Miracle-dz/UniFAR-FTUs huggingface dataset:
Human genetic background in susceptibility to tuberculosis

Tuberculosis (TB), especially in developing countries, is a major threat to human health. The pathogenesis of TB remains poorly understood, and <5%–10% of individuals infected with Mycobacterium tuberculosis (MTB) will develop clinical disease. The human genetic factors contributing to susceptibility or resistance to TB pathogenesis have been investigated by high-throughput and low-throughput association studies. Genetic polymorphisms of several genes including TLR, IGRM, VDR, ASAP1, AGMO, FOXP1, and UBLCP1 effect on the disease phenotype and also the outcome of TB treatment. Recently, microRNAs (miRNAs), which negatively regulated gene expression at the posttranscriptionally level, have gained increasing attention due to their altered expression in various human diseases, including some infections. They are crucial posttranscriptional regulators of immune response in both innate and adaptive immunity. It has been established in recent studies that the host immune response against MTB is regulated by many miRNAs, most of which are induced by MTB infection. Moreover, differential expression of miRNAs in TB patients may help distinguish between TB patients and healthy individuals or latent TB. In this review, we summarize and discuss the literature and highlight the role of selected single nucleotide polymorphisms and miRNAs that have been associated with TB infection.
Behavioral feeding strategy of frugivorous civets in a Bornean rainforest

We compared feeding behavior of frugivorous civets with those of other sympatric frugivores in a Bornean rainforest. For each frugivore residing in 3 different species of fig trees (Ficus variegata, F. fistulosa, and F. benjamina), we recorded temporal visitation patterns, visitation length, and time spent searching for fruit spanning 1,758 h of observations. The civets were strict nocturnal visitors, whereas the other 5 frugivorous species were diurnal visitors. Civets visited F. variegata, F. fistulosa, and F. benjamina trees until 51, 26, and 0.3 days before the end of fruiting, respectively, whereas the other frugivores visited the trees until 84.5, 32, and 1.2 days before the end of fruiting. Civets foraged longer in the F. variegata, F. fistulosa, and F. benjamina trees at each visit (119.17, 109.37, and 97.77 min, respectively) than did the other frugivores (39.88, 17.06, and 40.2 min). Civet search times (62.61, 27.55, and 18.9 s) were longer than those of the other frugivores (34.34, 12.46, and 9.49 s), and civet search duration significantly increased over time. Our results indicate that nocturnality is the most important factor in avoiding contest competition with larger diurnal frugivores, thus enabling long visits at feeding patches although long search times decreased feeding efficiency. Long foraging visits of civets would be advantageous in fruit-deficient environments and may compensate for their morpho-physiological disadvantages for frugivory.
Leveraging sequence‐based faecal microbial community survey data to identify alterations in gut microbiota among patients with Parkinson's disease

Parkinson's disease is a common degenerative disease of the elderly. Although the majority of studies have focused on the central nervous system (CNS) features of Parkinson's disease, recent findings suggest there is a functional link between the gut microbiome and the hallmarks of the disease. PubMed, Web of Science, EMBASE and other Chinese and English databases were searched for relevant literature. Studies on changes to intestinal microbiota in Parkinson's patients were retrieved and systematically reviewed. Quality filtering, clustering and species annotation were performed on 16s sequencing raw data from retrieved studies to achieve comparability across studies. Alpha‐diversity indices and a random effect model were used to analyse significantly altered microbiota. A total of nine studies were included in this retrospective analysis, four of which contained raw data. Alpha diversity was significantly different between control and Parkinson's disease patients in two of the four studies. Using the raw data from four individual studies, we observed differences in the phlya Bacteroidetes and Actinobacteria. Additionally, differences were observed between control and Parkinson's disease patients at the level of family (Prevotellacaea and Lactobacillaceae) and genus (Bifidobacterium and Clostridium). This study confirmed that changes in the microbiome are a consistent feature of Parkinson's disease patients and, therefore, may contribute to the onset of disease.
The Site of Release of the Diuretic Hormone in Rhodnius--A New Neurohaemal System in Insects

1. This investigation has sought the site of release of the diuretic hormone which appears in the haemolymph of larvae of Rhodnius shortly after they begin feeding. 2. Material possessing diuretic activity can be extracted in high concentration from those lengths of the peripheral abdominal nerves which lie just behind the mesothoracic ganglionic mass. 3. An electron microscope study of the mesothoracic ganglionic mass and the abdominal nerves which leave it has shown that the axons from the neurosecretory cell bodies in the ganglionic mass some of which at least have previously been shown to contain the diuretic hormone run out into the proximal lengths of the abdominal nerves where they branch. Some of the branches have been followed to neurosecretory axon endings which typically are packed with neurosecretory granules and lie immediately beneath the fibrous nerve sheath. 4. By constricting fed insects at various positions it has been possible to show that most of the diuretic hormone is released into the region close to but behind the mesothoracic ganglionic mass. 5. It is concluded that the release of diuretic hormone in Rhodnius is from a series of swollen neurosecretory axon endings dotted over the surface of those lengths of the peripheral abdominal nerves which lie close to the mesothoracic ganglionic mass.
Convulsive seizures and some behavioral comorbidities are uncoupled in the Scn1aA1783V Dravet syndrome mouse model

Dravet syndrome (Dravet) is a severe childhood epileptic encephalopathy. The disease begins with a febrile stage, characterized by febrile seizures with otherwise normal development. Progression to the worsening stage features recurrent intractable seizures and the presentation of additional nonepileptic comorbidities, including global developmental delay, hyperactivity, and motor deficits. Later in life, at the stabilization stage, seizure burden decreases, whereas Dravet‐associated comorbidities persist. To date, it remains debated whether the nonepileptic comorbidities result from severe epilepsy or represent an independent phenotypic feature.
Blood pressure measurement by coupling an external pressure and photo-plethysmographic signals

This work presents a new technique for the noninvasive measurement of systolic blood pressure (sBP), mean blood pressure (mBP) and diastolic blood pressure (dBP) using photo-plethysmographic (PPG) sensors when an artery is exposed to an external pressure. Two sets of experiments were performed: a first study combining PPG with an oscillometric device and the second combining PPG with a force/pressure sensor. These two experiments enable the estimation of BP values. PPG results were found to be comparable to oscillometric results, with bias and precision errors of -0.81 and 5.26 for sBP, 1.12 and 5.61 for mBP and 1.67 and 9.09 for dBP (n = 28). Furthermore, amplification over the brachial-to-finger path was found to be 1.02 ± 0.08 for mBP (n = 20) confirming that mBP does not undergo any amplification along the arterial tree whereas, for sBP, an amplification of 1.20 ± 0.12 (n = 7) was found for green wavelength, 1.35 ± 0.09 (n = 6) for red wavelength and 1.36 ± 0.09 (n = 6) for infrared wavelength. Thus, amplification for sBP is bigger for red and infrared wavelengths compared to green one.
Classify Motor Imagery by a Novel CNN with Data Augmentation*

The brain-computer interface (BCI) based on electroencephalography (EEG) converts the subject's intentions into control signals. For the BCI, the study of motor imagery has been widely used. In recent years, a classification method based on a convolutional neural network (CNNs) has been proposed. However, most of the existing methods use a single convolution scale on CNN, and another problem that affects the results is limited training data. To solve these problems, we propose a mixed-scale CNN architecture, and a data augmentation method is used to classify the EEG of motor imagery. After classifying the BCI competition IV dataset 2b, the average classification accuracy is 81.52%. Compared with the existing methods, our method has a better classification result. This method effectively solves the problems existing in the existing CNN-based motor imagery classification methods, and it improves the classification accuracy.
Pre-whitening and Null Projection as an Artifact Suppression Method for Electrocorticography Stimulation in Bi-Directional Brain Computer Interfaces

Electrocorticography (ECoG)-based bi-directional (BD) brain-computer interfaces (BCIs) are a forthcoming technology promising to help restore function to those with motor and sensory deficits. A major problem with this paradigm is that the cortical stimulation necessary to elicit artificial sensation creates strong electrical artifacts that can disrupt BCI operation by saturating recording amplifiers or obscuring useful neural signal. Even with state-of-the-art hardware artifact suppression methods, robust signal processing techniques are still required to suppress residual artifacts that are present at the digital back-end. Herein we demonstrate the effectiveness of a pre-whitening and null projection artifact suppression method using ECoG data recorded during a clinical neurostimulation procedure. Our method achieved a maximum artifact suppression of 21.49 dB and significantly increased the number of artifact-free frequencies in the frequency domain. This performance surpasses that of a more traditional independent component analysis methodology, while retaining a reduced complexity and increased computational efficiency.
Finite-Time Disturbance-Observer-Based Integral Terminal Sliding Mode Controller for Three-Phase Synchronous Rectifier

This article is concerned with the design of a finite-time disturbance-observer-based integral terminal sliding mode controller for the effective performance of three-phase synchronous rectifiers. The proposed control technique is developed based on the conventional synchronous reference frame model of the three-phase grid-connected converter, and the system dynamics is described in terms of a time-varying non-linear state equation. The variation of DC-load is considered as a disturbance. Therefore, a combination of a fast disturbance observer and an integral terminal sliding mode controller is utilized to produce the reference value of the direct axis for the current control loop. In this research, by employing Lyapunov stability theorem in the theoretical analysis and by numerical simulations, it is confirmed that the proposed closed-loop system is stable and the states converge to desired values in finite time even in the presence of load disturbance and control input saturation. The integral terminal sliding mode controller is utilized to maintain a robust performance along with a faster response of the converter. In order to demonstrate the performance ability of the proposed control scheme under real condition, an AC power source, impregnated with low order harmonics, is assumed. A real-time laboratory setup of the synchronous rectifier has been developed successfully, and the effective performance of the proposed control technique is fully proven.
Effective Influence Spreading in Temporal Networks With Sequential Seeding

The spread of influence in networks is a topic of great importance in many application areas. For instance, one would like to maximise the coverage, limiting the budget for marketing campaign initialisation and use the potential of social influence. To tackle this and similar challenges, more than a decade ago, researchers started to investigate the influence maximisation problem. The challenge is to find the best set of initially activated seed nodes in order to maximise the influence spread in networks. In typical approach we will activate all seeds in single stage, at the beginning of the process, while in this work we introduce and evaluate a new approach for seeds activation in temporal networks based on sequential seeding. Instead of activating all nodes at the same time, this method distributes the activations of seeds, leading to higher ranges of influence spread. The results of experiments performed using real and randomised networks demonstrate that the proposed method outperforms single stage seeding in 71% of cases by nearly 6% on average. Knowing that temporal networks are an adequate choice for modelling dynamic processes, the results of this work can be interpreted as encouraging to apply temporal sequential seeding for real world cases, especially knowing that more sophisticated seed selection strategies can be implemented by using the seed activation strategy introduced in this work.
Bio-Inspired Algorithms for Optimal Feature Selection in Motor Imagery-Based Brain-Computer Interface*

Recently, there is an increasing recognition that sensory feedback is critical for proper motor control. With the help of BCI, people with motor disabilities can communicate with their environments or control things around them by using signals extracted directly from the brain. The widely used non-invasive EEG based BCI system require that the brain signals are first preprocessed, and then translated into significant features that could be converted into commands for external control. To determine the appropriate information from the acquired brain signals is a major challenge for a reliable classification accuracy due to high data dimensions. The feature selection approach is a feasible technique to solving this problem, however, an effective selection method for determining the best set of features that would yield a significant classification performance has not yet been established for motor imagery (MI) based BCI. This paper explored the effectiveness of bio-inspired algorithms (BIA) such as Ant Colony Optimization (ACO), Genetic Algorithm (GA), Cuckoo Search Algorithm (CSA), and Modified Particle Swarm Optimization (M-PSO) on EEG and ECoG data. The performance of SVM classifier showed that M-PSO is highly efficacious with the least selected feature (SF), and converges at an acceptable speed in low iterations.
Validation of Diffeomorphic Registration on Cine Cardiac MR via Direct Frame-to-Frame Comparison with HARP Tracking on Tagged MR

Cardiac magnetic resonance (MR) tissue tagging offers an excellent solution for tracking deformation and is considered the reference standard for the quantification of strain. However, due to the requirements for a dedicated acquisition sequence and post-processing software, tagged MR acquisitions are performed much less frequently in routine clinical practice than the anatomical cine MR sequence. Using tagged MR as the reference standard, this study proposes an approach to evaluate a diffeomorphic image registration algorithm applied on cine MR images to compute the cardiac deformation. In contrast to previous evaluation methods that compared the final results, such as strain, computed from cine and tagged MR sequences, the proposed method performs a direct frame-to-frame comparison in the evaluation. To overcome the problem of misalignment between the tagged and cine MR images, the proposed approach performs transformations to and from the two-dimensional image pixel coordinates and three-dimensional space using the meta-information encoded in the MR images. Linear temporal interpolation is performed using the frame acquisition time since the last R-wave peak value of the electrocardiogram signal recorded in the meta-information. Several statistic measures are computed and reported for the registration error using the Euclidean distances between the corresponding set of points obtained using cine and tagged MR images.
Intermittent Purpura Development Associated with Leukocytoclastic Vasculitis Induced by Infliximab for Crohn's Disease

Anti-tumor necrosis factor (TNF) α agents, widely used for the treatment of Crohn's disease (CD), can sometimes induce skin-associated adverse events, which mainly include psoriasis-like eruptions, eczema, and cutaneous infections. In contrast, purpura caused by vasculitis is rarely seen. We herein report a unique case of leukocytoclastic vasculitis induced by infliximab administered for CD in which intermittent purpura development was noted. Fluorescent immunostaining showed no immunoglobulin A deposition on the vessel walls. No purpura was initially seen after starting infliximab, but it appeared approximately 10 months later; however, administration did not have to be discontinued, and the condition was later resolved. The present findings provide important details regarding vasculitis induced by anti-tumor necrosis factor-α agent administration.
Wide Input Range Differential Dual Band RF Energy Harvesting System for Battery-Less RFID Applications

A wide input range differential dual band RF energy harvesting system has been designed and presented for a battery less RFID applications. The proposed architecture includes an off-chip differential dual band antenna with differnetial dual band matching network followed by a CMOS cross coupled rectifier for RF-DC conversion which replaces a battery in several RFID applications. The proposed differential dual band matching network for the rectifier provides a peak efficiency of 60% at -12 dBm at 0.9 GHz and 64% at -16 dBm at 1.8 GHz. The proposed architecture achieves better efficiency for lower input power ranges from -20 dBm to 8 dBm which implemented using UMC 0. 18µm CMOS Technology.
A Hybrid Temporal Modeling Phoneme Recognized Network for Real-Time Speech Animation

Real-time speech animation is a compelling but challenging topic, where multimodal inputs such as audio, video or depth information, have been considered to leverage performance. However, in conditions that audio is the only available input, the quality of the outcome relies heavily on the real-time phoneme recognition. In this paper, we introduce a novel deep-learning based Realtime Phoneme Recognition Network architecture to leverage spatial, separate temporal and unified temporal modeling in the input audio data. Our network can achieve super performance in phoneme recognition. Our empirical results show that compared to the state-of-the-art algorithms, RealPRNet can achieve up to 20% PER improvement and 4% animation quality improvement based on the subject tested.
A New Multi-Objective Hybrid Flow Shop Scheduling Method to Fully Utilize the Residual Forging Heat

This paper aims to solve the problem of high energy consumption in forging production through energy-saving scheduling. By analyzing the flow shop characteristics of a forging workshop, an energy-efficient hybrid flow shop scheduling problem with forging tempering (EEHFSP-FT) is proposed. An energy-efficient scheduling model is established to simultaneously minimize both the completion time and energy consumption. In the scheduling model, constraints such as heating furnace capacity, required forging temperature, and required quenching temperature are taken into consideration. An energy-saving strategy of heat treatment with residual forging heat is adopted to address the problem of energy underutilization after forging. In order to use multi-objective optimization algorithms to solve the scheduling problems of charging and machine selection in forging production, encoding and decoding rules and evolutionary search strategies are designed. Finally, a case study on the flow shop of an automated forging center is analyzed. The validity of the proposed model is demonstrated by testing cases of different scales in conjunction with three different evolutionary algorithms. By analyzing the performance of the three algorithms, the algorithm suitable for solving the proposed model is determined.
A V-Net Based Deep Learning Model for Segmentation and Classification of Histological Images of Gastric Ablation

Gastric motility disorders are associated with bioelectrical abnormalities in the stomach. Recently, gastric ablation has emerged as a potential therapy to correct gastric dysrhythmias. However, the tissue-level effects of gastric ablation have not yet been evaluated. In this study, radiofrequency ablation was performed in vivo in pigs (n=7) at temperature-control mode (55-80°C, 5-10 s per point). The tissue was excised from the ablation site and routine H&E staining protocol was performed. In order to assess tissue damage, we developed an automated technique using a fully convolutional neural network to segment healthy tissue and ablated lesion sites within the muscle and mucosa layers of the stomach. The tissue segmentation achieved an overall Dice score accuracy of 96.18 ± 1.0 %, and Jacquard score of 92.77 ± 1.9 %, after 5-fold cross validation. The ablation lesion was detected with an overall Dice score of 94.16 ± 0.2 %. This method can be used in combination with high-resolution electrical mapping to define the optimal ablation dose for gastric ablation.Clinical Relevance—This work presents an automated method to quantify the ablation lesion in the stomach, which can be applied to determine optimal energy doses for gastric ablation, to enable clinical translation of this promising emerging therapy.
Measuring Current Source Output Impedance in EIT Systems while Attached to a Load*

A novel method for measuring the shunt impedance of current sources in Electrical Impedance Tomography (EIT) systems is introduced. In an EIT system, electrical currents with theoretical sum of zero, are applied to the body and any mismatch between the currents results in current going through an extra grounded electrode. Since the N − 1 current patterns applied in an N-electrode EIT system are orthogonal to each other, by introducing an additional linearly- independent current pattern, a system of linear equations can be established from which the unknown shunt impedances can be calculated. The framework of the proposed scheme is introduced and its effectiveness is validated through both simulation and practical implementation. The experimental results show that by measuring the shunt impedances with the proposed method and using those values to adjust the current sources, the current passing through the grounded electrode is significantly reduced.