bluuebunny/crossref_metadata_2025_split huggingface dataset:
Household air pollution in India and respiratory diseases: current status and future directions
Authors: Jindal Surinder K. , Aggarwal Ashutosh N. , Jindal Aditya 
Published: November 2019

Purpose of review
Combustion of solid cooking fuels employed by more than 3 billion people globally, contributes to approximately one third of ambient air pollution. In the recent past, the issue has drawn global attention because of its threat to the health of rural communities, particularly women and children. This review is focused on the evidence from India on effects of household air pollution (HAP) on respiratory health and interventions to replace the solid fuels.

Recent findings
HAP exposure is a major risk factor for increased respiratory symptoms, respiratory infections, and chronic obstructive pulmonary disease. In most studies, the odds ratio for the risk of development of respiratory disorders is more than one in HAP exposed individuals. HAP is also associated with increased risk of tuberculosis, asthma, mortality from cardio-respiratory illnesses, and nonrespiratory problems such as adverse pregnancy outcomes, prematurity, and low birth weight.

Summary
Household air pollution is a common but preventable risk factor for respiratory diseases. Replacement of solid cooking fuels with clean fuels such as LPG gas as exemplified by the ‘Ujjwala’ program of India is likely to be most effective intervention to reduce the HAP related disease burden.
Regional brain volumetric changes despite 2 years of treatment initiated during acute HIV infection
Authors: Kallianpur Kalpana J. , Jahanshad Neda , Sailasuta Napapon , Benjapornpong Khunthalee , Chan Phillip , Pothisri Mantana , Dumrongpisutikul Netsiri , Laws Elizabeth , Ndhlovu Lishomwa C. , Clifford Katherine M. , Paul Robert , Jagodzinski Linda , Krebs Shelly , Ananworanich Jintanat , Spudich Serena , Valcour Victor 
Published: November 2019

Objective:
To assess changes in regional brain volumes after 24 months among individuals who initiated combination antiretroviral therapy (cART) within weeks of HIV exposure.

Design:
Prospective cohort study of Thai participants in the earliest stages of HIV-1infection.

Methods:
Thirty-four acutely HIV-infected individuals (AHI; Fiebig I–V) underwent brain magnetic resonance (MR) imaging and MR spectroscopy at 1.5 T and immediately initiated cART. Imaging was repeated at 24 months. Regional brain volumes were quantified using FreeSurfer's longitudinal pipeline. Voxel-wise analyses using tensor-based morphometry (TBM) were conducted to verify regional assessments. Baseline brain metabolite levels, blood and cerebrospinal fluid biomarkers assessed by ELISA, and peripheral blood monocyte phenotypes measured by flow cytometry were examined as predictors of significant volumetric change.

Results:
Participants were 31 ± 8 years old. The estimated mean duration of infection at cART initiation was 15 days. Longitudinal analyses revealed reductions in volumes of putamen (P < 0.001) and caudate (P = 0.006). TBM confirmed significant atrophy in the putamen and caudate, and also in thalamic and hippocampal regions. In exploratory post-hoc analyses, higher baseline frequency of P-selectin glycoprotein ligand-1 (PSGL-1)-expressing total monocytes correlated with greater caudate volumetric decrease (ρ = 0.67, P = 0.017), whereas the baseline density of PSGL-1-expressing inflammatory (CD14+CD16+) monocytes correlated with putamen atrophy (ρ = 0.65, P = 0.022).

Conclusion:
Suppressive cART initiated during AHI may not prevent brain atrophy. Volumetric decrease appears greater than expected age-related decline, although examination of longitudinal change in demographically similar HIV-uninfected Thai individuals is needed. Mechanisms underlying progressive HIV-related atrophy may include early activation and enhanced adhesive and migratory capacity of circulating monocyte populations.
Using a Dual-Input Convolutional Neural Network for Automated Detection of Pediatric Supracondylar Fracture on Conventional Radiography
Authors: Choi Jae Won , Cho Yeon Jin , Lee Seowoo , Lee Jihyuk , Lee Seunghyun , Choi Young Hun , Cheon Jung-Eun , Ha Ji Young 
Published: November 2019

Objectives
This study aimed to develop a dual-input convolutional neural network (CNN)–based deep-learning algorithm that utilizes both anteroposterior (AP) and lateral elbow radiographs for the automated detection of pediatric supracondylar fracture in conventional radiography, and assess its feasibility and diagnostic performance.

Materials and Methods
To develop the deep-learning model, 1266 pairs of AP and lateral elbow radiographs examined between January 2013 and December 2017 at a single institution were split into a training set (1012 pairs, 79.9%) and a validation set (254 pairs, 20.1%). We performed external tests using 2 types of distinct datasets: one temporally and the other geographically separated from the model development. We used 258 pairs of radiographs examined in 2018 at the same institution as a temporal test set and 95 examined between January 2016 and December 2018 at another hospital as a geographic test set. Images underwent preprocessing, including cropping and histogram equalization, and were input into a dual-input neural network constructed by merging 2 ResNet models. An observer study was performed by radiologists on the geographic test set. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the model and human readers were calculated and compared.

Results
Our trained model showed an AUC of 0.976 in the validation set, 0.985 in the temporal test set, and 0.992 in the geographic test set. In AUC comparison, the model showed comparable results to the human readers in the geographic test set; the AUCs of human readers were in the range of 0.977 to 0.997 (P's > 0.05). The model had a sensitivity of 93.9%, a specificity of 92.2%, a PPV of 80.5%, and an NPV of 97.8% in the temporal test set, and a sensitivity of 100%, a specificity of 86.1%, a PPV of 69.7%, and an NPV of 100% in the geographic test set. Compared with the developed deep-learning model, all 3 human readers showed a significant difference (P's < 0.05) using the McNemar test, with lower specificity and PPV in the model. On the other hand, there was no significant difference (P's > 0.05) in sensitivity and NPV between all 3 human readers and the proposed model.

Conclusions
The proposed dual-input deep-learning model that interprets both AP and lateral elbow radiographs provided an accurate diagnosis of pediatric supracondylar fracture comparable to radiologists.
Images in Scalp Ultrasound Before and After Hair Transplant in Frontal Fibrosing Alopecia
Authors: Whittle Carolina , Castro Alex , Larrondo Jorge 
Published: November 2019

Abstract
Primary cicatricial alopecias are inflammatory scalp conditions that may lead to permanent hair loss and severe alopecia. Frontal fibrosing alopecia is one of the most frequent etiologies of primary cicatricial alopecias. Hair transplant could be a therapeutic option.
High-resolution ultrasound (US) is an imaging technique useful for evaluating scalp with adequate differentiation of the skin layers. Also, hair follicles are visualized.
The objectives are present presurgical and posttransplant scalp US studies on hair transplant.
Frontal fibrosing alopecia is a chronic entity that can progress to extensive irreversible alopecia. Hair transplant is a surgical option. On US, the hair follicles are visualized as thin hypoechoic lines. With Doppler color imaging, it is possible to evaluate vascularization. In this experience, we can observe the follicles of the compromised area before and after the hair transplant and the depth of the implanted follicles.
In conclusion, US impresses as a promissory technique for the imaging of hair transplant. More experience is necessary for a better approach of these findings.
Peripheral Serum Metabolomic Profiles Inform Antecedent Central Cognitive Impairment in Older Adults
Authors: Wang Jingye , Wei Runmin , Xie Guoxiang , Arnold Matthias , Louie Gregory , MahmoudianDehkordi Siamak , Blach Colette , Baillie Rebecca , Han Xianlin , De Jager Philip L. , Bennett David A. , Kaddurah-Daouk Rima , Jia Wei ,   for the Alzheimer’s Disease Metabolomics Consortium
Published: November 2019

AbstractThe incidence of Alzheimer’s disease (AD) increases with age and is a significant cause of worldwide morbidity and mortality. However, the metabolic perturbations behind the onset of AD remains unclear. In this study, we performed metabolite profiling in both brain (n = 109) and matching serum samples (n = 566) to identify differentially expressed metabolites and metabolic pathways associated with neuropathology and cognitive performance and to identify individuals at high risk of developing cognitive impairment. The abundances of six metabolites, GLCA, petroselinic acid, linoleic acid, myristic acid, palmitic acid, and palmitoleic acid as well as the DCA/CA ratio, along with the dysregulation scores of three metabolic pathways, primary bile acid biosynthesis, fatty acid biosynthesis, and biosynthesis of unsaturated fatty acids showed significant differences in diagnostic groups across both brain and serum (P-value < 0.05).Significant associations were observed between the levels of differential metabolites/pathways and cognitive performance, neurofibrillary tangles, and neuritic plaque burden. Metabolites abundances and personalized metabolic pathways scores were used to derive machine learning models that could be used to differentiate cognitively impaired persons from those without cognitive impairment (median of AUC = 0.772 for the metabolite level model; median of AUC = 0.731 for the pathway level model). Utilizing these two models on the entire baseline control group, we identified those who experienced cognitive decline (AUC = 0.804, sensitivity = 0.722, specificity = 0.749 for the metabolite level model; AUC = 0.778, sensitivity = 0.633, specificity = 0.825 for the pathway level model) and demonstrated their pre-AD onset prediction potentials. Our study provides a proof-of-concept that it is possible to discriminate antecedent cognitive impairment in older adults before the onset of overt clinical symptoms using metabolomics. Our findings, if validated in future studies, could enable the earlier detection and intervention of cognitive impairment that may halt its progression.
Antibiotic resistant and extended-spectrum β-lactamase producing faecal coliforms in wastewater treatment plant effluent
Authors: Smyth Cian , O’Flaherty Aidan , Walsh Fiona , Do Thi Thuy 
Published: November 2019

AbstractWastewater treatment plants (WWTPs) provide optimal conditions for the maintenance and spread of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). In this work we describe the occurrence of antibiotic resistant faecal coliforms and their mechanisms of antibiotic resistance in the effluent of two urban WWTPs in Ireland. Effluent samples were collected from two WWTPs in Spring and Autumn of 2015 and 2016. The bacterial susceptibility patterns to 13 antibiotics were determined. The phenotypic tests were carried out to identify AmpC or extended-spectrum β-lactamase (ESBL) producers. The presence of ESBL genes were detected by PCR. Plasmids carrying ESBL genes were transformed intoEscherichia coliDH5α recipient and underwent plasmid replicon typing to identify incompatibility groups. More than 90% of isolated faecal coliforms were resistant to amoxicillin and ampicillin, followed by tetracycline (up to 39.82%), ciprofloxacin (up to 31.42%) and trimethoprim (up to 37.61%). Faecal coliforms resistant to colistin and imipenem were detected in all effluent samples. Up to 53.98% of isolated faecal coliforms expressed a multi-drug resistance (MRD) phenotype. AmpC production was confirmed in 5.22% of isolates. The ESBL genes were confirmed for 11.84% of isolates (9.2% of isolates carriedblaTEM, 1.4%blaSHV-12, 0.2%blaCTX-M-1and 1%blaCTX-M-15). Plasmids extracted from 52 ESBL isolates were successfully transformed into recipientE. coli. The detected plasmid incompatibility groups included the IncF group, IncI1, IncHI1/2 and IncA/C. These results provide evidence that treated wastewater is polluted with ARB and MDR faecal coliforms and are sources of ESBL-producing, carbapenem and colistin resistantEnterobacteriaceae.ImportanceAntibiotic resistant bacteria (ARB) are an emerging environmental concern with a potential impact on human health. The results provide the evidence that treated wastewater is polluted with antibiotic resistant bacteria containing mobile resistance mechanisms of importance to clinical treatment of pathogens and multi-drug resistant (MDR) faecal coliforms. They are sources of relatively high proportions of ESBL-producingEnterobacteriaceae, and include carbapenem and colistin resistantEnterobacteriaceae.The significance of this study is the identification of the role of WWTPs as a potential control point to reduce or stop the movement of ESBL, MDR and colistin resistant bacteria into the environment from further upstream sources, such as human or animal waste.
Comparing optimization modeling approaches for the multi-mode resource-constrained multi-project scheduling problem
Authors: Kannimuthu Marimuthu , Raphael Benny , Ekambaram Palaneeswaran , Kuppuswamy Ananthanarayanan 
Published: November 2019

PurposeConstruction firms keep minimal resources to maintain productive working capital. Hence, resources are constrained and have to be shared among multiple projects in an organization. Optimal allocation of resources is a key challenge in such situations. Several approaches and heuristics have been proposed for this task. The purpose of this paper is to compare two approaches for multi-mode resource-constrained project scheduling in a multi-project environment. These are the single-project approach (portfolio optimization) and the multi-project approach (each project is optimized individually, and then heuristic rules are used to satisfy the portfolio constraint).Design/methodology/approachA direct search algorithm called Probabilistic Global Search Lausanne is used for schedule optimization. Multiple solutions are generated that achieve different trade-offs among the three criteria, namely, time, cost and quality. Good compromise solutions among these are identified using a multi-criteria decision making method, Relaxed Restricted Pareto Version 4. The solutions obtained using the single-project and multi-project approaches are compared in order to evaluate their advantages and disadvantages. Data from two sources are used for the evaluation: modified multi-mode resource-constrained project scheduling problem data sets from the project scheduling problem library (PSPLIB) and three real case study projects in India.FindingsComputational results prove the superiority of the single-project approach over heuristic priority rules (multi-project approach). The single-project approach identifies better solutions compared to the multi-project approach. However, the multi-project approach involves fewer optimization variables and is faster in execution.Research limitations/implicationsIt is feasible to adopt the single-project approach in practice; realistic resource constraints can be incorporated in a multi-objective optimization formulation; and good compromise solutions that achieve acceptable trade-offs among the conflicting objectives can be identified.Originality/valueAn integer programming model was developed in this research to optimize the multiple objectives in a multi-project environment considering explicit resource constraints and maximum daily costs constraints. This model was used to compare the performance of the two multi-project environment approaches. Unlike existing work in this area, the model used to predict the quality of activity execution modes is based on data collected from real construction projects.
Multi-skill project scheduling with skill evolution and cooperation effectiveness
Authors: Li Qian , Sun Qinshan , Tao Sha , Gao Xinglin 
Published: November 2019

Purpose
Recently, there has been increasing focus on the development of multi-skilled workforce in project management. The purpose of this paper is to investigate a multi-skill project scheduling problem (MSPSP), which combines project scheduling and multi-skill personnel assignment. The distinct features of skill evolution and cooperation effectiveness are considered in the problem to maximize the total project effectiveness and skill development simultaneously.

Design/methodology/approach
The Bi-objective non-linear integer programming (LIP) models are formulated for the problem using three types of skill development objective function: number of experts, total skill increment and “bottleneck” skill increment. Non-linear models are then linearized through several linearization techniques, and the ε-constraint method is used to convert the bi-objective models into single-objective models.

Findings
A construction project case is used to validate the proposed models. In comparison with models that do not consider skill evolution and cooperation effectiveness, the models proposed in this paper offer more realistic solutions and show better performance with regard to both project effectiveness and skill development.

Originality/value
This research extends the current MSPSP by considering skill evolution based on the “learning effect” as well as the influence of cooperation in an activity-based team, which are common phenomena in practice but seldom studied. LIP models formulated in this paper can be solved by any off-the-shelf optimization solver, such as CPLEX. Besides, the proposed LIP models can offer better project scheduling and personnel assignment plan, which would be of immense practical value in project management applications.
Does megaproject social responsibility improve the sustainability of the construction industry?
Authors: Ma Hanyang , Liu Zheming , Zeng Saixing , Lin Han , Tam Vivian W.Y. 
Published: November 2019

PurposeSince megaproject social responsibility (MSR) has received increasing attention in megaproject management and plays critical roles in megaproject practices, the purpose of this paper is to explore how MSR facilitates an improved sustainability of the construction industry.Design/methodology/approachBy integrating multiple theoretical perspectives of transaction cost theory, institutionalism and attention- and resource-based views, and by using survey data of Chinese megaprojects and construction enterprises, this paper offers a theoretical elaboration of and an empirical investigation into the impacts that MSR’s four dimensions exert on industrial improvement in economic sustainability and social responsibility.FindingsThe study’s empirical results indicate that MSR has positive impacts on improving the sustainability of the construction industry, and that such positive impacts are weakened by the interactions of primary stakeholders in the megaprojects but are strengthened by the interactions of secondary stakeholders.Practical implicationsThis paper suggests that managers and policymakers make efforts to governmental guidance, media monitoring and public participation in megaprojects, so as to limit the potential unethical behaviors in megaproject management and enhance the sociopolitical legitimacy that are essential for the sustainability of the construction industry.Originality/valueBy analyzing the industrial outcomes of MSR, this paper extends studies on the topic beyond the current literature’s focus on the antecedents of MSR, and it enriches the research on MSR stakeholders by elaborating on the contingent roles of the various stakeholders in megaproject management.
Pedagogical utility of oral discussion versus collaborative drafting
Authors: Heidari Darani Laya , Hosseinpour Nafiseh 
Published: November 2019

PurposeThe purpose of this paper is to investigate and compare the effects of group-to-whole student-led oral discussion and small-group collaborative drafting as pre-writing tasks on Iranian intermediate English as a foreign language (EFL) learners’ writing performance. Additionally, the difference between the writing components was examined.Design/methodology/approachTo achieve these objectives, a group of 120 intermediate EFL learners participated in a pretest–posttest study in which they were randomly assigned into two experimental groups and one control group. The students in all three groups were tasked with writing a textbook evaluation report for the pretest and posttest. The pre-writing process in the first experimental group consisted of a group-to-whole student-led oral discussion, while the second experimental group engaged in small-group collaborative drafting.FindingsThe results indicate that both pre-tasks were effective in improving the participants’ writing skill, while collaborative drafting was even more efficient. Furthermore, it was observed that more writing components improved through collaborative drafting. It is concluded, therefore, that the social atmosphere created through oral discussion and the scaffolding resulting from collaborative drafting can help in writing improvement.Research limitations/implicationsThe findings herein can have implications for first language (L1) composition instruction and second language (L2) writing teaching and, thus, underscoring the utility of the social constructivist approach to writing instruction.Originality/valueAs there has been no study conducted to explore the effects of group-to-whole student-led oral discussion on EFL learners’ writing skill and to compare its impacts to those of small-group collaborative drafting, the results of this study fill this gap in the literature.