bluuebunny/crossref_metadata_2025_split huggingface dataset:
Invalidating Water Injection Discontinuation Plan Through Comprehensive Surveillance Data Acquisition and Interpretation
Authors: Nalasavagan Kausalyah , Razmi Nur Adibah , Rahman Harun , Rosland Eadie Azahar , Nazwar Suhenri Kurniawan , Motaei Eghbal , Bibic Ovidiu Cristian , Hoo Lien Wei 
Published: April 2024

Abstract
Waterflooding technology has been a longstanding practice in the oil and gas industry, with its roots dating back to its development in the US in the 1940s. The fundamental concept involves replenishing drained spaces to maintain system pressure, although the complexity arises in reservoirs filled with heterogeneities. Beyond pressure preservation, waterflooding also enhances production through efficient sweeping. The success of waterflooding hinges on the dual metrics of pressure maintenance and production rate. However, quantifying this success is not always straightforward and can be obscured by the poor performance of individual producers.
This paper presents a real case study conducted at Field S, highlighting a meticulous evaluation of the impact of water injection. Despite the drilling of two peripheral water injectors in 2021, water breakthrough shortly after injection initiation led to a nearly 20% reduction in field production. Analysis of available data pointed to injection-induced fractures, inadequate cement behind casing channeling, and high permeability contrast in multiple sand lobes as potential sources of increased water cut observed in key wells. To address this, an intermittent flow strategy was implemented at the expense of the voidage replacement ratio (VRR), allowing the gas oil ratio (GOR) to develop and improve well lifting. Various diagnostics, including streamline analysis, injection fall-off, advanced well interference tests, and capacitance-resistant model analysis, were assessed.
Field S, following its field development plan, plans to add 52 infill wells within the next 5 years to its existing inventory of 15 wells. Reservoir simulation work has been endorsed to support continuous water injection for pressure maintenance before infill, aligning with the planned recovery factor. The robustness of the water injection program faced a challenge when management proposed its discontinuation after only 30 months of injection. This paper showcases a real case study demonstrating the utilization of multiple surveillance tools and diagnostic plots to validate waterflood efficiency, enabling Field S to persist with its water injection program.
The paper provides a comprehensive analysis of how a tailored surveillance program and data interpretation workflows could enable petroleum engineers to proactively diagnose and troubleshoot water injection programs more efficiently with minimal intervention. Additionally, it demonstrates the application of various water injection-related diagnostic plots from published papers on the Field S dataset and their compatibility index.
Leveraging Machine Learning for Predicting Gas Separation Efficiency of a Downhole Separator
Authors: Sharma A. , Osorio L. , Ashesh S. , Gupta I. , Burak T. , Konate N. , Karami H. 
Published: April 2024

AbstractArtificial lift systems, specifically Electrical Submersible Pumps and Sucker Rod Pumps, often face operational challenges due to high Gas-Oil Ratio, leading to premature tool failure and increased non-productive time. These issues underscore the necessity for efficient gas separation at upstream of the artificial lift pump to ensure uninterrupted operations. Downhole gas separators, situated upstream of the artificial lift pump, are designed to segregate gas from the fluid, reducing downtime. This study aims to predict the gas separation efficiency of the downhole gas separator using experimental data from a centrifugal separator and testing the generated models on experimental data for a gravity separator.To predict the separation efficiency of the downhole separator, multiple machine learning models were evaluated, and a comparative analysis was performed. The experimental test data obtained for the centrifugal packer-type separator was used for model training and experimental test data for the gravity separator was used for model testing (blind test). To conduct experiments, multiphase flow setup (31ft. (9.4m) long horizontal and 27ft. (8.2m) tall vertical section of multiphase flow setup) was used. In this study, seven regression methods, including multilinear (MLR), random forest (RF), support vector machine (SVM), ridge, lasso, k-nearest neighbor (KNN) and XGBoost, were examined to assess their ability to effectively predict the gas separation efficiency. Several performance metrics, including Root mean square error (RMSE), Mean absolute percentage error (MAPE) and R-squared were evaluated to select the efficient model.In-depth exploratory data analysis and feature engineering were performed to identify significant input parameters for model training. The scatterplot and correlation plot indicated the response variable were mostly affected by inlet liquid and gas volume flow. Therefore, liquid and gas volume flow at inlet were selected as two independent parameters to predict gas volume flow per minute at the outlet (GVFO) as the response variable. After data scaling, a comparative analysis of the seven incorporated regression models revealed that, the Random Forest model to be the most efficient for predicting GVFO, closely followed by the KNN model. The Random Forest model exhibits R-squared values of 96% and RMSE value of 112. Hyperparameter tuning and tenfold cross-validation ensured optimal values for tuning parameters and prevented oveffitting, respectively.This developed machine learning workflow enables the prediction of the gas separation efficiency of different type of downhole gas separator by using experimental test data from a centrifugal downhole separator. It provides critical findings through a comparative analysis of seven regression models. This research offers valuable insights that can aid in optimizing the performance of artificial lift systems.
Measuring algorithmic bias to analyze the reliability of AI tools that predict depression risk using smartphone sensed-behavioral data
Authors: Adler Daniel A. , Stamatis Caitlin A. , Meyerhoff Jonah , Mohr David C. , Wang Fei , Aranovich Gabriel J. , Sen Srijan , Choudhury Tanzeem 
Published: April 2024

Abstract

AI tools intend to transform mental healthcare by providing remote estimates of depression risk using behavioral data collected by sensors embedded in smartphones. While these tools accurately predict elevated symptoms in small, homogenous populations, recent studies show that these tools are less accurate in larger, more diverse populations. In this work, we show that accuracy is reduced because sensed-behaviors are unreliable predictors of depression across individuals; specifically the sensed-behaviors that predict depression risk are inconsistent across demographic and socioeconomic subgroups. We first identified subgroups where a developed AI tool underperformed by measuring algorithmic bias, where subgroups with depression were incorrectly predicted to be at lower risk than healthier subgroups. We then found inconsistencies between sensed-behaviors predictive of depression across these subgroups. Our findings suggest that researchers developing AI tools predicting mental health from behavior should think critically about the generalizability of these tools, and consider tailored solutions for targeted populations.
Structures of H5N1 influenza polymerase with ANP32B reveal mechanisms of genome replication and host adaptation
Authors: Fodor Ervin , Staller Ecco , Carrique Loïc , Swann Olivia , Fan Haitian , Keown Jeremy , Sheppard Carol , Barclay Wendy , Grimes Jonathan 
Published: April 2024

Abstract

Avian influenza A viruses (IAVs) pose a public health threat, as they are capable of triggering pandemics by crossing species barriers. Replication of avian IAVs in mammalian cells is hindered by species-specific variation in acidic nuclear phosphoprotein 32 (ANP32) proteins, which are essential for viral RNA genome replication. Adaptive mutations enable the IAV RNA polymerase (FluPolA) to surmount this barrier. Here, we present cryo-electron microscopy structures of monomeric and dimeric avian H5N1 FluPolA with human ANP32B. ANP32B interacts with the PA subunit of FluPolA in the monomeric form, at the site used for its docking onto the C-terminal domain of host RNA polymerase II during viral transcription. ANP32B acts as a chaperone, guiding FluPolA towards a ribonucleoprotein-associated FluPolA to form an asymmetric dimer—the replication platform for the viral genome. These findings offer insights into the molecular mechanisms governing IAV genome replication, while enhancing our understanding of the molecular processes underpinning mammalian adaptations in avian-origin FluPolA.
selenium and systemic lupus erythematosus (SLE): A double- blind randomised controlled trial
Authors: Abrishamkar Hengame , Helli Bizhan , Zilaei Marzieh , Rajaei Elham , Sheikhi Ali , Hosseini Seyed Ahmad 
Published: April 2024

Abstract

Objective: This randomized trial aimed to investigate the effects of selenium on patients diagnosed with systemic lupus erythematosus (SLE).
Design: 50 SLE patients were randomly assigned to receive daily either 200 μg of selenium supplement or a placebo for eight weeks. Both groups received general healthy lifestyle recommendations. Blood samples to measure the stress markers were assessed before and after the intervention. The systemic lupus erythematosus disease activity index (SLEDA) was used to assess the clinical signs of the patients before and after the intervention.
Results: Selenium supplementation compared with the control group significantly reduced serum high-sensitive reactive protein (CRP), Erythrocyte Sedimentation Rate (ESR), Malondialdehyde (MDA), and increased Glutathione peroxidase (GPX), and total antioxidant capacity (TAC). Except for ESR (p= 0.019), the p value of all of the said parameters was ˂0.001. Clinical manifestations of the disease such as arthritis (p= 0.012) and alopecia (p= 0.011) were also improved following the intervention.
Conclusion: A daily intake of 200 μg of selenium supplements for 8 weeks could significantly improve several metabolic markers and clinical manifestations in lupus patients.
To boldly go where no microRNAs have gone before: Spaceflight impact on risk for small-for-gestational-age infants
Authors: Corti Giada , Kim JangKeun , Enguita Francisco , Guarnieri Joseph , Grossman Lawrence , Costes Sylvain , Fuentealba Matias , Scott Ryan , Magrini Andrea , Sanders Lauren , Furman David , Calleja-Agius Jean , Mason Christopher , Galeano Diego , Bottini Massimo , Beheshti Afshin 
Published: April 2024

Abstract

In the era of renewed space exploration, comprehending the effects of the space environment on human health, particularly for deep space missions, is crucial. While extensive research exists on the impacts of spaceflight, there is a gap regarding female reproductive risks. We hypothesize that space stressors could have enduring effects on female health, potentially increasing risks for future pregnancies upon return to Earth, particularly related to small-for-gestational-age (SGA) fetuses. To address this, we identify a shared microRNA (miRNA) signature between SGA and the space environment, conserved across humans and mice. These miRNAs target genes and pathways relevant to diseases and development. Employing a machine learning approach, we identify potential FDA-approved drugs to mitigate these risks, including estrogen and progesterone receptor antagonists, vitamin D receptor antagonists, and DNA polymerase inhibitors. This study underscores potential pregnancy-related health risks for female astronauts and proposes pharmaceutical interventions to counteract the impact of space travel on female health.
Facial Skin Biophysical Multi-Parameter and Microbiome-Based Korean Skin Cutotype (KSC) Determination
Authors: Han Kyudong , Mun Seyoung , Jo HyungWoo , Heo Young Mok , Baek Chaeyun , Kim Hye-Been , Lee Haeun , Yun Kyeongeui , Jung Jinuk , Lee Wooseok , Jeon Dasom Jeon , Kang So Min , Kang Seunghyun , Choi Young-Bong , Han Sangjin , Kim Gabriel , Ahn Kung , Lee Dong Hun , Ahn Yongju , Lee Dong-Geol 
Published: April 2024

Abstract

The field of human microbiome research is rapidly expanding beyond the gut and into the facial skin care industry. However, there is still no established criterion to define the objective relationship between the microbiome and clinical trials for developing personalized skin solutions that consider individual diversity. In this study, we conducted an integrated analysis of skin measurements, clinical BSTI surveys, and the skin microbiome of 950 Korean subjects to examine the ideal skin microbiome-biophysical associations. By utilizing four skin biophysical parameters, we identified four distinct Korean Skin Cutotypes (KSCs) and categorized the subjects into three aging groups: the Young (under 34 years old), the Aging I group (35–50), and the Old group (over 51). To unravel the intricate connection between the skin's microbiome and KSC types, we conducted DivCom clustering analysis. This endeavor successfully classified 726 out of 740 female skin microbiomes into three subclusters: DC1-sub1, DC1-sub2, and DC2 with 15 core genera. To further amplify our findings, we harnessed the potent capabilities of the CatBoost boosting algorithm and achieved a reliable framework for predicting skin types based on microbial composition with an impressive average accuracy of 0.96 AUC value. Our study conclusively demonstrated that these 15 core genera could serve as objective indicators, differentiating the microbial composition among the aging groups. In conclusion, this study sheds light on the complex relationship between the skin microbiome and biophysical properties, and the findings provide a promising approach to advance the field of skincare, cosmetics, and broader microbial research.
Bergenia purpurascens is a non-specific ACE2 inhibitor
Authors: Shi Yongxia , Sun Xiaoning , Dai Dalan , Guo Hongyu , Mei Lijuan , Dai Jun , Yu Zhaoxia , Jiang Lei 
Published: April 2024

Abstract

Introduction
Bergenia purpurascens is a medicinal herb found in southwestern China. This article mainly explores its efficacy and mechanism in blocking ACE2 receptor.
Materials and methods
This study investigated the ability of water extract of Bergenia purpurascens to disrupt the formation of the complex between COVID-19 S1 RBD and ACE2. After establishing quality standards for the extract, it was shown to inhibit live virus infection of COVID-19 XBB1.5 variant using a Vero-E6 cell infection model. The mechanism by which the water extract disrupts the binding of S1 protein and ACE2 was studied using SPR. The composition of the extract was analyzed using UPLC-MS/MS, and active compounds were identified through in silico screening and real activity testing.
Results
The water extracts of Bergenia purpurascens exhibit significantly high inhibitory activity and are found to be stable. The prominent compounds constituting the extract are Phenylpropanoids and Carbohydrates, whose abundance accounts for over 60% of the composition. The extract is highly potent in inhibiting live COVID-19 XBB1.5 virus infection, having an IC50 value measuring 0.037mg/mL. Investigation of the extract's inhibitory mechanism reveals that it acts as a non-specific inhibitor of ACE2 by exhibiting a steady-state binding affinity (KD) of 7.9×10-7M. Seventy-three percent of the high-content compounds were found to be inactive. Active compounds were identified as a group of low-content compounds, represented by Ellagic acid, working in combination.
Conclusions
The water extract of Bergenia purpurascens is a good and cheap non-specific ACE2 inhibitor.
Identification of m6A-associated diagnostic biomarkers and subtypes for osteoporosis's disease diagnosis and risk prediction based on machine learning
Authors: Luo Shengzhong , Liu Jiahao , Miao Xinxin , Deng Jianjian , Wu Tianlong , Cheng Xigao 
Published: April 2024

Abstract

Background
Osteoporosis(OP) is a systemic skeletal dysfunction disorder that occurs in large numbers worldwide. The aim of our study was to screen potential m6A-related diagnostic markers and establish diagnostic predictive models to provide a reference framework for the diagnosis and treatment of OP.
Methods
GSE56815 and GSE35956 were retrieved from GEO. The m6A-related diagnostic markers for OP were identified through random forest algorithm screening of GSE56815, and subsequently validated for diagnostic efficacy in GSE35956. The mechanism exploration of diagnostic markers was conducted via GO and KEGG analyses based on GSEA. Immune cell infiltration estimation was performed using ssGSEA, with correlation analysis between immune cells and diagnostic markers. Consensus clustering produced two OP patient subtypes, which were compared using m6A scores.
Results
4 m6A-related diagnostic markers for OP have been selected by random forest. Utilizing these 4 diagnostic markers, an OP diagnostic model was successfully established, and its diagnostic value was validated using independent external data. The immune infiltration analysis of ssGSEA revealed that CD56 dim natural killer cell significantly infiltrated the OP samples, while the diagnostic markers were found to possess regulatory effects on diverse immune cells. OP samples were classified into two m6A subtypes through the concordance clustering, with type A having a higher m6A score than type B. The classification can provide more instructive assistance for the diagnosis of OP.
Conclusions
A diagnostic model for OP was constructed based on four m6A-related genes in the study, which provides significant references for diagnosis of OP and holds practical significance.
Loss-of-function in testis-specific serine/threonine protein kinase (TSSKs) triggers male infertility in an invasive moth
Authors: Yang Xueqing 
Published: April 2024

Abstract

Genetic control system at molecular level presents a promising and eco-friendly strategy for the management of pest and insect-transmitted diseases. Although considerable advancements have been achieved in gene drive applications targeting mosquitoes, endeavors to combat agricultural pests have been somewhat restricted. Here, we identified that the testis-specific serine/threonine kinases (TSSKs) family is uniquely expressed in the testes of Cydia pomonella, a prominent global invasive species. We further generated male moths with disrupted the expression of TSSKs and those with TSSKs completely knocked out using RNA interference and CRISPR/Cas 9 genetic editing techniques, resulting in significant disruptions in spermiogenesis, decreased sperm motility, and hindered development of fertilized oocytes. Further explorations into the underlying post-transcriptional regulatory mechanisms have revealed the involvement of lnc117962 as a competing endogenous RNA (ceRNA) for miR-3960, thereby regulating TSSKs. Notably, orchard trials have demonstrated that the release of male strains can effectively suppress population growth. Our findings indicate that targeting TSSKs could serve as a feasible avenue for managing C. pomonella populations, offering significant insights and potential strategies for controlling invasive pests through genetic sterile insect technique (gSIT) technology.
Prolonged Hypokalemia Long After Causative Factor Elimination in Pseudo-Bartter/Gitelman Syndrome
Authors: Kondo Atsushi , Horinouchi Tomoko , Inoki Yuta , Ichikawa Yuta , Tanaka Yu , Kitakado Hideaki , Ueda Chika , Sakakibara Nana , Nagano China , Nozu Kandai 
Published: April 2024

Abstract

Pseudo-Bartter/Gitelman syndrome (PBS/PGS) results from medication and lifestyle factors, causing hypokalemia and potential kidney dysfunction. Treatment involves eliminating the underlying causes, typically leading to rapid improvement. However, PBS/PGS findings may persist long after the removal of causative factors, and its pathogenesis remains unclear. This study focused on 49 cases diagnosed with PBS/PGS based on apparent causes; the cases had no pathogenic variants related to hereditary kidney diseases. They were categorized into two groups: the current group (n = 39), where causative factors persisted, and the past group (n = 10), where more than one year had elapsed since the elimination of the causative factors at the time of examination. A retrospective comparative analysis was conducted between these groups. All patients were female, except for two in the current group. The median time since the elimination of causes in the past group was 7.5 years. Hypokalemia and kidney dysfunction were observed in both groups without statistically significant differences. Both groups exhibited overactivation of renin-angiotensin systems. This study is the first to reveal the possibility of persistent PBS/PGS findings even after the removal of causative factors. While swift removal of the cause of PBS/PGS is crucial, long-term post-removal monitoring is essential to improve renal prognosis.
Vitamin D Binding Protein and Receptor Prevalence in a Large Population with Periodontitis: Genetic Polymorphisms and Transcriptomic Profiling
Authors: Nie Ziyan , Hu Xiaopan , Hu Peinan , Li Peiqiang , Zhou Haijing , Xie Xiaodong 
Published: April 2024

Abstract

Background: There is an ongoing controversy regarding the expression of vitamin D receptor (VDR) and binding protein (VDBP) genes, as well as their polymorphisms, in periodontitis. We examined eight single nucleotide polymorphisms (SNPs) and performed a transcriptome-level bioinformatics analysis to clarify their relationship with periodontitis.
Methods: To explore VDR and VDBP polymorphisms, 600 subjects were included, including 307 patients with chronic periodontitis (CP) and 293 healthy controls. Genomic DNA was extracted from peripheral venous blood collected from each subject. A MassARRAY system was used to detect genetic polymorphisms, including rs1544410G/A (BsmI), rs2228570C/T (FokI), rs7975232G/T (ApaI), rs731236T/C (TaqI), rs739837G/T, rs9729G/T, and rs3847987C/Ain the VDR gene, and rs7041A/C in the VDBP gene. Then, we analyzed transcriptome sequencing datas of gingival tissues from two single-cell transcriptome sequencing studies to identify differential expression profiles. The objective was to further explore the potential association between VDR gene and gingival tissues in individuals with chronic periodontitis (CP).
Results: The regression analysis model revealed a significant relationship between rs739837G/T (P = 0.044) and rs7041A/C (P = 0.032) polymorphisms and CP susceptibility. Subjects carrying the TT genotype of rs739837showed a decreased risk of developing CP compared to those carrying the GG+GTgenotype (OR = 0.53, 95% CI = 0.29–0.99). Individuals carrying the AC+CC genotype of rs7041 showed a reduced risk of developing CP compared to those with the AA genotype (OR = 0.70, 95% CI = 0.51–0.97). Furthermore, allele C of rs7041was found to have a protective effect against periodontitis (P = 0.033, OR = 0.7525, CI = 0.5793–0.9776). However, no association was found between CP susceptibility and six other 6 SNPs (rs1544410, rs2228570, rs7975232, rs9729, rs731236, and rs3847987). Differential levels of VDR transcription were observed in gingival tissues during CP.
Conclusions: VDR genetic variability and transcriptional expression are significant factors affecting susceptibility to CP. These findings suggested that rs739837 TT in VDR and rs7041 A/C in VDBP may be protective against periodontitis.