bluuebunny/crossref_metadata_2025_split huggingface dataset:
Forecasting cryptocurrency's buy signal with a bagged tree learning approach to enhance purchase decisions
Authors: Alsini Raed , Abu Al-Haija Qasem , Alsulami Abdulaziz A. , Alturki Badraddin , Alqurashi Abdulaziz A. , Mashat Mouhamad D. , Alqahtani Ali , Alhebaishi Nawaf 
Published: May 2024

IntroductionThe cryptocurrency market is captivating the attention of both retail and institutional investors. While this highly volatile market offers investors substantial profit opportunities, it also entails risks due to its sensitivity to speculative news and the erratic behavior of major investors, both of which can provoke unexpected price fluctuations.MethodsIn this study, we contend that extreme and sudden price changes and atypical patterns might compromise the performance of technical signals utilized as the basis for feature extraction in a machine learning-based trading system by either augmenting or diminishing the model's generalization capability. To address this issue, this research uses a bagged tree (BT) model to forecast the buy signal for the cryptocurrency market. To achieve this, traders must acquire knowledge about the cryptocurrency market and modify their strategies accordingly.Results and discussionTo make an informed decision, we depended on the most prevalently utilized oscillators, namely, the buy signal in the cryptocurrency market, comprising the Relative Strength Index (RSI), Bollinger Bands (BB), and the Moving Average Convergence/Divergence (MACD) indicator. Also, the research evaluates how accurately a model can predict the performance of different cryptocurrencies such as Bitcoin (BTC), Ethereum (ETH), Cardano (ADA), and Binance Coin (BNB). Furthermore, the efficacy of the most popular machine learning model in precisely forecasting outcomes within the cryptocurrency market is examined. Notably, predicting buy signal values using a BT model provides promising results.
An evaluation method for integrating EVs in distribution networks with clustering algorithms
Authors: Chao Lu , Yu Lu , Yihua Liu , Menghua Deng , Yanjun Chen , Ruochen Duan 
Published: May 2024

Introduction: With the installation of advanced metering infrastructures, the operation data of EVs in the distribution networks can be obtained with time intervals of seconds and minutes. Based on these operation data, the impacts of integrating EVs into the distribution networks can be calculated and discussed.Methods: In this paper, an improved clustering algorithm with a new distance index for the daily curves of different types of EVs was proposed. The different types of EVs can be classified into several typical groups and the required number of operation scenarios can be reduced. After reducing the large-scale database to typical clusters, research can be conducted on the characteristics of EVs specific to certain scenarios.Results and discussion: In this way, the capability of integrating different types of EVs into the distribution network, such as fast EV charging stations, slow EV charging stations, and EV bus charging stations, is assessed from the perspective of load capacity size. The proposed clustering algorithm was verified with practical operation data.
Identification and analysis of chemokine-related and NETosis-related genes in acute pancreatitis to develop a predictive model
Authors: Mo Shuangyang , Wu Wenhong , Luo Kai , Huang Cheng , Wang Yingwei , Qin Heping , Cai Huaiyang 
Published: May 2024

Background: Chemokines and NETosis are significant contributors to the inflammatory response, yet there still needs to be a more comprehensive understanding regarding the specific molecular characteristics and interactions of NETosis and chemokines in the context of acute pancreatitis (AP) and severe AP (SAP).Methods: To address this gap, the mRNA expression profile dataset GSE194331 was utilized for analysis, comprising 87 AP samples (77 non-SAP and 10 SAP) and 32 healthy control samples. Enrichment analyses were conducted for differentially expressed chemokine-related genes (DECRGs) and NETosis-related genes (DENRGs). Three machine-learning algorithms were used for the identification of signature genes, which were subsequently utilized in the development and validation of nomogram diagnostic models for the prediction of AP and SAP. Furthermore, single-gene Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were performed. Lastly, an interaction network for the identified signature genes was constructed.Results: We identified 12 DECRGs and 7 DENRGs, and enrichment analyses indicated they were primarily enriched in cytokine-cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and T cell receptor signaling pathway. Moreover, these machine learning algorithms finally recognized three signature genes (S100A8, AIF1, and IL18). Utilizing the identified signature genes, we developed nomogram models with high predictive accuracy for AP and differentiation of SAP from non-SAP, as demonstrated by area under the curve (AUC) values of 0.968 (95% CI 0.937–0.990) and 0.862 (95% CI 0.742–0.955), respectively, in receiver operating characteristic (ROC) curve analysis. Subsequent single-gene GESA and GSVA indicated a significant positive correlation between these signature genes and the proteasome complex. At the same time, a negative association was observed with the Th1 and Th2 cell differentiation signaling pathways.Conclusion: We have identified three genes (S100A8, AIF1, and IL18) related to chemokines and NETosis, and have developed accurate diagnostic models that might provide a novel method for diagnosing AP and differentiating between severe and non-severe cases.
PEARR tool training and implementation: building awareness of violence and human trafficking in a hospital system
Authors: Roe-Sepowitz Dominique , Bracy Kristen , Gibbs Holly , Stafford Rae Lynn , Bernardin Brooke , Stoklosa Hanni 
Published: May 2024

IntroductionHealth professionals have an opportunity to assist patients who are experiencing many types of violence, including human trafficking; however, current approaches are often not person-centered. The Provide privacy, Educate, Ask, Respect and Respond (PEARR) Tool, a recognized screening tool in the U.S., is a structured conversation guide for health professionals on how to provide trauma-sensitive assistance to patients who may be experiencing such violence, including human trafficking. This is the first study to evaluate the PEARR Tool and its use in hospital settings.MethodsA U.S.-based health system adopted the PEARR Tool as part of its Abuse, Neglect, and Violence policy and procedure. To support successful adoption, the health system also developed educational modules on human trafficking and trauma-informed approaches to patient care, including a module on the PEARR steps. In October 2020 and June 2021, a voluntary “PEARR Tool Training and Implementation Survey” was distributed to emergency department staff in three hospitals. The survey consisted of 22 questions: eight demographic and occupation related questions; five questions related to the education provided to staff; and, nine questions related to the use of the PEARR Tool in identifying and assisting patients.ResultsThe overall findings demonstrate a general increase in awareness about the prevalence of human trafficking, as well as a significant increase in awareness about the implementation of the PEARR Tool. However, the findings demonstrate that most respondents were not utilizing the PEARR Tool between October 2020 and June 2021. Most reported that the reason for this was because they had not suspected any of their patients to be victims of abuse, neglect, or violence, including human trafficking. Of those that had utilized the PEARR Tool, there was a marked increase in staff that reported its usefulness and ease of access when caring for patients.DiscussionThe COVID-19 pandemic posed many challenges during this study, including delays in staff education, changes in education format and delivery, and strains on staff. Initial data regarding the use of the PEARR Tool is promising; and additional research is recommended.
A simulation study: comparing independent component analysis and signal-space projection – source-informed reconstruction for rejecting muscle artifacts evoked by transcranial magnetic stimulation
Authors: Mutanen Tuomas Petteri , Ilmoniemi Ida , Atti Iiris , Metsomaa Johanna , Ilmoniemi Risto Juhani 
Published: May 2024

IntroductionThe combination of transcranial magnetic stimulation (TMS) and electroencephalography (EEG) allows researchers to explore cortico-cortical connections. To study effective connections, the first few tens of milliseconds of the TMS-evoked potentials are the most critical. Yet, TMS-evoked artifacts complicate the interpretation of early-latency data. Data-processing strategies like independent component analysis (ICA) and the combined signal-space projection–source-informed reconstruction approach (SSP–SIR) are designed to mitigate artifacts, but their objective assessment is challenging because the true neuronal EEG responses under large-amplitude artifacts are generally unknown. Through simulations, we quantified how the spatiotemporal properties of the artifacts affect the cleaning performances of ICA and SSP–SIR.MethodsWe simulated TMS-induced muscle artifacts and superposed them on pre-processed TMS–EEG data, serving as the ground truth. The simulated muscle artifacts were varied both in terms of their topography and temporal profiles. The signals were then cleaned using ICA and SSP–SIR, and subsequent comparisons were made with the ground truth data.ResultsICA performed better when the artifact time courses were highly variable across the trials, whereas the effectiveness of SSP–SIR depended on the congruence between the artifact and neuronal topographies, with the performance of SSP–SIR being better when difference between topographies was larger. Overall, SSP–SIR performed better than ICA across the tested conditions. Based on these simulations, SSP–SIR appears to be more effective in suppressing TMS-evoked muscle artifacts. These artifacts are shown to be highly time-locked to the TMS pulse and manifest in topographies that differ substantially from the patterns of neuronal potentials.DiscussionSelecting between ICA and SSP–SIR should be guided by the characteristics of the artifacts. SSP–SIR might be better equipped for suppressing time-locked artifacts, provided that their topographies are sufficiently different from the neuronal potential patterns of interest, and that the SSP–SIR algorithm can successfully find those artifact topographies from the high-pass-filtered data. ICA remains a powerful tool for rejecting artifacts that are not strongly time locked to the TMS pulse.
A novel acupuncture technique at the Zusanli point based on virtual reality and EEG: a pilot study
Authors: Zhang Yongheng , Hua Weicheng , Zhou Ziqiu , Zhu Haibin , Xiong Jiawei , Zhang Jianbin , Chen Duo , Guo Jiayang 
Published: May 2024

IntroductionAcupuncture is a Traditional Chinese Medicine (TCM) method that achieves therapeutic effects through the interaction of neurotransmitters and neural regulation. It is generally carried out manually, making the related process expert-biased. Meanwhile, the neural stimulation effect of acupuncture is difficult to track objectively. In recent years, virtual reality (VR) in medicine has been on the fast lane to widespread use, especially in therapeutic stimulation. However, the use of related technologies in acupuncture has not been reported.MethodsIn this work, a novel acupuncture stimulation technique using VR is proposed. To track the stimulation effect, the electroencephalogram (EEG) is used as the marker to validate brain activities under acupuncture.Results and discussionAfter statistically analyzing the data of 24 subjects during acupuncture at the “Zusanli (ST36)” acupoint, it has been determined that Virtual Acupuncture (VA) has at least a 63.54% probability of inducing similar EEG activities as in Manual Acupuncture (MA). This work may provide a new solution for researchers and clinical practitioners using Brain-Computer Interface (BCI) in acupuncture.
Cannabidiol is a behavioral modulator in BTBR mouse model of idiopathic autism
Authors: Shrader Sarah H. , Mellen Nicholas , Cai Jun , Barnes Gregory N. , Song Zhao-Hui 
Published: May 2024

IntroductionThe prevalence of Autism Spectrum Disorder (ASD) has drastically risen over the last two decades and is currently estimated to affect 1 in 36 children in the U.S., according to the center for disease control (CDC). This heterogenous neurodevelopmental disorder is characterized by impaired social interactions, communication deficits, and repetitive behaviors plus restricted interest. Autistic individuals also commonly present with a myriad of comorbidities, such as attention deficit hyperactivity disorder, anxiety, and seizures. To date, a pharmacological intervention for the treatment of core autistic symptoms has not been identified. Cannabidiol (CBD), the major nonpsychoactive constituent of Cannabis sativa, is suggested to have multiple therapeutic applications, but its effect(s) on idiopathic autism is unknown. We hypothesized that CBD will effectively attenuate the autism-like behaviors and autism-associated comorbid behaviors in BTBR T+Itpr3tf/J (BTBR) mice, an established mouse model of idiopathic ASD.MethodsMale BTBR mice were injected intraperitoneally with either vehicle, 20 mg/kg CBD or 50 mg/kg CBD daily for two weeks beginning at postnatal day 21 ± 3. On the final treatment day, a battery of behavioral assays were used to evaluate the effects of CBD on the BTBR mice, as compared to age-matched, vehicle-treated C57BL/6 J mice.ResultsHigh dose (50 mg/kg) CBD treatment attenuated the elevated repetitive self-grooming behavior and hyperlocomotion in BTBR mice. The social deficits exhibited by the control BTBR mice were rescued by the 20 mg/kg CBD treatment.DiscussionOur data indicate that different doses for CBD are needed for treating specific ASD-like behaviors. Together, our results suggest that CBD may be an effective drug to ameliorate repetitive/restricted behaviors, social deficits, and autism-associated hyperactivity.
Dropout in cognitive behavioral treatment in adults living with overweight and obesity: a systematic review
Authors: Neri Lenycia de Cassya Lopes , Mariotti Francesca , Guglielmetti Monica , Fiorini Simona , Tagliabue Anna , Ferraris Cinzia 
Published: May 2024

Obesity is a chronic, complex, and multifactorial disease resulting from the interaction of genetic, environmental, and behavioral factors. It is characterized by excessive fat accumulation in adipose tissue, which damages health and deteriorates the quality of life. Although dietary treatment can significantly improve health, high attrition is a common problem in weight loss interventions with serious consequences for weight loss management and frustration. The strategy used to improve compliance has been combining dietary prescriptions and recommendations for physical activity with cognitive behavioral treatment (CBT) for weight management. This systematic review determined the dropout rate and predictive factors associated with dropout from CBT for adults with overweight and obesity. The data from the 37 articles selected shows an overall dropout rate between 5 and 62%. The predictive factors associated with attrition can be distinguished by demographics (younger age, educational status, unemployed status, and ethnicity) and psychological variables (greater expected 1-year Body Mass Index loss, previous weight loss attempts, perceiving more stress with dieting, weight and shape concerns, body image dissatisfaction, higher stress, anxiety, and depression). Common reasons for dropping out were objective (i.e., long-term sickness, acute illness, and pregnancy), logistical, poor job conditions or job difficulties, low level of organization, dissatisfaction with the initial results, lack of motivation, and lack of adherence. According to the Mixed Methods Appraisal quality analysis, 13.5% of articles were classified as five stars, and none received the lowest quality grade (1 star). The majority of articles were classified as 4 stars (46%). At least 50% of the selected articles exhibited a high risk of bias. The domain characterized by a higher level of bias was that of randomization, with more than 60% of the articles having a high risk of bias. The high risk of bias in these articles can probably depend on the type of study design, which, in most cases, was observational and non-randomized. These findings demonstrate that CBT could be a promising approach for obesity treatment, achieving, in most cases, lower dropout rates than other non-behavioral interventions. However, more studies should be conducted to compare obesity treatment strategies, as there is heterogeneity in the dropout assessment and the population studied. Ultimately, gaining a deeper understanding of the comparative effectiveness of these treatment strategies is of great value to patients, clinicians, and healthcare policymakers.Systematic review registration: PROSPERO 2022 CRD42022369995 Available from: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42022369995.
The relationship between dietary intake of ω-3 and ω-6 fatty acids and frailty risk in middle-aged and elderly individuals: a cross-sectional study from NHANES
Authors: Yan Zhaoqi , Xu Yifeng , Li Keke , Zhang Wenqiang , Liu Liangji 
Published: May 2024

BackgroundFrailty is a complex clinical syndrome characterized by a decline in the functioning of multiple body systems and reduced adaptability to external stressors. Dietary ω-3 fatty acids are considered beneficial dietary nutrients for preventing frailty due to their anti-inflammatory and immune-regulating properties. However, previous research has yielded conflicting results, and the association between ω-6 fatty acids, the ω-6: ω-3 ratio, and frailty remains unclear. This study aims to explore the relationship between these factors using the National Health and Nutrition Examination Survey (NHANES) database.Materials and methodsSpecialized weighted complex survey design analysis software was employed to analyze data from the 2005–2014 NHANES, which included 12,315 participants. Multivariate logistic regression models and restricted cubic splines (RCS) were utilized to assess the relationship between omega intake and frailty risk in all participants. Additionally, a nomogram model for predicting frailty risk was developed based on risk factors. The reliability of the clinical model was determined by the area under the receiver operating characteristic (ROC) curve, calibration curves, and decision curve analysis (DCA).ResultsIn dietary ω-3 intake, compared to the T1 group (≤1.175 g/d), the T3 group’s intake level (&gt;2.050 g/d) was associated with approximately 17% reduction in frailty risk in model 3, after rigorous covariate adjustments (odds ratio (OR) = 0.83, 95% confidence interval (CI): (0.70, 0.99)). In dietary ω-6 intake, the T2 group’s intake level (&gt;11.423, ≤19.160 g/d) was associated with a 14% reduction in frailty risk compared to the T1 group (≤11.423 g/d) (OR: 0.86, 95% CI: 0.75, 1.00, p = 0.044). RCS results indicated a non-linear association between ω-3 and ω-6 intake and frailty risk. Both ROC and DCA curves demonstrated the stability of the constructed model and the effectiveness of an omega-rich diet in reducing frailty risk. However, we did not find a significant association between the ω-6: ω-3 ratio and frailty.ConclusionThis study provides support for the notion that a high intake of ω-3 and a moderate intake of ω-6 may contribute to reducing frailty risk in middle-aged and elderly individuals.
Compartmental analysis: a new approach to estimate protein breakdown and meal response in health and critical illness
Authors: Deutz Nicolaas E. P. , Engelen Mariëlle P. K. J. 
Published: May 2024

Purpose of reviewThis study aimed to discuss the use of the pulse stable isotope tracer approach to study changes in metabolism in healthy individuals and critically ill patients.Recent findings and conclusionWe found that in the postabsorptive state and healthy condition, intracellular protein breakdown and net intracellular protein breakdown, when calculated using the pulse tracer approach, are about double what has previously been reported using the more traditional primed-constant and continuous stable isotope approaches (600 versus 300 grams of protein/day). In critically ill patients, protein breakdown is even higher and calculated to be approximately 900 grams of protein/day, using the pulse tracer approach. Based on these data, we hypothesize that reducing protein breakdown in the postabsorptive state is key when trying to improve the condition of critically ill patients. Moreover, we also used the pulse tracer approach during feeding to better estimate the intracellular metabolic response to feeding. Our first observation is that endogenous protein breakdown does not seem to be reduced during feeding. We also have shown that when consuming a meal with a certain amount of protein, the biological value of that protein meal can be calculated with the pulse tracer approach. In conclusion, using the pulse stable isotope tracer approach to study protein kinetics in the postabsorptive state and during feeding expands our understanding of how dietary proteins can affect human protein metabolism. The intracellular protein synthesis stimulatory effect of a meal is an important factor to consider when calculating the exact protein requirements and needs, particularly in critical illness.
Production and quality evaluation of a novel γ-aminobutyric acid-enriched yogurt
Authors: Zhu Fei , Hu Sheng , Mei Lehe 
Published: May 2024

Objectiveγ-aminobutyric acid (GABA) is a neurotransmitter inhibitor that has beneficial effects on various health conditions such as hypertension, cognitive dysfunction, and anxiety. In this study, we investigated a novel yogurt naturally enriched with GABA using a Levilactobacillus brevis strain isolated in our laboratory; the specific optimum yogurt production conditions for this strain were determined.MethodsWe isolated an L. brevis strain and used it to produce yogurt naturally enriched with GABA. We explored the optimal conditions to enhance GABA yield, including fermentation temperature, inoculation amount, L-monosodium glutamate (L-MSG) concentration, fermentation time, and sucrose content. We also performed mixed fermentation with Streptococcus thermophilus and evaluated the quality of the yogurt.ResultsFollowing optimization (43°C, 8% inoculation amount, 1.5 g/L L-MSG, and 8% sucrose for 40 h of fermentation), the GABA yield of the yogurt increased by 2.2 times, reaching 75.3 mg/100 g. Mixed fermentation with S. thermophilus demonstrated favorable results, achieving a GABA yield akin to that found in some commercially available functional foods. Moreover, the viable microbe count in the GABA-enriched yogurt exceeded 1 × 108 cfu/mL, which is higher than that of commercial standards. The yogurt also exhibited a suitable water-holding capacity, viscosity, 3-week storage time, and favorable sensory test results.ConclusionThis study highlights the potential of naturally enriched GABA yogurt as a competitive commercial yogurt with beneficial health effects.