bluuebunny/crossref_metadata_2025_split huggingface dataset:
Complete Feed Based on Fermented Sago Waste Against the Consumption and Digestion of Goat
Authors: Fadly Fadly Hidayat Ilyas , Syahrir Syahriani , Islamiyati Rohmiyatul 
Published: June 2023

A common method used to improve the nutritional quality of feed is by fermentation method. The working principle is that fermentation activates the growth and metabolism of microorganism so that it can increase the digestibility of feed and produce the preferred aroma of livestock. This study aims to find out the effect of giving fermented sago waste on the consumption and digestibility of goat and cattle. Two feed treatments were tested on 12 goats. The results showed that the consumption and digestibility value of Dry Matter (DM) and Crude Protein (CP) showed no noticeable difference in goat cattle. The study concluded that complete feed-based fermented sago waste did not affect the consumption and digestibility of CP and DM of goat.
Keywords: Sago waste, completed feed, consumption, digestion, dry matter, crude protein
Women Are Underrepresented Among Principal Investigators of Hip and Knee Arthroplasty Clinical Trials in the United States
Authors: Silvestre Jason , Walker Jasmine J. , LaPorte Dawn M. , Nelson Charles L. 
Published: June 2023

Background:
Leadership of clinical trials confers national recognition and promotes academic advancement. We hypothesized that women would be underrepresented among principal investigators (PIs) of hip and knee arthroplasty clinical trials in the United States.

Methods:
A query of hip and knee arthroplasty clinical trials from 2015 to 2021 was performed on ClinicalTrials.gov. Clinical trials were included if they involved a U.S.-based orthopaedic-surgeon PI. We analyzed the sex representation of arthroplasty PIs among junior-level (assistant professor) and senior-level (associate or full professor) faculty. Participation-to-prevalence ratios (PPRs) were calculated by comparing the sex representation among arthroplasty PIs with that among academic arthroplasty faculty at institutions conducting hip and knee arthroplasty clinical trials. A PPR of <0.8 indicated underrepresentation and a PPR of >1.2 indicated overrepresentation.

Results:
A total of 157 clinical trials involving 192 arthroplasty PIs were included. Of these PIs, only 2 (1.0%) were women. PIs were mostly funded by academic institutions (66%) and industry (33%). U.S. federal sources funded a minority (1%) of PIs. Of the 243 male arthroplasty faculty who were eligible, 190 men (78.2%) served as PIs. In contrast, of the 17 female arthroplasty faculty who were eligible, only 2 women (11.8%) served as PIs (p < 0.001). Across the entire cohort of arthroplasty PIs, women were underrepresented (PPR = 0.16), whereas men were equitably represented (PPR = 1.06). Women were underrepresented at the assistant professor (PPR = 0.0), associate professor (PPR = 0.52), and full professor (PPR = 0.58) levels.

Conclusions:
Women were underrepresented among PIs of hip and knee arthroplasty clinical trials, which may lead to disparities in academic promotion and advancement. More research is needed to understand the potential barriers to female leadership of clinical trials. Greater awareness and engagement are needed to create sex equity in clinical trial leadership for hip and knee arthroplasty research.

Clinical Relevance:
The underrepresentation of women among arthroplasty PIs may lead to fewer options in surgical providers for patients and may limit access to musculoskeletal care for certain patient populations. A diverse arthroplasty workforce can promote attention to issues that disproportionately affect historically underrepresented and vulnerable patient populations.
Chronic Lower Back Pain in Weight Lifters
Authors: Ross Ruby , Han Julie , Slover James 
Published: June 2023

» Chronic lower back pain (LBP) is common in both nonathletes and weight lifters, but the diagnosis and treatment should be approached differently in these 2 populations based on the unique movement patterns causing the pain.» Injury rates of weight lifters are far less than those of contact sports, ranging from 1.0 to 4.4 injuries per 1,000 workout hours. However, the lower back was consistently one of the top 2 injury sites for weight lifters, accounting for anywhere from 23% to 59% of all injuries. LBP was most often associated with the squat or deadlift.» Guidelines for evaluating general LBP are applicable to weight lifters, including a thorough history and physical examination. However, the differential diagnosis will change based on the patient's lifting history. Of the many etiologies of back pain, weight lifters are most likely to be diagnosed with muscle strain or ligamentous sprain, degenerative disk disease, disk herniation, spondylolysis, spondylolisthesis, or lumbar facet syndrome.» Traditional recommended therapies include nonsteroidal anti-inflammatory drugs, physical therapy, and activity modification, which are often insufficient to resolve pain and prevent injury recurrence. Because most athletes will want to continue to lift weights, lifting-specific behavior modifications focused on improved technique and correcting mobility and muscular imbalances are important aspects of management in this patient population.
Resilience in Residency and Beyond
Authors: Dove James H. , Kutschke Michael J. , Fadale Paul D. , Akelman Edward 
Published: June 2023

» Distinct from the burnout and wellness continuum, resilience is a developed and refined characteristic that propels an individual toward personal and professional success.» We propose a clinical resilience triangle consisting of 3 components that define resilience: grit, competence, and hope.» Resilience is a dynamic trait that should be built during residency and constantly fortified in independent practice so that orthopaedic surgeons may acquire and hone the skills and mental fortitude required to take on the overwhelming challenges that we all inevitably face.
Recent Advances and Challenges of the Application of Artificial Intelligence to Predict Wellbore Instabilities during Drilling Operations
Authors: Kamgue Lenwoue Arnaud Regis , Li Zhonghui , Tang Chuanfu , Zhang Wuchang , Ding Shi , Hu Pengjie , Sun Wentie 
Published: June 2023

Summary
Artificial intelligence (AI) is revolutionizing several businesses across the world, and its implementation in drilling engineering has enhanced the performance of oil and gas companies. This paper reviews and analyzes the successful application of AI techniques to predict wellbore instabilities during drilling operations. First, a summary of the implementation of AI for the prediction of loss circulation, pipe stuck, and mud window is highlighted. Then, the recent innovations and challenges of the AI adoption in major drilling companies is presented. Finally, recommendations are provided to improve the integration of AI in the drilling industry. This analysis gives deep insight into the main publications and recent advances of the application of AI in drilling engineering and is expected to contribute to the further development of the drilling industry.
Atrial fibrillation and cardioversion in the critically ill – a post-hoc analysis of emergency department patients
Authors: Gupta Sophie , Schnaubelt Sebastian , Oppenauer Julia , Lutnik Martin , Cacioppo Filippo , Spiel Alexander , Domanovits Hans , Sulzgruber Patrick , Niederdöckl Jan 
Published: June 2023

Abstract
Objective:
In critically ill patients with atrial fibrillation (AF) standard treatment algorithms might not be applicable. Emergency departments (ED) play a crucial role in implementing individualized treatment approaches. The aim of this study was to evaluate the effectiveness of ED cardioversion algorithms based on lactate levels as a surrogate for severity of illness.
Design, Setting and Patients:
This retrospective study was conducted at the ED of the Medical University of Vienna, Austria, between 2012 and 2022. A consecutive series of 3,535 AF episodes were analyzed.
Measurement and main results:
The main outcome was cardioversion success depending on serum lactate levels [mmol/L]. Lactate levels were divided into quintiles (lac <1.1, 1.1 - 1.3, 1.4 - 1.7, 1.8 - 2.3 and > 2.3mmol/L) and into two groups using a cut-off of 4mmol/L. Overall CV success decreased with rising lactate levels (79% in lac < 1.1mmol/L vs. 61% in lac > 2.3mmol/L; p < 0.001). For electrical cardioversion [eCV] (n=1,119 cases; sinus rhythm [SR] achieved in 86%) success, no significant difference was seen in relation to lactate levels. However, for medical cardioversion [mCV] (n=759; 67% SR), conversion success decreased with increasing lactate levels (SR: lac < 1.1mmol/L 84% vs. lac > 2.3mmol/L 59%; p < 0.001).
Conclusion:
Overall cardioversion success was less likely with rising lactate levels; especially medical cardioversion success rates decreased. Therefore, AF in the critically ill may benefit from either electrical cardioversion, treatment of the underlying condition or primary rate control, rather than medical cardioversion attempts.
Longitudinal plasma proteomics reveals alveolar-capillary barrier disruption in critically ill COVID-19 patients
Authors: Duijvelaar Erik , Gisby Jack , Peters James , Bogaard Harm Jan , Aman Jurjan 
Published: June 2023

Abstract
The pathobiology of respiratory failure in COVID-19 consists of a complex interplay between direct viral cytopathic effects and a dysregulated host immune response. In a randomised clinical trial, imatinib treatment improved clinical outcomes associated with respiratory failure. Here, we performed longitudinal profiling of 6385 plasma proteins in 318 hospitalised patients to investigate the biological processes involved in critical COVID-19, and assess the effects of imatinib treatment. Nine proteins measured at hospital admission accurately predicted critical illness development. Next to dysregulation of inflammation, critical illness was characterised by pathways involving cellular adhesion, extracellular matrix turnover and tissue remodelling. Imatinib treatment attenuated protein perturbations associated with inflammation and extracellular matrix turnover. External RNA-sequencing data from the lungs of SARS-CoV-2 infected hamsters validated that imatinib exerts these effects in the pulmonary compartment. These findings implicate that the plasma proteome reflects alveolar capillary barrier disruption in critical COVID-19 which was attenuated with imatinib treatment.
Impacts of an abbreviated personal agency training with refugee women and their male partners on economic empowerment, gender-based violence, and mental health: a randomized controlled trial in Rwanda
Authors: Kalra Naira , Habumugisha Lameck , Shankar Anita 
Published: June 2023

Abstract
Introduction We assessed the impact of a personal agency-based training for refugee women and their male partners on their economic and social empowerment, rates of intimate partner violence (IPV), and non-partner violence (NPV).
Methods We conducted an individually randomized controlled trial with 1061 partnered women (aged 18–45) living in a refugee camp in Rwanda. Women received two days of training, and their partners received one day of training. The follow-up survey where all relevant outcomes were assessed was carried out at 6–9 months post-intervention.
Results At follow up, women in the intervention arm were more likely to report partaking in income generating activities (aIRR 1.27 (1.04–1.54), p < 0.05) and skill learning (aIRR 1.59 (1.39–1.82), p < 0.001) and reported a reduction in experience of physical or sexual NPV in the past six months (aIRR 0.65 (0.39–1.07), p < 0.09). While improved, no statistically significant impacts were seen on physical or sexual IPV (aIRR 0.80 (0.58–1.09), p = 0.16), food insecurity (β 0.98 (0.93 to 1.03), p = 0.396), or clean cookstove uptake (aIRR 0.95 (0.88 to 1.01), p = 0.113) in the past six months. We found statistically significant reduction in physical and sexual IPV amongst those experiencing IPV at baseline (aIRR 0.72 (0.50 to 1.02), p < 0.07). Small improvements in self-efficacy scores and our indicator of adapting to stress were seen in the intervention arm. Some challenges were also seen, such as higher prevalence of probable depression and/or anxiety (aIRR 1.79 (1.00-3.22), p = 0.05) and PTSD (aIRR 2.07 (1.10–3.91), p < 0.05) in the intervention arm compared to the control arm.
Conclusion Our findings echo previous research showing personal agency training can support economic well-being of women. We also find potentially promising impacts on gender-based violence. However, there is some evidence that integration of evidence-based mental health support is important when enhancing agency amongst conflict-affected populations.
Trial registration number
The trial was registered with ClinicalTrials.gov, Identifier: NCT04081441 on 09/09/2019.
Structure-Dynamics Correlation in Metallic Glass Revealed by 5-Dimensional Scanning Transmission Electron Microscopy
Authors: Mitsuishi Kazutaka , Iakoubovskii Konstantin , Kohara Shinji , Tsuchiya Koichi , Nakazawa Katsuaki 
Published: June 2023

Abstract
Dynamic and structural heterogeneities play an important role in glass transition phenomena and in the formation of amorphous structures. Since structure and dynamics are mutually related, it is expected that there exists some relation between them; however, this relation has not been characterized by a direct experiment. Elucidation of this relation is the key to identifying the structure responsible for the rapid freezing of atomic motion during the glass transition. In this study, we simultaneously observed the dynamic and structural heterogeneities near the glass transition temperature in Zr50Cu40Al10 using five-dimensional scanning transmission electron microscopy, which is capable of recording the spatiotemporal distribution of electron diffraction pattern. Dynamic and structural heterogeneities were visualized with sub-nanometer resolution upon heating in situ, and a spatial correlation between them was observed up to the glass transition temperature. Simultaneous measurements of dynamic and structural heterogeneities directly revealed that the ordered atomic structure had slow dynamics and that the order decreased with temperature.
Ensemble Model for Educational Data Mining Based on Synthetic Minority Oversampling Technique
Authors: R Manoharan , Stalin M.Subi , Loganathan Ganesh Babu , s Deepa , k Venkateswaran 
Published: June 2023

Abstract
Data mining in the classroom is a well-known field that involves data mining concepts, statistical analysis, and machine learning concepts, all of which are applied to educational data. These EDM processed data are frequently used to analyse various aspects of the business and process model. Existing and traditional process models involve the use of traditional statistical techniques to process data, which necessitates a significant amount of manual intervention for data modelling and pre-processing. To address the issues raised above, this paper proposes a novel technique that combines a machine learning model with statistical approaches. This machine learning combination combines various classifiers such as Decision Tree Logistic Regression, Random Forest, Multiplayer Perceptron, K Nearest Neighbor, Decision Tree, and so on. Because of the limited data availability, the information used in the observation is highly imbalanced. As a result, the above-mentioned technique is combined with the universally benchmarked model known as Synthetic Minority Oversampling Technique (SMOTE) to discuss issues related to class imbalance. In addition, the performance evaluation is statistically performed to demonstrate the efficacy of the suggested strategies. A technological college in India obtained the main student data collection, which included information on 6,807 students with characteristics. A synthetic minority oversampling approach sensor is used to handle the imbalanced data set. The model is calibrated using eight methodologies, which are then evaluated to determine the dimensions that will help produce the best suitable model to categorise a student based on his achievements.
Identification of anoikis-related lncRNAs and analysis of immune infiltration in patients with breast cancer
Authors: Deng Hao , Jin Yao , Luo Meng , Chen Huihui , Chen Yiding 
Published: June 2023

Abstract
Background
Breast cancer is a significant worldwide health concern with a complex etiology and multiple treatment approaches. Anoikis, a programmed cell death mechanism, has emerged as a critical regulatory pathway in biological processes like proliferation and migration in breast cancer. Long non-coding RNAs (lncRNAs), specifically those related to anoikis (arLncRNAs), have been linked to breast cancer development. Therefore, investigating the role of arLncRNAs in breast cancer is of utmost importance.
Methods
In this study, we collected RNA sequencing data from The Cancer Genome Atlas (TCGA) database and identified anoikis-related genes from GeneCards, which we then compared with the TCGA dataset. Using differential analysis, functional enrichment analysis, and single-factor Cox regression, we identified arLncRNAs that are significantly associated with breast cancer prognosis and constructed a reliable risk model.
Results
We identified eight arLncRNAs that are significantly associated with breast cancer prognosis: LINC01235, AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1, and AC091182.2. Among these, LINC01235 and AC091182.2 were identified as unfavorable prognostic factors, while the remaining six (AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1) were identified as favorable prognostic factors. Furthermore, we analyzed the relationship between cancer-related immune infiltration and the arLncRNAs risk model. Our results showed that the degree of immune infiltration was higher in the low-risk group.
Conclusions
We have identified eight arLncRNAs, namely LINC01235, AL136531.1, AL122010.1, LINC01871, MAPT-AS1, SEMA3B-AS1, ST7-AS1, and AC091182.2, which possess significant predictive value for breast cancer prognosis and response to immunotherapy. These arLncRNAs could be potentially exploited as clinical targets for the treatment of breast cancer associated with anoikis.
On the estimation of landslide intensity, hazard and density via data-driven models
Authors: Napoli Mariano Di , Tanyas Hakan , Castro-Camilo Daniela , Calcaterra Domenico , Cevasco Andrea , Martire Diego Di , Pepe Giacomo , Brandolini Pierluigi , Lombardo Luigi 
Published: June 2023

Abstract
Maps that attempt to predict landslide occurrences have essentially stayed the same since 1972. In fact, most of the geo-scientific efforts have been dedicated to improve the landslide prediction ability with models that have largely increased their complexity but still have addressed the same binary classification task. In other words, even though the tools have certainly changed and improved in fifty years, the geomorphological community addressed and still addresses landslide prediction via data-driven solutions by estimating whether a given slope is potentially stable or unstable. This concept corresponds to the landslide susceptibility, a paradigm that neglects how many landslides may trigger within a given slope, how large these landslides may be and what proportion of the given slope they may disrupt. The landslide intensity concept summarized how threatening a landslide or a population of landslide in a study area may be. Recently, landslide intensity has been spatially modeled as a function of how many landslides may occur per mapping unit, something, which has later been shown to closely correlate to the planimetric extent of landslides per mapping unit. In this work, we take this observation a step further as we use the relation between landslide intensity and planimetric extent to generate maps that predict the aggregated size of landslides per slope, and the proportion of the slope they may affect. Our findings suggest that it may be time for the geoscientific community as a whole, to expand the research efforts beyond the use of susceptibility assessment, in favor of more informative analytical schemes. In fact, our results show that landslide susceptibility can be also reliably estimated (AUC of 0.92 and 0.91 for the goodness-of-fit and prediction skill, respectively) as part of a Log-Gaussian Cox Process model, from which the intensity expressed as count per unit (Pearson correlation coefficient of 0.91 and 0.90 for the goodness-of-fit and prediction skill, respectively) can also be derived and then converted into how large a landslide or several coalescing ones may become once they trigger and propagate downhill. This chain of landslide intensity, hazard and density may lead to substantially improve decision-making processes related to landslide risk.
Interacting Topological Quantum Chemistry in 2D: Many-body Real Space Invariants
Authors: Bernevig B. Andrei , Song Zhida , Herzog-Arbeitman Jonah 
Published: June 2023

Abstract
The topological phases of non-interacting fermions have been classified by their symmetries, culminating in a modern electronic band theory where wavefunction topology can be obtained from momentum space. Recently, Real Space Invariants (RSIs) have provided a spatially local description of the global momentum space indices. The present work generalizes this real space classification to interacting 2D states. We construct many-body local RSIs as the quantum numbers of a set of symmetry operators on open boundaries, but which are independent of the choice of boundary. Using the U(1) particle number, they yield many-body fragile topological indices, which we use to identify which single-particle fragile states are many-body topological or trivial at weak coupling. To this end, we construct an exactly solvable Hamiltonian with single-particle fragile topology that is adiabatically connected to a trivial state through strong coupling. We then define global many-body RSIs on periodic boundary conditions. They reduce to Chern numbers in the band theory limit, but also identify strongly correlated stable topological phases with no single-particle counterpart. Finally, we show that the many-body local RSIs appear as quantized coefficients of Wen-Zee terms in the topological quantum field theory describing the phase.
Textural Image-Based Feature Prediction Model for Stochastic Streamflow Synthesis
Authors: Piran Shirin , Panu Umed 
Published: June 2023

Abstract
To address the challenge of obtaining reliable streamflow data for water resource management, this paper develops an encoding scheme to transform a streamflow time series into an 8-bit grayscale image where it is feasible to develop a correlation between neighbouring pixels to reveal patterns that are not easily discernable in traditional time series analysis. To extract relevant information from an encoded streamflow image, a novel textural feature extraction approach has been developed for synthesizing streamflow data. The developed textural feature extraction model can capture the simultaneous correlation in two dimensions of an encoded streamflow image, which is then transformed into the frequency domain using a discrete Fourier transform. The use of the power spectrum of the Fourier coefficients facilitates the synthesis of encoded streamflow images. The effectiveness of the model is evaluated using three case studies across Canada by comparing the properties of synthesized streamflow with the historical streamflow using the structural similarity (SSIM) index. Results show that the proposed model effectively synthesizes encoded streamflow with high SSIM values for the Fraser, Black, and South Saskatchewan Rivers. The results also affirm that the model reproduces the temporal dependence and correlation structure of the historical streamflow and the average of 30 synthesized realizations up to more than 100 lags. The null hypothesis tests support the conclusion that there is statistically no significant difference between the synthesized monthly streamflow time series and the historical time series. In addition, the entropy-based test also emphasizes that synthesized and historical streamflow are indistinguishable.