Miracle-dz/UniFAR-FTUs huggingface dataset:
Microbial Similarity and Preference for Specific Sites in Healthy Oral Cavity and Esophagus

Human microbial communities are highly complex ecosystems, but it remains unclear if microbial compositions have any similarity in distinct sites of the oral cavity and esophagus in particular. Clinical samples were collected from three niches (saliva, tongue dorsum and supragingival plaque) of the oral cavity and three segments (upper, middle, and lower) of the esophagus in 27 healthy individuals. Bacterial V3-V4 region of 16S rRNA gene in these samples was amplified and sequenced on Illumina sequencing platform, followed by data analysis using QIIME and LEfSe softwares. Highly diverse bacterial flora with 365 genera belonging to 29 phyla resided in the oral cavity and 594 genera belonging to 29 phyla in the esophagus. The phyla Proteobacteria, Firmicutes, Bacteroidetes, Actinobacteria, Fusobacteria, and TM7 were most abundant in both the oral cavity and the esophagus, but the phyla Actinobacteria and Bacteroidetes were preferable in the oral cavity and Firmicutes in the esophagus. The genera Streptococcus, Neisseria, Prevotella, Actinobacillus, and Veillonella were most abundant in both oral cavity and esophagus, but Neisseria was preferable in the oral cavity and Streptococcus in the esophagus. Different niche-specific bacterial signatures were found in the oral cavity, e.g., the class Flavobacteria in the supragingival plaque, class Bacteroides in the saliva and the class Clostridia in the tongue dorsum. By contrast, no site specific bacteria for three different segments of esophagus were found. However, high variability of microbial compositions between individuals was observed. In conclusion, this study confirmed microbial diversity at different taxonomic levels in healthy oral cavity and esophagus, and identified the site-preferable bacterial signatures in six niches of the upper digestive tract. These findings provide a critical baseline for future studies interpreting microbiome-related diseases.
LSTM-Based Zero-Velocity Detection for Robust Inertial Navigation

We present a method to improve the accuracy of a zero-velocity-aided inertial navigation system (INS) by replacing the standard zero-velocity detector with a long short-term memory (LSTM) neural network. While existing threshold-based zero-velocity detectors are not robust to varying motion types, our learned model accurately detects stationary periods of the inertial measurement unit (IMU) despite changes in the motion of the user. Upon detection, zero-velocity pseudo-measurements are fused with a dead reckoning motion model in an extended Kalman filter (EKF). We demonstrate that our LSTM-based zero-velocity detector, used within a zero-velocity-aided INS, improves zero-velocity detection during human localization tasks. Consequently, localization accuracy is also improved. Our system is evaluated on more than 7.5 km of indoor pedestrian locomotion data, acquired from five different subjects. We show that 3D positioning error is reduced by over 34% compared to existing fixed-threshold zero-velocity detectors for walking, running, and stair climbing motions. Additionally, we demonstrate how our learned zero-velocity detector operates effectively during crawling and ladder climbing. Our system is calibration-free (no careful threshold-tuning is required) and operates consistently with differing users, IMU placements, and shoe types, while being compatible with any generic zero-velocity-aided INS.
Novel rapid-acting antidepressants: molecular and cellular signaling mechanisms

Abstract Depression is a chronic, debilitating, and common illness. Currently available pharmacotherapies can be helpful but have several major drawbacks, including substantial rates of low or no response and a long therapeutic time lag. In pursuit of better treatment options, recent research has focussed on rapid-acting antidepressants, including the N-methyl-d-aspartate (NMDA) receptor (NMDAR) antagonist ketamine, which affects a range of signaling pathways in ways that are distinct from the mechanisms of typical antidepressants. Because ketamine and similar drugs hold the promise of dramatically improving treatment options for depressed patients, there has been considerable interest in developing new ways to understand how these compounds affect the brain. Here, we review the current understanding of how rapid-acting antidepressants function, including their effects on neuronal signaling pathways and neural circuits, and the research techniques being used to address these questions.
Structuring the synthesis of heap-manipulating programs

This paper describes a deductive approach to synthesizing imperative programs with pointers from declarative specifications expressed in Separation Logic. Our synthesis algorithm takes as input a pair of assertions—a pre- and a postcondition—which describe two states of the symbolic heap, and derives a program that transforms one state into the other, guided by the shape of the heap. Our approach to program synthesis is grounded in proof theory: we introduce the novel framework of Synthetic Separation Logic (SSL), which generalises the classical notion of heap entailment P ⊢ Q to incorporate a possibility of transforming a heap satisfying an assertion P into a heap satisfying an assertion Q. A synthesized program represents a proof term for a transforming entailment statement P ↝ Q, and the synthesis procedure corresponds to a proof search. The derived programs are, thus, correct by construction, in the sense that they satisfy the ascribed pre/postconditions, and are accompanied by complete proof derivations, which can be checked independently. We have implemented a proof search engine for SSL in a form of the program synthesizer called SuSLik. For efficiency, the engine exploits properties of SSL rules, such as invertibility and commutativity of rule applications on separate heaps, to prune the space of derivations it has to consider. We explain and showcase the use of SSL on characteristic examples, describe the design of SuSLik, and report on our experience of using it to synthesize a series of benchmark programs manipulating heap-based linked data structures.
Tractable Querying and Learning in Hybrid Domains via Sum-Product Networks

Probabilistic representations, such as Bayesian and Markov networks, are fundamental to much of statistical machine learning. Thus, learning probabilistic representations directly from data is a deep challenge, the main computational bottleneck being inference that is intractable. Tractable learning is a powerful new paradigm that attempts to learn distributions that support efficient probabilistic querying. By leveraging local structure, representations such as sum-product networks (SPNs) can capture high tree-width models with many hidden layers, essentially a deep architecture, while still admitting a range of probabilistic queries to be computable in time polynomial in the network size. The leaf nodes in SPNs, from which more intricate mixtures are formed, are tractable univariate distributions, and so the literature has focused on Bernoulli and Gaussian random variables. This is clearly a restriction for handling mixed discrete-continuous data, especially if the continuous features are generated from non-parametric and non-Gaussian distribution families. In this work, we present a framework that systematically integrates SPN structure learning with weighted model integration, a recently introduced computational abstraction for performing inference in hybrid domains, by means of piecewise polynomial approximations of density functions of arbitrary shape. Our framework is instantiated by exploiting the notion of propositional abstractions, thus minimally interfering with the SPN structure learning module, and supports a powerful query interface for conditioning on interval constraints. Our empirical results show that our approach is effective, and allows a study of the trade off between the granularity of the learned model and its predictive power.
MIDV-500: A Dataset for Identity Documents Analysis and Recognition on Mobile Devices in Video Stream

A lot of research has been devoted to identity documents analysis and recognition on mobile devices. However, no publicly available datasets designed for this particular problem currently exist. There are a few datasets which are useful for associated subtasks but in order to facilitate a more comprehensive scientific and technical approach to identity document recognition more specialized datasets are required. In this paper we present a Mobile Identity Document Video dataset (MIDV-500) consisting of 500 video clips for 50 different identity document types with ground truth which allows to perform research in a wide scope of document analysis problems. The paper presents characteristics of the dataset and evaluation results for existing methods of face detection, text line recognition, and document fields data extraction. Since an important feature of identity documents is their sensitiveness as they contain personal data, all source document images used in MIDV-500 are either in public domain or distributed under public copyright licenses. The main goal of this paper is to present a dataset. However, in addition and as a baseline, we present evaluation results for existing methods for face detection, text line recognition, and document data extraction, using the presented dataset.
Using Video-Reflexive Ethnography to Engage Hospital Staff to Improve Dementia Care

In this article, we discuss how video-reflexive ethnography may be useful in engaging staff to improve dementia care in a hospital medical unit. Seven patients with dementia were involved in the production of patient-story videos, and fifty members of staff (nurses, physicians, and allied health practitioners) participated in video-reflexive groups. We identified five substantial themes to describe how video-reflexive groups might contribute to enacting person-centered care for improving dementia care: (a) seeing through patients’ eyes, (b) seeing normal strange and surprised, (c) seeing inside and between, (d) seeing with others inspires actions, and (e) seeing with the team builds a culture of learning. Our findings suggest that video reflexivity is not only useful for staff engagement but also effective in enhancing team capacity to enact person-centered care in the hospital setting.
Bridging the Accuracy Gap for 2-bit Quantized Neural Networks (QNN)

Deep learning algorithms achieve high classification accuracy at the expense of significant computation cost. In order to reduce this cost, several quantization schemes have gained attention recently with some focusing on weight quantization, and others focusing on quantizing activations. This paper proposes novel techniques that target weight and activation quantizations separately resulting in an overall quantized neural network (QNN). The activation quantization technique, PArameterized Clipping acTivation (PACT), uses an activation clipping parameter $\alpha$ that is optimized during training to find the right quantization scale. The weight quantization scheme, statistics-aware weight binning (SAWB), finds the optimal scaling factor that minimizes the quantization error based on the statistical characteristics of the distribution of weights without the need for an exhaustive search. The combination of PACT and SAWB results in a 2-bit QNN that achieves state-of-the-art classification accuracy (comparable to full precision networks) across a range of popular models and datasets.
Perceptually Optimized Enhancement of Contrast and Color in Images

In this paper, we propose perceptually optimized enhancement of contrast and color in images using just-noticeable-difference (JND) transform and color constancy. We adopt JND transform to get JND map that represents the perceptual response of the human visual system (HVS). We utilize color constancy to estimate the light source color and be robust to color bias. First, we use a perceptual generalized equalization model for the optimization of both color and contrast based on color constancy and contrast enhancement, i.e. base image. Second, we generate JND map based on HVS response model from foreground and background luminance, called JND transform. Next, we update the JND map based on Weber’s law to boost perceptual response. Finally, we perform inverse JND transform from the base image and its JND map to produce the enhanced image highly correlated with the human visual perception. Experimental results show that the proposed method achieves good performance in contrast enhancement, color reproduction, and detail enhancement.
Deep Reinforcement Learning for Swarm Systems

Recently, deep reinforcement learning (RL) methods have been applied successfully to multi-agent scenarios. Typically, the observation vector for decentralized decision making is represented by a concatenation of the (local) information an agent gathers about other agents. However, concatenation scales poorly to swarm systems with a large number of homogeneous agents as it does not exploit the fundamental properties inherent to these systems: (i) the agents in the swarm are interchangeable and (ii) the exact number of agents in the swarm is irrelevant. Therefore, we propose a new state representation for deep multi-agent RL based on mean embeddings of distributions, where we treat the agents as samples and use the empirical mean embedding as input for a decentralized policy. We define different feature spaces of the mean embedding using histograms, radial basis functions and neural networks trained end-to-end. We evaluate the representation on two well-known problems from the swarm literature in a globally and locally observable setup. For the local setup we furthermore introduce simple communication protocols. Of all approaches, the mean embedding representation using neural network features enables the richest information exchange between neighboring agents, facilitating the development of complex collective strategies.
Biomarkers and predictors for functional and anatomic outcomes for small gauge pars plana vitrectomy and peeling of the internal limiting membrane in naïve diabetic macular edema: The VITAL Study

Purpose We aimed to investigate biomarkers and predictive factors for visual and anatomical outcome in patients with naïve diabetic macular edema (DME) who underwent small gauge pars plana vitrectomy (PPV) with internal limiting membrane (ILM) peeling as a first line treatment. Design Multicenter, retrospective, interventional study. Participants 120 eyes from 120 patients with naïve DME treated with PPV and ILM peeling with a follow up of 24 months. Methods Change in baseline best corrected visual acuity (BCVA) and central subfoveal thickness (CST) 1, 6, 12 and 24 months after surgery. Predictive value of baseline BCVA, CST, optical coherence tomography (OCT) features (presence of subretinal fluid (SRF) and photoreceptor damage), and time between DME diagnosis and surgery. Additional treatment for DME needed. Intra- and post-operative complications (cataract rate formation, increased intraocular pressure). Main outcome measures The correlation between baseline characteristics and BCVA response (mean change from baseline; categorized improvement ≥5 or ≥10; Early Treatment Diabetic Retinopathy Study (ETDRS) letters) 12 and 24 months after surgery. Results Mean BCVA was 0.66 ± 0.14 logMAR, 0.52 ± 0.21 logMAR, and 0.53 ± 0.21 logMAR (p<0.001) at baseline, 12 and 24 months, respectively. Shorter time from DME diagnosis until PPV (OR: 0.98, 95% CI: 0.97–0.99, p<0.001) was a predictor for good functional treatment response (area under the curve 0.828). For every day PPV is postponed, the patient’s chances to gain ≥5 letters at 24 months decrease by 1.8%. Presence of SRF was identified as an anatomical predictor of a better visual outcome, (OR: 6.29, 95% CI: 1.16–34.08, p = 0.033). Safety profile was acceptable. Conclusions Our results reveal a significant functional and anatomical improvement of DME 24 months after primary PPV, without the need for additional treatment. Early surgical intervention and presence of SRF predict good visual outcome. These biomarkers should be considered when treatment is chosen.
Human Liver Stem Cell-Derived Extracellular Vesicles Prevent Aristolochic Acid-Induced Kidney Fibrosis

With limited therapeutic intervention in preventing the progression to end-stage renal disease, chronic kidney disease (CKD) remains a global health-care burden. Aristolochic acid (AA) induced nephropathy is a model of CKD characterised by inflammation, tubular injury, and interstitial fibrosis. Human liver stem cell-derived extracellular vesicles (HLSC-EVs) have been reported to exhibit therapeutic properties in various disease models including acute kidney injury. In the present study, we aimed to investigate the effects of HLSC-EVs on tubular regeneration and interstitial fibrosis in an AA-induced mouse model of CKD. NSG mice were injected with HLSC-EVs 3 days after administering AA on a weekly basis for 4 weeks. Mice injected with AA significantly lost weight over the 4-week period. Deterioration in kidney function was also observed. Histology was performed to evaluate tubular necrosis, interstitial fibrosis, as well as infiltration of inflammatory cells/fibroblasts. Kidneys were also subjected to gene array analyses to evaluate regulation of microRNAs (miRNAs) and pro-fibrotic genes. The effect of HLSC-EVs was also tested in vitro to assess pro-fibrotic gene regulation in fibroblasts cocultured with AA pretreated tubular epithelial cells. Histological analyses showed that treatment with HLSC-EVs significantly reduced tubular necrosis, interstitial fibrosis, infiltration of CD45 cells and fibroblasts, which were all elevated during AA induced injury. At a molecular level, HLSC-EVs significantly inhibited the upregulation of the pro-fibrotic genes α-Sma, Tgfb1, and Col1a1 in vivo and in vitro. Fibrosis gene array analyses revealed an upregulation of 35 pro-fibrotic genes in AA injured mice. Treatment with HLSC-EVs downregulated 14 pro-fibrotic genes in total, out of which, 5 were upregulated in mice injured with AA. Analyses of the total mouse miRnome identified several miRNAs involved in the regulation of fibrotic pathways, which were found to be modulated post-treatment with HLSC-EVs. These results indicate that HLSC-EVs play a regenerative role in CKD possibly through the regulation of genes and miRNAs that are activated during the progression of the disease.
Study on the Binding Interaction of the α,α′,δ,δ′-Tetramethylcucurbit[6]uril With Biogenic Amines in Solution and the Solid State

1H NMR spectroscopy and MALDI-TOF mass spectrometry were utilized to examine the binding interaction of α,α′,δ,δ′-tetramethylcucurbit[6]uril (TMeQ[6]) and six biogenic amines (spermine, spermidine, 2-phenethylamine, tyramine, histamine, and tryptamine). Their 1H NMR spectra both at pD = 7 and pD = 3 revealed that four biogenic amines (spermine, spermidine, 2-phenethylamine, and histamine) can fit in the TMeQ[6] cavity, respectively, and other biogenic amines were located outside of the TMeQ[6] portal. In addition, a solid-state evaluation with single-crystal X-ray diffraction analysis showed the binding interaction of spermine, spermidine, 2-phenethylamine, and tyramine with TMeQ[6].
Trust-Based Collaborative Privacy Management in Online Social Networks

Online social networks have now become the most popular platforms for people to share information with others. Along with this, there is a serious threat to individuals’ privacy. One privacy risk comes from the sharing of co-owned data, i.e., when a user shares a data item that involves multiple users, some users’ privacy may be compromised, since different users generally have different opinions on who can access the data. How to design a collaborative management mechanism to deal with such a privacy issue has recently attracted much attention. In this paper, we propose a trust-based mechanism to realize collaborative privacy management. Basically, a user decides whether or not to post a data item based on the aggregated opinion of all involved users. The trust values between users are used to weight users’ opinions, and the values are updated according to users’ privacy loss. Moreover, the user can make a tradeoff between data sharing and privacy preserving by tuning the parameter of the proposed mechanism. We formulate the selecting of the parameter as a multi-armed bandit problem and apply the upper confidence bound policy to solve the problem. Simulation results demonstrate that the trust-based mechanism can encourage the user to be considerate of others’ privacy, and the proposed bandit approach can bring the user a high payoff.
Remember and Forget for Experience Replay

Experience replay (ER) is a fundamental component of off-policy deep reinforcement learning (RL). ER recalls experiences from past iterations to compute gradient estimates for the current policy, increasing data-efficiency. However, the accuracy of such updates may deteriorate when the policy diverges from past behaviors and can undermine the performance of ER. Many algorithms mitigate this issue by tuning hyper-parameters to slow down policy changes. An alternative is to actively enforce the similarity between policy and the experiences in the replay memory. We introduce Remember and Forget Experience Replay (ReF-ER), a novel method that can enhance RL algorithms with parameterized policies. ReF-ER (1) skips gradients computed from experiences that are too unlikely with the current policy and (2) regulates policy changes within a trust region of the replayed behaviors. We couple ReF-ER with Q-learning, deterministic policy gradient and off-policy gradient methods. We find that ReF-ER consistently improves the performance of continuous-action, off-policy RL on fully observable benchmarks and partially observable flow control problems.
COMT1 Silencing Aggravates Heat Stress-Induced Reduction in Photosynthesis by Decreasing Chlorophyll Content, Photosystem II Activity, and Electron Transport Efficiency in Tomato

Despite a range of initiatives to reduce global carbon emission, the mean global temperature is increasing due to climate change. Since rising temperatures pose a serious threat of food insecurity, it is important to further explore important biological molecules that can confer thermotolerance to plants. Recently, melatonin has emerged as a universal abiotic stress regulator that can enhance plant tolerance to high temperature. Nonetheless, such regulatory roles of melatonin were unraveled mainly by assessing the effect of exogenous melatonin on plant tolerance to abiotic stress. Here, we generated melatonin deficient tomato plants by silencing of a melatonin biosynthetic gene, CAFFEIC ACID O-METHYLTRANSFERASE 1 (COMT1), to unveil the role of endogenous melatonin in photosynthesis under heat stress. We examined photosynthetic pigment content, leaf gas exchange, and a range of chlorophyll fluorescence parameters. The results showed that silencing of COMT1 aggravated heat stress by inhibiting both the light reactions and the carbon fixation reactions of photosynthesis. The photosynthetic pigment content, light absorption flux, trapped energy flux, energy dissipation, density of active reaction center per photosystem II (PSII) cross-section, the photosynthetic electron transport rate, the maximum photochemical efficiency of PSII photochemistry, and the rate of CO2 assimilation all decreased in COMT1-silenced plants compared with that of non-silenced plants particularly under heat stress. However, exogenous melatonin alleviated heat-induced photosynthetic inhibition in both genotypes, indicating that melatonin is essential for maintaining photosynthetic capacity under stressful conditions. These findings provide genetic evidence on the vital role of melatonin in photosynthesis and thus may have useful implication in horticultural crop management in the face of climate change.