bluuebunny/crossref_metadata_2025_split huggingface dataset:
PSII-5 Endotoxin-Induced Inflammation on Immune Markers and Steroidogenesis in Granulosa Cells
Authors: Castro Briza , Austin Molly , Gifford Craig A , Hernandez Gifford Jennifer A 
Published: October 2022

Abstract
Reproductive performance is central to profitability in beef cattle operations. Pathogenic stress can result from gram-negative bacteria release of the lipopolysaccharide (LPS) endotoxin which has the potential to accumulate in follicular fluid and impair folliculogenesis and steroidogenesis, leading to reduced fertility. Elevated LPS concentrations observed in acute clinical bacterial infections are recognized to have detrimental effects on female fertility, however, definitive effects of low concentrations of LPS present in subclinical or undetectable infection remains to be fully elucidated. This study investigated the effect of varying concentrations of LPS on mechanisms regulating ovarian function and subsequent steroidogenesis. A granulosa cell line (KGN) was cultured with or without increasing doses of LPS (0.0001 to 10 µg/mL) for 30 min or 48 h. Cells collected 30 min after treatment were analyzed for abundance of phosphorylated proteins and cells collected 48 h after treatment were analyzed for total protein abundance and gene expression. Media were analyzed by radioimmunoassay for steroid hormone concentrations. Quantitative and semi-quantitative PCR was used to evaluate aromatase, beta-catenin, and cytokines IL-6 and IL-8, respectively. No differences were detected among LPS treatment groups compared to vehicle-treated controls for protein abundance of total beta-catenin (P = 0.99) or active beta-catenin lacking phosphorylation at serine 33/37 (P = 0.95) evaluated at 48 h. Similar results were observed in protein abundance of phosphorylated forms of beta-catenin at serine 552 (P = 0.98) and 675 (P = 0.85) evaluated at 30 min. Additionally, estradiol concentrations were not different at 0.0001 to 1.0 µg/mL LPS; however, estradiol was significantly decreased at 10 µg/mL LPS treatment compared to the vehicle-treated control (P = 0.03). Progesterone concentrations were also similar within 0.0001 to 1.0 µg/mL LPS treatment groups with a tendency to decrease at 10 µg/mL LPS treatment (P = 0.08) compared to the vehicle-treated control. Varying concentrations of LPS did not change mRNA expression of beta-catenin (P = 0.28), aromatase (P = 0.32), and IL-6 (P = 0.35). However, a significant increase in cytokine IL-8 mRNA expression was observed at 10 µg/mL LPS compared to the vehicle-treated control (P = 0.04). These findings suggest that inflammatory response against LPS at higher concentrations may influence distinct cytokine expression and steroidogenesis. Conversely, basal expression of key steroidogenic enzymes and ovarian steroidogenic modulators are less affected by low concentrations of LPS. Therefore, immune response of granulosa cells is dependent on concentration of LPS exposure that has the ability to impact fertility by modulating the ovarian hormonal and follicular environment.
PSII-12 Change in College Students’ Sense of Belonging as a Result of a Beef Cattle Production Tour
Authors: Drewery Merritt L , Cooper Jade , Wickersham Tryon A 
Published: October 2022

Abstract
Sense of belonging is a student’s sense of feeling accepted, valued, encouraged, and included by others in their discipline. Sense of belonging fundamentally influences behavior and is linked to academic and career outcomes. Recent projections indicate there will not be enough qualified college graduates for requisite STEM agriculture careers. Further, there is an ethnic disparity in STEM agriculture degree recipients. As Black and Hispanic students have higher attrition than White students in STEM, there is an opportunity to implement educational interventions to improve their sense of belonging and increase matriculation into the agriculture workforce. The objective of this study was to evaluate if an experiential learning opportunity, a five-day tour of the beef cattle industry, changed students’ sense of belonging with a focus on ethnicity. Procedures involving human subjects were approved by Texas State University IRB (#8309). Students from two universities attended a beef cattle industry tour in the Texas Panhandle in May 2022. Students toured facilities, interacted with industry professionals, and reflected on their experiences through daily entries in journals. Pre- and post-tests were administered via Qualtrics as identical questionnaire-based survey instruments immediately before and after the tour. Questions were adapted from previously validated instruments. Sense of belonging metrics were scored on a 5-point Likert scale where 1=Strongly disagree and 5=Strongly agree. Cronbach’s alpha for the pre-survey was 0.71 and for the post-survey was 0.74. Statistical analyses were conducted with SPSS v.26. Independent sample t-tests were used to evaluate the change from pre- to post-survey responses and one-way ANOVA was used to evaluate the difference in responses between ethnicities. Students (n = 21) were predominantly female (81%); attended Texas A&M University (67%) or Texas State University (33%); and were White (52%), Hispanic (33%), or Black (14%). We combined “Hispanic” and “Black” into a single independent variable to analyze differences between White (52%) and non-White (48%) students. Before the tour, there was a difference (P = 0.05) in sense of belonging between White (4.33 ± 0.16) and ethnic minority (3.73 ± 0.23) students such that White students had a stronger sense of belonging in agriculture. There was no change (P = 0.55) in White students’ sense of belonging as a result of the tour, from 4.33 ± 0.16 to 4.39 ± 0.44. However, there was a change (P ≤ 0.01) in ethnic minority students’ sense of belonging, from 3.73 ± 0.23 to 4.37 ± 0.27. Ultimately, participating in the tour increased Hispanic and Black, but not White, students’ sense of belonging. One potential benefit of implementing experiential learning opportunities in dynamic social environments is improving students’ sense of belonging, especially in careers/industries where ethnic minorities are underrepresented.
PSIII-7 Late-Breaking: Improving Heifer Development Programs Using Precision Supplementation
Authors: Dagel Anna , Menendez Hector M , Ehlert Krista , Olson Ken , Brennan Jameson R 
Published: October 2022

Abstract
Improving individual animal efficiency is important for livestock producers to run financially sustainable operations. Developing heifers to replace a cow in the herd is expensive and reducing costs during the growing and reproductive phases is critical to increase profitability. Precision livestock management technologies can help reduce feed waste and optimize individual animal intake on extensive rangeland systems. Precision supplementation for heifer development programs aims to provide proper nutrition management and develop heifers to meet target body weight without over or under conditioning. With conventional feeding methods, animals are able to over consume feed and may be overdeveloped. Therefore, the objective of our study was to compare differences in yearling heifer performance between conventional and precision supplementation on winter rangeland pastures. The study was conducted at the South Dakota State University Cottonwood Field Station, located near Philip, SD. Angus heifer calves (Bos taurus, n = 60) born in the spring of 2021were utilized for the project. Following fall weaning, initial weight was used to randomly assign heifers to one of two treatments: control (n = 30) or precision (n = 30). Treatment groups were separated into different pastures to graze dormant native range from November to June and were rotated monthly to reduce pasture influence. Both groups were offered 2.27 kg/hd/d dried distillers grain pellets; the control group was bunk fed, allowing for normal variation in consumption and the precision group was fed individually using the Super SmartFeed Producer. Individual daily BW was measured using SmartScale technology and used to calculate daily rate of gain. Heifers were supplemented to reach 60% of their mature body weight (MBW; 635 kg) at time of breeding. Heifers were bred in June using estrus synchronization with fixed time artificial insemination. Statistical analysis was conducted in Program R, which included an ANOVA to evaluate the differences in ADG between the control and precision treatments. Our results indicated a significant difference in average daily gain between the control (P &lt; 0.05), mean ADG of 1.94 and 1.76 lb/d, respectively. Further, the control group utilized 5,600 lb more feed compared to the precision group. The number of heifers that achieved estrous was similar for both control (27) and precision groups (25) based on visual qualitative assessment of heat detection patches. At completion of the winter grazing season, the control group was developed to 64.9% MBW with an average weight of 909.37 lb and the precision group reached 62.2% MBW with an average weight of 870.37 lb. Successful implementation of precision supplementation has the potential to develop more uniform heifers at breeding, reduce intake variation, and reduce supplement overconsumption.
A New Defense of Brain Death as the Death of the Human Organism
Authors: McGee Andrew , Gardiner Dale , Jansen Melanie 
Published: October 2022

Abstract
This paper provides a new rationale for equating brain death with the death of the human organism, in light of well-known criticisms made by Alan D Shewmon, Franklin Miller and Robert Truog and a number of other writers. We claim that these criticisms can be answered, but only if we accept that we have slightly redefined the concept of death when equating brain death with death simpliciter. Accordingly, much of the paper defends the legitimacy of redefining death against objections, before turning to the specific task of defending a new rationale for equating brain death with death as slightly redefined.
High frequency root dynamics: sampling and interpretation using replicated robotic minirhizotrons
Authors: Nair Richard , Strube Martin , Hertel Martin , Kolle Olaf , Rolo Victor , Migliavacca Mirco 
Published: October 2022

AbstractAutomating dynamic fine root data collection in the field is a longstanding challenge with multiple applications for co-interpretation and synthesis for ecosystem understanding. High frequency root data are only achievable with paired automated sampling and processing. However, automatic minirhizotron (root camera) instruments are still rare and data are often not collected in natural soils or analysed at high temporal resolution. Instruments must also be affordable for replication and robust under variable natural conditions. Here, we show a system built with off-the-shelf parts which samples at sub-daily resolution. We paired this with a neural network to analyse all images collected. We performed two mesocosm studies and two field trials alongside ancillary data collection (soil CO2 efflux, temperature, and moisture content, and ‘PhenoCam’-derived above-ground dynamics). We produce robust and replicated daily time series of root dynamics under all conditions. Temporal root changes were a stronger driver than absolute biomass on soil CO2 efflux in the mesocosm. Proximal sensed above-ground dynamics and below-ground dynamics from minirhizotron data were not synchronized. Root properties extracted were sensitive to soil moisture and occasionally to time of day (potentially relating to soil moisture). This may only affect high frequency imagery and should be considered in interpreting such data.
Infrastructure, health and urban planning: rethinking the past and exploring future possibilities as a response to Harris & De Leeuw (2022)
Authors: McManus Phil 
Published: October 2022

Abstract
All infrastructure is health infrastructure, but not everything should be labelled infrastructure. This distinction, given (renewed) awareness of infrastructure, volumetric urbanism and environmental factors, is important for understanding infrastructure and health relationships. Building on an etymology of infrastructure and a summary of volumetric urbanism, this article explores selected historical urbanist programs and their intersection with concerns about infrastructure and health. Understanding history should foster humility yet enable us to research and call out infrastructures that are deleterious to health, avoid displacing negative health impacts through infrastructure provision and promote infrastructures that simultaneously provide hope, enable physical and emotional regeneration and heal the planet.
Inactivated SARS-CoV-2 vaccine does not increase the risk of relapse in patients with clinically inactive adult-onset Still’s disease
Authors: Hong Xinyue , Pan Haoyu , Su Yutong , Hu Qiongyi , Sun Yue , Liu Honglei , Cheng Xiaobing , Ye Junna , Shi Hui , Meng Jianfen , Zhou Zhuochao , Jia Jinchao , Liu Tingting , Wang Mengyan , Chen Xia , Ma Yuning , Tang Zihan , Wang Fan , Zhang Hao , You Yijun , Zhu Dehao , Chen Longfang , Yang Chengde , Teng Jialin , Chi Huihui 
Published: October 2022

Abstract

Objective
A succession of cases have reported flares of adult-onset Still’s disease (AOSD) after vaccination against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), raising concerns. We aimed to investigate the impact of inactivated SARS-CoV-2 vaccines on disease activity in patients with AOSD.

Methods
We prospectively enrolled clinically inactive AOSD patients visiting the outpatient clinics of our department. The patients received SARS-CoV-2 vaccines (BBIBP-CorV, Sinopharm, Beijing, China) voluntarily. The occurrence of relapse in the participants was recorded during the follow-up period, and a propensity score matching (PSM) method was used to compare the relapse rates between vaccinated and unvaccinated patients. Localized and systemic symptoms were assessed in the vaccinated patients.

Results
A total of 122 patients with inactive AOSD were included, of which 49.2% (n = 60) voluntarily received the inactivated SARS-CoV-2 vaccine. The relapse rate did not increase significantly in vaccinated patients in comparison with unvaccinated patients (after PSM: 6.8% vs 6.8%), and no relapse occurred within 1 month after vaccination. No obvious adverse reactions were reported in 75.0% of the participants, and none of the patients reported severe reactions.

Conclusion
Increased disease activity or relapse following vaccination with inactivated SARS–CoV-2 was rare in patients with inactive AOSD. Local and systemic adverse reactions were found to be mild and self-limiting. These safety profiles of inactivated SARS–CoV-2 vaccines in patients with AOSD may assist in eliminating vaccine hesitancy and increase the vaccination rate against SARS-CoV-2.
CRISPR/Cas9‐mediated gene knockout of Sj16 in Schistosoma japonicum eggs upregulates the host‐to‐egg immune response
Authors: Zhang Lichao , Wang Lifu , Xiang Suoyu , Hu Yunyi , Zhao Siyu , Liao Yao , Zhu Zifeng , Wu Xiaoying 
Published: October 2022

AbstractSchistosomiasis is an important, neglected tropical disease. Schistosoma japonicum can evade host attacks by regulating the host's immunity, causing continuous infection. However, interactions between the host's immune system and S. japonicum are unclear. Our previous research found that the Sj16 protein isolated from S. japonicum has an anti‐inflammatory effect in the host. However, the role of Sj16 in the regulation of host immunity in S. japonicum infection is not clear. Here, we applied the CRISPR/Cas9 technique to knockout Sj16 in S. japonicum eggs and investigated the effect of Sj16 in regulating host immunity. We found egg viability decreased after Sj16 knockout. In addition, we found granulomatous inflammation increased, the T‐cell immune response enhanced and the immune microenvironment changed in mice model injected with Sj16‐knockout eggs by tail vein. These findings suggested that S. japonicum could regulate host immunity through Sj16 to evade the host immune attack and cause continuous infection. In addition, we confirmed the application of CRISPR/Cas9‐mediated gene reprogramming for functional genomics in S. japonicum.
Global drought trends and future projections
Authors: Vicente-Serrano Sergio M. , Peña-Angulo Dhais , Beguería Santiago , Domínguez-Castro Fernando , Tomás-Burguera Miquel , Noguera Iván , Gimeno-Sotelo Luis , El Kenawy Ahmed 
Published: October 2022

Drought is one of the most difficult natural hazards to quantify and is divided into categories (meteorological, agricultural, ecological and hydrological), which makes assessing recent changes and future scenarios extremely difficult. This opinion piece includes a review of the recent scientific literature on the topic and analyses trends in meteorological droughts by using long-term precipitation records and different drought metrics to evaluate the role of global warming processes in trends of agricultural, hydrological and ecological drought severity over the last four decades, during which a sharp increase in atmospheric evaporative demand (AED) has been recorded. Meteorological droughts do not show any substantial changes at the global scale in at least the last 120 years, but an increase in the severity of agricultural and ecological droughts seems to emerge as a consequence of the increase in the severity of AED. Lastly, this study evaluates drought projections from earth system models and focuses on the most important aspects that need to be considered when evaluating drought processes in a changing climate, such as the use of different metrics and the uncertainty of modelling approaches.
This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.
Using machine learning to identify novel hydroclimate states
Authors: Marvel Kate , Cook Benjamin I. 
Published: October 2022

Anthropogenic climate change is expected to alter drought risk in the future. However, droughts are not uncommon or unprecedented, as documented in tree-ring-based reconstructions of the summer average Palmer drought severity index (PDSI). Using an unsupervised machine-learning method trained on these reconstructions of pre-industrial climate, we identify outliers: years in which the spatial pattern of PDSI is unusual relative to ‘normal' variability. We show that in many regions, outliers are more frequently identified in the twentieth and twenty-first centuries. This trend is more pronounced when the regional drought atlases are combined into a single global dataset. By definition, outlier patterns at the 10% level are expected to occur once per decade, but from 1950 to 2000 more than 6 years per decade are identified as outliers in the global drought atlas (GDA). Extending the GDA through 2020 using an observational dataset suggests that anomalous global drought conditions are present in 80% of years in the twenty-first century. Our results indicate, without recourse to climate models, that the world is more frequently experiencing drought conditions that are highly unusual in the context of past natural climate variability.This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.
Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical–statistical forecasting
Authors: AghaKouchak A. , Pan B. , Mazdiyasni O. , Sadegh M. , Jiwa S. , Zhang W. , Love C. A. , Madadgar S. , Papalexiou S. M. , Davis S. J. , Hsu K. , Sorooshian S. 
Published: October 2022

Despite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known—i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models—i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models.
This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.
Benchmark worst droughts during the summer monsoon in India
Authors: Mishra Vimal , Mujumdar Milind , Mahto Shanti Shwarup 
Published: October 2022

During the summer monsoon (June–September) season, drought poses challenges for agricultural activities and water availability in India. We develop a framework considering the timing, areal coverage and severity that can be used for the assessment of meteorological droughts as the monsoon season progresses. We estimate the benchmark worst droughts within the monsoon season (June, July, August and September) using the long-term (1901–2020) gridded rainfall. The benchmark worst droughts were identified considering the extent and severity of drought using the Drought Severity Coverage Index (DSCI). The worst meteorological drought in June, July, August and September occurred in 1923, 2002, 1937 and 1907 with a return period of 68, 200, 147, 188 years, respectively. The worst drought in the entire summer monsoon season occurred in 1918, which had a return period of 238 years. The benchmark droughts during June 1923, July 2002 and monsoon 1918 were associated with the warm sea surface temperature over the equatorial Pacific Ocean. The other two droughts (1937 and 1907) were linked with the off-equatorial warming over the Indo-Pacific region. The leading mode of variability in the Standardized Precipitation Index obtained from empirical orthogonal function analysis showed that the western and central parts of the country are most affected by droughts during the summer monsoon season. The second leading mode exhibited declining trends in rainfall with a bipolar spatial variability centred over the Gangetic Plain and southern India. The estimated DSCI for a 2–500 return period can be used for the drought assessment during the monsoon season in India.This article is part of the Royal Society Science+ meeting issue ‘Drought risk in the Anthropocene’.