bluuebunny/crossref_metadata_2025_split huggingface dataset:
Which schools receive state-level support for local food purchases? Evidence from reimbursement incentive programs in Michigan and Oregon
Authors: O'Hara Jeffrey K. , Jablonski Becca B. R. , Plakias Zoë T. 
Published: March 2022

AbstractState-level reimbursement programs are increasingly being used to incentivize procurement of local foods by US K-12 school food authorities (SFAs), which are schools or school districts that administer a food service program. However, few studies have explored the characteristics of SFAs that are associated with applying for and receiving reimbursement incentives. We consider reimbursement incentive programs in two states, Oregon and Michigan. In 2018–2019, the school year we study, Oregon used an opt-in model in which all SFAs were eligible to receive reimbursement incentives. In contrast, Michigan used a competitive funding model in which only some SFAs were eligible to apply and only some SFAs that applied received support. Using data from the Farm to School Census, as well as data from the two states' Departments of Education, we estimate discrete choice regressions to explore the factors that are associated with SFAs' application for and receipt of these reimbursement incentives. We find that SFAs that opted into Oregon's procurement program are larger, in metropolitan areas, and more likely to purchase fruits and vegetables locally. Thus, the reimbursement incentives are directed toward SFAs with characteristics that complement F2S program development, instead of SFAs with greater structural impediments. In Michigan, we find that SFAs with past F2S experience and community support for F2S were the most likely to apply for reimbursement incentives. However, conditional on applying, the SFAs most likely to receive funding in Michigan were those located in rural areas, more likely to source meat and seafood locally, and more likely to source directly from producers. Thus, Michigan's support, which was more budget constrained, appeared to target SFAs with distance-based challenges and non-traditional procurement strategies.
Deep Learning for Multi‐Timescales Pacific Decadal Oscillation Forecasting
Authors: Qin Mengjiao , Du Zhenhong , Hu Linshu , Cao Wenting , Fu Zhiyi , Qin Lianjie , Wu Sensen , Zhang Feng 
Published: March 2022

AbstractVariations in the Pacific decadal oscillation (PDO) can influence marine ecosystems and regional climate phenomena. Accurate and long‐term forecasts of the PDO are therefore crucial for marine governance. This paper presents a novel seasonal gated recurrent unit (SGRU) model, based on deep learning, for forecasting the PDO at multiple time scales. The model first decomposes the complex and nonlinear PDO index time series into three separate components, each retaining a distinct pattern of PDO. Next, a three‐pathway GRU model is constructed to model and forecast the PDO index. A test applying the SGRU model to the period from 1979 to 2020 demonstrates its superiority over eight state‐of‐the‐art models in PDO forecasting. Additionally, the SGRU model can flexibly produce high‐performance forecasts at multiple time scales. In view of that physical and dynamical models rely on clear evolutionary mechanisms, the SGRU model overcomes the complexities of these models.
Increasing Concurrent Drought Probability in Global Main Crop Production Countries
Authors: Qi Wei , Feng Lian , Yang Hong , Liu Junguo 
Published: March 2022

AbstractUnderstanding concurrent drought events in global main croplands is crucially important for food security, effective adaptation to climate change and human well‐being. Yet, there is a lack of comprehensive studies on concurrent drought probability changes among main crop production countries on a global scale, especially for the future. Here, we studied concurrent drought among 26 main crop production countries for the time period 1861–2099. During the historical period, probability of concurrent moderate and severe drought among the countries was relatively low, and the maximum concurrent moderate and severe drought probability will double and triple under RCP8.5 extreme climate, respectively. Concurrent probability of moderate and severe drought between China and United States of America, Brazil and Russia will be at least 6% and 5% under RCP8.5, respectively, compared with zero in the historical period. Limiting RCPs to RCP2.6 can decrease the concurrent probability of severe drought at least 2% (at least 3% for RCP8.5).
A Dynamo Simulation Generating Saturn‐Like Small Magnetic Dipole Tilts
Authors: Yadav Rakesh Kumar , Cao Hao , Bloxham Jeremy 
Published: March 2022

AbstractAmong planetary dynamos, the magnetic field of Saturn stands out in its exceptional level of axisymmetry. One of its peculiar features is that the magnetic dipole mode is tilted with respect to the planetary rotation axis by only ≈0.007° or less. Numerical dynamo simulations performed in this context have had great difficulty in producing such small dipole tilt angles without introducing ad hoc ingredients such as a latitudinally varying heat flux pattern in the outer layers or stably stratified layers (SSLs). Here we present a numerical dynamo simulation that generates a highly axisymmetric dynamo with a dipole tilt of about ≈0.0008° on average. The model consists of a deep dynamo layer and an overlying low‐conductivity layer but without any SSLs. We highlight a novel mechanism where strong differential rotation generated in the atmospheric layer penetrates into the dynamo region, helping to maintain a very small magnetic dipole tilt.
Effects of Increased Drought in Amazon Forests Under Climate Change: Separating the Roles of Canopy Responses and Soil Moisture
Authors: Wey Hao‐Wei , Pongratz Julia , Nabel Julia E. M. S. , Naudts Kim 
Published: March 2022

AbstractThe Amazon forests are one of the largest ecosystem carbon pools on Earth. Although more frequent and prolonged future droughts have been predicted, the impacts have remained largely uncertain, as most land surface models (LSMs) fail to capture the vegetation drought responses. In this study, the ability of the LSM JSBACH to simulate the drought responses of leaf area index (LAI) and leaf litter production in the Amazon forests is evaluated against artificial drought experiments. Based on the evaluation, improvements are implemented, including a dependency of leaf growth on leaf carbon allocation and a better representation of drought‐dependent leaf shedding. The modified JSBACH is shown to capture the drought responses at two sites and across different regions of the basin. It is then coupled with an atmospheric model to simulate the carbon and biogeophysical feedbacks of drought under future climate. We separate the drought impacts into (a) the direct effect, resulting from drier soil and stomatal closure, which does not involve a change in canopy structure, and (b) the LAI effect, resulting from leaf shedding and involving canopy response. We show that the latter accounts for 35% of reduced land carbon uptake (9 ± 10 vs. 26 ± 7 g/m2/yr; mean ± 1 sd) and 12% of surface warming (0.09 ± 0.03 vs. 0.7 ± 0.07 K) during the late 21st century. A north‐south dipole of precipitation change is found, which is largely attributable to the direct effect. The results highlight the importance of incorporating drought deciduousness of tropical rainforests in LSMs to better simulate land‐atmosphere interactions in the future.
MESH1 knockdown triggers proliferation arrest through TAZ repression
Authors: Sun Tianai , Ding Chien-Kuang Cornelia , Zhang Yuning , Zhang Yang , Lin Chao-Chieh , Wu Jianli , Setayeshpour Yasaman , Coggins Si’Ana , Shepard Caitlin , Macias Everardo , Kim Baek , Zhou Pei , Gordân Raluca , Chi Jen-Tsan 
Published: March 2022

AbstractAll organisms are constantly exposed to various stresses, necessitating adaptive strategies for survival. In bacteria, the main stress-coping mechanism is the stringent response triggered by the accumulation of “alarmone” (p)ppGpp to arrest proliferation and reprogram transcriptome. While mammalian genomes encode MESH1—the homolog of the (p)ppGpp hydrolase SpoT, current knowledge about its function remains limited. We found MESH1 expression tended to be higher in tumors and associated with poor patient outcomes. Consistently, MESH1 knockdown robustly inhibited proliferation, depleted dNTPs, reduced tumor sphere formation, and retarded xenograft growth. These antitumor phenotypes associated with MESH1 knockdown were accompanied by a significantly altered transcriptome, including the repressed expression of TAZ, a HIPPO coactivator, and proliferative gene. Importantly, TAZ restoration mitigated many anti-growth phenotypes of MESH1 knockdown, including proliferation arrest, reduced sphere formation, tumor growth inhibition, dNTP depletion, and transcriptional changes. Furthermore, TAZ repression was associated with the histone hypo-acetylation at TAZ regulatory loci due to the induction of epigenetic repressors HDAC5 and AHRR. Together, MESH1 knockdown in human cells altered the genome-wide transcriptional patterns and arrested proliferation that mimicked the bacterial stringent response through the epigenetic repression of TAZ expression.
High drug-loaded microspheres enabled by controlled in-droplet precipitation promote functional recovery after spinal cord injury
Authors: Li Wei , Chen Jian , Zhao Shujie , Huang Tianhe , Ying Huiyan , Trujillo Claudia , Molinaro Giuseppina , Zhou Zheng , Jiang Tao , Liu Wei , Li Linwei , Bai Yuancheng , Quan Peng , Ding Yaping , Hirvonen Jouni , Yin Guoyong , Santos Hélder A. , Fan Jin , Liu Dongfei 
Published: March 2022

AbstractDrug delivery systems with high content of drug can minimize excipients administration, reduce side effects, improve therapeutic efficacy and/or promote patient compliance. However, engineering such systems is extremely challenging, as their loading capacity is inherently limited by the compatibility between drug molecules and carrier materials. To mitigate the drug-carrier compatibility limitation towards therapeutics encapsulation, we developed a sequential solidification strategy. In this strategy, the precisely controlled diffusion of solvents from droplets ensures the fast in-droplet precipitation of drug molecules prior to the solidification of polymer materials. After polymer solidification, a mass of drug nanoparticles is embedded in the polymer matrix, forming a nano-in-micro structured microsphere. All the obtained microspheres exhibit long-term storage stability, controlled release of drug molecules, and most importantly, high mass fraction of therapeutics (21.8–63.1 wt%). Benefiting from their high drug loading degree, the nano-in-micro structured acetalated dextran microspheres deliver a high dose of methylprednisolone (400 μg) within the limited administration volume (10 μL) by one single intrathecal injection. The amount of acetalated dextran used was 1/433 of that of low drug-loaded microspheres. Moreover, the controlled release of methylprednisolone from high drug-loaded microspheres contributes to improved therapeutic efficacy and reduced side effects than low drug-loaded microspheres and free drug in spinal cord injury therapy.
The core and accessory Hfq interactomes across Pseudomonas aeruginosa lineages
Authors: Trouillon Julian , Han Kook , Attrée Ina , Lory Stephen 
Published: March 2022

AbstractThe major RNA-binding protein Hfq interacts with mRNAs, either alone or together with regulatory small noncoding RNAs (sRNAs), affecting mRNA translation and degradation in bacteria. However, studies tend to focus on single reference strains and assume that the findings may apply to the entire species, despite the important intra-species genetic diversity known to exist. Here, we use RIP-seq to identify Hfq-interacting RNAs in three strains representing the major phylogenetic lineages ofPseudomonas aeruginosa. We find that most interactions are in fact not conserved among the different strains. We identify growth phase-specific and strain-specific Hfq targets, including previously undescribed sRNAs. Strain-specific interactions are due to different accessory gene sets, RNA abundances, or potential context- or sequence- dependent regulatory mechanisms. The accessory Hfq interactome includes most mRNAs encoding Type III Secretion System (T3SS) components and secreted toxins in two strains, as well as a cluster of CRISPR guide RNAs in one strain. Conserved Hfq targets include the global virulence regulator Vfr and metabolic pathways involved in the transition from fast to slow growth. Furthermore, we use rGRIL-seq to show that RhlS, a quorum sensing sRNA, activates Vfr translation, thus revealing a link between quorum sensing and virulence regulation. Overall, our work highlights the important intra-species diversity in post-transcriptional regulatory networks inPseudomonas aeruginosa.
Synthetic glycans control gut microbiome structure and mitigate colitis in mice
Authors: Tolonen Andrew C. , Beauchemin Nicholas , Bayne Charlie , Li Lingyao , Tan Jie , Lee Jackson , Meehan Brian M. , Meisner Jeffrey , Millet Yves , LeBlanc Gabrielle , Kottler Robert , Rapp Erdmann , Murphy Chris , Turnbaugh Peter J. , von Maltzahn Geoffrey , Liu Christopher M. , van Hylckama Vlieg Johan E. T. 
Published: March 2022

AbstractRelative abundances of bacterial species in the gut microbiome have been linked to many diseases. Species of gut bacteria are ecologically differentiated by their abilities to metabolize different glycans, making glycan delivery a powerful way to alter the microbiome to promote health. Here, we study the properties and therapeutic potential of chemically diverse synthetic glycans (SGs). Fermentation of SGs by gut microbiome cultures results in compound-specific shifts in taxonomic and metabolite profiles not observed with reference glycans, including prebiotics. Model enteric pathogens grow poorly on most SGs, potentially increasing their safety for at-risk populations. SGs increase survival, reduce weight loss, and improve clinical scores in mouse models of colitis. Synthetic glycans are thus a promising modality to improve health through selective changes to the gut microbiome.
Deep proteomic profiling unveils arylsulfatase A as a non-alcoholic steatohepatitis inducible hepatokine and regulator of glycemic control
Authors: Montgomery Magdalene K. , Bayliss Jacqueline , Nie Shuai , De Nardo William , Keenan Stacey N. , Miotto Paula M. , Karimkhanloo Hamzeh , Huang Cheng , Schittenhelm Ralf B. , Don Anthony S. , Ryan Andrew , Williamson Nicholas A. , Ooi Geraldine J. , Brown Wendy A. , Burton Paul R. , Parker Benjamin L. , Watt Matthew J. 
Published: March 2022

AbstractNon-alcoholic steatohepatitis (NASH) and type 2 diabetes are closely linked, yet the pathophysiological mechanisms underpinning this bidirectional relationship remain unresolved. Using proteomic approaches, we interrogate hepatocyte protein secretion in two models of murine NASH to understand how liver-derived factors modulate lipid metabolism and insulin sensitivity in peripheral tissues. We reveal striking hepatokine remodelling that is associated with insulin resistance and maladaptive lipid metabolism, and identify arylsulfatase A (ARSA) as a hepatokine that is upregulated in NASH and type 2 diabetes. Mechanistically, hepatic ARSA reduces sulfatide content and increases lysophosphatidylcholine (LPC) accumulation within lipid rafts and suppresses LPC secretion from the liver, thereby lowering circulating LPC and lysophosphatidic acid (LPA) levels. Reduced LPA is linked to improvements in skeletal muscle insulin sensitivity and systemic glycemic control. Hepatic silencing of Arsa or inactivation of ARSA’s enzymatic activity reverses these effects. Together, this study provides a unique resource describing global changes in hepatokine secretion in NASH, and identifies ARSA as a regulator of liver to muscle communication and as a potential therapeutic target for type 2 diabetes.
Characteristics and treatment patterns of patients with asthma on multiple-inhaler triple therapy in Spain
Authors: Barrecheguren Miriam , Monteagudo Monica , Miravitlles Marc , Flor Xavier , Núñez Alexa , Osorio Jeisson , Muñoz Xavier , Ojanguren Iñigo 
Published: March 2022

AbstractThe aim of this observational, retrospective study was to describe characteristics, treatment patterns, and adherence among patients with asthma who initiated multiple-inhaler triple therapy (MITT) in Catalonia, Spain. This study used data of patients initiating MITT in 2016 from the SIDIAP (Information System for Research in Primary Care) database, which covers ~80% of the Catalonian population (5.8 million). Of 1,204 patients initiating MITT, 361 (30.0%) stepped down (discontinued ≥ 1 and continued ≥1 MITT component) and 89 (7.4%) stopped all three components of MITT for a period of 60 days during the following 12 months. In the follow-up period, 196 (16.3%) patients were considered adherent to MITT (>0.8 proportion of days covered [PDC]), with a mean (standard deviation) PDC of 0.52 (0.51) days. Given the low adherence and substantial rates of step down/discontinuation among patients initiating MITT, there is an urgent need to implement strategies to improve treatment adherence/persistence.
Field experiments underestimate aboveground biomass response to drought
Authors: Kröel-Dulay György , Mojzes Andrea , Szitár Katalin , Bahn Michael , Batáry Péter , Beier Claus , Bilton Mark , De Boeck Hans J. , Dukes Jeffrey S. , Estiarte Marc , Holub Petr , Jentsch Anke , Schmidt Inger Kappel , Kreyling Juergen , Reinsch Sabine , Larsen Klaus Steenberg , Sternberg Marcelo , Tielbörger Katja , Tietema Albert , Vicca Sara , Peñuelas Josep 
Published: March 2022

AbstractResearchers use both experiments and observations to study the impacts of climate change on ecosystems, but results from these contrasting approaches have not been systematically compared for droughts. Using a meta-analysis and accounting for potential confounding factors, we demonstrate that aboveground biomass responded only about half as much to experimentally imposed drought events as to natural droughts. Our findings indicate that experimental results may underestimate climate change impacts and highlight the need to integrate results across approaches.
Affective touch perception and longing for touch during the COVID-19 pandemic
Authors: Meijer Larissa L. , Hasenack B. , Kamps J. C. C. , Mahon A. , Titone G. , Dijkerman H. C. , Keizer A. 
Published: March 2022

AbstractInterpersonal touch and affective touch play a crucial role in social interactions and have a positive influence on mental health. The social distancing regulations implemented during the COVID-19 pandemic have reduced the ability to engage in interpersonal touch. This could cause longing for touch, and it might subsequently alter the way in which affective touch is perceived. To investigate this, we conducted an online survey and included 1982 participants, which contained questions regarding the COVID-19 regulations, longing for touch, and the perceived pleasantness of affective and non-affective touch. Results showed that participants reported feelings of longing for touch. This significantly increased with the duration and severity of the COVID-19 regulations. In addition, participants who experienced more longing for touch rated videos of affective and non-affective touch as more pleasant. Current results provide insight in the impact of sudden and prolonged COVID-19 regulations and show that increasing the duration and severity of these regulations is associated with a higher desire for touch, which is associated with increased perceived pleasantness of observing touch.
Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms
Authors: Essam Yusuf , Huang Yuk Feng , Ng Jing Lin , Birima Ahmed H. , Ahmed Ali Najah , El-Shafie Ahmed 
Published: March 2022

AbstractFloods and droughts are environmental phenomena that occur in Peninsular Malaysia due to extreme values of streamflow (SF). Due to this, the study of SF prediction is highly significant for the purpose of municipal and environmental damage mitigation. In the present study, machine learning (ML) models based on the support vector machine (SVM), artificial neural network (ANN), and long short-term memory (LSTM), are tested and developed to predict SF for 11 different rivers throughout Peninsular Malaysia. SF data sets for the rivers were collected from the Malaysian Department of Irrigation and Drainage. The main objective of the present study is to propose a universal model that is most capable of predicting SFs for rivers within Peninsular Malaysia. Based on the findings, the ANN3 model which was developed using the ANN algorithm and input scenario 3 (inputs consisting of previous 3 days SF) is deduced as the best overall ML model for SF prediction as it outperformed all the other models in 4 out of 11 of the tested data sets; and obtained among the highest average RMs with a score of 3.27, hence indicating that the model is very adaptable and reliable in accurately predicting SF based on different data sets and river case studies. Therefore, the ANN3 model is proposed as a universal model for SF prediction within Peninsular Malaysia.
Prediction of hearing recovery in unilateral sudden sensorineural hearing loss using artificial intelligence
Authors: Lee Min Kyu , Jeon Eun-Tae , Baek Namyoung , Kim Jeong Hwan , Rah Yoon Chan , Choi June 
Published: March 2022

AbstractDespite the significance of predicting the prognosis of idiopathic sudden sensorineural hearing loss (ISSNHL), no predictive models have been established. This study used artificial intelligence to develop prognosis models to predict recovery from ISSNHL. We retrospectively reviewed the medical data of 453 patients with ISSNHL (men, 220; women, 233; mean age, 50.3 years) who underwent treatment at a tertiary hospital between January 2021 and December 2019 and were followed up after 1 month. According to Siegel’s criteria, 203 patients recovered in 1 month. Demographic characteristics, clinical and laboratory data, and pure-tone audiometry were analyzed. Logistic regression (baseline), a support vector machine, extreme gradient boosting, a light gradient boosting machine, and multilayer perceptron were used. The outcomes were the area under the receiver operating characteristic curve (AUROC) primarily, area under the precision-recall curve, Brier score, balanced accuracy, and F1 score. The light gradient boosting machine model had the best AUROC and balanced accuracy. Together with multilayer perceptron, it was also significantly superior to logistic regression in terms of AUROC. Using the SHapley Additive exPlanation method, we found that the initial audiogram shape is the most important prognostic factor. Machine/deep learning methods were successfully established to predict the prognosis of ISSNHL.