planning horizon increased, the relative emphasis on weaning weight or hot carcass weight, which affected revenue, decreased while the relative emphasis on stayability and mature weight increased. As the phenotypic mean for hot carcass weight approached and surpassed a predetermined discount threshold, the relative emphasis decreased before increasing again, once the mean weight surpassed the threshold. Rank correlations between indexes with different sale endpoints was 0.71 ± 0.1. Within a slaughter endpoint, re-ranking occurred between short and long planning horizons (r = 0.78 ± 0.09) while that of a weaning endpoint was less substantial (r = 0.85 ± 0.10). Jacard index scores between indexes with different planning horizons ranged from 39.7% to 87.9% and from 47.9% to 78.7% for weaning and carcass endpoints, respectively, for the top 5% of selection candidates. These results illustrate that the determination of a planning horizon can impact the rank of selection candidates and increases in net profit.

Decoding selectivity: computational insights into AKR1B1 and AKR1B10 inhibition.

Understanding selectivity mechanisms of inhibitors towards highly homologous proteins is of paramount importance in the design of selective candidates. Human aldo-keto reductases (AKRs) pertain to a superfamily of monomeric oxidoreductases, which serve as NADPH-dependent cytosolic enzymes to catalyze the reduction of carbonyl groups to primary and secondary alcohols using electrons from NADPH. Among AKRs, AKR1B1 is emerging as a promising target for cancer treatment and diabetes, despite its high structural similarity with AKR1B10, which leads to severe adverse events. Therefore, it is crucial to understand the selectivity mechanisms of AKR1B1 and AKR1B10 to discover safe anticancer candidates with optimal therapeutic efficacy. In this study, multiple computational strategies, including sequence alignment, structural comparison, Protein Contacts Atlas analysis, molecular docking, molecular dynamics simulation, MM-GBSA calculation, alanine scanning mutagenesis and pharmacophore modeling analysis were employed to comprehensively understand the selectivity mechanisms of AKR1B1/10 inhibition based on selective inhibitor lidorestat and HAHE. This study would provide substantial evidence in the design of potent and highly selective AKR1B1/10 inhibitors in future.

Analysis of actionable gene fusions in a large cohort of Chinese patients with colorectal cancer.

The prevalence of gene fusion is extremely low in unselected patients with colorectal cancer (CRC). Published data on gene fusions are limited by relatively small sample sizes, with a primary focus on Western populations. This study aimed to analyse actionable gene fusions in a large consecutive Chinese CRC population. This study included 5,534 consecutive CRC patients from the Genecast database. Genomic profiling was performed using a panel of 769 cancer-related genes. Data for 34 CRC patients with actionable gene fusions were also collected from cBioPortal and ChimerSeq. Among 5,534 CRC patients, 54 (0.98%) had actionable gene fusions, with NTRK1/2/3 being the most common fusion (0.38%), accounting for 38.9% (21/54) of those with fusions. Actionable gene fusion enrichment was higher in patients with microsatellite instability-high (MSI-H) (6.7% vs. 0.5%, P < 0.001), RAS/BRAF wildtype (2.0% vs. 0.2%, P < 0.001) and RNF43 mutation (7.7% vs. 0.4%, P < 0.001) than in patients with microsatellite stability/MSI-low, RAS/BRAF mutation and RNF43 wildtype, respectively. When these markers were combined, the fusion detection rate increased. Among patients with RAS/BRAF wildtype and MSI-H, fusions were detected in 20.3% of patients. The fusion detection rate further increased to 37.5% when RNF43 mutation was added. The fusion detection rate was also higher in colon cancer than in rectal cancer. No significant differences in clinical or molecular features were found in patients with actionable gene fusions between the Genecast, cBioPortal, and ChimerSeq databases. Approximately 1% of the unselected Chinese CRC population carries actionable gene fusions, mostly involving NTRK. Actionable gene fusions are more prevalent in MSI-H, RAS/BRAF wildtype, or RNF43-mutated CRC, as well as in colon cancer. Mapping of these molecular markers can markedly increase the fusion detection rate, which can help clinicians select candidates for fusion testing and targeted therapy.

Synthesis of a benzothiazole-based structure as a selective colorimetric-fluorogenic cyanate chemosensor

Abstract In this study, a new heterocyclic compound incorporating a benzothiazole moiety was specifically designed for the detection of cyanate anions, employing a hydrogen bonding mechanism. Through strategic integration of triazine and phenylenediamine cyclic groups into the compound’s structure, intramolecular hydrogen bonding interactions between the donor and acceptor sites were enhanced, leading to exceptional sensitivity towards cyanate anions. Utilizing the amino-type excited-state intramolecular proton transfer phenomenon, this new compound exhibited dual signals and achieved a significant Stokes’ shift via proton transfer, coupled with aggregation-induced emission properties. This unique combination resulted in visible color changes and an impressive fluorescence response, offering a promising solution for the sensitive detection of cyanate ions in critical environmental matrices. Cyanate detection at low concentrations by this as-synthesized compound (L1), accompanied by a distinct color change and a gradual fluorescence increase upon incremental cyanate addition demonstrated L1’s selectivity, as confirmed in the presence of various competing anions F−, Cl−, Br−, I−, ClO 3 − {\text{ClO}}_{3}^{-} , ClO 4 − {\text{ClO}}_{4}^{-} , NO 3 − {\text{NO}}_{3}^{-} , BrO 3 − {\text{BrO}}_{3}^{-} , CN− and CNO−. Spectrofluorometric investigations demonstrated that L1 shows significant potential as a selective cyanate anion detection candidate.

Longitudinal genomic analyses of automatically-recorded vaginal temperature in lactating sows under heat stress conditions based on random regression models

BackgroundAutomatic and continuous recording of vaginal temperature (TV) using wearable sensors causes minimal disruptions to animal behavior and can generate data that enable the evaluation of temporal body temperature variation under heat stress (HS) conditions. However, the genetic basis of TV in lactating sows from a longitudinal perspective is still unknown. The objectives of this study were to define statistical models and estimate genetic parameters for TV in lactating sows using random regression models, and identify genomic regions and candidate genes associated with HS indicators derived from automatically-recorded TV.ResultsHeritability estimates for TV ranged from 0.14 to 0.20 over time (throughout the day and measurement period) and from 0.09 to 0.18 along environmental gradients (EG, − 3.5 to 2.2, which correspond to dew point values from 14.87 to 28.19 ˚C). Repeatability estimates of TV over time and along EG ranged from 0.57 to 0.66 and from 0.54 to 0.77, respectively. TV measured from 12h00 to 16h00 had moderately high estimates of heritability (0.20) and repeatability (0.64), indicating that this period might be the most suitable for recording TV for genetic selection purposes. Significant genotype-by-environment interactions (GxE) were observed and the moderately high estimates of genetic correlations between pairs of extreme EG indicate potential re-ranking of selection candidates across EG. Two important genomic regions on chromosomes 10 (59.370–59.998 Mb) and16 (21.548–21.966 Mb) were identified. These regions harbor the genes CDC123, CAMK1d, SEC61A2, and NUDT5 that are associated with immunity, protein transport, and energy metabolism. Across the four time-periods, respectively 12, 13, 16, and 10 associated genomic regions across 14 chromosomes were identified for TV. For the three EG classes, respectively 18, 15, and 14 associated genomic windows were identified for TV, respectively. Each time-period and EG class had uniquely enriched genes with identified specific biological functions, including regulation of the nervous system, metabolism and hormone production.ConclusionsTV is a heritable trait with substantial additive genetic variation and represents a promising indicator trait to select pigs for improved heat tolerance. Moderate GxE for TV exist, indicating potential re-ranking of selection candidates across EG. TV is a highly polygenic trait regulated by a complex interplay of physiological, cellular and behavioral mechanisms.

How pedigree errors affect genetic evaluations and validation statistics

Pedigrees used in genetic evaluations contain errors. Because of such errors, assumptions regarding the relatedness among individuals in genetic evaluation models are wrong. Consequences of that have been investigated in earlier studies focusing on models that did not account for genomic information yet. The objective of this work was to investigate the effects of pedigree errors on the results from genetic evaluations using the single-step model, and the effect of such effects on results from validation studies with forward prediction. We used a real pedigree (n = 361,980) and real genotypes (n = 25,950) of Fleckvieh cattle, sampled in a way to provide a good consistency between pedigree and genomic relationships. Given the real pedigree and genotypes, true breeding values (TBV) were simulated to have a covariance structure equal to the matrix H assumed in a single-step model. Based on TBV, phenotypes were simulated with a heritability of 0.25. Genetic evaluations were conducted with a conventional animal model (i.e., without genomic information) and a single-step animal model under scenarios using either the correct pedigree or a pedigree containing 5%, 10%, or 20% of wrong records. Wrong records were simulated by randomly assigning wrong sires to nongenotyped females. The increasing rates of pedigree errors led to decreasing correlations between TBV and EBV and lower standard deviations of predictions. Less variation was observed because pedigree errors operate actually as a random exchange of daughters among bulls, making them look more similar to each other than they actually are. This occurs of course only when animals have progeny. Therefore, this decreased variation was more pronounced for progeny tested bulls than for young selection candidates. In a forward prediction validation scenario, the stronger decrease in variation when animals get progeny caused an apparent inflation of early predictions. This phenomenon may contribute to the usually observed problem of inflation of early predictions observed in validation studies.

Ridge regression and deep learning models for genome-wide selection of complex traits in New Mexican Chile peppers

BackgroundGenomewide prediction estimates the genomic breeding values of selection candidates which can be utilized for population improvement and cultivar development. Ridge regression and deep learning-based selection models were implemented for yield and agronomic traits of 204 chile pepper genotypes evaluated in multi-environment trials in New Mexico, USA.ResultsAccuracy of prediction differed across different models under ten-fold cross-validations, where high prediction accuracy was observed for highly heritable traits such as plant height and plant width. No model was superior across traits using 14,922 SNP markers for genomewide selection. Bayesian ridge regression had the highest average accuracy for first pod date (0.77) and total yield per plant (0.33). Multilayer perceptron (MLP) was the most superior for flowering time (0.76) and plant height (0.73), whereas the genomic BLUP model had the highest accuracy for plant width (0.62). Using a subset of 7,690 SNP loci resulting from grouping markers based on linkage disequilibrium coefficients resulted in improved accuracy for first pod date, ten pod weight, and total yield per plant, even under a relatively small training population size for MLP and random forest models. Genomic and ridge regression BLUP models were sufficient for optimal prediction accuracies for small training population size. Combining phenotypic selection and genomewide selection resulted in improved selection response for yield-related traits, indicating that integrated approaches can result in improved gains achieved through selection.ConclusionsAccuracy values for ridge regression and deep learning prediction models demonstrate the potential of implementing genomewide selection for genetic improvement in chile pepper breeding programs. Ultimately, a large training data is relevant for improved genomic selection accuracy for the deep learning models.

The Emergence of the Academic Candidate: Evaluation as textual dramaturgy

Our contribution sheds light on the dramaturgies of evaluation that precede candidate selection in academic organizations. The dramaturgies unfold across committee meetings, reviews, and reports that funnel the pool of candidates into a shortlist of prospective members. Because they are prolonged and not all stages involve copresence, the continuity and consistency of evaluative processes is a central dramaturgical problem. It highlights the constitutive role of written documents for the continuity and consistency of organizational evaluation processes. We marshal evidence from a comparative study on academic candidacy in two organizational settings: grantmakers, who select candidates for funding, and universities, who select candidates for professorships. Drawing on archived records produced in the context of research grant applications and professorial recruitments between 1950 and 2000, we distinguish two regimes of textual agency throughout the processes of evaluation: documents structure the process of candidate selection throughout dramaturgical stages, and they act as relays that transfer assessments of human actors across dramaturgical stages and time. In addition, by focusing on organizational access and showing how organizations make people before even hiring them, we draw attention to the emergence of a highly scripted dramatic figure in academic life: the candidate.

Investigating the impact of non-additive genetic effects in the estimation of variance components and genomic predictions for heat tolerance and performance traits in crossbred and purebred pig populations

BackgroundNon-additive genetic effects are often ignored in livestock genetic evaluations. However, fitting them in the models could improve the accuracy of genomic breeding values. Furthermore, non-additive genetic effects contribute to heterosis, which could be optimized through mating designs. Traits related to fitness and adaptation, such as heat tolerance, tend to be more influenced by non-additive genetic effects. In this context, the primary objectives of this study were to estimate variance components and assess the predictive performance of genomic prediction of breeding values based on alternative models and two independent datasets, including performance records from a purebred pig population and heat tolerance indicators recorded in crossbred lactating sows.ResultsIncluding non-additive genetic effects when modelling performance traits in purebred pigs had no effect on the residual variance estimates for most of the traits, but lower additive genetic variances were observed, especially when additive-by-additive epistasis was included in the models. Furthermore, including non-additive genetic effects did not improve the prediction accuracy of genomic breeding values, but there was animal re-ranking across the models. For the heat tolerance indicators recorded in a crossbred population, most traits had small non-additive genetic variance with large standard error estimates. Nevertheless, panting score and hair density presented substantial additive-by-additive epistatic variance. Panting score had an epistatic variance estimate of 0.1379, which accounted for 82.22% of the total genetic variance. For hair density, the epistatic variance estimates ranged from 0.1745 to 0.1845, which represent 64.95–69.59% of the total genetic variance.ConclusionsIncluding non-additive genetic effects in the models did not improve the accuracy of genomic breeding values for performance traits in purebred pigs, but there was substantial re-ranking of selection candidates depending on the model fitted. Except for panting score and hair density, low non-additive genetic variance estimates were observed for heat tolerance indicators in crossbred pigs.

Genetic analysis of the blood transcriptome of young healthy pigs to improve disease resilience

BackgroundDisease resilience is the ability of an animal to maintain productive performance under disease conditions and is an important selection target. In pig breeding programs, disease resilience must be evaluated on selection candidates without exposing them to disease. To identify potential genetic indicators for disease resilience that can be measured on selection candidates, we focused on the blood transcriptome of 1594 young healthy pigs with subsequent records on disease resilience. Transcriptome data were obtained by 3’mRNA sequencing and genotype data were from a 650 K genotyping array.ResultsHeritabilities of the expression of 16,545 genes were estimated, of which 5665 genes showed significant estimates of heritability (p < 0.05), ranging from 0.05 to 0.90, with or without accounting for white blood cell composition. Genes with heritable expression levels were spread across chromosomes, but were enriched in the swine leukocyte antigen region (average estimate > 0.2). The correlation of heritability estimates with the corresponding estimates obtained for genes expressed in human blood was weak but a sizable number of genes with heritable expression levels overlapped. Genes with heritable expression levels were significantly enriched for biological processes such as cell activation, immune system process, stress response, and leukocyte activation, and were involved in various disease annotations such as RNA virus infection, including SARS-Cov2, as well as liver disease, and inflammation. To estimate genetic correlations with disease resilience, 3205 genotyped pigs, including the 1594 pigs with transcriptome data, were evaluated for disease resilience following their exposure to a natural polymicrobial disease challenge. Significant genetic correlations (p < 0.05) were observed with all resilience phenotypes, although few exceeded expected false discovery rates. Enrichment analysis of genes ranked by estimates of genetic correlations with resilience phenotypes revealed significance for biological processes such as regulation of cytokines, including interleukins and interferons, and chaperone mediated protein folding.ConclusionsThese results suggest that expression levels in the blood of young healthy pigs for genes in biological pathways related to immunity and endoplasmic reticulum stress have potential to be used as genetic indicator traits to select for disease resilience.

Single Nucleotide Polymorphism R25C and R25H on Leptin gene and their association with body weight and measurements of Pasundan cattle

Abstract. Hilmia N, Ramdani D, Hidayat R, Widyastuti R, Hernaman I, Dudi, Edianingsih P, Arifin J. 2023. Single Nucleotide Polymorphism R25C and R25H on Leptin gene and their association with body weight and measurements of Pasundan cattle. Biodiversitas 24: 6310-6315. The Leptin gene is a potential candidate for genetic selection in livestock. Nonsynonimous Single Nucleotide Polymorphisms (SNP) Arg25Cys/R25C in Leptin gene associated with fat deposition mainly. This study aimed to identify polymorphism of Leptin Gene based on SNPs of Pasundan cattle and their association with body weight and measurements. This research used 64 DNA samples along with the corresponding body weight and measurement data from Pasundan cattle. About 9 and 7 DNA samples from Bali and Ongole cross (PO) cattle respectively, were also collected as comparisons. The DNA along 620 bp was multiplied by PCR. The SNPs in the Leptin gene on exon two were identified, followed by direct sequencing of the PCR product. The sequencing results were analyzed by BioEdit and Molecular Evolutionary Genetic Analysis (MEGA) 4.0. The General Linear Model analyzed the association between SNPs and body weight and measurements. The results showed there were two nonsynonymous SNP in the Leptin gene on exon two, i.e. g.1047C&gt;T/Arg25Cys/R25C (T allele) that changed amino acid Arginine to Cysteine and g.1048G&gt;A/Arg25His/R25H (A allele) which changed Arginine to Histidine. The frequency of the C allele in the Leptin gene on Pasundan cattle (0.54) was higher than mutation alleles, i.e. T ( 0.29) and A allele (0.17). There were six genotypes: CC (0.266), CT (0.390), CA (0.156), TT, TA, and AA 0.063 respectively. The Leptin gene mutation at the 25th position of amino acids, which changed Arginin to Cystein or Histidin (R25C and R25H), did not affect body weight and measurement in Pasundan Cattle. Therefore, nonsynonymous SNP R25C and R25H could not be used as marker genetic selection in Pasundan cattle.

Identifying molecular tags selectively retained on the surface of brain endothelial cells to generate artificial targets for therapy delivery

Current strategies to identify ligands for brain delivery select candidates based on preferential binding to cell-membrane components (CMC) on brain endothelial cells (EC). However, such strategies generate ligands with inherent brain specificity limitations, as the CMC (e.g., the transferrin receptor TfR1) are also significantly expressed on peripheral EC. Therefore, novel strategies are required to identify molecules allowing increased specificity of therapy brain delivery. Here, we demonstrate that, while individual CMC are shared between brain EC and peripheral EC, their endocytic internalization rate is markedly different. Such differential endocytic rate may be harnessed to identify molecular tags for brain targeting based on their selective retention on the surface of brain EC, thereby generating ‘artificial’ targets specifically on the brain vasculature. By quantifying the retention of labelled proteins on the cell membrane, we measured the general endocytic rate of primary brain EC to be less than half that of primary peripheral (liver and lung) EC. In addition, through bio-panning of phage-displayed peptide libraries, we unbiasedly probed the endocytic rate of individual CMC of liver, lung and brain endothelial cells. We identified phage-displayed peptides which bind to CMC common to all three endothelia phenotypes, but which are preferentially endocytosed into peripheral EC, resulting in selective retention on the surface of brain EC. Furthermore, we demonstrate that the synthesized free-form peptides are capable of generating artificial cell-surface targets for the intracellular delivery of model proteins into brain EC with increasing specificity over time. The developed identification paradigm, therefore, demonstrates that the lower endocytic rate of individual CMC on brain EC can be harnessed to identify peptides capable of generating ‘artificial’ targets for the selective delivery of proteins into the brain vasculature. In addition, our approach identifies brain-targeting peptides which would have been overlooked by conventional identification strategies, thereby increasing the repertoire of candidates to achieve specific therapy brain delivery.

Target-Based Design, Synthesis, and Biological Evaluation of Novel 1,2,4-Triazolone Derivatives as Potential nAChR Modulators.

Novel agrochemicals have been successfully developed using target-based drug design (TBDD). To discover a novel, efficient, and highly selective nicotinic insecticide candidate, we developed a unified pharmacological model using TBDD by studying the binding modes of 11 nicotinic acetylcholine receptor (nAChR) modulators with acetylcholine binding protein (AChBP) targets for the first time. This model was used to design and develop a series of 1,2,4-triazolone derivatives. Bioassays demonstrated excellent insecticidal activities against Aphis glycines of compounds 4k (LC50 = 4.95 mg/L) and 4q (LC50 = 3.17 mg/L), and low toxicities to Apis mellifera. Additionally, compound 4q was stably bound to Aplysia californica AChBP, which was consistent with the pharmacological model obtained via molecular docking and molecular dynamics simulations. Therefore, compound 4q could be a potential lead candidate targeting nAChR. The explicit pharmacological model of nAChR modulators with Ac-AChBP in this study may facilitate the future rational design of eco-friendly nicotinic insecticides.

Abstract C161: Identification of a reversible selective FGFR2 clinical development candidate with potency against gatekeeper and molecular brake mutations

Abstract Approved FGFR inhibitors have demonstrated responses in patients that harbor FGFR genetic alterations but show reduced activity in patients with gatekeeper and molecular brake mutations. In addition, efficacy of these agents may be reduced due to inhibition of FGFR1 and associated dose-limiting hyperphosphatemia. To address these issues, a novel series of reversible FGFR2 inhibitors with activity against clinically relevant mutations and selectivity over FGFR1 has been identified. The inhibition of FGFR2 phosphorylation in engineered cell lines demonstrated activity toward wild-type FGFR2, molecular brake mutations N549H/K, and gatekeeper mutations V564I/F/L. Selectivity of these inhibitors against FGFR1 could lead to fewer dose limiting toxicities compared to approved FGFR inhibitors. From this series, the identification of the clinical development candidate as a potent selective reversible FGFR2 inhibitor is described. In vivo characterization to be presented includes dose escalating rodent pharmacokinetics (PK), second species PK, and efficacy in AN3 CA tumor bearing mouse models. Citation Format: John Fischer, Karyn Bouhana, Richard Brizendine, Mark Chicarelli, Brad Fell, Jennifer Fulton, Anna Guarnieri, Leyla Haygood, Ravi Jalluri, Amber Johnson, Keith Koch, Erika Koslov-Davino, Max Mejia, Rob Rieger, John Robinson, Mareli Rodriguez, Francis Sullivan, Yang Wang, Shannon Winski, Silas Wood, Yeyun Zhou. Identification of a reversible selective FGFR2 clinical development candidate with potency against gatekeeper and molecular brake mutations [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr C161.

Reducing gender bias in the evaluation and selection of future leaders: The role of decision-makers' mindsets about the universality of leadership potential.

Extensive research has documented organizational decision-makers' preference for men over women when they evaluate and select candidates for leadership positions. We conceptualize a novel construct-mindsets about the universality of leadership potential-that can help reduce this bias. People can believe either that only some individuals have high leadership potential (i.e., a nonuniversal mindset) or that most individuals have high leadership potential (i.e., a universal mindset). Five studies investigated the relationship between these mindsets and decision-makers' gender biases in leader evaluation and selection decisions. The more senior government officials in China held a universal mindset, the less they showed gender bias when rating their subordinates' leadership capability (Study 1). Working adults in the United Kingdom who held a more universal mindset exhibited less gender bias when evaluating and selecting job candidates for a leadership position (Study 2). In an experiment, Singaporean students exposed to a universal mindset exhibited less gender bias when evaluating and selecting candidates than those exposed to a nonuniversal mindset (Study 3). Another experiment with working adults in China replicated this pattern and added a control condition to confirm the directionality of the effect (Study 4). Last, Study 5 showed that a more universal mindset was associated with less gender bias particularly among decision-makers with stronger gender stereotypes in the domain of leadership. This research demonstrates that, although they are seemingly unrelated to gender, mindsets about the universality of leadership potential can influence the extent to which people express gender bias in the leadership context. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Virtual network embedding method based on node delay perception

AbstractWireless virtual network uses software defined network and network function virtualisation technologies to create multiple logically isolated virtual networks on a physical wireless network. Wireless virtual network can improve the utilisation of wireless resources to meet the requirements of different services. Delay is an important performance indicator, which has strict requirements for delay‐sensitive services such as video conferencing and online games. In this study, a virtual network embedding method based on node delay perception (VNE‐NDP) is proposed, which considers both the node and link resources as well as the embedding delay requirements of virtual networks. The virtual network embedding method based on node delay perception consists of two phases: virtual node embedding and virtual link embedding. In the virtual node embedding phase, a physical node sorting method based on node delay perception (PNS‐NDP) is proposed. In PNS‐NDP, the node deployment delay is introduced into the node sorting algorithm for the first time. The authors select candidate physical nodes for each virtual node according to their resource availability and delay performance, which can greatly reduce the VN embedding delay without sacrificing too much other performance. In the virtual link embedding phase, a shortest path algorithm with bandwidth and link deployment delay constraints to find feasible physical paths for each virtual link is used. In addition, the VN embedding (VNE) deployment time is set as a new evaluation index. Simulation results show that compared with other VNE methods, VNE‐NDP can achieve higher success rate, revenue‐to‐expenditure ratio, and lower deployment delay.