Miracle-dz/UniFAR-FTUs huggingface dataset:
A dysbiotic microbiome triggers TH17 cells to mediate oral mucosal immunopathology in mice and humans

Combined patient and animal model studies implicate microbiota-triggered TH17 cells as disease drivers and therapeutic targets in periodontitis. Probing periodontitis pathology Periodontitis is a common inflammatory disease that can lead to bone loss. Dutzan et al. used a mouse model of ligature-induced periodontitis and samples from patients to demonstrate that TH17 cells drive disease. People naturally deficient in TH17 cells were less likely to develop periodontitis. These pathogenic cells were dependent on the microbiome, and targeting TH17 cells with a small-molecule inhibitor dampened inflammation and bone loss in mice. Their results reveal mechanisms behind the immunopathology and could lead to effective treatments for this disease. Periodontitis is one of the most common human inflammatory diseases, yet the mechanisms that drive immunopathology and could be therapeutically targeted are not well defined. Here, we demonstrate an expansion of resident memory T helper 17 (TH17) cells in human periodontitis. Phenocopying humans, TH17 cells expanded in murine experimental periodontitis through local proliferation. Unlike homeostatic oral TH17 cells, which accumulate in a commensal-independent and interleukin-6 (IL-6)–dependent manner, periodontitis-associated expansion of TH17 cells was dependent on the local dysbiotic microbiome and required both IL-6 and IL-23. TH17 cells and associated neutrophil accumulation were necessary for inflammatory tissue destruction in experimental periodontitis. Genetic or pharmacological inhibition of TH17 cell differentiation conferred protection from immunopathology. Studies in a unique patient population with a genetic defect in TH17 cell differentiation established human relevance for our murine experimental studies. In the oral cavity, human TH17 cell defects were associated with diminished periodontal inflammation and bone loss, despite increased prevalence of recurrent oral fungal infections. Our study highlights distinct functions of TH17 cells in oral immunity and inflammation and paves the way to a new targeted therapeutic approach for the treatment of periodontitis.
Evaluation of Convolutional Neural Network Architectures for Chart Image Classification

Many information visualization techniques map abstract data into visual representations to present the underlying information in a more understandable way. However, when data is not available, automatic methods are necessary to extract the underlying data from the chart images. This paper focuses on chart image classification, which is a fundamental step of chart analysis and data extraction. The performance of most conventional classifiers depends on the feature extraction technique applied, usually requiring a domain-specific design for creating the features. In this context, Convolutional Neural Network (CNN) has gained attention not only because it has achieved good results in many computer vision tasks but also because it can learn representations of images without the design of a feature extractor specific for a domain problem. Thereby, this paper proposes an evaluation of CNN architectures (VGG19, Resnet-50, and Inception-V3), comparing them with conventional classifiers (HOG features combined with KNN, Naive Bayes, Random Forest and Support Vector Machine). An artificially generated dataset of 10 classes of chart images was used for training and testing. Furthermore, another dataset composed of chart images collected from the internet was used for validation. The results show that Resnet-50 and Inception-V3 performed best achieving an accuracy of 77.76% and 76.77% respectively on the validation set. The CNN models outperformed the conventional methods, where the best combination was HOG+SVM, achieving 45.03 %. Moreover, some similar chart types were expected to be misclassified, and they were discussed based on the confusion matrix.
Privacy-Preserving Incentive Mechanisms for Mobile Crowdsensing

Ubiquitous smartphones with various built-in sensors have boosted development of many crowdsensing applications in recent years. To achieve good performance with this class of applications, a large number of participating users with sensed data are needed. Two important considerations for improving user participation in crowdsensing are the incentive mechanism for motivating users to contribute their sensing capabilities, and the techniques for protecting user privacy. In this article, we survey the works that address these issues by integrating privacy techniques with incentive mechanisms. We also advocate two future research directions to enhance incentive mechanisms for mobile crowdsensing.
18F-FDG-PET uptake in non-infected total hip prostheses

Background and purpose — 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) can be used in the diagnostic work-up of a patient with suspected periprosthetic joint infection (PJI) but, due to a lack of accurate interpretation criteria, this technique is not routinely applied. Since the physiological uptake pattern of FDG around a joint prosthesis is not fully elucidated, we determined the physiological FDG uptake in non-infected total hip prostheses. Patients and methods — Patients treated with primary total hip arthroplasty (1995–2016) who underwent a FDG-PET/CT for an indication other than a suspected PJI were retrospectively evaluated. Scans were both visually and quantitatively analyzed. Semi-quantitative analysis was performed by calculating maximum and peak standardized uptake values (SUVmax and SUVpeak) by volume of interests (VOIs) at 8 different locations around the prosthesis. Results — 58 scans from 30 patients were analyzed. In most hips, a diffuse heterogeneous uptake pattern around the prosthesis was observed (in 32/38 of the cemented prostheses, and in 16/20 of the uncemented prostheses) and most uptake was located around the neck of the prosthesis. The median SUVmax in the cemented group was 2.66 (95% CI 2.51–3.10) and in the uncemented group 2.87 (CI 2.65–4.63) (Median difference = –0.36 [CI –1.2 to 0.34]). In uncemented prostheses, there was a positive correlation in time between the age of the prosthesis and the FDG uptake (rs = 0.63 [CI 0.26–0.84]). Interpretation — Our study provides key data to develop accurate interpretation criteria to differentiate between physiological uptake and infection in patients with a prosthetic joint.
Image Dehazing for Object Recognition using Faster RCNN

Object recognition in hazy conditions is quite difficult due to illumination variance. The challenge arises in finding out features from such images. Herein, we have proposed the method to deal with such images. The input image needs to be dehazed before applying the recognition algorithms. On the other hand, dehazing a non-hazy image makes it dark resulting in loss of features. Hence, a decision is to be made whether or not the image should be dehazed before recognition. Also, for a very dense haze, even dehazing doesn’t help in object recognition. In order to tackle this issue, this paper presents a novel method to quantitatively estimate the amount of haze in the image – also termed as haze degree – using dark channel prior of the input images. We compared our values with the existing method using FRIDA dataset. The estimated haze degree is used to decide whether input image need to be dehazed or not. We use DehazeNet and Faster RCNN for dehazing and recognition, respectively. We test our method on real time hazy images to set a threshold on haze degree to classify the image as light, moderate or densely hazed. We used the Static Scenes dataset from Color Hazy Images for Comparison (CHIC) database to obtain the threshold values.
Cross-Functional Teams That Grok It: The Collective Empathic Understanding of Product Requirements

Software development has become increasingly software 'product' development, without the authoritative 'customer' stakeholder role that many requirements engineering processes assume exists in some form. Many progressive software product companies today are empowering cross-functional product teams to own their product – to collectively understand the product context, the true product needs, and manage its on-going evolution. Some teams do this better than others and neither established requirements elicitation and validation processes nor conventional team leadership practices explain the reasons for the differences. This research inquires into how cross-functional product teams, as a collective, create and nurture a shared mental model that accurately represents the external product domain and its realities. The research also examines how teams use that collective understanding to shape development plans, internal and external communications, new team member onboarding, etc. The aim of this research is to develop substantive theory that describes how self-directed, cross-functional product development teams take an empathic-based approach to discovering, understanding, and maintaining a deep, collective understanding of the domain and true product needs. This theory will support software product development leaders in creating rich-er conditions for teams to grok.
Cyclone Track Prediction with Matrix Neural Networks

Although machine learning and statistical methods have been extensively used to study cyclones, the prediction of cyclone trajectories remains a challenging problem. Matrix neural networks have the ability of handling spatial correlations in the data which made them suitable for image recognition tasks. Cyclone trajectories are defined by the latitude and the longitudes coordinates as a temporal sequence. Matrix neural networks are suitable for track prediction as the dataset can be conveniently given as input without vectorization that could result in loss of correlation of the spatial information. In this paper, matrix neural networks are used for cyclone track prediction for the South Indian Ocean. The results show that matrix neural networks have the ability to preserve spatial correlation that empower them to make a better prediction when compared to prominent recurrent neural network architectures.
Adaptive Dynamic Checkpointing for Safe Efficient Intermittent Computing

Energy-harvesting devices have the potential to be the foundation of emerging, sensor-rich application domains where the use of batteries is infeasible, such as in space and civil infrastructure. Programming on an energy-harvesting device is difficult because the device operates only intermittently, as energy is available. Intermittent operation requires the programmer to reason about energy to understand data consistency and forward progress of their program. Energy varies with input and environment, making intermittent programming difficult. Existing systems for intermittent execution provide an unfamiliar programming abstraction and fail to adapt to energy changes forcing a compromise of either performance or assurance of forward progress. This paper presents Chinchilla, a compiler and runtime system that allows running unmodified C code efficiently on an energy-harvesting device with little additional programmer effort and no additional hardware support. Chinchilla overprovisions code with checkpoints to assure the system makes progress, even with scarce energy. Chinchilla disables checkpoints dynamically to efficiently adapt to energy conditions. Experiments show that Chinchilla improves programmability, is performant, and makes it simple to statically check the absence of non-termination. Comparing to two systems from prior work, Alpaca and Ratchet, Chinchilla makes progress when Alpaca cannot, and has 125% mean speedup against Ratchet.
Cross-platform mechanical characterization of lung tissue

Published data on the mechanical strength and elasticity of lung tissue is widely variable, primarily due to differences in how testing was conducted across individual studies. This makes it extremely difficult to find a benchmark modulus of lung tissue when designing synthetic extracellular matrices (ECMs). To address this issue, we tested tissues from various areas of the lung using multiple characterization techniques, including micro-indentation, small amplitude oscillatory shear (SAOS), uniaxial tension, and cavitation rheology. We report the sample preparation required and data obtainable across these unique but complimentary methods to quantify the modulus of lung tissue. We highlight cavitation rheology as a new method, which can measure the modulus of intact tissue with precise spatial control, and reports a modulus on the length scale of typical tissue heterogeneities. Shear rheology, uniaxial, and indentation testing require heavy sample manipulation and destruction; however, cavitation rheology can be performed in situ across nearly all areas of the lung with minimal preparation. The Young’s modulus of bulk lung tissue using microindentation (1.9±0.5 kPa), SAOS (3.2±0.6 kPa), uniaxial testing (3.4±0.4 kPa), and cavitation rheology (6.1±1.6 kPa) were within the same order of magnitude, with higher values consistently reported from cavitation, likely due to our ability to keep the tissue intact. Although cavitation rheology does not capture the non-linear strains revealed by uniaxial testing and SAOS, it provides an opportunity to measure mechanical characteristics of lung tissue on a microscale level on intact tissues. Overall, our study demonstrates that each technique has independent benefits, and each technique revealed unique mechanical features of lung tissue that can contribute to a deeper understanding of lung tissue mechanics.
Simple Categorization of Human Figure Drawings at 5 Years of Age as an Indicator of Developmental Delay

ABSTRACT Purpose: To elucidate the association between developmental stage of human figure drawing(HFD) and fine motor control, visual perception, and further investigate its potential to be used for screening developmental delay. Methods: Participants were 301 children at 5½ years of age, 176 born preterm and 125 at term, whose HFDs were categorized into six developmental stages. Motor-Free-Visual-Perception Test, Movement-ABC, Performance Intelligence Quotient (PIQ: Wechsler Scale), and the Visual-Motor Integration test were used. Fine motor functions were explored using ImageJ. Results: Age-expected HFDs were drawn by 87% of the children, while 13%, mostly preterm boys, drew immature ones. Stages of HFD were related to both PIQ and Movement-ABC. Visuomotor control and visual perception significantly explained the HFD. The sensitivity and specificity of HFD as a screening tool was moderate to good. Conclusions: HFD is influenced by visual perception and visuomotor control and can be used for screening developmental delay at preschool age.
Object Classification in Thermal Images using Convolutional Neural Networks for Search and Rescue Missions with Unmanned Aerial Systems

In recent years, the use of Unmanned Aerial Systems (UAS) has become commonplace in a wide variety of tasks due to their relatively low cost and ease of operation. In this paper, we explore the use of UAS in maritime Search And Rescue (SAR) missions by using experimental data to detect and classify objects at the sea surface. The objects are chosen as common objects present in maritime SAR missions: a boat, a pallet, a human, and a buoy. The data consists of thermal images and a Gaussian Mixture Model (GMM) is used to discriminate foreground objects from the background. Then, bounding boxes containing the object are defined and used to train a Convolutional Neural Network (CNN). The CNN achieves the average accuracy of 92.5% when evaluating a testing dataset.
Shear Waves Reveal Viscoelastic Changes in Skeletal Muscles After Hemispheric Stroke

We investigated alterations in material properties such as elasticity and viscoelasticity of stroke-affected muscles using ultrasound induced shear waves and mechanical models. We used acoustic radiation force to generate shear waves along fascicles of biceps muscles and measured their propagation velocity. The shear wave data were collected in muscles of 13 hemiplegic stroke survivors under passive conditions at 90°, 120°, and 150° elbow flexion angles. In a viscoelastic medium, as opposed to a purely elastic medium, the shear wave propagation velocity depends on the frequency content of the induced wave. Therefore, in addition to the shear wave group velocity (GpV), we also estimated a frequency-dependent phase velocity (PhV). We found significantly higher GpVs and PhVs in stroke-affected muscles ( ${p} < {0.05}$ ). The velocity data were used to estimate shear elasticity and viscosity using an elastic and viscoelastic material models. A pure elastic model showed increased shear elasticity in stroke-affected muscles ( ${p} < {0.05}$ ). The Voigt model estimates of viscoelastic properties were also significantly different between the stroke-impaired and non-impaired muscles. We observed significantly larger model-estimated viscosity values on the stroke-affected side at elbow flexion angles of 120° and 150°. Furthermore, the creep behavior (tissue strain resulting from the application of sudden constant stress) of the model was also different between muscles of the paretic and non-paretic side. We speculate that these changes are associated with the structural disruption of muscles after stroke and may potentially affect force generation from muscle fibers as well as transmission of force to tendons.
Modeling Skeletal Muscle Laminopathies Using Human Induced Pluripotent Stem Cells Carrying Pathogenic LMNA Mutations

Laminopathies are a clinically heterogeneous group of disorders caused by mutations in LMNA. The main proteins encoded by LMNA are Lamin A and C, which together with Lamin B1 and B2, form the nuclear lamina: a mesh-like structure located underneath the inner nuclear membrane. Laminopathies show striking tissue specificity, with subtypes affecting striated muscle, peripheral nerve, and adipose tissue, while others cause multisystem disease with accelerated aging. Although several pathogenic mechanisms have been proposed, the exact pathophysiology of laminopathies remains unclear, compounded by the rarity of these disorders and lack of easily accessible cell types to study. To overcome this limitation, we used induced pluripotent stem cells (iPSCs) from patients with skeletal muscle laminopathies such as LMNA-related congenital muscular dystrophy and limb-girdle muscular dystrophy 1B, to model disease phenotypes in vitro. iPSCs can be derived from readily accessible cell types, have unlimited proliferation potential and can be differentiated into cell types that would otherwise be difficult and invasive to obtain. iPSC lines from three skeletal muscle laminopathy patients were differentiated into inducible myogenic cells and myotubes. Disease-associated phenotypes were observed in these cells, including abnormal nuclear shape and mislocalization of nuclear lamina proteins. Nuclear abnormalities were less pronounced in monolayer cultures of terminally differentiated skeletal myotubes than in proliferating myogenic cells. Notably, skeletal myogenic differentiation of LMNA-mutant iPSCs in artificial muscle constructs improved detection of myonuclear abnormalities compared to conventional monolayer cultures across multiple pathogenic genotypes, providing a high-fidelity modeling platform for skeletal muscle laminopathies. Our results lay the foundation for future iPSC-based therapy development and screening platforms for skeletal muscle laminopathies.
Ultrasonic Processing of Aluminum–Magnesium Alloys

This study evaluated the effect of UltraSonic Treatment (UST) on a range of Al–Mg alloys. Previous research was carried out on single magnesium compositions. However, the amount and type of the alloy addition are known to affect the grain size even under UST, and the aim of this study was to determine whether or not alloy composition plays a similar role in the case of Al–Mg alloys. By testing binary Al–Mg alloys cast under regular casting conditions and under the presence of an ultrasonic field, it was found that while the addition of Mg solute is important, the amount of solute has little effect when UST is applied. It was observed that the grain size was barely affected by extra solute additions in this condition. This is due to the application of UST during solidification, which resulted in a dramatic reduction in the size of the nucleation free zone thus promoting many more successful nucleation events. Acoustic streaming is proposed as the main cause of this reduction in grain size.
Zinc Chloride Exposure Inhibits Brain Acetylcholine Levels, Produces Neurotoxic Signatures, and Diminishes Memory and Motor Activities in Adult Zebrafish

In this study, we evaluated the acute (24, 48, 72, and 96 h) and chronic (21 days) adverse effects induced by low doses (0.1, 0.5, 1, and 1.5 mg/L) of zinc chloride (ZnCl2) exposure in adult zebrafish by using behavioral endpoints like three-dimensional (3D) locomotion, passive avoidance, aggression, circadian rhythm, and predator avoidance tests. Also, brain tissues were dissected and subjected to analysis of multiple parameters related to oxidative stress, antioxidant responses, superoxide dismutase (SOD), neurotoxicity, and neurotransmitters. The results showed that ZnCl2-exposed fishes displayed decreased locomotor behavior and impaired short-term memory, which caused an Alzheimer’s Disease (AD)-like syndrome. In addition, low concentrations of ZnCl2 induced amyloid beta (amyloid β) and phosphorylated Tau (p-Tau) protein levels in brains. In addition, significant induction in oxidative stress indices (reactive oxygen species (ROS) and malondialdehyde (MDA)), reduction in antioxidant defense system (glutathione (GSH), GSH peroxidase (GSH-Px) and SOD) and changes in neurotransmitters were observed at low concentrations of ZnCl2. Neurotoxic effects of ZnCl2 were observed with significant inhibition of acetylcholine (ACh) activity when the exposure dose was higher than 1 ppm. Furthermore, we found that zinc, metallothionein (MT), and cortisol levels in brain were elevated compared to the control group. A significantly negative correlation was observed between memory and acetylcholinesterase (AChE) activity. In summary, these findings revealed that exposure to ZnCl2 affected the behavior profile of zebrafish, and induced neurotoxicity which may be associated with damaged brain areas related to memory. Moreover, our ZnCl2-induced zebrafish model may have potential for AD-associated research in the future.
A Compact Encoding for Efficient Character-level Deep Text Classification

This paper puts forward a new text to tensor representation that relies on information compression techniques to assign shorter codes to the most frequently used characters. This representation is language-independent with no need of pretraining and produces an encoding with no information loss. It provides an adequate description of the morphology of text, as it is able to represent prefixes, declensions, and inflections with similar vectors and are able to represent even unseen words on the training dataset. Similarly, as it is compact yet sparse, is ideal for speed up training times using tensor processing libraries. As part of this paper, we show that this technique is especially effective when coupled with convolutional neural networks (CNNs) for text classification at character-level. We apply two variants of CNN coupled with it. Experimental results show that it drastically reduces the number of parameters to be optimized, resulting in competitive classification accuracy values in only a fraction of the time spent by one-hot encoding representations, thus enabling training in commodity hardware.