Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Transcriptomics Identifies Novel Genes and Pathways Involved in Post-Traumatic Osteoarthritis Development and Progression

Anterior cruciate ligament (ACL) injuries often result in post-traumatic osteoarthritis (PTOA). To better understand the molecular mechanisms behind PTOA development following ACL injury, we profiled ACL injury-induced transcriptional changes in knee joints of three mouse strains with varying susceptibility to OA: STR/ort (highly susceptible), C57BL/6J (moderately susceptible) and super-healer MRL/MpJ (not susceptible). Right knee joints of the mice were injured using a non-invasive tibial compression injury model and global gene expression was quantified before and at 1-day, 1-week, and 2-weeks post-injury using RNA-seq. Following injury, injured and uninjured joints of STR/ort and injured C57BL/6J joints displayed significant cartilage degeneration while MRL/MpJ had little cartilage damage. Gene expression analysis suggested that prolonged inflammation and elevated catabolic activity in STR/ort injured joints, compared to the other two strains may be responsible for the severe PTOA phenotype observed in this strain. MRL/MpJ had the lowest expression values for several inflammatory cytokines and catabolic enzymes activated in response to ACL injury. Furthermore, we identified several genes highly expressed in MRL/MpJ compared to the other two strains including B4galnt2 and Tpsab1 which may contribute to enhanced healing in the MRL/MpJ. Overall, this study has increased our knowledge of early molecular changes associated with PTOA development.
Korean Journal for Food Science of Animal Resources

The concern about the possibility of food can be a vehicle for the transmission of Clostridium difficile to humans has been raised recently due to the similarities among the strains isolated from patients, foods and food animals. In this study, therefore, the prevalence of C. difficile was investigated in beef and chicken meat products collected from 57 different butcher shops, markets and fast food restaurants in Sakarya province of Turkey. Two out of 101 samples (1.98%) was positive for C. difficile indicating a very low prevalence. The pathogen was isolated from an uncooked meatball sample and a cooked meat döner sample, whereas not detected in chicken meat samples. The meatball isolate was resistant to vancomycin and tetracycline, while the cooked meat döner isolate was resistant to vancomycin and metronidazole. Both isolates were sensitive to moxifloxacin and clindamycin. Toxins A and B were not detected. This study reveals the presence of C. difficile in further processed beef products in Turkey.
The Role of IL-33/ST2 Pathway in Tumorigenesis

Cancer is initiated by mutations in critical regulatory genes; however, its progression to malignancy is aided by non-neoplastic cells and molecules that create a permissive environment known as the tumor stroma or microenvironment (TME). Interleukin 33 (IL-33) is a dual function cytokine that also acts as a nuclear factor. IL-33 typically resides in the nucleus of the cells where it is expressed. However, upon tissue damage, necrosis, or injury, it is quickly released into extracellular space where it binds to its cognate receptor suppression of tumorigenicity 2 (ST2)L found on the membrane of target cells to potently activate a T Helper 2 (Th2) immune response, thus, it is classified as an alarmin. While its role in immunity and immune-related disorders has been extensively studied, its role in tumorigenesis is only beginning to be elucidated and has revealed opposing roles in tumor development. The IL-33/ST2 axis is emerging as a potent modulator of the TME. By recruiting a cohort of immune cells, it can remodel the TME to promote malignancy or impose tumor regression. Here, we review its multiple functions in various cancers to better understand its potential as a therapeutic target to block tumor progression or as adjuvant therapy to enhance the efficacy of anticancer immunotherapies.
Sparse Tikhonov-Regularized Hashing for Multi-Modal Learning

This paper mainly focuses on the role of regularization in Multi-Modal Learning (MML). Existing MML studies devote most of the efforts in maximizing the consensus of models from cues of different modalities. However, regularization methods are still far from fully explored. To fill in this gap, we propose a compact and efficient coding solution, termed by sparse Tikhonov-Regularized Hashing (STRH). The STRH enforces both the $\ell_{0}$-norm induced sparsity constraints and the Tikhonov regularization on the binary solution vectors which maximize cross-modal correlation. In addition, we raise the concerns on the challenging testing scenario of ‘Multi-modal Learning and Single-modal Prediction’ (MLSP). Finally, we demonstrate that the STRH is an efficient hashing solutions by showing its superiority under the MLSP scenario.
Mixture of Deep Regression Networks for Head Pose Estimation

Accurate and robust head pose estimation is a challenging computer vision task. In most existing methods, single-modal RGB or depth images are directly used for head pose estimation. The obvious drawbacks of these methods are two fold: (1) Traditional shallow models are not good at learning representative features. (2) They are single-modal approaches, resulting in sensitivity to noise. As such, in this work we propose a novel multi-modal regression model for head pose estimation, named mixture of deep regression networks (MoDRN). It only uses good examples for one modality to learn sub-network parameters. Thus, the sub-networks tend to be better trained and more robust to noise, making significant improved performance in their combination. Experiments on public datasets such as BIWI and BU-3DFE show the effectiveness of our approach.
Regularized Gradient Descent Training of Steered Mixture of Experts for Sparse Image Representation

The Steered Mixture-of-Experts (SMoE) framework targets a sparse space-continuous representation for images, videos, and light fields enabling processing tasks such as approximation, denoising, and coding. The underlying stochastic processes are represented by a Gaussian Mixture Model, traditionally trained by the Expectation-Maximization (EM) algorithm. We instead propose to use the MSE of the regressed imagery for a Gradient Descent optimization as primary training objective. Further, we extend this approach with regularization terms to enforce desirable properties like the sparsity of the model or noise robustness of the training process. Experimental evaluations show that our approach outperforms the state-of-the-art consistently by 1.5 dB to 6.1 dB PSNR for image representation.
Heart Rate Variability During Early Mobilization in Patients with Acute Ischemic Stroke

Autonomic dysfunction is one of the predictors of poor outcome in patients with acute ischemic stroke. We compared the heart rate variability (HRV) during early mobilization in patients with or without neurological deterioration (ND). We enrolled 7 acute ischemic patients with ND and 14 without ND and measured their HRV in the rest and mobilization by electrocardiography. There was a significant difference in sympathetic nervous activity during mobilization between the 2 groups. However, no significant differences in blood pressure, heart rate, and parasympathetic nerve activity were observed. In patients with acute ischemic stroke, it is likely that the increase in sympathetic nervous activity during mobilization is associated with ND.
An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging

There exist various types of information in retail food packages, including food product name, ingredients list and use by date. The correct recognition and coding of use by dates is especially critical in ensuring proper distribution of the product to the market and eliminating potential health risks caused by erroneous mislabelling. The latter can have a major negative effect on the health of consumers and consequently raise legal issues for suppliers. In this work, an end-to-end architecture, composed of a dual deep neural network based system is proposed for automatic recognition of use by dates in food package photos. The system includes: a Global level convolutional neural network (CNN) for high-level food package image quality evaluation (blurry/clear/missing use by date statistics); a Local level fully convolutional network (FCN) for use by date ROI localisation. Post ROI extraction, the date characters are then segmented and recognised. The proposed framework is the first to employ deep neural networks for end-to-end automatic use by date recognition in retail packaging photos. It is capable of achieving very good levels of performance on all the aforementioned tasks, despite the varied textual/pictorial content complexity found in food packaging design.
Integrative Histologic and Bioinformatics Analysis of BIRC5/Survivin Expression in Oral Squamous Cell Carcinoma

Survivin is a well-known protein involved in the inhibition of apoptosis in many different cancer types. The aim of this study was to perform an integrated bioinformatic and histologic analysis in order to study the expression and prognostic role of Survivin and its related gene BIRC5 in oral cancer. Publicly available databases were accessed via Gene Expression Omnibus and Oncomine, in addition raw data from The Cancer Genome Atlas (TCGA) were also obtained in order to analyze the rate of gene mutation, expression and methylation in patients with oral squamous cells carcinoma (OSCC). Immunohistochemistry (IHC) was also performed in order to evaluate the nuclear and cytoplasmic expression of Survivin and their correlation with cell proliferation in samples from OSCC patients. Results of this study revealed that Survivin is rarely mutated in OSCC samples and upregulated when compared to non-cancerous tissue. A negative correlation between the methylation of the island cg25986496 and BIRC5 mRNA expression was detected from TCGA data. IHC staining revealed that cytoplasmic (and not nuclear) expression of Survivin is associated with poor overall survival in OSCC patients, while the nuclear expression correlates with higher proliferation rate. In addition, data from TCGA database revealed that BIRC5 gene expression is an independent prognostic factor for OSCC patients.
Milk and Plasma Lutein and Zeaxanthin Concentrations in Chinese Breast-Feeding Mother–Infant Dyads With Healthy Maternal Fruit and Vegetable Intake*

Abstract Objectives: While others have reported that milk from coastal Chinese women contains high levels of lutein and zeaxanthin, no research has determined the corresponding infant plasma response. Whether infant plasma levels increase commensurately provides important guidance for supplementation of these increasingly intriguing carotenoids in breast-feeding mothers and formula-fed infants. Methods: Fifty-six mother–infant pairs with a maternal diet rich in eggs, green leafy vegetables, and fruit were enrolled between 6 and 16 weeks of lactation. Milk samples and blood samples from both the mother and infant were collected at entry. Maternal 3-day dietary records and a second milk sample were collected 1 to 3 weeks later. Results: Mean milk lutein concentrations in samples 1 and 2 were 6.5 and 7.7 μg/dL (range 1–22.5 μg/dL), and for zeaxanthin, 1.6 and 1.7 μg/dL (range 1–5.9 μg/dL). Lutein concentrations in infant plasma (18.2 μg/dL) were similar to those in maternal plasma (21.6 μg/dL); zeaxanthin was lower than lutein in both maternal (3.1 μg/dL) and infant (2.9 μg/dL) plasma. Infant and maternal mean plasma lutein and zeaxanthin concentrations were higher than those in both milk samples 1 and 2 (lutein, 6.9 and 8.2 μg/dL; zeaxanthin, 1.9 and 2.0 μg/dL). Infant plasma lutein and zeaxanthin concentrations positively correlated with those in milk sample 1 (lutein, r2 = 0.15, p = 0.004; zeaxanthin, r2 = 0.21, p < 0.001). Conclusions: Together, these results reveal that high milk concentrations of lutein and zeaxanthin driven by healthy maternal intakes of xanthophyll rich foods are associated with high infant plasma concentrations. These findings will be useful for determining appropriate lutein fortification strategies. Clinical Study.gov registration number: NCT01669655.
Multivariate GBLUP Improves Accuracy of Genomic Selection for Yield and Fruit Weight in Biparental Populations of Vaccinium macrocarpon Ait

The development of high-throughput genotyping has made genome-wide association (GWAS) and genomic selection (GS) applications possible for both model and non-model species. The exploitation of genome-assisted approaches could greatly benefit breeding efforts in American cranberry (Vaccinium macrocarpon) and other minor crops. Using biparental populations with different degrees of relatedness, we evaluated multiple GS methods for total yield (TY) and mean fruit weight (MFW). Specifically, we compared predictive ability (PA) differences between univariate and multivariate genomic best linear unbiased predictors (GBLUP and MGBLUP, respectively). We found that MGBLUP provided higher predictive ability (PA) than GBLUP, in scenarios with medium genetic correlation (8–17% increase with corg~0.6) and high genetic correlations (25–156% with corg~0.9), but found no increase when genetic correlation was low. In addition, we found that only a few hundred single nucleotide polymorphism (SNP) markers are needed to reach a plateau in PA for both traits in the biparental populations studied (in full linkage disequilibrium). We observed that higher resemblance among individuals in the training (TP) and validation (VP) populations provided greater PA. Although multivariate GS methods are available, genetic correlations and other factors need to be carefully considered when applying these methods for genetic improvement.
Improving Response Time of Active Speaker Detection Using Visual Prosody Information Prior to Articulation

Natural multi-party interaction commonly involves turning one’s gaze towards the speaker who has the ﬂoor. Implementing virtual agents or robots who are able to engage in natural conversations with humans therefore requires enabling machines to exhibit this form of communicative behaviour. This task is called active speaker detection. In this paper, we propose a method for active speaker detection using visual prosody (lip and head movements) information before and after speech articulation to decrease the machine response time; and also demonstrate the discriminating power of visual prosody before and after speech articulation for active speaker detection. The results show that the visual prosody information one second before articulation is helpful in detecting the active speaker. Lip movements provide better results than head movements, and fusion of both improves accuracy. We have also used visual prosody information of the ﬁrst second of the speech utterance and found that it provides more accurate results than one second before articulation. We conclude that the fusion of lip movements from both regions (the ﬁrst one second of speech and the one second before articulation) improves the accuracy for active speaker detection.
Detection and Tracking of Astral Microtubules in Fluorescence Microscopy Images

In this paper we explore detection and tracking of astral micro-tubules, a sub-population of microtubules which only exists during and immediately before mitosis and aids in the spindle orientation by connecting it to the cell cortex. Its analysis can be useful to determine the presence of certain diseases, such as brain pathologies and cancer. The proposed algorithm focuses on overcoming the problems regarding fluorescence microscopy images and microtubule behaviour by using various image processing techniques and is then compared with three existing algorithms, tested on consistent sets of images.
Dentin Bond Strength of Experimental Composites Containing Bioactive Glass: Changes During Aging for up to 1 Year.

PURPOSE To investigate dentin bond strength of experimental composites based on a bis-GMA/TEG-DMA composite filled with a varying amount (0 to 40 wt%) of bioactive glass 45S5 (BG) at a total filler content of 70 wt%. MATERIALS AND METHODS Specimens for shear bond strength testing (diameter = 3.12 mm, height = 3 mm) were bonded to human dentin using a two-step self-etch adhesive and subjected to aging in water at 37°C for 1 week, 1 month, 3 months, 6 months, and 1 year. A total of 600 specimens were prepared (6 materials × 5 aging times × 20 specimens per experimental group). Bond strength was tested by loading specimens in a universal testing machine at a constant crosshead speed of 0.5 mm/min until fracture. Reliability analysis was performed using Weibull statistics. RESULTS Experimental composites with a low BG content (up to 5 wt%) showed dentin bond strength and reliability comparable to those of the commercial reference composite. A further increase in the BG amount diminished both bond strength and reliability. The bond strength decline was linearly dependent on the amount of BG when observed within each aging time. One-year aging in water caused no deterioration of bond strength, but diminished bond reliability. The reliability after 1-year aging was similar among all composites, including the commercial reference composite. CONCLUSION The variation in BG amount diminished the bond strength and reliability in a dose-dependent manner.
Image Aesthetics Assessment Using Composite Features from off-the-Shelf Deep Models

Deep convolutional neural networks have recently achieved great success on image aesthetics assessment task. In this paper, we propose an efficient method which takes the global, local and scene-aware information of images into consideration and exploits the composite features extracted from corresponding pretrained deep learning models to classify the derived features with support vector machine. Contrary to popular methods that require fine-tuning or training a new model from scratch, our training-free method directly takes the deep features generated by off-the-shelf models for image classification and scene recognition. Also, we analyzed the factors that could influence the performance from two aspects: the architecture of the deep neural network and the contribution of local and scene-aware information. It turns out that deep residual network could produce more aesthetics-aware image representation and composite features lead to the improvement of overall performance. Experiments on common large-scale aesthetics assessment benchmarks demonstrate that our method outperforms the state-of-the-art results in photo aesthetics assessment.
Perceptual and Automatic Evaluations of the Intelligibility of Speech Degraded by Noise Induced Hearing Loss Simulation

This study aims at comparing perceptual and automatic intelligibility measures on degraded speech. It follows a previous study that designed a novel approach for the automatic prediction of Age-Related Hearing Loss (ARHL) effects on speech intelligibility. In this work, we adapted this approach to a different type of hearing disorder: the Noise Induced Hearing Loss (NIHL), i.e., hearing loss caused by noise exposure at work. Thus, we created a speech corpus made of both isolated words and short sentences pronounced by three speakers (male, female and child) and we simulated different levels of NIHL. A repetition task has been carried out with 60 participants to collect perceptual intelligibility scores. Then, an Automatic Speech Recognition (ASR) system has been designed to predict the perceptual scores of intelligibility. The perceptual evaluation showed similar effects of NIHL simulation on the male, female and child speakers. In addition, the automatic intelligibility measure, based on automatic speech recognition scores, was proven to well predict the effects of the different severity levels of NIHL. Indeed, high correlation coefficients were obtained between the automatic and perceptual intelligibility measures on both speech repetition tasks: 0.94 for isolated words task and 0.97 for sentences task.
Military genitourinary injuries: past, present, and future

Genitourinary (GU) injuries among military service members are as old as war itself. The frequency and clinical picture of GU injuries sustained in conflicts over the ages have evolved along with the constantly changing nature of combat. In this review, we discuss changing trends in the diagnosis and management of GU injuries over the past century and introduce areas of innovation which could impact the care of casualties who sustain GU trauma in the future.
Accuracy of three-dimensional cephalograms generated using a biplanar imaging system

Objective Biplanar imaging systems allow for simultaneous acquisition of lateral and frontal cephalograms. The purpose of this study was to compare measurements recorded on three-dimensional (3D) cephalograms constructed from two-dimensional conventional radiographs and biplanar radiographs generated using a new biplanar imaging system with those recorded on cone-beam computed tomography (CBCT)-generated cephalograms in order to evaluate the accuracy of the 3D cephalograms generated using the biplanar imaging system. Methods Three sets of lateral and frontal radiographs of 15 human dry skulls with prominent facial asymmetry were obtained using conventional radiography, the biplanar imaging system, and CBCT. To minimize errors in the construction of 3D cephalograms, fiducial markers were attached to anatomical landmarks prior to the acquisition of radiographs. Using the 3D Ceph™ program, 3D cephalograms were constructed from the images obtained using the biplanar imaging system (3D cephbiplanar), conventional radiography (3D cephconv), and CBCT (3D cephcbct). A total of 34 measurements were obtained compared among the three image sets using paired t-tests and Bland–Altman plotting. Results There were no statistically significant differences between the 3D cephbiplanar and 3D cephcbct measurements. In addition, with the exception of one measurement, there were no significant differences between the 3D cephcbct and 3D cephconv measurements. However, the values obtained from 3D cephconv showed larger deviations than those obtained from 3D cephbiplanar. Conclusions The results of this study suggest that the new biplanar imaging system enables the construction of accurate 3D cephalograms and could be a useful alternative to conventional radiography.
Pathogen-Derived Extracellular Vesicle-Associated Molecules That Affect the Host Immune System: An Overview

Recently, the interest in extracellular vesicles (EVs) released by pathogens like bacteria, fungi, and parasites has rapidly increased. Many of these pathogens actively modulate the immune responses of their host and there is accumulating evidence that pathogen-derived EV contribute to this process. The effects of pathogen-derived EV on the host immune system have been attributed to proteins, lipids, nucleic acids, and glycans contained in, or present on these EV. For example, toxins in bacterial EV can modulate pathogen clearance and antigen presentation, while EV-associated polysaccharides are potential vaccine targets because they induce protective immune responses. Furthermore, parasite EV-associated microRNA may increase parasite survival via host gene repression, and the lipid A moiety of LPS in bacteria-derived EV induces strong pro-inflammatory responses. Research on pathogen EV-associated molecules may pave new avenues to combat infectious diseases by immune intervention. This review provides an overview of the current knowledge of EV-associated molecules released by extracellular pathogens and their effects on the host immune system. The current focus and future hotspots of this rapidly expanding field will be highlighted and discussed.