Diffuse Neutron Scattering Study of Magnetic Correlations in half-doped La0.5Ca0.5-xSrxMnO3 (x = 0.1, 0.3 and 0.4) Manganites

The short range ordered magnetic correlations have been studied in half doped La0.5Ca0.5-xSrxMnO3 (x = 0.1, 0.3 and 0.4) compounds by polarized neutron scattering technique. On doping Sr2+ for Ca2+ ion, these compounds with x = 0.1, 0.3, and 0.4 exhibit CE-type, mixture of CE-type and A-type, and A-type antiferromagnetic ordering, respectively. Magnetic diffuse scattering is observed in all the compounds above and below their respective magnetic ordering temperatures and is attributed to magnetic polarons. The correlations are primarily ferromagnetic in nature above T\_N, although a small antiferromagnetic contribution is also evident. Additionally, in samples x = 0.1 and 0.3 with CE-type antiferromagnetic ordering, superlattice diffuse reflections are observed indicating correlations between magnetic polarons. On lowering temperature below T\_N the diffuse scattering corresponding to ferromagnetic correlations is suppressed and the long range ordered antiferromagnetic state is established. However, the short range ordered correlations indicated by enhanced spin flip scattering at low Q coexist with long range ordered state down to 3K. In x = 0.4 sample with A-type antiferromagnetic ordering, superlattice diffuse reflections are absent. Additionally, in comparison to x = 0.1 and 0.3 sample, the enhanced spin flip scattering at low Q is reduced at 310K, and as temperature is reduced below 200K, it becomes negligibly low. The variation of radial correlation function, g(r) with temperature indicates rapid suppression of ferromagnetic correlations at the first nearest neighbor on approaching TN. Sample x = 0.4 exhibits growth of ferromagnetic phase at intermediate temperatures (~ 200K). This has been further explored using SANS and neutron depolarization techniques.Comment: 13 pages, 12 figures, To appear in Physical Review

On the role of different Skyrme forces and surface corrections in exotic cluster-decay

We present cluster decay studies of $^{56}$Ni$^*$ formed in heavy-ion collisions using different Skyrme forces. Our study reveals that different Skyrme forces do not alter the transfer structure of fractional yields significantly. The cluster decay half-lives of different clusters lies within \pm 10% for PCM and \pm 15% for UFM.Comment: 13 pages,6 figures and 1 table; in press Pramana Journal of Physics (2010

Astrophysical S_{17}(0) factor from a measurement of d(7Be,8B)n reaction at E_{c.m.} = 4.5 MeV

Angular distribution measurements of $^2$H($^7$Be,$^7$Be)$^2$H and $^2$H($^7$Be,$^8$B)$n$ reactions at $E_{c.m.}\sim$~4.5 MeV were performed to extract the astrophysical $S_{17}(0)$ factor using the asymptotic normalization coefficient (ANC) method. For this purpose a pure, low emittance $^7$Be beam was separated from the primary $^7$Li beam by a recoil mass spectrometer operated in a novel mode. A beam stopper at 0$^{\circ}$ allowed the use of a higher $^7$Be beam intensity. Measurement of the elastic scattering in the entrance channel using kinematic coincidence, facilitated the determination of the optical model parameters needed for the analysis of the transfer data. The present measurement significantly reduces errors in the extracted $^7$Be(p,$\gamma$) cross section using the ANC method. We get $S_{17}$~(0)~=~20.7~$\pm$~2.4 eV~b.Comment: 15 pages including 3 eps figures, one figure removed and discussions updated. Version to appear in Physical Review

Identifying sources, pathways and risk drivers in ecosystems of Japanese Encephalitis in an epidemic-prone north Indian district

Japanese Encephalitis (JE) has caused repeated outbreaks in endemic pockets of India. This study was conducted in Kushinagar, a highly endemic district, to understand the human-animal-ecosystem interactions, and the drivers that influence disease transmission. Utilizing the ecosystems approach, a cross-sectional, descriptive study, employing mixed methods design was employed. Four villages (two with pig-rearing and two without) were randomly selected from a high, a medium and a low burden (based on case counts) block of Kushinagar. Children, pigs and vectors were sampled from these villages. A qualitative arm was incorporated to explain the findings from the quantitative surveys. All human serum samples were screened for JE-specific IgM using MAC ELISA and negative samples for JE RNA by rRT-PCR in peripheral blood mononuclear cells. In pigs, IgG ELISA and rRT-PCR for viral RNA were used. Of the 242 children tested, 24 tested positive by either rRT-PCR or MAC ELISA; in pigs, 38 out of the 51 pigs were positive. Of the known vectors, Culex vishnui was most commonly isolated across all biotopes. Analysis of 15 blood meals revealed human blood in 10 samples. Univariable analysis showed that gender, religion, lack of indoor residual spraying of insecticides in the past year, indoor vector density (all species), and not being vaccinated against JE in children were significantly associated with JE positivity. In multivariate analysis, only male gender remained as a significant risk factor. Based on previous estimates of symptomatic: asymptomatic cases of JE, we estimate that there should have been 618 cases from Kushinagar, although only 139 were reported. Vaccination of children and vector control measures emerged as major control activities; they had very poor coverage in the studied villages. In addition, lack of awareness about the cause of JE, lack of faith in the conventional medical healthcare system and multiple referral levels causing delay in diagnosis and treatment emerged as factors likely to result in adverse clinical outcomes

Cardiac-Oxidized Antigens Are Targets of Immune Recognition by Antibodies and Potential Molecular Determinants in Chagas Disease Pathogenesis

Trypanosoma cruzi elicits reactive oxygen species (ROS) of inflammatory and mitochondrial origin in infected hosts. In this study, we examined ROS-induced oxidative modifications in the heart and determined whether the resultant oxidized cardiac proteins are targets of immune response and of pathological significance in Chagas disease. Heart biopsies from chagasic mice, rats and human patients exhibited, when compared to those from normal controls, a substantial increase in protein 4-hydroxynonenal (4-HNE), malondialdehyde (MDA), carbonyl, and 3-nitrotyrosine (3-NT) adducts. To evaluate whether oxidized proteins gain antigenic properties, heart homogenates or isolated cardiomyocytes were oxidized in vitro and one- or two-dimensional gel electrophoresis (2D-GE)/Western blotting (WB) was performed to investigate the proteomic oxidative changes and recognition of oxidized proteins by sera antibodies in chagasic rodents (mice, rats) and human patients. Human cardiomyocytes exhibited LD50 sensitivity to 30 µM 4-HNE and 100 µM H2O2 at 6 h and 12 h, respectively. In vitro oxidation with 4-HNE or H2O2 resulted in a substantial increase in 4-HNE- and carbonyl-modified proteins that correlated with increased recognition of cardiac (cardiomyocytes) proteins by sera antibodies of chagasic rodents and human patients. 2D-GE/Western blotting followed by MALDI-TOF-MS/MS analysis to identify cardiac proteins that were oxidized and recognized by human chagasic sera yielded 82 unique proteins. We validated the 2D-GE results by enzyme-linked immunosorbent assay (ELISA) and WB and demonstrated that oxidation of recombinant titin enhanced its immunogenicity and recognition by sera antibodies from chagasic hosts (rats and humans). Treatment of infected rats with phenyl-α-tert-butyl nitrone (PBN, antioxidant) resulted in normalized immune detection of cardiac proteins associated with control of cardiac pathology and preservation of heart contractile function in chagasic rats. We conclude that ROS-induced, cardiac-oxidized antigens are targets of immune recognition by antibodies and molecular determinants for pathogenesis during Chagas disease

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

INTRODUCTION: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further. Prediction models developed using AI or ML techniques are often labelled as a 'black box' and little is known about their methodological and reporting quality. Therefore, this comprehensive systematic review aims to evaluate the reporting quality, the methodological conduct, and the risk of bias of prediction model studies that applied ML techniques for model development and/or validation. METHODS AND ANALYSIS: A search will be performed in PubMed to identify studies developing and/or validating prediction models using any ML methodology and across all medical fields. Studies will be included if they were published between January 2018 and December 2019, predict patient-related outcomes, use any study design or data source, and available in English. Screening of search results and data extraction from included articles will be performed by two independent reviewers. The primary outcomes of this systematic review are: (1) the adherence of ML-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD), and (2) the risk of bias in such studies as assessed using the Prediction model Risk Of Bias ASsessment Tool (PROBAST). A narrative synthesis will be conducted for all included studies. Findings will be stratified by study type, medical field and prevalent ML methods, and will inform necessary extensions or updates of TRIPOD and PROBAST to better address prediction model studies that used AI or ML techniques. ETHICS AND DISSEMINATION: Ethical approval is not required for this study because only available published data will be analysed. Findings will be disseminated through peer-reviewed publications and scientific conferences. SYSTEMATIC REVIEW REGISTRATION: PROSPERO, CRD42019161764

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.

OBJECTIVE: To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties. DESIGN: Systematic review. DATA SOURCES: PubMed from 1 January 2018 to 31 December 2019. ELIGIBILITY CRITERIA: Articles reporting on the development, with or without external validation, of a multivariable prediction model (diagnostic or prognostic) developed using supervised machine learning for individualised predictions. No restrictions applied for study design, data source, or predicted patient related health outcomes. REVIEW METHODS: Methodological quality of the studies was determined and risk of bias evaluated using the prediction risk of bias assessment tool (PROBAST). This tool contains 21 signalling questions tailored to identify potential biases in four domains. Risk of bias was measured for each domain (participants, predictors, outcome, and analysis) and each study (overall). RESULTS: 152 studies were included: 58 (38%) included a diagnostic prediction model and 94 (62%) a prognostic prediction model. PROBAST was applied to 152 developed models and 19 external validations. Of these 171 analyses, 148 (87%, 95% confidence interval 81% to 91%) were rated at high risk of bias. The analysis domain was most frequently rated at high risk of bias. Of the 152 models, 85 (56%, 48% to 64%) were developed with an inadequate number of events per candidate predictor, 62 handled missing data inadequately (41%, 33% to 49%), and 59 assessed overfitting improperly (39%, 31% to 47%). Most models used appropriate data sources to develop (73%, 66% to 79%) and externally validate the machine learning based prediction models (74%, 51% to 88%). Information about blinding of outcome and blinding of predictors was, however, absent in 60 (40%, 32% to 47%) and 79 (52%, 44% to 60%) of the developed models, respectively. CONCLUSION: Most studies on machine learning based prediction models show poor methodological quality and are at high risk of bias. Factors contributing to risk of bias include small study size, poor handling of missing data, and failure to deal with overfitting. Efforts to improve the design, conduct, reporting, and validation of such studies are necessary to boost the application of machine learning based prediction models in clinical practice. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42019161764

Influence of porosity and fibre diameter on the degradation of chitosan fibre-mesh scaffolds and cell adhesion

The state of the art approaches for tailoring the degradation of chitosan scaffolds are based on altering the chemical structure of the polymer. Nevertheless, such alterations may lead to changes in other properties of scaffolds, such as the ability to promote cell adhesion. The aim of this study was to investigate the influence of physical parameters such as porosity and fibre diameter on the degradation of chitosan fibre-mesh scaffolds, as a possible way of tailoring the degradation of such scaffolds. Four sets of scaffolds with distinct fibre diameter and porosity were produced and their response to degradation and cell adhesion was studied. The degradation study was carried out at 37"C in a lysozyme solution for five weeks. The extent of degradation was expressed as percentage of weight loss of the dried scaffolds after lysozyme treatment. Cell adhesion was assessed by Confocal Microscopy. The results have shown that the scaffolds with higher porosity degrade faster and that, within the same range of porosity, the fibres with smaller diameter degrade slightly faster. Furthermore, the morphological differences between the scaffolds did not affect the degree of cell adhesion, and the cells were observed throughout the thickness of all four types of scaffold

Energy consumption in chemical fuel-driven self-assembly

Nature extensively exploits high-energy transient self-assembly structures that are able to perform work through a dissipative process. Often, self-assembly relies on the use of molecules as fuel that is consumed to drive thermodynamically unfavourable reactions away from equilibrium. Implementing this kind of non-equilibrium self-assembly process in synthetic systems is bound to profoundly impact the fields of chemistry, materials science and synthetic biology, leading to innovative dissipative structures able to convert and store chemical energy. Yet, despite increasing efforts, the basic principles underlying chemical fuel-driven dissipative self-assembly are often overlooked, generating confusion around the meaning and definition of scientific terms, which does not favour progress in the field. The scope of this Perspective is to bring closer together current experimental approaches and conceptual frameworks. From our analysis it also emerges that chemically fuelled dissipative processes may have played a crucial role in evolutionary processes