bluuebunny/crossref_metadata_2025_split huggingface dataset:
Towards population-independent, multi-disease detection in fundus photographs
Authors: Matta Sarah , Lamard Mathieu , Conze Pierre-Henri , Le Guilcher Alexandre , Lecat Clément , Carette Romuald , Basset Fabien , Massin Pascale , Rottier Jean-Bernard , Cochener Béatrice , Quellec Gwenolé 
Published: July 2023

AbstractIndependent validation studies of automatic diabetic retinopathy screening systems have recently shown a drop of screening performance on external data. Beyond diabetic retinopathy, this study investigates the generalizability of deep learning (DL) algorithms for screening various ocular anomalies in fundus photographs, across heterogeneous populations and imaging protocols.The following datasets are considered: OPHDIAT (France, diabetic population), OphtaMaine (France, general population), RIADD (India, general population) and ODIR (China, general population). Two multi-disease DL algorithms were developed: a Single-Dataset (SD) network, trained on the largest dataset (OPHDIAT), and a Multiple-Dataset (MD) network, trained on multiple datasets simultaneously. To assess their generalizability, both algorithms were evaluated whenever training and test data originate from overlapping datasets or from disjoint datasets. The SD network achieved a mean per-disease area under the receiver operating characteristic curve (mAUC) of 0.9571 on OPHDIAT. However, it generalized poorly to the other three datasets (mAUC < 0.9). When all four datasets were involved in training, the MD network significantly outperformed the SD network (p = 0.0058), indicating improved generality. However, in leave-one-dataset-out experiments, performance of the MD network was significantly lower on populations unseen during training than on populations involved in training (p < 0.0001), indicating imperfect generalizability.
Important roles of Hif1a in maternal or adult BPA exposure induced pancreatic injuries
Authors: Liu Huiping , Zhou Yongnian , Li Yike , Gong Zhihua 
Published: July 2023

AbstractBisphenol A (BPA) is a monomer to produce polycarbonate plastics and can be released into the environment through human activities, leading to its accumulation in animals, plants and humans through direct contact or environmental exposure. Epidemiological studies have reported that BPA exposure is associated with metabolic disorders. The pancreas is an important endocrine organ and plays an important role in metabolic disorders. To explore the possible long-term effects of BPA exposure on neonatal health, bioinformatic methods were used to identify differentially expressed genes (DEGs) by comparing the neonatal pancreas after maternal exposure to BPA with the adult pancreas after direct exposure to BPA. Two datasets about BPA exposure and pancreatic abnormality, GSE82175 and GSE126297 in Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI) were collected. Control (or BPA-exposed) offspring (maternal exposure) and Control (or BPA-exposed) adults (direct exposure) were defined as Control (or BPA) groups. The results showed that BPA disturbed the normal function of the pancreas in both offspring and adults, with offspring showing higher susceptibility to BPA than adults. Seventeen insulin secretion-related DEGs (Stxbp5l, Fam3d, Mia3, Igf1, Hif1a, Aqp1, Kif5b, Tiam1, Map4k4, Cyp51, Pde1c, Rab3c, Arntl, Clock, Edn3, Kcnb1, and Krt20) in the BPA group were identified, and 15 regulator DEGs (Zfp830, 4931431B13Rik, Egr1, Ddit4l, Cep55, G530011O06Rik, Hspa1b, Hspa1a, Cox6a2, Ibtk, Banf1, Slc35b2, Golt1b, Lrp8, and Pttg1) with opposite expression trends and a regulator gene Cerkl with the similar expression trend in the Control and BPA groups were identified. Hif1α might be an important molecular target for pancreatic cancer caused by BPA exposure, and pregnancy is a critical window of susceptibility to BPA exposure.
Sleep disorders among patients suffering from road traffic injuries in an urban setting of Vietnam: an exploratory study
Authors: Vu Hai Minh , Tran Tung Hoang , Dang Anh Kim , Hoang Trong Nang , Nguyen Cuong Tat , Nguyen Huong Lan Thi , Latkin Carl A. , Ho Cyrus S. H. , Ho Roger C. M. 
Published: July 2023

AbstractSleep quality is an important indicator of treatment outcome for patients with traffic accident injuries. In Vietnam, the impacts of injury on sleep status are usually amplified in urban areas due to disproportionate distribution of mental care services between the city and less developed settings. Our study investigated deterioration in sleep quality and identified associated demographic factors among traffic injury patients in an small urban setting of Vietnam. A cross-sectional study was conducted among 408 patients in one provincial hospital and five district hospitals in Thai Binh, Vietnam from October to December 2018. A structured questionnaire was designed based on 3 standardized scales: Health-related Quality of Life, the Pittsburgh Sleep Quality Index and the Kessler Scale. Face-to-face interviews and medical records were conducted by trained health professionals on patients hospitalized in the Trauma—Orthopedic/Burn Department and Surgery and General Department. About 16.9% of respondents had sleep disturbances, and there was a statistically significant difference between age group (p < 0.01), education level (p < 0.01), and monthly household income (p < 0.01) between participants who with and without sleep disturbances. Furthermore, more than half (50.7%) of respondents sleep less than 5 h per day, while 18.7% of the sampled also reported that the habitual sleep efficiency was below 85%. Current results indicated that people being female, suffering from traumatic brain injury, being comatose at hospitalization, and having higher psychological distress scores were more likely to suffer from sleep problems. Our study is one of the first evidence in Vietnam to assess sleep disturbances in road traffic injury patients and their correlated factors. It is important to identify patients who are at risk of sleep disturbances based on socio-demographic and clinical characteristics, as well as psychological distress status. Therefore, a holistic approach should be taken to include sleep quality and psychological state in the treatment process and outcome assessment for road traffic injury patients.
Glutathione trisulfide prevents lipopolysaccharide-induced retinal inflammation via inhibition of proinflammatory cytokine production in glial cells
Authors: Tawarayama Hiroshi , Umeki Kota , Inoue-Yanagimachi Maki , Takahashi Naoki , Hasegawa Hirokazu , Himori Noriko , Tsuda Satoru , Kunikata Hiroshi , Akaike Takaaki , Nakazawa Toru 
Published: July 2023

AbstractWe aimed to investigate the impact of glutathione trisulfide (GSSSG) on lipopolysaccharide (LPS)-induced inflammation in retinal glia. Inflammatory responses in mouse-derived glial cells and Wistar rat retinas were stimulated with administration of LPS. Cell survival and proinflammatory cytokine production were examined using the Calcein-AM assay, and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and enzyme-linked immunosorbent assay (ELISA), respectively. Retinal microglia were visualized with immunohistochemistry for Iba1. Administration of LPS (10 µg/mL) or GSSSG (less than 100 µM) did not affect survival of cultured primary Müller cells and established microglial cells (BV-2). RT-qPCR and ELISA indicated that GSSSG inhibited LPS-induced gene upregulation and protein secretion of proinflammatory cytokines in these glial cells and rat retinas. GSSSG inhibited LPS-induced activation of TGF-β-activated kinase 1 (TAK1), which is an upstream kinase of NF-κB, in BV-2 cells. Finally, in vivo experiments indicated that intravitreal administration of GSSSG but not its relative glutathione disulfide (GSSG) inhibited LPS (500 ng)-induced accumulation of Iba1-immunopositive microglia in rat retinas. Taken together, GSSSG has the potential to prevent pathogenesis of inflammation-associated ocular diseases by inhibiting proinflammatory cytokine expression in retinal glial cells.
Exploring biomarkers of premature ovarian insufficiency based on oxford nanopore transcriptional profile and machine learning
Authors: Yu Zhaoyang , Li Mujun , Peng Weilong 
Published: July 2023

AbstractPremature ovarian insufficiency (POI) is a reproductive endocrine disorder characterized by infertility and perimenopausal syndrome, with a highly heterogeneous genetic etiology and its mechanism is not fully understood. Therefore, we utilized Oxford Nanopore Technology (ONT) for the first time to characterize the full-length transcript profile, and revealed biomarkers, pathway and molecular mechanisms for POI by bioinformatics analysis and machine learning. Ultimately, we identified 272 differentially expressed genes, 858 core genes, and 25 hub genes by analysis of differential expression, gene set enrichment, and protein–protein interactions. Seven candidate genes were identified based on the intersection features of the random forest and Boruta algorithm. qRT-PCR results indicated that COX5A, UQCRFS1, LCK, RPS2 and EIF5A exhibited consistent expression trends with sequencing data and have potential as biomarkers. Additionally, GSEA analysis revealed that the pathophysiology of POI is closely associated with inhibition of the PI3K-AKT pathway, oxidative phosphorylation and DNA damage repair, as well as activation of inflammatory and apoptotic pathways. Furthermore, we emphasize that downregulation of respiratory chain enzyme complex subunits and inhibition of oxidative phosphorylation pathways play crucial roles in the pathophysiology of POI. In conclusion, our utilization of long-read sequencing has refined the annotation information within the POI transcriptional profile. This valuable data provides novel insights for further exploration into molecular regulatory networks and potential biomarkers associated with POI.
Fatty acid metabolism changes in association with neurobehavioral deficits in animal models of fetal alcohol spectrum disorders
Authors: Hwang Hye Mee , Kawasawa Yuka Imamura , Basha Aiesha , Mohammad Shahid , Ito Mariko , Hashimoto-Torii Kazue 
Published: July 2023

AbstractFetal alcohol spectrum disorders (FASD) show behavioral problems due to prenatal alcohol exposure (PAE). A previous study reports changes in gene expressions linked to fatty acid (FA) metabolism in the cerebral cortex of the PAE mouse model. We find an increase of palmitic acid and arachidonic acid in phospholipid in the cerebral cortex of PAE at postnatal day 30. The increase of palmitic acid is consistent with increase of the producing enzyme, Fasn (fatty acid synthase). Decrease of 26:6 FA is also consistent with the increase of the enzyme which uses 26:6 as a substrate for making very long chain FAs, Elovl4 (elongation of very long chain fatty acids protein 4). However, there is no increase in the elongated products. Rather, lipid droplets (LDs) accumulated in the brain. Although FA-associated metabolic measurements are not affected by PAE, the abundance of FA-related gut microbiota is altered. This suggests that the gut microbiome could serve as a tool to facilitate uncovering the brain pathophysiology of FASD and a potential target to mitigate neurobehavioral problems.
Disruption prediction for future tokamaks using parameter-based transfer learning
Authors: Zheng Wei , Xue Fengming , Chen Zhongyong , Chen Dalong , Guo Bihao , Shen Chengshuo , Ai Xinkun , Wang Nengchao , Zhang Ming , Ding Yonghua , Chen Zhipeng , Yang Zhoujun , Shen Biao , Xiao Bingjia , Pan Yuan 
Published: July 2023

AbstractTokamaks are the most promising way for nuclear fusion reactors. Disruption in tokamaks is a violent event that terminates a confined plasma and causes unacceptable damage to the device. Machine learning models have been widely used to predict incoming disruptions. However, future reactors, with much higher stored energy, cannot provide enough unmitigated disruption data at high performance to train the predictor before damaging themselves. Here we apply a deep parameter-based transfer learning method in disruption prediction. We train a model on the J-TEXT tokamak and transfer it, with only 20 discharges, to EAST, which has a large difference in size, operation regime, and configuration with respect to J-TEXT. Results demonstrate that the transfer learning method reaches a similar performance to the model trained directly with EAST using about 1900 discharge. Our results suggest that the proposed method can tackle the challenge in predicting disruptions for future tokamaks like ITER with knowledge learned from existing tokamaks.
Multi-model assessment identifies livestock grazing as a major contributor to variation in European Union land and water footprints
Authors: Vanham Davy , Bruckner Martin , Schwarzmueller Florian , Schyns Joep , Kastner Thomas 
Published: July 2023

AbstractFood systems are the largest users of land and water resources worldwide. Using a multi-model approach to track food through the global trade network, we calculated the land footprint (LF) and water footprint (WF) of food consumption in the European Union (EU). We estimated the EU LF as 140–222 Mha yr−1 and WF as 569–918 km3 yr−1. These amounts are 5–7% of the global LF and 6–10% of the global WF of agriculture, with the EU representing 6% of the global population. We also calculated the global LF of livestock grazing, accounting only for grass eaten, to be 1,411–1,657 Mha yr−1, and the global LF of agriculture to be 2,809–3,014 Mha yr−1, which is about two-thirds of what the Food and Agriculture Organization Statistics (FAOSTAT) database reports. We discuss here the different methods for calculating the LF for livestock grazing, underscoring the need for a consistent methodology when monitoring the food LF and WF reduction goals set by the EU’s Farm To Fork Strategy.
Factors Impacting Implementation of Universal Screening of Developmental Language Disorder in Public Schools
Authors: Christopulos Tyler T. , Redmond Sean M. 
Published: July 2023

Purpose:
Developmental language disorder (DLD) is an underidentified neurodevelopmental disorder that affects, on average, one out of 11 kindergarten-age children (ages 4–6 years). Children with DLD can face academic, behavioral, psychiatric, emotional, and social challenges. Universal screening is seen as an effective way for public school districts to increase DLD identification rates. However, little is known about factors impacting implementation of school-based universal screenings for DLD. We partnered with a large suburban school district in the Intermountain West region of the United States to gather detailed perspectives from school personnel regarding the barriers and facilitators to the implementation of universal screening in their district.

Method:
Using a two-phase mixed-methods design, we first conducted focus groups to identify potential barriers and facilitators to universal screening. We then used the qualitative data from the first phase to develop a 20-item survey to assess agreement with the focus group results among a wider group of district speech-language pathologists and kindergarten teachers from the school district.

Results:
Our survey showed moderate levels of agreement with our focus group results. In particular, school personnel showed high levels of support for universal screening for DLD, with interesting interplay across various factors: (a) the negative impact of unmanageable workload on personnel under both referral- and universal-based identification formats, (b) the preference for paraprofessionals to administer screenings, (c) the role that Response to Intervention programs may play in offsetting workloads associated with universal screenings, and (d) the need for increased awareness and education about child language development and impairment among general education teachers and the public.

Conclusion:
Recommendations for incorporating these factors into more useful and applicable collaborative research-based efforts are presented.

Supplemental Material:

https://doi.org/10.23641/asha.23661876
Neural dynamics for improving optimiser in deep learning with noise considered
Authors: Su Dan , Stanimirović Predrag S. , Han Ling Bo , Jin Long 
Published: July 2023

AbstractAs deep learning evolves, neural network structures become increasingly sophisticated, bringing a series of new optimisation challenges. For example, deep neural networks (DNNs) are vulnerable to a variety of attacks. Training neural networks under privacy constraints is a method to alleviate privacy leakage, and one way to do this is to add noise to the gradient. However, the existing optimisers suffer from weak convergence in the presence of increased noise during training, which leads to a low robustness of the optimiser. To stabilise and improve the convergence of DNNs, the authors propose a neural dynamics (ND) optimiser, which is inspired by the zeroing neural dynamics originated from zeroing neural networks. The authors first analyse the relationship between DNNs and control systems. Then, the authors construct the ND optimiser to update network parameters. Moreover, the proposed ND optimiser alleviates the non‐convergence problem that may be suffered by adding noise to the gradient from different scenarios. Furthermore, experiments are conducted on different neural network structures, including ResNet18, ResNet34, Inception‐v3, MobileNet, and long and short‐term memory network. Comparative results using CIFAR, YouTube Faces, and R8 datasets demonstrate that the ND optimiser improves the accuracy and stability of DNNs under noise‐free and noise‐polluted conditions. The source code is publicly available at https://github.com/LongJin‐lab/ND.
Photovoltaic hosting capacity improvement based on the economic comparison between curtailment and network upgrade
Authors: Fatima Samar , Püvi Verner , Pourakbari‐Kasmaei Mahdi , Lehtonen Matti 
Published: July 2023

AbstractThe upcoming network investment decisions and regulatory framework adopted by distribution system operators (DSOs) are most likely to be impacted by the integration of fluctuating distributed generation. The economical hosting capacity (HC) improvement method is investigated in this paper as a trade‐off between curtailment and upgrade using a Monte Carlo simulation procedure. The associated costs of both methods are vital indicators for network operators that are trying to maximize the HC and minimize cost. In addition, the breakeven point where curtailment and upgrade costs intersect is the decisive point at which network upgrade becomes sensible as marginal curtailment cost exceeds upgrade cost. A shift in global climate conditions can impact the photovoltaic (PV) levels that motivate network operators to investigate PV penetration, especially in colder climate regions. Thus, the primary objective of this paper is to investigate the shift of the breakeven point to guide DSOs to either adopt PV curtailment as a temporary measure or grid upgrade as a risk aversion strategy considering Finnish climate and load patterns. The real‐time load and PV generation data are utilized for the simulations to consider the dynamic performance indication of three Finnish distribution networks. Curtailment remains a low‐cost option to obtain a percentage HC rise of 13%, 7%, and 8% for rural, suburban, and urban regions, respectively, beyond which curtailment compensation cost surpasses upgrade cost. In essence, PV curtailment serves as an immediate and least‐cost solution to relieve network violations and defer network investment until the HC level (118%, 106%, 97%) making the upgrade a practical option afterwards.
A physical model for the calculation of the corona onset voltage gradient of AC spacers
Authors: Huang Shilong , Mao Jie , Yan Xiaoliang , Xue Weiye , Zhang Zhongyuan , Xie Liang , Liu Yunpeng 
Published: July 2023

AbstractBy correcting the photon area factor (PAF) to suit the structure of the spacer and redefining the effective ionization region of the process of negative corona onset, a physical model for the calculation of COG of AC spacers is established. The validity and practicability of the established physical model are validated by the corona onset test results of spacers in both low‐altitude and high‐altitude areas. The numerical results pertaining to PAF and effective ionization region are consistent with the theoretical analysis. With the established physical model as a basis, the COG of the spacer is calculated under different geometrical structure parameters and ambient conditions. The results imply that changing the curvature of the spacer edge has a more significant impact on the COG of the AC spacer than changing the curvature of the spacer end and thickness H. The decrease in the length of the ionization region and the rapid attenuation of spatial electric field intensity of the spacer with smaller geometric parameters lead to an increase in the COG.
Early Postoperative Results in Robotic-Arm-Assisted Total Knee Replacement versus Conventional Technique: First Latin American Experience
Authors: Figueroa David , Guiloff Rodrigo , Prado Tomas , Figureoa Loreto , Sotomayor Juan Jose , Alarcon Alberto , Figueroa Francisco , Vaisman Alex , Calvo Rafael 
Published: July 2023

AbstractEarly results with robotic-arm-assisted total knee arthroplasty (TKA) are encouraging; nevertheless, literature might be unrepresentative, as it comes mostly from American, European, and Asian countries. There is limited experience and no comparative clinical reports in Latin America, a region of mainly low- and middle-income countries with limited access to these promising technologies. This study aims to compare the early postoperative results of the first Latin American experience with robotic-arm-assisted TKA versus conventional TKA. A cohort study was performed, including 181 consecutive patients (195 knees) with advanced symptomatic knee osteoarthritis (OA) undergoing primary TKA between March 2016 and October 2019. The cohort included 111 consecutive patients (123 knees) undergoing conventional TKA, followed by 70 consecutive patients (72 knees) undergoing robotic-arm-assisted TKA. The same surgical team (surgeon 1 and surgeon 2) performed all procedures. Patients with previous osteotomy, posttraumatic OA, and revision components were not considered. The same anesthetic and rehabilitation protocol was followed. The investigated clinical outcomes (for the first 60 postoperative days) were: surgical tourniquet time, time to home discharge, time to ambulation, postoperative daily pain (Visual Analog Scale), opioid use, range of motion, blood loss, complications, and postoperative mechanical axis. The early clinical postoperative results of this first Latin American comparative experience of robotic-arm-assisted TKA versus conventional technique showed lower opioids requirements and faster functional recovery of ambulation in those patients operated with the robotic system; nevertheless, surgical times were higher, without differences in total postoperative complications and other clinical outcomes.
Development of Novel Catalytic Direct Syntheses of N-Unsubstituted Ketimines and Their Applications to One-Pot Reactions
Authors: Morimoto Hiroyuki , Ohshima Takashi , Kondo Yuta 
Published: July 2023

AbstractHerein, we summarize our recent efforts toward developing catalytic methods for the synthesis of N-unsubstituted ketimines and their applications to one-pot reactions for producing various nitrogen-containing compounds. This account provides detailed background, optimization, scope, and mechanistic information. We hope this work will stimulate future studies on the implementation of N-unsubstituted ketimines.1	Introduction2	Scandium-Catalyzed Synthesis of N-Unsubstituted Ketimines3	Tetra-n-butylammonium Fluoride Catalyzed Synthesis of N-Unsubstituted Ketimines4	Conclusion