bluuebunny/crossref_metadata_2025 huggingface dataset:
Tumor biomechanics as a novel imaging biomarker to assess response to immunotherapy in a murine glioma model
Authors: Streibel Yannik , Breckwoldt Michael O. , Hunger Jessica , Pan Chenchen , Fischer Manuel , Turco Verena , Boztepe Berin , Fels-Palesandro Hannah , Scheck Jonas G. , Sturm Volker , Karimian-Jazi Kianush , Agardy Dennis A. , Annio Giacomo , Mustapha Rami , Soni Shreya S. , Alasa Abdulrahman , Weidenfeld Ina , Rodell Christopher B. , Wick Wolfgang , Heiland Sabine , Winkler Frank , Platten Michael , Bendszus Martin , Sinkus Ralph , Schregel Katharina
Published: July 2024

Glioblastoma is the most common and aggressive primary malignant brain tumor with poor prognosis. Novel immunotherapeutic approaches are currently under investigation. Even though magnetic resonance imaging (MRI) is the most important imaging tool for treatment monitoring, response assessment is often hampered by therapy-related tissue changes. As tumor and therapy-associated tissue reactions differ structurally, we hypothesize that biomechanics could be a pertinent imaging proxy for differentiation. Longitudinal MRI and magnetic resonance elastography (MRE) were performed to monitor response to immunotherapy with a toll-like receptor 7/8 agonist in orthotopic syngeneic experimental glioma. Imaging results were correlated to histology and light sheet microscopy data. Here, we identify MRE as a promising non-invasive imaging method for immunotherapy-monitoring by quantifying changes in response-related tumor mechanics. Specifically, we show that a relative softening of treated compared to untreated tumors is linked to the inflammatory processes following therapy-induced re-education of tumor-associated myeloid cells. Mechanistically, combined effects of myeloid influx and inflammation including extracellular matrix degradation following immunotherapy form the basis of treated tumors being softer than untreated glioma. This is a very early indicator of therapy response outperforming established imaging metrics such as tumor volume. The overall anti-tumor inflammatory processes likely have similar effects on human brain tissue biomechanics, making MRE a promising tool for gauging response to immunotherapy in glioma patients early, thereby strongly impacting patient pathway.
Clinical efficacy of pre-trained large language models through the lens of aphasia
Authors: Cong Yan , LaCroix Arianna N. , Lee Jiyeon
Published: July 2024

The rapid development of large language models (LLMs) motivates us to explore how such state-of-the-art natural language processing systems can inform aphasia research. What kind of language indices can we derive from a pre-trained LLM? How do they differ from or relate to the existing language features in aphasia? To what extent can LLMs serve as an interpretable and effective diagnostic and measurement tool in a clinical context? To investigate these questions, we constructed predictive and correlational models, which utilize mean surprisals from LLMs as predictor variables. Using AphasiaBank archived data, we validated our models’ efficacy in aphasia diagnosis, measurement, and prediction. Our finding is that LLMs-surprisals can effectively detect the presence of aphasia and different natures of the disorder, LLMs in conjunction with the existing language indices improve models’ efficacy in subtyping aphasia, and LLMs-surprisals can capture common agrammatic deficits at both word and sentence level. Overall, LLMs have potential to advance automatic and precise aphasia prediction. A natural language processing pipeline can be greatly benefitted from integrating LLMs, enabling us to refine models of existing language disorders, such as aphasia.
Presurgical management of ulnar nerve entrapment in patients with and without diabetes mellitus
Authors: Andersson Stina , Zimmerman Malin , Perez Raquel , Rydberg Mattias , Dahlin Lars B.
Published: July 2024

Diabetes mellitus (DM) is a risk factor for the development of ulnar nerve entrapment (UNE). Differences in surgical outcomes for UNE between patients with and without DM have been reported, but studies on presurgical management are scarce. This study aimed to evaluate the presurgical management of UNE in patients with (DM diagnosis < 2 years of UNE diagnosis) and without DM regarding the level of care for diagnosis and the proportion that proceeds to surgery. Data from 6600 patients with UNE between 2004 and 2019 were included from the Skåne Health Care Register (SHR) and cross-linked with data from the Swedish National Diabetes Register (NDR). The group with UNE and DM was compared to the group with UNE without DM and prevalence ratios were calculated using Cox regression analysis. Patients with DM more often got their first UNE diagnosis in secondary care than in primary care (643/667, 96% vs. 5361/5786; 93%). Patients with and without DM, regardless of the type of DM, presence of retinopathy, or HbA1c levels, were surgically treated for UNE to the same extent (258/667, 39% of patients with DM vs. 2131/5786, 37% of patients without DM). The proportion of surgically treated was significantly lower among patients diagnosed with UNE in primary care compared to secondary care (59/449, 13% vs. 2330/5786, 38%). Men were more often surgically treated than women (1228/3191, 38% vs.1161/3262 36%). Patients with UNE and DM are surgically treated to the same extent as patients with UNE but without DM and are more likely to be diagnosed in specialized care.
A privacy-preserving platform oriented medical healthcare and its application in identifying patients with candidemia
Authors: Yuan Siyi , Xu Song , Lu Xiao , Chen Xiangyu , Wang Yao , Bao Renyi , Sun Yunbo , Xiao Xiongjian , Su Longxiang , Long Yun , Li Linfeng , He Huaiwu
Published: July 2024

Federated learning (FL) has emerged as a significant method for developing machine learning models across multiple devices without centralized data collection. Candidemia, a critical but rare disease in ICUs, poses challenges in early detection and treatment. The goal of this study is to develop a privacy-preserving federated learning framework for predicting candidemia in ICU patients. This approach aims to enhance the accuracy of antifungal drug prescriptions and patient outcomes. This study involved the creation of four predictive FL models for candidemia using data from ICU patients across three hospitals in China. The models were designed to prioritize patient privacy while aggregating learnings across different sites. A unique ensemble feature selection strategy was implemented, combining the strengths of XGBoost’s feature importance and statistical test p values. This strategy aimed to optimize the selection of relevant features for accurate predictions. The federated learning models demonstrated significant improvements over locally trained models, with a 9% increase in the area under the curve (AUC) and a 24% rise in true positive ratio (TPR). Notably, the FL models excelled in the combined TPR + TNR metric, which is critical for feature selection in candidemia prediction. The ensemble feature selection method proved more efficient than previous approaches, achieving comparable performance. The study successfully developed a set of federated learning models that significantly enhance the prediction of candidemia in ICU patients. By leveraging a novel feature selection method and maintaining patient privacy, the models provide a robust framework for improved clinical decision-making in the treatment of candidemia.
Hemodynamics of short-duration light-intensity physical exercise in the prefrontal cortex of children: a functional near-infrared spectroscopy study
Authors: Naito Takashi , Oka Koichiro , Ishii Kaori
Published: July 2024

Identifying the types of exercise that enhance cerebral blood flow is crucial for developing exercise programs that enhance cognitive function. Nevertheless, few studies have explored the amount of light-intensity, short-duration exercises that individuals can easily perform on cerebral blood flow, particularly in children. We examined the effects of these exercises on the hemodynamics of the prefrontal cortex (PFC) using functional near-infrared spectroscopy. Participants comprised 41 children (aged 12.1 ± 1.5 years, 37% female) who engaged in seven light-intensity exercises, with each movement performed in two patterns lasting 10 or 20 s. Changes in oxygenated hemoglobin (oxy-Hb) levels at rest and during exercise were compared using analysis of covariance, with sex and age as covariates. Significant increases in oxy-Hb were observed in multiple regions of the PFC during all forms of exercise (including dynamic and twist stretching [66.6%, 8/12 regions, η2 = 0.07–0.27], hand and finger movements [75.0%, 9/12 regions, η2 = 0.07–0.16], and balance exercises (100.0%, 6/6 regions, η2 = 0.13–0.25]), except for static stretching with monotonic movements. This study implies that short-duration, light-intensity exercises, provided that they entail a certain degree of cognitive and/or physical demands, can activate the PFC and increase blood flow.
Asymmetric analogous hyperbola model of overburden movement and its verification
Authors: Jiashun Liu , Yang Wang , Jianping Zuo , Zuoqi Wu , Yunjiang Sun , Zhiyong Zheng
Published: July 2024

The extraction of underground coal resources induces the fracture and movement of overlying strata, leading to geological hazards such as surface deformation, cracks, and even subsidence. Utilizing the analogous hyperbola model of overlying strata movement, we conducted a mechanical analysis to examine the asymmetric fracture mechanism resulting from coal seam mining in thick loose strata. An asymmetric analogous hyperbola model was established by introducing distinct virtual half-axis lengths (b). The thickness impact of thick loose layers (H) and bedrock layer (h) on the asymmetric movement of overlying rock during mining was also discussed. Similarity model tests were conducted to research the migration characteristics and surface subsidence patterns of overburdened rock and thick loose layers at different mining stages and validate the hypothesis of asymmetric overburdened rock migration. Additionally, the discrete element numerical model for thick and loose layers mining was established by using UDEC and discussed the asymmetric analogous hyperbola behaviour of overburden movement and surface subsidence. The comparison results show that the established asymmetric hyperbolic model can effectively predict the movement law of overlying strata and surface subsidence characteristics. Therefore, the proposed model can provide valuable theoretical support for predicting the movement patterns of overburden under thick loose layers and mitigating surface subsidence disasters.
AI models predicting breast cancer distant metastasis using LightGBM with clinical blood markers and ultrasound maximum diameter
Authors: Tan Yang , Zhang Wen-hai , Huang Zhen , Tan Qi-xing , Zhang Yue-mei , Wei Chang-yuan , Feng Zhen-Bo
Published: July 2024

Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.
A novel optimization rainfall coupling model based on stepwise decomposition technique
Authors: Zheng Zhiwen , Zhang Xianqi , Yin Qiuwen , Liu Fang , Ren He , Zhao Ruichao
Published: July 2024

Traditional decomposition integration models decompose the original sequence into subsequences, which are then proportionally divided into training and testing periods for modeling. Decomposition may cause data aliasing, then the decomposed training period may contain part of the test period data. A more effective method of sample construction is sought in order to accurately validate the model prediction accuracy. Semi-stepwise decomposition (SSD), full stepwise decomposition (FSD), single model semi-stepwise decomposition (SMSSD), and single model full stepwise decomposition (SMFSD) techniques were used to create the samples. This study integrates Variational Mode Decomposition (VMD), African Vulture Optimization Algorithm (AVOA), and Least Squares Support Vector Machine (LSSVM) to construct a coupled rainfall prediction model. The influence of different VMD parameters α is examined, and the most suitable stepwise decomposition machine learning coupled model algorithm for various stations in the North China Plain is selected. The results reveal that SMFSD is relatively the most suitable tool for monthly precipitation forecasting in the North China Plain. Among the predictions for the five stations, the best overall performance is observed at Huairou Station (RMSE of 18.37 mm, NSE of 0.86, MRE of 107.2%) and Jingxian Station (RMSE of 24.74 mm, NSE of 0.86, MRE of 51.71%), while Hekou Station exhibits the poorest performance (RMSE of 25.11 mm, NSE of 0.75, MRE of 173.75%).
Validation of the Hungarian version of the 6-item turnover intention scale among elderly care workers
Authors: Németh Zsanett , Deák Petra , Szűcs Réka , Makai Alexandra , Hock Márta
Published: July 2024

This research examines the psychometric characteristics and reliability of the 6-item turnover intention scale (TIS-6) by Bothma and Roodt (SA J Hum Resour Manag 11:a507, 2013) on a Hungarian sample. The internal validity of the TIS-6 was assessed using data from 269 Hungarian elderly care institution workers. Confirmatory factor analysis was performed to analyse the structural validity. Convergent and discriminant validity were examined with questions on job characteristics and using the Maslach Burnout Inventory and Effort-Reward Imbalance Scale. IBM SPSS 28.0 software was used for the statistical analysis, and the results were considered significant at p < 0.05. The internal consistency of the questionnaire's scale proved to be acceptable (α = 0.826). Convergent validity was confirmed by the relationships between the components of the questionnaire and burnout (rs = 0.512; p < 0.001; rs = 0.419; p < 0.001) and workplace stress (rs = 0.565; p < 0.001; rs = 0.310; p < 0.001). There were significant differences between the TIS-6 scores among the groups with different degrees of burnout (p < 0.001), which indicated adequate discriminant validity of the questionnaire. The structural validity of the questionnaire was acceptable, and the scale questions fit well. The Hungarian version of the TIS-6 scale is a valid and reliable tool for assessing turnover intention among elderly care institution workers in Hungary.
Quantifying impairment and disease severity using AI models trained on healthy subjects
Authors: Yu Boyang , Kaku Aakash , Liu Kangning , Parnandi Avinash , Fokas Emily , Venkatesan Anita , Pandit Natasha , Ranganath Rajesh , Schambra Heidi , Fernandez-Granda Carlos
Published: July 2024

Automatic assessment of impairment and disease severity is a key challenge in data-driven medicine. We propose a framework to address this challenge, which leverages AI models trained exclusively on healthy individuals. The COnfidence-Based chaRacterization of Anomalies (COBRA) score exploits the decrease in confidence of these models when presented with impaired or diseased patients to quantify their deviation from the healthy population. We applied the COBRA score to address a key limitation of current clinical evaluation of upper-body impairment in stroke patients. The gold-standard Fugl-Meyer Assessment (FMA) requires in-person administration by a trained assessor for 30-45 minutes, which restricts monitoring frequency and precludes physicians from adapting rehabilitation protocols to the progress of each patient. The COBRA score, computed automatically in under one minute, is shown to be strongly correlated with the FMA on an independent test cohort for two different data modalities: wearable sensors (ρ = 0.814, 95% CI [0.700,0.888]) and video (ρ = 0.736, 95% C.I [0.584, 0.838]). To demonstrate the generalizability of the approach to other conditions, the COBRA score was also applied to quantify severity of knee osteoarthritis from magnetic-resonance imaging scans, again achieving significant correlation with an independent clinical assessment (ρ = 0.644, 95% C.I [0.585,0.696]).
Deep learning for multi-type infectious keratitis diagnosis: A nationwide, cross-sectional, multicenter study
Authors: Li Zhongwen , Xie He , Wang Zhouqian , Li Daoyuan , Chen Kuan , Zong Xihang , Qiang Wei , Wen Feng , Deng Zhihong , Chen Limin , Li Huiping , Dong He , Wu Pengcheng , Sun Tao , Cheng Yan , Yang Yanning , Xue Jinsong , Zheng Qinxiang , Jiang Jiewei , Chen Wei
Published: July 2024

The main cause of corneal blindness worldwide is keratitis, especially the infectious form caused by bacteria, fungi, viruses, and Acanthamoeba. The key to effective management of infectious keratitis hinges on prompt and precise diagnosis. Nevertheless, the current gold standard, such as cultures of corneal scrapings, remains time-consuming and frequently yields false-negative results. Here, using 23,055 slit-lamp images collected from 12 clinical centers nationwide, this study constructed a clinically feasible deep learning system, DeepIK, that could emulate the diagnostic process of a human expert to identify and differentiate bacterial, fungal, viral, amebic, and noninfectious keratitis. DeepIK exhibited remarkable performance in internal, external, and prospective datasets (all areas under the receiver operating characteristic curves > 0.96) and outperformed three other state-of-the-art algorithms (DenseNet121, InceptionResNetV2, and Swin-Transformer). Our study indicates that DeepIK possesses the capability to assist ophthalmologists in accurately and swiftly identifying various infectious keratitis types from slit-lamp images, thereby facilitating timely and targeted treatment.
Accuracy of dental implant placement using different dynamic navigation and robotic systems: an in vitro study
Authors: Xu Zonghe , Zhou Lin , Han Bin , Wu Shuang , Xiao Yanjun , Zhang Sihui , Chen Jiang , Guo Jianbin , Wu Dong
Published: July 2024

Computer-aided implant surgery has undergone continuous development in recent years. In this study, active and passive systems of dynamic navigation were divided into active dynamic navigation system group and passive dynamic navigation system group (ADG and PDG), respectively. Active, passive and semi-active implant robots were divided into active robot group, passive robot group and semi-active robot group (ARG, PRG and SRG), respectively. Each group placed two implants (FDI tooth positions 31 and 36) in a model 12 times. The accuracy of 216 implants in 108 models were analysed. The coronal deviations of ADG, PDG, ARG, PRG and SRG were 0.85 ± 0.17 mm, 1.05 ± 0.42 mm, 0.29 ± 0.15 mm, 0.40 ± 0.16 mm and 0.33 ± 0.14 mm, respectively. The apical deviations of the five groups were 1.11 ± 0.23 mm, 1.07 ± 0.38 mm, 0.29 ± 0.15 mm, 0.50 ± 0.19 mm and 0.36 ± 0.16 mm, respectively. The axial deviations of the five groups were 1.78 ± 0.73°, 1.99 ± 1.20°, 0.61 ± 0.25°, 1.04 ± 0.37° and 0.42 ± 0.18°, respectively. The coronal, apical and axial deviations of ADG were higher than those of ARG, PRG and SRG (all P < 0.001). Similarly, the coronal, apical and axial deviations of PDG were higher than those of ARG, PRG, and SRG (all P < 0.001). Dynamic and robotic computer-aided implant surgery may show good implant accuracy in vitro. However, the accuracy and stability of implant robots are higher than those of dynamic navigation systems.
A point cloud segmentation framework for image-based spatial transcriptomics
Authors: Defard Thomas , Laporte Hugo , Ayan Mallick , Soulier Juliette , Curras-Alonso Sandra , Weber Christian , Massip Florian , Londoño-Vallejo José-Arturo , Fouillade Charles , Mueller Florian , Walter Thomas
Published: July 2024

Recent progress in image-based spatial RNA profiling enables to spatially resolve tens to hundreds of distinct RNA species with high spatial resolution. It presents new avenues for comprehending tissue organization. In this context, the ability to assign detected RNA transcripts to individual cells is crucial for downstream analyses, such as in-situ cell type calling. Yet, accurate cell segmentation can be challenging in tissue data, in particular in the absence of a high-quality membrane marker. To address this issue, we introduce ComSeg, a segmentation algorithm that operates directly on single RNA positions and that does not come with implicit or explicit priors on cell shape. ComSeg is applicable in complex tissues with arbitrary cell shapes. Through comprehensive evaluations on simulated and experimental datasets, we show that ComSeg outperforms existing state-of-the-art methods for in-situ single-cell RNA profiling and in-situ cell type calling. ComSeg is available as a documented and open source pip package at https://github.com/fish-quant/ComSeg.
Integrative analysis of genomic and epigenomic regulation reveals miRNA mediated tumor heterogeneity and immune evasion in lower grade glioma
Authors: Yang Zhen , Liu Xiaocen , Xu Hao , Teschendorff Andrew E. , Xu Lingjie , Li Jingyi , Fu Minjie , Liu Jun , Zhou Hanyu , Wang Yingying , Zhang Licheng , He Yungang , Lv Kun , Yang Hui
Published: July 2024

The expression dysregulation of microRNAs (miRNA) has been widely reported during cancer development, however, the underling mechanism remains largely unanswered. In the present work, we performed a systematic integrative study for genome-wide DNA methylation, copy number variation and miRNA expression data to identify mechanisms underlying miRNA dysregulation in lower grade glioma. We identify 719 miRNAs whose expression was associated with alterations of copy number variation or promoter methylation. Integrative multi-omics analysis revealed four subtypes with differing prognoses. These glioma subtypes exhibited distinct immune-related characteristics as well as clinical and genetic features. By construction of a miRNA regulatory network, we identified candidate miRNAs associated with immune evasion and response to immunotherapy. Finally, eight prognosis related miRNAs were validated to promote cell migration, invasion and proliferation through in vitro experiments. Our study reveals the crosstalk among DNA methylation, copy number variation and miRNA expression for immune regulation in glioma, and could have important implications for patient stratification and development of biomarkers for immunotherapy approaches.
RECORD, a high-throughput, customizable system that unveils behavioral strategies leveraged by rodents during foraging-like decision-making
Authors: Ibáñez Alcalá Raquel J. , Beck Dirk W. , Salcido Alexis A. , Davila Luis D. , Giri Atanu , Heaton Cory N. , Villarreal Rodriguez Kryssia , Rakocevic Lara I. , Hossain Safa B. , Reyes Neftali F. , Batson Serina A. , Macias Andrea Y. , Drammis Sabrina M. , Negishi Kenichiro , Zhang Qingyang , Umashankar Beck Shreeya , Vara Paulina , Joshi Arnav , Franco Austin J. , Hernandez Carbajal Bianca J. , Ordonez Miguel M. , Ramirez Felix Y. , Lopez Jonathan D. , Lozano Nayeli , Ramirez Abigail , Legaspy Linnete , Cruz Paulina L. , Armenta Abril A. , Viel Stephanie N. , Aguirre Jessica I. , Quintanar Odalys , Medina Fernanda , Ordonez Pablo M. , Munoz Alfonzo E. , Martínez Gaudier Gustavo E. , Naime Gabriela M. , Powers Rosalie E. , O’Dell Laura E. , Moschak Travis M. , Goosens Ki A. , Friedman Alexander
Published: July 2024

Translational studies benefit from experimental designs where laboratory organisms use human-relevant behaviors. One such behavior is decision-making, however studying complex decision-making in rodents is labor-intensive and typically restricted to two levels of cost/reward. We design a fully automated, inexpensive, high-throughput framework to study decision-making across multiple levels of rewards and costs: the REward-COst in Rodent Decision-making (RECORD) system. RECORD integrates three components: 1) 3D-printed arenas, 2) custom electronic hardware, and 3) software. We validated four behavioral protocols without employing any food or water restriction, highlighting the versatility of our system. RECORD data exposes heterogeneity in decision-making both within and across individuals that is quantifiably constrained. Using oxycodone self-administration and alcohol-consumption as test cases, we reveal how analytic approaches that incorporate behavioral heterogeneity are sensitive to detecting perturbations in decision-making. RECORD is a powerful approach to studying decision-making in rodents, with features that facilitate translational studies of decision-making in psychiatric disorders.
Low-dose ionizing radiation generates a hormetic response to modify lipid metabolism in Chlorella sorokiniana
Authors: Stanić Marina , Jevtović Mima , Kovačević Snežana , Dimitrijević Milena , Danilović Luković Jelena , McIntosh Owen A. , Zechmann Bernd , Lizzul Alessandro Marco , Spasojević Ivan , Pittman Jon K.
Published: July 2024

Algal biomass is a viable source of chemicals and metabolites for various energy, nutritional, medicinal and agricultural uses. While stresses have commonly been used to induce metabolite accumulation in microalgae in attempts to enhance high-value product yields, this is often very detrimental to growth. Therefore, understanding how to modify metabolism without deleterious consequences is highly beneficial. We demonstrate that low-doses (1–5 Gy) of ionizing radiation in the X-ray range induces a non-toxic, hormetic response in microalgae to promote metabolic activation. We identify specific radiation exposure parameters that give reproducible metabolic responses in Chlorella sorokiniana caused by transcriptional changes. This includes up-regulation of >30 lipid metabolism genes, such as genes encoding an acetyl-CoA carboxylase subunit, phosphatidic acid phosphatase, lysophosphatidic acid acyltransferase, and diacylglycerol acyltransferase. The outcome is an increased lipid yield in stationary phase cultures by 25% in just 24 hours, without any negative effects on cell viability or biomass.
Enhancing the expressivity of quantum neural networks with residual connections
Authors: Wen Jingwei , Huang Zhiguo , Cai Dunbo , Qian Ling
Published: July 2024

In noisy intermediate-scale quantum era, the research on the combination of artificial intelligence and quantum computing has been greatly developed. Here we propose a quantum circuit-based algorithm to implement quantum residual neural networks, where the residual connection channels are constructed by introducing auxiliary qubits to data-encoding and trainable blocks in quantum neural networks. We prove that when this particular network architecture is applied to a l-layer data-encoding, the number of frequency generation forms extends from one, namely the difference of the sum of generator eigenvalues, to $${{{{{{{\mathcal{O}}}}}}}}({l}^{2})$$
O

(

l

2

)

, and the flexibility in adjusting Fourier coefficients can also be improved. It indicates that residual encoding can achieve better spectral richness and enhance the expressivity of various parameterized quantum circuits. Extensive numerical demonstrations in regression tasks and image classification are offered. Our work lays foundation for the complete quantum implementation of classical residual neural networks and offers a quantum feature map strategy for quantum machine learning.
Carrier transport mechanisms of titanium nitride and titanium oxynitride electron-selective contact in silicon heterojunction solar cells
Authors: Liu Chenxi , Wang Yang , Liu Jinpei , Ma Runze , Liu Hao , Wang Qi , Fu Yujun , Liu Qiming , He Deyan
Published: July 2024

It is widely accepted that an effective carrier-selective contact is indispensable for high performance crystalline silicon (c-Si) solar cells. However, the properties of these carrier-selective contact materials significantly differ from c-Si in terms of band gap, work function, lattice constant. Consequently, this gives rise to challenges such as band discontinuity and suspended bonds at the interface, which subsequently impact the specific carrier transport process and potentially lead to a reduction primarily in the fill factor at the device level. Titanium nitride (TiN) and titanium oxynitride (TiOxNy) have been employed as an electron-selective contact in both c-Si and perovskite solar cells, demonstrating their effectiveness in enhancing the performance of these devices. Based on the detailed characterizations of the band alignment, the carrier transport mechanisms are analyzed using multiple models, and the theoretical results are basically self-consistent through the verification of variable temperature experiments. These analytical methods can also provide solutions for analyzing the band structure and transport mechanism of diverse heterojunctions, ultimately contributing to the design and optimization of semiconductor heterojunction devices.
Particle-hole mixed Bogoliubov quasiparticles and Cooper instability in single-unit-cell FeSe/SrTiO3 films
Authors: Wei Zhiyuan , Li Shaozhi , Liu Bo , Sun Xiupeng , Hu Yinqi , Sun Shuai , Peng Shuting , Luo Yang , Huai Linwei , Shen Jianchang , Wang Bingqian , Miao Yu , Ou Zhipeng , Wang Yao , Jiang Kun , He Junfeng
Published: July 2024

In conventional superconductors, Bogoliubov quasiparticles and Cooper instability provide a paradigm to describe the superconducting state and the superconducting transition, respectively. However, whether these concepts can be adapted to describe Fe-based superconductors requires rigorous examinations from experiments. Here, we report angle-resolved photoemission studies on single-layer FeSe films grown on SrTiO3 substrate. Due to the improved clarity, our results reveal both particle and hole branches of the energy band with clear quasiparticles. The dispersion and coherence factors are extracted, which unveil the particle-hole mixed Bogoliubov quasiparticles in the superconducting state of the FeSe/STO films. Effective pairing susceptibility is also deduced as a function of temperature, which indicates the persistence of Cooper instability in Fe-based superconductors.
Human land occupation regulates the effect of the climate on the burned area of the Brazilian Cerrado
Authors: Segura-Garcia Carlota , Bauman David , S. Arruda Vera L. , C. Alencar Ane A. , Oliveras Menor Imma
Published: July 2024

Human activities and climate change are transforming fire regimes globally. The interaction between these two drivers is poorly understood, yet critical if we aim at predicting how biomes will respond to novel fire regimes. In the Brazilian Cerrado, altered fire regimes are threatening its unique biodiversity and ecosystem functioning. Here, using geospatial data for the period 1985-2020 and a causal inference framework to design Bayesian statistical models, we demonstrate that a larger human presence in the landscape (≥40% land-use area) reduces the Cerrado’s burned area and hinders its responsiveness to climate; while climatic effects only become apparent in landscapes with little human presence, where hotter and drier conditions increase burned area. Finally, we find spatially heterogeneous burned area trends over time, with increases associated to climate change in landscapes that have remained mostly intact, and decreases caused by anthropic expansion. Both diverging trends have important implications for the conservation of the Cerrado as land-use expansion and climate change continue to unfold.
Environmental governance is critical for mitigating human displacement due to weather-related disasters
Authors: Meng Sisi
Published: July 2024

The impact of climate change is increasingly evident through intensified and more frequent weather-related events, resulting in a higher likelihood of disaster-induced displacement. In response, effective adaptation and resilience-building measures implemented through environmental governance are crucial. Here, we investigate the relationship between environmental governance (measured by the Notre Dame Country Index) and weather-related disaster displacements. Using a panel dataset of 92 countries from 2010 to 2020, Granger non-causality tests reveal a strong bidirectional relationship between environmental governance and disaster displacement. The relationship is primarily driven by governance readiness in the higher-income group, while both governance and economic readiness are crucial in the middle-income group. Notably, all three readiness components appear significant in the lower-income group, emphasizing the imperative for comprehensive disaster management strategies that address economic development, governmental effectiveness, and social conditions. These findings underscore the importance of effective environmental governance for climate change adaptation and sustainable development goals.
Efficient deep learning-based automated diagnosis from echocardiography with contrastive self-supervised learning
Authors: Holste Gregory , Oikonomou Evangelos K. , Mortazavi Bobak J. , Wang Zhangyang , Khera Rohan
Published: July 2024

Background

Advances in self-supervised learning (SSL) have enabled state-of-the-art automated medical image diagnosis from small, labeled datasets. This label efficiency is often desirable, given the difficulty of obtaining expert labels for medical image recognition tasks. However, most efforts toward SSL in medical imaging are not adapted to video-based modalities, such as echocardiography.

Methods

We developed a self-supervised contrastive learning approach, EchoCLR, for echocardiogram videos with the goal of learning strong representations for efficient fine-tuning on downstream cardiac disease diagnosis. EchoCLR pretraining involves (i) contrastive learning, where the model is trained to identify distinct videos of the same patient, and (ii) frame reordering, where the model is trained to predict the correct of video frames after being randomly shuffled.

Results

When fine-tuned on small portions of labeled data, EchoCLR pretraining significantly improves classification performance for left ventricular hypertrophy (LVH) and aortic stenosis (AS) over other transfer learning and SSL approaches across internal and external test sets. When fine-tuning on 10% of available training data (519 studies), an EchoCLR-pretrained model achieves 0.72 AUROC (95% CI: [0.69, 0.75]) on LVH classification, compared to 0.61 AUROC (95% CI: [0.57, 0.64]) with a standard transfer learning approach. Similarly, using 1% of available training data (53 studies), EchoCLR pretraining achieves 0.82 AUROC (95% CI: [0.79, 0.84]) on severe AS classification, compared to 0.61 AUROC (95% CI: [0.58, 0.65]) with transfer learning.

Conclusions

EchoCLR is unique in its ability to learn representations of echocardiogram videos and demonstrates that SSL can enable label-efficient disease classification from small amounts of labeled data.
Zeolitic imidazolate framework-8 as a high-affinity adsorbent for dispersive solid-phase extraction in the analysis of plant growth regulators in fruits
Authors: Xia Lian , Yuan Can , He Tianqiang , Cui Jiaxin
Published: July 2024

An effective d-SPE method for sensitive detection of plant growth regulators (PGRs) was developed based on the strong affinity between ZIF-8 and PGRs due to the synergistic effects of π–π interaction, van der Waals force, H-bond and surface effect.
Selective lactic acid synthesis via ethylene glycol electrooxidation in borate buffer
Authors: Koul Adarsh , Chandra Shubhadeep , Schuhmann Wolfgang
Published: July 2024

Efficient and selective electrochemical oxidation of biomass-derived ethylene glycol to lactic acid is achieved over nickel catalyst by controlling the pH of the electrolyte solution.
Taming 2,2′-biimidazole ligands in trivalent chromium complexes
Authors: Chong Julien , Benchohra Amina , Besnard Céline , Guénée Laure , Rosspeintner Arnulf , Cruz Carlos M. , Jiménez Juan-Ramón , Piguet Claude
Published: July 2024

Moving from homoleptic [Cr(biimidazole)3]3+ toward heteroleptic [Cr(phen)2(biimidazole)]3+ restores solubility, stability and pH-dependent near infrared phosphorescence at the molecular level.
Electrospinning of sustainable polymers from biomass for active food packaging
Authors: Topuz Fuat , Uyar Tamer
Published: July 2024

This review outlines the latest developments in using electrospun materials derived from sustainable, biomass-sourced polymers for active food packaging applications.
Effect of intramolecular energy transfer in a dual-functional molecular dyad on the performance of solution-processed TADF OLEDs
Authors: Kwon Na Yeon , Kwak Haeun , Kim Ha Yeon , Park Su Hong , Park Jin Young , Kang Min Ji , Koh Chang Woo , Park Sungnam , Cho Min Ju , Choi Dong Hoon
Published: July 2024

An asymmetric molecular dyad emitter, Cy-tmCPBN, comprising a host unit (tmCP) and a multiple resonance (MR) emitter (CzBN) connected via cyclohexane, demonstrates promising performance for highly efficient solution-processable MR-TADF-OLEDs.
Selective oxidative coupling of methane to ethane with oxygen using an Au/Zn2Ti3O8 photocatalyst under mild conditions
Authors: Huang Qiuying , Cai Junjian , Wei Fen , Fan Yaming , Liang Zheng , Liu Kunlong , Lu Xue Feng , Ding Zhengxin , Wang Sibo
Published: July 2024

An Au/Zn2Ti3O8 Schottky junction photocatalyst exhibits high performance for the selective methane oxidation, in which the Au sites stabilize the ˙CH3 intermediate to facilitate the coupling reaction for high ethane selectivity.
Enhancing energy storage performances of Bi0.5Na0.5TiO3-based dielectric ceramics via modulating polymorphic polar nanoregions
Authors: Dong Xiaoyan , Wu Xiaojun , Lv Xiang , Wu Jiagang
Published: July 2024

A high recoverable energy density (Wrec) of 7.0 J cm−3 and a large efficiency (η) of 81.5% were achieved in a Bi0.5Na0.5TiO3-based lead-free relaxor by modulating polymorphic polar nanoregions.
A dual benchmarking study of facial forgery and facial forensics
Authors: Pham Minh Tam , Huynh Thanh Trung , Nguyen Thanh Tam , Nguyen Thanh Toan , Nguyen Thanh Thi , Jo Jun , Yin Hongzhi , Hung Nguyen Quoc Viet
Published: July 2024

In recent years, visual facial forgery has reached a level of sophistication that humans cannot identify fraud, which poses a significant threat to information security. A wide range of malicious applications have emerged, such as deepfake, fake news, defamation or blackmailing of celebrities, impersonation of politicians in political warfare, and the spreading of rumours to attract views. As a result, a rich body of visual forensic techniques has been proposed in an attempt to stop this dangerous trend. However, there is no comprehensive, fair, and unified performance evaluation to enlighten the community on best performing methods. The authors present a systematic benchmark beyond traditional surveys that provides in‐depth insights into facial forgery and facial forensics, grounding on robustness tests such as contrast, brightness, noise, resolution, missing information, and compression. The authors also provide a practical guideline of the benchmarking results, to determine the characteristics of the methods that serve as a comparative reference in this never‐ending war between measures and countermeasures. The authors’ source code is open to the public.
High‐isolation single‐layer dual‐mode substrate‐integrated waveguide diplexers for millimetre‐wave applications
Authors: Liu Qing , Zhou Dong‐Fang , Gong Ke , An Na , Zhang De‐Wei , Qian Hang
Published: July 2024

New coupling scheme substrate‐integrated waveguide (SIW) diplexers based on dual‐mode cavities are proposed for millimetre‐wave applications. Dual‐mode SIW cavities are simultaneously employed in common input and coupling cavity to produce filtering transmission poles and transmission zero (TZ). A common input SIW cavity with two orthogonal modes is employed instead of the traditional T‐junction for circuit miniaturisation, insertion‐loss reduction, and intrinsically high isolation. A critical dual‐mode SIW cavity with diagonally distributed modes has been employed in each channel to realise a controllable stopband TZ for high selectivity of passband's skirt. To further improve channel isolation and passbands' selectivity, a SIW diplexer based on dual‐ and single‐mode cavities is proposed, and four‐pole responses for each passband channel are designed. For the verification, two millimetre‐wave diplexers have been fabricated and tested.
SubDLe: Identification of substructures in cosmological simulations with deep learning
Authors: Esposito Michela , Borgani Stefano , Murante Giuseppe
Published: July 2024

Context. The identification of substructures within halos in cosmological hydrodynamical simulations is a fundamental step to identify the simulated counterparts of real objects, namely galaxies. For this reason, substructure finders play a crucial role in extracting relevant information from the simulation outputs. In general, they are based on physically motivated definitions of substructures, performing multiple steps of particle-by-particle operations, and for this reason they are computationally expensive.

Aims. The purpose of this work is to develop a fast algorithm to identify substructures, especially galaxies, in simulations. The final aim, besides a faster production of subhalo catalogs, is to provide an algorithm fast enough to be applied with a fine time cadence during the evolution of the simulations. Having access to galaxy catalogs while the simulation is evolving is indeed necessary for sub-resolution models based on the global properties of galaxies.

Methods. In this context, machine learning methods offer a wide range of automated tools for fast analysis of large data sets. So, we chose to apply the architecture of a well-known fully convolutional network, U-Net, for the identification of substructures within the mass density field of the simulation. We have developed SubDLe (Substructure identification with Deep Learning), an algorithm that combines a 3D generalization of U-Net and a Friends-of-Friends algorithm, and trained it to reproduce the identification of substructures performed by the SubFind algorithm in a set of zoom-in cosmological hydrodynamical simulations of galaxy clusters. For the feasibility study presented in this work, we have trained and tested SubDLe on galaxy clusters at z = 0, using a NVIDIA P100 GPU. We focused our tests on the version of the algorithm working on the identification of purely stellar substructures, stellar SubDLe.

Results. Our stellar SubDLe proved very efficient in identifying most of the galaxies, 82% on average, in a set of 12 clusters at z = 0. In order to prove the robustness of the method, we also performed some tests at z = 1 and increased the resolution of the input density grids. The average time taken by our SubDLe to analyze one cluster is about 70 s, around a factor 30 less than the typical time taken by SubFind in a single computing node.

Conclusions. Our stellar SubDLe is capable of identifying the majority of galaxies in the challenging high-density environment of galaxy clusters in short computing times. This result has interesting implications in view of the possibility of integrating fast subhalo finders within simulation codes, which can take advantage of accelerators available in state-of-the-art computing nodes.
When societalization stalls: the semantics of code switching and the work of the soft legal realm
Authors: Moore Sarah
Published: July 2024

The dominant sociological view is that there are too many public crises. This article points to a different problem, namely the tendency for societalization to stall and for too few public crises to be fully realized. Anchored in a detailed analysis of the response to the UK Metropolitan police killing of Jean Charles de Menezes in 2005 and the Grenfell Tower fire in 2017, the article explores how crisis-events unfold, from their early conception in media coverage through to their treatment as events to be investigated and reviewed. A particular point of focus is the role of a soft legal realm in handling the second of these phases of societalization. In sketching out the work, principles and functions of the soft legal realm, the article suggests that we conceive of this as an interstitial institution, one that, in the early twenty-first century, has become predominantly responsible for the translation and treatment of would-be public crises in the UK, and beyond. The article ends by identifying a set of key threats to the legitimacy of this institution in managing the messy border relations of the civil and non-civil sphere.
In search of global sociology: a critical overview of competing research agendas
Authors: Roudometof Victor
Published: July 2024

This article critically examines the idea of global sociology in relationship to the discipline of Sociology. While the discipline initially emerged in Western societies, in the post-1945 era, the field extended its reach to the rest of the world. The intellectual project of global sociology emerged in the 1960s and has been on the agenda of the International Sociological Association since at least 1990. Delving deeper than the mere notion of a sociology that is applied to the world, global sociology requires the re-examination of the role of core sociological concepts and narratives, especially regarding the extent to which these reflect Eurocentric preoccupations. Global modernisation (alongside Global Studies) and Postcolonial Sociology, two of the most widely known research fields claiming global intent, are examined as to whether they provide meaningful blueprints for global sociology. Both offer promising insights but also suffer from important drawbacks. Instead of attempting to unearth new grand narratives that would displace the narrative of Western modernity, though, it might be possible to envision a plurality of such narratives coming from different regions of the globe. Sociology can thus be conceived not as exclusively global but also as local and glocal.