Miracle-dz/UniFAR-FTUs huggingface dataset:
Model evaluation based on the negative predictive value for interval-censored survival outcomes

In many cohort studies, time to events such as disease recurrence is recorded in an interval-censored format. An important objective is to predict patient outcomes. Clinicians are interested in predictive covariates. Prediction rules based on the receiver operating characteristic curve alone are not related to the survival endpoint. We propose a model evaluation strategy to leverage the predictive accuracy based on negative predictive functions. Our proposed method makes very few assumptions and only requires a working model to obtain the regression coefficients. A nonparametric estimate of the predictive accuracy provides a simple and flexible approach for model evaluation to interval-censored survival outcomes. The implementation effort is minimal, therefore this method has an increased potential for immediate use in biomedical data analyses. Simulation studies and a breast cancer trial example further illustrate the practical advantages of this approach.
Rheology of Concentrated Emulsions of Spherical Droplets

Abstract We propose a viscosity model accounting for experiments of emulsions of two immiscible liquids at arbitrary volume fractions. The model is based on a recursive-differential method formulated in terms of the appropriate scaling variable which emerges from an analysis of excluded volume effects in the system. This variable, called the effective filling fraction, incorporates the geometrical information of the system which determines the maximum packing and reduces to the bare filling fraction for infinitely diluted emulsions. The agreement of our model for the viscosity with experiments and previous theories is good for all the range of volume fractions and viscosity ratios.
Calculated CT Volumes of Lacrimal Glands in Normal Caucasian Orbits

Introduction: The objective was to describe the range of normal volumes for the lacrimal gland calculated from CT. Methods: A retrospective review with institutional review board approval of 293 CT scans of 586 orbits was performed. Patients were included if they were Caucasian and aged 18 years or older. Orbits were excluded if there was a disease or trauma. OsiriX software was used to outline the lacrimal gland in consecutive axial slices and to calculate the volume. Inter-rater agreement was assessed in a subset of 30 randomly selected orbits by observers of different levels of training using the intraclass correlation coefficient (ICC). Results: Two hundred sixty orbits of 187 patients were included. The mean volume of the lacrimal gland was 0.696cm3 in right orbits (SD = 0.261) and 0.649cm3 in left orbits (SD = 0.231), with no significant difference between right and left (p= 0.125). The mean volume was 0.680cm3 in men (SD = 0.241) and 0.662cm3 in women (SD = 0.260), with no significant difference between men and women (p = 0.564). There was an inverse relationship between gland volume and age (Pearson r= −0.428 right orbits and −0.469 left orbits). Of the 73 bilateral patients, right and left orbits correlated well (Pearson r = 0.712). Agreement was good among the observers (ICC = 0.727). Conclusions: This is the first study to report the range of normal volume for Caucasian lacrimal glands measured on CT scans. The volume of the lacrimal gland decreases with age, and there is no gender or laterality difference.
Numerical study of a continuous casting process with electromagnetic brake

This dissertation investigates the e ect of electromagnetic braking and gas injection on the uid ow in a continuous casting slab mold numerically and makes veri cations on basis of a small Liquid Metal Model for Continuous Casting of steel (mini-LIMMCAST). Numerical calculations were performed by means of the software package CFX with an implemented RANS-SST turbulence model. The non-isotropic nature of the MHD turbulence was taken into account by speci c modi cations of the turbulence model. The numerical results were validated by ow measurements at the mini-LIMMCAST facility. Numerical simulations disclose the damping e ect on the ow closely depending on the wall conductance ratio. In addition, speci c modi cations of the turbulence model play a crucial role in reconstructing the peculiar phenomenon of an excitation of nonsteady, nonisotropic, large-scale ow perturbations caused by the application of the DC magnetic eld. Chapter 1 consists of four parts: research objectives; motivation for the thesis; literature review; a short introduction to the research background; a brief view of magnetohydrodynamic fundamentals; as well as description of experimental setup. Chapter 2 gives a description of the mathematical modelling. By virtue of the introduction to turbulence models, RANS-SST turbulence model is chosen in present work. Considering the in uence of magnetic eld on the turbulence, a speci c modi cation of the magnetic eld on turbulence model is addressed. Chapter 3 introduces the choice of mathematical models and the implementation of a speci c modi cation of the magnetic eld in uence on the turbulence model in CFX, and then making two tests for this MHD model. Chapter 4 is focused on the e ect of an electromagnetic brake on the turbulent melt ow in a continuous casting mold. The comparison between our numerical calculations and the experimental results displays a good agreement; in particular, the peculiar phenomenon of an excitation of nonsteady, nonisotropic, large-scale ow perturbations caused by the application of the DC magnetic eld is reconstructed successfully. Another important result of our study is the feature that the electrical boundary conditions, namely the wall conductivity ratio, have a serious in uence on the mold ow while it is exposed to an external magnetic eld. Chapter 5 is concentrated on the investigation of bubble-driven liquid metal ows with external static magnetic eld and the mathematical model veri cation. The calculations are able to reproduce a striking feature of a horizontal magnetic eld found in the range of moderate Hartmann numbers revealing that such a steady transverse magnetic eld may destabilize the ow and cause distinct oscillations of the liquid velocity. Chapter 6 addresses the study of ow eld in a slab mold with the applications of magnetic eld and gas injection. An Euler-Euler approach has been employed to investigate the e ects of argon gas and static magnetic eld on the ow pattern in a slab mold. Chapter 7 summaries and draws some main conclusions from current work.
The Impact of Community Based Health Insurance in Enhancing Better Accessibility and Lowering the Chance of Having Financial Catastrophe Due to Health Service Utilization

The Lao population mostly relies on out-of-pocket expenditures for health care services. This study aims to determine the role of community-based health insurance in making health care services accessible and in preventing financial catastrophe resulting from personal payment for inpatient services. A cross-sectional study design was applied. Data collection involved 126 insured and 126 uninsured households in identical study sites. Two logistic regression models were used to predict and compare the probability of hospitalization and financial catastrophe that occurred in both insured and uninsured households within the previous year. The findings show that insurance status does not significantly improve accessibility and financial protection against catastrophic expenditure. The reason is relatively simple, as catastrophic health expenditure refers to a total out-of-pocket payment equal to or more than 40% of household income minus subsistence. When household income declines as a result of inability to work due to illness, the 40% threshold is quickly reached. Despite this, results suggest that insured households are not significantly better off under community-based health insurance. However, compared to uninsured households, insured households do have better accessibility and a lower probability of reaching the financial catastrophe threshold.
Sensorimotor network alterations in children and youth with prenatal alcohol exposure

Children with prenatal alcohol exposure (PAE) often have impaired sensorimotor function. While altered brain structure has been noted in sensorimotor areas, the functional brain alterations remain unclear. This study aims to investigate sensorimotor brain networks in children and youth with PAE using resting‐state functional magnetic resonance imaging (rs‐fMRI). A parcellation‐based network analysis was performed to identify brain networks related to hand/lower limb and face/upper limb function in 59 children and youth with PAE and 50 typically developing controls. Participants with PAE and controls had similar organization of the hand and face areas within the primary sensorimotor cortex, but participants with PAE had altered functional connectivity (FC) between the sensorimotor regions and the rest of the brain. The sensorimotor regions in the PAE group showed less connectivity to certain hubs of the default mode network and more connectivity to areas of the salience network. Overall, our results show that despite similar patterns of organization in the sensorimotor network, subjects with PAE have increased FC between this network and other brain areas, perhaps suggesting overcompensation. These alterations in the sensorimotor network lay the foundation for future studies to evaluate interventions and treatments to improve motor function in children with PAE.
PrgK, a Multidomain Peptidoglycan Hydrolase, Is Essential for Conjugative Transfer of the Pheromone-Responsive Plasmid pCF10

ABSTRACT Peptidoglycan (PG) hydrolases associated with bacterial type IV secretion systems (T4SSs) are thought to generate localized lesions in the PG layer to facilitate assembly of the translocation channel. The pheromone-responsive plasmid pCF10 of Enterococcus faecalis encodes a putative cell wall hydrolase, PrgK, and here we report that a prgK deletion abolished functionality of the pCF10-encoded T4SS as monitored by pCF10 conjugative transfer. Expression in trans of wild-type prgK fully complemented this mutation. PrgK has three potential hydrolase motifs resembling staphylococcal LytM, soluble lytic transglycosylase (SLT), and cysteine-, histidine-dependent amidohydrolase/peptidase (CHAP) domains. Complementation analyses with mutant alleles established that PrgK bearing two hydrolase domains in any combination supported near-wild-type plasmid transfer, and PrgK bearing a single hydrolase domain supported at least a low level of transfer in filter matings. When exported to the Escherichia coli periplasm, each domain disrupted cell growth, and combinations of domains additionally induced cell rounding and blebbing and conferred enhanced sensitivity to osmotic shock. Each domain bound PG in vitro, but only the SLT domain exhibited detectable hydrolase activity, as shown by zymographic analyses and release of fluorescent PG fragments. Genes encoding three T4SS-associated, putative hydrolases, Lactococcus lactis CsiA, Tn925 Orf14, and pIP501 TraG, partially complemented the ΔprgK mutation. Our findings establish that PrgK is an essential component of the pCF10-encoded Prg/Pcf T4SS and that its hydrolase domains coordinate their activities for full PrgK function. PrgK is indispensable for plasmid transfer in liquid matings, suggestive of a role in formation or stabilization of mating junctions.
Are One-Time Contributors Different? A Comparison to Core and Periphery Developers in FLOSS Repositories

Context: Free/Libre Open Source Software (FLOSS) communities consist of different types of contributors. Core contributors and peripheral contributors work together to create a successful project, each playing a different role. One-Time Contributors (OTCs), who are on the very fringe of the peripheral developers, are largely unstudied despite offering unique insights into the development process. In a prior survey, we identified OTCs and discovered their motivations and barriers. Aims: The objective of this study is to corroborate the survey results and provide a better understand of OTCs. We compare OTCs to other peripheral and core contributors to determine whether they are distinct. Method: We mined data from the same code-review repository used to identify survey respondents in our previous study. After identifying each contributor as core, periphery, or OTC, we compared them in terms of patch size, time interval from submission to decision, the nature of their conversations, and patch acceptance rates. Results: We identified a continuum between core developers and OTCs. OTCs create smaller patches, face longer time intervals between patch submission and rejection, have longer review conversations, and face lower patch acceptance rates. Conversely, core contributors create larger patches, face shorter time intervals for feedback, have shorter review conversations, and have patches accepted at the highest rate. The peripheral developers fall in between the OTCs and the core contributors. Conclusion: OTCs do, in fact, face the barriers identified in our prior survey. They represent a distinct group of contributors compared to core and peripheral developers.
Performance of concrete with binary and ternary cement blends

Among the many factors that govern the durability and performance of concrete in service, type of cement receives greater attention. This paper describes the characteristics of cementitious systems required to meet the diverse requirements of strength and durability of concrete and highlights the advantages of part replacement of OPC by fl y ash, granulated slag and silica fume – either singly or in combination in ternary blends. Examples of successful application are cited.
Carbon Dioxide Emission from Maize Straw Incubated with Soil under Various Moisture and Nitrogen Levels

°C for 51 days. CO2-C evolved was regularly recorded for all treatments during entire incubation period. Results showed that the mixing of straw with soil accelerated decomposition rates and enhanced cumulative CO2-C production. The incorporation of straw brought about 50% increase in the cumulative CO2-C production as compared with controls. About 45% of added maize straw C was mineralized to CO2-C in 51 days. We conclude that incorporation of straw into soil along with the addition of N and moisture levels significantly affected CO2-C evolution, cumulative CO2-C, C mineralization and soil organic carbon deposition. The CO2 emission was in positive correlation with (R 2 =0.99) N, moisture and incubation time (days). The straw returning into soil may enhance carbon pools and, thus will improve soil and environmental quality.
Deep learning and its application to medical image segmentation

One of the most common tasks in medical imaging is semantic segmentation. Achieving this segmentation automatically has been an active area of research, but the task has been proven very challenging due to the large variation of anatomy across different patients. However, recent advances in deep learning have made it possible to significantly improve the performance of image recognition and semantic segmentation methods in the field of computer vision. Due to the data driven approaches of hierarchical feature learning in deep learning frameworks, these advances can be translated to medical images without much difficulty. Several variations of deep convolutional neural networks have been successfully applied to medical images. Especially fully convolutional architectures have been proven efficient for segmentation of 3D medical images. In this article, we describe how to build a 3D fully convolutional network (FCN) that can process 3D images in order to produce automatic semantic segmentations. The model is trained and evaluated on a clinical computed tomography (CT) dataset and shows state-of-the-art performance in multi-organ segmentation.
The Virtual Mouse Brain: A Computational Neuroinformatics Platform to Study Whole Mouse Brain Dynamics

Visual Abstract Connectome-based modeling of large-scale brain network dynamics enables causal in silico interrogation of the brain’s structure-function relationship, necessitating the close integration of diverse neuroinformatics fields. Here we extend the open-source simulation software The Virtual Brain (TVB) to whole mouse brain network modeling based on individual diffusion magnetic resonance imaging (dMRI)-based or tracer-based detailed mouse connectomes. We provide practical examples on how to use The Virtual Mouse Brain (TVMB) to simulate brain activity, such as seizure propagation and the switching behavior of the resting state dynamics in health and disease. TVMB enables theoretically driven experimental planning and ways to test predictions in the numerous strains of mice available to study brain function in normal and pathological conditions.
A Novel Diterpenoid Suppresses Osteoclastogenesis and Promotes Osteogenesis by Inhibiting Ifrd1‐Mediated and IκBα‐Mediated p65 Nuclear Translocation

Osteoporosis develops because of impaired bone formation and/or excessive bone resorption. Although the pharmacological treatment of osteoporosis has been extensively developed, alternative treatments are still needed. Here, we showed that oridonin (ORI), a diterpenoid isolated from Rabdosia rubescens, can suppress osteoclastogenesis and enhance osteogenesis. ORI inhibited the receptor activator of nuclear factor‐κB (NF‐κB) ligand (RANKL)‐induced osteoclast formation and bone resorption through the inhibition of p65 nuclear translocation. ORI‐induced inhibition of this translocation led to an increase in osteoblast differentiation and mineralization through the promotion of Smad1/Smad5 phosphorylation. Further analyses demonstrated that the inhibition of p65 nuclear translocation is due to the suppression of IκBα phosphorylation and the induced proteasomal degradation of interferon‐related development regulator 1 (Ifrd1), a transcriptional corepressor that is involved in the suppression of NF‐κB nuclear translocation. Moreover, mice treated with ORI at catabolic and anabolic windows showed a considerable attenuation of ovariectomy (OVX)‐induced osteoporosis. Taken together, our findings reveal that ORI protects against OVX‐induced bone loss via inhibiting osteoclastic bone resorption but enhancing osteoblastic bone formation through abolishing both Ifrd1‐mediating and IκBα‐mediated p65 nuclear translocation. These results show the potential of ORI for treatment of osteoporosis and highlight Ifrd1 as a another novel promising target for anti‐osteoporotic drugs. © 2017 American Society for Bone and Mineral Research
High-throughput characterization of the functional impact of IgG Fc glycan aberrancy in juvenile idiopathic arthritis

Juvenile idiopathic arthritis (JIA) encompasses all forms of chronic idiopathic arthritis that arise before age 16. Previous studies have found JIA to be associated with lower Fc galactosylation of circulating IgG, but the overall spectrum of glycan changes and the net impact on IgG function are unknown. Using ultra performance liquid chromatography (UPLC), we compared IgG glycosylation in 54 subjects with recent-onset untreated JIA with 98 healthy pediatric controls, paired to biophysical profiling of affinity for 20 IgG receptors using a high-throughput multiplexed microsphere assay. Patients with JIA exhibited an increase in hypogalactosylated and hyposialylated IgG glycans, but no change in fucosylation or bisection, together with alteration in the spectrum of IgG ligand binding. Supervised machine learning demonstrated a robust capacity to discriminate JIA subjects from controls using either glycosylation or binding data. The binding signature was driven predominantly by enhanced affinity for Fc receptor like protein 5 (FcRL5), a noncanonical Fc receptor expressed on B cells. Affinity for FcRL5 correlated inversely with galactosylation and sialylation, a relationship confirmed through enzymatic manipulation. These results demonstrate the capacity of combined structural and biophysical IgG phenotyping to define the overall functional impact of IgG glycan changes and implicate FcRL5 as a potential cellular sensor of IgG glycosylation.
End-to-End Scene Text Recognition in Videos Based on Multi Frame Tracking

Text detection and recognition in scene images and videos attract much attention in computer vision recently. However, most existing text detection and recognition methods only focus on static images. In this paper an end-to-end scene text recognition method based on multi frame tracking is proposed for text in videos, in which temporal information is employed to improve performance. First, an end-to-end text recognition method based on a unified deep neural network is used to detect and recognize text in each frame of the input video. Then, multi frame text tracking is employed through associations of texts in current frame and several previous frames to obtain final results. Experiments on ICDAR datasets demonstrate that the proposed method outperforms the state-of-the-art methods in end-to-end video text recognition.
Similarity-Aware Spectral Sparsification by Edge Filtering

In recent years, spectral graph sparsification techniques that can compute ultra-sparse graph proxies have been extensively studied for accelerating various numerical and graph-related applications. Prior nearly-linear-time spectral sparsification methods first extract low-stretch spanning tree from the original graph to form the backbone of the sparsifier, and then recover small portions of spectrally-critical off-tree edges to the spanning tree to significantly improve the approximation quality. However, it is not clear how many off-tree edges should be recovered for achieving a desired spectral similarity level within the sparsifier. Motivated by recent graph signal processing techniques, this paper proposes a similarity-aware spectral graph sparsification framework that leverages efficient spectral off-tree edge embedding and filtering schemes to construct spectral sparsifiers with guaranteed spectral similarity (relative condition number) level. An iterative graph densification scheme is introduced to facilitate efficient and effective filtering of off-tree edges for highly ill-conditioned problems. The proposed method has been validated using various kinds of graphs obtained from public domain sparse matrix collections relevant to VLSI CAD, finite element analysis, as well as social and data networks frequently studied in many machine learning and data mining applications.
Machine learning on FPGAs to face the IoT revolution

FPGAs have been rapidly adopted for acceleration of Deep Neural Networks (DNNs) with improved latency and energy efficiency compared to CPU and GPU-based implementations. High-level synthesis (HLS) is an effective design flow for DNNs due to improved productivity, debugging, and design space exploration ability. However, optimizing large neural networks under resource constraints for FPGAs is still a key challenge. In this paper, we present a series of effective design techniques for implementing DNNs on FPGAs with high performance and energy efficiency. These include the use of configurable DNN IPs, performance and resource modeling, resource allocation across DNN layers, and DNN reduction and re-training. We showcase several design solutions including Long-term Recurrent Convolution Network (LRCN) for video captioning, Inception module for FaceNet face recognition, as well as Long Short-Term Memory (LSTM) for sound recognition. These and other similar DNN solutions are ideal implementations to be deployed in vision or sound based IoT applications.