bluuebunny/crossref_metadata_2025_split huggingface dataset:
Assessing resident needs and faculty perceptions to improve training in rheumatology
Authors: He Lauren , Saygin Didem , Leverenz David , Quimson Laarni , Martin Shannon , Ko Kichul 
Published: November 2022

Abstract
Background
Internal medicine (IM) residents lack confidence in rheumatology. Due to the wide variety of topics in rheumatology, identifying the most important subjects to learn during training is vital to create future interventions to increase confidence and knowledge. The preferred teaching modality for both attendings/fellows and residents is not known.
Methods
An electronic survey was distributed to all IM residents, rheumatology fellows, and rheumatology faculty at the University of Chicago during the 2020–2021 academic year. Residents reported self-confidence levels on 10 rheumatology topics, while rheumatology attendings/fellows were asked to rank these from most to least important to learn during IM residency. All groups were asked preferred teaching modality.
Results
Median confidence level [interquartile range] among residents for caring for patients with rheumatological conditions was 6 [3.6–7.5] for inpatient and 5 [3.7–6.5] for outpatient settings (10 being very confident). Attendings and fellows identified the most important topics to learn during the rheumatology rotation as ordering and interpreting autoimmune serologies and musculoskeletal exam. Both attendings/fellows and residents preferred bedside teaching in the inpatient setting and case-based learning in the outpatient setting.
Conclusions
While some disease-specific topics such as autoimmune serologies were identified as important rheumatology topics for IM residents to learn, more practical topics like musculoskeletal exam skills were also deemed important. This highlights the need for comprehensive interventions that focus on more than standardized exam topics alone to improve rheumatology confidence in IM residents. There are different preferences of teaching styles in various clinical settings.
Medical Image Captioning via Generative Pretrained Transformers
Authors: Selivanov Alexander , Rogov Oleg , Chesakov Daniil , Shelmanov Artem , Fedulova Irina , Dylov Dmitry V. 
Published: November 2022

Abstract
The automatic clinical caption generation problem is referred to as proposed model combining the analysis of frontal chest X-Rayscans with structured patient information from the radiology records. We combine two language models, the Show-Attend-Telland the GPT-3, to generate comprehensive and descriptive radiology records. The proposed combination of these models generates a textual summary with the essential information about pathologies found, their location, and the 2D heatmaps localizing each pathology on the original X-Ray scans. The proposed model is tested on two medical datasets, the Open-I,MIMIC-CXR, and the general-purpose MS-COCO. The results measured with the natural language assessment metrics provetheir efficient applicability to the chest X-Ray image captioning.
High selectivity of frequency induced transcriptional responses
Authors: Givre Alan , Colman-Lerner Alejandro , Ponce-Dawson Silvina 
Published: November 2022

Abstract
Cells detect changes in their environment and generate responses. Often, gene expression is modified. In this paper we use information theory and a simple transcription model to analyze whether the resulting gene expression serves to identify extracellular stimuli and assess their intensity when they are encoded in the amplitude, duration or frequency of a transcription factor’s nuclear concentration. We find, for all cases, that about three ranges of input strengths can be distinguished and that maximum information transmission occurs for fast and high activation threshold promoters. The three input modulation modes differ in the sensitivity to changes in the promoters parameters. Frequency modulation is the most sensitive and duration modulation, the least. This is key for signal identification: there are promoter parameters that yield a relatively high information transmission for duration or amplitude modulation and a much smaller value for frequency modulation. The reverse situation cannot be found with a single promoter transcription model. Thus, pulses of transcription factors can selectively activate the “frequency-tuned” promoter while prolonged nuclear accumulation would activate several promoters simultaneously. Frequency modulation is therefore highly selective and better suited than the other encoding modes for signal identification without requiring other mediators of the transduction.
CubeSat in-orbit validation of in situ performance by high fidelity radiation modelling
Authors: Lenart Arpad , Sivasankaran Srihari , Hidding Bernhard , Oi Daniel , Ling Alexander , Neilson Peter 
Published: November 2022

Abstract
Space based quantum technologies are essential building blocks for global quantum networks. However, the optoelectronic components and devices used are susceptible to radiation damage. The SpooQy-1 CubeSat mission demonstrated polarization-based quantum entanglement correlations using avalanche photodiodes for single-photon detection. While predicting instrument performance remains a challenging part of space missions, here, we report on how in-situ dark count rate trends of two silicon Geiger-mode avalanche photodiodes (GM-APD) may be predicted using high-fidelity radiation modelling techniques. This aids the diagnosis of unexpected trends in instrument performance as we were able to support the claim that differences in radiation shielding was a major contributor to the observed in-orbit data. While low-fidelity modelling is limited to lifetime predictions, implementing high-fidelity radiation modelling can have applications beyond this for low-earth orbit CubeSats.
Simple live-imaging method for viewing the first cleavage of mouse embryos that does not require genetic manipulation
Authors: Okabe Motonari , Shirasawa Hiromitsu , Goto Mayumi , Iwasawa Takuya , Sakaguchi Taichi , Fujishima Akiko , Onodera Yohei , Makino Kenichi , Miura Hiroshi , Kumazawa Yukiyo , Takahashi Kazumasa , Terada Yukihiro 
Published: November 2022

Abstract
Time-lapse incubators have become increasingly popular in assisted reproductive technology, allowing for the observation of the developmental process, which may be useful in the selection of human embryos suitable for transplantation. Dynamic morphological changes of chromosomes and the cytoskeleton occur during early embryonic development, including in humans, and abnormalities such as embryonic chromosomal aneuploidy occur when development does not proceed normally. Chromosome and cytoskeletal dynamics are difficult to observe with time-lapse bright field monitoring. However, in recent years, live-cell imaging techniques have been used to analyse these dynamics by injecting fluorescently labelled cytoskeletal proteins or mRNA encoding fluorescein probes. These require complicated procedures and necessitate mechanical invasion of cells. Here, we introduced a fluorescence-labelled probe with cell-membrane permeability that specifically adheres to DNA and to the cytoskeleton as imaged in an incubator-integrated time-lapse confocal laser microscope observation system. This platform enabled us to analyse, in detail, the dynamics of chromosomes, microtubules, and microfilaments from the fertilized pronuclear zygote, through first cleavage, to 2-cell stage embryo. This method is simple and does not require genetic manipulation, and its application can be expected to provide novel insights into embryonic development in many mammals, including humans.
UV radiation: plant responses and an in-depth mechanism of sustainability under climatic extremities
Authors: Bera Kuntal , Ball Kakan , Ghosh Subir , Sadhukhan Sanjoy , Dutta Puspendu 
Published: November 2022

Abstract
Light is the primary element that plants need to grow, and UV rays, the harmful part of solar radiation, can decrease plant growth by slowing down the metabolic rate of photosynthesis. There are three different types of ultraviolet (UV) radiation, each with a distinct waveband: UV-A (315–400 nm), UV-B (280–315 nm), and UV-C (280 nm). Wavelength, intensity, and exposure to light have a big impact on plant quality and growth. Photoreceptors such as UV Resistance Locus 8 (UVR8), which promotes adaptation to UV-B stress, control how plants interact with UV radiation. While it is well established that UV in large amounts can harm production and quality metrics, some studies suggest that UV in moderate doses may promote biomass growth and the creation of beneficial chemicals that primarily absorb UV. It is well known that UV radiation causes differences in plant architecture, which are significant in ornamental crops and raise their economic value. Increased resistance to insects and diseases and a reduction in postharvest quality depletion are two effects of abiotic stress brought on by UV exposure. This review emphasises how UV may affect plant quality, growth, photomorphogenesis, and resistance to abiotic and biotic stress.
The unrecognized role of fidelity in effectiveness-implementation hybrid trials: simulation study and guidance for implementation researchers
Authors: Trutschel Diana , Blatter Catherine , Simon Michael , Holle Daniela , Reuther Sven , Brunkert Thekla 
Published: November 2022

Abstract
Background

Effectiveness-implementation hybrid designs are a relatively new approach to evaluate efficacious interventions in real world settings while concurrently gathering information on the implementation. Intervention fidelity can significantly influence the effectiveness of an intervention during implementation. However little guidance exists for applied researchers conducting effectiveness-implementation hybrid trials regarding the impact of fidelity on intervention effects and power.

Methods

We conducted a simulation study based on parameters from a clinical example study. For the simulation we explored parallel and stepped-wedge cluster randomized trials (CRTs) and hypothetical patterns of fidelity increase during implementation: slow, linear and fast. Based on fixed design parameters, i.e. the number of clusters (C = 6), time points (T = 7) and patients per cluster (n = 10) we used linear mixed models to estimate the intervention effect and calculated the power for different fidelity patterns. Further, we conducted a sensitivity analysis to compare outcomes based on different assumptions for the intracluster-correlation coefficient and the cluster size.

Results

Ensuring high fidelity from the beginning is central to achieve accurate intervention effect estimates in stepped-wedge and parallel CRTs. The importance of high fidelity in the earlier stages is more emphasized in stepped-wedge designs than in parallel CRTs. In contrast, if the increase of fidelity is too slow despite relatively high starting levels, the study will likely be underpowered and the intervention effect estimates will also be biased. This effect is more accentuated in parallel CRTs, here reaching 100% fidelity within the next measurement points is crucial.

Conclusions

This study discusses the importance of intervention fidelity for the study`s power and highlights different recommendations to deal with low fidelity in parallel and stepped-wedge CRTs from a design perspective. Applied researchers should consider the detrimental effect of low fidelity in their evaluation design. Overall, there are fewer options to adjust the trial design after the fact in parallel CRT as compared to stepped-wedge CRTs. Particular emphasis should be placed on the selection of contextually relevant implementation strategies.
Transrectal versus transperineal prostate biopsy in detection of prostate cancer: a retrospective study based on 452 patients
Authors: Lu Mengxin , Luo Yi , Wang Yongzhi , Yu Jingtian , Zheng Hang , Yang Zhonghua 
Published: November 2022

Abstract
Background Transrectal (TR) ultrasound guided prostate biopsy and transperineal (TP) ultrasound guided prostate biopsy are the two most commonly used methods to detect prostate cancer, the detection rate of the two biopsy approaches may differ in patients with different clinical characteristics. Here we aimed to compare the prostate cancer detection rate and positive rate of biopsy cores between TR and TP prostate biopsy in patients with different clinical characteristics.
Methods We retrospectively analyzed and compared the clinical data of 452 patients underwent TR or TP prostate biopsy in our hospital from June 2017 to September 2021. And patients were stratified according to several clinical characteristic (serum PSA level, prostate volume, PSA density, PI-RADS score, T stage and ISUP grade), cancer detection rate and positive rate of biopsy cores were compared in different stratified groups.
Results There was no significant difference in age, PSA level, prostate volume, and PSA density between the TR and TP groups. TR group had a higher overall cancer detection rate and positive rate of biopsy cores than TP group. Further subgroup analysis showed that TR group had a higher cancer detection rate in patients with PI-RADS score of > 3, prostate volumes > 30 mL, and that the TR group had a higher positive rate of biopsy cores among the patients with T3-T4 stages, while TP group had a higher positive rates of biopsy cores among the patients with T1-T2 stages.
Conclusions TR approach may have advantage in patients with PI-RADS score of > 3, prostate volumes > 30 mL and T3-T4 stages, while TP approach may have advantage in patients with T1-T2 stages.
Parametric and Non-parametric Analysis on RHMFO based Optimal Detection of BorderGateway Protocol Anomalies
Authors: Mala Sunita. , Chairperson India Department of ISE Sharnbasava University Kalaburgi Suj 
Published: November 2022

Abstract
For secure and effective Internet access, the Border Gateway Protocol (BGP) must be able to recognize and stop strange coincidences in real time. Regardless, over the past ten years, more study has focused on spotting BGP abnormalities; because of the rise of new bizarre behaviour from both hackers and network configuration errors, it continues to be more challenging. This work goes with the parametric and non parametric analysis of RHMFO based Optimal Detection of BGP Anomalieswith two major steps (i) Feature Extraction (ii) Anomaly Detection". Initially, extensive features such "statistical features, higher order statistical features, and correntropy features" are extracted during the feature extraction stage. For the detection process, an optimized DBN is proposed to define the presence of attack. Here, a hybrid optimization model known as RHMFO is introduced to fine-tune the weight of DBN in order to enhance the detection accuracy. The traditional Rider Optimization Method (ROA) and the MFO algorithm are conceptually combined to create the suggested RHMFO paradigm. Finally, in this paper, parametric and non-parametric analysis is performed. By varying the parameters of RHMFO, the performance of the suggested work is evaluated.
Towards Trustworthy Human Suspicious Activity Detection from Surveillance Videos Using Deep Learning
Authors: Buttar Ahmed Mateen , Bano Mahnoor , Akbar Muhammad Azeem , Gumaei Abdu H. 
Published: November 2022

Abstract
In today’s world suspicious or unusual activities express threat and danger to others. For the prevention from various security issues an automatic video detection system is very important. The study objective is to create an intelligent system that will take a video stream as input and detect what kind suspicious activity is happening in that particular video to reduce the time that consume on watching video. It is difficult to consecutively monitor cameras videos that recorded in public places for the detection any abnormal event so an automatic video detection system is needed for that purpose. For that purpose, deep learning-based model is the best approach. In this work we use three models Convolutional neural network (CNN) model GRU model and ConvLSTM model. These models are trained on the same dataset of 6 suspicious activities of humans that are (Running, Punching, Falling, Snatching, Kicking and Shooting). The dataset consist of various video related to each activity. Different deep learning techniques are applied in the proposed work that are preprocessing, data annotation model training and classification. The frames are extracted from the source video and then features are calculated through model known as Inception v3 which is a variant of Convolutional Neural Network. On the same dataset the CNN model attains 91.55% accuracy the ConvLSTM model attain 88.73% accuracy and the GRU model attain 84.01% accuracy. The performance of proposed models are evaluated using confusion matrix, f1-score, precision, and recall. The proposed model proved better than other models in terms of performance and accuracy. The findings of this study prove helpful unusual event by examining the abnormal behaviour of person.
Inferior vena cava distensibility from subcostal and trans-hepatic imaging using both M- mode or artificial intelligence: a prospective study on mechanically ventilated patients. Short Title: Subcostal vs transhepatic IVC evaluation
Authors: Sanfilippo Filippo , Via Luigi La , Dezio Veronica , Amelio Paolo , Genoese Giulio , Messina Antonio , Robba Chiara , Noto Alberto 
Published: November 2022

Abstract
Background: Variation of inferior vena cava (IVC) is used to predict fluid-responsiveness, but the IVC visualization with standard sagittal approach (SC, subcostal) cannot be always achieved. In such cases, coronal trans-hepatic (TH) window may offer an alternative, but the interchangeability of IVC measurements in SC and TH is not fully established. Further, artificial intelligence (AI) with automated border detection may be of clinical value but it needs validation.
Methods: Prospective observational study in mechanically ventilated patients with pressure-controlled mode. Primary outcome was the IVC distensibility (IVCd) in SC and TH imaging, with measurements taken both in M-Mode or with AI software. We calculated mean bias, limits of agreement (LoA), and intra-class correlation (ICC) coefficient.
Results: Thirty-three patients were included. Feasibility rate was 87.9% and 81.8% for SC and TH visualization, respectively. Comparing imaging from the same anatomical site acquired with different modalities (M-Mode vs AI), we found the following IVCd differences: 1)SC: mean bias -3.1%, LoA [-20.1;13.9], ICC=0.65; 2)TH: mean bias -2.0%, LoA [-19.3;15.4], ICC=0.65. When comparing the results obtained from the same modality but from different sites (SC vs TH), IVCd differences were: 3)M-Mode: mean bias 1.1%, LoA [-6.9;9.1], ICC=0.54; 4)AI: mean bias 2.0%, LoA [-25.7;29.7], ICC=0.32.
Conclusions: In patients mechanically ventilated, AI software shows good accuracy (modest overestimation) and moderate correlation as compared to M-mode assessment of IVCd, both for SC and TH windows. However, precision seems suboptimal with wide LoA. The comparison of M-Mode or AI between different sites yields similar results but with weaker correlation.
Body composition predicts hypertension using machine learning methods: A Cohort Study
Authors: Nematollahi Mohammad Ali , Jahangiri Soodeh , Asadollahi Arefeh , Salimi Maryam , Dehghan Azizallah , Mashayekh Mina , Roshanzamir Mohamad , Gholamabbas Ghazal , Alizadehsani Roohallah , Bazrafshan Mehdi , Bazrafshan Hanieh , Drissi Hamed Bazrafshan , Islam Sheikh Mohammed Shariful 
Published: November 2022

Abstract
Introduction: We used machine learning methods to investigate if body composition indices predict hypertension.
Methods: Data from a cohort study was used, and 4663 records were included (2156 were male, 1099 with hypertension, with the age range of 35-70 years old). Body composition analysis was done using bioelectrical impedance analysis (BIA); weight, basal metabolic rate, total and regional fat percentage (FATP), and total and regional fat-free mass (FFM) were measured. We used machine learning methods such as Support Vector Classifier, Decision Tree, Stochastic Gradient Descend Classifier, Logistic Regression, Gaussian Naïve Bayes, K-Nearest Neighbor, Multi-Layer Perceptron, Random Forest, Gradient Boosting, Histogram-based Gradient Boosting, Bagging, Extra Tree, Ada Boost, Voting and Stacking to classify the investigated cases and find the most relevant features to hypertension.
Results: FATP, AFFM, BMR, FFM, TRFFM, AFATP, LFATP, and older age were the top features in hypertension prediction. Arm FFM, basal metabolic rate, total FFM, Trunk FFM, leg FFM, and male gender were inversely associated with hypertension, but total FATP, arm FATP, leg FATP, older age, trunk FATP, and female gender were directly associated with hypertension. Ensemble methods such as voting and stacking had the best performance for hypertension prediction. Stacking showed an accuracy rate of 79%.
Conclusion: By using machine learning methods, we found that BIA-derived body composition indices predict hypertension with an acceptable accuracy.
CAG (cytarabine, aclarubicin and granulocyte colony-stimulating factor) regimen for core binding factor acute myeloid leukaemia with measurable residual disease
Authors: Shen Yao-jia , Zhang Yi , Chang Jie , Wang Hua-feng , Zhu Li , Ye Xing-nong , Jin Jie , Zhu Hong-hu 
Published: November 2022

Abstract
Background: Acute myeloid leukaemia (AML) with t (8;21) or inv (16), called core binding factor (CBF) AML, has a favourable prognosis. However, some CBF-AML patients have persistent measurable residual disease (MRD) and are more likely to relapse after standard chemotherapy treatment. The CAG regimen, composed of cytarabine, aclarubicin and granulocyte colony-stimulating factor, has been proven to be effective and safe in treating refractory AML patients.
Method: We performed a retrospective study to evaluate the efficacy of the CAG regimen to eliminate MRD detected by RUNX1::RUNX1T1 and CBFβ::MYH11 transcript levels by quantitative polymerase chain reaction (Q-PCR) among 23 patients. Molecular response was defined as the ratio of fusion transcript after treatment to that before treatment greater than or equal to 0.5.
Result: The molecular response rate and median decrease ratio of fusion transcripts at themolecular level of the CAG regimen were 52.5% and 0.53, respectively. Among the 15 patients who had a poor molecular response to the high/intermediate-dose cytarabine regimen, the median decrease ratios of transcripts at the molecular level of high/intermediate-dose cytarabine and CAG were 1.55 and 0.53 (P=0.028), respectively, and 6 of 15 patients achieved amolecular response to CAG (40%). The median disease-free survival was 18 months, and the overall survival rate at three years among all patients was 72.7%±10.7%. The common treatment-related adverse events were nausea (100%), thrombocytopenia (39%) and neutropenia (37.5%).
Conclusion: The CAG regimen may have activity in CBF-AML patients and could provide a new option for patients who have a poor molecular response to high/intermediate-dose cytarabine.