bluuebunny/crossref_metadata_2025_split huggingface dataset:
An improved protocol for bacteria identification by MALDI-TOF MS directly from positive blood cultures
Authors: Cruz Sara , Abreu David , Gomes Rosário , Martins-Oliveira Inês , Silva-Dias Ana , Perez-Viso Blanca , Cantón Rafael , Pina-Vaz Cidália 
Published: December 2023

AbstractFASTinov® developed a rapid antimicrobial susceptibility test that includes the purification of a bacterial suspension directly from positive blood cultures (BC). In order to streamline laboratory workflow, the use of the bacterial suspension obtained through FASTinov® sample prep was tested for identification (ID) by matrix absorption laser deionization–time of flight mass spectrometry (MALDI-TOF MS) (Bruker) in 364 positive BC, and its accuracy assessed comparing with the MALDI-TOF MS ID of the next-day subcultured colonies. FASTinov sample prep was highly reliable for rapid ID directly from BC with proportion of agreement of 94.9% for Gram-positive and 96.3% for Gram-negative bacteria.
C-reactive protein levels, the prognostic nutritional index, and the lactate dehydrogenase-to-lymphocyte ratio are important prognostic factors in primary central nervous system lymphoma: a single-center study of 223 patients
Authors: Zuo Jinyi , Lei Ting , Zhong Shuai , Zhou Jiajun , Liu Rui , Wu Chenxing , Li Shouwei 
Published: December 2023

AbstractPrimary central nervous system lymphoma (PCNSL) is a rare and highly aggressive type of extranodal non-Hodgkin lymphoma (NHL), and the prognosis is poor. Currently, the most used prognostic models are the Memorial Sloan-Kettering Cancer Center (MSKCC) and International Extranodal Lymphoma Study Group (IELSG) scores; however, their predictive effects are changing with increasing incidence and changing treatment regimens. A growing body of evidence has demonstrated that inflammatory and nutritional markers are factors that can determine tumor prognosis. Therefore, the aim of this study was to identify and validate novel prognostic factors for PCNSL. Clinical information was collected from 223 patients with PCNSL. Patients younger than 18 years of age were excluded. Progression-free survival (PFS) and overall survival (OS) were used as endpoints, and receiver operating characteristic (ROC) curve analyses were conducted to determine the cutoff values for the inflammatory indicators. Correlations between variables and PFS or OS were assessed using univariate and multivariate analyses, and positive indicators were selected for survival analysis. A prognostic nutritional index (PNI) < 49.38 was associated with worse PFS (p = 0.003), and outcomes significantly differed between patients with a PNI ≥ 49.38 and < 49.38 (p < 0.001). Age < 60 years (p < 0.001) and C-reactive protein (CRP) levels < 3.14 (p = 0.001) were associated with better OS. In elderly patients (≥ 60 years), a lactate dehydrogenase-to-lymphocyte ratio (LLR) < 95.69 (p = 0.021) was associated with better OS, and the outcome significantly differed between patients with an LLR ≥ 95.69 and LLR < 95.69 (p = 0.015). The PNI and CRP levels are prognostic factors for PCNSL, and CRP was the first time shown to be a prognosis factor of PCNSL. In elderly patients with PCNSL, the LLR can predict prognosis.
What We Have Learned About Social Integration: Conclusion
Authors: Grunow Daniela , Sachweh Patrick , Schimank Uwe , Traunmüller Richard 
Published: December 2023

AbstractThis chapter concludes the special issue on social integration and makes three additional contributions. First, we discuss how the featured articles employ the concept of social integration that we have proposed in the introduction. We find clear references to the four ingredients of social integration that we have suggested: consensus, trust, conformity and cooperation. Many articles also agree with the multi-level nature of social integration. Second, we synthesise the answers that the featured articles provide for the open questions we have posed: how social integration is generated and how it is linked to societal outcomes, both normatively desirable and undesirable. Although most authors in this special issue start from the premise that social integration is inherently desirable, our conceptual lens also brings in the “dark side” of social integration. Third, we identify two further aspects that deserve more attention in future research: a rigorous analysis of how the mechanisms of social integration operate on the micro-, meso- and macro-levels of society; and more attention to the transnational interdependencies in which national modes of social integration are embedded.
Endosulfan residues and farmers’ replacement behaviors of endosulfan in the north-west inland cotton region
Authors: Zhou Shuyan , Zhang Yang , Wang Jingjing , Cheng Shikun , Zhuo Fuyan , Hong Yun 
Published: December 2023

AbstractWe assessed the situation of endosulfan residues in cotton fields after the endosulfan ban came into effect and the current knowledge, attitude, and practice (KAP) of cotton farmers on the phase-out of endosulfan and the application of alternative technologies. Topsoil samples (n = 91) of cotton fields were collected from the major cotton-producing areas in China, namely the north-west inland cotton region, and the endosulfan residues were analyzed. A KAP survey was carried out for cotton farmers, and 291 questionnaires were distributed. The influences of gender, age, education background, cotton planting years, publicity and training, income sources, and other factors on cotton farmers’ KAP were analyzed. The results showed that endosulfan sulfate was the main endosulfan residue in the soil, followed by β-endosulfan and α-endosulfan, the average residual contents were 0.569, 0.139, and 0.060 µg/kg, respectively. The results of the KAP study showed that cotton farmers scored low on knowledge about the phase-out of endosulfan and the application of alternative technologies but high on attitude and practice. The number of family members, years of cotton planting, age, and the cotton-planting area had different degrees of influence on KAP scores. The training could significantly improve the KAP scores of cotton farmers; training should be more targeted and designed reasonably for key groups, such as men and the population under 30, followed by training them to use pesticides safely. For large-scale cotton growers, training should focus on green prevention and control technologies.
Factors Influencing Medication Adherence Among Adults Living with Diabetes and Comorbidities: a Qualitative Systematic Review
Authors: Gow Kendall , Rashidi Amineh , Whithead Lisa 
Published: December 2023

Abstract
Purpose of review
Medication adherence plays an important role in improving health outcomes related to diabetes and comorbidity. The potential factors influencing medication adherence and how they contribute to health behaviors have not been synthesized to date. This review synthesized qualitative studies that identified factors influencing medication adherence among adults living with diabetes and comorbidity.

Recent findings
Twenty-eight findings were extracted and synthesized into four themes: perceived support, lack of knowledge, medication issues, and the importance of routine. The findings highlight the factors that support medication adherence and areas that can be targeted to support and promote medication adherence. The findings also support the potential role of healthcare providers in supporting people living with diabetes and comorbidity to adhere to and maintain medication regimes.

Summary
Several factors were identified that are amenable to intervention within the clinical practice setting and have the potential to enhance medication adherence and improve health outcomes for people living with diabetes and comorbidities. The development of acceptable and effective interventions could have a positive effect on medication adherence and health outcomes.
Enhancing Strength-Ductility Synergy of CoCrNi-Based Medium-Entropy Alloy Through Coherent L12 Nanoprecipitates and Grain Boundary Precipitates
Authors: Li Leilei , Song Kaikai , Gao Qingwei , Zhou Changshan , Liu Xiaoming , Wang Yaocen , Bai Xiaojun , Cao Chongde 
Published: December 2023

AbstractThe L12-strengthened Co34Cr32Ni27Al4Ti3 medium-entropy alloy (MEA) with precipitations of grain boundaries has been developed through selective laser melting (SLM) followed by cold rolling and annealing, exhibiting excellent strength-ductility synergy. The as-printed alloy exhibits low yield strength (YS) of ~ 384 MPa, ultimate tensile strength (UTS) of ~ 453 MPa, and uniform elongation (UE) of 1.5% due to the existence of the SLM-induced defects. After cold rolling and annealing, the YS, UTS, and UE are significantly increased to ~ 739 MPa, ~ 1230 MPa, and ~ 47%, respectively. This enhancement primarily originates from the refined grain structure induced by cold rolling and annealing. The presence of coherent spherical γ' precipitates (L12 phases) and Al/Ti-rich precipitates at the grain boundaries, coupled with increased lattice defects such as dislocations, stacking faults, and ultrafine deformation twins, further contribute to the property’s improvement. Our study highlights the potential of SLM in producing high-strength and ductile MEA with coherent L12 nanoprecipitates, which can be further optimized through subsequent rolling and annealing processes. These findings offer valuable insights for the development of high-performance alloys for future engineering applications.
A Semantic Enhanced Course Recommender System via Knowledge Graphs for Limited User Information Scenarios
Authors: Sanguino Juan , Manrique Ruben , Mariño Olga 
Published: December 2023

AbstractThe Universal Declaration of Human Rights states that “everyone has the right to education” and it must be “equally accessible to all”. Aligned with these principles are online learning platforms that offer free online courses that require no or minimal information from their students to democratize learning. Despite the benefits, the lack of user information challenges traditional recommendation systems and, when applied to education, impacts student experiences. This paper addresses the problem of limited student information in course recommendation systems. We tackle the problem by generating a semantically enriched vector-based representation of course content using an Open Knowledge Graph (DBpedia) and Topic Modeling methods, such as LDA and LSA and by collecting tracking events from log systems (Google Analytics) to create a hybrid recommendation system using content-based and collaborative filtering strategies for low-information scenarios. Our experiments use GCFGlobal.org, an online learning platform offering free self-paced online courses to 100 million people, to validate our approach. Results indicate that the proposed approach outperforms the previous works in the field contributing to the creation of fairer recommendation systems.
Neural nonnegative matrix factorization for hierarchical multilayer topic modeling
Authors: Haddock Jamie , Will Tyler , Vendrow Joshua , Zhang Runyu , Molitor Denali , Needell Deanna , Gao Mengdi , Sadovnik Eli 
Published: December 2023

AbstractWe introduce a new method based on nonnegative matrix factorization, Neural NMF, for detecting latent hierarchical structure in data. Datasets with hierarchical structure arise in a wide variety of fields, such as document classification, image processing, and bioinformatics. Neural NMF recursively applies NMF in layers to discover overarching topics encompassing the lower-level features. We derive a backpropagation optimization scheme that allows us to frame hierarchical NMF as a neural network. We test Neural NMF on a synthetic hierarchical dataset, the 20 Newsgroups dataset, and the MyLymeData symptoms dataset. Numerical results demonstrate that Neural NMF outperforms other hierarchical NMF methods on these data sets and offers better learned hierarchical structure and interpretability of topics.
Combined gas effect on the oil reservoir in a dual-well system
Authors: Ali Imran , Gubanov Sergey Igorevich , Alexandrovna Olkhovskaya Valeria , Moiseevich Shterenberg Alexander , Silantieva Anastasia Mikhailovna , Siddiqui Mohammad Nahid 
Published: December 2023

AbstractNowadays, high-viscosity oil reservoirs are located in a sphere of extra attention with the demands of special technologies. To maximize high-viscosity oil production advanced action technologies are used involving thermal and many other chemical methods. The article describes the results of a laboratory experiment to determine the rheological properties of high-viscosity oil, which determine the optimal temperature of the reservoir heating. A new method of thermobaric layer treatment; including heat transfer agent injection by a dual-well system; is suggested (injection to a vertical shaft and a heated oil production from a side shaft). A new steam injection steam-gas injection is proposed. The optimal layer warming temperature was 50 °C. The results of the calculations of the radius of the reservoir heating to the target temperature, which allowed for optimizing oil production are also discussed. Efficiency and warming area dependence on a heat-transfer agent to a layer injection time are also identified and presented. It was observed that thermo-physical calculations using the data of a laboratory experiment are useful in the well-design, as it allowed to determine the parameters of the technology and the position of the bottom hole about the source of heating. The optimal bottom hole spacing between the production side shaft and the vertical shaft was found to be 76.0 m. The optimal temperature of 50 °C was reached 10 days after the warning started.
Convolution Neural Network Bidirectional Long Short-Term Memory for Heartbeat Arrhythmia Classification
Authors: Alkhawaldeh Rami S. , Al-Ahmad Bilal , Ksibi Amel , Ghatasheh Nazeeh , Abu-Taieh Evon M. , Aldehim Ghadah , Ayadi Manel , Alkhawaldeh Samar M. 
Published: December 2023

AbstractArrhythmia is a heart condition that poses a severe threat to life and requires prompt medical attention. One of the challenges in detecting arrhythmias accurately is that incorrect diagnoses can have severe consequences. In light of this, it is critical to develop a solution that is both effective and reliable. In this study, we propose a residual Convolution Neural Network Bidirectional Long Short-Term Memory (DeepResidualBiLSTM) model for classifying Arrhythmia types, which addresses the vanishing gradient problem and captures the relevant features in the signals’ long dependencies. The model is characterized by its simplicity, stability, and ability to extract meaningful features effectively. Using two well-known datasets, the experimental results demonstrate exceptional accuracy, precision, and recall values of approximately 99.4% at the early stage of 20 epoch training. Furthermore, the model demonstrates a remarkable ability to discriminate between Arrhythmia classes under varying thresholds using the ROC curve metric, with a high value, in most cases, of 100% for accurately detecting positive cases.
Carbon taxation in Singapore's semiconductor sector: a mini-review on GHG emission metrics and reporting
Authors: Li Yuanzhe , Wang Yan , Chong Daphne , Xu Zhongqi , Li Luzi , Hu Yuchun 
Published: December 2023

AbstractThe threat of climate change has catalyzed global endeavors to curb greenhouse gas emissions, with carbon taxation emerging as a pivotal policy instrument. Singapore, akin to Taiwan, has embraced this tool, and its ramifications on their semiconductor industry are both profound and multifaceted. At the outset, the imposition of carbon taxes inevitably escalates production costs for semiconductor firms, compelling them to offset their carbon footprint financially. This escalation, in turn, poses a risk of eroding the industry's competitive edge, nudging firms to contemplate the prospect of migrating to locales with more lenient carbon taxation regimes. However, in juxtaposition to these challenges, carbon taxation unveils a silver lining. It instigates semiconductor entities to recalibrate their operations, infusing energy-efficient technologies and pivoting towards renewable energy avenues. Such transitions not only attenuate their carbon emissions but also curtail their financial burden arising from carbon taxation. This manuscript elucidates a panoramic landscape of both policy innovations and technological strides specific to Singapore's semiconductor arena. It aims to be an instrumental compass for stakeholders, delineating pathways for achieving optimal eco-financial equilibrium in the sector.
Graphical Abstract
Effects of niacin on apo A1 and B levels: a systematic review and meta-analysis of randomised controlled trials
Authors: Saboori Somayeh , Yousefi Rad Esmaeil , Tammam Jonathan , Thondre Pariyarath Sangeetha , Coe Shelly 
Published: December 2023

AbstractNiacin has been investigated for its potential impact on lipid metabolism and cardiovascular health. This meta-analysis aims to systematically evaluate the effects of niacin interventions on apo A1 and apo B levels, key regulators of lipoprotein metabolism and markers of cardiovascular risk. A comprehensive search of the literature was performed on five databases of PubMed, Scopus, Web of Science, Embase and Cochrane library, from inception up to 15 July 2023. This search identified 1452 publications, from which twelve randomised controlled trials met the inclusion criteria. The intervention dosages ranged from 500 to 3000 mg/d, and the study durations spanned from 6 to 102·8 weeks. The niacin intervention demonstrated a significant reduction in apo B levels (weighted mean differences (WMD): −24·37 mg/dl, P = 0·01). Subgroup analyses indicated that intervention duration played a role, with trials of ≤ 16 weeks showing a greater reduction in apo B. Regarding apo A1, niacin significantly increased its levels (WMD: 8·23 mg/dl, P < 0·001). Subgroup analyses revealed that the beneficial effects of niacin on apo A1 were observed at a dosage of > 1500 mg/d (P < 0·001), and extended-release niacin was more effective compared with other forms (P < 0·001). According to the Begg’s regression test, no publication bias was observed in this systematic review and meta-analysis. This meta-analysis highlights niacin’s potential role in improving lipid profiles and cardiovascular health. Further well-designed clinical trials are needed to elucidate and confirm optimal dosages and durations of niacin interventions for influencing apo A1 and B.
Visibility and exploitation in social networks
Authors: Galimullin Rustam , Pedersen Mina Young 
Published: December 2023

AbstractSocial media is not a neutral channel. How visible information posted online is depends on many factors such as the network structure, the emotional volatility of the content, and the design of the social media platform. In this paper, we use formal methods to study the visibility of agents and information in a social network, as well as how vulnerable the network is to exploitation. We introduce a modal logic to reason about a social network of agents that can follow each other, post, and share information. We show that by imposing some simple rules on the system, a potentially malicious agent can take advantage of the network construction to post an unpopular opinion that may reach many agents. The network is presented both in static and dynamic forms. We prove completeness, expressivity, and model checking problem complexity results for the corresponding logical systems.
Evaluation der deutschsprachigen Version des Regulatory Emotional Self-Efficacy Fragebogens für Jugendliche
Authors: Gutzweiler Raphael , Schug Sira , Lohoff Judith , In-Albon Tina 
Published: December 2023

Zusammenfassung: Theoretischer Hintergrund: Die Überzeugung, dass die eigenen Emotionen reguliert werden können, zählt als Voraussetzung für eine erfolgreiche Emotionsregulation und damit für die psychische Gesundheit. Methode: In drei Stichproben (Jugend, Schüler_innen, klinische Stichprobe) wurde bei 509 Jugendlichen und jungen Erwachsenen ( M = 16.55 Jahren, SD = 2.72, Range = 13 – 23 Jahre, 66.6 % weiblich) eine angepasste Fragebogenversion des Regulatory Emotional Self-Efficacy (RESE-D; Caprara et al., 2008 ) zur Erfassung der Selbstwirksamkeit im Umgang mit Emotionen evaluiert. Mittels Multigruppenfaktorenanalyse wurde die Faktorenstruktur des RESE-D, Messinvarianz, Reliabilität und Validität in Bezug zu weiteren Fragebögen überprüft. Ergebnisse: Jugendliche der klinischen Stichprobe zeigten eine signifikant geringere Selbstwirksamkeit in der Emotionsregulation als die Schulstichprobe (differenzielle Validität). Die Reliabilität (interne Konsistenz Cronbachs α zwischen .71 und .82 für die Subskalen) und konvergente Validität des RESE-D konnte mittels signifikanter Korrelationen zu Konstrukten wie Emotionsregulation, allgemeiner Selbstwirksamkeit, Lebensqualität und erhöhter Ängstlichkeit aufgezeigt werden. Schlussfolgerungen: Mit dem RESE-D liegt ein reliabler, valider und mit zehn Items ökonomischer Fragebogen für das Jugendalter vor.
Model of Storm Surge Maximum Water Level Increase in a Coastal Area Using Ensemble Machine Learning and Explicable Algorithm
Authors: Sun Kun , Pan Jiayi 
Published: December 2023

AbstractThis study proposes a novel, new ensemble model (NEM) designed to simulate the maximum water level increases caused by storm surges in a frequently cyclone‐affected coastal water of Hong Kong, China. The model relies on storm and water level data spanning 1978–2022. The NEM amalgamates three machine learning algorithms: Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and XGBoost (XGB), employing a stacking technique for integration. Six parameters, determined using the Random Forest and Recursive Feature Elimination algorithms (RF‐RFE), are used as input features for the NEM. These parameters are the nearest wind speed, gale distance, nearest air pressure, minimum distance, maximum pressure drop within 24 hr, and large wind radius. Model assessment results suggest that the NEM exhibits superior performance over RF, GBDT, and XGB, delivering high stability and precision. It reaches a coefficient of determination (R2) up to 0.95 and a mean absolute error (MAE) that fluctuates between 0.08 and 0.20 m for the test data set. An interpretability analysis conducted using the SHapley Additive exPlanations (SHAP) method shows that gale distance and nearest wind speed are the most significant features for predicting peak water level increases during storm surges. The results of this study could provide practical implications for predictive models concerning storm surges. These findings present essential tools for the mitigation of coastal disasters and the improvement of marine disaster warning systems.
How the Hunga Tonga—Hunga Ha'apai Water Vapor Cloud Impacts Its Transport Through the Stratosphere: Dynamical and Radiative Effects
Authors: Niemeier Ulrike , Wallis Sandra , Timmreck Claudia , van Pham Trang , von Savigny Christian 
Published: December 2023

AbstractThe eruption of the Hunga Tonga—Hunga Ha'apai (HTHH) volcano on 15 January 2022 injected about 150 Tg of water vapor (H2O), roughly 10% of the background stratospheric H2O content, to altitudes above 50 km. Simulations of the spatial evolution of the H2O cloud with the ICON‐Seamless model are very close to observations from the Aura Microwave Limb Sounder. The vertical transport of the H2O cloud had three phases: an initial subsidence phase, a stable phase, and a rising phase. Radiative cooling of H2O clearly affects the transport of the H2O cloud, as demonstrated with passive tracers, and is the main driver within the subsidence phase. It also counteracts the large‐scale rising motion in the tropics, leading to the stable phase, and modulates the large‐scale transport of the H2O cloud for about 9 months. This holds for different QBO phases, where the H2O cloud differs mainly in its vertical extent.