bluuebunny/crossref_metadata_2025_split huggingface dataset:
Sleep Stage Classification For Medical Purposes: Machine Learning Evaluation For Imbalanced Data
Authors: Pardamean Bens , Budiarto Arif , Mahesworo Bharuno , Hidayat Alam Ahmad , Sudigyo Digdo 
Published: January 2022

Abstract
Background: Sleep is commonly associated with physical and mental health status. Sleep quality can be determined from the dynamic of sleep stages during the night. Data from the wearable device can potentially be used as predictors to classify the sleep stage. Robust Machine Learning (ML) model is needed to learn the pattern within wearable data to be associated with the sleep-wake classification, especially to handle the imbalanced proportion between wake and sleep stages. In this study, we incorporated a publicy available dataset consists of three features captured from a consumer wearable device and the labelled sleep stages from a polysomnogram. We implemented Random Forest, Support Vector Machine , Extreme Gradiet Boosting Tree, Densed Neural Network (DNN), and Long Short-Term Memory (LSTM), complemented by three strategies to handle the imbalanced data problem. Results: In total, we included more than 24,815 rows of preprocessed data from 31 samples. The proportion of minority-majority data is 1:10. In classifying this extreme imbalanced data, the DNN model was found to have the best performance compared to the previous best model, which is based on basic Multi-Layer Perceptron. Our best model successfully achieved a 12% higher specificity score (prediction score for minority class) and 1% improvement on the sensitivity score (prediction score for majority class) by including all features in the model. This achievement was affected by the implementation of custom class weight and oversampling strategy. In contrast, when we only used two features, XGB achieved a specificity improvement only by 1%, while keeping the sensitivity at the same level.Conclusions: The non-linear operation within the DNN model could successfully learn the hidden pattern from the combination of three features. Additionally, the class weight parameter avoided the model ignoring the minority class by giving more weight for this class in the loss function. The feature engineering process seemed to obscure the time-series characteristics within the data. This is why LSTM, as one of the best methods for time-series data, failed to perform well in this classification task.
Secured - Quantum Key Distribution (SQKD) for solving Side Channel Attack to enhance Security, based on Shifting &amp; Binary Conversion for Securing Data (SBSD) Frameworks
Authors: N Gopinath , D Prayla Shyry 
Published: January 2022

Abstract
Network security is critical for both personal and business networks. Most homes with high – speed internet have one or more wireless routers, which can be hacked if not adequately secured. Even though, if more number of solutions were addressed for security, still the security is challenging one in networks.Quantum Key Distribution was proposed to enhance security in the past literature. In this QKD, the secret message was converted in to Q-bits. Through this side channel, there is a chance to hack the data by the Eavesdropper which cannot be identified by the receiver side. So, receiver will send the acknowledgement to the sender for sending encrypted data in the classical channel.From this, the hacker can easily fetch the encrypted data from the classical channel. To address this issue, Security in Quantum side Channel (SQSC) framework has been proposed in which Shifting and Binary Conversions (SBC) algorithm has been implemented. This proposed security model attains good performance to a greater extent.
Development of a Combined Bulging-Piercing Technique to Reduce Forming Load for a Long Semi-Hollow Stepped Part
Authors: Nakeenopakun Nara , Olarnrithinun Sutee , Aue-u-lan Yingyot 
Published: January 2022

Abstract
This paper aims to develop a new forming technique to manufacture a long semi-hollow stepped part. Traditionally, hot backward extrusion is used. This technique is not suitable, because it requires a very high forming load acting on the die and punch especially at the contact between punch and workpiece. As a result, the service life of the punch is very low. Therefore, a new technique to overcome this problem is needed. A combined bulging-piercing technique was proposed and developed in this research. The main concept of this technique is to bulge the part by upsetting the workpiece between the punch and the counter-punch to generate high frictional contact pressure which will help to restrain the material sliding down to the die cavity during the piercing step. In other words, this technique utilizes frictional force at the die-workpiece interface to reduce the forming load of the punch. Finite element modeling was employed to investigate and determine the suitable level of the bulging which can reduce the forming load without generating any significantly high force to the counter-punch. Only experiments with the minimum forming load were selected and implemented to validate this concept, because other conditions with high load will risk to damage the punch and the machine press of the product line. The results show that this technique can reduce the forming load by almost 40%, and also control a good concentricity of the part and reduce the wall thickness variation.
Effects of Rifampicin On Pyrotinib Maleate Pharmacokinetics In Healthy Subjects
Authors: Cai Ming-min , Dou Ting , Tang Lu , Sun Qiu-yue , Zhai Zi-hong , Wang Hui-ping , Qian Wei 
Published: January 2022

Abstract
Purpose: Pyrotinib (PTN) is primarily metabolized by cytochrome P450 (CYP)3A4 isozyme. Rifampicin (RIF) is a strong CYP3A4 inducer. Thus, the effect of oral RIF on PTN pharmacokinetics (PK) was evaluated to provide dose recommendation when co-administered.Method: This phase I, open-label study investigated the effects of steady-state RIF administration on single-dose PK of PTN, in 18 healthy participants who received PTN 400 mg single doses on days 1 and 13, and were administrated with RIF 600 mg qd on days 6-16. Each dose for RIF was administrated on an empty stomach, PTN were administrated orally in the morning 30 min after the start of the standard meal. Serial PK samples for PTN were collected on day 1 and day 13. Plasma PTN PK parameters were determined with non-compartmental analysis. Geometric least-squares mean ratios (GMRs) and 90% confidence intervals (CIs) were generated by the mixed-effected model for within-subject treatment comparisons. Safety assessments were performed throughout the study.Results: Eighteen subjects were enrolled and 15 completed the study. RIF significantly reduced PTN exposure: GMRs (90 % CI) for PTN + RIF versus PTN alone were 0.04 (0.034,0.049), 0.04 (0.037,0.054), and 0.11 (0.09,0.124) for area under the curve from time zero to time of last quantifiable concentration (AUC0-t), area under the curve from time zero to infinity (AUC0-∞ ), and maximum observed plasma concentration(Cmax), respectively. PTN alone and co-administered with RIF was well tolerated.Conclusion: Concurrent administration of PTN and RIF was associated with significantly decreased systemic exposure to PTN. The findings suggest that concomitant strong CYP3A4 inducers should be avoided during PTN treatment. Concurrent administration of PTN and RIF was well tolerated.
Disparities in Lung Protective Ventilation in the United States
Authors: Malnoske Michelle , Quill Caroline , Barwise Amelia , Pietropaoli Anthony 
Published: January 2022

Abstract
Background: Lung-protective ventilation is often used in critically ill patients with acute respiratory failure, including those without acute respiratory distress syndrome. While disparities exist in the delivery of critical care based on gender, race, and insurance status, it is unknown whether there are disparities in the use of lung-protective ventilation. The objective of our study was to determine whether gender-, racial / ethnic-, or insurance status-based disparities exist in the use of lung-protective ventilation for critically ill mechanically ventilated patients in the United States (U.S.).Methods: This was a secondary data analysis of the U.S. Critical Illness and Injury Trials Group Critical Illness Outcomes Study, a prospective multi-center cohort study conducted from 2010 - 2012. The dependent variable of interest was the proportion of patients receiving tidal volume > 8 mL/kg predicted body weight (PBW). The independent variables of interest were gender, insurance status, and race / ethnicity. Results: Our primary analysis included 1,595 mechanically ventilated patients from 59 intensive care units (ICUs) in the U.S. Women were more likely to receive tidal volumes > 8 ml/kg PBW than men (odds ratio [OR] = 3.25, 95% confidence interval [CI] = 2.58 – 4.09), though this relationship was substantially weakened after adjusting for gender differences in height (OR = 1.26 95% CI = 0.94 – 1.71). The underinsured were significantly more likely to receive tidal volume > 8 ml/kg PBW than the insured in multivariable analysis (odds ratio = 1.54, 95% confidence interval = 1.16 – 2.04). The prescription of > 8 ml/kg PBW tidal volume did not differ by racial or ethnic categories. Conclusions: In this prospective nationwide cohort of critically ill mechanically ventilated patients, women and the underinsured were less likely than their comparators to receive lung protective ventilation, with no apparent differences based on race / ethnicity alone. Differences in height between men and women do not fully explain this disparity. Future research should evaluate whether implicit bias affects tidal volume choice and other management decisions in critical care.
Borrowers satisfaction evaluation from Microfinance institution by Using FCEM
Authors: Yunxian Yan , Hassan Munawar , Iqbal Shafqat , Hassan Shahbaz Gul 
Published: January 2022

Abstract
Boundless researchers have made efforts to assess the impact of Microfinance on poverty reduction both positively and negatively, but the perception of borrowers about the effectiveness of Microfinance has not been yet found. This study adopts the Fuzzy Comprehensive Evaluation Method (FCEM) in conjunction with the Analytical Hierarchy Process (AHP) to evaluate the level of satisfaction of borrowers with the products and services of microfinance institutions at different criterion levels. The goal is to assess the level of satisfaction for each criterion level under the degree of satisfaction of the borrower about the product and services provided by Microfinance Institution (MFI). Findings show that the claim of MFI was false because study evidence makes it clear that borrowers are not satisfied with the product and services of MFI. In addition to the literature, this study also highlighted the weakness of the MFI product and services. Thus, both the government and the MFIs can improve their performance and change their policies for the welfare of the borrowers.
Can we prevent the neuroinflammation and neuroprogression produced by oxidative stress in euthymic bipolar patients, using mood stabilizers?
Authors: Lima Daniela Delwing-de Lima Delwing-de , Cyrino Luiz Arthur Rangel , Ferreira Gabriela Kozuchovski Ferreira , Magro Débora Delwing-Dal Magro Delwing-Dal , Calegari Claudia Regina Calegari , Cabral Heloisi Cabral , Cavichioli Natalia Cavichioli , Ramos Silvia Aparecida Ramos , Ullmann Oliver Matheus Ullmann , Mayer Yasmin Mayer , Pscheidt Luana Carla , Schramm Maria Augusta Schramm , Tomasi Maria Cecília Tomasi , Stammerjohann Felipe Luis Schmoller Stammerjohann , Delmonego Larissa Delmonego , Packer Maria Helena Packer , Fiamoncini Heloiza Fiamoncini 
Published: January 2022

Abstract
Bipolar Disorder (BD) is associated with systemic toxicity, represented by changes in the biomarkers, associated with mood episodes, leading to neurological damage, which may reflect on cognitive functions and functionality, and the progression of the disease. We aimed to analyze the effect of four biomarkers superoxide dismutase (SOD), catalase (CAT), glutathione peroxidase (GSH-Px) enzymes, and thiobarbituric acid reactive substances (TBA-RS) related to oxidative stress in BD and to correlate them with cognitive functions and functionality. We studied 50 bipolar patients type I/II, in the euthymic phase, which was divided into two subgroups with 25 patients, (≤3 years and ≥10 years of diagnosis, from the first episode of mania), and 25 control patients. To analyze frontal cognitive functions and functionality, we used the tests: Frontal Assessment Battery (FAB) and Functioning Assessment Short Test (FAST), respectively. The FAST test scores results were ≤3 years (20.63±8.21), ≥10 years (27.80±12.50), and control group (9.80±5.94), p<0.001. Changes occurred in all domains. The FAB test scores were ≤3 years (14.64±2.48), ≥10 years (12.44±2.78), and control group (15.84±1.55), p<0.001, and showed lower scores in four domains. The oxidative stress showed an increase in TBA-RS levels: ≤3 years (3.18±1.17), ≥10 years (3.01±1.03) compared to the control group (1.62±0.28), p<0.0001; and in the CAT enzyme activity we found, ≤3 years (8.10±4.18), ≥10 years (9.41±4.95), compared to the control group (3.47±0.77), p<0.0001. Even during the euthymic phase, bipolar patients showed and maintained an increase in CAT activity and lipid peroxidation with significant changes in the FAB and FAST tests in different groups of patients, demonstrating impairment in cognitive functions and functionality since disease onset.
Optimal Ambulatory Vital Sign Targets in Pulmonary Hypertension Pregnant Women via Time-Dependent Survival Analysis
Authors: liu renhuai , zheng ziyu , su binxiao 
Published: January 2022

Abstract
Background: Pulmonary hypertension (PH) can cause complications in pregnant women due to significant hemodynamic fluctuation or right heart failure as well as death during pregnancy and postpartum. Those in critical condition would be sent to the intensive care unit (ICU) for observation and treatment. However, evidence to suggest the safe target vital signs is limited and none specific to pregnancy with PH.Methods: This retrospective study of consecutive obstetric patients with PH admitted to ICU of the First Affiliated Hospital of Air Force Military Medical University of China, from January 2011 to May 2020, consisted of 92 cases analyzed using time-dependent Cox regression to consider the dynamic features of vital signs. Results: 7/92 maternal deaths occurred. Most of these deaths occurred within the first three days of admission to the ICU. The vital signs for survival were stable and normal compared to death. Three vital signs were identified as risk factors in the maternal in-hospital mortality model via backward selection: SpO2（HR，0.93；95%CI，0.88-0.97；P=0.003）, heart rate（HR，0.94；95%CI，0.90-0.99；P=0.027）, and mean arterial pressure (MAP) （HR，1.09；95%CI，1.00-1.18；P=0.045）. Log of relative hazard ratios of mortality is linearly negatively related to SpO2 value with a U-shaped correlation with heart rate and MAP (both lower and higher values were associated with high mortality). The optimal range of SpO2 <73%, MAP was 65–95 mmHg, and heart rate was 59–125 beats per minute (bpm). Further exploration showed that the cumulative and the longest consecutive time of abnormal vital signs also affect the outcome. For example, SpO2<73% accumulated for 5 h or continuously up to 2 h increases mortality.Conclusions: Pregnant women with PH who died in the hospital experienced long-term abnormal fluctuations in MAP, heart rate, and SpO2 during ICU stay. Maintaining SpO2>73%, MAP at 65–95mmHg, and heart rate at 59–125 bpm can significantly reduce in-hospital maternal mortality. The effects of the abnormal SpO2, heart rate, and MAP on in-hospital maternal mortality should be combined with the cumulative time and the longest duration.Trial Registry: ChiCTR2100046637.
Comparison of the Diagnostic Value Between Triglyceride-Glucose Index and Triglyceride-to-High Density Lipoprotein Cholesterol Ratio in MAFLD Patients: A Retrospective Cross-Sectional Study
Authors: Liu Zhi , He He , Dai Yuzhao , Liao Shenling , An Zhenmei , Li Shuangqing 
Published: January 2022

Abstract
Background The triglyceride and glucose index (TyG) and triglyceride-to-high density lipoprotein cholesterol ratio (TG/HDL-C) were found to be substitute markers of insulin resistance (IR). We aimed to compare the efficacy of the two indicators in the diagnosis of Metabolic-Associated Fatty Liver Disease (MAFLD), which was rarely covered in the literature, and to construct a novel disease diagnosis model.Methods A retrospective cross-sectional study was carried out in West China Hospital of Sichuan University and 229 people (97 MAFLD and 132 Non-MAFLD) were included. Biochemical indexes were collected and analyzed by logistic regression to screen out indicators that expressed differently in MAFLD patients and healthy controls and incorporate them into a diagnostic model. MAFLD was diagnosed by Ultrasound.Results After adjusting for age, gender and BMI, Serum ALT, AST, AST/ALT (A/A), FPG, Cys-C, URIC, TG, HDL-C, ALP, GGT, nonHDL-C, LDL-C/HDL-C, nonHDL-C/HDL-C, TG/HDL-C, TC/HDL-C, TyG and TyG-BMI were risk factors of MAFLD through binary logistics regression analysis. The odds ratio of TG/HDL-C and TyG were 5.387 (95%CI: 2.986-9,718) and 107.945 (95% CI: 25.824-451.222). In identifying MAFLD, TyG, TG/HDL-C and TG were found to be the most vital indexes by the random forest method and the area under the curve (AUC) of them are all greater than 0.9 respectively. In addition, the combination of gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI had a great diagnostic efficiency for MAFLD.Conclusions TyG and TG/HDL-C were potential risk factors for MAFLD and the former made a better performance in diagnosing MAFLD. The combination of gender, BMI, ALT, TG, HDL-C, TyG and TyG-BMI improved the diagnostic capability of MAFLD.
A Genome-wide Association Study to Identify Candidate Genes for Metabolic Disorders in Offspring by in Vitro Fertilization
Authors: Liu Lihong , HA Siyao , Li MingQing , Li Zhiling 
Published: January 2022

Abstract
BackgroundIn vitro fertilization (IVF) processes increase offspring's short-term and long-term health risks, but their mechanisms remain unclear. MethodsWe conducted a bibliometric analysis to determine the landscape of IVF offspring health. Subsequently, a bioinformatics method was utilized to identify the co-genes properties and biological function mechanisms of IVF and type 2 diabetes mellitus (T2DM). Finally, we predicted compounds against key targets and performed multiple validations of the mechanisms underlying IVF offspring health risks. ResultsWe identified 15 genes associated with T2DM, and their biological functions are primarily associated with lipid metabolism. We also identified the properties of co-genes, modified characteristics, identified 3 SNPs sites, and determined the three core genes, APOA1, APOB, and APOE, which were mainly correlated with metabolic and cardiovascular diseases. In addition, we predicted drugs that may improve metabolic abnormalities in IVF offspring. ConclusionsThe impact of aberrant lipid metabolism in offspring after IVF therapy warrants additional investigation, particularly in terms of long-term health consequences and possible mechanisms.
Comparative Analysis of Improved of FCM Algorithms in the Segmentation of Retinal Blood Vessels
Authors: Mehidi Imane , Belkhiat Djamel Eddine Chouaib , Jabri Dalel 
Published: January 2022

Abstract
The main purpose of identifying and locating the vessels of the retina is to specify the various tissues from the vascular structure of the retina (which could be differencied between wide or tight) of the background of the fundus image. There exist several segmentation techniques that are spreading to divide the retinal vessels, depending on the issues and complexity of the retinal images. Fuzzy c-means is one of the most often used algorithms for retinal image segmentation due to its effectiveness and speed. This paper analyzes the performance of improved FCM algorithms for retinal image segmentation in terms of their ability and capability in segmenting and isolating blood vessels. The process we followed in our paper consists of two phases. Firstly, the pre-processing phase, where the green channel is taken for the color image of the retina. Contrast enhancement is performed through CLAHE , proceeded by applying bottom-hat filtering to bottom-hat filtering is applied with the purpose to define the region of interest. Secondly, in the segmentation phase the obtained image is segmented using FCM algorithms. The algorithms chosen for this study are: FCM, EnFCM, SFCM, FGFCM, FRFCM, DSFCM_N, FCM_SICM and, SSFCM performed on DRIVE and STARE databases. Experiments accomplished on DRIVE and STARE databases demonstrate that the DSFCM_N algorithm achieves better results on the DRIVE database, whereas the FGFCM algorithm provides better results on the STARE database in term of accuracy. Concerning time consumption. The FRFCM algorithm requires less time than other algorithms in the segmentation of retinal images.
Machine Learning Applied to Datasets of Human Activity Recognition:
Data Analysis in Health Care
Authors: Patricia Ariza-Colpas Paola , Enrico Vicario , Shariq Butt Aziz , De la Hoz Franco Emiro , Alberto Piñeres-Melo Marlon , Isabel Oviedo-Carrascal Ana , Tariq Muhammad Imran , Restrepo Johanna Karina García , Fulvio Patara 
Published: January 2022

Background:
In order to remain active and productive, older adults with poor health require
a combination of advanced methods of visual monitoring, optimization, pattern recognition, and learning,
which provide safe and comfortable environments and serve as a tool to facilitate the work of family
members and workers, both at home and in geriatric homes. Therefore, there is a need to develop
technologies to provide these adults autonomy in indoor environments.

Objective:
This study aimed to generate a prediction model of daily living activities through classification
techniques and selection of characteristics in order to contribute to the development in this area of
knowledge, especially in the field of health. Moreover, the study aimed to accurately monitor the activities
of the elderly or people with disabilities. Technological developments allow predictive analysis of
daily life activities, contributing to the identification of patterns in advance in order to improve the
quality of life of the elderly.

Method:
The vanKasteren, CASAS Kyoto, and CASAS Aruba datasets were used to validate a predictive
model capable of supporting the identification of activities in indoor environments. These datasets
have some variation in terms of occupation and the number of daily living activities to be identified.

Results:
Twelve classifiers were implemented, among which the following stand out: Classification via
Regression, OneR, Attribute Selected, J48, Random SubSpace, RandomForest, RandomCommittee,
Bagging, Random Tree, JRip, LMT, and REP Tree. The classifiers that show better results when identifying
daily life activities are analyzed in the light of precision and recall quality metrics. For this
specific experimentation, the Classification via Regression and OneR classifiers obtain the best results.

Conclusion:
The efficiency of the predictive model based on classification is concluded, showing the
results of the two classifiers, i.e., Classification via Regression and OneR, with quality metrics higher
than 90% even when the datasets vary in occupation and number of activities.