bluuebunny/crossref_metadata_2025_split huggingface dataset:
Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis
Authors: Jamal Salma , Khubaib Mohd. , Gangwar Rishabh , Grover Sonam , Grover Abhinav , Hasnain Seyed E. 
Published: March 2020

AbstractTuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disease necessitating novel diagnosis and treatment strategies. High-throughput sequencing methods generate a large amount of data which could be exploited in determining multi-drug resistant (MDR-TB) associated mutations. The present work is a computational framework that uses artificial intelligence (AI) based machine learning (ML) approaches for predicting resistance in the genes rpoB, inhA, katG, pncA, gyrA and gyrB for the drugs rifampicin, isoniazid, pyrazinamide and fluoroquinolones. The single nucleotide variations were represented by several sequence and structural features that indicate the influence of mutations on the target protein coded by each gene. We used ML algorithms - naïve bayes, k nearest neighbor, support vector machine, and artificial neural network, to build the prediction models. The classification models had an average accuracy of 85% across all examined genes and were evaluated on an external unseen dataset to demonstrate their application. Further, molecular docking and molecular dynamics simulations were performed for wild type and predicted resistance causing mutant protein and anti-TB drug complexes to study their impact on the conformation of proteins to confirm the observed phenotype.
Quantum speed limit based on the bound of Bures angle
Authors: Wu Shao-xiong , Yu Chang-shui 
Published: March 2020

AbstractIn this paper, we investigate the unified bound of quantum speed limit time in open systems based on the modified Bures angle. This bound is applied to the damped Jaynes-Cummings model and the dephasing model, and the analytical quantum speed limit time is obtained for both models. As an example, the maximum coherent qubit state with white noise is chosen as the initial states for the damped Jaynes-Cummings model. It is found that the quantum speed limit time in both the non-Markovian and the Markovian regimes can be decreased by the white noise compared with the pure state. In addition, for the dephasing model, we find that the quantum speed limit time is not only related to the coherence of initial state and non-Markovianity, but also dependent on the population of initial excited state.
Modeling Differential Effects of Maternal Dietary Patterns across Severity Levels of Preterm Birth Using a Partial Proportional Odds Model
Authors: Mitku Aweke A. , Zewotir Temesgen , North Delia , Jeena Prakash , Naidoo Rajen N. 
Published: March 2020

AbstractPreterm birth is a common cause of death worldwide of children under the age of five years. This condition is linked with short and long term neonatal morbidity and mortality. Maternal nutrition during pregnancy has a profound effect on fetal growth and development and subsequently also on the incidence of preterm birth. The aim of this study was to assess the differential effect of dietary patterns of pregnant women across ordered levels of preterm birth. Dietary assessments were performed using a food frequency questionnaire, presented to 687 pregnant women, in the “Mother and Child in the Environment” birth cohort during the period of 2013 to 2017. Each pregnancy resulted in a live birth. Eight dietary patterns were extracted, using exploratory factor analysis. The partial proportional odds model was employed to model severity levels of preterm birth. The partial proportional odds model has been recognized to be a flexible approach since it allows the effect of predictor variables to vary across categories of the ordinal response variable of interest. Women with increased consumption of vegetable-rich foods showed a reduced risk of very to moderately preterm birth incidence (AOR = 0.73, 95% CI = (0.531, 0.981), p = 0.036). Lower odds of very/moderately preterm birth compared to late preterm or term birth were observed for women following “nuts and rice foods” dietary pattern (AOR = 0.25, 95% CI = (0.099, 0.621), p = 0.003). High dietary consumption of starch foods dietary pattern (AOR = 2.09, 95% CI = (1.158, 3.769), p = 0.014) was associated with the most severe level of preterm birth outcome incidence, i.e. very/moderately preterm birth. The partial proportional odds modeling allowed the description of the effect of maternal dietary patterns across the different severity levels of preterm birth.
Cardiac muscle regulatory units are predicted to interact stronger than neighboring cross-bridges
Authors: Kalda Mari , Vendelin Marko 
Published: March 2020

AbstractStrong interactions between cross-bridges (XB) and regulatory units (RU) lead to a steep response of cardiac muscle to an increase in intracellular calcium. We developed a model to quantitatively assess the influence of different types of interactions within the sarcomere on the properties of cardiac muscle. In the model, the ensembles consisting of cross-bridge groups connected by elastic tropomyosin are introduced, and their dynamics is described by a set of partial differential equations. Through large scans in the free energy landscape, we demonstrate the different influence of RU-RU, XB-XB, and XB-RU interactions on the cooperativity coefficient of calcium binding, developed maximal force, and calcium sensitivity. The model solution was fitted to reproduce experimental data on force development during isometric contraction, shortening in physiological contraction, and ATP consumption by acto-myosin. On the basis of the fits, we quantified the free energy change introduced through RU-RU and XB-XB interactions and showed that RU-RU interaction leads to ~ 5 times larger change in the free energy profile of the reaction than XB-XB interaction. Due to the deterministic description of muscle contraction and its thermodynamic consistency, we envision that the developed model can be used to study heart muscle biophysics on tissue and organ levels.
Dual-energy CT perfusion imaging for differentiating WHO subtypes of thymic epithelial tumors
Authors: Yu Chunhai , Li Ting , Zhang Ruiping , Yang Xiaotang , Yang Zhao , Xin Lei , Zhao Zhikai 
Published: March 2020

AbstractTo evaluate the role of conventional contrast-enhanced CT (CECT) imaging and dual-energy spectral CT (DECT) perfusion imaging in differentiating the WHO histological subtypes of thymic epithelial tumours (TETs). Eighty-eight patients with TETs who underwent DECT perfusion scans (n = 51) and conventional CT enhancement scans (n = 37) using a GE Discovery CT750 HD scanner were enrolled in this study. The mean maximal contrast-enhanced range (mean CEmax) and the perfusion and spectral parameters of the lesions were analysed. Among the six WHO subtypes (Type A, AB, B1, B2, and B3 thymoma and thymic carcinoma), the mean CEmax values and most of the perfusion and spectral parameter values of Type A and Type AB were significantly higher than those of the other subtypes (all P < 0.05), and there was no difference among Type B1, B2 and B3 (all P > 0.05). The mean CEmax value was not different between Type B (including Type B1, B2, and B3) and thymic carcinoma (P = 1.000). The PS, IC, NIC and λHU values in the optimal venous phase of thymic carcinoma were higher than those of Type B (all P < 0.05). The parameters of conventional CECT imaging and DECT perfusion imaging can help identify the subtype of TETs, especially those of DECT perfusion imaging in type B thymomas and thymic carcinomas.
A new approach to comprehensively evaluate the morphological properties of the human femoral head: example of application to osteoarthritic joint
Authors: Ryan M. , Barnett L. , Rochester J. , Wilkinson J. M. , Dall’Ara E. 
Published: March 2020

AbstractOsteoarthritis affects the morphological properties of the femoral head. The goal of this study was to develop a method to elucidate whether these changes are localised to discrete regions, or if the reported trends in microstructural changes may be identified throughout the subchondral bone of the human femoral head. Whole femoral heads extracted from osteoarthritic (n = 5) and healthy controls (n = 5) underwent microCT imaging 39 μm voxel size. The subchondral bone plate was virtually isolated to evaluate the plate thickness and plate porosity. The trabecular bone region was divided into 37 volumes of interest spatially distributed in the femoral head, and bone morphometric properties were determined in each region. The study showed how the developed approach can be used to study the heterogeneous properties of the human femoral head affected by a disease such as osteoarthritis. As example, in the superior femoral head osteoarthritic specimens exhibited a more heterogeneous micro-architecture, with trends towards thicker cortical bone plate, higher trabecular connectivity density, higher trabecular bone density and thicker structures, something that could only be observed with the newly developed approach. Bone cysts were mostly confined to the postero-lateral quadrants extending from the subchondral region into the mid trabecular region. Nevertheless, in order to generalise these findings, a larger sample size should be analysed in the future. This novel method allowed a comprehensive evaluation of the heterogeneous micro-architectural properties of the human femoral head, highlighting effects of OA in the superior subchondral cortical and trabecular bone. Further investigations on different stages of OA would be needed to identify early changes in the bone.
Siamese neural networks for continuous disease severity evaluation and change detection in medical imaging
Authors: Li Matthew D. , Chang Ken , Bearce Ben , Chang Connie Y. , Huang Ambrose J. , Campbell J. Peter , Brown James M. , Singh Praveer , Hoebel Katharina V. , Erdoğmuş Deniz , Ioannidis Stratis , Palmer William E. , Chiang Michael F. , Kalpathy-Cramer Jayashree 
Published: March 2020

AbstractUsing medical images to evaluate disease severity and change over time is a routine and important task in clinical decision making. Grading systems are often used, but are unreliable as domain experts disagree on disease severity category thresholds. These discrete categories also do not reflect the underlying continuous spectrum of disease severity. To address these issues, we developed a convolutional Siamese neural network approach to evaluate disease severity at single time points and change between longitudinal patient visits on a continuous spectrum. We demonstrate this in two medical imaging domains: retinopathy of prematurity (ROP) in retinal photographs and osteoarthritis in knee radiographs. Our patient cohorts consist of 4861 images from 870 patients in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) cohort study and 10,012 images from 3021 patients in the Multicenter Osteoarthritis Study (MOST), both of which feature longitudinal imaging data. Multiple expert clinician raters ranked 100 retinal images and 100 knee radiographs from excluded test sets for severity of ROP and osteoarthritis, respectively. The Siamese neural network output for each image in comparison to a pool of normal reference images correlates with disease severity rank (ρ = 0.87 for ROP and ρ = 0.89 for osteoarthritis), both within and between the clinical grading categories. Thus, this output can represent the continuous spectrum of disease severity at any single time point. The difference in these outputs can be used to show change over time. Alternatively, paired images from the same patient at two time points can be directly compared using the Siamese neural network, resulting in an additional continuous measure of change between images. Importantly, our approach does not require manual localization of the pathology of interest and requires only a binary label for training (same versus different). The location of disease and site of change detected by the algorithm can be visualized using an occlusion sensitivity map-based approach. For a longitudinal binary change detection task, our Siamese neural networks achieve test set receiving operator characteristic area under the curves (AUCs) of up to 0.90 in evaluating ROP or knee osteoarthritis change, depending on the change detection strategy. The overall performance on this binary task is similar compared to a conventional convolutional deep-neural network trained for multi-class classification. Our results demonstrate that convolutional Siamese neural networks can be a powerful tool for evaluating the continuous spectrum of disease severity and change in medical imaging.
Machine learning prediction of incidence of Alzheimer’s disease using large-scale administrative health data
Authors: Park Ji Hwan , Cho Han Eol , Kim Jong Hun , Wall Melanie M. , Stern Yaakov , Lim Hyunsun , Yoo Shinjae , Kim Hyoung Seop , Cha Jiook 
Published: March 2020

AbstractNationwide population-based cohort provides a new opportunity to build an automated risk prediction model based on individuals’ history of health and healthcare beyond existing risk prediction models. We tested the possibility of machine learning models to predict future incidence of Alzheimer’s disease (AD) using large-scale administrative health data. From the Korean National Health Insurance Service database between 2002 and 2010, we obtained de-identified health data in elders above 65 years (N = 40,736) containing 4,894 unique clinical features including ICD-10 codes, medication codes, laboratory values, history of personal and family illness and socio-demographics. To define incident AD we considered two operational definitions: “definite AD” with diagnostic codes and dementia medication (n = 614) and “probable AD” with only diagnosis (n = 2026). We trained and validated random forest, support vector machine and logistic regression to predict incident AD in 1, 2, 3, and 4 subsequent years. For predicting future incidence of AD in balanced samples (bootstrapping), the machine learning models showed reasonable performance in 1-year prediction with AUC of 0.775 and 0.759, based on “definite AD” and “probable AD” outcomes, respectively; in 2-year, 0.730 and 0.693; in 3-year, 0.677 and 0.644; in 4-year, 0.725 and 0.683. The results were similar when the entire (unbalanced) samples were used. Important clinical features selected in logistic regression included hemoglobin level, age and urine protein level. This study may shed a light on the utility of the data-driven machine learning model based on large-scale administrative health data in AD risk prediction, which may enable better selection of individuals at risk for AD in clinical trials or early detection in clinical settings.
High step‐up double input converter with soft switching and reduced number of semiconductors
Authors: Dobakhshari Sina Salehi , Fathi Seyyed Hamid , Milimonfared Jafar 
Published: March 2020

A novel semi‐isolated high step‐up double‐input converter with current‐fed inputs is introduced in this study. The proposed converter can operate in double‐input mode and single‐input mode. It can manage the collected power from both inputs effectively. In addition, it makes a gentle transition between the modes, without the need for an abrupt change in pulse pattern, and does not utilise a breaker switch. Meanwhile, the converter preserves soft‐switching as well as a few number of semiconductors (three switches and three diodes). These items have improved the efficiency of the converter to 94.5%. Moreover, it is a high‐voltage gain (more than 13). Also, both input ports are current‐fed, so the input currents are smoothed. These features make the converter compatible with hybrid renewable energy applications. Furthermore, all components are engaged in both modes, which results in an improved component's utilisation factor. A thorough analysis has been conducted to prove the characteristics mentioned above of the proposed converter. Theoretical analysis has been verified by simulation and experimental results of a 200 W prototype.
Überlegungen zur kieferorthopädischen Therapie von Lippen-Kiefer-Gaumenspalten
Authors: Pichelmayer Margit 
Published: March 2020

ZusammenfassungBezüglich der kieferorthopädischen Behandlung von Lippen-, Kiefer- und Gaumenspalten gibt es viele verschiedenen Philosophien. Derzeit existiert kein international einheitliches Konzept. Die grundlegenden Überlegungen, die Behandlung dieser sehr häufigen angeborenen Fehlbildungen betreffend, sind jedoch sehr ähnlich, da sie sich automatisch aus den Defiziten von Weichteilen, Knochen und Zähnen ergeben. Ein nicht unwesentlicher Aspekt ist dabei der Umgang mit Abweichungen von der natürlichen Symmetrie, die in erster Linie bei einseitigen Spalten zu finden sind, die generell häufiger vorkommen als die doppelseitigen Fehlbildungen.
Clinical, Molecular, Functional, and Structural Characterization of
CYP17A1 Mutations in Brazilian Patients with 17-Hydroxylase
Deficiency
Authors: Coeli-Lacchini Fernanda Borchers , Mermejo Livia M. , Bodoni Aline Faccioli , Elias Lucila Leico Kagohara , Silva Jr Wilson Araújo , Antonini Sonir R. , Moreira Ayrton C. , de Castro Margaret 
Published: March 2020

Abstract17-Hydroxylase-deficiency (17OHD) is a rare form of congenital adrenal
hyperplasia. The aim of the work was to study clinical, biochemical, and the
follow up of 17OHD patients and evaluate the function and structure of
CYP17A1 mutations. Brazilian patients (three 46, XX and four 46,
XY; 17±1.9 years) with combined 17-hydroxylase/17,20-lyase
deficiency were evaluated. CYP17A1 gene was sequenced. Functional
analysis was performed transfecting COS7 cells, which were exposed to
progesterone or 17α-hydroxypregnolone substrates. Hormones were
determined by RIA or LC-MS/MS. Three-dimensional structural modeling
was performed by Modeller software. All patients presented prepubertal
female external genitalia, primary amenorrhea, hypergonadotrophic
hypogonadism, hypokalemic hypertension, decreased cortisol, and increased
ACTH and corticosterone levels. Five patients presented previously described
mutations: p.W406R/p.W406R,
IVS2–2A>C/p.P428L, and p.P428L/p.P428L. Two
patients presented the compound heterozygous
p.G478S/p.I223Nfs*10 mutations, whose CYP17A1 activity and
the three dimensional structural modeling are originally studied in this
paper. CYP17A1 activity of p.G478S was 13 and 58% against
progesterone and 17-hydroxypregnenolone, respectively. The
p.I223Nfs*10 caused a truncated inactive protein. Three-dimensional
p.G478S structural modeling showed different internal hydrophobic
interaction with W313 and created an additional chain side contact with L476
residue. Due to the rarity of 17OHD, the long term follow up
(15.3±3.1 years) of our patients will help endocrinologists on the
management of patients with 17OHD. The mutation
p.G478S/pI223Nfs*10 led to severe 17OHD and impaired CYP17A1
structure and function. The integration of in silico and in vitro analysis
showed how the amino acid changes affected the CYP17A1 activity and
contributed to clarify the molecular interactions of CYP17A1.
Hearing Secret Harmonies: Balint and The Re-Imagining of Medicine
Authors: Elder Andrew 
Published: March 2020

AbstractThe author uses three poems – all by the same poet who was a respiratory physician – to describe the role the imagination in Balint groups, profoundly psychoanalytic in principle, in re-imagining medicine. He states that doctors emerge from medical school ‘one-eyed’ but can slowly move to restore ‘binocular vision’ through emotional insight and greater awareness of relationships gained through participation in a Balint group. He cites Enid Balint’s concept of ‘imaginative perception’ and describes a case which brings many of the themes of the talk together including unconscious communication and the nature of ‘time’ in the GP’s consulting room.
Association between Maternal Serum Hormones along the Maternal–Fetal Hypothalamic–Pituitary–Adrenal Axis and Successful Vaginal Delivery Measured Prior to Labor Induction
Authors: Cawyer Chase R. , Lobashevksy Elena , Corley-Topham Glenda , Anderson Sarah , Owen John , Subramaniam Akila 
Published: March 2020

Abstract
Objective This study aimed to evaluate if maternal serum hormones along the maternal–fetal hypothalamic–pituitary–adrenal (HPA) axis, when drawn prior to labor induction, differed between women who delivered vaginally and those who underwent cesarean.
Study Design This was a prospective observational study at a single perinatal center performed from August 2017 to May 2018. Nulliparous women with uncomplicated singleton pregnancies ≥39 weeks had maternal serum collected prior to induction. Corticotrophin-releasing hormone (CRH) was measured by ELISA; dehydroepiandrosterone sulfate (DHEA-S), cortisol, estriol (E3) estradiol (E2), and progesterone (P4) were measured by chemiluminescent reaction. Mean analyte concentrations as well as three ratios (E2/P4, E3/P4, and E2/E3) were compared between women who had a vaginal versus cesarean delivery. Logistic regression was used to model the relationship between CRH and the odds of vaginal birth. We estimated that a sample size of 66 would have 90% power to detect a 25% difference in mean CRH levels assuming a vaginal:cesarean ratio of 2:1 with a baseline CRH concentration of 140 (standard deviation = 36) pg/mL.
Results Of the 88 women who had their serum analyzed, 27 (31%) underwent cesarean. Mean maternal serum CRH levels were similar between the vaginal delivery and cesarean groups (122.6 ± 95.2 vs. 112.3 ± 142.4, p = 0.73). Similarly, there were no significant differences in any other maternal serum analytes or ratios. Logistic regression showed a nonsignificant odds ratio for successful vaginal birth (p = 0.69) even when evaluating only the 16 women who had a cesarean for an arrest disorder (p = 0.08).
Conclusion In low-risk nulliparous women undergoing full-term labor induction, there were no differences noted in a broad array of other maternal-fetal HPA-axis hormones between women who had a vaginal or cesarean delivery.
Anomalous thickness-dependent electrical conductivity in van der Waals layered transition metal halide, Nb3Cl8
Authors: Yoon Jiho , Lesne Edouard , Sklarek Kornelia , Sheckelton John , Pasco Chris , Parkin Stuart S P , McQueen Tyrel M , Ali Mazhar N 
Published: March 2020

Abstract
Understanding the electronic transport properties of layered, van der Waals transition metal halides (TMHs) and chalcogenides is a highly active research topic today. Of particular interest is the evolution of those properties with changing thickness as the 2D limit is approached. Here, we present the electrical conductivity of exfoliated single crystals of the TMH, cluster magnet, Nb3Cl8, over a wide range of thicknesses both with and without hexagonal boron nitride (hBN) encapsulation. The conductivity is found to increase by more than three orders of magnitude when the thickness is decreased from 280 µm to 5 nm, at 300 K. At low temperatures and below ∼50 nm, the conductance becomes thickness independent, implying surface conduction is dominating. Temperature dependent conductivity measurements indicate Nb3Cl8 is an insulator, however, the effective activation energy decreases from a bulk value of 310 meV to 140 meV by 5 nm. X-ray photoelectron spectroscopy (XPS) shows mild surface oxidation in devices without hBN capping, however, no significant difference in transport is observed when compared to the capped devices, implying the thickness dependent transport behavior is intrinsic to the material. A conduction mechanism comprised of a higher conductivity surface channel in parallel with a lower conductivity interlayer channel is discussed.