bluuebunny/crossref_metadata_2025 huggingface dataset:
India’s Child Brides and Youngest Mothers: Beyond Popular Perceptions
Authors: Saha Apala
Published: December 2019

Over one million girls in India are married off before they turn 15 years of age, and more than four hundred thousand among them bear children. This article aims to identify and locate regionally the youngest cohort of child brides and adolescent mothers. It seeks to highlight the associated complexities and challenges of this feature in the country using Census of India, 2011 data. The analysis shows that, at first level, this phenomenon represents in a broad east-west regional pattern; in the second level of disaggregation, it emerges in rural Rajasthan and urban parts of Gujarat and Maharashtra and, in the third level, it emerges in particular pockets like Bhilwara, Ajmer, Chittaurgarh, Tonk, Ahmedabad, Kheda and Gandhinagar. This article shows that the incidence of the phenomenon of child brides does not reflect a clear linkage with the regional location of population groups such as north India or rural India. The popular characterisation of this feature on regional lines fails to spot its incidence across the country and undermines the gravity of the issue in almost every district.
Health Conditions, Medication and Hospitalisation Preferences of Elderly in Kerala
Authors: Sanitha V. P. , Parida J. K. , Awasthi I. C.
Published: December 2019

This article intends to study the health conditions, medication and hospitalisation preferences, and the determinants of medication preferences of the elderly population in Kerala. It is based on the primary data collected from a sample of 801 households and three old-age homes (OAHs) in three districts of the state, namely Kozhikode, Ernakulum and Trivandrum. The evidence suggests that the elderly population are vulnerable to various chronic diseases and they tend to spend more on healthcare. In the absence of public provision of effective healthcare services in Kerala, the elderly spend out-of-pocket on health expenses and choose private hospitals for effective and better treatment. However, those who prefer government hospitals mostly belong to poor and marginalised groups, including the inmates of OAHs. With regard to the type of medicines, the elderly often prefer Ayurveda and Homeopathy to Allopathy medicines. Based on these findings, we suggest that the government intervention in terms of more health care facilities and health insurance schemes for the elderly is necessary to improve their quality of life.
Female Labour Force Participation in African Countries: An Empirical Analysis
Authors: Idowu Omowumi O. , Owoeye Taiwo
Published: December 2019

This article investigates female labour force participation (FLFP) in a panel comprising 20 selected African countries across five sub-regional groupings, over the period 1990–2018. It analyses the determinants of demand and supply of FLFP, thereby contributing to the ongoing debate on the female labour participation in African countries. The study sources data from the World Bank Data Bank and uses seemingly unrelated regression as an estimation technique after confirming the stationarity of the time series properties of all variables and the static analysis of the models. The analysis suggests that the GDP growth rate and inequality are positive determinants of female labour demand, while wages, GDP per capita growth and poverty are negative determinants. It also shows evidence for an inverted U-shaped relationship between female labour supply and economic development in Africa, while education, fertility, GDP per capita growth, manufacturing growth rate and culture were contributing factors to this relationship. It is therefore suggested that policymakers in Africa should accelerate the process of formalising African economies to encourage an increase in female labour participation.
Blended Learning: Balancing the Best of Both Worlds for Adult Learners
Authors: McKenna Kelly , Gupta Kalpana , Kaiser Leann , Lopes Tobin , Zarestky Jill
Published: December 2019

Blended, or hybrid, courses have often been touted as the ideal way to facilitate learning as they allow learners to interact in both face-to-face and online settings, thereby experiencing the “best of both worlds.” In practice, that ideal learning setting is more difficult to achieve. While blended courses have the potential to integrate the most beneficial aspects of each modality, such course design requires diligence to successfully navigate a balance and ensure the optimal delivery mode is considered for each learning scenario. In this article, the authors share the most current research on blended learning for adults, including benefits and drawbacks, various blended models, the results of an empirical study comparing two blended designs, and conclude with a practitioner tool to guide decision-making and achieve the appropriate balance of online and face-to-face and ultimately realize the best of both worlds for adult learners.
Low-density polyethylene films incorporated with silver nanoparticles to promote antimicrobial efficiency in food packaging
Authors: Brito Sabrina da Costa , Bresolin Joana D , Sivieri Kátia , Ferreira Marcos D
Published: December 2019

Technological innovations in packaging are intended to prevent microbiological contaminations for ensuring food safety and preservation. In this context, researchers have investigated the antimicrobial effect of low-density polyethylene films incorporated with the following concentrations of silver nanoparticles: 1.50, 3.75, 7.50, 15.00, 30.00, 60.00, and 75.00 µg/ml. The films were characterized using field emission gun scanning electron microscopy, Fourier transform infrared spectroscopy, X-ray diffraction, thermogravimetry, and differential scanning calorimetry. From the results of these techniques, it could be concluded that the silver nanoparticles incorporated in the low-density polyethylene films did not influence their physical, chemical, and thermal properties. The direct contact assays, shake-flask assays, and bacterial images obtained using scanning electron microscopy were used to analyze the antimicrobial activity of the films. In the microbial analyses, it was verified that the nanostructured films exhibited antimicrobial properties against all the microorganisms studied, although more notably for fungi and Gram-negative bacteria than the Gram-positive bacteria. Moreover, it was discovered that the packages, in which silver nanoparticles were incorporated, inhibited the growth and reproduction of bacterial cells during the early stages. These results suggest that the extruded low-density polyethylene films incorporated with silver nanoparticles may be an essential tool for improving food quality and safety.
Elaboration of a spontaneous gluten-free sourdough with a mixture of amaranth, buckwheat, and quinoa flours analyzing microbial load, acidity, and pH
Authors: Carbó Rosa , Gordún Elena , Fernández Antía , Ginovart Marta
Published: December 2019

Pseudocereals are gluten-free, nutrient-dense raw materials that are being considered for the production of gluten-free products, especially bread. This study proposes a gluten-free sourdough formula based on equal amounts of amaranth, buckwheat, and quinoa with a dough yield of 250, and an elaboration method to obtain ripe sourdough. Sourdough was characterized in terms of microbiology, pH, and total titratable acidity. The established protocol made it possible to obtain a spontaneous ripe sourdough with lactic acid bacteria populations of 9.60 ± 0.02 log CFU/g and total yeasts and non- Saccharomyces yeast populations (lysine positive) of 7.91 ± 0.15 and 7.52 ± 0.10 log CFU/g, respectively. Great pH stability and total titratable acidity were maintained in the ripe sourdough phase, with values of 4.04 ± 0.02 and 18.39 ± 0.56 ml NaOH 0.1 M/10 g, respectively, at the time of the next refreshment. The use of this sourdough could be an interesting alternative for the production of not only gluten-free bread but also other gluten-free products.
Correlation of Placental Magnetic Resonance Imaging With Histopathologic Diagnosis: Detection of Aberrations in Structure and Water Diffusivity
Authors: Bockoven Crystal , Gastfield Roland D , Victor Thomas , Venkatasubramanian Palamadai N , Wyrwicz Alice M , Ernst Linda M
Published: December 2019

Objective

Noninvasive methods to identify placental pathologic conditions are being sought in order to recognize these conditions at an earlier stage leading to improved clinical interventions and perinatal outcomes. The objective of this study was to examine fixed tissue slices of placenta by T2- and diffusion-weighted magnetic resonance imaging (MRI) and correlate the images with placental pathologic findings defined by routine gross and histologic examination.

Methods

Four formalin-fixed placentas with significant placental pathology (maternal vascular malperfusion, chronic villitis of unknown etiology, and massive perivillous fibrin deposition) and 2 histologically normal placentas were evaluated by high-resolution MRI. Representative placental slices were selected (2 cm long and 10 mm wide) and rehydrated. Imaging was performed on a Bruker Avance 14.1 T microimager. Diffusion-weighted images were acquired from 16 slices using slice thickness 0.5 mm and in-plane resolution approximately 100 µm × 100 µm. T2 maps were obtained from the same slices. T2 relaxation time and apparent diffusion coefficient (ADC) were acquired from representative regions of interest and compared between normal and diseased placentas.

Results

In T2- and diffusion-weighted images, the placental microstructure differed subjectively between diseased and normal placentas. Furthermore, diseased placentas showed statistically significantly longer mean T2 relaxation times and generally higher mean ADC.

Conclusion

Diffusion- and T2-weighted MRI can potentially be used to detect significant placental pathology by using T2 relaxation time and ADC as markers of altered placental microstructure.
Blood Biomarkers of Parenchymal Damage in Ischemic Stroke Patients Treated With Revascularization Therapies
Authors: Piccardi Benedetta , Biagini Silvia , Iovene Veronica , Palumbo Vanessa
Published: December 2019

Purpose:

Postischemic reperfusion injury may exacerbate cerebral damage and capillary dysfunction, leading to brain edema (BE), hemorrhagic transformation (HT), necrosis, and injury from free radicals with subsequent infarct growth (IG). Several plasmatic biomarkers involved in the ischemic cascade have been studied in relation to radiological and clinical outcomes of reperfusion injury in ischemic stroke with heterogeneous results. This article provides a brief overview of the contribution of circulating biomarkers to the pathophysiology of parenchymal damage in ischemic stroke patients treated with revascularization therapies.

Methods:

We included full reports with measurements of plasma markers in patients with acute ischemic stroke treated with revascularization therapies.

Findings:

Our research included a large number of observational studies investigating a possible role of circulating biomarkers in the development of parenchymal damage after acute stroke treatments. To make the results clearer, we divided the review in 4 sections, exploring the relation of different biomarkers with each of the indicators of parenchymal damage (HT, BE, IG, recanalization).

Discussion and conclusion:

Definite conclusions are difficult to draw because of heterogeneity across studies. However, our review seems to confirm an association between some circulating biomarkers (particularly matrix metalloproteinase-9) and occurrence of parenchymal damage in ischemic stroke patients treated with revascularization therapies.
When we increase fear, do we dampen hope? Using narrative persuasion to promote human papillomavirus vaccination in China
Authors: Liu Sixiao , Yang Janet Z , Chu Haoran
Published: December 2019

This study examines the use of narrative persuasion in promoting human papillomavirus vaccination. Compared to non-narrative message, the narrative message increased perceived severity of human papillomavirus, which influenced vaccination intention through heightened fear. The narrative message also dampened hope and subsequently decreased vaccination intention. This research attests to the effectiveness of narrative persuasion in vaccination promotion, but also offers important caveat for message design—egocentric efficacy information is crucial in narrative persuasion designed to motivate preventive health behaviors.
Adaptive comfort-oriented vehicle lateral control with online controller adjustments according to driver behaviour and look-ahead dynamics
Authors: Mirbeygi Moghaddam Amirreza , Ghaffari Ali , Khodayari Alireza
Published: December 2019

This paper presents a comfort-oriented adaptive fuzzy-model predictive control strategy to control the lateral motion of a vehicle with the steering angle as the input while preventing sudden changes and unwanted motions. This is reached by utilizing three main contributions: an adaptive fuzzy model based on look-ahead dynamics, limiting the controller to the acceptable range of states to the driver and introducing an adjustment factor to the controller's cost function. Using adaptive-fuzzy models to describe the vehicle lateral dynamics and driver behaviour for the purpose of the control-oriented model enables this method of control to advantage from the low computational effort of the models while maintaining the accuracy and adaptive properties which are crucial to the performance and robustness of the system during the manoeuvres. Also, the characteristics of each driver's behaviour modify the controller towards a more comfort-oriented drive with means of the cost function and constraints. The comparative simulation indicates that the developed method leads to a comfort-oriented drive, in that the vehicle's states stay within the limits of the driver behaviour range, the fluctuations are insignificant and the control strategy is more accurate than previous methods.
Uncertainty quantification from raw measurements to post-processed data: A general methodology and its application to a homogeneous-charge compression–ignition engine
Authors: Pochet Maxime , Jeanmart Hervé , Contino Francesco
Published: December 2019

Internal combustion engines have been improved for many decades. Yet, complex phenomena are now resorted to, for which any optimum might be unstable: noise, low-temperature heat release timing, stratification, pollutant sweet spots, and so on. In order to make reliable statements on an improvement, one must specify the uncertainty related to it. Still, uncertainty quantification is generally missing in the piston engine experimental literature. Therefore, we detailed a mathematical methodology to obtain any engine parameter uncertainty and then used it to derive the uncertainty expressions of the physical quantities of the most generic homogeneous-charge compression–ignition research engine (mass-flow-induced mixture with [Formula: see text] fuel). We then applied those expressions on an existing hydrogen homogeneous-charge compression–ignition test bench. This includes the uncertainty propagation chain from sensor specifications, user calibrations, intake control, in-cylinder processes, and post-processing techniques. Directly measured physical quantities have uncertainties of around 1%, depending on the sensor quality (e.g. pressure, volume), but indirectly measured quantities relying on modelled parameters have uncertainties higher than 5% (e.g. wall heat losses, in-cylinder temperature, gross heat release, pressure rise rate). Other findings that such an analysis can bring relate, for example, to the physical quantities driving the uncertainty and to the ones that can be neglected. In the case of the homogeneous-charge compression–ignition engine considered, the effects of blow-by, bottle purity and air moisture content were found negligible; the post-processing for effective compression ratio, effective in-cylinder temperature, and top dead centre offset were found essential; and the pressure and volume uncertainties were found to be the main drivers to a large extent. The obtained numeric values serve the general purpose of alerting the experimenter on uncertainty order of magnitudes. The developed methodology shall be used and adapted by the experimenter willing to study the uncertainty propagation in their setup or willing to assess the adequacy of a sensor performance.
Increasing the roundness of deformed cylinder liner in internal combustion engines by using a non-circular liner profile
Authors: Alshwawra Ahmad , Pasligh Henning , Hansen Hauke , Dinkelacker Friedrich
Published: December 2019

Increasing the efficiency of internal combustion engines is of major interest for reduced greenhouse gas emission. A significant improvement potential is given with the reduction of friction losses. Here, especially the friction between the piston ring and the cylinder liner is of interest. This article describes a study with the target to enhance the piston ring–cylinder liner conformation through increasing the roundness of the deformed liner during the warm operation state. The approach is based on the assumption that a non-circular liner in the cold state can deform due to thermal and mechanical stresses toward a circular shape under typical hot operation conditions. To test this hypothesis, a computational model for a gasoline engine was built and simulated using advanced finite element methods. The simulation describes the deformation process of the liner from the thermal and mechanical stresses. First, the deformation of a circular liner is simulated, showing asymmetric deformations of up to 30 µm in the warm state for the cylinder positioned at the end of the four-cylinder bank. As experimental data are readily available, a comparison was possible, showing good agreement. Then, three liner configurations with non-circular shape in the cold stage are investigated. For an elliptically shaped configuration, a nearly circular-shaped liner is reached under typical operation conditions. This numerical approach shows the potential for reduced friction of the piston–liner arrangement within internal combustion engines. The planned next step is the extension of this method to three-dimensional shape aspects and the application to the geometry of our test engine of our lab where friction can be measured in detail with a floating-liner measurement system.
DDX5 Silencing Suppresses the Migration of Basal cell Carcinoma Cells by Downregulating JAK2/STAT3 Pathway
Authors: Quan Zhe , Zhang Bei-bei , Yin Fang , Du Jiru , Zhi Yuan-ting , Xu Jin , Song Ningjing
Published: December 2019

Basal cell carcinoma is driven by the aberrant activation of hedgehog signaling. DEAD (Asp-Glu-Ala-Asp) box protein 5 is frequently overexpressed in human cancer cells and associated with the tumor growth and invasion. The purpose of this study was to investigate the role of DEAD (Asp-Glu-Ala-Asp) box protein 5 in the growth, migration, and invasion of basal cell carcinoma. The role of DEAD (Asp-Glu-Ala-Asp) box protein 5 was detected by quantitative real-time polymerase chain reaction, Western blot, and terminal deoxynucleotidyl transferase-mediated deoxyuridine triphosphate nick-end labeling assay in basal cell carcinoma cells. The associations between JAK2/STAT3 pathway and DEAD (Asp-Glu-Ala-Asp) box protein 5 were analyzed in basal cell carcinoma cells. Results showed that DEAD (Asp-Glu-Ala-Asp) box protein 5 is overexpressed in basal cell carcinoma cells. DEAD (Asp-Glu-Ala-Asp) box protein 5 knockdown inhibited the migration and invasion of basal cell carcinoma cells. DEAD (Asp-Glu-Ala-Asp) box protein 5 knockdown increased the apoptosis of basal cell carcinoma cells induced by tunicamycin. Results found that DEAD (Asp-Glu-Ala-Asp) box protein 5 knockdown increased JAK2 and STAT3 expression in basal cell carcinoma cells. JAK2 inhibitor decreased STAT3 expression and abolished the inhibitory effects of DEAD (Asp-Glu-Ala-Asp) box protein 5 silencing on migration and invasion in basal cell carcinoma cells. In conclusion, these results indicate that DEAD (Asp-Glu-Ala-Asp) box protein 5 is a potential target for inhibiting basal cell carcinoma cells growth, migration, and invasion by downregulating JAK2/STAT3 pathway.
Aerodynamics in motorsports
Authors: Katz Joseph
Published: December 2019

Motor racing, like other popular forms of competitive sports, requires physical fitness, concentration, and vigorous preparation and training. Although progress in technology may dominate the race, governing bodies are continuously updating the rulebooks to keep the human factor dominant in winning races. On the other hand, vehicle performance depends on elements such as the engine, tires, suspension, road, and aerodynamics. In recent years, however, vehicle aerodynamics has gained increased attention, mainly due to the utilization of the negative lift (downforce) principle, yielding several significant performance improvements. The importance of drag reduction and improved fuel efficiency are easily understood by the novice observer and are still at the center of racing vehicle design. Interestingly, however, generating downforce by the vehicle usually increases its drag but improves average speed in closed circuits. Consequently, various methods to generate downforce such as inverted wings, diffusers, and vortex generators will be discussed. Also, generic trends connecting a vehicle’s shape to its aerodynamics are presented, followed by more specific race-car examples. Due to the complex geometry of these vehicles, the aerodynamic interaction between the various body components is significant, resulting in vortex flows and wing shapes which may be different than those used on airplanes.
Management of a decompensated acute-on-chronic intracranial venous sinus thrombosis
Authors: Serna Candel Carmen , Hellstern Victoria , Beitlich Tania , Aguilar Pérez Marta , Bäzner Hansjörg , Henkes Hans
Published: December 2019

A 34-year-old female patient presented during the 10th week of her second gravidity with headache, nausea and vomiting 2 weeks before admission. Her medical history was remarkable for a heterozygous factor V Leiden mutation, elevated lipoprotein A, and a cerebral venous thrombosis (CVT) after oral contraceptive intake 15 years before. Magnetic resonance imaging (MRI) suggested acute and massive intracranial sinus thrombosis. Despite full-dose anticoagulation, the patient deteriorated clinically and eventually became comatose. Now, MRI/magnetic resonance angiography revealed vasogenic edema of both thalami, of the left frontal lobe, and of the head of the caudate nucleus, with venous stasis and frontal petechial hemorrhage. She was referred for endovascular treatment. Diagnostic angiography confirmed a complete superficial and deep venous sinus occlusion. Endovascular access to the straight and superior sagittal sinus was possible, but neither rheolysis nor balloon angioplasty resulted in recanalization of the venous sinuses. Monitored heparinization was continued and antiaggregation was initiated. The patient remained comatose for another 5 days and MRI showed progress of the cytotoxic edema. On day 6, infusion of eptifibatide at body-weight-adapted dosage was started. The following day, the patient improved and slowly regained consciousness. MRI confirmed regression of the edema. The eptifibatide infusion was continued for a total of 14 days. Thereafter two doses of 180 mg ticagrelor per os (PO) daily were started. The patient remained on acetylsalicylic acid (ASA), ticagrelor, and enoxaparin on an unchanged dosage regimen. She was discharged home 26 days after the endovascular treatment without serious neurological deficit, with the pregnancy intact. At the 30th week of pregnancy the dosage of ASA was reduced to 300 mg once PO daily. Cesarian delivery was carried out at the 38th week of pregnancy. The newborn was completely healthy. Ultima ratio therapeutic options for severe intracranial venous sinus thrombosis refractory to anticoagulation are discussed, with an emphasis on platelet-function inhibition.
Association of Single-Nucleotide Polymorphisms With Chronic Rhinosinusitis in a Southwestern Han Chinese Population: A Replication Study
Authors: Xu Yang , Zheng Yongbo , Cao Min , Yang Wen , Ren Jianjun , Song Yao , Cheng Danni , Wang Jing , Huang Ligao , Xu Wei , Zhao Yu , Liu Geoffrey
Published: December 2019

Background

Chronic rhinosinusitis (CRS) is a multifactorial inflammatory disease. The role of genetic variations of related genes in the development of CRS and severity of symptoms is unknown in Southwestern Chinese populations.

Objective

We selected candidate CRS-related genetic polymorphisms and evaluated the associations that were different according to the presence of nasal polyp, asthma, and allergic rhinitis (AR) in a Southwestern Chinese population. Detailed phenotypes were compared among different genotypes.

Methods

In 452 CRS patients and 591 healthy controls, clinico-epidemiological information was collected and 23 previously reported CRS-related single-nucleotide polymorphisms (SNPs) were genotyped. Genotypes were determined using a Sequenom MassARRAY SNP genotyping system. Clinical disease measures including the sinonasal outcome test, visual analogue scale (VAS), and symptom severity VAS were evaluated for each patient. The association between CRS, genotypes, asthma, AR, and symptoms was analyzed. The effect of sex, age, body mass index, and status of smoking was considered.

Results

Statistically significant genotypic association with CRS was observed with an IL1RL1 genetic polymorphism (rs13431828; odds ratio [OR] = 1.45; 95% confidence interval [CI], 1.06–1.99; P = .02). Similar association was observed with rs13431828 in subgroups of CRS with nasal polyps (OR = 1.53; 95% CI, 1.03–2.29; P = .04), asthma (OR = 2.08; 95% CI, 1.14–3.79; P = .02), and AR (OR = 1.59; 95% CI, 1.06–2.39; P = .02). No significant association with other SNPs was observed. The evaluated genetic polymorphisms were not associated with clinical symptom scores.

Conclusion

This study replicated rs13431828 as being associated with CRS in Southwestern Chinese. rs13431828 was also significantly associated with CRS patients who have concurrent allergic nasal diseases.
Successful and unsuccessful recruitment and retainment strategies in a UK multicentre drug trial for a rare chronic pain condition which performed above target
Authors: Bisla Jatinder , Ambler Gareth , Frank Bernhard , Gulati Sumit , Hocken Poppy , James Mairi , Kelly Joanna , Keshet-Price Jocelyn , McCabe Candy , McGylnn Deborah , Padfield Nick , Pang David , Pout Gill , Sanders Mark , Serpell Mick , Shenker Nicholas , Shoukrey Karim , Wesley Samuel , Weston Margaret , White-Alao Beverly , Wyatt Lynne , Murphy Caroline , Goebel Andreas
Published: December 2019

Introduction:

Recruitment into trials in rare chronic pain conditions can be challenging, so such trials consequently are underpowered or fail.

Methods:

Drawing from our experience in conducting, to date, the largest academic trial in a rare chronic pain condition, complex regional pain syndrome, we have identified recruitment and retention strategies for successful trial conduct.

Results:

We present 13 strategies grouped across the categories of ‘setting the recruitment rate’, ‘networking’, ‘patient information’, ‘trial management’ and ‘patient retention’. Moreover, six recruitment risks are also discussed. A conservative recruitment estimate, based on audits of newly referred patients to the trial centres without taking into account availability of ‘old’ patients or recruitment from outside centres, and assuming a 55% patient refusal rate yielded accurate numbers.

Conclusion:

Appreciation of these identified recruitment challenges and opportunities may contribute to supporting prospective investigators when they design clinical trials for chronic pain patient population groups where it has been historically difficult to conduct high-quality and robust clinical trials.
Actionable Analysis: Toward a Jurisdictional Evaluation of Primary Care Access in the Community Context
Authors: Ford Mary M. , Weisbeck Kirsten , Kerker Bonnie , Cohen Louise
Published: December 2019

Primary care is the foundation of health care systems and has potential to alleviate inequities in population health. We examined multiple measures of adult primary care access, health status, and socioeconomic position at the New York City Council District level—a unit of analysis both relevant to and actionable by local policymakers. The results showed significant associations between measures of primary care access and health status after adjustment for socioeconomic factors. We found that an increase of 1 provider per 10 000 people was associated with a 1% decrease in diabetes rates and a 5% decrease in rates of adults without an influenza immunization. Furthermore, higher rates of primary care providers in high-poverty districts accepted Medicaid and had Patient-Centered Medical Home recognition, increasing constituent accessibility. Our findings highlight the significant contribution of primary care access to community health; policies and resource allocation must prioritize primary care facility siting and provider recruitment in low-access areas.
Is It All About the Fascia? A Systematic Review and Meta-analysis of the Prevalence of Extramuscular Connective Tissue Lesions in Muscle Strain Injury
Authors: Wilke Jan , Hespanhol Luiz , Behrens Martin
Published: December 2019

Background:

The fascia has been demonstrated to represent a potential force transmitter intimately connected to the underlying skeletal muscle. Sports-related soft tissue strains may therefore result in damage to both structures.

Purpose:

To elucidate the prevalence of connective tissue lesions in muscle strain injury and their potential impact on return-to-play (RTP) duration.

Study Design:

Systematic review; Level of evidence, 3.

Methods:

Imaging studies describing frequency, location, and extent of soft tissue lesions in lower limb muscle strain injuries were identified by 2 independent investigators. Weighted proportions (random effects) were pooled for the occurrence of (1) myofascial or fascial lesions, (2) myotendinous lesions, and (3) purely muscular lesions. Study quality was evaluated by means of an adapted Downs and Black checklist, which evaluates reporting, risk of bias, and external validity.

Results:

A total of 16 studies (fair to good methodological quality) were identified. Prevalence of strain injury on imaging studies was 32.1% (95% CI, 24.2%-40.4%) for myofascial lesions, 68.4% (95% CI, 59.6%-76.6%) for myotendinous lesions, and 12.7% (95% CI, 3.0%-27.7%) for isolated muscular lesions. Evidence regarding associations between fascial damage and RTP duration was mixed.

Conclusion:

Lesions of the collagenous connective tissue, namely the fascia and the tendinous junction, are highly prevalent in athletic muscle strain injuries. However, at present, their impact on RTP duration is unclear and requires further investigation.
Influence of reinforcement and processing on aluminum matrix composites modified by stir casting route
Authors: Rozhbiany Fakhir Aziz Rasul , Jalal Shawnim Rashied
Published: December 2019

In the present study, the individual and combined effect of reinforcement on aluminum (Al; 6063) alloy is discussed. These Al metal matrix composites with individual and multiple reinforcements are finding increased application in aerospace, automobile industry, underwater machines, and transportation application, due to improved mechanical and tribological properties like strong, stiff, wear, and impact resistance. Al 6063 alloy reinforced with different elements such as two types of ceramic mortar ash (MA) and nanofibrillated composite (NFC), industrial waste met coke ash (MCA), and agro waste straw ash with the constant rate of 5 wt% for each reinforced element. The results show a significant effect on the mechanical properties such as tensile strength and hardness. Damping characteristic improved by mixing of MA and NFC together and the results shown that the damping characteristics can be good for MCA reinforcement. Logarithmic decrement slowly increased by adding all types of reinforced metal together. NFC has high resistance to wear loss followed by MA and MCA but for mixing all types of reinforcements together resulted in good improvement in wear loss. Microstructure analysis of Al matrix composites is the small grain size and uniform distribution with good particulate matrix interface bonding.
Retraction Note: Flexible Field Emitter for X-ray Generation by Implanting CNTs into Nickel Foil
Authors: Sun Bin , Wang Yan , Ding Guifu
Published: December 2019

The Editors-in-Chief have retracted this article [1] because it significantly overlaps with previously published articles by the same authors [2, 3].
Learning misclassification costs for imbalanced classification on gene expression data
Authors: Lu Huijuan , Xu Yige , Ye Minchao , Yan Ke , Gao Zhigang , Jin Qun
Published: December 2019

Background

Cost-sensitive algorithm is an effective strategy to solve imbalanced classification problem. However, the misclassification costs are usually determined empirically based on user expertise, which leads to unstable performance of cost-sensitive classification. Therefore, an efficient and accurate method is needed to calculate the optimal cost weights.

Results

In this paper, two approaches are proposed to search for the optimal cost weights, targeting at the highest weighted classification accuracy (WCA). One is the optimal cost weights grid searching and the other is the function fitting. Comparisons are made between these between the two algorithms above. In experiments, we classify imbalanced gene expression data using extreme learning machine to test the cost weights obtained by the two approaches.

Conclusions

Comprehensive experimental results show that the function fitting method is generally more efficient, which can well find the optimal cost weights with acceptable WCA.
Detecting overlapping protein complexes in weighted PPI network based on overlay network chain in quotient space
Authors: Zhao Jie , Lei Xiujuan
Published: December 2019

Background

Protein complexes are the cornerstones of many biological processes and gather them to form various types of molecular machinery that perform a vast array of biological functions. In fact, a protein may belong to multiple protein complexes. Most existing protein complex detection algorithms cannot reflect overlapping protein complexes. To solve this problem, a novel overlapping protein complexes identification algorithm is proposed.

Results

In this paper, a new clustering algorithm based on overlay network chain in quotient space, marked as ONCQS, was proposed to detect overlapping protein complexes in weighted PPI networks. In the quotient space, a multilevel overlay network is constructed by using the maximal complete subgraph to mine overlapping protein complexes. The GO annotation data is used to weight the PPI network. According to the compatibility relation, the overlay network chain in quotient space was calculated. The protein complexes are contained in the last level of the overlay network. The experiments were carried out on four PPI databases, and compared ONCQS with five other state-of-the-art methods in the identification of protein complexes.

Conclusions

We have applied ONCQS to four PPI databases DIP, Gavin, Krogan and MIPS, the results show that it is superior to other five existing algorithms MCODE, MCL, CORE, ClusterONE and COACH in detecting overlapping protein complexes.
Ranking near-native candidate protein structures via random forest classification
Authors: Wu Hongjie , Huang Hongmei , Lu Weizhong , Fu Qiming , Ding Yijie , Qiu Jing , Li Haiou
Published: December 2019

Background

In ab initio protein-structure predictions, a large set of structural decoys are often generated, with the requirement to select best five or three candidates from the decoys. The clustered central structures with the most number of neighbors are frequently regarded as the near-native protein structures with the lowest free energy; however, limitations in clustering methods and three-dimensional structural-distance assessments make identifying exact order of the best five or three near-native candidate structures difficult.

Results

To address this issue, we propose a method that re-ranks the candidate structures via random forest classification using intra- and inter-cluster features from the results of the clustering. Comparative analysis indicated that our method was better able to identify the order of the candidate structures as comparing with current methods SPICKR, Calibur, and Durandal. The results confirmed that the identification of the first model were closer to the native structure in 12 of 43 cases versus four for SPICKER, and the same as the native structure in up to 27 of 43 cases versus 14 for Calibur and up to eight of 43 cases versus two for Durandal.

Conclusions

In this study, we presented an improved method based on random forest classification to transform the problem of re-ranking the candidate structures by an binary classification. Our results indicate that this method is a powerful method for the problem and the effect of this method is better than other methods.
Predicting drug-target interactions from drug structure and protein sequence using novel convolutional neural networks
Authors: Hu ShanShan , Zhang Chenglin , Chen Peng , Gu Pengying , Zhang Jun , Wang Bing
Published: December 2019

Background

Accurate identification of potential interactions between drugs and protein targets is a critical step to accelerate drug discovery. Despite many relative experimental researches have been done in the past decades, detecting drug-target interactions (DTIs) remains to be extremely resource-intensive and time-consuming. Therefore, many computational approaches have been developed for predicting drug-target associations on a large scale.

Results

In this paper, we proposed an deep learning-based method to predict DTIs only using the information of drug structures and protein sequences. The final results showed that our method can achieve good performance with the accuracies up to 92.0%, 90.0%, 92.0% and 90.7% for the target families of enzymes, ion channels, GPCRs and nuclear receptors of our created dataset, respectively. Another dataset derived from DrugBank was used to further assess the generalization of the model, which yielded an accuracy of 0.9015 and an AUC value of 0.9557.

Conclusion

It was elucidated that our model shows improved performance in comparison with other state-of-the-art computational methods on the common benchmark datasets. Experimental results demonstrated that our model successfully extracted more nuanced yet useful features, and therefore can be used as a practical tool to discover new drugs.

Availability

http://deeplearner.ahu.edu.cn/web/CnnDTI.htm.
NmSEER V2.0: a prediction tool for 2′-O-methylation sites based on random forest and multi-encoding combination
Authors: Zhou Yiran , Cui Qinghua , Zhou Yuan
Published: December 2019

Background

2′-O-methylation (2′-O-me or Nm) is a post-transcriptional RNA methylation modified at 2′-hydroxy, which is common in mRNAs and various non-coding RNAs. Previous studies revealed the significance of Nm in multiple biological processes. With Nm getting more and more attention, a revolutionary technique termed Nm-seq, was developed to profile Nm sites mainly in mRNA with single nucleotide resolution and high sensitivity. In a recent work, supported by the Nm-seq data, we have reported a method in silico for predicting Nm sites, which relies on nucleotide sequence information, and established an online server named NmSEER. More recently, a more confident dataset produced by refined Nm-seq was available. Therefore, in this work, we redesigned the prediction model to achieve a more robust performance on the new data.

Results

We redesigned the prediction model from two perspectives, including machine learning algorithm and multi-encoding scheme combination. With optimization by 5-fold cross-validation tests and evaluation by independent test respectively, random forest was selected as the most robust algorithm. Meanwhile, one-hot encoding, together with position-specific dinucleotide sequence profile and K-nucleotide frequency encoding were collectively applied to build the final predictor.

Conclusions

The predictor of updated version, named NmSEER V2.0, achieves an accurate prediction performance (AUROC = 0.862) and has been settled into a brand-new server, which is available at http://www.rnanut.net/nmseer-v2/ for free.
A systematic evaluation of copy number alterations detection methods on real SNP array and deep sequencing data
Authors: Luo Fei
Published: December 2019

Background

The Copy Number Alterations (CNAs) are discovered to be tightly associated with cancers, so accurately detecting them is one of the most important tasks in the cancer genomics. A series of CNAs detection methods have been proposed and new ones are still being developed. Due to the complexity of CNAs in cancers, no CNAs detection method has been accepted as the gold standard caller. Several evaluation works have made attempts to reveal typical CNAs detection methods’ performance. Limited by the scale of evaluation data, these different comparison works don’t reach a consensus and the researchers are still confused on how to choose one proper CNAs caller for their analysis. Therefore, it needs a more comprehensive evaluation of typical CNAs detection methods’ performance.

Results

In this work, we use a large-scale real dataset from CAGEKID consortium to evaluate total 12 typical CNAs detection methods. These methods are most widely used in cancer researches and always used as benchmark for the newly proposed CNAs detection methods. This large-scale dataset comprises of SNP array data on 94 samples and the whole genome sequencing data on 10 samples. Evaluations are comprehensively implemented in current scenarios of CNAs detection, which include that detect CNAs on SNP array data, on sequencing data with tumor and normal matched samples and on sequencing data with single tumor sample. Three SNP based methods are firstly ranked. Subsequently, the best SNP based method’s results are used as benchmark to compare six matched samples based methods and three single tumor sample based methods in terms of the preprocessing, recall rate, Jaccard index and segmentation characteristics.

Conclusions

Our survey thoroughly reveals 12 typical methods’ superiority and inferiority. We explain why methods show specific characteristics from a methodological standpoint. Finally, we present the guiding principle for choosing one proper CNAs detection method under specific conditions. Some unsolved problems and expectations are also addressed for upcoming CNAs detection methods.
Using discriminative vector machine model with 2DPCA to predict interactions among proteins
Authors: Li Zhengwei , Nie Ru , You Zhuhong , Cao Chen , Li Jiashu
Published: December 2019

Background

The interactions among proteins act as crucial roles in most cellular processes. Despite enormous effort put for identifying protein-protein interactions (PPIs) from a large number of organisms, existing firsthand biological experimental methods are high cost, low efficiency, and high false-positive rate. The application of in silico methods opens new doors for predicting interactions among proteins, and has been attracted a great deal of attention in the last decades.

Results

Here we present a novelty computational model with the adoption of our proposed Discriminative Vector Machine (DVM) model and a 2-Dimensional Principal Component Analysis (2DPCA) descriptor to identify candidate PPIs only based on protein sequences. To be more specific, a 2DPCA descriptor is employed to capture discriminative feature information from Position-Specific Scoring Matrix (PSSM) of amino acid sequences by the tool of PSI-BLAST. Then, a robust and powerful DVM classifier is employed to infer PPIs. When applied on both gold benchmark datasets of Yeast and H. pylori, our model obtained mean prediction accuracies as high as of 97.06 and 92.89%, respectively, which demonstrates a noticeable improvement than some state-of-the-art methods. Moreover, we constructed Support Vector Machines (SVM) based predictive model and made comparison it with our model on Human benchmark dataset. In addition, to further demonstrate the predictive reliability of our proposed method, we also carried out extensive experiments for identifying cross-species PPIs on five other species datasets.

Conclusions

All the experimental results indicate that our method is very effective for identifying potential PPIs and could serve as a practical approach to aid bioexperiment in proteomics research.
Prediction of protein structural classes by different feature expressions based on 2-D wavelet denoising and fusion
Authors: Wang Shunfang , Wang Xiaoheng
Published: December 2019

Background

Protein structural class predicting is a heavily researched subject in bioinformatics that plays a vital role in protein functional analysis, protein folding recognition, rational drug design and other related fields. However, when traditional feature expression methods are adopted, the features usually contain considerable redundant information, which leads to a very low recognition rate of protein structural classes.

Results

We constructed a prediction model based on wavelet denoising using different feature expression methods. A new fusion idea, first fuse and then denoise, is proposed in this article. Two types of pseudo amino acid compositions are utilized to distill feature vectors. Then, a two-dimensional (2-D) wavelet denoising algorithm is used to remove the redundant information from two extracted feature vectors. The two feature vectors based on parallel 2-D wavelet denoising are fused, which is known as PWD-FU-PseAAC. The related source codes are available at https://github.com/Xiaoheng-Wang12/Wang-xiaoheng/tree/master.

Conclusions

Experimental verification of three low-similarity datasets suggests that the proposed model achieves notably good results as regarding the prediction of protein structural classes.
Classification of adaptor proteins using recurrent neural networks and PSSM profiles
Authors: Khanh Le Nguyen Quoc , Nguyen Quang H. , Chen Xuan , Rahardja Susanto , Nguyen Binh P.
Published: December 2019

Background

Adaptor proteins are carrier proteins that play a crucial role in signal transduction. They commonly consist of several modular domains, each having its own binding activity and operating by forming complexes with other intracellular-signaling molecules. Many studies determined that the adaptor proteins had been implicated in a variety of human diseases. Therefore, creating a precise model to predict the function of adaptor proteins is one of the vital tasks in bioinformatics and computational biology. Few computational biology studies have been conducted to predict the protein functions, and in most of those studies, position specific scoring matrix (PSSM) profiles had been used as the features to be fed into the neural networks. However, the neural networks could not reach the optimal result because the sequential information in PSSMs has been lost. This study proposes an innovative approach by incorporating recurrent neural networks (RNNs) and PSSM profiles to resolve this problem.

Results

Compared to other state-of-the-art methods which had been applied successfully in other problems, our method achieves enhancement in all of the common measurement metrics. The area under the receiver operating characteristic curve (AUC) metric in prediction of adaptor proteins in the cross-validation and independent datasets are 0.893 and 0.853, respectively.

Conclusions

This study opens a research path that can promote the use of RNNs and PSSM profiles in bioinformatics and computational biology. Our approach is reproducible by scientists that aim to improve the performance results of different protein function prediction problems. Our source code and datasets are available at https://github.com/ngphubinh/adaptors.