Bayer (United States)

The continuous growth in both the number of phenotypic records and the range of traits included in beef cattle genetic evaluations poses substantial statistical and computational challenges for the estimation of genetic and residual (co)variance matrices required for breeding value estimation. Structural equation models (SEM), implemented using either factor analysis (FA) or recursive model (REC) structures, provide a flexible framework to model genetic and residual (co)variance matrices while yielding more parsimonious and computationally efficient parameterizations. Here, SEM was applied to estimate parameters for growth and ultrasound-measured carcass traits in beef cattle. The dataset comprised 2942 animals, and six traits were evaluated using standard multiple-trait mixed models (SMTM) and SEM. We considered FA and REC models implemented with six alternative parameterizations, in which random effects were represented as linear combinations of fewer unobservable random variables. Relative to the SMTM, both the model with two factors in the genetic covariance matrix (FA2G) and the model in which six recursive effects were constrained to zero in the residual covariance matrix (REC1) demonstrated a strong ability to capture genetic variability, as reflected by comparable heritability estimates. Correlations between estimated breeding values (EBV) for the same traits across models were consistently high, ranging from 0.94 to 1.00, indicating strong agreement among model estimates. The FA2G model was the most parsimonious in terms of the effective number of parameters (pD), with 431.2 pD, corresponding to a reduction of 25.3 parameters relative to the SMTM. The REC1 model also emerged as a competitive alternative for this dataset, exhibiting a lower pD (443.6) than the SMTM (456.5) and the most favorable deviance information criterion among all models evaluated (e.g., 37,868.6 for REC1 versus 37,874.7 for SMTM). Overall, these results demonstrate that mixed-effects multi-trait models for beef cattle genetic evaluation can be effectively implemented using FA or REC structures, which provide parsimonious representations of the underlying covariance patterns while maintaining high agreement in EBV.

Abstract Objectively selecting genotypes using genomic predictions for multiple traits warrants more empirical research. This study investigated how deployment of genomic prediction within the Clemson sorghum [ Sorghum bicolor (L.) Moench] breeding program would help to develop hybrids adapted to the eastern United States. Note that 9 years (2016–2024) of data collected on 1790 F 1 hybrids for grain yield, anthracnose resistance, days to anthesis, plant height, and test weight were used to calculate best linear unbiased estimates. Hybrid phenotypic data, in conjunction with genotype data for 1835 parents (983 R‐lines and 852 B‐lines), were used to train and test genomic prediction models. A model with general combining ability (GCA) effects only and a second with GCA and specific combining ability effects were used to estimate the breeding value of new parents and specific hybrid combinations, respectively. Prediction accuracy ( r ) using three cross‐validation strategies found that mean accuracies were high for plant height and moderate for grain yield, anthracnose resistance, days to anthesis, and test weight. An independent culling method using genomic prediction values for plant height and anthracnose was implemented to retroactively select seed and pollinator parents for testcrossing and specific hybrid combinations of superior seed and pollinator parents. Genomic selection of new F 4:5 parents resulted in approximately half of the total families within each fertility pool having one or more progeny advance to testcrossing, thereby avoiding overrepresentation of just a few pedigrees at the testcross phenotyping stage. Meanwhile, removing hybrids predicted above a height threshold and with an unacceptable level of anthracnose resistance led to 514 retained hybrids that had a significantly higher observed grain yield than the mean yield of 381 discarded hybrids. This tandem genomic selection scheme can advance the regional adaptation of individual fertility pools to support sorghum hybrid development for the eastern United States.

Seed health testing is a cornerstone of global food security, yet traditional diagnostic workflows often struggle to balance speed, sensitivity, and regulatory confidence under low-prevalence and heterogeneous seed lot conditions. This review synthesizes recent advances in molecular diagnostics (PCR, qPCR, LAMP, and digital PCR), non-destructive imaging technologies (hyperspectral, X-ray, and thermal imaging), and data-driven analytical approaches for pathogen detection in seeds. Emphasis is placed on the practical integration of these tools within high-throughput, ISO/IEC 17025-compliant laboratory workflows, highlighting their respective strengths, limitations, and roles in risk-based decision-making. Comparative discussions address cost, sensitivity, turnaround time, and field deployability across diagnostic platforms, supported by crop- and pathogen-specific examples. Emerging approaches such as CRISPR-based biosensing, advanced sequencing, and imaging-assisted analytics are discussed in the context of validation, regulatory acceptance, and operational feasibility. By focusing on implementation rather than conceptual frameworks, this review provides a pragmatic reference for laboratories, regulators, and seed companies seeking to modernize seed health testing while safeguarding trade integrity and biosecurity.

Determine animal performance and eco-nomic net returns for 6 bermudagrass-based pasture graz-ing systems in the US Southern Great Plains. Animal performance data from a 4-yr split-plot grazing study conducted in south-central Oklahoma were used to evaluate 6 bermudagrass-based stocker cattle grazing systems: bermudagrass forage with 112 kg N/ha (100 lb/acre) and a feed supplement under continuous stocking (BNSC), bermudagrass forage with 112 kg N/ha and a feed supplement grazed in a 28-d rest rotation (BNSR), bermudagrass forage without N fer-tilizer with a feed supplement under continuous stocking (BSC), bermudagrass forage without N fertilizer with a feed supplement grazed in a 28-d rest rotation (BSR), ber-mudagrass forage interseeded with grazing-tolerant alfalfa under continuous stocking (BAC), and bermudagrass for-age interseeded with grazing-tolerant alfalfa grazed in a 28-d rest rotation (BAR). Enterprise budgets and mixed effect models were used to estimate means for measures of animal performance and net returns. Total gain was greater in the BNSC system (324 kg/ha) than the BNSR and BAR sys-tems with gains of 307 and 299 kg/ha. Interseeded alfalfa with rotational grazing (BAR) had the greatest net re-turn of $44/ha ($18/acre). The next-best system was BAC with a net return of −$56/ha. The conventional systems (BNSC and BNSR) had low net returns due to fertilizer and supplemental feed costs, and rotational systems (BSR and BNSR) had low net returns due to greater water, fenc-ing, and labor costs. The bermudagrass systems with interseeded alfalfa performed better eco-nomically than the conventional bermudagrass systems. The alfalfa rotational system was more economical than conventional continuously stocked systems because giving alfalfa time to recover from preferential grazing, extended its stand life.

BackgroundDigital health refers to the field of knowledge and practice associated with the development and use of digital technologies to improve clinical practice and health outcomes. Knowledge of digital health technology is becoming essential for all nurses and health providers.ObjectiveThis study aims to present the results of the systematic reviews that were used to inform the recommendations in a best practice guideline (BPG) following the GRADE (Grading of Recommendations, Assessment, Development, and Evaluation) approach. Reviews focused on digital health education for nurses and health providers, peer champion models, and the use of predictive analytics in digital health environments.MethodsThe BPG team, in collaboration with a panel of 17 experts, conducted 5 systematic reviews to address 5 recommendation questions. Systematic searches looked for relevant studies published in English from January 2017 to July 2022 from 10 databases. The GRADE approach was used to synthesize and evaluate the quality of evidence, ensuring the guideline aligned with international reporting standards.ResultsA total of 18 articles across 4 systematic reviews met the inclusion criteria. From these reviews, 4 corresponding recommendations were drafted for nurses and health providers. The strength of the recommendations was determined through discussion and consensus by the expert panel using the GRADE approach. Among all, 1 systematic review resulted in no recommendation due to insufficient evidence.ConclusionsThe BPG on digital health provides 4 evidence-based recommendations for nurses and health providers on how to incorporate digital health technologies into clinical practice. This BPG is intended to be used across all health care settings.

Pentosan polysulfate (PPS) is an approved drug for the treatment of interstitial cystitis in humans and osteoarthritis in animals. This semisynthetic highly sulfated polysaccharide shares structural similarities with heparin and also interacts with platelet factor 4 (PF4), the key protein implicated in thrombocytopenia, a serious side effect of heparin administration. Thrombocytopenia arises from an immune response to structural features of multimeric complexes of heparin and PF4, although the prediction of disease progression in patients is complicated by the variable polyclonal and polyspecific response. The potential risk of provoking a similar response to PPS or materials derivatized with PPS, which could include subcutaneous or intravenous applications for other therapeutic goals, therefore needs to be assessed. In the absence of a clear proxy measurement for the risk of PPS to induce HIT, the ability of PPS and its fractions to interact with PF4 was examined from a broad structural perspective, employing orthogonal techniques, which were compared with unfractionated heparins (UFHs) and low-molecular-weight heparins (LMWHs). Zeta potential analysis, isothermal titration microcalorimetry, and circular dichroism showed that PPS interacts with PF4 in a manner dependent on its molecular weight, exhibiting behavior intermediate between that of LMHW and UFH. The interaction of PPS size-separated fractions with PF4 also exhibited a dependence on M w; higher M w corresponding to stronger interactions, and the same trend was confirmed by atomic force microscopy. Interestingly, despite PPS forming complexes with PF4, and the complexes formed with PPS fractions being smaller than those formed with UFH and LMWH, enzyme immunoassay studies nevertheless demonstrated the formation of antigenic complexes. Since PPS provokes comparable interactions with PPS, the results suggest that close monitoring of potential thrombocytopenia effects will be necessary when considering PPS dosing, especially for intravenous applications.

Abstract Biomass sorghum [ Sorghum bicolor (L.) Moench] is an alternative biofuel feedstock to maize [ Zea mays (L.)]. One advantage of biomass sorghum over maize is relatively lower nitrogen (N) requirement and its ability to tolerate late planting or fit into double cropping systems. To evaluate the most economic rate of N (MERN) for biomass sorghum in the Mid‐Atlantic region, a 3‐year (2017, 2018, and 2019) field trial was conducted at two locations (Holland and Hare) in Southeastern Virginia. Treatments were two planting dates (early [EP] versus late [LP]) and seven N rates (0, 56, 112, 168, 224, 280, and 336 kg ha −1 ) conducted in six environments. Data on dry matter yield of biomass sorghum, plant height, stem diameter, susceptibility to foliar diseases, and stalk lodging were recorded. Between the two planting dates, EP had 52%, 19%, and 16% higher dry matter, plant height, and stem diameter, respectively, compared to LP. However, LP exhibited lower disease pressure in four of 6 site‐years and lodging severity likely in response to weather conditions. Nitrogen rates between 112 and 224 kg ha −1 significantly improved dry matter, plant height, and stem diameter, with lower disease and lodging severity. At the MERN of 123 kg ha −1 , dry matter yield (20.66 Mg ha −1 ), plant height (454 cm), disease (61%), and lodging severity (34%) were least as compared to other treatments. Application of N at 123 kg ha −1 can provide the highest economic return while reducing the potential for N loss in the Mid‐Atlantic region.

Introduction: Sleep disturbances associated with menopause (SDM) are common and bothersome, but there are currently no specifically licensed treatments, and studies thus far have used different methodologies to measure sleep quality. Among those, digital health technologies (DHTs) present an innovative approach that supports patient-centric drug development by providing insights into how a patient responds to treatment in real-world settings. DHTs therefore may offer a solution to provide unobtrusive objective measurement of SDM. Here we describe the joint development of a novel DHT-derived endpoint for assessing sleep quality in menopausal women through a collaborative approach from evidence generation to analytical, clinical, and usability validation based on regulatory guidance. Methods: To demonstrate the fit-for-purpose of the novel DHT-derived endpoint, Bayer (drug developer), Sleepiz AG (DHT provider), and DEEP Measures (collaboration platform provider) partnered and applied established frameworks to leverage prior work while compiling comprehensive data, conducting a gap analysis, and curating evidence in the DEEP Measures collaboration platform based on and in preparation for discussions with health authorities. Initial regulatory feedback from health authorities provided useful input and supported the study design on the incorporation of the DHT-derived endpoint into the clinical development program of elinzanetant. Through collaborative efforts between the drug developer and the DHT provider, the novel DHT-derived endpoint (Sleepiz One+ for continuous, home-based measurement of wake after sleep onset in SDM and other sleep parameters) was implemented as an exploratory endpoint in a phase 2 pilot study where data to demonstrate fit-for-purpose were generated and validated against polysomnography, the gold-standard objective measure for sleep. The study outcomes alongside the results of the gap analyses and leveraging prior work were then structured systematically in the DEEP Measures platform. Data were organized according to the DEEP Stack model (which included information on the measurement definition, target solution profile, and instrumentation), and these facilitated the integration of our outputs directly into the regulatory package used for following health authority interactions to drive the acceptance of the novel endpoint. Conclusion: We outline how various stakeholders collaborated to leverage prior evidence, interacted with regulatory authority, and incorporated a novel DHT-derived endpoint into clinical development programs. Evidence and data generated in the present project have the potential to build the basis for further endpoint and DHT development and validation.

Compassion training (CT) has been used in medical education to enhance patient care and decrease provider burnout. A growing number of institutions incorporate humanistic practices into anatomy classes; however, there is no report of CT ever being incorporated. The authors hypothesized that implementing CT during a pre-clinical anatomy course, a program called Compassionatomy, would help students to identify with body donors, practice compassion contemplative exercises during and outside of anatomy labs, and maintain high empathy levels throughout the academic year. All first-year medical students at UC San Diego enrolled in 2022/2023 experienced Compassionatomy, which included combinations of CT during the 7 most emotionally challenging dissections. Longitudinal study participants (n = 36) completed 20 session evaluations-after each of 7 sessions with and 13 sessions without CT. Students' connection with the body donor, assessed by the Inclusion of Other in Self scale, increased over the year (p < 0.001) with more connection after CT than non-CT sessions (p = 0.048). This increase was associated with higher self-rated positive emotional valence (p = 0.03) but not arousal (p = 0.56). In-session engagement with compassion practices increased over time (p = 0.04). More engagement in contemplative practices outside anatomy was reported in the weeks following CT than non-CT sessions (p = 0.02). Empathy levels, assessed by the Jefferson Scale of Empathy (JSE), remained high across the year (p = 0.22) and did not differ between students in the cohort receiving Compassionatomy and the previous year's cohort who had not experienced Compassionatomy (p = 0.15). Results suggest that incorporation of CT into anatomy labs has benefits for enhancing connection with the body donor, the students' "first patient."

To quantify the burden of sleep disturbances on health-related quality of life (HRQoL) and mental well-being in perimenopausal and postmenopausal women with/without co-occurring vasomotor symptoms (VMS). Perimenopausal and postmenopausal women aged 40 to 65 years who participated in the National Health and Wellness 2019/2021 (US; N=27,621) and 2017/2020 cross-sectional surveys (France, Germany, Italy, Spain, UK; N=20,220) were included. Patient-reported outcomes were HRQoL (Short-Form Health Survey physical and mental component summary scores, EuroQol Visual Analogue Scale, EQ-5D-5L), depression (Patient Health Questionnaire-9), and anxiety (Generalized Anxiety Disorder-7 assessment). Associations between self-reported sleep disturbances and/or VMS and study outcomes were evaluated using generalized multivariable linear regression. Among perimenopausal women, sleep disturbances were reported by 61.7% (US) and 60.6% (Europe) with VMS, and 38.0% (US) and 40.8% (Europe) without VMS. Among postmenopausal women, sleep disturbances were reported by 66.7% (US) and 63.4% (Europe) with VMS, and 44.5% (US) and 40.9% (Europe) without VMS. Compared with women with neither symptom, perimenopausal and postmenopausal women with sleep disturbances had worse HRQoL ( P <0.001) and higher (worse) depression and anxiety scores ( P <0.05 perimenopausal, P <0.001 postmenopausal) irrespective of VMS. In addition, among postmenopausal women, those with sleep disturbances alone had worse HRQoL and higher (worse) depression and anxiety scores than those with VMS alone ( P <0.001). Sleep disturbance was common among perimenopausal and postmenopausal women irrespective of VMS, and independently associated with negative effects on HRQoL, depression, and anxiety. Effective treatments for sleep disturbances and VMS in menopausal women are needed to mitigate the associated burden and improve well-being.