Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure and role for active site lid of lactate monooxygenase from Mycobacterium smegmatis

Lactate monooxygenase (LMO) catalyzes the FMN‐dependent “coupled” oxidation of lactate and O2 to acetate, carbon dioxide, and water, involving pyruvate and hydrogen peroxide as enzyme‐bound intermediates. Other α‐hydroxy acid oxidase family members follow an “uncoupled pathway,” wherein the α‐keto acid product quickly dissociates before the reduced flavin reacts with oxygen. Here, we report the structures of Mycobacterium smegmatis wild‐type LMO and a wild‐type‐like C203A variant at 2.1 Å and 1.7 Å resolution, respectively. The overall LMO fold and active site organization, including a bound sulfate mimicking substrate, resemble those of other α‐hydroxy acid oxidases. Based on structural similarity, LMO is similarly distant from lactate oxidase, glycolate oxidase, mandelate dehydrogenase, and flavocytochrome b2 and is the first representative enzyme of its type. Comparisons with other α‐hydroxy acid oxidases reveal that LMO has a longer and more compact folded active site loop (Loop 4), which is known in related flavoenzymes to undergo order/disorder transitions to allow substrate/product binding and release. We propose that LMO's Loop 4 has an enhanced stability that is responsible for the slow product release requisite for the coupled pathway. We also note electrostatic features of the LMO active site that promote substrate binding. Whereas the physiological role of LMO remains unknown, we document what can currently be assessed of LMO's distribution in nature, including its unexpected occurrence, presumably through horizontal gene transfer, in halophilic archaea and in a limited group of fungi of the genus Beauveria.
Polyethylene Oxide and Silicon-Substituted Hydroxyapatite Composite: A Biomaterial for Hard Tissue Engineering in Orthopedic and Spine Surgery

Background: Tissue engineering and biomaterials have made it possible to innovate bone treatments for orthopedic and spine problems. The aim of this study is to develop a novel polyethylene oxide (PEO)/silicon-substituted hydroxyapatite (Si-HA) composite to be used as a scaffold for hard tissue engineering in orthopedic and spine procedures. Materials and Methods: The composite was fabricated through the electrospinning technique. The applied voltage (5 kV) and PEO concentration (5%) were fixed. Processing parameters such as the flow rates (20 μl/min and 50 μl/min), distances from capillary tube to the collector (130 mm and 180 mm), spinning time (10 min and 20 min), and concentration of Si-HA (0.2% and 0.6%) were explored to find the optimum conditions to produce fine composite fibers. Results: Scanning electron microscope images showed that 5% PEO, 5% PEO/0.2% Si-HA, and 5% PEO/0.6% Si-HA fibers were successively produced. Flow rates and working distances showed significant influence on the morphology of the polymeric and composite fibers. A high flow rate (50 μl/min) and a larger working distance (180 mm) resulted in larger fibers. The comparison between the mean fiber diameter of 5% PEO/0.2% Si-HA and 5% PEO/0.6% Si-HA showed to be significantly different. As the Si-HA concentration increased, certain fibers were having particles of Si-HA that were not properly integrated into the polymer matrix. Conclusions: Synthesis of a novel biomaterial for hard tissue scaffold through electrospinning was successful. In general, PEO/Si-HA fibers produced have the desired characteristics to mimic the extracellular matrix of bone.
Picomolar zinc binding modulates formation of Bcl10-nucleating assemblies of the caspase recruitment domain (CARD) of CARD9

The caspase recruitment domain–containing protein 9 (CARD9)–B-cell lymphoma/leukemia 10 (Bcl10) signaling axis is activated in myeloid cells during the innate immune response to a variety of diverse pathogens. This signaling pathway requires a critical caspase recruitment domain (CARD)–CARD interaction between CARD9 and Bcl10 that promotes downstream activation of factors, including NF-κB and the mitogen-activated protein kinase (MAPK) p38. Despite these insights, CARD9 remains structurally uncharacterized, and little mechanistic understanding of its regulation exists. We unexpectedly found here that the CARD in CARD9 binds to Zn2+ with picomolar affinity—a concentration comparable with the levels of readily accessible Zn2+ in the cytosol. NMR solution structures of the CARD9–CARD in the apo and Zn2+-bound states revealed that Zn2+ has little effect on the ground-state structure of the CARD; yet the stability of the domain increased considerably upon Zn2+ binding, with a concomitant reduction in conformational flexibility. Moreover, Zn2+ binding inhibited polymerization of the CARD9–CARD into helical assemblies. Here, we also present a 20-Å resolution negative-stain EM (NS-EM) structure of these filamentous assemblies and show that they adopt a similar helical symmetry as reported previously for filaments of the Bcl10 CARD. Using both bulk assays and direct NS-EM visualization, we further show that the CARD9–CARD assemblies can directly template and thereby nucleate Bcl10 polymerization, a capacity considered critical to propagation of the CARD9–Bcl10 signaling cascade. Our findings indicate that CARD9 is a potential target of Zn2+-mediated signaling that affects Bcl10 polymerization in innate immune responses.
Stereo Generation from a Single Image Using Deep Residual Network

In this paper, we propose a framework to generate stereoscopic content from a single image using the relative depth label predicted from deep residual network. Specifically, our framework first obtains a coarse relative depth label from the network and refines it to painting depth by sampling and interpolation, then an unsupervised clustering algorithm is employed to separate pixels of different depths into different layers to generate stereoscopic images. Experimental results with good visual effects demonstrate that the proposed method can be generally applied in both outdoor and indoor scenes. Meanwhile the quantitative results on relative depth estimation from a single image are comparable to state-of-the-art. Further experiments show the application possibility of our method in VR and panorama.
Comparison of Unsupervised Modulation Filter Learning Methods for ASR

The widespread deployment of automatic speech recognition (ASR) system in consumer centric applications such as voice interaction and voice search demands the need for noise robustness in such systems. One approach to this problem is to achieve the desired robustness in speech representations used in the ASR. Motivated from studies on robust human speech recognition, we analyse the unsupervised data-driven temporal modulation ﬁlter learning for robust feature extraction. In this paper, we compare various unsupervised models for data driven ﬁlter learning like convolutional autoencoder (CAE), generative adversarial network (GAN) and convolutional restricted Boltzmann machine (CRBM). The unsupervised models are designed to learn a set of ﬁlters from long temporal trajectories of speech sub-band energy. The ﬁlters learnt from these models are used for modulation ﬁltering of the input spectrogram before the ASR training. The ASR experiments are performed on Wall Street Journal (WSJ) Aurora-4 database with clean and multi condition training setup. The experimental results obtained from the modulation ﬁltered representations shows considerable robustness to noise, channel distortions and reverber-ant conditions compared to other feature extraction methods. Among the three approaches compared in this paper, the GAN approach provides the most consistent improvements in ASR accuracy in different training scenarios.
Long Distance Voice Channel Diagnosis Using Deep Neural Networks

In long distance telephone networks, it is time-consuming to detect and locate the problematic devices. Although hints could be given from the types of distortion in the test calls, it is tedious to manually classify the distortion types from a large number of calls. In this paper, we present our work on using a deep neural network-based classiﬁer to automatically detect and identify the type of distortion which often occurs in long distance calls. We veriﬁed our approach with data from real telecommunication networks and the results showed that our approach can achieve an average recall rate of 71% in classiﬁcation. We believe our method can lead to a huge reduction of manpower and time in long distance voice channel troubleshooting.
Neural Network-Based Estimation of Distortion Sensitivity for Image Quality Prediction

Due to its computational simplicity, the PSNR is a popular and widely used image quality measure, although it correlates poorly with perceived visual quality. Distortion sensitivity, a reference image specific property, can be used to compensate for the lack of perceptual relevance of the PSNR. Based on the functional mapping between perceptual and computational quality a deep convolutional neural network is used to estimate patchwise distortion sensitivity. The local estimates are used for an imagewise perceptual adaptation of the PSNR. The performance of the proposed estimation approach is evaluated on the LIVE and TID2013 databases and shows comparable or superior performance as compared to benchmark image quality measures.
An Interlocutor-Modulated Attentional LSTM for Differentiating between Subgroups of Autism Spectrum Disorder

Recalling and discussing personal emotional experiences is one of the key procedures in assessing complex affect processing of individuals with Autism Spectrum Disorder (ASD). This procedure is a standard subpart of a diagnostic interview to assess ASD the Autism Diagnostic Observation Schedule (ADOS). Previous work has demonstrated that the behavior features computed from this procedure in ADOS possess discriminative information between the three distinct ASD subgroups: Autistic Disorder (AD), High Functioning Autism (HFA), and Asperger Syndrome (AS). In this work, we propose an interlocutor-modulated attentional long short term memory network (IM-aLSTM) that models the ASD individual’s acoustic features with a novel interlocutor-modulated attention mechanism. Our IM-aLSTM achieves ASD subgroup categorization accuracy of 66.5%, which is a 14% absolute improvement over baseline method on the same database. Our analyses further indicate that the attention weights are concentrated more on interaction segments where the ASD individual is being asked to recall and discuss his/her own negative emotional experiences.
Subpixel-Level-Accurate Algorithm for Removing Double-Layered Reflections from a Single Image

When photographers take pictures through glasses, image quality is sometimes degraded by window reflections. In this paper, we propose an algorithm to enhance the effectiveness of removing double-layered reflections from a single image. First, we apply subpixel-level autocorrelation to calculate the shift kernel of reflections. Secondly, a Gaussian-mixture-model (GMM) based layer separation technique is applied to estimate the initial transmission and reflection layers. In the refinement stage, we propose to add another GMM optimization flow to obtain the accurate transmission layer. Finally, a color and brightness adjustment step in Lab domain is used to provide better image quality. The qualitative and quantitative results show that the proposed method is very robust and effective.
A Case Study on the Importance of Belief State Representation for Dialogue Policy Management

A key component of task-oriented dialogue systems is the belief state representation, since it directly affects the policy learning efﬁciency. In this paper, we propose a novel, binary, compact, yet scalable belief state representation. We compare the standard verbose belief state representation (268 dimensions) with the domain-independent representation (57 dimensions) and the proposed representation (13 or 4 dimensions). To test those representations, the recently introduced Advantage Actor Critic (A2C) algorithm is exploited. The latter has not been tested before for any representation apart from the verbose one. We study the effect of the belief state representation within A2C under 0%, 15%, 30%, and 45% semantic error rate and conclude that the novel binary representation in general outperforms both the domain-independent and the verbose belief state representation. Further, the robustness of the binary representation is tested under more realistic scenarios with mismatched semantic error rates, within the A2C and DQN algorithms. The results indicate that the proposed compact, binary representation performs better or similarly to the other representations, being an efﬁcient and promising alternative to the full belief.
Common Crucial Feature for Crowdsourcing Based Mobile Visual Location Recognition

Crowdsourcing provides a novel and effective way of constructing a location image database for mobile visual location recognition. Compared with traditional location image databases, a crowdsourced database has richer information for location images, with various angles, times, distances and weathers, providing great potential for high recognition accuracy. However, it is inevitable to have various disturbances on these location images, hindering the potential. To address this challenge, we first propose a Common Crucial Feature (CCF) detection algorithm to exclude unimportant visual features from crucial features. To achieve a good balance between the efficiency and accuracy, we further propose a CCF based Visual Hash Bits (VHB) scheme to encode CCF features into hash bits to vote for most matching images. Extensive experiments are conducted on a crowdsourced dataset with 9,064 location images, demonstrating that our scheme outperforms other state-of-the-art schemes.
Deep Manifold-to-Manifold Transforming Network

In this paper, we propose an end-to-end deep manifold-to-manifold transforming network (DMT-Net), which makes SPD matrices flow from one Riemannian manifold to another more discriminative one. For discriminative feature learning, two specific layers on manifolds are developed: (i) the local SPD convolutional layer, (ii) the non-linear SPD activation layer, where positive definiteness is satisfied for both two layers. Further, to relieve computational burden of kernels on relative large-scale data, we design a batch-kernelized layer to favor batchwise kernel optimization of deep networks. Specifically, one reference set dynamically changing with the network training is introduced to break the limitation of memory size. We evaluate our proposed method on action recognition datasets, where input signals are popularly modeled as SPD matrices. The experimental results demonstrate that our DMT-Net is more competitive than state-of-the-art methods.
History of ZIKV Infections in India and Management of Disease Outbreaks

Zika virus (ZIKV) is an emerging arbovirus infection endemic in multiple countries spread from Asia, Africa to the Americas and Europe. Previously known to cause rare and fairly benign human infections, ZIKV has become a major international public health emergency after being linked to unexpected neurological complications, that includes fetal brain damage/death and microcephaly in babies born to infected mothers and Guillain-Barre syndrome (GBS) in adults. It appears that a single genetic mutation in the ZIKV genome, likely acquired during explosive ZIKV outbreak in French Polynesia (2013), made virus causing mild disease to target fetus brain. The Aedes mosquitoes are found to be the main carrier of ZIKV, passing the virus to humans. Originally isolated from patients in Africa in 1954 (African lineage), virus disseminated to Southeast Asia (Asian lineage), establishing new endemic foci, including one in India. Numerous cases of ZIKV infection have been reported in several locations in India and neighboring countries like Pakistan and Bangladesh since mid of the last century, suggesting that the virus reached this part of Asia soon after it was first discovered in Uganda in 1947. Although, the exact means by which ZIKV was introduced to India remains unknown, it appears that the ZIKV strain circulating in India possibly belongs to the “Asian lineage,” which has not yet been associated with microcephaly and other neurological disorders. However, there still exists a threat that the contemporary ZIKV virulent strain from South America, carrying a mutation can return to Asia, posing a potential crisis to newborns and adult patients. Currently there is no specific vaccine or antiviral medication to combat ZIKV infection, thus, vector control and continuous monitoring of potential ZIKV exposure is essential to prevent the devastating consequences similar to the ones experienced in Brazil. However, the major obstacle faced by Indian healthcare agencies is that most cases of ZIKV infection have been reported in rural areas that lack access to rapid diagnosis of infection. In this review, we attempt to present a comprehensive analysis of what is currently known about the ZIKV infection in India and the neighboring countries.
Scalable Multi-Consistency Feature Matching with Non-Cooperative Games

Correspondence selection aiming at seeking correct relationships between two images is a fundamental and critical task in computer vision. This paper attempts to select consistent correspondences in the context of dynamic scenarios where multiple matching consistencies are normally incorporated. To this end, we present a grid-based game-theoretic matching (Grid-GTM) method which is divided into three processes, i.e., grid matching, local games and enrichment. Specifically, grid matching translates the multi-consistency problem into several independent single-consistency problems to decrease difficulties of selection and boost the efficiency. Local games extended under the guidance of a novel payoff function guarantee that mismatches are effectively removed. Enrichment is added to recover correct matches neglected by local games. Crucially, our approach achieves the state-of-the-art performance compared with seven algorithms in comprehensive evaluations. In addition, we construct a dataset that involves multiple consistencies under three different scenes in this paper.
Clostridium difficile Biofilm: Remodeling Metabolism and Cell Surface to Build a Sparse and Heterogeneously Aggregated Architecture

Clostridium difficile is an opportunistic entero-pathogen causing post-antibiotic and nosocomial diarrhea upon microbiota dysbiosis. Although biofilms could contribute to colonization, little is known about their development and physiology. Strain 630Δerm is able to form, in continuous-flow micro-fermentors, macro-colonies and submersed biofilms loosely adhesive to glass. According to gene expression data, in biofilm/planktonic cells, central metabolism is active and fuels fatty acid biosynthesis rather than fermentations. Consistently, succinate is consumed and butyrate production is reduced. Toxin A expression, which is coordinated to metabolism, is down-regulated, while surface proteins, like adhesins and the primary Type IV pili subunits, are over-expressed. C-di-GMP level is probably tightly controlled through the expression of both diguanylate cyclase-encoding genes, like dccA, and phosphodiesterase-encoding genes. The coordinated expression of genes controlled by c-di-GMP and encoding the putative surface adhesin CD2831 and the major Type IV pilin PilA1, suggests that c-di-GMP could be high in biofilm cells. A Bacillus subtilis SinR-like regulator, CD2214, and/or CD2215, another regulator co-encoded in the same operon as CD2214, control many genes differentially expressed in biofilm, and in particular dccA, CD2831 and pilA1 in a positive way. After growth in micro-titer plates and disruption, the biofilm is composed of robust aggregated structures where cells are embedded into a polymorphic material. The intact biofilm observed in situ displays a sparse, heterogeneous and high 3D architecture made of rods and micro-aggregates. The biofilm is denser in a mutant of both CD2214 and CD2215 genes, but it is not affected by the inactivation of neither CD2831 nor pilA1. dccA, when over-expressed, not only increases the biofilm but also triggers its architecture to become homogeneous and highly aggregated, in a way independent of CD2831 and barely dependent of pilA1. Cell micro-aggregation is shown to play a major role in biofilm formation and architecture. This thorough analysis of gene expression reprogramming and architecture remodeling in biofilm lays the foundation for a deeper understanding of this lifestyle and could lead to novel strategies to limit C. difficile spread.
Robust Scoring and Ranking of Object Tracking Techniques

Object tracking is an active research area and numerous techniques have been proposed recently. To evaluate a new tracker, its performance is compared against existing ones typically by averaging its quality based on a performance measure, over all test video sequences. Such averaging is, however, not representative as it does not account for outliers (or similarities) between trackers. This paper presents a framework for scoring and ranking of trackers using uncorrelated quality metrics (overlap ratio and failure rate), coupled with a robust estimator (median absolute deviation) against outliers. Ten different performing trackers are scored and ranked using the proposed framework on a public benchmark of 100 sequences. The obtained results show that our framework well highlights and distinguishes the relative performance of each tracker.
Geometric Surface-Based Tracking Control of a Quadrotor UAV under Actuator Constraints

This paper presents contributions on nonlinear tracking control systems for a quadrotor unmanned micro aerial vehicle. New controllers are proposed based on nonlinear surfaces composed by tracking errors that evolve directly on the nonlinear configuration manifold thus inherently including in the control design the nonlinear characteristics of the SE(3) configuration space. In particular geometric surface-based controllers are developed, and through rigorous stability proofs they are shown to have desirable closed loop properties that are almost global. A region of attraction, independent of the position error, is produced and its effects are analyzed. A strategy allowing the quadrotor to achieve precise attitude tracking while simultaneously following a desired position command and complying to actuator constraints in a computationally inexpensive manner is derived. This important contribution differentiates this work from existing Geometric Nonlinear Control System solutions (GNCSs) since the commanded thrusts can be realized by the majority of quadrotors produced by the industry. The new features of the proposed GNCSs are illustrated by numerical simulations of aggressive maneuvers and a comparison with a GNCSs from the bibliography.
Leveraging Second-Order Log-Linear Model for Improved Deep Learning Based ASR Performance

Gaussian generative models have been shown to be equivalent to discriminative log-linear models under weak assumptions for acoustic modeling in speech recognition systems. In this paper, we note that the output layer of deep learning model consists of a ﬁrst-order log-linear model, also known as logistic regression, which induces a set of homoscedastic distributions in the generative model space, resulting in linear decision boundaries. We leverage the above equivalence to make the deep learning models more expressive by replacing the ﬁrst order log-linear model with a second-order model, which leads to heteroscedastic distributions, as a result, the linear decision boundaries are replaced with quadratic ones. We observe that the proposed architecture yields a signiﬁcant improvement in speech recognition accuracy compared to the conventional model having a comparable number of parameters. Relative improvement of 8.37% and 3.92% in word error rate (WER) is obtained for shallow and deep feed-forward networks respectively. Moreover, with Long Short-Term Memory (LSTM) networks with projection matrix, we obtain signiﬁcant relative improvement in WER over the standard architecture.