Miracle-dz/UniFAR-FTUs huggingface dataset:
Necrotizing Pneumonia in Children: Early Recognition and Management

Necrotizing pneumonia (NP) is an uncommon complicated pneumonia with an increasing incidence. Early recognition and timely management can bring excellent outcomes. The diagnosis of NP depends on chest computed tomography, which has radiation damage and may miss the optimal treatment time. The present review aimed to elaborate on the reported predictors for NP. The possible pathogenesis of Streptococcus pneumoniae, Staphylococcus aureus, Mycoplasma pneumoniae and coinfection, clinical manifestations and management were also discussed. Although there is still a long way for these predictors to be used in clinical, it is necessary to investigate early predictors for NP in children.
Adjoint-Method Retrievals of Low-Altitude Wind Fields from Single-Doppler Wind Data

Abstract The simple adjoint method of Qiu and Xu is extended and used to retrieve the low-altitude horizontal wind field from single-Doppler radial wind data measured during the Phoenix II field experiment. Since the extended method uses only the radial momentum equation on a low-altitude horizontal plane with a weak nondivergence constraint for the horizontal winds, the pressure gradient and vertical advection are treated as an unknown residual forcing. The test results show that (i) the method can retrieve the low-altitude time-mean (or running mean) horizontal winds (averaged over a period of several sequential radar scans) from single-Doppler radial wind data; (ii) retrieving the time-mean part of the unknown residual forcing term improves the wind retrieval; (iii) the detailed data treatments and proper settings of the weights considered in Xu et at. remain useful for improving the retrieval.
Multibubbles Segmentation and Characteristic Measurement in Gas-Liquid Two-Phase Flow

Gas-liquid two-phase flow is a typical flow, and bubble characteristic measurement is of great importance to discover flow mechanism and guide the practical fluid mechanical engineering. In this paper, a virtual stereo vision measurement system mainly consists of a high-speed camera, and two optical reflector sets was established, and bubble images in gas-liquid two-phase flow were captured by the optimized virtual stereo vision sensor. Overlapping bubbles segmentation is indispensable for the images, and an effective multibubbles segmentation method was proposed. Firstly the convexities of the overlapped area were identified based on the chain code difference, and the pseudoconcave points were removed based on the concave length constraint. According to the matching principle of concave points, the segmentation area was clarified, and the overlapping bubbles were segmented effectively. Therefore, the modality and motion feature parameters of bubbles were estimated, and three-dimensional bubble trajectories and velocity vector field were reconstructed according to the measurement model of virtual stereo vision. The experimental results show that the segmentation and characteristic measurement method of multibubbles is valid and with high precision.
A Bayesian approach for estimating antiviral efficacy in HIV dynamic models

Abstract The study of HIV dynamics is one of the most important developments in recent AIDS research. It has led to a new understanding of the pathogenesis of HIV infection. Although important findings in HIV dynamics have been published in prestigious scientific journals, the statistical methods for parameter estimation and model-fitting used in those papers appear surprisingly crude and have not been studied in more detail. For example, the unidentifiable parameters were simply imputed by mean estimates from previous studies, and important pharmacological/clinical factors were not considered in the modelling. In this paper, a viral dynamic model is developed to evaluate the effect of pharmacokinetic variation, drug resistance and adherence on antiviral responses. In the context of this model, we investigate a Bayesian modelling approach under a non-linear mixed-effects (NLME) model framework. In particular, our modelling strategy allows us to estimate time-varying antiviral efficacy of a regimen during the whole course of a treatment period by incorporating the information of drug exposure and drug susceptibility. Both simulated and real clinical data examples are given to illustrate the proposed approach. The Bayesian approach has great potential to be used in many aspects of viral dynamics modelling since it allow us to fit complex dynamic models and identify all the model parameters. Our results suggest that Bayesian approach for estimating parameters in HIV dynamic models is flexible and powerful.
Reflectance full Mueller matrix polarimetry for microstructural validation of diffusion magnetic resonance imaging

Knowledge of fiber microstructure and orientation in the brain is critical for understanding the pathogenesis and progression of neurodegenerative diseases such as Alzheimer’s disease. Diffusion magnetic resonance imaging (dMRI) is a noninvasive imaging modality that can generate mappings of nerve fiber orientation. Due to rigorous levels of mathematical modeling involved in reconstructing dMRI data; and limited spatial resolution, there arises a need to validate the biological accuracy of collected dMRI data. Polarized light imaging (PLI) has been shown to have potential for microstructural validation due to the anisotropy in many biological tissues, particularly in myelin sheaths surrounding nerve fibers in the brain. Using PLI for this purpose is appealing because it is directly sensitive to tissue structure and can be done at high resolution. While several studies have had success using PLI for fiber mapping, continuing to advance this modality, particularly reflectance based PLI systems, could provide a valuable avenue for in vivo neural imaging. In order to reach the full potential of reflectance PLI systems, some key questions remain such as the ability of PLI to resolve crossing fibers, and the sensitivity of reflectance PLI to fiber inclination. Tissue phantoms are one potential method to isolate these issues in order to investigate them. In this proceeding, a five-wavelength reflectance Mueller matrix polarimeter is used for imaging of promising PLI tissue phantoms as well as regions of interest in fixed ferret brain samples. The retardance, diattenuation and depolarization mappings are derived from the Mueller matrix and studied in order to assess the sensitivity of this polarimeter configuration to different fiber orientations.
A Unified Modeling Method of UAVs Cooperative Target Assignment by Complex Multi-constraint Conditions

The multi-UAVs cooperative target assignment problem (MUCTAP) is a condition-complicated multi-models and multi-constraint combinatorial optimization problem, and it difficult to get a feasible solution by unified processing method. In order to solve this problem, a uniform model has been built for all kinds of unmanned aerial vehicles (UAVs) target assignment situations in this paper. We also design the unified processing method so that it can handle different types of MUCTAP. Considering the 3D environmental character, we present a method for MUCTAP by using spatially vertical section to compute the cost of flight path length, and apply the cost matrix of flight path length to optimize the target assignment algorithm. We also add the collaborative constraint relation to compute, so that it can improve the target assignment algorithm s accuracy. Simulation results show that this model can adapt to a variety of target assignment problems, and it has a high universality and accuracy. So it can effectively accomplish the task of MUCTAP.
Physiological characteristics and virulence gene composition of selected serovars of seafood-borne Salmonella enterica

Background and Aim: All serotypes of Salmonella enterica are considered potentially pathogenic. However, the non-typhoidal Salmonella (NTS) serotypes vary considerably in terms of pathogenicity and the severity of infections. Although diverse serotypes of NTS have been reported from tropical seafood, their sources, physiological characteristics, and virulence potentials are not well understood. This study aimed to compare the physiological characteristics of selected serovars of Salmonella from seafood and investigate possible variations in the distribution of known genes within the pathogenicity islands. Materials and Methods: A series of biochemical tests, including carbohydrate fermentation and amino acid decarboxylation tests were performed to physiologically compare the isolates. The genetic characterization with respect to putative virulence genes was done by screening for genes associated with Salmonella pathogenicity island (SPI) I–V, as well as the toxin- and prophage-associated genes by polymerase chain reaction. Results: Irrespective of serotypes, all the isolates uniformly harbored the five SPIs screened in this study. However, some virulence genes, such as the avrA, sodC, and gogB were not detected in all Salmonella isolates. The biochemical profiles of Salmonella serotypes were highly conserved except for variations in inositol fermentation and citrate utilization. All the isolates of this study were weak biofilm formers on polystyrene surfaces. Conclusion: The pathogenicity profiles of environmental NTS isolates observed in this study suggest that they possess the virulence machinery necessary to cause human infections and therefore, urgent measures to contain Salmonella contamination of seafood are required to ensure the safety of consumers.
Local Structure in Social Networks

This chapter is part of a continuing research series and reports work that is collaborative in every respect. The order of our names on this and our previous reports is alphabetical. National Science Foundation Grants GS-39778 to Carnegie-Mellon University and GJ-1 154X2 to the National Bureau of Economic Research, Inc., provided financial support. We are grateful to James A. Davis, J. Richard Dietrich, and Christopher Winship for aid in conducting this research and to Richard Hill for computer programing. This chapter was written when Paul Holland was with the Computer Research Center for Economics and Management Science of the National Bureau of Economic Research, Inc.
Temperature-induced epitaxial growth modes of para-sexiphenyl on Au(111)

We focus on the epitaxial growth of para-sexiphenyl on a Au 111 single-crystal surface. The films are prepared by molecular beam epitaxy at different temperatures 300 K, 330 K, and 430 K , and their thicknesses are 30 nm. X-ray diffraction investigations of these films show that the growth is strongly dependent on the substrate temperature. Lying molecules are always observed and lead to sexiphenyl crystals with their ± 21-3 plane parallel to the surface which corresponds to flat-on molecules with respect to the substrate. The in-plane alignment of the crystallites is determined by pole figure measurements, at which it is shown that it varies with the growth temperature. While standing molecules, which are only observed at 430 K, are aligned arbitrarily, lying molecules lead to preferred in-plane alignments. It is shown that the type of epitaxy changes from a molecule alignment along distinct gold directions on samples grown at 430 K and 330 K to an alignment of crystallites on the 300-K film, which is obviously driven by a lattice match. In the latter case the molecule long axes are tilted by 3° to the 11-2 direction of the gold surface. This experimentally observed in-plane alignment could be verified by a lattice mismatch calculation. The excellent agreement between experiment and calculated results reveals that in the latter case the bulk structure of the film is retained down to the interface and that its alignment is affected by the lattice match of the interface surface lattices.
DR‐NAP: Data reduction strategy using neural adaptation phenomenon in wireless sensor networks

Internet of Things (IoT) has got significant popularity among the researchers' community as they have been applied in numerous application domains. Most of the IoT applications are implemented with the help of wireless sensor networks (WSNs). These WSNs use different sensor nodes with a limited battery power supply. Hence, the energy of the sensor node is considered as one of the primary constraints of WSN. Besides, data communication in WSN dissipates more energy than processing the data. In most WSNs applications, the sensed data generated from the same location sensor nodes are identical or time‐series/periodical data. This redundant data transmission leads to more energy consumption. To reduce the energy consumption, a data reduction strategy using neural adaptation phenomenon (DR‐NAP) has been proposed to decrease the communication energy in routing data to the BS in WSN. The neural adaptation phenomenon has been utilized for designing a simple data reduction scheme to decrease the amount of data transmitted. In this way, the sensor node energy is saved and the lifetime of the network is enhanced. The proposed approach has been implanted in the existing gravitational search algorithm (GSA)‐based clustered routing for WSN. The sensed data are transmitted to CH and BS using DR‐NAP. Real sensor data from the Intel Berkeley Research lab have been used for conducting the experiments. The experiment results show 47.82% and 51.96% of improvement in network lifetime when compared with GSA‐based clustered routing and clustering scheme using Canada Geese Migration Principle (CS‐CGMP) for routing, respectively.
Temporal dynamics of geothermal microbial communities in Aotearoa-New Zealand

Microbial biogeography studies, in particular for geothermal-associated habitats, have focused on spatial patterns and/or individual sites, which have limited ability to describe the dynamics of ecosystem behaviour. Here, we report the first comprehensive temporal study of bacterial and archaeal communities from an extensive range of geothermal features in Aotearoa-New Zealand. One hundred and fifteen water column samples from 31 geothermal ecosystems were taken over a 34-month period to ascertain microbial community stability (control sites), community response to both natural and anthropogenic disturbances in the local environment (disturbed sites) and temporal variation in spring diversity across different pH values (pH 3, 5, 7, 9) all at a similar temperature of 60–70°C (pH sites). Identical methodologies were employed to measure microbial diversity via 16S rRNA gene amplicon sequencing, along with 44 physicochemical parameters from each feature, to ensure confidence in comparing samples across timeframes. Our results indicated temperature and associated groundwater physicochemistry were the most likely parameters to vary stochastically in these geothermal features, with community abundances rather than composition more readily affected by a changing environment. However, variation in pH (pH ±1) had a more significant effect on community structure than temperature (±20°C), with alpha diversity failing to adequately measure temporal microbial disparity in geothermal features outside of circumneutral conditions. While a substantial physicochemical disturbance was required to shift community structures at the phylum level, geothermal ecosystems were resilient at this broad taxonomic rank and returned to a pre-disturbed state if environmental conditions re-established. These findings highlight the diverse controls between different microbial communities within the same habitat-type, expanding our understanding of temporal dynamics in extreme ecosystems.
Periodic multivariate normal hidden markov models for the analysis of water quality time series

The modelling of multivariate riverine water quality time series poses some challenging problems including: weak dependency between observations; nonlinearity; non‐Normality; seasonality and missing data. We demonstrate that periodic multivariate Normal hidden Markov models (MNHMMs) are appropriate tools to analyse riverine water quality time series. We introduce a fully Bayesian inference procedure for this class of models, where the number of hidden states of the Markov process is unknown and reversible jump Markov chain Monte Carlo (RJMCMC) methods are developed. We present a case study using long‐term dissolved inorganic nitrogen time series measured in three Scottish rivers. Our results show the strength of the hidden Markov multistate approach for analysing long‐term multivariate riverine water quality time series. Copyright © 2010 John Wiley & Sons, Ltd.
Brain Tumour Segmentation Using S-Net and SA-Net

Image segmentation is an application area of computer vision and digital image processing that partitions a digital image into multiple image regions or segments. This process involves extracting a set of contours from the input digital image so that pixels belonging to a region share some common characteristics or computed properties, such as color, texture, or intensity. The application domain of image segmentation is widespread and includes video surveillance, object detection, traffic control system, and medical imaging. The application of image segmentation techniques in the field of medical imaging can be further subcategorized into virtual surgery simulation, diagnosis, a study of anatomical structures, measurement of tissue volumes, location of tumours, and other pathologies. In this study, we have proposed two new Convolutional Neural Network (CNN)-based models: (a) S-Net and (b) SA-Net (S-Net with attention mechanism) to perform image segmentation tasks in the field of medical imaging, especially to generate segmentation masks for brain tumours if present in brain Medical Resonance Imaging (MRI) scans. Both proposed models were developed by considering U-Net as the base architecture. The newly proposed models have leveraged the concept of ‘Merge Block’ to infuse both the local and global context and ‘Attention Block’ to focus on the region of interest having a specific object. Additionally, it uses techniques, such as data augmentation to utilize the available annotated samples more efficiently. The proposed models achieved a Dice Similarity Coefficient (DSC) measures of 0.78 and 0.81 for the High-Grade Glioma (HGG) and Low-Grade Glioma (LGG) datasets, respectively.
Effectiveness and safety of monoclonal antibodies against amyloid-beta vis-à-vis placebo in mild or moderate Alzheimer's disease

Backgrounds and objectives Currently, no consensus has been reached on the therapeutic implications of monoclonal antibodies against amyloid-beta (Aβ) in Alzheimer's disease (AD). This study aimed to examine the effectiveness and safety of monoclonal antibodies against Aβ as a whole and also to determine the superiority of individual antibodies vis-à-vis placebo in mild or moderate AD. Methods Literature retrieval, article selection, and data abstraction were performed independently and in duplicate. Cognition and function were appraised by the Mini-Mental State Examination (MMSE), Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog), Disability Assessment for Dementia (DAD), and Clinical Dementia Rating Scale-Sum of Boxes (CDR-SB). Effect sizes are expressed as standardized mean difference (SMD) with a 95% confidence interval (CI). Results Twenty-nine articles involving 108 drug-specific trials and 21,383 participants were eligible for synthesis. Of the four assessment scales, only CDR-SB was significantly reduced after using monoclonal antibodies against Aβ relative to placebo (SMD: −0.12; 95% CI: −0.2 to −0.03; p = 0.008). Egger's tests indicated a low likelihood of publication bias. At individual levels, bapineuzumab was associated with a significant increase in MMSE (SMD: 0.588; 95% CI: 0.226–0.95) and DAD (SMD: 0.919; 95% CI: 0.105–1.943), and a significant decrease in CDR-SB (SMD: −0.15; 95% CI: −0.282–0.018). Bapineuzumab can increase the significant risk of serious adverse events (OR: 1.281; 95% CI: 1.075–1.525). Conclusion Our findings indicate that monoclonal antibodies against Aβ can effectively improve instrumental activities of daily life in mild or moderate AD. In particular, bapineuzumab can improve cognition and function, as well as activities of daily life, and meanwhile, it triggers serious adverse events.
Metal-rich globular clusters in the Galaxy. III - The 'X-ray' globular cluster NGC 6441. IV - A color-magnitude diagram for NGC 6304

From a photoelectric and photographic investigation in the UBV system of the very compact, low galactic latitude, southern globular cluster NGC 6441, characteristics in the C-M diagram that are in accord with observations of the most metal-rich globular clusters in the Galaxy have been found. After an extensive discussion of the problem of isolating the cluster stars from the field stars, and of the problem of determining E (B--V), E (B--V) =0.46+-0.15 mag is adopted. If M/subV/,=+0.9 mag, then (m-M)$sub 0$=14.7+-0.3 mag. This makes NGC 6441, one of the intrinsically brightest globular clusters in the Galaxy, consistent with the large mass determined recently by Illingworth. However, no unusual features were detected in its C-M diagram that might account for its appearing within the error box of the 3U 1746--37 X-ray source, except possibly the anomalous position of V6 in the diagram. It is also found that NGC 6441 has a normal M/L ratio, and that, if our adopted reddening is correct, it is at perigalacticon. (AIP)
BPR: An Erasure Coding Batch Parallel Repair Approach in Distributed Storage Systems

Today, Erasure Coding is one of the most significant techniques widely used in distributed systems because it can improve reliability for large amounts of data with low storage overhead. However, when the distributed system encounters a large number of data loss in stripes and requires batch-stripes data recovery, current data recovery methods either repeat the single-stripe recovery method or only optimize partial stripe recovery when recovering large-scale stripes, which incurs heavy upload and download repair traffics and imbalanced load, affecting the efficiency of fault recovery and wasting additional resources. In this paper, we propose BPR, an Erasure Coding batch parallel repair approach for distributed storage systems. BPR reduces cross-rack network transfer time and increases recovery throughput by classifying the stripes and recovering the data of stripes in batches through the forward and reverse parallel data recovery. The experiment results show that for large-scale stripes recovery, BPR reduces the cross-rack network transfer time by up to 10% and increases the recovery throughput by up to 8% compared with the rPDL in some scenarios.
Two-photon voltage imaging denoising by self-supervised learning

High-speed low-light two-photon voltage imaging is an emerging tool to simultaneously monitor neuronal activity from a large number of neurons. However, shot noise dominates pixel-wise measurements and the neuronal signals are difficult to be identified in the single-frame raw measurement. We developed a self-supervised deep learning framework for voltage imaging denoising, DeepVID, without the need for any high-SNR measurements. DeepVID infers the underlying fluorescence signal based on independent temporal and spatial statistics of the measurement that is attributable to shot noise. DeepVID achieved a 15-fold improvement in SNR when comparing denoised and raw image data.