bluuebunny/crossref_metadata_2025_split huggingface dataset:
When antimicrobial stewardship begins with microbiological test requests: the case of asymptomatic bacteriuria
Authors: Imlay Hannah , Thorpe Alistair , Vaughn Valerie M. 
Published: August 2024

Purpose of review
We aim to review the rationale, methods, and experiences with diagnostic stewardship targeted at urinary tract infection (UTI) and related urinary syndromes.

Recent findings
In the last 18 months, several articles have demonstrated the impact of diagnostic stewardship interventions at limiting inappropriate diagnosis of UTIs or inappropriate antibiotic-prescribing, targeting the urinary tract. Antimicrobial stewardship programs may create and implement interventions at the point of urine test ordering, urine test resulting, or at the point of prescribing antibiotics after results have returned. Specific design and implementation of stewardship interventions depends on context. To maximize their impact, interventions should be accompanied by education and garner buy-in from providers.

Summary
Diagnostic stewardship can decrease unnecessary antibiotics and inappropriate diagnosis of UTI with multifaceted interventions most likely to be effective. Remaining questions include how to reduce ASB treatment in new populations, such as those with immune compromise, and persistent unknowns regarding UTI diagnosis and diagnostics.
The End of Race-Conscious Admissions
Authors: Nimmer Kaitlyn F. , Collins Reagan A. , Clarke Callisia N. 
Published: August 2024

Abstract:
The end of race-conscious admissions poses a significant challenge to the recruitment of underrepresented in medicine surgical trainees and surgeons in the United States. These developing limitations underscore the importance of retention within the academic surgical pathway as we seek to diversify the physician workforce in order to best serve our patients.
A Novel Immunodeficient Hyperglycemic Mouse Carrying the Ins1 Akita Mutation for Xenogeneic Islet Cell Transplantation
Authors: Nakano Kenta , Goto Motohito , Fukuda Satsuki , Yanobu-Takanashi Rieko , Yabe Shigeharu G. , Shimizu Yukiko , Sakuma Tetsushi , Yamamoto Takashi , Shimoda Masayuki , Okochi Hitoshi , Takahashi Riichi , Okamura Tadashi 
Published: August 2024

Background.
For patients who have difficulty controlling blood glucose even with insulin administration, xenogeneic islet cells, including human stem cell-derived pancreatic islets (hSC-islet) and porcine islets, have garnered attention as potential solutions to challenges associated with donor shortages. For the development of diabetes treatment modalities that use cell transplantation therapy, it is essential to evaluate the efficacy and safety of transplanted cells using experimental animals over the long term.

Methods.
We developed permanent diabetic immune-deficient mice by introducing the Akita (C96Y) mutation into the rodent-specific Insulin1 gene of NOD/Shi-scid IL2rγcnull (NOG) mice (Ins1C96Y/C96Y
NOG). Their body weight, nonfasting blood glucose, and survival were measured from 4 wk of age. Insulin sensitivity was assessed via tolerance tests. To elucidate the utility of these mice in xenotransplantation experiments, we transplanted hSC-islet cells or porcine islets under the kidney capsules of these mice.

Results.
All male and female homozygous mice exhibited persistent severe hyperglycemia associated with β-cell depletion as early as 4 wk of age and exhibited normal insulin sensitivity. These mice could be stably engrafted with hSC-islets, and the mice that received porcine islet grafts promptly exhibited lowered blood glucose levels, maintaining blood glucose levels below the normal glucose range for at least 52 wk posttransplantation.

Conclusions.
The Ins1

C96Y/C96Y
NOG mouse model provides an effective platform to assess both the efficacy and safety of long-term xenograft engraftment without the interference of their immune responses. This study is expected to contribute essential basic information for the clinical application of islet cell transplantation.
Dorsoventral comparison of intraspecific polymorphisms in the butterfly wing pattern using a convolutional neural network
Authors: Amino Kai , Hirakasa Tsubasa , Yago Masaya , Matsuo Takashi 
Published: August 2024

ABSTRACTButterfly wing patterns exhibit notable differences between the dorsal and ventral surfaces, and morphological analyses of them have provided insights into the ecological and behavioural characteristics of wing colour patterns. Conventional methods for dorsoventral comparisons are constrained by the need for homologous patches or shared features between two surfaces, limiting their applicability across species. We used a convolutional neural network (CNN)-based analysis, which can compare images of the dorsal and ventral surfaces without focusing on homologous patches or features, to detect dorsoventral bias in intraspecific polymorphisms such as sexual dimorphism (SD) and female-limited mimetic polymorphism (FMP). Using specimen images of 29 butterfly species from the Yaeyama Islands, Japan, we first showed that the level of SD calculated by CNN-based analysis corresponded well with traditional assessments of SD, demonstrating the validity of the method. Dorsoventral biases were widely detected, particularly in SD, which tended to bias dorsally. This supports the conventional hypothesis that sexual selection acts more strongly on the dorsal surface. In contrast, the FMP analysis showed no significant bias. Our findings highlight CNN-based analysis as a versatile technique for dorsoventral comparisons, suggesting that broader species sampling could reveal general patterns of selection acting differentially on the two surfaces.
Sensitivities in associating land-system archetypes with sustainability metrics: Insights from simulations
Authors: Varma Varun , Evans Paul M , Bütikofer Luca , Goodwin Cecily E D , Pywell Richard F , Redhead John W , Storkey Jonathan , Bullock James M , Mead Andrew 
Published: August 2024

AbstractArchetypes of land- and socio-ecological systems, generated using unsupervised classification methods, enable the assimilation of complex environmental and socio-economic information. Such simplification has considerable potential to feed into decision support systems for sustainability planning. But, the usefulness of archetypes depends on how well they relate to sustainability criteria, such as ecosystem service (ES) delivery, that are external to the input datasets employed for archetype generation. Sensitivities in such post-hoc association analyses, and the associated utility of the archetype framework in a decision support context, remain unexplored. Here we emulated post-hoc association analysis procedures using simulated socio-ecological datasets and ES response variables. Our simulations revealed a substantial influence on analysis performance from (1) the number of variables used as inputs in archetype generation, (2) the correlation structure of input datasets, (3) the type and distribution of input variables, and (4) the functional form (linear or non-linear) characterising the relationship between ES variables and their predictors. We observed near-identical performance when archetypes were generated using K-means clustering and Self-Organising Maps (SOMs) – two commonly used archetype classification methods. Further, better archetype classifier performance did not guarantee better discrimination of ES value distributions between archetypes. Our results suggest that designing a framework to generate archetypes for sustainability planning, and the selected methodological choices therein, should place greater emphasis on what the archetypes will be used for in downstream analyses, and not focus solely on archetype classifier performance. This would better ensure the identification of archetypes adaptable to a diverse array of sustainability indicators and sufficiently robust for monitoring decision outcomes over time.
mosGraphGPT: a foundation model for multi-omic signaling graphs using generative AI
Authors: Zhang Heming , Huang Di , Chen Emily , Cao Dekang , Xu Tim , Dizdar Ben , Li Guangfu , Chen Yixin , Payne Philip , Province Michael , Li Fuhai 
Published: August 2024

AbstractGenerative pretrained models represent a significant advancement in natural language processing and computer vision, which can generate coherent and contextually relevant content based on the pre-training on large general datasets and fine-tune for specific tasks. Building foundation models using large scale omic data is promising to decode and understand the complex signaling language patterns within cells. Different from existing foundation models of omic data, we build a foundation model,mosGraphGPT, for multi-omic signaling (mos) graphs, in which the multi-omic data was integrated and interpreted using a multi-level signaling graph. The model was pretrained using multi-omic data of cancers in The Cancer Genome Atlas (TCGA), and fine-turned for multi-omic data of Alzheimer’s Disease (AD). The experimental evaluation results showed that the model can not only improve the disease classification accuracy, but also is interpretable by uncovering disease targets and signaling interactions. And the model code are uploaded via GitHub with link:https://github.com/mosGraph/mosGraphGPT
Enhanced pollen tube performance at high temperature contributes to thermotolerant fruit production in tomato
Authors: Yimga Ouonkap Sorel V. , Palaniappan Meenakshisundaram , Pryze Kelsey , Jong Emma , Ali Mohammad Foteh , Styler Benjamin , Almasaud Rasha Althiab , Harkey Alexandria F. , Reid Robert W. , Loraine Ann E. , Smith Steven E. , Muday Gloria K. , Pease James B. , Palanivelu Ravishankar , Johnson Mark A. 
Published: August 2024

AbstractRising temperature extremes during critical reproductive periods threaten the yield of major grain and fruit crops. Flowering plant reproduction depends on development of sufficient numbers of pollen grains and on their ability to generate a cellular extension, the pollen tube, which elongates through the pistil to deliver sperm cells to female gametes for double fertilization. These critical phases of the life cycle are sensitive to temperature and limit productivity under high temperature (HT). Previous studies have investigated the effects of HT on pollen development, but little is known about how HT applied during the pollen tube growth phase affects fertility. Here, we used tomato as a model fruit crop to determine how HT affects the pollen tube growth phase, taking advantage of cultivars noted for fruit production in exceptionally hot growing seasons. We found that exposure to HT solely during the pollen tube growth phase limits fruit biomass and seed set more significantly in thermosensitive cultivars than in thermotolerant cultivars. Importantly, we found that pollen tubes from the thermotolerant Tamaulipas cultivar have enhanced growthin vivoandin vitrounder HT. Analysis of the pollen tube transcriptome’s response to HT allowed us to develop hypotheses for the molecular basis of cellular thermotolerance in the pollen tube and we define two response modes (enhanced induction of stress responses, and higher basal levels of growth pathways repressed by heat stress) associated with reproductive thermotolerance. Importantly, we define key components of the pollen tube stress response identifying enhanced ROS homeostasis and pollen tube callose synthesis and deposition as important components of reproductive thermotolerance in Tamaulipas. Our work identifies the pollen tube growth phase as a viable target to enhance reproductive thermotolerance and delineates key pathways that are altered in crop varieties capable of fruiting under HT conditions.Abstract Figure
Clinical efficacy landscaping in genetic obesity: A meta-analysis in Prader Willi syndrome (PWS)
Authors: Sarkar Manish , Horsten Henning von , Milunov Dimitrije , Lefebvre Nathalie Barreto , Saha Soham 
Published: August 2024

AbstractGenetic obesity such as Prader Willi syndrome (PWS) is a multifaceted condition influenced by various elements, including genetic predisposition, environmental factors, and behavioral components. This meta-analysis explored the reported efficacy of therapeutics in clinical trials for PWS by evaluating clinical endpoints reached in the course of the study and the adverse events observed for each. We looked at GLP1 receptor mediated and non-GLP1 receptor mediated therapies and compared their performance in study arms across time and standard of care. In addition, we estimated the present market shares across different obesity and diabetes assets which have been tested against PWS. In conclusion, the study points to two key readouts. First, existing obesity and diabetes assets are not effective for genetic obesity such as PWS. The unmet needs remain high. Second, the markets for obesity and diabetes are in a stage of expansion. A collaborative approach to therapy development for genetic obesity is required to improve the quality of life for affected individuals.
iSODA: A Comprehensive Tool for Integrative Omics Data Analysis in Single- and Multi-Omics Experiments
Authors: Olivier-Jimenez Damien , Derks Rico J. E. , Harari Oscar , Cruchaga Carlos , Ali Muhammad , Ori Alessandro , Di Fraia Domenico , Cabukusta Birol , Henrie Andy , Giera Martin , Mohammed Yassene 
Published: August 2024

AbstractOmics technologies including genomics, proteomics, metabolomics, and lipidomics allow profound insights into health and disease. Thanks to plummeting costs of continuously evolving omics analytical platforms, research centers collect multi-omics data more routinely. They are, however, confronted with the lack of a versatile software solution to harmoniously analyze single-omics data and merge and interpret multi-omics data. We have developed iSODA, an interactive web-based application for the analysis of single-as well as multi-omics omics data. The software tool emphasizes intuitive, interactive visualizations designed for user-driven data exploration. Researchers can filter and normalize their datasets and access a variety of functions ranging from simple data visualization like volcano plots and PCA, to advanced functional analyses like enrichment analysis for proteomics and saturation analysis for lipidomics. For insights from integrated multi-omics, iSODA incorporates Multi-Omics Factor Analysis – MOFA, and Similarity Network Fusion – SNF. All results are presented in interactive plots with the possibility of downloading plots and associated data. The ability to adapt the imported data on-the-fly allows for tasks such as removal of outlier samples or failed features, various imputation strategies, or data normalization. The modular design allows for extensions with new analyses and plots. The software is accessible underhttp://isoda.online/.Abstract FigureGraphical summary for iSODA showcasing some application examples, the data import, the single-omics and multi-omics modules.
The genome assembly ofRhabditoides inermisfrom a complex microbial community reveals further evidence for parallel gene family expansions across multiple nematodes
Authors: Rödelsperger Christian , Röseler Waltraud , Athanasouli Marina , Wighard Sara , Herrmann Matthias , Sommer Ralf J. 
Published: August 2024

AbstractBackgroundFree-living nematodes such asCaenorhabditis elegansandPristionchus pacificusare powerful model systems for linking specific traits to their underlying genetic basis. To trace the evolutionary history of a candidate gene, a robust phylogenomic framework is indispensable.ResultsIn this work, we generated a near chromosome-scale genome assembly of the nematodeRhabditoides inermiswhich had previously been proposed as the sister group of the family Diplogastridae to whichP. pacificusbelongs. The genome was assembled from a complex microbial community that consists of multiple bacteria and a fungus of the genusVanrija. TheR. inermisgenome has five chromosomes that likely arose from recent fusions of different Nigon elements. Phylogenomic analysis groupedR. inermiswithin a clade includingC. elegans,Mesorhabditis belariand other rhabditids and thus, did not support a sister group relationship betweenR. inermisand the family Diplogastridae. Comparative genomic analyses identified abundant lineage-specific orthogroups which reveal evidence for parallel expansions of environmentally responsive gene families.ConclusionsOur work demonstrates the value of theR. inermisgenome as a resource for future phylogenomic analysis and for studying gene family evolution.
Clonal evolutionary analysis reveals patterns of malignant transformation in pancreatic cancer from Intraductal Papillary Mucinous IPMN Neoplasms (IPMN)
Authors: Pea Antonio , He Xiaotong , Upstill-Goddard Rosie , Luchini Claudio , Santana Leonor Patricia Schubert , Dreyer Stephan , Duthie Fraser , Jamieson Nigel B. , McKay Colin J. , Dickson Euan J. , Pulvirenti Alessandra , Rebus Selma ,   Genomics Innovation Alliance,   Scottish Genome Partnership, Piccoli Paola , Sperandio Nicola , Lawlor Rita T. , Milella Michele , Froeling Fieke E. M. , Salvia Roberto , Scarpa Aldo , Bainkin Andrew V. , Wedge David C. , Chang David K. 
Published: August 2024

AbstractIntraductal papillary mucinous neoplasms (IPMNs) are critical precursors to pancreatic ductal adenocarcinoma (PDAC), a highly lethal cancer due to late detection and rapid progression. Using multi-region whole-genome and transcriptome sequencing, we traced the evolution of PDAC from IPMN, constructing detailed phylogenetic trees to provide insights into subclonal architectures and progression pathways. Our analysis identified two distinct evolutionary trajectories: one driven by a single ancestral clone, and another involving multiple independent ancestral clones, potentially influencing the timing and nature of PDAC onset. We further explored the roles of mutational signatures and structural variants (SVs) in promoting clonal evolution. Complementing these genomic findings, our transcriptomic analysis revealed unique gene expression profiles and variations in the immune landscape, correlating with the different progression stages from IPMN to PDAC. These insights reveal the complex molecular dynamics of IPMN progression to PDAC, highlighting the need to refine early detection and treatment strategies.
Predicting reproductive phenology of wind-pollinated trees via PlanetScope time series
Authors: Song Yiluan , Katz Daniel S.W. , Zhu Zhe , Beaulieu Claudie , Zhu Kai 
Published: August 2024

AbstractAirborne pollen triggers allergic reactions which result in public health consequences. A better understanding of flowering and pollen phenology could improve airborne pollen predictions and reduce pollen exposure. Data on the timing of flowering and pollen release are needed to improve models of airborne pollen concentrations, but existingin-situdata collection efforts are expensive and spatially sparse. Satellite-based estimates of plant phenology could potentially enable large-scale data collection, but it is difficult to detect the reproductive phenology of wind-pollinated flowers from space. Here, we infer the reproductive phenology of wind-pollinated plants usingPlanetScopetime series with a spatial resolution of 3 m and a daily revisit cycle, complemented byin-situflower and pollen observations, leveraging the correlation between vegetative and reproductive phenology. On the individual tree level, we extracted PlanetScope-derived green-up time and validated its correlation to flowering time using flower observations in a national-scale observatory network. Scaling up to the city level, we developed a novel approach to characterize pollen phenology from PlanetScope-derived vegetative phenology, by optimizing two tuning parameters: the threshold of green-up or green-down and the time lag between green-up/down and flowering. We applied this method to seven cities in the US and seven key wind-pollinated tree genera, calibrated by measurements of airborne pollen concentrations. Our method characterized pollen phenology accurately, not only in-sample (Spearman correlation: 0.751, nRMSE: 13.5%) but also out-of-sample (Spearman correlation: 0.691, nRMSE: 14.5%). Using the calibrated model, we further mapped the pollen phenology landscape within cities, showing intra-urban heterogeneity. Using high spatiotemporal resolution remote sensing, our novel approach enables us to infer the flowering and pollen phenology of wind-pollinated plant taxa on a large scale and a fine resolution, including areas with limited priorin-situflower and pollen observations. The use of PlanetScope time series therefore holds promise for developing process-based pollen models and targeted public health strategies to mitigate the impact of allergenic pollen exposure.
Myeloid-instructed CD14+CD4+T cells within the tumor microenvironment are associated with TNF⍺ signaling and prolonged survival in non-small cell lung cancer
Authors: Marceaux Claire , Yokote Kenta , Gayevskiy Velimir , Batey Daniel , Tarasova Ilariya , Choux Laurie , Riley Lucy , Ribera Nina Tubau , Hywood Jack , Christie Michael , Antippa Phillip , Speed Terence P , Rogers Kelly L. , Phipson Belinda , Asselin-Labat Marie-Liesse 
Published: August 2024

AbstractImmune cells from the adaptive and innate immune systems coexist in the tumor immune microenvironment (TME) to mediate tumor cell immunosurveillance and prevent tumor relapse. Relapse-free survival after surgical resection is heterogeneous in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), the two major subtypes of non-small cell lung cancer. Yet, little is known about the composition and spatial organisation of the TME that could explain the different prognoses for lung cancer patients. Using spatial multi-omics analyses, we show that LUAD and LUSC have distinct TMEs and that neutrophil-enriched tumors are associated with worse survival, while the accumulation of myeloid-instructed CD14+T cells in the tumor core was associated with prolonged patient survival. CD14+CD4+T cells express prototypic markers of myeloid cells and were found to be clonally expanded tumor-specific activated T cells. TNFα signaling was activated in tumors with high infiltration of CD14+CD4+T cells in the tumor core. Our results demonstrate that innate immune cells impact adaptive immune cell activity to support the deployment of discrete T cell populations with anti-tumor activity.