Miracle-dz/UniFAR-FTUs huggingface dataset:
Network Medicine in the Age of Biomedical Big Data

Network medicine is an emerging area of research dealing with molecular and genetic interactions, network biomarkers of disease, and therapeutic target discovery. Large-scale biomedical data generation offers a unique opportunity to assess the effect and impact of cellular heterogeneity and environmental perturbations on the observed phenotype. Marrying the two, network medicine with biomedical data provides a framework to build meaningful models and extract impactful results at a network level. In this review, we survey existing network types and biomedical data sources. More importantly, we delve into ways in which the network medicine approach, aided by phenotype-specific biomedical data, can be gainfully applied. We provide three paradigms, mainly dealing with three major biological network archetypes: protein-protein interaction, expression-based, and gene regulatory networks. For each of these paradigms, we discuss a broad overview of philosophies under which various network methods work. We also provide a few examples in each paradigm as a test case of its successful application. Finally, we delineate several opportunities and challenges in the field of network medicine. We hope this review provides a lexicon for researchers from biological sciences and network theory to come on the same page to work on research areas that require interdisciplinary expertise. Taken together, the understanding gained from combining biomedical data with networks can be useful for characterizing disease etiologies and identifying therapeutic targets, which, in turn, will lead to better preventive medicine with translational impact on personalized healthcare.
Sleep Disturbance Worsens Lower Urinary Tract Symptoms: The Nagahama Study.

PURPOSE Multiple factors are associated with lower urinary tract symptoms and sleep disturbance is well known to be associated with nocturia. The association between sleep disturbance and lower urinary tract symptoms has not been sufficiently elucidated. We evaluated the impact of sleep disturbance on lower urinary tract symptoms, especially voiding symptoms, in the Nagahama Study. MATERIALS AND METHODS The Nagahama Study is a prospective cohort study of healthy individuals. Among them study participants 50 years old or older who completed the I-PSS (International Prostate Symptom Score) questionnaire and a sleep problem questionnaire were included in this analysis and followed for 5 years. Sleep disturbance was stratified into poor sleep quality, sleep restriction and sleep medication use. We analyzed the association between sleep disturbance and worsening of lower urinary tract symptoms adjusted by various clinical factors on multivariate logistic regression analysis. RESULTS Among the 5,297 participants the prevalence of lower urinary tract symptoms at baseline was 23% and the prevalence rates of poor sleep quality, sleep restriction and sleep medication were 20%, 6% and 7%, respectively. Participants with poor sleep quality, sleep restriction or sleep medication use had a significantly greater incidence of lower urinary tract symptoms and voiding symptoms than those without such characteristics (p <0.01) Poor sleep quality and sleep medication use were significantly associated with worsening lower urinary tract symptoms as defined by an I-PSS score increase of 4 or greater (OR 1.23, p = 0.03, and 1.67, p <0.01), and with worsening voiding symptoms, defined as an increase of 3 or greater (OR 1.35, p <0.005, and 1.85, p <0.001, respectively). CONCLUSIONS Sleep disturbance was significantly associated with the presence of lower urinary tract symptoms in a cross-sectional manner and with worsening lower urinary tract symptoms longitudinally.
A DESIGN METHOD FOR CONTRA ROTATING PROPELLERS BASED ON EXACT LIFTING SURFACE CORRECTION

The paper presents some theoretical extensions of the lifting line design method for optimum contra-rotating propellers developed by the Marine CFD Group of DINAV. The original method and computational design program, in fact, has been enhanced in some fundamental aspects to be directly applied into practical design activities of commercial CRP sets for stern or podded drives, without further empirical corrections or adaptations. Now the case of propellers with non-optimum circulation along the radius can be solved and the main hydrodynamic parameters of blade sections can be corrected with an exact lifting surface theory. The main theoretical/numerical developments of the improved design method are given in the paper as well as the results obtained in an exemplary application to the design case of a real case of CRP set for a high speed motor boat with modern podded propulsion. The different propeller geometries found by using a non-optimum loading distribution and with the exact lifting surface corrections, are critically discussed also with respect to the different number of blades. From the discussion, some guidelines about the application of the method for the design of fast CR propellers and the relevance of using exact lifting surface corrections are finally drawn in the conclusions.
Role of Lung Ultrasound in Diagnosing and Grading Pulmonary Congestion in Heart Failure Patients

We enrolled 58 cardiomyopathic patients and 20 normal individuals admitted to Dar Al-Fouad hospital complaining of NYHA class III-IV, with evidence of pulmonary congestion. All patients underwent lung ultrasound. A comparison of the LUS results was done to 20 COPD patients with no history of cardiac disease. The mean age of control was 33.80; the mean age of patients was 54.03. The mean ejection fraction in the control group is 61.40, with the lowest ejection fraction 58%. The mean ejection fraction in the subject group 40.72, with the lowest ejection fraction 22% A statistical difference in lung ultrasound B lines total count (Sum) was found between the control and subjects. The mean number of B lines in the control group is 1.9, with the lowest and highest count 0 and 4 respectively. The mean number of B lines in the subject group is 54.95 with the lowest and highest count 13 and 130 respectively. The number of B-lines correlated well with E/e’ value with a positive correlation coefficient (r=0.837 p<0.0001) and a positive linear curve, which indicates its usefulness in diagnosing hemodynamic and pulmonary congestion. The number of B-lines correlated well with NT-proBNP >2000 pg/mL for congestion with a positive correlation coefficient (r=0.638, p<0.0001) and a positive linear curve, which indicates its usefulness in diagnosing pulmonary congestion. When compared to results of COPD patients, the difference was significant with ease of differentiation using lung ultrasound.
Interobserver Agreement for Mismatch Repair Protein Immunohistochemistry in Endometrial and Nonserous, Nonmucinous Ovarian Carcinomas

Immunohistochemistry (IHC) for mismatch repair (MMR) proteins is an established test to identify Lynch syndrome (LS) in patients with colorectal cancer and is being increasingly used to identify LS in women with endometrial and/or nonserous ovarian cancer (OC). We assessed interobserver agreement in the interpretation of MMR-IHC on endometrial and ovarian carcinomas. The study consisted of 73 consecutive endometrial cancers (n=48) and nonserous, nonmucinous epithelial OCs (n=25). Six pathologists from 2 cancer centers, one with and the other without, previous experience in interpreting MMR-IHC, evaluated MLH1, MSH2, MSH6, and PMS2 stains. Before the study, an experienced pathologist led a review of 9 teaching cases. A decision tool was developed as a guide in MMR-IHC interpretation. Staining was interpreted as intact, deficient, or equivocal for each protein. Interobserver agreement for the patient MMR status was categorized as “almost perfect” with &kgr;=0.919 (95% CI, 0.863-0.976). All observers were in agreement in 66 (92%) tumors. Four of the less experienced pathologists had at least 1 discrepant interpretation. There were 6 discordant cases: 3 MMR-deficient cases and 2 MMR-intact cases by majority opinion were called equivocal by at least 1 observer, and 1 MMR-deficient case by majority opinion was interpreted as MMR intact by 1 pathologist. Only the latter case (1/73 patients, 1.4%) had an unequivocal disagreement that could affect patient management. Issues associated with discordant interpretation included heterogeneous staining, intratumoral lymphocytes, regional reduced internal control tissue staining, and scattered absent/weak staining adjacent to tumor cells with strong nuclear staining.
Convolutional neural network for cell classification using microscope images of intracellular actin networks

Automated cell classification is an important yet a challenging computer vision task with significant benefits to biomedicine. In recent years, there have been several studies attempted to build an artificial intelligence-based cell classifier using label-free cellular images obtained from an optical microscope. Although these studies showed promising results, such classifiers were not able to reflect the biological diversity of different types of cell. While in terms of malignant cell, it is well-known that intracellular actin filaments are altered substantially. This is thought to be closely related to the abnormal growth features of tumor cells, their ability to invade surrounding tissues and also to metastasize. Therefore, being able to classify different types of cell based on their biological behaviors using automated technique is more advantageous. This article reveals the difference in the actin cytoskeleton structures between breast normal and cancer cells, which may provide new information regarding malignant changes and be used as additional diagnostic marker. Since the features cannot be well detected by human eyes, we proposed the application of convolutional neural network (CNN) in cell classification based on actin-labeled fluorescence microscopy images. The CNN was evaluated on a large number of actin-labeled fluorescence microscopy images of one human normal breast epithelial cell line and two types of human breast cancer cell line with different levels of aggressiveness. The study revealed that the CNN performed better in the cell classification task compared to a human expert.
Biological responses in mullet Mugil liza juveniles fed with guar gum supplemented diets

This study aimed to evaluate the effects of adding guar gum, a non-starch polysaccharide, to the diet of Mugil liza juveniles. The juveniles (mean weight = 0.38 ± 0.01 g) were fed one of three diets with increasing supplement levels (4%, 8%, and 12%) and a control diet without additional gum for 60 days, in order to evaluate the effects on zootechnical performance, proximate composition, liver parameters, morphological alterations to the intestinal tract, and modulation of gastrointestinal microbiota. The animals fed 8% and 12% gum presented a significantly lower mean final weight, weight gain, specific growth rate, food intake rate, and protein intake rate than the control. Adding gum to the diets also reduced the dry matter, crude protein, and carcass fat levels. All treatments with added gum resulted in increased liver glycogen, and the cholesterol levels were significantly reduced in fish fed 4% and 8% supplement levels. No intestinal morphological alterations were observed in the animals. However, a modulating effect was noted on the microbial community, altering the bacterial quantity and composition throughout the tract segments. The use of guar gum is not recommended in Mugil liza diets, at least above 4%. © 2015 Elsevier B.V. All rights reserved.
Laplacian embedded sparse subspace clustering

Subspace clustering, which aims at yielding a low-dimensional structure of high-dimensional data, is a fundamental clustering problem. Sparse subspace clustering (SSC) achieves state-of-the-art clustering performances by imposing sparse constraint on the coefficient matrix. However, most SSCs do not exploit the intrinsic relationship or prior information embedded in data. In this paper, we propose Laplacian embedded sparse subspace clustering, in which the intrinsic relationship of data is enforced by introducing a graph Laplacian regularization term into the clustering model. Then a symmetric constraint is imposed on the sparse representation to guarantee weight consistency for each pair of data points. To further offset the instability and control smoothness, a consistency penalty term is utilized to encourage the sequential property of data. Finally, the inexact augmented Lagrange multipliers (ALM) technique is adopted to solve the optimization problem. Experimental results on real-world data sets demonstrate the superior performance of the proposed algorithm over state-of-the-art methods.
Unbiased Recursive Partitioning to Stratify Patients with Acute Traumatic Spinal Cord Injuries: External Validity in an Observational Cohort Study

Abstract Clinical trials of novel therapies for acute spinal cord injury (SCI) are challenging because variability in spontaneous neurologic recovery can make discerning actual treatment effects difficult. Unbiased Recursive Partitioning regression with Conditional Inference Trees (URP-CTREE) is a novel approach developed through analyses of a large European SCI database (European Multicenter Study about Spinal Cord Injury). URP-CTREE uses early neurologic impairment to predict achieved motor recovery, with potential to optimize clinical trial design by optimizing patient stratification and decreasing sample sizes. We performed external validation to determine how well a previously reported URP-CTREE model stratified patients into distinct homogeneous subgroups and predicted subsequent neurologic recovery in an independent cohort. We included patients with acute cervical SCI level C4–C6 from a prospective registry at a quaternary care center from 2004–2018 (n = 101) and applied the URP-CTREE model and evaluated Upper Extremity Motor Score (UEMS) recovery, considered correctly predicted when final UEMS scores were within a pre-specified threshold of 9 points from median; sensitivity analyses evaluated the effect of timing of baseline neurological examination. We included 101 patients, whose mean times from injury baseline and follow-up examinations were 6.1 days (standard deviation [SD] 17) and 235.0 days (SD 71), respectively. Median UEMS recovery was 7 points (interquartile range 2–12). One of the predictor variables was not statistically significant in our sample; one group did not fit progressively improving UEMS scores, and three of five groups had medians that were not significantly different from adjacent groups. Overall accuracy was 75%, but varied from 82% among participants whose examinations occurred at <12 h, to 64% at 12–24 h, and 58% at >24 h. A previous URP-CTREE model had limited ability to stratify an independent into homogeneous subgroups. Overall accuracy was promising, but may be sensitive to timing of baseline neurological examinations. Further evaluation of external validity in incomplete injuries, influence of timing of baseline examinations, and investigation of additional stratification strategies is warranted.
Automated brain tumor segmentation using cascaded bootstrapping model

Brain tumor segmentation is an essential task in image based brain tumor analysis. Many deep learning based (DL) brain tumor segmentation methods have been proposed. They usually segment the brain tumor into three different sub-regions, i.e., the edema (ED), the enhancing tumor (ET) and the necrotic (NCR) from input multi-modal brain images. Some of them treat the brain tumor segmentation as a multi-label classification task, where voxels in the input brain images are classified into one of the four classes (i.e., 3 sub-regions + 1 background) simultaneously. Currently proposed methods decompose the multi-label classification task into separate binary classification tasks, each of which is responsible for classifying one of the three sub-regions. Since binary classification is easier to be optimized than multi-label classification, more accurate segmentation can be achieved by binary classification. However, most of binary classification based methods are not end-to-end, i.e., each DL model is trained to segment one tumor sub-regions separately, and correlations among them are ignored. To solve this issue, in this paper, we propose an end-to-end cascaded bootstrapping DL model, which considers the correlations among different binary classification tasks. Our model contains three levels, through which the whole tumor (WT), the tumor core (TC) and ET are segmented in a cascaded way. More important, each level estimates the corresponding segmentation using the features learnt from the previous level. Moreover, a multi-view fusion is adapted to further boost the segmentation performance. We evaluate our method using BRATS 2017 dataset, and the experimental results show excellent performance on the BRATS 2017 validation set.
VKORC1 and CYP2C9 Polymorphisms: A Case Report in a Dutch Family with Pulmonary Fibrosis

Here, we describe a Dutch family with idiopathic pulmonary fibrosis (IPF). We hypothesized that there might be an association between the presence of Vitamin K epoxide reductase complex 1 (VKORC1) and/or cytochrome P450 2C9 (CYP2C9) variant alleles and the early onset of IPF in the members of this family. VKORC1 (rs9923231 and rs9934438) and CYP2C9 (rs1799853 and rs1057910) were genotyped in this family, which includes a significant number of pulmonary fibrosis patients. In all family members, at least one of the variant alleles tested was present. The presence of the VKORC1 variant alleles in all of the IPF cases and CYP2C9 variants in all but one, which likely leads to a phenotype that is characterized by the early onset and progressive course of IPF. Our findings indicate a role of these allelic variants in (familial) IPF. Therefore, we suggest that the presence of these variants, in association with other pathogenic mutations, should be evaluated during genetic counselling. Our findings might have consequences for the lifestyle of patients with familial IPF in order to prevent the disease from becoming manifest.
The double-edged sword of femtosecond laser-induced periodic surface structures for sub-diffraction and high-efficient nanotexturing

Laser-induced periodic surface structures (LIPSS) have gained lots of attention for the rich physics and potentials in subdiffraction nanostructuring. Herein, we report new aspects of LIPSS to uniformly extend the periodicity to macro, or conversely suppress the periodicity to obtain freeform nanostructures. We have focused on the electron excitation, effective surface permittivity modifications, and plasmonic standing wave ablation for the structure origination and evolution. A plasmonic nanoimprinting model in long range and a nanohole-based light field enhancement in the nearfield are proposed, which are in good accord with the experiments. The nanotextured surface is obtained in a large area by light tailoring method with a cylindrical lens focusing and scanning. Besides, a critical power control method to confine the light field in nanoregion are conducted to obtain the freeform nanostructures, which have potential applications in birefringent optics and nanoscience.
The role of urban design in childhood obesity: A case study in Lisbon, Portugal

We hypothesize that physical features of the urban environment might enhance or prevent childhood obesity. Thus, this study's main goal was to verify if there is an association between the neighborhood urban design and childhood obesity—independent of well‐known childhood obesity determinants, such as, father's level of schooling (as a proxy for socioeconomic status), and parent's weight status.
Multi-user dynamic spectrum access based on reinforcement learning

The quantity of radio communication service is growing up with the development of wireless communication technology. While limited spectrum allocation, inefficient utilization of frequency band and plenty of unused spectrum have led to spectrum scarcity. To solve this issue, we study dynamic spectrum access technique to maximize the utilization of multi-channel wireless network. we assume that N users sharing K channels, and the users can choose any channel to transmit. each user selects a channel and transmits a packet with a certain attempt probability. After each time slot, every user receives a binary observation which indicates the result of their transmission. We aim to find an optimal strategy for spectrum access, which maximizes channel utilization in a partial observation scheme without message exchange or online coordination between users. Because of the larger scale of state space and incomplete partial observation, acquiring the optimal solution is computationally expensive in general. To solve this issue, we develop a partial observation dynamic spectrum access algorithm based on deep reinforcement leaning. We take experiments to show the strong performance of the algorithm which can increase the channel utilization to 90% in partial observation scheme.
Railway fastener defect detection based on deep convolutional networks

Fasteners are the important components of railway system, which can be used to fix the tracks to the sleepers and reduce the likelihood of derailment. Nowadays, the extensively used approaches for the automatic detection of defective fasteners are vision-based approaches. However, they are not robust and efficient enough to be applied in reality. To solve this problem, this paper applies deep convolutional networks for the automatic detection of fastener defect and proposes a two-stage fastener defect detection framework. The framework is composed of a CenterNet-based fastener localization module and a VGG-based defect classification module. Besides,we innovatively introduce an attention mechanism named CBAM into localization network and an adaptive weighted softmax loss in classification network training procedure to elevate the accuracy of both modules. The experiment result shows that both methods have obviously improved the performance of the fastener defect detection system. The proposed localization network has a better accuracy-speed trade-off with 99.94% AP at 63 FPS on the test set. In addition, the proposed defect classification network has the best accuracy (up to 98.10%) on the test set and can be used to classify up to 5 categories of defects.
SissiExpression: Towards intuitive visualization and interactive analysis of medical domain knowledge

This paper explores the methods and applications of intuitive visualization and interactive analysis of medical domain knowledge to help medical experts editing it autonomously. SissiExpression system is developed using C# and WPF technology, in which, medical knowledge is modeled as expression. The application of knowledge and execution process is clear and visible, the work of debugging and maintenance is convenient, so the result can be understood and gain recognition by experts. Expressions and knowledge are saved in XML file format, decoupled from program framework, subsequent maintenance, adding new knowledge and functions, can be achieved agilely, conveniently, with better cost control. Appropriate knowledge edition can be chosen flexibly according to demand, medical experts can possess their personal executable knowledge base. This program can help medical experts to mitigate human-computer interaction obstacles to some extent, both medical and information experts can focus on their own profession and tasks.