Miracle-dz/UniFAR-FTUs huggingface dataset:
Affine Invariant Image Comparison Under Repetitive Structures

We focus on the problem of affine invariant image comparison in the presence of noise and repetitive structures. The classic scheme of keypoints, descriptors and matcher is used. A local field of image gradient orientation is used as descriptor and two matchers are proposed, based on the a-contrario theory, for handling repetitive structures. The affine invariance is obtained by affine simulations. The proposed methods achieve state-of-the-art performances under repetitive structures.
IgG4-Related Sclerosing Cholangitis and Primary Sclerosing Cholangitis

Sclerosing cholangitis (SC) is defined as a condition with progressive stenosis and destruction of the bile ducts due to diffuse inflammation and fibrosis and currently includes three categories: primary sclerosing cholangitis (PSC), secondary cholangitis, and IgG4-related sclerosing cholangitis (IgG4-SC). SC categories share similar clinical features, such as cholestasis. Patients with SC present with cholestatic symptoms, including jaundice and pruritus, and blood tests reveal elevation of cholestatic enzymes. Cholangiography, endoscopic or magnetic, is inevitably required for making a diagnosis. Although the presentation of IgG4-SC and PSC are similar, the comorbidities, treatment response, and outcomes differ significantly, and therefore, it is strongly advisable to be familiar with these two diseases to make a correct diagnosis. Differentiation of cholangiocarcinoma from IgG4-SC and PSC is also extremely important. In this review, the clinical characteristics, comorbidities, treatment and outcomes of IgG4-SC and PSC will be outlined based on experience mainly from Japan.
Light YOLO for High-Speed Gesture Recognition

This paper proposes an efficient model named Light YOLO for hand gesture recognition on the embedded platforms. Light YOLO improves accuracy, speed, and model size, in three aspects. To deal with the small scale gestures in practical applications, we strengthen the YOLOv2 with a spatial refinement module to obtain fine-grained features. To accelerate the refined network, we propose a selective-dropout channel pruning approach to prune the redundancy convolution kernels in the network. Moreover, we introduce a dataset for hand gesture recognition in complex scenes. The experimental results on this dataset show that the proposed Light YOLO significantly improve the YOLOv2 network, i.e., accuracy from 96.80% to 98.06%, speed form 40PFS to 125FPS, and size form 250M to 4MB.
Contextual Speech Recognition in End-to-end Neural Network Systems Using Beam Search

Recent work has shown that end-to-end (E2E) speech recognition architectures such as Listen Attend and Spell (LAS) can achieve state-of-the-art quality results in LVCSR tasks. One beneﬁt of this architecture is that it does not require a separately trained pronunciation model, language model, and acoustic model. However, this property also introduces a drawback: it is not possible to adjust language model contributions separately from the system as a whole. As a result, inclusion of dynamic, contextual information (such as nearby restaurants or upcoming events) into recognition requires a different approach from what has been applied in conventional systems. We introduce a technique to adapt the inference process to take advantage of contextual signals by adjusting the output likelihoods of the neural network at each step in the beam search. We apply the proposed method to a LAS E2E model and show its effectiveness in experiments on a voice search task with both artiﬁcial and real contextual information. Given optimal context, our system reduces WER from 9.2% to 3.8%. The results show that this technique is effective at incorporating context into the prediction of an E2E system.
How Respiratory Syncytial Virus Genotypes Influence the Clinical Course in Infants Hospitalized for Bronchiolitis

Background We aimed to study respiratory syncytial virus (RSV) genotype distribution, clinical presentation, and disease severity in infants with bronchiolitis from RSV subtypes and new RSV genotypes. Methods We prospectively enrolled previously healthy term infants less than 1 year old hospitalized for bronchiolitis in an Italian university hospital over 12 epidemic seasons. In 312 nasopharyngeal washings positive for RSV, we sequenced the viral genotype and analyzed this according to patient data. Strain-specific RSV loads were quantified for 273 specimens. Results From 2005-2006 to 2011-2012, the RSV-A genotype NA1 predominated, and was replaced in 2012 by the novel ON1. All infants infected with RSV subtype B were genotype BA. Stratifying data according to genotypes NA1, ON1, and BA showed that NA1-infected infants were the youngest and had the most severe clinical course. Conversely, BA-infected infants had less severe symptoms and more frequently had eosinophilia and a family history of asthma. Infants with the ON1 genotype had a milder clinical course than those with NA1 and more risk factors for asthma, despite having the highest viral loads. Conclusion The disease course in infants hospitalized for acute RSV bronchiolitis may depend on the RSV genotype.
Tertiary Lymphoid Structures: Autoimmunity Goes Local

Tertiary lymphoid structures (TLS) are frequently observed in target organs of autoimmune diseases. TLS present features of secondary lymphoid organs such as segregated T and B cell zones, presence of follicular dendritic cell networks, high endothelial venules and specialized lymphoid fibroblasts and display the mechanisms to support local adaptive immune responses toward locally displayed antigens. TLS detection in the tissue is often associated with poor prognosis of disease, auto-antibody production and malignancy development. This review focuses on the contribution of TLS toward the persistence of the inflammatory drive, the survival of autoreactive lymphocyte clones and post-translational modifications, responsible for the pathogenicity of locally formed autoantibodies, during autoimmune disease development.
Serum Zinc Level and Eating Behaviors in Children Receiving Zinc Supplements without Physician Prescription

Background: The aim of the study was to compare the serum zinc level and eating behaviors in 2–8-year-old children with and without arbitrarily zinc supplementation. Materials and Methods: This case–control study was conducted from December 2015 to December 2017 in Isfahan, Iran. The case group consisted of seventy children, aged 2–8 years, who have received zinc supplement without physician prescription; the controls were an equal number of age-matched children who did not receive any supplement. The serum zinc level was measured, and eating behaviors were identified using Children's Eating Behavior Questionnaire (CEBQ). Results: There was no significant difference in serum zinc level between two groups (P = 0.18). Some differences in CEBQ subscales were identified between the groups studied. In the control group, the subscale of enjoyment of food was higher than the case group (P < 0.001). In the case group, the subscales of food fussiness and satiety responsiveness were higher than controls (P < 0.001). Conclusion: In this study, serum zinc levels were not significantly different between the two groups, and arbitrarily zinc supplementation does not play an important role in improving anorexia subscales.
The Energetic Cost of Reproduction and Its Effect on Optimal Life-History Strategies

Trade-offs in energy allocation between growth, reproduction, and survival are at the core of life-history theory. While age-specific mortality is considered to be the main determinant of the optimal allocation, some life-history strategies, such as delayed or skipped reproduction, may be better understood when also accounting for reproduction costs. Here, we present a two-pool indeterminate grower model that includes survival and energetic costs of reproduction. The energetic cost sets a minimum reserve required for reproduction, while the survival cost reflects increased mortality from low postreproductive body condition. Three life-history parameters determining age-dependent energy allocation to soma, reserve, and reproduction are optimized, and we show that the optimal strategies can reproduce realistic emergent growth trajectories, maturation ages, and reproductive outputs for fish. The model predicts maturation phase shifts along the gradient of condition-related mortality and shows that increased harvesting will select for earlier maturation and higher energy allocation to reproduction. However, since the energetic reproduction cost sets limits on how early an individual can mature, an increase in fitness at high harvesting can only be achieved by diverting most reserves into reproduction. The model presented here can improve predictions of life-history responses to environmental change and human impacts because key life-history traits such as maturation age and size, maximum body size, and size-specific fecundity emerge dynamically.
Event Segmentation Deficits in ADHD

Event segmentation is the automatic cognitive process of chunking ongoing information into meaningful events. Event segmentation theory (EST) proposes that event segmentation is a grouping process fundamental to normal, everyday perceptual processing, taking a central role in attention and action control. The neurocognitive deficits observed among individuals with ADHD overlap those involved in event segmentation, but to date no research has examined event segmentation in the context of ADHD. Objective: The goal of this study was to document the event segmentation deficits of individuals with ADHD. Method: Seventy-five undergraduates with ADHD and seventy-nine without ADHD performed an event segmentation task. Results: Results revealed that undergraduates with ADHD identify significantly more large events. Conclusion: These findingssuggest explicit disturbances in the event model and updating system among those with ADHD. Future research directions include further elucidating these deficits with more varied stimuli and establishing associations with functional impairments.
Lattice-free State-level Minimum Bayes Risk Training of Acoustic Models

Lattice-free maximum mutual information (LF-MMI) training, which enables MMI-based acoustic model training without any lattice generation procedure, has recently been proposed. Although LF-MMI showed high accuracy in many tasks, its MMI criterion does not necessarily maximize the speech recognition accuracy. In this work, we propose a lattice-free state-level minimum Bayes risk training (LF-sMBR), which maximizes state-level expected accuracy without relying on a lattice generation procedure. As is the case with the LF-MMI, LF-sMBR avoids redundant lattice generation by exploiting forward-backward calculation on phone N-gram space, which enables a much simpler and faster training based on an sMBR criterion. We found that special care for silence phones was essential for improving the accuracy by LF-sMBR. In our experiments on the AMI, CSJ, and Librispeech corpora, LF-sMBR achieved small but consistent improvements over LF-MMI AMs, showing state-of-the-art results for each test set.
A cancer vaccine with dendritic cells differentiated with GM-CSF and IFNα and pulsed with a squaric acid treated cell lysate improves T cell priming and tumor growth control in a mouse model

Introduction: Ovarian cancer is one of the most lethal gynecologic cancers. Relapses after remission are common, hence novel strategies are urgently needed. Our group has previously developed a vaccination approach based on dendritic cells pulsed with HOCl-oxidized tumor lysates. Here we investigate the improvement of this vaccine strategy using squaric acid treatment of cancer cells during tumor lysate preparation and by differentiating dendritic cells in the presence of GM-CSF and IFNα. Methods: Induction of cell death by squaric acid treatment was assessed with propidium iodide (PI) and Annexin V in ID8 tumor cells. High mobility group box 1 (HMGB1) immunogenic status was analyzed using a western blot-based method, as previously described. For immunological tests, ID8 cells expressing ovalbumin (ova-ID8) were treated with squaric acid before cell lysis. DCs prepared with the canonical GM-CSF and IL-4 differentiation cocktail or IFNα and GM-CSF were pulsed with tumor cell lysates and further matured in the presence of IFNγ and LPS (4-DCs and α-DCs respectively). DCs were then used in co-culture assays with ova-specific T cells and IFNγ and IL-4 secretion measured by ELISA. DC phenotypes were characterized by FACS. Finally, DCs were tested in an ovarian cancer mouse model measuring body weight and animal survival. Results: Squaric acid treatment of mouse ovarian cancer cells induced tumor cell death as well as preserve HMGB1, a crucial Damage-associated molecular pattern (DAMP) signal, in its active reduced form. Squaric acid treatment of ID8-ova cells increased IFNγ and decreased IL-4 production from ova-specific T cells in co-culture experiments, promoting a more immunogenic cytokine secretion pattern. DCs differentiated in the presence of IFNα induced a considerable decrease in IL-4 production compared to canonical 4-DCs, without affecting IFNγ release. DC phenotyping demonstrated a more mature and immunogenic phenotype for IFNα-differentiated DCs. Vaccination in tumor-bearing mice showed that IFNα-differentiated DCs pulsed with squaric acid-treated lysates were the most potent at delaying tumor growth, improving animal survival. Conclusion: We identified squaric acid as a novel immunogenic treatment of tumor cells for cancer vaccines particularly efficient in prolonging animal survival when used in combination with IFNα-differentiated DCs. These promising results support future efforts for the clinical translation of this approach.
Identification of Novel Quantitative Trait Loci Linked to Crown Rot Resistance in Spring Wheat

Crown rot (CR), caused by various Fusarium species, is a major disease in many cereal-growing regions worldwide. Fusarium culmorum is one of the most important species, which can cause significant yield losses in wheat. A set of 126 advanced International Maize and Wheat Improvement Center (CIMMYT) spring bread wheat lines were phenotyped against CR for field crown, greenhouse crown and stem, and growth room crown resistance scores. Of these, 107 lines were genotyped using Diversity Array Technology (DArT) markers to identify quantitative trait loci linked to CR resistance by genome-wide association study. Results of the population structure analysis grouped the accessions into three sub-groups. Genome wide linkage disequilibrium was large and declined on average within 20 cM (centi-Morgan) in the panel. General linear model (GLM), mixed linear model (MLM), and naïve models were tested for each CR score and the best model was selected based on quarantine-quarantine plots. Three marker-trait associations (MTAs) were identified linked to CR resistance; two of these on chromosome 3B were associated with field crown scores, each explaining 11.4% of the phenotypic variation and the third MTA on chromosome 2D was associated with greenhouse stem score and explained 11.6% of the phenotypic variation. Together, these newly identified loci provide opportunity for wheat breeders to exploit in enhancing CR resistance via marker-assisted selection or deployment in genomic selection in wheat breeding programs.
CLARITY-BPA academic laboratory studies identify consistent low-dose Bisphenol A effects on multiple organ systems.

Bisphenol A (BPA) is a high-production chemical used in a variety of applications worldwide. While BPA has been documented as an endocrine-disrupting chemical (EDC) having adverse health-related outcomes in multiple studies, risk assessment for BPA has lagged due to reliance on guideline toxicology studies over academic ones with end-points considered more sensitive and appropriate. To address current controversies on BPA safety, the United States National Institute of Environmental Health Sciences (NIEHS), the National Toxicology Program (NTP) and the Food and Drug Administration (FDA) established the Consortium Linking Academic and Regulatory Insights on BPA Toxicity (CLARITY-BPA) using the NCTR Sprague-Dawley rats. The goal of CLARITY-BPA is to perform a traditional regulatory toxicology study (Core study) in conjunction with multiple behavioural, molecular and cellular studies by academic laboratories focused on previously identified BPA-sensitive organ systems (Academic studies). Combined analysis of the data from both study types will be undertaken by the NTP with the aim of resolving uncertainties on BPA toxicity. To date, the Core study has been completed and a draft report released. Most of the academic studies have also been finalized and published in peer-reviewed journals. In light of this important milestone, the PPTOX-VI meeting held in the Faroe Islands, 27-30 May 2018 devoted a plenary session to CLARITY-BPA with presentations by multiple investigators with the purpose of highlighting key outcome. This MiniReview synthesizes the results of three academic studies presented at this plenary session, evaluates recently published findings by other CLARITY-BPA academic studies to provide an early combined overview of this emerging data and places this in the context of the Core study findings. This co-ordinated effort revealed a plethora of significant BPA effects across multiple organ systems and BPA doses with non-monotonic responses across the dose range utilized. Remarkably consistent across most studies, including the Core study, are low-dose effects (2.5, 25 and 250 μg BPA/kg body-weight). Collectively, the findings highlighted herein corroborate a significant body of evidence that documents adverse effects of BPA at doses relevant to human exposures and emphasizes the need for updated risk assessment analysis.
Single-Channel Dereverberation Using Direct MMSE Optimization and Bidirectional LSTM Networks

Dereverberation is useful in hands-free communication and voice controlled devices for distant speech acquisition. Single-channel dereverberation can be achieved by applying a time-frequency (TF) mask to the short-time Fourier transform (STFT) representation of a reverberant signal. Recent approaches have used deep neural networks (DNNs) to estimate such masks. Previously proposed DNN-based mask estimation methods train a DNN to minimize the mean-squared-error (MSE) between the desired and estimated masks. Recent TF mask estimation methods for signal separation directly minimize instead the MSE between the desired and estimated STFT magnitudes. We apply this direct optimization concept to dereverberation. Moreover, as reverberation exceeds the duration of a single STFT frame, we propose to use a bidirectional long short-term memory (LSTM) network which is able to take the relation between multiple STFT frames into account. We evaluated our method for different reverberation times and source-microphone distances using simulated as well as measured room impulse responses of different rooms. An evaluation of the proposed method and a comparison with a state-of-the-art method demonstrate the superiority of our approach and its robustness to different acoustic conditions.
Levels of Proinflammatory Cytokines, Oxidative Stress, and Tissue Damage Markers in Patients with Acute Heart Failure with and without Cardiorenal Syndrome Type 1

Background: Cardiorenal syndrome type 1 (CRS type 1) is characterized by a rapid worsening of cardiac function leading to acute kidney injury (AKI). Inflammation and oxidative stress seem to play a pivotal role in its pathophysiology. In this in vivo study, we examined the putative role of inflammation and humoral markers in the pathogenesis of the CRS type 1. Methods: We enrolled 53 patients with acute heart failure (AHF); 17 of them developed AKI (CRS type 1). The cause of AKI was presumed to be related to cardiac dysfunction after having excluded other causes. We assessed the plasma levels of proinflammatory cytokines (TNF-α, IL-6, IL-18, sICAM, RANTES, GMCSF), oxidative stress marker (myeloperoxidase, MPO), brain natriuretic peptide (BNP), and neutrophil gelatinase-associated lipocalin (NGAL) in AHF and CRS type 1 patients. Results: We observed a significant increase in IL-6, IL-18, and MPO levels in CRS type 1 group compared to AHF (p < 0.001). We found higher NGAL at admission in the CRS type 1 group compared to the AHF group (p = 0.008) and a positive correlation between NGAL and IL-6 (Spearman’s rho = 0.45, p = 0.003) and between IL-6 and BNP (Spearman’s rho = 0.43, p = 0.004). We observed lower hemoglobin levels in CRS type 1 patients compared to AHF patients (p < 0.05) and inverse correlation between hemoglobin and cytokines (IL-6: Spearman’s rho = –0.38, p = 0.005; IL-18: Spearman’s rho = –0.32, p = 0.02). Conclusion: Patients affected by CRS type 1 present increased levels of proinflammatory cytokines and oxidative stress markers, increased levels of tissue damage markers, and lower hemoglobin levels. All these factors may be implicated in the pathophysiology of CRS type 1 syndrome.
Non-Local Kalman: A Recursive Video Denoising Algorithm

In this article we propose a new recursive video denoising method with high performance. The method is recursive and uses only the current frame and the previous denoised one. It considers the video as a set of overlapping temporal patch trajectories. Following a Bayesian approach each trajectory is modeled as linear dynamic Gaussian model and denoised by a Kalman filter. To estimate its parameters, similar patches are grouped and their trajectories are considered as sharing the same model parameters. The filtering is mainly temporal; non-local spatial similarity is only used to estimate the parameters. This temporally causal method obtains results comparable (in terms of PSNR and SSIM) to state-of-the-art methods using several frames per frame denoised, but with a higher temporal consistency.
Information Encoding by Deep Neural Networks: What Can We Learn?

The recent advent of deep learning techniques in speech technology and in particular in automatic speech recognition has yielded substantial performance improvements. This suggests that deep neural networks (DNNs) are able to capture structure in speech data that older methods for acoustic modeling, such as Gaussian Mixture Models and shallow neural networks fail to uncover. In image recognition it is possible to link representations on the first couple of layers in DNNs to structural properties of images, and to representations on early layers in the visual cortex. This raises the question whether it is possible to accomplish a similar feat with representations on DNN layers when processing speech input. In this paper we present three different experiments in which we attempt to untangle how DNNs encode speech signals, and to relate these representations to phonetic knowledge, with the aim to advance conventional phonetic concepts and to choose the topology of a DNNs more efficiently. Two experiments investigate representations formed by auto-encoders. A third experiment investigates representations on convolutional layers that treat speech spectrograms as if they were images. The results lay the basis for future experiments with recursive networks.