bluuebunny/crossref_metadata_2025_split huggingface dataset:
Multidimensional optimization for accelerating light-powered biocatalysis in Rhodopseudomonas palustris
Authors: Zhang Yang , Meng Wenchang , He Yuting , Chen Yuhui , Shao Mingyu , Yuan Jifeng 
Published: August 2023

Abstract
Background
Whole-cell biocatalysis has been exploited to convert a variety of substrates into high-value bulk or chiral fine chemicals. However, the traditional whole-cell biocatalysis typically utilizes the heterotrophic microbes as the biocatalyst, which requires carbohydrates to power the cofactor (ATP, NAD(P)H) regeneration.
Results
In this study, we sought to harness purple non-sulfur photosynthetic bacterium (PNSB) as the biocatalyst to achieve light-driven cofactor regeneration for cascade biocatalysis. We substantially improved the performance of Rhodopseudomonas palustris-based biocatalysis by using a highly active and conditional expression system, blocking the side-reactions, controlling the feeding strategy, and attenuating the light shading effect. We found that 50 mM ferulic acid could be completely converted to vanillyl alcohol in the recombinant strain, reaching 770.8 mg/g-cdw vanillyl alcohol. In addition, > 99.9% conversion of p-coumaric acid to p-hydroxybenzyl alcohol (620.7 mg/g-cdw) was similarly achieved under light-anaerobic conditions. Moreover, we examined the isoprenol utilization pathway (IUP) for pinene synthesis and 188 mg/g-cdw pinene with 92% conversion rate from 30 mM isoprenol was obtained.
Conclusions
Taken together, these results suggested that R. palustris could be a promising host for light-powered biotransformation, which offers an efficient approach for synthesizing value-added chemicals in a green and sustainable manner.
Probing the Biology of Zinc Alpha 2-Glycoprotein and the role it plays in cachexia
Authors: Pandit Parth 
Published: August 2023

Abstract
Background
Cachexia is a metabolic disease characterized by significant weight loss and muscle wasting, and it is responsible for a substantial number of cancer-related deaths. Zinc-alpha-2-glycoprotein (ZAG) plays a role in the regulation of weight and body fat through the metabolism of lipids and glucose. In healthy individuals, ZAG promotes homeostasis by inducing the breakdown of adipose tissue, leading to reduced fat storage and overall weight. However, in various carcinomas and cancer patients, upregulation of ZAG is observed, resulting in rapid weight loss. Mutations in ZAG, specifically the amino acids Tryptophan 148, Arginine 73, Phenylalanine 101, and Isoleucine 76, have been identified and replaced with Alanine. The effects of these mutants, as well as the wild-type ZAG, can be investigated through experimental studies. Additionally, ZAG is believed to have a lipid binding site that may be crucial for its function.
Methods
To assess the impact of ZAG variants on lipolysis, a lipolysis colorimetric kit was used to measure their effects on 3T3 adipose cells. This allowed for the determination of the β-adrenoreceptor signalling pathways involved in the lipolytic effect of ZAG. The lipolysis experiments were conducted over a specific time period, as the duration of treatment can be a limiting factor influencing lipolysis.
Results
Among the ZAG variants tested, the Tryptophan-Alanine mutant exhibited increased lipolysis after 1 hour of treatment compared to other variants. These findings suggest that the specific mutations in ZAG can influence its lipolytic activity.
Conclusion
Identifying the ligand(s) and understanding the interactions between them and ZAG is crucial in advancing our knowledge of the mechanisms underlying ZAG's function. Ultimately, the development of therapeutics targeting ZAG could provide cancer patients with treatment options to attenuate weight loss, leading to improved prognoses and a better quality of life.
Rheumatoid arthritis and heart failure: A bidirectional two-sample Mendelian randomization study
Authors: Yang Yan , Cui Fenfang , xu Min , He Qin , Liu Ying , Lan Ying , Zhang Xiqian , zheng yaxian , Zhang Xu 
Published: August 2023

Abstract

Background:
Extensive observational studies suggested an association between rheumatoid arthritis (RA) and heart failure (HF). Nevertheless, whether there was a solid causal association was still unclear because of confounding factors. Therefore, a bidirectional two-sample Mendelian randomization (MR) study was performed to investigate whether genetically predicted RA is related to the risk of HF and vice versa.
Methods:
RA data were obtained from large-scale genome-wide association studies (GWASs), including 5,539 cases and 20,169 controls. Data on HF were obtained from the FINNGEN biobank, including 13,087 cases and 195,091 controls of European ancestry. The inverse-variance weighted method, weighted median and MR-Egger were used for estimating and verifying the possible causal effect of RA and HF. The Funnel plots, leaveone-out analyses and MR-Egger regression were used in the sensitivity analysis.
Results:
Genetic susceptibilities of RA increased the risk of HF (OR=1.02; 95% CI 1.00-1.04, p =0.03), while HF did not increase the risk of RA.
Conclusion:
Genetic liability to RA is associated with a higher risk of HF. On the other direction, HF is not related to RA. Our research supports the clinical need for screening HF in patients with RA, which has milestone significance in exploring whether to provide appropriate HF treatment for RA patients.
Explainable predictions of a machine learning model to forecast the postoperative length of stay for severe patients: Machine Learning Model Development and Evaluation
Authors: Cho Ha Na , Ahn Imjin , Gwon Hansle , Kang Hee Jun , Kim Yunha , Seo Hyeram , Choi Heejung , Kim Minkyoung , Han Jiye , Kee Gaeun , Park Seohyun , Jun Tae Joon , Kim Young-Hak 
Published: August 2023

Abstract

Background
Predicting the length of stay in advance will not only benefit the hospitals both clinically and financially but enable healthcare providers to better decision-making for improved quality of care. More importantly, understanding the length of stay of severe patients who require general anesthesia is key to enhancing health outcomes.
Objective
Here, we aim to discover how machine learning can support resource allocation management and decision-making resulting from the length of stay prediction.
Methods
A retrospective cohort study was conducted from January 2018 to October 2020. A total cohort of 240,000 patients’ medical records was collected. The data were collected exclusively for preoperative variables to accurately analyze the predictive factors impacting the postoperative length of stay. The main outcome of this study is an analysis of the length of stay (in days) after surgery until discharge. The prediction was performed with ridge regression, random forest, XGBoost, and multi-layer perceptron neural network models.
Results
The XGBoost resulted in the best performance with an average error within 3 days. Moreover, we explain each feature’s contribution over the XGBoost model and further display distinct predictors affecting the overall prediction outcome at the patient level. The risk factors that most importantly contributed to the stay after surgery were as follows: a direct bilirubin laboratory test, department change, calcium chloride medication, gender, and diagnosis with the removal of other organs. Our results suggest that healthcare providers take into account the risk factors such as the laboratory blood test, distributing patients, and the medication prescribed prior to the surgery.
Conclusion
We successfully predicted the length of stay after surgery and provide explainable models with supporting analyses. In summary, we demonstrate the interpretation with the XGBoost model presenting insights on preoperative features and defining higher risk predictors to the length of stay outcome. Our development in explainable models supports the current in-depth knowledge for the future length of stay prediction on electronic medical records that aids the decision-making and facilitation of the operation department.
Experimental assessment and prediction of design parameter influences on vacuum-based granular grippers
Authors: Wacker Christian , Dierks Niklas , Kwade Arno , Dröder Klaus 
Published: August 2023

Abstract
Innovative soft robotic grippers, such as granular grippers, enable the automated handling of a wide spectrum of different geometries, increasing the flexibility and robustness of industrial production systems. Granular grippers vary in their design as well as the configuration, which affects the specific characteristics and capabilities regarding grippable objects. Relevant aspects are the selection of granulates and membranes, as they affect the deformability. This results in influences on the achievable gripping forces, which vary with the gripped objects geometry. The present research investigates the influences of different configurations on the achievable gripping forces for an innovative vacuum-based granular gripper concept. Specifically, the focus lies on design as well as configuration parameters, which could influence the achievable gripping force. Influencing parameters are determined based on a literature review of similar gripping concepts. Various adjustment possibilities are identified, such as materials of granulates or membranes. The possible configuration options are experimentally analyzed with a one-factor-at-a-time approach. The possibility of modelling their interrelations is examined with approaches for linear models and compared to interpolations based on Machine Learning. Especially the granulate filling level and the membrane configuration exhibit the largest influences, which were best predicted with the approach based on artificial neural networks. A selection of an optimized gripper configuration for a specified object set as well as possible further developments such as a continuous expandability of the approaches and integrations with simulations are discussed. As a result of these analyses, this research provides methodologies for an optimized selection of a gripper configuration for an improved object-specific achievable gripping force and allows for more efficient handling processes with the examined type of vacuum-based granular gripper.
Impact of Diverse Agroforestry Systems on Soil Carbon Inventories and Microbial Activities in Indo-Gangetic Plains of Samastipur, Bihar
Authors: Singh Nongmaithem Raju , Raizada A. , Singh Ashish , Devi N. Peetambari , Singh Soibam Lanabir , Saurabh Kirti , Lamare D. M. , Singh L. Netajit , Pebam Rocky 
Published: August 2023

Abstract
Land use significantly influenced in soil organic carbon (SOC) and its intricate dynamics, subsequently influencing various soil biochemical processes, as well as soil microbial and metabolic indices. This investigation aimed effect of different agroforestry systems (AFS) with diverse intercrops on soil carbon and pools and microbial properties up to 30 cm soil depth. The total organic carbon (TOC) content across the AFS in surface soils (0-15cm) ranged 8.75 to 11.45 g kg− 1. Poplar based AFS had 10.8 to 23.6% higher (p < 0.05) TOC while lowest in Semal (8.75g kg− 1). TOC decreased − 19 to -30.3% in subsurface (15-30cm) compared to surface soils. Very labile carbon (F1), labile carbon (F2), less labile carbon (F3) and non-labile carbon (F4) constituted 38.5, 15.3, 20.3 and 25.9% of TOC in surface soils and 34.1, 14.3, 22.1 and 29.4% in subsurface soils. Poplar based AFS had higher Basal Respiration (25.2%) while lowest (0.475 µg CO2-C g− 1 h− 1) in Mango. Poplar recorded higher Carbon Management Index in surface (209.73%) and subsurface (178.5%). Principal component analysis has shown the first two PCs represented 82.69% of the total variation wherein TOC was the most influential factor in PC1 and microbial metabolic quotient (qCO2) in PC2. Overall, Poplar based AFS outperformed among AFSs. So, therefore, we suggest, promotion of poplar-based systems to restore soil carbon and microbial properties as well as overall soil health while ensuring nutritional security of the Indo-Gangetic plains of Samastipur.
A new intuitionistic fuzzy time series method based on the bagging of decision trees and principal component analysis
Authors: Yücesoy Erdinc , Egrioglu Erol , Bas Eren 
Published: August 2023

Abstract
Intuitionistic fuzzy time series methods provide a good alternative to the forecasting problem. It is possible to use the historical values of the time series as well as the membership and non-membership values obtained for the historical values as effective factors in improving the forecasting performance. In this study, a high order single variable intuitionistic fuzzy time series reduced forecasting model is first introduced. A new forecasting method is proposed for the solution of the forecasting problem in which the functional structure between the historical information of the intuitionistic time series and the forecast is obtained by bagging of decision trees based on the high order single variable intuitionistic fuzzy time series reduced forecasting model. In the proposed method, the intuitionistic fuzzy c-means clustering method is used to create intuitionistic fuzzy time series. To create a simpler functional structure with Bagging of decision trees, the input data from lagged variables, memberships, and non-membership values are subjected to dimension reduction by principal component analysis. The performance of the proposed method is compared with popular forecasting methods in the literature for ten different time series randomly obtained from the S&P500 stock market. According to the results of the analyses, the forecasting performance of the proposed method is better than both classical forecasting methods and some popular shallow and deep neural networks.
Evaluation of the Binding Properties of A New Phenylurea Appended Carbazole Compound to Pepsin/Trypsin by Computational and Multi-Spectral Analysis
Authors: Gökoğlu Elmas , Doyuran Bensu , Özen Gülşah , Duyar Halil , Taskin-Tok Tugba , Seferoğlu Zeynel 
Published: August 2023

Abstract

A novel carbazole compound, named 1-(9-ethyl-9H-carbazol-3-yl)-3-phenylurea (Cpu) was synthesized and its binding properties with protease enzymes (pepsin and trypsin) has been examined by steady-state fluorescence measurements, UV/vis absorption, infrared (FT-IR) and circular dicroism (CD) spectroscopies and also computational methods. The fluorescence experimental results indicated that the quenching mechanism of enzyme by Cpu is static process. The thermodynamic parameters (both negative ΔH/ΔS) and molecular docking results suggested that the binding of Cpu to pepsin/trypsin were driven by hydrogen bonds and van der Waals forces. Based on Förster’s theory, the binding distance (r) between pepsin/trypsin and Cpu was calculated to be 3.072/2.784 nm, which implies that non-radiative energy transfer occurs from enzyme to Cpu. Furthermore, absorption, CD, and FT-IR spectral analysis provided an evidence that the presence of Cpu induced notable changes in the secondary structures and microenvironmental of both pepsin and trypsin, supporting its significant influence on these enzymes.
Identification and in vitro characterization of a novel porcine parvovirus 6 in Russia
Authors: Komina Alina , Anoyatbekova Afshona , Krasnikov Nikita , Yuzhakov Anton 
Published: August 2023

Abstract
Porcine parvovirus 6 (PPV6) was first identified in aborted swine fetuses in China in 2014. Since its identification, an increased number of PPV6 cases have been reported in many countries with developed pig breeding. In this study, the first identification of porcine parvovirus 6 in Russia, its phylogenetic analysis, and its characterization in vitro are reported. During the investigation, 521 serum samples collected from pigs of different ages from seven regions of the Russian Federation were tested. In four regions, the DNA of the virus was detected. The overall prevalence of porcine parvovirus 6 in Russia was 9.4%. Fattening pigs were the group with the most frequent detection of the virus genome. Phylogenetic analysis of the Russian isolate detected in a domestic boar indicated high homology with strains from Spain. In vitro studies revealed that PPV6 mostly replicates in SPEV and SK cell cultures and, to a lesser extent, in ST. Our results demonstrated that PPV6 induced typical apoptotic features in cells, including DNA fragmentation, chromatin margination, nuclear condensation, pyknosis of nuclei, symplast formation, and various pathological mitoses.
Estimation of TBM Penetration rate using Gradient Boosting-based Algorithms
Authors: Ghorbani Ebrahim , Adoko Amoussou Coffi , Yagiz Saffet 
Published: August 2023

Abstract
TBM performance prediction from the rate of penetration (ROP) point of view has yet to draw a lot of attention since it is one of the main challenges for mechanized excavation with tunnel boring machines (TBMs). In this study, five algorithms, Gradient Boosting (GB), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), AdaBoost (AB), and CatBoost (CB) have been conducted to predict the ROP based on the Gradient Boosting theory. Six tunnel cases from different projects were examined to obtain the aim of the research. Dataset developed using those tunnel datasets includes Uniaxial compressive strength (UCS), Rock Type, Distance between Plane of Weakness (DPW), and TBM-related parameter of thrust force (TF). Mentioned Gradient Boosting algorithms were performed to obtain the most accurate results for the study. The developed models showed that XGBoost outperformed the other models, followed by the CatBoost model according to seven different evaluation metrics used to rank the models. After parameter tuning, the GB model outperformed others while those were not improved very much. By using the overall ranking according to the metrics and considering the parameter tuning time, XGBoost and CatBoost presented the first two best performances. Through SHAP values and dependency plots, the features and importance of the inputs showed the TF has the highest impact on the ROP, followed by UCS, Rock Type, and DPW. It is concluded that the XGBoost and CatBoost algorithms could be used for modeling to obtain the TBM penetration for similar rock types.
Radiographic texture analysis of the hard tissue changes following socket preservation with allograft and xenograft materials for dental implantation: A randomized clinical trial
Authors: Bayat Narges , Ghavimi Mohammad Ali , Rahimipour Kasra , Razi Sedigheh , Esmaeili Farzad 
Published: August 2023

Abstract
Objectives: This study aimed to assess the hard tissue changes following socket preservation with allograft and xenograft materials for dental implantation by texture analysis (TA) using cone-beam computed tomography (CBCT).
Materials and Methods: This prospective clinical trial was conducted on 25 patients who required the extraction of carious mandibular posterior teeth and their subsequent replacement with dental implants. The patients were categorized into three groups: (I) no socket preservation, (II) socket preservation with xenograft material, and (III) socket preservation with allograft material. Four months after tooth extraction, the patients were recalled for preoperative assessment before dental implantation, and CBCT scans were obtained. MaZda software was used to compare homogeneity, contrast, and texture complexity on axial CBCT sections among the three groups.
Results: Significant differences existed among the three groups in all parameters (P<0.05) except for the mean correlation parameter (P>0.05). The results showed no significant difference between the no graft and xenograft groups regarding contrast and differential (dif.) entropy (P>0.05). Also, no significant difference was found between the xenograft and allograft groups regarding the dif. variance, and also between the no graft and allograft groups regarding the inverse difference moment(InvDfMom) and dif. variance parameters (P>0.05). All other pairwise comparisons revealed significant differences (P<0.05).
Conclusion: TA can be used for quantification of radiographic changes of bone following socket preservation, and potentially accelerate the process of decision-making for dental implant treatment.
Noninferiority Clinical Trial of Adapted START NOW Psychotherapy for Outpatient Opioid Treatment
Authors: Truong Albert , Kablinger Anita , Hartman Cheri , Hartman David , West Jennifer , Hanlon Alexandra , Lozano Alicia , McNamara Robert , Seidel Richard , Trestman Robert 
Published: August 2023

Abstract
Background
Medications for opioid use disorder (MOUD) such as buprenorphine is effective for treating opioid use disorder (OUD). START NOW (SN) is a manualized, skills-based group psychotherapy originally developed and validated for the correctional population and has been shown to result in reduced risk of disciplinary infractions and future psychiatric inpatient days with a dose response effect. We investigate whether adapted START NOW is effective for treating OUD in a MOUD office-based opioid treatment (OBOT) setting in this non-inferiority clinical trial.
Methods
Patients enrolled in once weekly buprenorphine/suboxone MOUD OBOT were eligible for enrollment in this study. Participants were cluster-randomized, individually-randomized, or not randomized into either START NOW psychotherapy or treatment-as-usual (TAU) for 32 weeks of therapy. Treatment effectiveness was measured as the number of groups attended, treatment duration, intensity of attendance, and overall drug use as determined by drug screens.
Results
137 participants were quasi-randomized to participate in SN (n = 79) or TAU (n = 58). Participants receiving START NOW psychotherapy, when compared to TAU, had comparable number of groups attended (16.5 vs. 16.7, p = 0.80), treatment duration in weeks (24.1 vs. 23.8, p = 0.62), and intensity defined by number of groups attended divided by the number of weeks to last group (0.71 vs. 0.71, p = 0.90). SN compared to TAU also had similar rates of any positive drug screen result (81.0% vs. 91.4%, p = 0.16). This suggests that adapted START NOW is noninferior to TAU, or the standard of care at our institution, for treating opioid use disorder.
Conclusion
Adapted START NOW is an effective psychotherapy for treating OUD when paired with buprenorphine/naloxone in the outpatient group therapy setting. Always free and publicly available, START NOW psychotherapy, along with its clinician manual and training materials, are easily accessible and distributable and may be especially useful for low-resource settings in need of evidence-based psychotherapy.