bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hysteresis Behaviour of Precast Shear Wall – Slab Connection under Reverse Cyclic Loading
Authors: Arthi S , Jaya K P 
Published: October 2020

Abstract
An experimental study focused on precast shear wall-slab joint failure is presented in this paper. For the precast specimen, the shear wall and slab are precast members and were connected with cast-in situ concrete using dowel bars. The specimen was tested under reverse cyclic loading and the structural behaviour of the connection was evaluated. The parameters considered are cracking pattern, hysteresis behaviour, energy dissipation and ductility. This study also aimed to develop 3-D finite element (FE) model of precast shear wall – slab dowel connection using ABAQUS software in order to investigate the joint failure of the specimen such as damage and hysteresis behaviour. The non-linear behaviour of concrete was defined using concrete damaged plasticity (CDP) model and the interactions between the precast members were modelled using cohesive element. FE model was then validated against the experimental results of precast dowel connection which are vulnerable to wall-slab joint shear failure. The failure pattern of the tested specimen showed the “Strong joint-Weak member” concept. The comparison between experimental and numerical results showed that the FE model developed was capable of simulating the seismic performance and the joint failure of precast shear wall-slab connection using dowel bars.
Deep Learning-based Metamodeling Technique for Nonlinear Seismic Response Quantification
Authors: Kundu Anirban , Chakraborty Subrata 
Published: October 2020

Abstract
Classical physics-based numerical techniques such as finite element method (FEM) usually takes a huge computational resource and time for simulation-based uncertainties in structural analysis. Especially, it is expensive when nonlinear time history analysis is involved. The metamodeling technique becomes an alternative with an ability to predict the time history response of both elastic and inelastic structural system in a data-driven fashion. Various machine learning and deep learning-based techniques have been attempted to have their limitations in handling huge and sequential data while it comes to predict the whole response time history given stochastic ground motion acceleration. In this regard, Long Short Term Memory (LSTM) approach is found to be useful. The present study used LSTM deep learning network based metamodeling approach for nonlinear seismic response history approximation. The novelty of the approach lies in its capability of capturing record to record variability even for an inelastic structure. This metamodel can work with the desired level of accuracy with very limited data. The proposed approach has shown satisfactory results to approximate seismic response of a nonlinear single degree of freedom system.
Failure Mode Recognition of Columns Using Artificial Neural Network
Authors: Edward Chippy , Balu A S 
Published: October 2020

Abstract
Columns are one of the most vital segments in bridgessince its post-seismic behaviour is of much importance. The retrofitting methods and rehabilitation strategies of bridges mainly rely on the identification of the failure mode of columns. It has been witnessed in various studies on columns that the mode of failure highly depends on section and material properties and there is no specific boundary between the modes, which makes their identification more sophisticated. This paper uses an artificial neural network to predict the modes of failure by analysing the effects of such soft computing methods. In this study, machine- learning models were generated from the experimental data of 253 columns of rectangular cross-section and its accuracy of failure mode prediction was evaluated by considering failure modes mainly flexure, flexure-shear, and shear. The optimal input parameters have also been evaluated for the machine-learning algorithm that enhances the efficiency of failure mode prediction.
A Review on Strut and Tie Methods
Authors: Bhanu Kannan C , Ganesan N , Indira P V 
Published: October 2020

Abstract
Strut and tie method (STM) provides simplified solutions to complicated design problems. It was an evolutionary discovery which lead to an improved understanding of shear and flexural deformations of the structures. STM is applicable to the regions of the structure where beam theory is not valid. Numerous studies were conducted in this field, and various modifications are being attached to the conventional STM. Indeterminate models replaced determinate models, and nonlinear analysis replaced linear elastic analysis. New ideas like automatic generation of models based on optimisation techniques, simplified inelastic analysis using secant stiffness method and 3-dimensional analysis were also introduced. This paper summarises some of these studies and discusses their merits and limitations. Some suggestions to simplify these complicated methods are also included.
Optimal Rate of Printing of 3D Printed Concrete Columns and Walls to Avoid Buckling
Authors: Phadnis K P , Shariff M N , Menon D 
Published: October 2020

Abstract
Extrusion based construction of concrete structures has been identified as one of the alternative construction technologies which reduces the construction costs, delays in construction and the material consumption. The most attractive feature of 3D printed constructions is the ease of developing intricate forms and shapes (which otherwise require skilled manpower). Since this is a relatively new technology, not much research has been done with regard to its structural performance and stability. One of the major concerns is the possibility of buckling (under its self-weight) of the column or wall during the extrusion process. This sets a limitation of the acceptable height, thickness and rate of extrusion process. In this study simple ways (second order analysis using matrix methods) have been explored to determine the critical height and the optimal rate of extrusion. Matrix methods have inherent limitations due to the simplification adopted by ignoring the higher-order terms in Taylor’s series. The study uses the evolution of modulus of elasticity of concrete as proposed in literature. The analysis method has been validated using pertinent buckling tests reported in literature. Parameters such as modulus of elasticity and length are considered to determine the critical height to thickness of the wall and the optimal rate of extrusion. The problem is solved using the MATLAB software. This method requires minimal input from the user such as material properties, geometry and printing speed. The analysis method used in this study provides a useful design tool to estimate the limiting parameters of the structural walls and columns manufactured through the process of 3D printing.
A Comparative Study of Aerodynamic Coefficients on Tall Structures using Experimental Studies with International Codes and Standards
Authors: Nair Shalini R , Rooby Jessy , Branesh R John 
Published: October 2020

Abstract
This paper illustrates the comparison of aerodynamic forces and coefficients on rectangular tall buildings. The forces and coefficients were obtained using Wind Tunnel Experiment and various international code provisions and standards. Rectangular tall building models selected for the study were made of acrylic sheets with various geometric scales. The models were tested using low-speed wind tunnel for various angles of incidents of wind varying from 0° to 90°. Drag coefficients obtained from experiments were compared with values of mean drag coefficients obtained from codes and practices, (a) IS: 875(Part3): Wind Loads on Buildings and Structures (India, 2015), (b) AS/NZS1170.2:2011, the combined Australia/New Zealand Standard on Wind Actions (Standards Australia,2011); (c) ASCE7–10 (ASCE, 2010); and (d) HKCoP-2019, the Code of Practice on Wind Effects in Hong Kong, (Buildings Department HongKong, 2019). Buildings with the same plan aspect ratio show the same aerodynamic behaviour. Wind tunnel study over-predicts the coefficient values compared to standards and practices. The average coefficient of drag values from codes is in good agreement with wind tunnel data with a deviation of less than 15%. The study also includes an examination of parameters such as pitching moment and its coefficient.
A fuller picture of COVID-19 prognosis: the added value of vulnerability measures to predict mortality in hospitalised older adults
Authors: Aliberti Márlon Juliano Romero , Covinsky Kenneth E , Garcez Flavia Barreto , Smith Alexander K , Curiati Pedro Kallas , Lee Sei J , Dias Murilo Bacchini , Melo Victor José Dornelas , Rego-Júnior Otávio Fortes do , Richinho Valéria de Paula , Jacob-Filho Wilson , Avelino-Silva Thiago J 
Published: October 2020

Abstract

Background
Although coronavirus disease 2019 (COVID-19) disproportionally affects older adults, the use of conventional triage tools in acute care settings ignores the key aspects of vulnerability.

Objective
This study aimed to determine the usefulness of adding a rapid vulnerability screening to an illness acuity tool to predict mortality in hospitalised COVID-19 patients.

Design
Cohort study.

Setting
Large university hospital dedicated to providing COVID-19 care.

Participants
Participants included are 1,428 consecutive inpatients aged ≥50 years.

Methods
Vulnerability was assessed using the modified version of PRO-AGE score (0–7; higher = worse), a validated and easy-to-administer tool that rates physical impairment, recent hospitalisation, acute mental change, weight loss and fatigue. The baseline covariates included age, sex, Charlson comorbidity score and the National Early Warning Score (NEWS), a well-known illness acuity tool. Our outcome was time-to-death within 60 days of admission.

Results
The patients had a median age of 66 years, and 58% were male. The incidence of 60-day mortality ranged from 22% to 69% across the quartiles of modified PRO-AGE. In adjusted analysis, compared with modified PRO-AGE scores 0–1 (‘lowest quartile’), the hazard ratios (95% confidence interval) for 60-day mortality for modified PRO-AGE scores 2–3, 4 and 5–7 were 1.4 (1.1–1.9), 2.0 (1.5–2.7) and 2.8 (2.1–3.8), respectively. The modified PRO-AGE predicted different mortality risk levels within each stratum of NEWS and improved the discrimination of mortality prediction models.

Conclusions
Adding vulnerability to illness acuity improved accuracy of predicting mortality in hospitalised COVID-19 patients. Combining tools such as PRO-AGE and NEWS may help stratify the risk of mortality from COVID-19.
Is there a dose–response relationship between musical instrument playing and later-life cognition? A cohort study using EPIC-Norfolk data
Authors: Walsh Sebastian , Luben Robert , Hayat Shabina , Brayne Carol 
Published: October 2020

Abstract

Introduction
Musical instrument playing provides intellectual stimulation, which is hypothesised to generate cognitive reserve that protects against cognitive impairment. Studies to date have classified musicianship as a binary entity. This investigation draws on the dataset of the European Prospective Investigation of Cancer Norfolk study to examine the effect of frequency of playing on later-life cognition.

Methods
We compared three categorisations of self-reported musical playing frequency in late mid-life (12-month period) against cognitive performance measured after a 4–11 year delay, adjusted for relevant health and social confounders. Logistic regression models estimated the adjusted association between frequency of musical playing and the likelihood of being in the top and bottom cognitive deciles.

Results
A total of 5,693 participants (745 musicians) provided data on music playing, cognition and all co-variables. Classification of musicianship by frequency of playing demonstrated key differences in socio-demographic factors. Musicians outperformed non-musicians in cognition generally. Compared with non-musicians, frequent musicians had 80% higher odds of being in the top cognitive decile (OR 1.80 [95% CI 1.19–2.73]), whereas musicians playing at any frequency had 29% higher odds (95% CI 1.03–1.62). There was evidence of a threshold effect, rather than a linear dose–response relationship.

Discussion
This study supports a positive association between late mid-life musical instrument playing and later-life cognition, although causation cannot be assumed. Musicians playing frequently demonstrated the best cognition. ‘Musicians’ are a heterogeneous group and frequency of music playing seems a more informative measure than binary classification. Ideally, this more nuanced measure would be collected for different life course phases.
Review of nonopioid multimodal analgesia for surgical and trauma patients
Authors: George Stephy , Johns Meagan 
Published: October 2020

AbstractPurposePain is a frequent finding in surgical and trauma patients, and effective pain control remains a common challenge in the hospital setting. Opioids have traditionally been the foundation of pain management; however, these agents are associated with various adverse effects and risks of dependence and diversion.SummaryIn response to the rising national opioid epidemic and the various risks associated with opioid use, multimodal pain management through use of nonopioid analgesics such as acetaminophen, nonsteroidal anti-inflammatory drugs, α 2 agonists, N-methyl-d-aspartate (NMDA) receptor antagonists, skeletal muscle relaxants, sodium channel blockers, and local anesthetics has gained popularity recently. Multimodal analgesia has synergistic therapeutic effects and can decrease adverse effects by enabling use of lower doses of each agent in the multimodal regimen. This review discusses properties of the various nonopioid analgesics and encourages pharmacists to play an active role in the selection, initiation, and dose-titration of multimodal analgesia. The choice of nonopioid agents should be based on patient comorbidities, hemodynamic stability, and the agents’ respective adverse effect profiles. A multidisciplinary plan for management of pain should be formulated during transitions of care and is an area of opportunity for pharmacists to improve patient care.ConclusionMultimodal analgesia effectively treats pain while decreasing adverse effects. There is mounting evidence to support use of this strategy to decrease opioid use. As medication experts, pharmacists can play a key role in the selection, initiation, and dose-titration of analgesic agents based on patient-specific factors.
Dexmedetomidine as add-on sedation to reduce continuous infusion sedative use in mechanically ventilated patients
Authors: Park Jung Hoon , Derry Katrina L , Owens Robert L 
Published: October 2020

Abstract

Purpose
To characterize the impact of add-on dexmedetomidine therapy on baseline continuous infusion sedative use.

Methods
A retrospective, single-center, chart review–based study was conducted to assess outcomes of and potential predictors of response to add-on dexmedetomidine therapy in mechanically ventilated intensive care unit (ICU) patients who were already receiving continuous infusions of sedatives. Patients were defined as complete, partial, or nonresponders to add-on dexmedetomidine therapy if initial sedative infusion rates were reduced by 100%, by 50% to 99%, and by less than 50%, respectively, at 6 and 24 hours after initiation of dexmedetomidine.

Results
Among the 100 patients included in the study sample, there were 54 complete responders, 21 partial responders, and 25 nonresponders to dexmedetomidine add-on therapy at 6 hours after dexmedetomidine initiation; at 24 hours, there were 65 complete and 12 partial responders and 23 nonresponders. Of the variables tested (ie, baseline characteristics, opioid and antipsychotic use, hemodynamic parameters), none differentiated between complete or partial responders and nonresponders. Ventilator time, ICU length of stay (LOS), and hospital LOS after add-on dexmedetomidine therapy initiation were shorter among both partial responders and complete responders vs nonresponders (median, 1.1 days vs 4.1 days [P = 0.01], 7.0 days vs 14.1 days [P = 0.20], and 11.0 vs 17.0 days [P = 0.58], respectively), with only ventilator time being significantly different.

Conclusion
Add-on dexmedetomidine therapy can obviate or reduce the need for alternate sedation in as many as 75% of mechanically ventilated ICU patients. However, the addition of dexmedetomidine does not allow the reduction of alternate sedation in a substantial minority of patients, and failure to respond to dexmedetomidine can be identified as early at 6 hours after add-on therapy initiation. In the absence of clear predictors of response to dexmedetomidine, these data suggest empiric trials of dexmedetomidine can be considered but should be time-limited.
Treatment of chronic hepatitis C virus infection with crushed ledipasvir/sofosbuvir administered through a percutaneous endoscopic gastrostomy tube in a patient with HIV coinfection
Authors: Huffman Vanessa , Andrade Diana C , Sherman Elizabeth , Niu Jianli , Eckardt Paula A 
Published: October 2020

Abstract

Purpose
Ledipasvir/sofosbuvir is an oral combination therapy containing fixed doses of direct-acting antiviral agents indicated for the treatment of hepatitis C virus (HCV) infection. Currently there are limited data on the clinical efficacy of crushed ledipasvir/sofosbuvir administered via feeding tube.

Summary
This case report discusses the successful treatment of chronic HCV genotype 1b infection with crushed ledipasvir/sofosbuvir administered through a percutaneous endoscopic gastrostomy (PEG) tube in a patient with human immunodeficiency virus (HIV) coinfection and high-grade sarcoma who had severe swallowing difficulties. The patient received crushed ledipasvir/sofosbuvir daily for a total of 12 weeks. At 12 weeks the patient had achieved a sustained virologic response.

Conclusion
Currently, ledipasvir/sofosbuvir is available only as a tablet, with limited pharmacokinetic data available to guide clinicians on use of the fixed-dose combination medication in crushed form. This case report highlights our experience treating a patient with HCV/HIV coinfection through administration of crushed ledipasvir/sofosbuvir via PEG tube, which we found to be a safe and effective therapeutic option.
DeepBL: a deep learning-based approach for in silico discovery of beta-lactamases
Authors: Wang Yanan , Li Fuyi , Bharathwaj Manasa , Rosas Natalia C , Leier André , Akutsu Tatsuya , Webb Geoffrey I , Marquez-Lago Tatiana T , Li Jian , Lithgow Trevor , Song Jiangning 
Published: October 2020

Abstract
Beta-lactamases (BLs) are enzymes localized in the periplasmic space of bacterial pathogens, where they confer resistance to beta-lactam antibiotics. Experimental identification of BLs is costly yet crucial to understand beta-lactam resistance mechanisms. To address this issue, we present DeepBL, a deep learning-based approach by incorporating sequence-derived features to enable high-throughput prediction of BLs. Specifically, DeepBL is implemented based on the Small VGGNet architecture and the TensorFlow deep learning library. Furthermore, the performance of DeepBL models is investigated in relation to the sequence redundancy level and negative sample selection in the benchmark dataset. The models are trained on datasets of varying sequence redundancy thresholds, and the model performance is evaluated by extensive benchmarking tests. Using the optimized DeepBL model, we perform proteome-wide screening for all reviewed bacterium protein sequences available from the UniProt database. These results are freely accessible at the DeepBL webserver at http://deepbl.erc.monash.edu.au/.
Single-cell RNA-seq data semi-supervised clustering and annotation via structural regularized domain adaptation
Authors: Chen Liang , He Qiuyan , Zhai Yuyao , Deng Minghua 
Published: October 2020

Abstract

Motivation
The rapid development of single-cell RNA sequencing (scRNA-seq) technologies allows us to explore tissue heterogeneity at the cellular level. The identification of cell types plays an essential role in the analysis of scRNA-seq data, which, in turn, influences the discovery of regulatory genes that induce heterogeneity. As the scale of sequencing data increases, the classical method of combining clustering and differential expression analysis to annotate cells becomes more costly in terms of both labor and resources. Existing scRNA-seq supervised classification method can alleviate this issue through learning a classifier trained on the labeled reference data and then making a prediction based on the unlabeled target data. However, such label transference strategy carries with risks, such as susceptibility to batch effect and further compromise of inherent discrimination of target data.

Results
In this article, inspired by unsupervised domain adaptation, we propose a flexible single cell semi-supervised clustering and annotation framework, scSemiCluster, which integrates the reference data and target data for training. We utilize structure similarity regularization on the reference domain to restrict the clustering solutions of the target domain. We also incorporates pairwise constraints in the feature learning process such that cells belonging to the same cluster are close to each other, and cells belonging to different clusters are far from each other in the latent space. Notably, without explicit domain alignment and batch effect correction, scSemiCluster outperforms other state-of-the-art, single-cell supervised classification and semi-supervised clustering annotation algorithms in both simulation and real data. To the best of our knowledge, we are the first to use both deep discriminative clustering and deep generative clustering techniques in the single-cell field.

Availabilityand implementation
An implementation of scSemiCluster is available from https://github.com/xuebaliang/scSemiCluster.

Supplementary information
Supplementary data are available at Bioinformatics online.
Distort, Extort, Deceive and Exploit: Exploring the Inner Workings of a Romance Fraud
Authors: Carter Elisabeth 
Published: October 2020

AbstractRomance fraud is a crime where the fraudster must strike a balance between the romantic and financial aspects of the communication for their criminal intent to remain hidden. This discourse analytic research examines the setup of information early in the interaction, the use of visceral language and isolation as key tactics of exploitation enabling the distortion of reality and manipulation of power. With demands shrouded in a health narrative, and secrecy urged for the preservation of the relationship and the victim’s happiness, this research reveals how the language of this financially and emotionally devastating crime involves grooming strategies akin to coercive control and domestic violence and abuse and exposes the inaccuracies of popular narratives surrounding victims and in awareness-raising and crime prevention strategies.