bluuebunny/crossref_metadata_2025_split huggingface dataset:
Technological scopes of companies in automotive semiconductor technology: Focusing on CPC codes in patents
Authors: Chin Kyounghwan , Lee Heesang 
Published: December 2024

Abstract

This study expanded the research beyond the automotive industry to five industries, including the general IT industry and automotive semiconductor industry subsectors, and analyzed the development status of automotive semiconductor technology. This study considered 418,853 patents from 45 leading companies in five industry subsectors over 11 years (2010–2020). This period marked the advent of autonomous and electric vehicles (EVs). First, the patents held by the companies and the cooperative patent classification (CPC) codes contained in the patents were extracted to conduct a quantitative and qualitative analysis of the technologies. The general IT and integrated device manufacturer (IDM) industries have shown the highest number of patents and growth, with Samsung and Rivian leading in patents and patent growth, respectively, among the companies analyzed. Next, CPCs were classified into technology groups (TG), allowing the considered company's technological directions to be identified. The results demonstrated that five groups of companies are engaged in developing sensor and communication technologies belonging to TG04 and TG05. Furthermore, the number of CPCs per patent (CP Value), which was employed to assess new entrants’ capability beyond just patent count analysis, facilitated identifying and comparing new entrants with high potential, such as Mobileye. This study offers insights on a company’s patent applications and decisions in technological fields for future research and development.
Genome-wide meta-analyses of non-response to antidepressants identify novel loci and potential drugs
Authors: Koch Elise , Jürgenson Tuuli , Einarsson Guðmundur , Mitchell Brittany , Harder Arvid , García-Marín Luis M. , Krebs Kristi , Lin Yuhao , Shadrin Alexey , Xiong Ying , Frei Oleksandr , Lu Yi , Hägg Sara , Renteria Miguel , Medland Sarah , Wray Naomi , Martin Nicholas , Hübel Christopher , Breen Gerome , Thorgeirsson Thorgeir , Stefansson Hreinn , Stefansson Kari , Lehto Kelli , Milani Lili , Andreassen Ole , O`Connell Kevin 
Published: December 2024

Abstract

Antidepressants exhibit a considerable variation in efficacy, and increasing evidence suggests that individual genetics contribute to antidepressant treatment response. Here, we combined data on antidepressant non-response measured using rating scales for depressive symptoms, questionnaires of treatment effect, and data from electronic health records, to increase statistical power to detect genomic loci associated with non-response to antidepressants in a total sample of 135,471 individuals prescribed antidepressants (25,255 non-responders and 110,216 responders). We performed genome-wide association meta-analyses, genetic correlation analyses, leave-one-out polygenic prediction, and bioinformatics analyses for genetically informed drug prioritization. We identified two novel loci (rs1106260 and rs60847828) associated with non-response to antidepressants and showed significant polygenic prediction in independent samples. Genetic correlation analyses show positive associations between non-response to antidepressants and most psychiatric traits, and negative associations with cognitive traits and subjective well-being. In addition, we investigated drugs that target proteins likely involved in mechanisms underlying antidepressant non-response, and shortlisted drugs that warrant further replication and validation of their potential to reduce depressive symptoms in individuals who do not respond to first-line antidepressant medications. These results suggest that meta-analyses of GWAS utilizing real-world measures of treatment outcomes can increase sample sizes to improve the discovery of variants associated with non-response to antidepressants.
ENSO as an Emergent Constraint on the Pattern Effect
Authors: Hanke Tyler , Proistosescu Cristian 
Published: December 2024

Abstract

As the spatial pattern of warming in sea surface temperatures evolves, it leads to changes in Earth's net radiative feedback and effective climate sensitivity. The magnitude of the change in feedback attributable to changes in regional temperature patterns is known as the “pattern effect”. This pattern effect has been mostly attributed to variability in low cloud radiative effect (CRE), and exhibits significant variance in models while being poorly constrained from observations. Here we use ENSO as an emergent constraint on the pattern effect. We find strong linear relationships between modeled ENSO feedbacks and their respective pattern effects, especially for net CRE. Observed ENSO feedbacks lie within the middle of the model ensemble and suggest a pattern effect magnitude of 1.00 +/- 0.35 W/m^2/K when calculated relative to preindustrial conditions. Model-simulated pattern effects also exhibit similar spatial distributions as the pattern diversity of ENSO, hinting at similar physics governing the two phenomena.
China’s agricultural non-point source pollution control: Policy logic, spatiotemporal characteristics and trend prediction
Authors: zou lilin , liang yifan , yang yuanyuan 
Published: December 2024

Abstract

Agricultural non-point source pollution (ANPSP) control is essential for advancing sustainable, low-carbon agricultural development and accelerating the establishment of a robust agricultural economy. However, existing research has paid limited attention to the policy rationale underlying ANPSP mitigation efforts, with insufficient exploration of the spatiotemporal characteristics of various agricultural pollutants across provinces nationwide and their projected future trends. This paper comprehensively analyzed aforesaid aspects through theoretical deductions, quantitative assessments, and predictive trend modeling. The research findings revealed that the policy formulation logic behind China’s ANPSP control followed a framework of “legislating first, adjusting next, and implementing subsequently.” Across different stages, the implementation logic manifested distinct operational modes and determining factors, while deviations arose from inducements within political incentives, promotion-based incentives, and fiscal incentives. From 1978 to 2022, pollution loads of chemical oxygen demand, total nitrogen, and total phosphorus in China agricultural production sector increased overall, though their emission intensity gradually decreased; livestock production and fertilizer application persisted as the primary sources of ANPSP. The ANPSP status across regions in China demonstrated considerable stability, with significant spatial spillover effects wherein the pollution status of adjacent units influenced the state of adjacent units, though the direction and degree of these effects vary. Projections indicate distinct evolutionary trends for various agricultural pollutants. Consequently, direct policy recommendations include establishing a comprehensive ANPSP control policy framework, advancing regionally differentiated pollution control strategies, enhancing dynamic monitoring of ANPSP, and implementing coordinated control measures across regions.
Integrative Omics reveals genetic basis and TaMYB7-A1’s function in wheat WUE and drought resilience
Authors: Xiao Jun , Zhou Yuxin , Wang Hao , Qiao Yunzhou , Zhao Peng , Cao Yuan , Liu Xuemei , Yang Yiman , Lin Xuelei , Xu Shengbao , Dong Baodi , Wang Dongzhi 
Published: December 2024

Abstract

Improving water use efficiency (WUE) and drought resistance in wheat is critical for ensuring global food security under changing climate conditions. Here, we integrated multi-omic data, including population-scale phenotyping, transcriptomics, and genomics, to dissect the genetic and molecular mechanisms underlying WUE and drought resilience in wheat. Genome-wide association studies (GWAS) revealed 8,135 SNPs associated with WUE-related traits, identifying 258 conditional and non-conditional QTLs, many of which co-localized with known drought-resistance genes. Pan-transcriptome analysis uncovered tissue-specific expression patterns, core and unique gene functions, and dynamic sub-genomic biases in response to drought. eQTL mapping pinpointed 146,966 regulatory loci, including condition-specific hotspots enriched for genes involved in water regulation, osmoregulation, and photosynthesis. Integration of Weighted gene co-expression network analysis (WGCNA), Summary-data-based Mendelian Randomization (SMR) and GWAS, eQTLs identified 207 candidate causal genes as key regulators for WUE-related traits in wheat, such as TaMYB7-A1. Functional analyses found that TaMYB7-A1 enhances drought tolerance by promoting root growth, reducing oxidative stress, and improving osmotic regulation, enabling better water access and survival under stress. It also increases photosynthesis efficiency and WUE, boosting yield under drought without compromising performance in well-watered conditions, making it ideal target for breeding. Our findings provide a comprehensive omic framework for understanding the genetic architecture of WUE and drought resistance, offering valuable targets for breeding resilient wheat varieties.
Baicalin inhibits infectious bronchitis virus via Mitochondrial Antiviral Signaling protein-mediated signaling
Authors: He Jiongjie , Guo Huilin , Wang Ling , Wang Shengyi 
Published: December 2024

Abstract

The inhibitory effect of baicalin on IBV replication is closely related to expression of the mitochondrial antiviral signaling protein (MAVS). However, the exact mechanisms underlying this relationship remain unclear. In this study, we revealed the essential role of MAVS in mediating inhibition of IBV replication by baicalin in chicken embryonic kidney (CEK) cells. The results showed that baicalin significantly upregulated the expression of MAVS in a dose-dependent manner. Additionally, baicalin could maintain mitochondrial function through MAVS. Further investigation revealed that baicalin increased the expression of mitofusin-1 (Mfn1), anti-apoptotic mitochondrial import receptor subunit TOM70 (TOM70), voltage-dependent anion channel 1 (VDAC1), autophagy-related 5 (Atg5) and autophagy-related 12 (Atg12). Overexpression of Mfn1, VDAC1, TOM70, Atg5 and Atg12 enhanced baicalin-mediated interferon-β (IFN-β) expression, and downregulation of these proteins attenuated baicalin-mediated IFN-β expression. In MAVS-knockout cells, the overexpression of Mfn1, mitofusin-2 (Mfn2), VDAC1, TNFR1-associated death domain protein, Fas-associated death domain protein, Atg5 and Atg12 did not affect baicalin-mediated IFN-β expression. These findings indicate that baicalin promotes mitochondrial fusion, increases mitochondrial permeability and cellular energy metabolism, and accelerates autophagy. Baicalin could upregulate the expression of IFN-β via Mfn1, VDAC1, TOM70, Atg5 and Atg12, with this upregulation dependent on the expression of MAVS.
Risk prediction model for acute stroke in elderly population in cold regions based on machine learning
Authors: Li Jing , Mou Shasha , Du Jieyu , Song Miaomiao , Miao Shidi , Cheng Ke 
Published: December 2024

Abstract

Cerebrovascular disease (CVD) profoundly affects the health and quality of life of elderly individuals, with stroke being its most prevalent manifestation, characterized by high incidence, disability, and mortality rates. In Northeast China, harsh winter conditions further exacerbate the risk of stroke. This study leverages machine learning (ML) to develop a predictive model for assessing acute stroke risk among the elderly in cold regions, aiming to enable early identification of high-risk individuals.Significant risk factors for acute stroke onset were identified through univariate and multivariate logistic regression (LR) analyses. Input variables were selected based on expert clinical recommendations and a comprehensive literature review. Three machine learning algorithms—LR, extreme gradient boosting (XGBoost), and random forest (RF)—were compared for their clinical prediction performance, with the best-performing algorithm used to construct the risk prediction model. A line chart was employed to visualize the probability of acute stroke in this population.The findings highlight diabetes history, coronary heart disease(CHD), systolic blood pressure(SBP), neutrophil count(NEUT#), total bile acid (TBA), fasting blood glucose (FBG), and homocysteine(Hcy) as independent risk factors for acute stroke in elderly individuals in cold regions. In contrast, prealbumin(PA), albumin (ALB), and high-density lipoprotein cholesterol (HDL-C) emerged as potential protective factors. The LR-based model developed in this study demonstrated robust performance in predicting acute stroke risk, providing accurate and individualized risk assessments for the target population.
Ice cores from the Allan Hills, Antarctica show relatively stable atmospheric CO2 and CH4 levels over the last 3 million years
Authors: Peterson Julia Marks , Shackleton Sarah , Higgins John , Severinghaus Jeffrey , Yan Yuzhen , Buizert Christo , Kalk Michael , Beaudette Ross , Hishamunda Valens , Carter Austin , Kurbatov Andrei , Epifanio Jenna , Morgan Jacob , Bender Mike , Brook Ed 
Published: December 2024

Abstract

The role greenhouse gases play in the evolution of Earth's climate over the last 3 million years is uncertain beyond the continuous ice core record (800,000 years). Here, we present new snapshots of carbon dioxide (CO2) and methane (CH4) between 3.1 and 0.4 million years ago (Ma) from shallow ice cores drilled in the Allan Hills Blue Ice Area (BIA). In the oldest ice (>1 Ma), mixing and thinning have attenuated the glacial-interglacial variability, and we reconstruct long-term averages. The data indicate that CO2 and CH4 levels in the early Pleistocene are within the range of variability observed for the last 0.8 Ma. Across the Pleistocene, no significant change in mean CH4 is observed but we find a small, 25 ppm decline in CO2 from 2.9 to 1.2 Ma followed by stable mean CO2 across the mid-Pleistocene Transition. In late Pliocene samples (ranging from 2.8-3.1 Ma), trapped air is impacted by the addition of CO2 from respired organic matter. Corrections using the stable carbon isotopes of CO2 indicate that atmospheric CO2 in these late Pliocene samples is within the range measured in the early Pleistocene (<300 ppm). Observed changes in greenhouse gases are small relative to both local and global cooling observed in the same ice cores (Shackleton et al., 2024a, b) and independent records from marine sediments, suggesting that other components of Earth’s climate system contributed to global cooling over the last 3 million years.
Mechanically-Adaptive, Resveratrol-Eluting Neural Probes for Improved Intracortical Recording Performance and Stability
Authors: Mueller Natalie N. , Ocoko Mali Ya Mungu , Kim Youjoung , Li Kate , Gisser Kaela , Glusauskas Gabriele , Lugo Isabella , Dernelle Peter , Hermoso Anna Clarissa , Wang Jaime , Duncan Jonathan , Capadona Jeffrey R. , Hess-Dunning Allison 
Published: December 2024

Abstract

Intracortical microelectrodes are used for recording activity from individual neurons, providing both a valuable neuroscience tool and an enabling medical technology for individuals with motor disabilities. Standard neural probes carrying the microelectrodes are rigid silicon-based structures that can penetrate the brain parenchyma to interface with the targeted neurons. Unfortunately, within weeks after implantation into the brain, neural recording quality from microelectrodes degrades, owing largely to a neuroinflammatory response. Key contributors to the neuroinflammatory response include mechanical mismatch at the device-tissue interface and a state of oxidative stress. We developed a mechanically-adaptive, resveratrol-eluting (MARE) neural probe to mitigate both mechanical mismatch and oxidative stress and thereby promote improved neural recording quality and longevity. In this work, we demonstrate that compared to rigid silicon controls, highly-flexible MARE probes exhibit improved recording performance, more stable impedance, and a healing tissue response. With further optimization, MARE probes can serve as long-term, robust neural probes for brain-machine interface applications.
Comparison of Ensemble Techniques for Early Prediction of Alzhiemer Disease
Authors: Orlunwo Placida Orochi , Onuodu Friday Eleonu 
Published: December 2024

Abstract

Alzheimer's disease (AD) is a progressive neurological condition characterized by a loss in cognitive functions, with no disease-modifying medication now available. It is crucial for early detection and treatment of Alzheimer's disease before clinical manifestation. The stage between cognitively healthy older persons and AD is known as mild cognitive impairment (MCI). To predict the transition from one-stage MCI to probable AD, five ensemble learning approach was used (Stacking, Gradient boost Bagging, Adaptive boost and Voting), an integrated model that combines not only cross-sectional neuroimaging biomarkers at baseline but also longitudinal cerebrospinal fluid (CSF) and cognitive performance biomarkers from the Alzheimer's Disease Neuroimaging Initiative cohort (ADNI). The adaptive boost, stacking and bagging ensemble approach has shown potential to identify those at risk of developing Alzheimer's disease, this would benefit them the most from a clinical trial or to use as a stratification approach inside clinical trials.
Adaptive hybrid potential evapotranspiration (PET) prediction method based on automatic machine learning
Authors: Chenjia Zhang , Tianxin Xu , Yan Zhang , Abullimiti Abdu Kaimu , Yutong Zhang , Lirui Guo , Daokun Ma 
Published: December 2024

Abstract

In arid areas, estimation of crop water demand through potential evapotranspiration (PET) forecast has a guiding effect on water-saving irrigation, to cope with the crisis of water shortage. Neural network-based PET prediction methods is considered to have huge application potential because of its small prediction error. However, the physical conditions and data quality in different regions make the choice of neural network different, making it difficult to provide a general PET prediction method. So an adaptive hybrid model based on automatic machine learning for short-term PET prediction is proposed coupling neural network and PET formula. Process is divided into two stages: learning and forecasting. Learning stage includes three modules: meteorological data reconstructing, adaptive data set generation and adaptive hybrid model (PET calculation formula + neural network) selecting. Forecast stage includes two modules: adaptive data set generation and rolling prediction. 105 standard weather stations in Xinjiang were used as data sets (43 of them had missing data) to test model. According to modules, networks and PET formulas used in the prediction process, corresponding labels were generated in each dataset forecast result. Ratio of training set and test set for each data set was 8:2. Grid search was used to optimize the best hyperparameter combination. In test set, the average absolute error (MAE) and average squared error (MSE) of the model prediction were 0.338mm and 0.270, achieving high prediction accuracy. The mean prediction error is smaller to any single mixed model. We demonstrate that the neural network applicability varies among the used data sources, and Gate Recurrent Unit (GRU) and 1 Dimension convolutional neural network (1DCNN) are more suitable for the selected datasets, while Long Short Term Memory network (LSTM) and Multilayer Perceptron (MLP) are not applicable. Combined with the analysis of the labels, We find evidences that applicability of neural networks and PET formulas is independent of geographic region and degree of drought. In 2023, method of rolling prediction for 1-15 days is verified, and the verification results show that PET prediction error based on neural network is significantly smaller than useing weather forecast data to calculate PET. In addition, by comparison,we determined that adaptive input length can effectively reduce the prediction error, MAE was 27.52% smaller than fixed input length, and MSE was 45.76% smaller than fixed input length. The proposed method realized the automatic machine learning of PET forecast, can predict PET more accurately, and can be further expanded by adding neural networks and PET formulas to improve its generalization ability.
Examination of new clinical dental caries in school children using real intra oral photos with artificial intelligence model YOLO-V8x
Authors: Fadilah Rina Putri Noer , Rikmasari Rasmi , Akbar Saiful , Setiawan Arlette Suzy Puspa Pertiwi 
Published: December 2024

Abstract

Background Dental caries is one of the most common chronic diseases in school-aged children, with a prevalence above 80% in Indonesia. Traditional diagnostic practices are time-consuming and dependent on the number of healthcare professionals available, and as such, have led to the creation of AI-based alternatives, such as the HI Bogi application. This research used the YOLO-v8 model to assist in the detection of dental caries, which is faster, more efficient, and highly accurate, and can be developed to enhance existing dental health programs in schools in IndonesiaMaterials and Methods A dataset of 3,221 JPG images labeled using the ICDAS D0 to D1 method was prepared and processed using Roboflow labeling software, and the images were resized to 640×640 pixels to standardize the input for model training. The dataset was divided into training (2,266 images), validation (635 images), and testing (320 images) subsets. The YOLOv8x algorithm was used for deep learning and the performance of the model was evaluated using confusion matrix analysis to calculate True Positive (TP), False Positive (FP), and False Negative (FN) values. Statistical Mann–Whitney tests were conducted to compare the classification accuracy between the AI model and dentists across the ICDAS categories, whereas a diagnostic speed test assessed the efficiency of the AI model relative to dentists.Results The YOLO-v8 model showed encouraging results, with a recall of 41.1%, accuracy of 72.6%, and mAP of 45.8%. P-values of 0.301 for D1, 0.690 for D2, 0.621 for D3, 0.693 for D4, 0.634 for D5, and 0.302 for D6 were obtained from comparative testing with dentists. These results showed no significant variations in the sensitivity, specificity, PPV, or NPV among the ICDAS categories (p > 0.05). Furthermore, AI performed much faster than the dentists during the examinations (p = 0.000). AI may improve the efficiency and efficacy of early caries diagnosis as demonstrated by these findings.Conclusion The YOLO-v8 model integrated into the application showed promising results in early caries detection, comparable to those of dentists across all ICDAS criteria. AI significantly outperformed dentists in terms of examination speed and completed tasks four times faster. Future research should explore transformer-based models to improve accuracy and expand the datasets to enhance the ability of the model to identify diverse caries categories, including rare lesions.