bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sequence stratigraphy of the Eocene deep-water organic-rich mudstones in the Fushun Basin, Northeast China
Authors: Xu Yinbo , Sun Pingchang , Li Yuanji , Yao Shuqing , Zhang Jiaqiang , Liu Zhaojun , Li Feng , Tong Lihua 
Published: December 2023

AbstractSequence stratigraphy study in mudstone and oil shale strata is difficulty but critical for the analysis of the quality unconventional oil and gas reservoirs. This contribution investigates the sequence stratigraphy of the Eocene thick organic-rich fine-grained sediments in the Fushun basin, according to the organic geochemical data measured in laboratory and predicted by the amplitude difference the resistivity and the density curve when they are overlapping (which was called ΔlogR). The sampling interval of ΔlogR was 0.125 m. The results show that the total organic carbon, the oil yield, and the petroleum potential of the Eocene mudstones prove a good linear correlation between values measured in lab and values predicted by the ΔlogR model. A total of 21 parasequences are identified in mudstones and can be divided into two cycles, a fully developed lower cycle and an underdeveloped upper cycle. There are four organic matter accumulation stages in a fully developed cycle, including two stages in the uprising half-cycle characterized by low values and rising values of the predicted geochemical data, respectively, followed by two stages in the declining half cycle characterized by high values and decreasing values of the predicted geochemical data, respectively. This contribution provides a basis for a further analysis of the characteristics of organic matter cycles in the oil shale strata of the Fushun Basin and areas with similar situation.
A Review and Benchmark on State-of-the-Art Steel Defects Detection
Authors: Chazhoor Anthony Ashwin Peter , Ho Edmond S. L. , Gao Bin , Woo Wai Lok 
Published: December 2023

AbstractSteel, a critical material in construction, automobile, and railroad manufacturing industries, often presents defects that can lead to equipment failure, significant safety risks, and costly downtime. This research aims to evaluate the performance of state-of-the-art object detection models in detecting defects on steel surfaces, a critical task in industries such as railroad and automobile manufacturing. The study addresses the challenges of limited defect data and lengthy model training times. Five existing state-of-the-art object detection models (faster R-CNN, deformable DETR, double head R-CNN, Retinanet, and deformable convolutional network) were benchmarked on the Northeastern University (NEU) steel dataset. The selection of models covers a broad spectrum of methodologies, including two-stage detectors, single-stage detectors, transformers, and a model incorporating deformable convolutions. The deformable convolutional network achieved the highest accuracy of 77.28% on the NEU dataset following a fivefold cross-validation method. Other models also demonstrated notable performance, with accuracies within the 70–75% range. Certain models exhibited particular strengths in detecting specific defects, indicating potential areas for future research and model improvement. The findings provide a comprehensive foundation for future research in steel defect detection and have significant implications for practical applications. The research could improve quality control processes in the steel industry by automating the defect detection task, leading to safer and more reliable steel products and protecting workers by removing the human factor from hazardous environments.
Patient predictors of pathogenic versus commensal Gram-positive bacilli organisms isolated from blood cultures
Authors: Sharma Arjun , Elligsen Marion , Daneman Nick , Lam Philip W. 
Published: December 2023

Abstract

Objective:
Gram-positive bacilli represent a diverse species of bacteria that range from commensal flora to pathogens implicated in severe and life-threatening infection. Following the isolation of Gram-positive bacilli from blood cultures, the time to species identification may take upward of 24 hours, leaving clinicians to conjecture whether they may represent a contaminant (inadvertent inoculation of commensal flora) or pathogenic organism. In this study, we sought to identify patient variables that could help predict the isolation of contaminant versus pathogenic Gram-positive bacilli from blood cultures.

Design:
Retrospective cohort study.

Settings:
One quaternary academic medical center affiliated with the University of Toronto.

Patients:
Adult inpatients were admitted to hospital over a 5-year period (May 2014 to December 2019).

Methods:
A total of 260 unique Gram-positive bacilli blood culture results from adult inpatients were reviewed and analyzed in both a univariable and multivariable model.

Results:
Malignancy (aOR 2.78, 95% CI 1.33–5.91, p = 0.007), point increments in the Quick Sepsis Related Organ Failure Assessment score for sepsis (aOR 2.25, 95% CI 1.50–3.47, p < 0.001), peptic ulcer disease (aOR 5.63, 95% CI 1.43–21.0, p = 0.01), and the receipt of immunosuppression prior to a blood culture draw (aOR 3.80, 95% CI 1.86–8.01, p < 0.001) were associated with an increased likelihood of speciating pathogenic Gram-positive bacilli from blood cultures such as Clostridium species and Listeria monocytogenes.

Conclusion:
Such predictors can help supplement a clinician’s assessment on determining when empirical therapy is indicated when faced with Gram-positive bacilli from blood cultures and may direct future stewardship interventions for responsible antimicrobial prescribing.
All aboard the ChatGPT steamroller: Top 10 ways to make artificial intelligence work for healthcare professionals
Authors: Non Lemuel R. 
Published: December 2023

Abstract
Chat Generative Pre-trained Transformer (ChatGPT), the flagship generative artificial intelligence (AI) chatbot by OpenAI, is transforming many things in medicine, from healthcare and research to medical education. It is anticipated to integrate in many aspects of the medical industry, and we should brace for this inevitability and use it to our advantage. Here are proposed ways you can use ChatGPT in medicine with some specific use cases in antimicrobial stewardship and hospital epidemiology.
Spatiotemporal self-supervised pre-training on satellite imagery improves food insecurity prediction
Authors: Cartuyvels Ruben , Fierens Tom , Coppieters Emiel , Moens Marie-Francine , Sileo Damien 
Published: December 2023

Abstract
Global warming will cause unprecedented changes to the world. Predicting events such as food insecurities in specific earth regions is a valuable way to face them with adequate policies. Existing food insecurity prediction models are based on handcrafted features such as population counts, food prices, or rainfall measurements. However, finding useful features is a challenging task, and data scarcity hinders accuracy. We leverage unsupervised pre-training of neural networks to automatically learn useful features from widely available Landsat-8 satellite images. We train neural feature extractors to predict whether pairs of images are coming from spatially close or distant regions on the assumption that close regions should have similar features. We also integrate a temporal dimension to our pre-training to capture the temporal trends of satellite images with improved accuracy. We show that with unsupervised pre-training on a large set of satellite images, neural feature extractors achieve a macro F1 of 65.4% on the Famine Early Warning Systems network dataset—a 24% improvement over handcrafted features. We further show that our pre-training method leads to better features than supervised learning and previous unsupervised pre-training techniques. We demonstrate the importance of the proposed time-aware pre-training and show that the pre-trained networks can predict food insecurity with limited availability of labeled data.
Organising fragmented labour: the case of migrant workers at Helpling in Berlin
Authors: Niebler Valentin , Animento Stefania 
Published: December 2023

AbstractResearch on collective organisation of migrant platform workers is mostly concerned with ride-hailing and delivery logistics, where explicit forms of collective action have been visible. This paper addresses the issue of the self-organisation of migrant workers on cleaning platforms through a case study of Helpling cleaners in Berlin. In the paper, we ask why the attempts for organising workers have failed to scale up beyond the informal exchange of information and tactics among the workers. Our article argues that the spatial dispersion of work across the city, lack of occupational identity, and the legal framework of work, make worker organising difficult for cleaners in Berlin. Still, these factors do not lead to an absence of collective practices. Helpling workers in our study gather in online groups, can receive help and exchange in a community centre, and have been in touch with political groups. Based on the case study, the article discusses potentials and hurdles for the development of collective counter-power.
Researching a History of Epidemics in Sierra Leone during the Coronavirus Pandemic
Authors: M’bayo Tamba E. 
Published: December 2023

AbstractThis article provides a narrative about archival research experience in Sierra Leone as the coronavirus outbreak spread globally in early 2020. Coincidentally, the research concerned the country’s history of epidemics since 1787, when Freetown, its first city, accommodated freed Blacks repatriated from Britain and the Americas. As Sierra Leone prepared for another disease outbreak after Ebola in 2014, leaving or staying in Freetown (after seven months into a ten-month Fulbright US Scholar term) had health and research outcomes at stake. Historicizing the pandemic while engaging personal/social memory in historical accounts, the article highlights containment measures adopted against epidemics/pandemics across time.
Typing of Leishmania isolates from vectors and leporids of the Madrid (Spain) outbreak
Authors: Fernández-Arévalo Anna , González Estela , Ballart Cristina , Martín-Martín Inés , Tebar Silvia , Muñoz Carme , Jiménez Maribel , Molina Ricardo , Gállego Montserrat 
Published: December 2023

Abstract
In 2009, a large outbreak of leishmaniasis, associated with environmental changes, was declared near Madrid (Spain), in which Phlebotomus perniciosus was the vector, whereas the main reservoirs were hares and rabbits. Analysis of isolates from humans, vectors and leporids from the focus identified the Leishmania infantum ITS-Lombardi genotype. However, multilocus enzyme electrophoresis (MLEE), the reference technique for Leishmania typing, and sequencing of the hsp70 gene, a commonly used marker, were not performed. In the present study, 19 isolates from P. perniciosus (n = 11), hares (n = 5) and rabbits (n = 3) from the outbreak area, all characterized as ITS-Lombardi in previous studies, were analysed by MLEE and hsp70 sequencing. The hsp70 results confirmed that all the analysed strains are L. infantum. However, by MLEE, 4 different zymodemes of L. infantum were identified based on variable mobilities of the NP1 enzyme: MON-34 (NP1100, n = 11), MON-80 (NP1130, n = 6), MON-24 (NP1140, n = 1) and MON-331 (NP1150, n = 1). The relative frequency of these zymodemes does not correspond to their usual occurrence in Spain. Moreover, MON-34 and MON-80 were found in P. perniciosus, hares and rabbits for the first time. These findings continue to provide insights into the outbreak and call for further studies with a higher number of strains.
Laboring Femininities: Skill, Body, and Class-making Among Beauty Workers in India
Authors: Banerjee Supurna 
Published: December 2023

AbstractTea plantation workers in India have historically been a part of the feminized workforce, constituting somewhat exceptionally formal labor in a country with high informalization of women's employment. In the past decade, however, a combined fallout of neo-liberalization and globalization contextualized within the local history of varying phases of incorporation, accumulation/dispossession and shifting relations of production brought about a crisis in the tea plantations leading to closures, retrenchment, and casualization. The women workers from tea plantations joined the burgeoning casualized urban labor force. Through ethnography and interviews I traced women workers from tea plantations in West Bengal, India, who migrated to the beauty industry in Hyderabad and Delhi-NCR. The paper focuses on the construction of women's labor in the beauty industry with continuities and contrasts from the tea plantations to understand the makings of gendered labor and skill. The women's frequent invocation of femininity as skill foregrounds the woman's body as central to woman's labor and the workplace but also provides a scope to unsettle understanding of femininity as a specific and naturalized concept. Using the lens of migration from one sector of feminized labor to another, this paper interrogates the production of the feminine worker and the workplace in different but related contexts. Their reflections on their work, skill, and workplace allows us an insight into the ways in which the body as the woman and the worker is deployed as skilled/natural and how they themselves co-construct, negotiate, and subvert the construction of femininity and feminine labor in the workplace.
Force-based organization and control scheme for the non-prehensile cooperative transportation of objects
Authors: Rosenfelder Mario , Ebel Henrik , Eberhard Peter 
Published: December 2023

AbstractOver decades of robotics research, cooperative object transportation has been studied as a meaningful model problem for robotic networks because it possesses a variety of crucial challenges. Although these challenges are demanding, the cooperation of multiple robots has the potential to solve automation problems that are beyond the scope of an individual robot. So far, the model problem has mostly been addressed by explicitly controlling the robots’ positions. However, the position-based approach suffers from some intrinsic detriments, for example, the lack of explicit feedback between robots and object. Moreover, it remains an open question how many robots shall be employed to ensure a successful transportation. This paper’s purpose is to overcome these challenges using a novel force-based approach taking into account the robots’ actual manipulation capabilities, that is, the exerted forces. Using cost-efficient hardware, the interaction forces are measured and, what is more, explicitly controlled by a highly responsive onboard controller. Employing a tailored software architecture, the novel force-based scheme, useful for robotic manipulation beyond the benchmark problem, is probably the most flexible of its kind regarding the number of robots and the object’s shape. The controller’s functionality and performance as well as the scheme’s versatility are demonstrated by several hardware experiments.
Non-native tone categorization and word learning across a spectrum of L1 tonal statuses
Authors: Laméris Tim Joris , Llompart Miquel , Post Brechtje 
Published: December 2023

AbstractAdults differ in the ease with which they acquire lexical tones in a non-native language. Individual differences have been attributed to several factors, such as the role that pitch plays in a learner's L1 to signal lexical meaning (L1 tonal status), the shape of the tones to be acquired (tone types), as well as extralinguistic factors (such as musical experience and working memory). Here, we ask whether learners from a spectrum of L1 tonal statuses (Dutch, Swedish and Japanese, and Thai) differ in their tone word learning facility, whilst we simultaneously investigate the effects of tone type, and musical experience and working memory. Our findings suggest that above and beyond L1 tonal status, the strongest predictor of tone word learning was pre-lexical tone processing (measured by a tone categorization task), although the strength of the link between pre-lexical and lexical processing may be modulated by L1 tonal status.
QUALITATIVE UNCERTAINTY PRINCIPLE ON CERTAIN LIE GROUPS
Authors: CHATTOPADHYAY ARUP , GIRI DEBKUMAR , SRIVASTAVA R. K. 
Published: December 2023

AbstractIn this article, we study the recent development of the qualitative uncertainty principle on certain Lie groups. In particular, we consider that if the Weyl transform on certain step-two nilpotent Lie groups is of finite rank, then the function has to be zero almost everywhere as long as the nonvanishing set for the function has finite measure. Further, we consider that if the Weyl transform of each Fourier–Wigner piece of a suitable function on the Heisenberg motion group is of finite rank, then the function has to be zero almost everywhere whenever the nonvanishing set for each Fourier–Wigner piece has finite measure.
Observational Evidence of the Four‐Wave Interaction Between Magnetospheric Whistler Mode Waves
Authors: Yang Qiwu , Liu Si , Yang Hongming , Gao Zhonglei , Xiao Fuliang , He Qian 
Published: December 2023

AbstractWhistler mode wave is a crucial emission which can significantly affect the electron dynamics in the magnetosphere. The nonlinear three‐wave interaction between whistler mode waves has been frequently observed, while the four‐wave interaction between these waves is seldom reported. Here, we present two multiband whistler mode wave events by Van Allen Probes, in which the relatively weak wave bands occur exactly corresponding to the strong wave bands. We find that the frequencies of the weak and strong bands satisfy the frequency matching conditions of the four‐wave interaction, and the wave normal angles of the weak bands are almost within the possible ranges calculated from the wave vector matching conditions. Additionally, the cross‐correlation coefficients between the amplitudes of the weak bands and that of strong bands approach 0.80. These results indicate that the weak bands are very likely to be produced by the four‐wave interaction between whistler mode waves. Similar to the three‐wave interaction, the four‐wave interaction can extend the frequency of whistler mode waves from close to 0.5fce to a wider range in both the lower (0.26fce) and upper (0.78fce) bands, and thus potentially play an important role in the electron dynamics.
Intensive Outpatient Treatment of Pediatric Rumination Syndrome
Authors: Kroon Van Diest Ashley M. 
Published: December 2023

Objective: Rumination syndrome is a disorder of gut–brain interaction that involves effortless postprandial regurgitation of gastric contents. It frequently occurs in otherwise healthy children and adolescents, and is associated with significant physical and psychological consequences. Treatment of Rumination syndrome is largely behavioral, and more severe illness was traditionally treated with intensive inpatient treatment. While largely successful, inpatient treatment is very costly and frequently denied by insurance. Method: As such, the intensive outpatient program (IOP) for treating Rumination syndrome was developed. This paper describes the novel IOP approach to treating pediatric Rumination syndrome in detail. Results: Data on both treatment and cost-effectiveness of the IOP are reviewed, demonstrating that IOP is as effective yet significantly cheaper than intensive inpatient treatment. The tiered method of care used within this model, adaptation to telehealth, and translatability of the program to other providers and institutions are also reviewed. Conclusions: Intensive outpatient treatment for pediatric Rumination syndrome is an effective form of treatment that is significantly less costly than inpatient care, more readily approved by insurance, and more easily translated to telehealth or other institutions and providers for use. Implications for Impact Statement The intensive outpatient program for treating pediatric Rumination syndrome is effective while costing significantly less than previously relied upon inpatient treatment and is described in detail in this article. Adaptation to telehealth as well as ability of other providers or institutions to use this model to treat pediatric Rumination syndrome is reviewed.
Single-cell transcriptional landscape of long non-coding RNAs orchestrating mouse heart development
Authors: Ramos Thaís A. R. , Urquiza-Zurich Sebastián , Kim Soo Young , Gillette Thomas G. , Hill Joseph A. , Lavandero Sergio , do Rêgo Thaís G. , Maracaja-Coutinho Vinicius 
Published: December 2023

AbstractLong non-coding RNAs (lncRNAs) comprise the most representative transcriptional units of the mammalian genome. They are associated with organ development linked with the emergence of cardiovascular diseases. We used bioinformatic approaches, machine learning algorithms, systems biology analyses, and statistical techniques to define co-expression modules linked to heart development and cardiovascular diseases. We also uncovered differentially expressed transcripts in subpopulations of cardiomyocytes. Finally, from this work, we were able to identify eight cardiac cell-types; several new coding, lncRNA, and pcRNA markers; two cardiomyocyte subpopulations at four different time points (ventricle E9.5, left ventricle E11.5, right ventricle E14.5 and left atrium P0) that harbored co-expressed gene modules enriched in mitochondrial, heart development and cardiovascular diseases. Our results evidence the role of particular lncRNAs in heart development and highlight the usage of co-expression modular approaches in the cell-type functional definition.
Lesion detection in women breast’s dynamic contrast-enhanced magnetic resonance imaging using deep learning
Authors: Saikia Sudarshan , Si Tapas , Deb Darpan , Bora Kangkana , Mallik Saurav , Maulik Ujjwal , Zhao Zhongming 
Published: December 2023

AbstractBreast cancer is one of the most common cancers in women and the second foremost cause of cancer death in women after lung cancer. Recent technological advances in breast cancer treatment offer hope to millions of women in the world. Segmentation of the breast’s Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is one of the necessary tasks in the diagnosis and detection of breast cancer. Currently, a popular deep learning model, U-Net is extensively used in biomedical image segmentation. This article aims to advance the state of the art and conduct a more in-depth analysis with a focus on the use of various U-Net models in lesion detection in women’s breast DCE-MRI. In this article, we perform an empirical study of the effectiveness and efficiency of U-Net and its derived deep learning models including ResUNet, Dense UNet, DUNet, Attention U-Net, UNet++, MultiResUNet, RAUNet, Inception U-Net and U-Net GAN for lesion detection in breast DCE-MRI. All the models are applied to the benchmarked 100 Sagittal T2-Weighted fat-suppressed DCE-MRI slices of 20 patients and their performance is compared. Also, a comparative study has been conducted with V-Net, W-Net, and DeepLabV3+. Non-parametric statistical test Wilcoxon Signed Rank Test is used to analyze the significance of the quantitative results. Furthermore, Multi-Criteria Decision Analysis (MCDA) is used to evaluate overall performance focused on accuracy, precision, sensitivity, F$$_1$$

1

-score, specificity, Geometric-Mean, DSC, and false-positive rate. The RAUNet segmentation model achieved a high accuracy of 99.76%, sensitivity of 85.04%, precision of 90.21%, and Dice Similarity Coefficient (DSC) of 85.04% whereas ResNet achieved 99.62% accuracy, 62.26% sensitivity, 99.56% precision, and 72.86% DSC. ResUNet is found to be the most effective model based on MCDA. On the other hand, U-Net GAN takes the least computational time to perform the segmentation task. Both quantitative and qualitative results demonstrate that the ResNet model performs better than other models in segmenting the images and lesion detection, though computational time in achieving the objectives varies.