Large-Sample Genome-Wide Association Study of Resistance to Retained Placenta in U.S. Holstein Cows.

A genome-wide association study of resistance to retained placenta (RETP) using 632,212 Holstein cows and 74,747 SNPs identified 200 additive effects with p-values < 10-8 on thirteen chromosomes but no dominance effect was statistically significant. The regions of 87.61-88.74 Mb of Chr09 about 1.13 Mb in size had the most significant effect in LOC112448080 and other highly significant effects in CCDC170 and ESR1, and in or near RMND1 and AKAP12. Four non-ESR1 genes in this region were reported to be involved in ESR1 fusions in humans. Chr23 had the largest number of significant effects that peaked in SLC17A1, which was involved in urate metabolism and transport that could contribute to kidney disease. The PKHD1 gene contained seven significant effects and was downstream of another six significant effects. The ACOT13 gene also had a highly significant effect. Both PKHD1 and ACOT13 were associated with kidney disease. Another highly significant effect was upstream of BOLA-DQA2. The KITLG gene of Chr05 that acts in utero in germ cell and neural cell development, and hematopoiesis was upstream of a highly significant effect, contained a significant effect, and was between another two significant effects. The results of this study provided a new understanding of genetic factors underlying RETP in U.S. Holstein cows.

Visualization of CO2 electrolysis using optical coherence tomography.

Electrolysers offer an appealing technology for conversion of CO2 into high-value chemicals. However, there are few tools available to track the reactions that occur within electrolysers. Here we report an electrolysis optical coherence tomography platform to visualize the chemical reactions occurring in a CO2 electrolyser. This platform was designed to capture three-dimensional images and videos at high spatial and temporal resolutions. We recorded 12 h of footage of an electrolyser containing a porous electrode separated by a membrane, converting a continuous feed of liquid KHCO3 to reduce CO2 into CO at applied current densities of 50-800 mA cm-2. This platform visualized reactants, intermediates and products, and captured the strikingly dynamic movement of the cathode and membrane components during electrolysis. It also linked CO production to regions of the electrolyser in which CO2 was in direct contact with both membrane and catalyst layers. These results highlight how this platform can be used to track reactions in continuous flow electrochemical reactors.

Reactive Carbon Capture Enables CO2 Electrolysis with Liquid Feedstocks.

ConspectusThe electrochemical reduction of carbon dioxide (CO2RR) is a promising strategy for mitigating global CO2 emissions while simultaneously yielding valuable chemicals and fuels, such as CO, HCOO-, and C2H4. This approach becomes especially appealing when integrated with surplus renewable electricity, as the ensuing production of fuels could facilitate the closure of the carbon cycle. Despite these advantages, the realization of industrial-scale electrolyzers fed with CO2 will be challenged by the substantial energy inputs required to isolate, pressurize, and purify CO2 prior to electrolysis.To address these challenges, we devised an electrolyzer capable of directly converting reactive carbon solutions (e.g., a bicarbonate-rich eluent that exits a carbon capture unit) into higher value products. This "reactive carbon electrolyzer" operates by reacting (bi)carbonate with acid generated within the electrolyzer to produce CO2 in situ, thereby facilitating CO2RR at the cathode. This approach eliminates the need for expensive CO2 recovery and compression steps, as the electrolyzer can then then coupled directly to the CO2 capture unit.This Account outlines our endeavors in developing this type of electrolyzer, focusing on the design and implementation of materials for electrocatalytic (bi)carbonate conversion. We highlight the necessity for a permeable cathode that allows the efficient transport of (bi)carbonate ions while maintaining a sufficiently high catalytic surface area. We address the importance of the supporting electrolyte, detailing how (bi)carbonate concentration, counter cations, and ionic impurities impact selectivity for products formed in the electrolyzer. We also catalog state-of-the-art performance metrics for reactive carbon electrolyzers (i.e., Faradaic efficiency, full cell voltage, CO2 utilization efficiency) and outline strategies to bridge the gap between these values and those required for commercial operation Collectively, these findings contribute to the ongoing efforts to realize industrial-scale electrochemical reactors for CO2 conversion, bringing us closer to a sustainable and closed-loop carbon cycle.

Notalgia Paresthetica Responding Positively to Chiropractic Spinal Manipulation: A Case Report.

Notalgia paresthetica (NP) is a chronic cutaneous neuropathy characterized by localized pruritus and pain, numbness, and/or paresthesia, often linked to degenerative cervicothoracic changes. Treatment options for NP are limited. This case report details a 54-year-old woman with a six-year history of right-sided periscapular pruritus and cervicothoracic discomfort who presented to a chiropractor upon referral with a prior diagnosis of NP. Prior topical treatments yielded minimal relief. Radiographs revealed degenerative spinal changes at C5/6 and C6/7 which correlated with her periscapular symptom distribution. The patient responded positively to chiropractic spinal manipulative therapy (SMT), focusing on the cervicothoracic region, coupled with myofascial release. Symptoms significantly improved after a single SMT session and resolved after a second session, with no pruritus returning over one-month follow-up. While this case highlights the potential benefits of SMT for NP, further research is needed to explore the effectiveness of this treatment.

Novel Use of Clonidine Patch to Treat Tizanidine Withdrawal.

Tizanidine is commonly prescribed for muscle spasticity and pain. Yet, withdrawal is rarely reported. Tizanidine stimulates presynaptic α-2 adrenergic and imidazoline receptors decreasing norepinephrine release. Abrupt cessation can cause withdrawal. Current treatment strategies include tapering oral tizanidine or substituting oral clonidine. A 52-year-old male with a history of hypertension, diabetes, coronary artery disease, and chronic back pain presented with altered mental status, agitation, hypertensive emergency (blood pressure: 250/145 mmHg), and tachycardia. The patient had been prescribed tizanidine for chronic back pain for two years and had recently run out with suspicion of misuse. Tizanidine withdrawal was diagnosed, and he improved with 0.1 mg oral clonidine three times daily weaned over five days while hospitalized. One month later the patient was admitted for persistent hypertension, tachycardia, diaphoresis, and anxiety. Alpha-2 agonist withdrawal was again diagnosed. Utilizing a clonidine patch taper may offer a reasonable approach in patients with tizanidine withdrawal.

Adapted large language models can outperform medical experts in clinical text summarization.

Analyzing vast textual data and summarizing key information from electronic health records imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown promise in natural language processing (NLP) tasks, their effectiveness on a diverse range of clinical summarization tasks remains unproven. Here we applied adaptation methods to eight LLMs, spanning four distinct clinical summarization tasks: radiology reports, patient questions, progress notes and doctor-patient dialogue. Quantitative assessments with syntactic, semantic and conceptual NLP metrics reveal trade-offs between models and adaptation methods. A clinical reader study with 10 physicians evaluated summary completeness, correctness and conciseness; in most cases, summaries from our best-adapted LLMs were deemed either equivalent (45%) or superior (36%) compared with summaries from medical experts. The ensuing safety analysis highlights challenges faced by both LLMs and medical experts, as we connect errors to potential medical harm and categorize types of fabricated information. Our research provides evidence of LLMs outperforming medical experts in clinical text summarization across multiple tasks. This suggests that integrating LLMs into clinical workflows could alleviate documentation burden, allowing clinicians to focus more on patient care.

Participatory investigation of goat farmers' breeding practices, trait preference, and selection criteria in Burkina Faso.

The community-based breeding program (CBBP) is an innovative approach recommended for genetic improvement and sustainable use of animal genetic resources in extensive farming systems. Successful implementation of this approach requires an understanding of the characteristics of production systems, breeding objectives, and farmers' trait preference. This study aimed to identify the selection criteria of goat farmers in rural areas of Burkina Faso and their potential implications in establishing CBBP. Following focus group discussions, a well-structured questionnaire was designed and administered to 372 randomly selected goat farmers in two different agro-ecological zones. A list of traits obtained during focus group discussions was provided to farmers individually, and they were asked to rank the ones they preferentially use to select breeding animals. Statistical tests were conducted to compare data between the two agro-ecological zones. The results showed that the average goat flock per household was higher (P < 0.05) in the Sudanian (15.68 ± 13.76), compared to the Sudano-Sahelian area (12.93 ± 13.3). Adult females were the dominant age-sex group in both areas. Reasons for culling, keeping breeding bucks, and castration practice were significantly different (P < 0.05) among agro-ecological zones. The most important common criterion for selection in the two zones was body size, coat color, and growth rate for the bucks and does, while fertility (0.06) parameters including twining ability (0.18), kidding frequency (0.11), and mothering ability (0.15) were furthermore considered for breeding does selection. These findings provide valuable insights for developing CBBPs tailored to goat production in the study areas.

Indirect H2O2 synthesis without H2.

Industrial hydrogen peroxide (H2O2) is synthesized using carbon-intensive H2 gas production and purification, anthraquinone hydrogenation, and anthrahydroquinone oxidation. Electrochemical hydrogenation (ECH) of anthraquinones offers a carbon-neutral alternative for generating H2O2 using renewable electricity and water instead of H2 gas. However, the H2O2 formation rates associated with ECH are too low for commercialization. We report here that a membrane reactor enabled us to electrochemically hydrogenate anthraquinone (0.25 molar) with a current efficiency of 70% at current densities of 100 milliamperes per square centimeter. We also demonstrate continuous H2O2 synthesis from the hydrogenated anthraquinones over the course of 48 h. This study presents a fast rate of electrochemically-driven anthraquinone hydrogenation (1.32 ± 0.14 millimoles per hour normalized per centimeter squared of geometric surface of electrode), and provides a pathway toward carbon-neutral H2O2 synthesis.

Current challenges and future of agricultural genomes to phenomes in the USA.

Dramatic improvements in measuring genetic variation across agriculturally relevant populations (genomics) must be matched by improvements in identifying and measuring relevant trait variation in such populations across many environments (phenomics). Identifying the most critical opportunities and challenges in genome to phenome (G2P) research is the focus of this paper. Previously (Genome Biol, 23(1):1-11, 2022), we laid out how Agricultural Genome to Phenome Initiative (AG2PI) will coordinate activities with USA federal government agencies expand public-private partnerships, and engage with external stakeholders to achieve a shared vision of future the AG2PI. Acting on this latter step, AG2PI organized the "Thinking Big: Visualizing the Future of AG2PI" two-day workshop held September 9-10, 2022, in Ames, Iowa, co-hosted with the United State Department of Agriculture's National Institute of Food and Agriculture (USDA NIFA). During the meeting, attendees were asked to use their experience and curiosity to review the current status of agricultural genome to phenome (AG2P) work and envision the future of the AG2P field. The topic summaries composing this paper are distilled from two 1.5-h small group discussions. Challenges and solutions identified across multiple topics at the workshop were explored. We end our discussion with a vision for the future of agricultural progress, identifying two areas of innovation needed: (1) innovate in genetic improvement methods development and evaluation and (2) innovate in agricultural research processes to solve societal problems. To address these needs, we then provide six specific goals that we recommend be implemented immediately in support of advancing AG2P research.

A Million-Cow Validation of a Chromosome 14 Region Interacting with All Chromosomes for Fat Percentage in U.S. Holstein Cows.

A genome-wide association study (GWAS) of fat percentage (FPC) using 1,231,898 first lactation cows and 75,198 SNPs confirmed a previous result that a Chr14 region about 9.38 Mb in size (0.14-9.52 Mb) had significant inter-chromosome additive × additive (A×A) effects with all chromosomes and revealed many new such effects. This study divides this 9.38 Mb region into two sub-regions, Chr14a at 0.14-0.88 Mb (0.74 Mb in size) with 78% and Chr14b at 2.21-9.52 Mb (7.31 Mb in size) with 22% of the 2761 significant A×A effects. These two sub-regions were separated by a 1.3 Mb gap at 0.9-2.2 Mb without significant inter-chromosome A×A effects. The PPP1R16A-FOXH1-CYHR1-TONSL (PFCT) region of Chr14a (29 Kb in size) with four SNPs had the largest number of inter-chromosome A×A effects (1141 pairs) with all chromosomes, including the most significant inter-chromosome A×A effects. The SLC4A4-GC-NPFFR2 (SGN) region of Chr06, known to have highly significant additive effects for some production, fertility and health traits, specifically interacted with the PFCT region and a Chr14a region with CPSF1, ADCK5, SLC52A2, DGAT1, SMPD5 and PARP10 (CASDSP) known to have highly significant additive effects for milk production traits. The most significant effects were between an SNP in SGN and four SNPs in PFCT. The CASDSP region mostly interacted with the SGN region. In the Chr14b region, the 2.28-2.42 Mb region (138.46 Kb in size) lacking coding genes had the largest cluster of A×A effects, interacting with seventeen chromosomes. The results from this study provide high-confidence evidence towards the understanding of the genetic mechanism of FPC in Holstein cows.

External validation, radiological evaluation, and development of deep learning automatic lung segmentation in contrast-enhanced chest CT.

OBJECTIVES: There is a need for CT pulmonary angiography (CTPA) lung segmentation models. Clinical translation requires radiological evaluation of model outputs, understanding of limitations, and identification of failure points. This multicentre study aims to develop an accurate CTPA lung segmentation model, with evaluation of outputs in two diverse patient cohorts with pulmonary hypertension (PH) and interstitial lung disease (ILD). METHODS: This retrospective study develops an nnU-Net-based segmentation model using data from two specialist centres (UK and USA). Model was trained (n = 37), tested (n = 12), and clinically evaluated (n = 176) on a diverse 'real-world' cohort of 225 PH patients with volumetric CTPAs. Dice score coefficient (DSC) and normalised surface distance (NSD) were used for testing. Clinical evaluation of outputs was performed by two radiologists who assessed clinical significance of errors. External validation was performed on heterogenous contrast and non-contrast scans from 28 ILD patients. RESULTS: A total of 225 PH and 28 ILD patients with diverse demographic and clinical characteristics were evaluated. Mean accuracy, DSC, and NSD scores were 0.998 (95% CI 0.9976, 0.9989), 0.990 (0.9840, 0.9962), and 0.983 (0.9686, 0.9972) respectively. There were no segmentation failures. On radiological review, 82% and 71% of internal and external cases respectively had no errors. Eighteen percent and 25% respectively had clinically insignificant errors. Peripheral atelectasis and consolidation were common causes for suboptimal segmentation. One external case (0.5%) with patulous oesophagus had a clinically significant error. CONCLUSION: State-of-the-art CTPA lung segmentation model provides accurate outputs with minimal clinical errors on evaluation across two diverse cohorts with PH and ILD. CLINICAL RELEVANCE: Clinical translation of artificial intelligence models requires radiological review and understanding of model limitations. This study develops an externally validated state-of-the-art model with robust radiological review. Intended clinical use is in techniques such as lung volume or parenchymal disease quantification. KEY POINTS: • Accurate, externally validated CT pulmonary angiography (CTPA) lung segmentation model tested in two large heterogeneous clinical cohorts (pulmonary hypertension and interstitial lung disease). • No segmentation failures and robust review of model outputs by radiologists found 1 (0.5%) clinically significant segmentation error. • Intended clinical use of this model is a necessary step in techniques such as lung volume, parenchymal disease quantification, or pulmonary vessel analysis.

Perceptions of Data Set Experts on Important Characteristics of Health Data Sets Ready for Machine Learning: A Qualitative Study.

Importance: The lack of data quality frameworks to guide the development of artificial intelligence (AI)-ready data sets limits their usefulness for machine learning (ML) research in health care and hinders the diagnostic excellence of developed clinical AI applications for patient care. Objective: To discern what constitutes high-quality and useful data sets for health and biomedical ML research purposes according to subject matter experts. Design, Setting, and Participants: This qualitative study interviewed data set experts, particularly those who are creators and ML researchers. Semistructured interviews were conducted in English and remotely through a secure video conferencing platform between August 23, 2022, and January 5, 2023. A total of 93 experts were invited to participate. Twenty experts were enrolled and interviewed. Using purposive sampling, experts were affiliated with a diverse representation of 16 health data sets/databases across organizational sectors. Content analysis was used to evaluate survey information and thematic analysis was used to analyze interview data. Main Outcomes and Measures: Data set experts' perceptions on what makes data sets AI ready. Results: Participants included 20 data set experts (11 [55%] men; mean [SD] age, 42 [11] years), of whom all were health data set creators, and 18 of the 20 were also ML researchers. Themes (3 main and 11 subthemes) were identified and integrated into an AI-readiness framework to show their association within the health data ecosystem. Participants partially determined the AI readiness of data sets using priority appraisal elements of accuracy, completeness, consistency, and fitness. Ethical acquisition and societal impact emerged as appraisal considerations in that participant samples have not been described to date in prior data quality frameworks. Factors that drive creation of high-quality health data sets and mitigate risks associated with data reuse in ML research were also relevant to AI readiness. The state of data availability, data quality standards, documentation, team science, and incentivization were associated with elements of AI readiness and the overall perception of data set usefulness. Conclusions and Relevance: In this qualitative study of data set experts, participants contributed to the development of a grounded framework for AI data set quality. Data set AI readiness required the concerted appraisal of many elements and the balancing of transparency and ethical reflection against pragmatic constraints. The movement toward more reliable, relevant, and ethical AI and ML applications for patient care will inevitably require strategic updates to data set creation practices.

Updating test-day milk yield factors for use in genetic evaluations and dairy production systems: a comprehensive review.

Various methods have been proposed to estimate daily yield from partial yields, primarily to deal with unequal milking intervals. This paper offers an exhaustive review of daily milk yields, the foundation of lactation records. Seminal advancements in the late 20th century concentrated on two main adjustment metrics: additive additive correction factors (ACF) and multiplicative correction factors (MCF). An ACF model provides additive adjustments to two times AM or PM milk yield, which then becomes the estimated daily yields, whereas an MCF is a ratio of daily yield to the yield from a single milking. Recent studies highlight the potential of alternative approaches, such as exponential regression and other nonlinear models. Biologically, milk secretion rates are not linear throughout the entire milking interval, influenced by the internal mammary gland pressure. Consequently, nonlinear models are appealing for estimating daily milk yields as well. MCFs and ACFs are typically determined for discrete milking interval classes. Nonetheless, large discrete intervals can introduce systematic biases. A universal solution for deriving continuous correction factors has been proposed, ensuring reduced bias and enhanced daily milk yield estimation accuracy. When leveraging test-day milk yields for genetic evaluations in dairy cattle, two predominant statistical models are employed: lactation and test-day yield models. A lactation model capitalizes on the high heritability of total lactation yields, aligning closely with dairy producers' needs because the total amount of milk production in a lactation directly determines farm revenue. However, a lactation yield model without harnessing all test-day records may ignore vital data about the shapes of lactation curves needed for informed breeding decisions. In contrast, a test-day model emphasizes individual test-day data, accommodating various intervals and recording plans and allowing the estimation of environmental effects on specific test days. In the United States, the patenting of test-day models in 1993 used to restrict the use of test-day models to regional and unofficial evaluations by the patent holders. Estimated test-day milk yields have been used as if they were accurate depictions of actual milk yields, neglecting possible estimation errors. Its potential consequences on subsequent genetic evaluations have not been sufficiently addressed. Moving forward, there are still numerous questions and challenges in this domain.

Organizational Factors in Clinical Data Sharing for Artificial Intelligence in Health Care.

Importance: Limited sharing of data sets that accurately represent disease and patient diversity limits the generalizability of artificial intelligence (AI) algorithms in health care. Objective: To explore the factors associated with organizational motivation to share health data for AI development. Design, Setting, and Participants: This qualitative study investigated organizational readiness for sharing health data across the academic, governmental, nonprofit, and private sectors. Using a multiple case studies approach, 27 semistructured interviews were conducted with leaders in data-sharing roles from August 29, 2022, to January 9, 2023. The interviews were conducted in the English language using a video conferencing platform. Using a purposive and nonprobabilistic sampling strategy, 78 individuals across 52 unique organizations were identified. Of these, 35 participants were enrolled. Participant recruitment concluded after 27 interviews, as theoretical saturation was reached and no additional themes emerged. Main Outcome and Measure: Concepts defining organizational readiness for data sharing and the association between data-sharing factors and organizational behavior were mapped through iterative qualitative analysis to establish a framework defining organizational readiness for sharing clinical data for AI development. Results: Interviews included 27 leaders from 18 organizations (academia: 10, government: 7, nonprofit: 8, and private: 2). Organizational readiness for data sharing centered around 2 main constructs: motivation and capabilities. Motivation related to the alignment of an organization's values with data-sharing priorities and was associated with its engagement in data-sharing efforts. However, organizational motivation could be modulated by extrinsic incentives for financial or reputational gains. Organizational capabilities comprised infrastructure, people, expertise, and access to data. Cross-sector collaboration was a key strategy to mitigate barriers to access health data. Conclusions and Relevance: This qualitative study identified sector-specific factors that may affect the data-sharing behaviors of health organizations. External incentives may bolster cross-sector collaborations by helping overcome barriers to accessing health data for AI development. The findings suggest that tailored incentives may boost organizational motivation and facilitate sustainable flow of health data for AI development.

Oxygen-Resistant CO2 Reduction Enabled by Electrolysis of Liquid Feedstocks.

Electrolytic CO2 reduction fails in the presence of O2. This failure occurs because the reduction of O2 is thermodynamically favored over the reduction of CO2. Consequently, O2 must be removed from the CO2 feed prior to entering an electrolyzer, which is expensive. Here, we show that the use of liquid bicarbonate feedstocks (e.g., aqueous 3.0 M KHCO3), rather than gaseous CO2 feedstocks, enables efficient and selective CO2 reduction without additional procedures for removing O2. This effect is made possible because liquid bicarbonate solutions, which serve as a liquid CO2 carrier, deliver high concentrations of captured CO2 to the cathode, while the low solubility of O2 in aqueous media maintains a low O2 concentration at the same cathode surface. Consequently, electrolyzers fed with liquid bicarbonate feedstocks create an environment at the cathode that favors the reduction of CO2 over O2. We validate this claim by electrochemically converting CO2 into CO with reaction selectivities of 65% at 100 mA cm-2 using a 3.0 M KHCO3 solution bubbled with 100% CO2 or 100% O2. Similar experiments performed with a gaseous CO2 feedstock showed that merely 0.5% of O2 in the feedstock reduced CO selectivity by >90% after 1 h of electrolysis. Our findings demonstrate that a liquid bicarbonate feedstock enables efficient CO2 reduction without the need for expensive O2 removal steps.

Clinical Text Summarization: Adapting Large Language Models Can Outperform Human Experts.

Sifting through vast textual data and summarizing key information from electronic health records (EHR) imposes a substantial burden on how clinicians allocate their time. Although large language models (LLMs) have shown immense promise in natural language processing (NLP) tasks, their efficacy on a diverse range of clinical summarization tasks has not yet been rigorously demonstrated. In this work, we apply domain adaptation methods to eight LLMs, spanning six datasets and four distinct clinical summarization tasks: radiology reports, patient questions, progress notes, and doctor-patient dialogue. Our thorough quantitative assessment reveals trade-offs between models and adaptation methods in addition to instances where recent advances in LLMs may not improve results. Further, in a clinical reader study with ten physicians, we show that summaries from our best-adapted LLMs are preferable to human summaries in terms of completeness and correctness. Our ensuing qualitative analysis highlights challenges faced by both LLMs and human experts. Lastly, we correlate traditional quantitative NLP metrics with reader study scores to enhance our understanding of how these metrics align with physician preferences. Our research marks the first evidence of LLMs outperforming human experts in clinical text summarization across multiple tasks. This implies that integrating LLMs into clinical workflows could alleviate documentation burden, empowering clinicians to focus more on personalized patient care and the inherently human aspects of medicine.