Miracle-dz/UniFAR-FTUs huggingface dataset:
Advanced Platelet-Rich Fibrin and Connective Tissue Graft for Treating Marginal Tissue Recessions: A Randomized, Controlled Split-Mouth Study

Objectives: This study aimed to evaluate and compare the clinical outcomes of advanced platelet-rich fibrin (A-PRF) and connective tissue graft (CTG) in treating marginal tissue recessions. Materials & Methods: Fifteen patients with isolated bilateral maxillary gingival recessions were recruited for the study, with 30 defects. The defects were classified as Miller's class I/II gingival recession on the canine or premolar region. Patients were randomly divided into two groups, each receiving one of the two treatment techniques (A-PRF or CTG) on a different side of the maxilla in a split-mouth design. Clinical parameters such as recession height (RH), recession width (RW), probing pocket depth (PPD), clinical attachment level (CAL), a width of attached gingiva (WAG), and keratinized tissue height (KTH) were evaluated at baseline, 3, and 6 months. Changes in biotype, Recession Esthetic Score (RES), and Visual Analogue Score-Esthetics (VAS-E) were also evaluated at 6 months. Trial Registration: Ethics approval number (Helsinki): PHRC/HC/877/21 and registered at the Clinical Trials Registry under the number NCT05267015 Results: At the end of 6 months, there was a statistically significant reduction in RH and RW in both groups, with the mean RC% of 69.2±22.91, and 88.66±33.18 in Groups I and II, respectively. Intergroup analysis showed statistically significant differences in recession parameters between groups at 3 and 6 months, with better outcomes for the CTG group. Conclusions: This study demonstrates that A-PRF and CTG effectively manage gingival recession defects. However, CTG resulted in better clinical outcomes in terms of reduction in recession height and width.
Successful Treatment of Encephalitis Following SARS-Cov-2 Infection: A Case Report

Background: The SARS-CoV-2 invades the cells by attachment of virus spike proteins (S1, S2) to cell membrane and engages angiotensin-converting enzyme 2 (ACE2), which is highly expressed in the epithelium of cerebral vasculature. Here, we describe a patient with encephalitis following SARS-CoV-2 infection. Case presentation: A 77-year-old male patient presented with mild cough and coryza lasting for eight days without a prior history of underlying disease or neurologic disorder. Oxygen saturation (SatO2) was decreased and behavioral changes, confusion, and headaches were started within three days prior to admission. Bilateral ground glass opacifications and consolidations were noted on chest CT scan. Lymphopenia, highly elevated D-Dimer and ferritin were noted in laboratory results. Brain CT and MRI showed no changes regarding encephalitis. Cerebrospinal fluid was collected as the symptoms persisted. The results of SARS-CoV-2 RNA RT-PCR from CSF and nasopharyngeal samples were positive. The combination therapy with remdesivir, interferon beta-1alpha and methylprednisolone was started. Due to deterioration of the patient’s status and SatO2, he was admitted to the ICU and intubated. Tocilizumab, dexamethasone, and mannitol were started. The patient was extubated on the 16th day of ICU admission. The patient’s level of consciousness and SatO2 were improved. He was discharged from the hospital a week later. Conclusion: RT-PCR of CSF sample along with brain imaging can help with diagnosis when encephalitis due to SARS-CoV-2 is suspected. However, no changes regarding encephalitis may be seen on brain CT or MRI. Combination therapy with antivirals, interferon beta, corticosteroids, and tocilizumab can help patients recover in these conditions.
Biogenic selenium nanoparticles: trace element with promising anti-toxoplasma effect

ABSTRACT Toxoplasmosis is an opportunistic infection caused by the coccidian Toxoplasma gondii which represents a food and water contaminant. The available chemotherapeutic agents for toxoplasmosis are limited and the choice is difficult when considering the side effects. Selenium is an essential trace element. It is naturally found in dietary sources, especially seafood, and cereals. Selenium and selenocompounds showed anti-parasitic effects through antioxidant, immunomodulatory, and anti-inflammatory mechanisms. The present study evaluated the potential efficacy of environmentally benign selenium nanoparticles (SeNPs) against acute toxoplasmosis in a mouse model. SeNPs were fabricated by nanobiofactory Streptomyces fulvissimus and characterized by different analytical techniques including, UV-spectrophotometry, transmission electron microscopy, EDX, and XRD. Swiss albino mice were infected with Toxoplasma RH strain in a dose of 3500 tachyzoites in 100 μl saline to induce acute toxoplasmosis. Mice were divided into five groups. Group I: non-infected, non-treated, group II: infected, non-treated, group III: non-infected, treated with SeNPs, group IV: infected, treated with co-trimoxazole (sulfamethoxazole/trimethoprim) and group V: infected, treated with SeNPs. There was a significant increase in survival time in the SeNPs-treated group and minimum parasite count was observed compared to untreated mice in hepatic and splenic impression smears. Scanning electron microscopy showed tachyzoites deformity with multiple depressions and protrusions, while transmission electron microscopy showed excessive vacuolization and lysis of the cytoplasm, especially in the area around the nucleus and the apical complex, together with irregular cell boundary and poorly demarcated cell organelles. The present study demonstrated that the biologically synthesized SeNPs can be a potential natural anti-Toxoplasma agent in vivo.
The Role of Lung Ultrasound in Systemic Sclerosis

Background In systemic sclerosis (SSc), high-resolution computed tomography (HRCT) of the chest is the standard criterion for the diagnosis of interstitial lung disease (ILD). However, recent evidence suggests that lung ultrasound (LUS) can also detect ILD, without radiation exposure. Thus, our goal was to perform a systematic review, aiming to clarify the role of LUS in the detection of ILD in SSc. Methods A systematic review was carried out in PubMed and EMBASE (PROSPERO register number CRD42022293132), to identify studies that compared LUS with HRCT in the detection of ILD in patients with SSc. Risk of bias was assessed with the QUADAS-2 () tool. Results Three hundred seventy-five publications were identified. After screening, 13 were included in the final analysis. No study presented high risk of bias. Lung ultrasound protocol was highly heterogeneous between authors, specifically concerning transducer, intercostal spaces evaluated, exclusion criteria, and definition of positive LUS. Most authors evaluated the presence of B-lines as a surrogate of ILD, with only 4 focusing on pleural changes. A positive correlation between LUS findings and ILD detected by HRCT was reported. Results also revealed high sensitivity (74.3%–100%) but variable specificity (16%–99%). Positive predictive value varied between 16% and 95.1%, and negative predictive value between 51.7% and 100%. Conclusion Lung ultrasound is sensitive in the detection of ILD, but specificity must be optimized. The value of pleural evaluation also requires further investigation. Moreover, a consensus is needed to define a uniform LUS protocol to implement in future investigations.
The prevalence of bleeding disorders in women with proven endometriosis: results of a screening study.

Objective: To establish the rate of bleeding disorders amongst women with confirmed endometriosis and clarify predictive features on history. Methods: Eight-two women with confirmed diagnosis of endometriosis were recruited from a tertiary womens hospital. General bleeding tendency was evaluated with a modified internationally standardised questionnaire, and a bleeding score was calculated. Menstrual loss was evaluated by obtaining current and heaviest-ever menses profile using Pictorial Blood loss Assessment Chart(PBAC). Forty-one women satisfied criteria for a significant bleeding tendency and underwent coagulation tests including Prothrombin time(PT), activated partial thromboplastin time(aPTT), Platelet Function Analyser-100(PFA-100) and von Willebrand factor(vWF)/Factor VIII(FVIII) studies. Results : The prevalence of abnormal screening haemostasis tests for the population was 17.72% (95% CI 10.04-27.94) for all tests, 8.54%(95% CI 3.50-16.80) for vWF tests, and 12.99%( 95% CI 6.41-22.59) for PFA-100. Receiver Operator Characteristic curve analysis demonstrated a very good performance of the bleeding score as a diagnostic test for haemostatic abnormalities for nulliparous women with endometriosis. Bleeding symptoms individually were not predictive. Logistic regression suggested the combination of mittleschmerz, cutaneous bleeding symptoms, heavy menstrual bleeding and prolonged bleeding from minor wounds as predictive for haemostatic abnormalities (p=0.0197). Conclusion: The prevalence of abnormal bleeding tests in women with endometriosis on preliminary testing was higher than the population rate of bleeding abnormalities and thus people with histologically proven endometriosis warrant a higher index of suspicion with respect to testing for mild bleeding disorders.
Influence of Body Position on the Motor Development of Preterm Infants: A Randomized Clinical Trial

Background: To analyze the influence of body position on the motor development of preterm infants in the first year of life corrected for prematurity. Methods: This controlled, randomized, open trial included 30 preterm infants randomly assigned to one of the following three groups: prone group (n = 9), supine group (n = 10), and control group (n = 11). Intervention: Motor development was assessed at four time points using the Alberta Infant Motor Scale: first outpatient visit and at 4, 8 and 12 months corrected age. Results: In the third assessment at 8 months corrected age, the supine group exhibited better motor development than the other groups (p = 0.02). In the control group, the number of infants with normal development decreased from 11 (100%) in the first assessment to 5 (45.45%) in the last assessment. Most infants of mothers who received guidance on body positioning achieved normal motor development in the first year of life (63.1%). Greater dispersion from normal Alberta Infant Motor Scale scores was observed in infants at 8 and 12 months of age. Conclusions: Guidance on body positioning of preterm infants at home appears to have a positive influence in the first year of life. Child care strategies after hospitalization should be supported to permit full development of the child.
Experimental and Numerical Study of Shock Wave Interaction with Perforated Plates

The flow developed behind shock wave transmitted through a screen or a perforated plat is initially highly unsteady and nonuniform. It contains multiple shock reflections and interactions with vortices shed from the open spaces of the barrier The present paper studies experimentally and theoretically/numerically the flow and wave pattern resulted from the interaction of an incident shock wave with a few different types of barriers, all having the same porosity but different geometries
Trainability Preserving Neural Pruning

Many recent works have shown trainability plays a central role in neural network pruning -- unattended broken trainability can lead to severe under-performance and unintentionally amplify the effect of retraining learning rate, resulting in biased (or even misinterpreted) benchmark results. This paper introduces trainability preserving pruning (TPP), a scalable method to preserve network trainability against pruning, aiming for improved pruning performance and being more robust to retraining hyper-parameters (e.g., learning rate). Specifically, we propose to penalize the gram matrix of convolutional filters to decorrelate the pruned filters from the retained filters. In addition to the convolutional layers, per the spirit of preserving the trainability of the whole network, we also propose to regularize the batch normalization parameters (scale and bias). Empirical studies on linear MLP networks show that TPP can perform on par with the oracle trainability recovery scheme. On nonlinear ConvNets (ResNet56/VGG19) on CIFAR10/100, TPP outperforms the other counterpart approaches by an obvious margin. Moreover, results on ImageNet-1K with ResNets suggest that TPP consistently performs more favorably against other top-performing structured pruning approaches. Code: https://github.com/MingSun-Tse/TPP.
Verifying the Union of Manifolds Hypothesis for Image Data

Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimensional structure in data of interest; this existence is posited by the manifold hypothesis, which states that the data lies on an unknown manifold of low intrinsic dimension. In this paper, we argue that this hypothesis does not properly capture the low-dimensional structure typically present in image data. Assuming that data lies on a single manifold implies intrinsic dimension is identical across the entire data space, and does not allow for subregions of this space to have a different number of factors of variation. To address this deficiency, we consider the union of manifolds hypothesis, which states that data lies on a disjoint union of manifolds of varying intrinsic dimensions. We empirically verify this hypothesis on commonly-used image datasets, finding that indeed, observed data lies on a disconnected set and that intrinsic dimension is not constant. We also provide insights into the implications of the union of manifolds hypothesis in deep learning, both supervised and unsupervised, showing that designing models with an inductive bias for this structure improves performance across classification and generative modelling tasks. Our code is available at https://github.com/layer6ai-labs/UoMH.
Guarded Policy Optimization with Imperfect Online Demonstrations

The Teacher-Student Framework (TSF) is a reinforcement learning setting where a teacher agent guards the training of a student agent by intervening and providing online demonstrations. Assuming optimal, the teacher policy has the perfect timing and capability to intervene in the learning process of the student agent, providing safety guarantee and exploration guidance. Nevertheless, in many real-world settings it is expensive or even impossible to obtain a well-performing teacher policy. In this work, we relax the assumption of a well-performing teacher and develop a new method that can incorporate arbitrary teacher policies with modest or inferior performance. We instantiate an Off-Policy Reinforcement Learning algorithm, termed Teacher-Student Shared Control (TS2C), which incorporates teacher intervention based on trajectory-based value estimation. Theoretical analysis validates that the proposed TS2C algorithm attains efficient exploration and substantial safety guarantee without being affected by the teacher's own performance. Experiments on various continuous control tasks show that our method can exploit teacher policies at different performance levels while maintaining a low training cost. Moreover, the student policy surpasses the imperfect teacher policy in terms of higher accumulated reward in held-out testing environments. Code is available at https://metadriverse.github. io/TS2C.
Diagnosing Model Performance Under Distribution Shift

Prediction models can perform poorly when deployed to target distributions different from the training distribution. To understand these operational failure modes, we develop a method, called DIstribution Shift DEcomposition (DISDE), to attribute a drop in performance to different types of distribution shifts. Our approach decomposes the performance drop into terms for 1) an increase in harder but frequently seen examples from training, 2) changes in the relationship between features and outcomes, and 3) poor performance on examples infrequent or unseen during training. These terms are defined by fixing a distribution on $X$ while varying the conditional distribution of $Y \mid X$ between training and target, or by fixing the conditional distribution of $Y \mid X$ while varying the distribution on $X$. In order to do this, we define a hypothetical distribution on $X$ consisting of values common in both training and target, over which it is easy to compare $Y \mid X$ and thus predictive performance. We estimate performance on this hypothetical distribution via reweighting methods. Empirically, we show how our method can 1) inform potential modeling improvements across distribution shifts for employment prediction on tabular census data, and 2) help to explain why certain domain adaptation methods fail to improve model performance for satellite image classification.
Optimal probabilistic forecasts for risk management

This paper explores the implications of producing forecast distributions that are optimized according to scoring rules that are relevant to financial risk management. We assess the predictive performance of optimal forecasts from potentially misspecified models for i) value-at-risk and expected shortfall predictions; and ii) prediction of the VIX volatility index for use in hedging strategies involving VIX futures. Our empirical results show that calibrating the predictive distribution using a score that rewards the accurate prediction of extreme returns improves the VaR and ES predictions. Tail-focused predictive distributions are also shown to yield better outcomes in hedging strategies using VIX futures.
Reservoir computing based on solitary-like waves dynamics of liquid film flows: A proof of concept

Several theoretical works have shown that solitons —waves that self-maintain constant shape and velocity as they propagate— can be used as a physical computational reservoir, a concept where machine learning algorithms designed for digital computers are replaced by analog physical systems that exhibit nonlinear dynamical behaviour. Here we propose and experimentally validate a novel reservoir computing (RC) system that for the first time employs solitary-like (SL) waves propagating on the surface of a liquid film flowing over an inclined surface. We demonstrate the ability of the SL wave RC system (SLRC) to forecast chaotic time series, also producing experimental evidence of the possibility to combine RC with nonlinear vector autoregression techniques not only in a computer program but also in a physical system.
Bi-parametric prostate MR image synthesis using pathology and sequence-conditioned stable diffusion

We propose an image synthesis mechanism for multi-sequence prostate MR images conditioned on text, to control lesion presence and sequence, as well as to generate paired bi-parametric images conditioned on images e.g. for generating diffusion-weighted MR from T2-weighted MR for paired data, which are two challenging tasks in pathological image synthesis. Our proposed mechanism utilises and builds upon the recent stable diffusion model by proposing image-based conditioning for paired data generation. We validate our method using 2D image slices from real suspected prostate cancer patients. The realism of the synthesised images is validated by means of a blind expert evaluation for identifying real versus fake images, where a radiologist with 4 years experience reading urological MR only achieves 59.4% accuracy across all tested sequences (where chance is 50%). For the first time, we evaluate the realism of the generated pathology by blind expert identification of the presence of suspected lesions, where we find that the clinician performs similarly for both real and synthesised images, with a 2.9 percentage point difference in lesion identification accuracy between real and synthesised images, demonstrating the potentials in radiological training purposes. Furthermore, we also show that a machine learning model, trained for lesion identification, shows better performance (76.2% vs 70.4%, statistically significant improvement) when trained with real data augmented by synthesised data as opposed to training with only real images, demonstrating usefulness for model training.
Ternary Quantization: A Survey

Inference time, model size, and accuracy are critical for deploying deep neural network models. Numerous research efforts have been made to compress neural network models with faster inference and higher accuracy. Pruning and quantization are mainstream methods to this end. During model quantization, converting individual float values of layer weights to low-precision ones can substantially reduce the computational overhead and improve the inference speed. Many quantization methods have been studied, for example, vector quantization, low-bit quantization, and binary/ternary quantization. This survey focuses on ternary quantization. We review the evolution of ternary quantization and investigate the relationships among existing ternary quantization methods from the perspective of projection function and optimization methods.
Statistical-Computational Tradeoffs in Mixed Sparse Linear Regression

We consider the problem of mixed sparse linear regression with two components, where two real $k$-sparse signals $\beta_1, \beta_2$ are to be recovered from $n$ unlabelled noisy linear measurements. The sparsity is allowed to be sublinear in the dimension, and additive noise is assumed to be independent Gaussian with variance $\sigma^2$. Prior work has shown that the problem suffers from a $\frac{k}{SNR^2}$-to-$\frac{k^2}{SNR^2}$ statistical-to-computational gap, resembling other computationally challenging high-dimensional inference problems such as Sparse PCA and Robust Sparse Mean Estimation; here $SNR$ is the signal-to-noise ratio. We establish the existence of a more extensive computational barrier for this problem through the method of low-degree polynomials, but show that the problem is computationally hard only in a very narrow symmetric parameter regime. We identify a smooth information-computation tradeoff between the sample complexity $n$ and runtime for any randomized algorithm in this hard regime. Via a simple reduction, this provides novel rigorous evidence for the existence of a computational barrier to solving exact support recovery in sparse phase retrieval with sample complexity $n = \tilde{o}(k^2)$. Our second contribution is to analyze a simple thresholding algorithm which, outside of the narrow regime where the problem is hard, solves the associated mixed regression detection problem in $O(np)$ time with square-root the number of samples and matches the sample complexity required for (non-mixed) sparse linear regression; this allows the recovery problem to be subsequently solved by state-of-the-art techniques from the dense case. As a special case of our results, we show that this simple algorithm is order-optimal among a large family of algorithms in solving exact signed support recovery in sparse linear regression.
Unsupervised Recycled FPGA Detection Using Symmetry Analysis

Recently, recycled field-programmable gate arrays (FPGAs) pose a significant hardware security problem due to the proliferation of the semiconductor supply chain. Ring oscillator (RO) based frequency analyzing technique is one of the popular methods, where most studies used the known fresh FPGAs (KFFs) in machine learning-based detection, which is not a realistic approach. In this paper, we present a novel recycled FPGA detection method by examining the symmetry information of the RO frequency using unsupervised anomaly detection method. Due to the symmetrical array structure of the FPGA, some adjacent logic blocks on an FPGA have comparable RO frequencies, hence our method simply analyzes the RO frequencies of those blocks to determine how similar they are. The proposed approach efficiently categorizes recycled FPGAs by utilizing direct density ratio estimation through outliers detection. Experiments using Xilinx Artix-7 FPGAs demonstrate that the proposed method accurately classifies recycled FPGAs from 10 fresh FPGAs by x% fewer computations compared with the conventional method.