bluuebunny/crossref_metadata_2025_split huggingface dataset:
Fe, Mn Content in Water of Non-Centralized Water Supply in the Tyumen Urban Agglomeration
Authors: Belyanovskaya Alexandra , Soldatova Evgenia , Krestyannikova Elizaveta 
Published: April 2024

Abstract
The article presents an approach that could be adapted for other regions or serve as an illustration of local geological anomalies. The methodology included analysis of Fe and Mn content, pH, total hardness and permanganate index of 159 water samples collected from the Tyumen urban agglomeration. These parameters were compared with published data on water composition from other regions of Russia. It was found that the tap water from the Tyumen urban agglomeration has a predominantly neutral pH (5-8); the average content of organic matter (according to permanganate index) is less than 5 ppm) in all considered objects. The average water hardness does not exceed the permissible values (6 ppm); the calculated Ca2+ - Mg2+ contents are 75 and 45 mg-eq/l respectively. The average concentration of iron in water from non-centralized water supply sources and private wells within the Tyumen agglomeration is 1.9 MPC (maximum permissible concentration), while manganese levels are at 1.2 MPC. The LCIA method was used to assess the impact of Fe and Mn on freshwater ecosystems. An increased risk of non-cancerous health effects in the population utilizing well water for drinking purposes was identified, due to high concentrations of iron.
Pathological Underestimation of Core Needle Biopsy Risks in Ductal Carcinoma In Situ Breast Cancer Diagnosis: a Predictive Nomogram
Authors: Xie Shiyang , Wang Yaxuan , Song Jian , Guo Jiaru , Wang Xu , Cui Mengyao 
Published: April 2024

Abstract
Purpose
The study aimed to investigate the predictors of pathological underestimation (PU) in patients with DCIS diagnosed preoperatively by ultrasonocentesis and to create a nomogram to predict the risk of PU, providing a risk assessment tool that can assist clinicians in their surgical decision-making.
Methods
This retrospective study collected data of 309 patients with DCIS from the First Hospital of China Medical University between June 2012 and June 2022. Univariate and multivariate analyses were used in this training cohort to select independent risk factors that affect the PU risks in DCIS patients diagnosed by ultrasound-guided hollow needle aspiration biopsy (US-CNB), and a nomogram was established. The internal validation method was used as the validation cohort to verify the model.
Results
While IBC patients showed significant BI RADS mass differences by US (P = 0.029), linear/segmental calcification (P < 0.001), microinvasion (P = 0.002), and menstruation showed critical differences (P = 0.057). When we compared lymph node metastasis (LNM) patients with non-LNM patients, the former group showed significant abnormal lymph node differences by US (P < 0.001), ER (P = 0.003), PR (P = 0.022), Ki-67 (P = 0.005), PDW (P = 0.0039), and NLR (P = 0.016). In the IBC and LNM nomogram, the C-statistic was 0.814 (95% CI: 0.766–0.863) and 0.780 (95% CI: 0.642–0.917) respectively. The calibration curve showed that the nomogram was well calibrated, and the mean absolute calibration error was 0.029 and 0.019 respectively.
Conclusions
We created a nomogram predicting the likelihood of PU in DCIS patients diagnosed with US-CNB. Risk stratification with this nomogram could develop standardized practices to optimize DCIS patient management.
Predict key biomarkers of Alcohol Dependence in Nucleus accumbens by Weighted gene co-expression network analysis
Authors: Shi Xugang , Xu Bing , He Taiping , Shi Jinghong , Zhang Lijun 
Published: April 2024

Abstract

Alcohol dependence (AD) is a complication behavioral disease interacted by a number of genes and environmental factors with high heritability. The main point of the study was to identify the regulatory mechanism exerted by the nucleus accumbens (NAc) in AD. We not only performed DEGs analysis but also carried WGCNA analysis in GSE62699 dataset, and then we used online tools for gene enrichment analysis of the DEGs and hub genes, and explored possible transcription factors. We conducted DEG analysis first, and then opted the top 25% of genes with the maximum variance to perform WGCNA analysis. Finally, through GO and KEGG analysis, we found that acute inflammation is the most important biological pathway in DEGs. Then signal transduction and cell communication are the main results of BP in WGCNA. We used the hub gene to explore the transcription factor (TF) which may have a key role using the web tool–miRNet, and found that TP53 and NFKB1 are the important TFs in NAc. From the result, the miRNAs and TFs may serve as potential biomarkers and treatment targets of AD, and all these findings may be a theoretical basis to explore the regulatory mechanisms of AD.
Exploring Social Impairment in Those with Opioid Use Disorder: Linking Impulsivity, Childhood Trauma, and the Prefrontal Cortex
Authors: Arruda Thais , Sinko Laura , Regier Paul , Tufanoglu Altona , Curtin Adrian , Teitelman Anne , Ayaz Hasan , Cronholm Peter , Childress Anna Rose 
Published: April 2024

Abstract
Background
Challenges with social functioning, which is a hallmark of opioid use disorder (OUD), are a drawback in treatment adherence and maintenance. Yet, little research has explored the underlying mechanisms of this impairment. Impulsivity, a known risk factor for OUD, and corresponding neural alterations may be at the center of this issue. Childhood adversity, which has been linked to both impulsivity and poorer treatment outcomes, could also affect this relationship. This study aims to understand the relationship between impulsivity and social functioning in those recovering from OUD. Differences in the prefrontal cortex will be analyzed, as well as potential moderating effects of childhood trauma.
Methods
Participants with (N = 16) and without (N = 19) social impairment completed a survey (e.g., social functioning, Barrat’s Impulsivity Scale, Adverse Childhood Experiences (ACEs) and cognitive tasks while undergoing neuroimaging. Functional near infrared spectroscopy (fNIRS), a modern, portable, wearable and low-cost neuroimaging technology, was used to measure prefrontal cortex activity during a behavioral inhibition task (Go/No-Go task).
Results
Those who social functioning survey scores indicated social impairment (n = 16) scored significantly higher on impulsivity scale (t(33)= -3.4, p < 0.01) and reported more depressive symptoms (t(33) = -2.8, p < 0.01) than those reporting no social impairment (n = 19). Social functioning was negatively correlated with impulsivity (r=-0.7, p < 0.001), such that increased impulsivity corresponded to decreased social functioning. Childhood trauma emerged as a moderator of this relationship, but only when controlling for the effects of depression, B=-0.11, p = 0.023. Although both groups had comparable Go/No-Go task performance, the socially impaired group displayed greater activation in the dorsolateral (F(1,100.8) = 7.89, p < 0.01), ventrolateral (F(1,88.8) = 7.33, p < 0.01), and ventromedial (F(1,95.6) = 7.56, p < 0.01) prefrontal cortex during impulse control.
Conclusion
In addition to being more impulsive, individuals with social impairment exhibited differential activation in the prefrontal cortex when controlling responses. Furthermore, the impact of impulsivity on social functioning varies depending on ACEs demonstrating that it must be considered in treatment approaches. These findings have implications for addressing social needs and impulsivity of those in recovery, highlighting the importance of a more personalized, integrative, and trauma-informed approach to intervention.
Neurological manifestations of acute SARS-CoV-2 infection in pediatric patients: a 3-year study
Authors: Vivisenco Iolanda Cristina , Lescaie Andreea , Dragomirescu Ana , Ionita Ioana Catalina , Florescu Irina , Ciocea Bogdan , Grama Andreea Rodica , Craciun Maria-Dorina , Chivu Carmen-Daniela , Ulmeanu Coriolan Emil , Nitescu Viorela Gabriela 
Published: April 2024

Abstract

Purpose
This study analyzed the neurological manifestation profiles of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across pandemic waves in pediatric patients.
Methods
This retrospective study analyzed the data of patients aged 0–18 years who were diagnosed with acute SARS-CoV-2 infection and admitted to a pediatric tertiary hospital between March 1, 2020, and February 28, 2023. The presence of neurological manifestations was established based on the symptoms noted in each patient chart. The relationships between neurological manifestations and pandemic waves or age groups were assessed using the chi-square test.
Results
This study included 1677 patients. Neurological manifestations were noted in 10% (n = 168) of patients with a 3.2 years median age (interquartile range: 1–11.92). Neurological manifestations were significantly associated with the pandemic waves (p = 0.006) and age groups (p < 0.001). Seizures were noted in 4.2% of cases and reached an increasing frequency over time (p = 0.001), but were not associated with age groups. Febrile seizures accounted for the majority of seizures. Headache was reported in 2.6% of cases and had similar frequencies across the pandemic waves and age groups. Muscular involvement was noted in 2% of cases and reached a decreasing frequency over time (p < 0.001) and showed different frequencies among the age groups.
Conclusions
Neurological manifestations of acute SARS-CoV-2 infection exhibit distinct patterns, depending on the pandemic wave and patient age group. The Wuhan and Omicron waves involved the nervous system more often than the other waves; however, this was probably through different mechanisms.
Awareness and practice of dietary recommendations for cancer prevention among participants of the 2023 Korean National Cancer Prevention for Dietary Awareness and Practice Survey
Authors: Choi Yoonjoo , Jung Hyein , Kim Byungmi 
Published: April 2024

Abstract

Background
Most cancers are preventable, especially by improving dietary habits, which account for a large proportion of cancer causes. However, several factors influence a person’s dietary behaviors, and it can be challenging to implement healthy behaviors. Individuals with poor dietary behaviors should be encouraged to improve them and those making efforts should be encouraged to adopt more active steps to prevent cancer. Therefore, to identify the proportion of Korean adults who followed the recommended guidelines for dietary factors to prevent cancer risk and those with less practice, we surveyed Korean adults using an Internet-based questionnaire.
Methods
The 2023 Korean National Cancer Prevention for Dietary Awareness and Practice Survey was a cross-sectional online survey conducted during September 4–25, 2023, and included adults aged 20–69 years. The survey included questions on sociodemographic and lifestyle factors to assess the practice of five dietary recommendations: “consume sufficient amounts of fruits and vegetables,” “balance your diet with a wide range of healthy foods,” “limit your salt intake from all sources,” “avoid burnt or charred foods,” and “avoid alcohol consumption.”
Results
Among the 4,000 participants, 82.6% were aware that unbalanced diet is a risk factor for cancer. Being physically active and having nutritional education were associated with a higher odds ratio (OR) for always practicing the five dietary recommendations. Participants with a history of chronic disease had a lower OR for consuming a balanced diet (OR: 0.822) and limited salt intake (OR: 0.771). Compared with those who did not have a chronic disease, participants with hypertension, dyslipidemia, or type 2 diabetes had a lower OR of always limiting salt intake (0.654, 0.657, and 0.595, respectively). Participants with hypertension or dyslipidemia had ORs of 29% and 27% of always avoiding alcohol consumption, respectively. Those with dyslipidemia had ORs of 32% and 31% for always consuming a balanced diet and avoiding burnt or charred food, respectively.
Conclusions
Despite high awareness of dietary cancer prevention, individuals with chronic diseases, especially dyslipidemia, showed poor adherence to cancer-preventive dietary practices. Our findings highlight the need for targeted interventions to improve the dietary habits of this atrisk population.
Study of a solid-state reactor for vinegar with rotary drum and validation of its physicochemical indexes
Authors: Yang wen , Liu hui-peng , Hong hou-sheng 
Published: April 2024

Abstract

In response to the shortcomings of low automation, large footprint, and low conversion rate in the traditional solid-state vinegar fermentation process, a drum type solid-state reactor was studied by combining traditional solid-state fermentation with mechanized equipment. The equipment is mainly composed of four parts: transmission system, automatic feeding and discharging, stirring ventilation and temperature control, and fumigation and vinegar pouring system. It can automatically feed and discharge, inoculate, ferment, fumigation and vinegar pouring, and other functions. This article mainly elaborates on solid-state fermentation for vinegar production, introduces the operating equipment and drum reactors in this field, and verifies whether there are differences in the main physicochemical indicators of reactor fermentation for vinegar production compared to conventional equipment.
Relationship between oxidative balance score and all-cause mortality in patients with nonalcoholic fatty liver disease
Authors: zhang peng , Wang Yun-Sheng , Gao Zhao-Xing , Hu Xiao , Ge Man , Xu Yi-Qing , He Tian , Liu Lei , Pan Hai-Feng 
Published: April 2024

Abstract
Background and Aims:
The Oxidative Balance Score (OBS) emerges as a novel index for assessing the state of oxidative stress. Previous studies have reported the possible links between oxidative stress and the development of non-alcoholic fatty liver disease (NAFLD), but the relationship between OBS and NAFLD remains unclear.
Methods
Data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2018 was used in this study, and survival data was obtained from the National Death Index (NDI). The OBS was determined by calculating dietary nutrient consumption and lifestyle practices. The US Fatty Liver Index (USFLI) served as the basis for diagnosing NAFLD. Cox regression and restricted cubic spline (RCS) methods were employed to evaluate the associations between OBS and the risk of all-cause mortality in NAFLD patients.
Results
Fully adjusted Cox regression analysis indicated that higher total OBS (hazards ratio [HR]: 0.97, 95%CI: 0.95, 0.99) and dietary OBS (hazards ratio [HR]: 0.97, 95%CI: 0.95, 0.99)were associated with the lower risks of all-cause mortality in NAFLD (both P < 0.05). In the categorical analysis of the OBS, findings revealed that, as compared to the lowest quartile of OBS, the top quartile's adjusted HR for total and dietary OBS was 0.55 (95% CI: 0.34, 0.88) and 0.55(95% CI: 0.33, 0.94), respectively (both P < 0.05). Subgroup analysis indicated an inverse relationship between total/dietary OBS and mortality from all causes only in females with NAFLD.
Conclusion
The increase in OBS was correlated with a reduced risk of all-cause mortality in NAFLD, suggesting the advantages of adopting an antioxidant-rich diet and lifestyle behaviors in managing NAFLD.
Development of Multi-objective Optimization model integrating Construction Time, Cost, Quality and Risk using Ant Colony Algorithm
Authors: M Renuka S , N Sai Krishna , Avudaiappan Siva , Calquín Danny Lobos , González Marcos Díaz 
Published: April 2024

Abstract
It is a well-known fact that in a complex construction project that involves resources and operations, estimating the duration and cost of each activity is a challenge. In addition to time and cost, other parameters like quality and risk also have to be optimized to ensure the success of a project. Lack of quality and risk measures may affect productivity and the quality of workmanship. Therefore, it is important to engage quality and risk parameters in the planning stage, along with time and cost. The main aim of this paper is to integrate quality and risk into a scheduling model and to develop a multi-objective scheduling model to solve the time-cost trade-off problems. To achieve this aim, the data of fifteen large-scale residential, commercial, and industrial projects concerning time, cost, quality, and risk were collected. Then, to optimize this data, a project model was developed using the Python-based ACO algorithm. Then, by using the ACO algorithm, optimal time and cost solutions were found. The model has been validated by comparing the proposed model results with the actual time and cost of the project. A correlation analysis was done to study the interrelationship among the time, cost, quality, and risk components. The solutions were also tested with sensitivity analysis, and Pareto-optimal solutions were compared with previous solutions. Finally, it is inferred that the estimated time and costs obtained using the ACO algorithm were approximately equal to the actual time and costs obtained by taking quality and risk constraints into account. Hence, in the planning stage itself, the time and cost can be predicted using the proposed model for various scenarios of quality and risk.
Machine Learning Techniques with Fairness for Prediction of Completion of Drug and Alcohol Rehabilitation
Authors: Roberts-Licklider Karen , Trafalis Theodore 
Published: April 2024

Abstract
The aim of this study is to look at predicting whether a person will complete a drug and alcohol rehabilitation program and the number of times a person attends. The study is based on demographic data obtained from Substance Abuse and Mental Health Services Administration (SAMHSA) from both admissions and discharge data from drug and alcohol rehabilitation centers in Oklahoma. Demographic data is highly categorical which led to binary encoding being used and various fairness measures being utilized to mitigate bias of nine demographic variables. Kernel methods such as linear, polynomial, sigmoid, and radial basis functions were compared using support vector machines at various parameter ranges to find the optimal values. These were then compared to methods such as decision trees, random forests, and neural networks. Synthetic Minority Oversampling Technique Nominal (SMOTEN) for categorical data was used to balance the data with imputation for missing data. The nine bias variables were then intersectionalized to mitigate bias and the dual and triple interactions were integrated to use the probabilities to look at worst case ratio fairness mitigation. Disparate Impact, Statistical Parity difference, Conditional Statistical Parity Ratio, Demographic Parity, Demographic Parity Ratio, Equalized Odds, Equalized Odds Ratio, Equal Opportunity, and Equalized Opportunity Ratio were all explored at both the binary and multiclass scenarios.
Scaling Limits of the Wasserstein information matrix on Gaussian mixture models
Authors: Zhao Jiaxi , Li Wuchen 
Published: April 2024

Abstract
We study the Wasserstein metric on the Gaussian mixture models (GMMs), which is defined as the pullback of the full Wasserstein metric on the space of smooth probability distributions with finite second moments. A class of Wasserstein metrics on probability simplices over one-dimensional bounded homogeneous lattices is derived via a scaling limit of the Wasserstein metric on GMMs. Specifically, for a sequence of GMMs whose variances tend to zero, we prove that the limit of the Wasserstein metric exists after certain renormalization. Generalizations of this metric in general GMMs are established, including inhomogeneous lattice models whose lattice gaps are not the same, extended GMMs whose mean parameters of Gaussian components can also change, and the second-order metric containing high-order information of the scaling limit. We further study the Wasserstein gradient flows on GMMs. Numerical examples demonstrate the effectiveness of the proposed GMM models for approximating Wasserstein gradient flows.
Scalable interpolation of satellite altimetry data with probabilistic machine learning
Authors: Gregory William , MacEachern Ronald , Takao So , Lawrence Isobel , Nab Carmen , Deisenroth Mark , Tsamados Michel 
Published: April 2024

Abstract

In this work, we present a new open-source Python programming library for performing efficient interpolation of non-stationary satellite altimetry data, using scalable Gaussian Process (GP) techniques. We showcase the library, GPSat, by using data from the CryoSat-2, Sentinel-3A, and Sentinel-3B radar altimeters, to generate complete maps of daily 50 km2-gridded Arctic sea ice radar freeboard. Relative to a previous GP interpolation scheme, we find that GPSat offers a 504× computational speedup, with less than 4 mm difference on the derived freeboards, on average. We then demonstrate the scalability of GPSat through freeboard interpolation at 5 km2 grid resolution, and Sea-Level Anomalies (SLA) at the resolution of the altimeter footprint. Validation of this novel high resolution radar freeboard product shows strong agreement with airborne data, with a linear correlation of 0.66. Footprint-level SLA interpolation also shows improvements in predictive skill over linear regression, which is a standard approach used in sea ice altimetry data processing. We suggest that GPSat could overcome the computational bottlenecks faced in many altimetry-based interpolation routines. This could in turn lead to improved observational estimates of ocean topography and sea ice thickness, and also further critical understanding of ocean and sea ice variability over short spatio-temporal scales.
Farmer’s Touch: Harnessing Technologies to Enhance Crop Production
Authors: Vasamsetty Mohana Siri , Buradakavi Yamuna Devi , Battula Vishal , Jayanthy Prahashit , Veluri Ravi Kishore 
Published: April 2024

Abstract
The objective of every agricultural endeavour is to achieve optimal output and productivity under varying conditions, thereby conserving precious resources, energy, and reducing production costs. Modern agricultural practices focus on the detailed monitoring of crop conditions by assessing variables like soil quality, plant vitality, the impact of fertilizers and pesticides, irrigation levels, and overall yield. Precision Agriculture is defined as a farm management approach that utilizes information and technology to pinpoint, analyse, and address the variability found within fields to enhance yield, profitability, sustainability, and environmental protection, all while cutting down on costs. This field employs advanced sensor technologies and analytical tools to boost crop production and support management decisions. Our project aims to provide guidance to both commercial farmers and individuals interested in gardening on the necessary steps and precautions for improving crop yield. This comprehensive project will offer advice across various sectors, including soil management, pest control, equipment, and marketing, using a technology stack that comprises a Full-Stack web application, a Machine Learning-powered Chatbot integrated via Flask API with a translation feature, and SQL. The primary objective of this project is to deliver virtual blog content and web-based assistance for enhanced crop growth and yield.