bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Contribution of Psychological Resilience and Job Meaningfulness to Well-being of Working Cancer Survivors
Authors: Zheng Dianhan , Marbut Alexander R. , Zhang Jing , O’Keefe Louise C. 
Published: May 2022

Background: Although studies suggest that cancer survivors face workplace obstacles, to date there has been little empirical research regarding the personal and environmental factors that can help cancer survivors adjust to work. The purpose of this study was to examine how working survivors’ resilience and job meaningfulness were related to their well-being outcomes, including lower cancer-related intrusive thoughts, fatigue, and presenteeism. Methods: We recruited 200 full-time employed cancer survivors from online participant panels using Qualtrics. Participants responded to an online survey that measured their resilience, job meaningfulness, job-related psychological distress, and well-being outcomes. We conducted descriptive statistical analysis, confirmatory factor analysis, and moderated mediated analysis to examine the psychological process in which resilience and job meaning are associated with cancer survivors’ mental health and work outcomes. Findings: The relationship between cancer survivors’ resilience and their well-being outcomes depended on job meaningfulness. For survivors whose jobs were not highly meaningful, their resilience was related to reduced job-related psychological distress, which, in turn, was related to lower intrusive thoughts, fatigue, and presenteeism. For survivors with highly meaningful jobs, they did not need to rely on resilience to protect them from workplace psychological distress and other negative outcomes. Conclusion/Application to Practice: It is important for working cancer survivors to develop resilience, especially when they do not perceive their work as highly meaningful. Successful resilience-building interventions can buffer the negative impact of low job meaningfulness and help working survivors achieve better outcomes. In addition, organizations can actively help enrich survivors’ jobs to increase perceived meaningfulness.
A Cross-Country Network Analysis of Resilience Systems in Young Adults
Authors: Jefferies Philip , Höltge Jan , Fritz Jessica , Ungar Michael 
Published: May 2022

Multisystemic resilience has been conceptualised as involving a constellation of protective factors which operate at different levels to promote adaptation and thriving despite experiences of adversity. We used network modelling to discover how protective factors at two different systemic levels (intrapersonal strengths and social-ecological resources) interrelate, drawing on survey data from 5283 emerging adults ( M = 24.53 years; 52% female) in Brazil, China, Indonesia, Russia, Thailand, the US and Vietnam. Results indicated that the level of connectivity within and between protective factor levels was similar between the countries, but that there was substantial variation in the specific interrelations among protective factors (both within and between levels), including the presence of some country-specific negative interrelations between protective factors at different levels. The findings support the importance of cultural context in studies of resilience, with implications for the development of appropriate resilience-building interventions for this age group.
Machine Learning Predicts Recurrent Lumbar Disc Herniation Following Percutaneous Endoscopic Lumbar Discectomy
Authors: Ren GuanRui , Liu Lei , Zhang Po , Xie ZhiYang , Wang PeiYang , Zhang Wei , Wang Hui , Shen MeiJi , Deng LiTing , Tao YuAo , Li Xi , Wang JiaoDong , Wang YunTao , Wu XiaoTao 
Published: May 2022

Study Design Retrospective study. Objectives To develop machine learning (ML) models to predict recurrent lumbar disc herniation (rLDH) following percutaneous endoscopic lumbar discectomy (PELD). Methods We retrospectively analyzed 1159 patients who had undergone single-level PELD for lumbar disc herniation (LDH) between July 2014 to December 2019 at our institution. Various preoperative imaging variables and demographic metrics were brought in analysis. Student’s t test and Chi-squared test were applied for univariate analysis, which were feature selection for ML models. We established ML models to predict rLDH: Artificial neural networks (ANN), Extreme Gradient Boost classifier (XGBoost), KNeighborsClassifier (KNN), Decision tree classifier (Decision Tree), Random forest classifier (Random Forest), and support vector classifier (SVC). Results A total 130 patients (11.22%) were diagnosed as rLDH in 1159 patients. Recurrence occurred within 10.25 ± 11.05 months. Body mass index (BMI) ( P = .027), facet orientation (FO) ( P < .001), herniation type ( P = .012), Modic changes ( P = .004), and disc calcification ( P = .013) are significant factors in univariate analysis ( P < .05). Extreme Gradient Boost classifier, Random Forest, ANN showed fine area under the curve, .9315, .9220, and .8814 respectively. Conclusion We developed a deep learning and 2 ensemble models with fine performance in prediction of rLDH following PELD. Predicting re-herniation before surgery has the potential to optimize decision-making and meaningfully decrease the rates of rLDH following PELD. Our ML model identified higher BMI, lower FO, Modic changes, disc calcification in a non-protrusive region, and herniation type (noncontained herniation) as significant features for predicting rLDH.
How to Optimize Pedicle Screw Parameters for the Thoracic Spine? A Biomechanical and Finite Element Method Study
Authors: Solitro Giovanni F. , Welborn Michelle C. , Mehta Ankit I. , Amirouche Farid 
Published: May 2022

Study Design Pedicle screw study. Objective The selection of pedicle screw parameters usually involves the surgeon’s analysis of preoperative CT imaging along with anatomical landmarks and tactile examination. However, there is minimal consensus on a standardized guideline for selection methods on pedicle screws. We aimed to determine the effects of thoracic screw diameter to pedicle width on pullout strength determined by cortical bone purchase. Methods Biomechanical study performed with human cadaveric thoracic vertebrae and experimentally validated three-dimensional finite element model instrumented with pedicle screws of various diameters. We used a variable (SD/PW) ratio to express the screw selection. We hypothesized a positive correlation between the pullout load determined by the bone purchase and the SD/PW. This relationship was first investigated in a validated finite element model considering bone purchase related to the strength of an upper thoracic vertebra. Then, the correlation to the entire spine is evaluated. Results The failure load ranged from 371.3 to 1601.0 N, respectively, for 3 and 6 mm screws. The determinant coefficient was increased to R2=.421 when a linear relationship between pullout load and the SD/PW ratio was used. The peak loads of 1216 and 1288N were found for an SD/PW ratio of .83. Conclusion We have found that the screw pullout load is more correlated to SD/PW than other pedicle measures for a maximized SD/PW ratio of .83. This particular value should be considered the upper limit of the indicated SD/PW ratio and a means to determine the optimal screw diameter to enhance pullout strength.
The Relationship of Radiographic Parameters and Morphological Changes at Various Stages of Degeneration of the Lumbar Facet Joints: Cadaver Study
Authors: Byvaltsev Vadim A , Kalinin Andrei A , Shepelev Valerii V , Pestryakov Yurii Ya , Biryuchkov Mikhail Y , Jubaeva Bagdat A , Boddapati Venkat , Lehman Ronald A , Riew K Daniel 
Published: May 2022

Study Design Cadaveric specimens. Objective To perform a pathomorphological analysis of the degree of facet joint (FJ) degeneration utilizing fresh cadaveric models and correlating these structural changes with imaging findings. Methods L1-L5 FSU including all tissue between the anterior longitudinal ligament to the posterior spinal structures were obtained on 28 patients at a mean of 5.7 hours post-mortem. The samples were fixed in an agar medium and CT and MRI were performed. The level of FJ degeneration was identified based on prior classifications Osteoarthritis Research Society International (OARSI), as was the facet angle and tropism. Pathomorphological assessment including articular cartilage cell density was performed according to prior established methodology. Results Radiographically, a direct association was identified between FJ degeneration and patient age. Facet angle and tropism did not significantly vary by patient age. Pathomorphologically, there was a decrease in the cellular density of articular cartilage with increasing patient age. Similarly, there was a significant direct correlation between radiographic degree of degenerative changes in FJs with the age of cadavers and the degree of degeneration of FJs according to the morphological classification of OARSI, as well as a significant inverse correlation with cell density. Conclusion A comprehensive assessment of various signs of FJ degeneration using cadaveric material has established that, based on radiographic imaging, it is possible to assess the microstructural state of FJ, including at an early stage of the disease. This data may be useful for surgeons in guiding therapeutic strategies based on individual biometric parameters of the FJ.
Metformin for treatment of cytopenias in children and young adults with Fanconi anemia
Authors: Pollard Jessica A. , Furutani Elissa , Liu Shanshan , Esrick Erica , Cohen Laurie E. , Bledsoe Jacob , Liu Chih-Wei , Lu Kun , de Haro Maria Jose Ramirez , Surrallés Jordi , Malsch Maggie , Kuniholm Ashley , Galvin Ashley , Armant Myriam , Kim Annette S. , Ballotti Kaitlyn , Moreau Lisa , Zhou Yu , Babushok Daria , Boulad Farid , Carroll Clint , Hartung Helge , Hont Amy , Nakano Taizo , Olson Tim , Sze Sei-Gyung , Thompson Alexis A. , Wlodarski Marcin W. , Gu Xuesong , Libermann Towia A. , D’Andrea Alan , Grompe Markus , Weller Edie , Shimamura Akiko 
Published: May 2022

Abstract
Fanconi anemia (FA), a genetic DNA repair disorder characterized by marrow failure and cancer susceptibility. In FA mice, metformin improves blood counts and delays tumor development. We conducted a single institution study of metformin in nondiabetic patients with FA to determine feasibility and tolerability of metformin treatment and to assess for improvement in blood counts. Fourteen of 15 patients with at least 1 cytopenia (hemoglobin &lt; 10 g/dL; platelet count &lt; 100 000 cells/µL; or an absolute neutrophil count &lt; 1000 cells/µL) were eligible to receive metformin for 6 months. Median patient age was 9.4 years (range 6.0-26.5 ). Thirteen of 14 subjects (93%) tolerated maximal dosing for age; 1 subject had dose reduction for grade 2 gastrointestinal symptoms. No subjects developed hypoglycemia or metabolic acidosis. No subjects had dose interruptions caused by toxicity, and no grade 3 or higher adverse events attributed to metformin were observed. Hematologic response based on modified Myelodysplastic Syndrome International Working Group criteria was observed in 4 of 13 evaluable patients (30.8%; 90% confidence interval, 11.3-57.3). Median time to response was 84.5 days (range 71-128 days). Responses were noted in neutrophils (n = 3), platelets (n = 1), and red blood cells (n = 1). No subjects met criteria for disease progression or relapse during treatment. Correlative studies explored potential mechanisms of metformin activity in FA. Plasma proteomics showed reduction in inflammatory pathways with metformin. Metformin is safe and tolerable in nondiabetic patients with FA and may provide therapeutic benefit. This trial was registered at as #NCT03398824.
RETRACTED ARTICLE: Conductive Gels: Properties and Applications of Nanoelectronics
Authors: Trung Nguyen Dinh , Huy Dinh Tran Ngoc , Jade Catalan Opulencia Maria , Lafta Holya A. , Abed Azher M. , Bokov Dmitry Olegovich , Shomurodov Kahramon , Van Thuc Master Hoang , Thaeer Hammid Ali , Kianfar Ehsan 
Published: May 2022

AbstractConductive gels are a special class of soft materials. They harness the 3D micro/nanostructures of gels with the electrical and optical properties of semiconductors, producing excellent novel attributes, like the formation of an intricate network of conducting micro/nanostructures that facilitates the easy movement of charge carriers. Conductive gels encompass interesting properties, like adhesion, porosity, swelling, and good mechanical properties compared to those of bulk conducting polymers. The porous structure of the gels allows the easy diffusion of ions and molecules and the swelling nature provides an effective interface between molecular chains and solution phases, whereas good mechanical properties enable their practical applications. Due to these excellent assets, conductive gels are promising candidates for applications like energy conversion and storage, sensors, medical and biodevices, actuators, superhydrophobic coatings, etc. Conductive gels offer promising applications, e.g., as soft sensors, energy storage, and wearable electronics. Hydrogels with ionic species have some potential in this area. However, they suffer from dehydration due to evaporation when exposed to the air which limits their applications and lifespan. In addition to conductive polymers and organic charge transfer complexes, there is another class of organic matter called “conductive gels” that are used in the organic nanoelectronics industry. The main features of this family of organic materials include controllable photoluminescence, use in photon upconversion technology, and storage of optical energy and its conversion into electricity. Various parameters change the electronic and optical behaviors of these materials, which can be changed by controlling some of the structural and chemical parameters of conductive gels, their electronic and optical behaviors depending on the applications. If the conjugated molecules with π bonds come together spontaneously, in a relative order, to form non-covalent bonds, they form a gel-like structure that has photoluminescence properties. The reason for this is the possibility of excitation of highest occupied molecular orbital level electrons of these molecules due to the collision of landing photons and their transfer to the lowest unoccupied molecular orbital level. This property can be used in various nanoelectronic applications such as field-effect organic transistors, organic solar cells, and sensors to detect explosives. In this paper, the general introduction of conductive or conjugated gels with π bonds is discussed and some of the physical issues surrounding electron excitation due to incident radiation and the mobility of charge carriers, the position, and role of conductive gels in each of these applications are discussed.
Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases
Authors: Gupta Chirag , Chandrashekar Pramod , Jin Ting , He Chenfeng , Khullar Saniya , Chang Qiang , Wang Daifeng 
Published: May 2022

AbstractIntellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the “big data” revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.
Slope aspect affects the soil microbial communities in karst tiankeng negative landforms
Authors: Jiang Cong , Zhu Sufeng , Feng Jie , Shui Wei 
Published: May 2022

Abstract
Background
Karst tiankeng is a large-scale negative surface terrain, and slope aspects affect the soil conditions, vegetation and microbial flora in the tiankeng. However, the influence of the slope aspect on the soil microbial community in tiankeng has not been elucidated.

Methods
In this study, metagenomic sequencing technology was used to analyze the soil microbial community structure and functional potentials on the shady and sunny slopes of karst tiankeng.

Results
The Shannon–Wiener diversity of microbial communities on shady slope (SHS) was significantly higher than that on sunny slope (SUS). Although the composition of dominant phyla on shady slope (SHS) and sunny slope (SUS) was similar, there were significant differences in beta-diversity. The linear discriminate analysis (LDA) results showed that biomarkers mainly belongs to Actinobacteria, Chloroflexi and Proteobacteria. Functional pathways and CAZy (Carbohydrate-Active Enzymes) genes also had a remarkable response to slope aspect change. LEfSe results indicated several biomarker pathways in sunny slope involved in human disease. Moreover, the abundance of CAZy genes was higher in shady slope and had stronger ability in decomposing litter. The microbial communities were mainly correlation with the vegetation characteristics (species richness and coverage) and soil properties (SOC and pH).

Conclusions
These results indicate slope aspect has a pronounced influence on microbial community composition, structure and function at karst tiankeng. In the future, the conservation of karst tiankeng biodiversity should pay more attention to topographical factors.
Evidence of multiple genome duplication events in Mytilus evolution
Authors: Corrochano-Fraile Ana , Davie Andrew , Carboni Stefano , Bekaert Michaël 
Published: May 2022

AbstractBackgroundMolluscs remain one significantly under-represented taxa amongst available genomic resources, despite being the second-largest animal phylum and the recent advances in genomes sequencing technologies and genome assembly techniques. With the present work, we want to contribute to the growing efforts by filling this gap, presenting a new high-quality reference genome forMytilus edulisand investigating the evolutionary history within the Mytilidae family, in relation to other species in the class Bivalvia.ResultsHere we present, for the first time, the discovery of multiple whole genome duplication events in the Mytilidae family and, more generally, in the class Bivalvia. In addition, the calculation of evolution rates for three species of the Mytilinae subfamily sheds new light onto the taxa evolution and highlights key orthologs of interest for the study ofMytilusspecies divergences.ConclusionsThe reference genome presented here will enable the correct identification of molecular markers for evolutionary, population genetics, and conservation studies. Mytilidae have the capability to become a model shellfish for climate change adaptation using genome-enabled systems biology and multi-disciplinary studies of interactions between abiotic stressors, pathogen attacks, and aquaculture practises.
Functional annotation of creeping bentgrass protein sequences based on convolutional neural network
Authors: Jiang Han-Yu , He Jun 
Published: May 2022

Abstract
Background
Creeping bentgrass (Agrostis soionifera) is a perennial grass of Gramineae, belonging to cold season turfgrass, but has poor disease resistance. Up to now, little is known about the induced systemic resistance (ISR) mechanism, especially the relevant functional proteins, which is important to disease resistance of turfgrass. Achieving more information of proteins of infected creeping bentgrass is helpful to understand the ISR mechanism.

Results
With BDO treatment, creeping bentgrass seedlings were grown, and the ISR response was induced by infecting Rhizoctonia solani. High-quality protein sequences of creeping bentgrass seedlings were obtained. Some of protein sequences were functionally annotated according to the database alignment while a large part of the obtained protein sequences was left non-annotated. To treat the non-annotated sequences, a prediction model based on convolutional neural network was established with the dataset from Uniport database in three domains to acquire good performance, especially the higher false positive control rate. With established model, the non-annotated protein sequences of creeping bentgrass were analyzed to annotate proteins relevant to disease-resistance response and signal transduction.

Conclusions
The prediction model based on convolutional neural network was successfully applied to select good candidates of the proteins with functions relevant to the ISR mechanism from the protein sequences which cannot be annotated by database alignment. The waste of sequence data can be avoided, and research time and labor will be saved in further research of protein of creeping bentgrass by molecular biology technology. It also provides reference for other sequence analysis of turfgrass disease-resistance research.