Miracle-dz/UniFAR-FTUs huggingface dataset:
On the interactions of the receptor-binding domain of SARS-CoV-1 and SARS-CoV-2 spike proteins with monoclonal antibodies and the receptor ACE2

A new betacoronavirus named SARS-CoV-2 has emerged as a new threat to global health and economy. A promising target for both diagnosis and therapeutics treatments of the new disease named COVID-19 is the coronavirus (CoV) spike (S) glycoprotein. By constant-pH Monte Carlo simulations and the PROCEEDpKa method, we have mapped the electrostatic epitopes for four monoclonal antibodies and the angiotensin-converting enzyme 2 (ACE2) on both SARS-CoV-1 and the new SARS-CoV-2 S receptor binding domain (RBD) proteins. We also calculated free energy of interactions and shown that the S RBD proteins from both SARS viruses binds to ACE2 with similar affinities. However, the affinity between the S RBD protein from the new SARS-CoV-2 and ACE2 is higher than for any studied antibody previously found complexed with SARS-CoV-1. Based on physical chemical analysis and free energies estimates, we can shed some light on the involved molecular recognition processes, their clinical aspects, the implications for drug developments, and suggest structural modifications on the CR3022 antibody that would improve its binding affinities for SARS-CoV-2 and contribute to address the ongoing international health crisis.
Co-evolutionary analysis reveals a conserved dual binding interface between extracytoplasmic function (ECF) σ factors and class I anti-σ factors

Extracytoplasmic function σ factors (ECFs) belong to the most abundant signal transduction mechanisms in bacteria. Amongst the diverse regulators of ECF activity, class I anti-σ factors are the most important signal transducers in response to internal and external stress conditions. Despite the conserved secondary structure of the class I anti-σ factor domain (ASDI) that binds and inhibits the ECF under non-inducing conditions, the binding interface between ECFs and ASDIs is surprisingly variable between the published co-crystal structures. In this work, we provide a comprehensive computational analysis of the ASDI protein family and study the different contact themes between ECFs and ASDIs. To this end, we harness the co-evolution of these diverse protein families and predict covarying amino acid residues as likely candidates of an interaction interface. As a result, we find two common binding interfaces linking the first α-helix of the ASDI to the DNA binding region in the σ4 domain of the ECF, and the fourth α-helix of the ASDI to the RNA polymerase (RNAP) binding region of the σ2 domain. The conservation of these two binding interfaces contrasts with the apparent quaternary structure diversity of the ECF/ASDI complexes, partially explaining the high specificity between cognate ECF and ASDI pairs. Furthermore, we suggest that the dual inhibition of RNAP- and DNA-binding interfaces are likely a universal feature of other ECF anti-σ factors, preventing the formation of non-functional trimeric complexes between σ/anti-σ factors and RNAP or DNA. Significance In the bacterial world, extracytoplasmic function σ factors (ECFs) are the most widespread family of alternative σ factors, mediating many cellular responses to environmental cues, such as stress. This work uses a computational approach to investigate how these σ factors interact with class I anti-σ factors – the most abundant regulators of ECF activity. By comprehensively classifying the anti-σs into phylogenetic groups and by comparing this phylogeny to the one of the cognate ECFs, the study shows how these protein families have co-evolved to maintain their interaction over evolutionary time. These results shed light on the common contact residues that link ECFs and anti-σs in different phylogenetic families and set the basis for the rational design of anti-σs to specifically target certain ECFs. This will help to prevent the cross-talk between heterologous ECF/anti-σ pairs, allowing their use as orthogonal regulators for the construction of genetic circuits in synthetic biology.
The missing role of gray matter in studying brain controllability

Brain controllability properties are normally derived from the white matter fiber tracts in which the neural substrate of the actual energy consumption, namely the gray matter, has been widely ignored. Here, we study the relationship between gray matter volume of regions across the whole cortex and their respective control property derived from the structural architecture of the white matter fiber tracts. The data suggests that the ability of white fiber tracts to exhibit control at specific nodes not only depends on the connection strength of the structural connectome but additionally strongly depends on gray matter volume at the host nodes. Our data indicates that connectivity strength and gray matter volume interact with respect to the brain’s control properties, such that gray matter exerts the great impact in regions with high structural connectivity. Disentangling effects of the regional gray matter volume and connectivity strength, we found that frontal and sensory areas play crucial roles in controllability. Together these results suggest that structural and regional properties of the white matter and gray matter provide complementary information in studying the control properties of the intrinsic structural and functional architectural of the brain.
Diffusion MRI of the Unfolded Hippocampus

The hippocampus is implicated in numerous neurological disorders and the ability to detect subtle or focal hippocampal abnormalities earlier in disease progression could significantly improve the treatment of patients. Ex vivo studies with ultra-high field have revealed that diffusion MRI (dMRI) can reveal microstructural variations within the hippocampal subfields and lamina, and may also be sensitive to intra-hippocampal pathways. However, translation to lower resolution in vivo dMRI studies of the hippocampus is challenging due to its complicated geometry. One novel way to overcome some of these obstacles is by transforming the usual Cartesian coordinates in an MRI image to coordinates that are crafted to curve themselves according to the complicated geometry of the hippocampus. This procedure allows us to virtually unfold the hippocampus into a thin sheet. In this work, we introduce an algorithm to map diffusion MRI data to this sheet, allowing us to overcome the difficulties associated with the hippocampus’ complicated geometry. We demonstrate how our method can be readily integrated into existing implementations of traditional tractography methods and how it leads to enhancements in the resulting tracts. Further, our results on high quality in vivo dMRI acquisitions show that unfolding the hippocampus leads to a more anatomically plausible modelling of the connectivity of the hippocampus as probed by probabilistic tractography, revealing key elements of the polysynaptic pathway and anterior-posterior connectivity gradients.
Evaluation of Neuropharmacological Effects of Different Chemical Extracts of Flemingia Stricta (Roxb.) Leaves

Background Traditional preparation of the leaf of Flemingia stricta (Fabaceae) Roxb. , a medicinal plant of the Indian subcontinent, has been used for treatment of different diseases as herbal preparation. Our purpose was to analyze Neuropharmacological effects of different chemical extracts of Flemingia stricta Roxb. as particular form of behavioral inhibition that occurs in response to novel environmental events. Methods In present study, the anxiogenic activity of crude extracts of Flemingia stricta leaves was determined using standard animal behavioral models, such as hole cross and open field; Sedative property and anxiolytic potential were assessed by conducting thiopental sodium induced sleeping times tests and elevated plus maze test respectively. Results The crude extracts at both dose (200 and 400mg/kg) exhibited a significant (P<0.05, P<0.01) dose-dependent suppression of motor activity and exploratory activity of mice in both open field and hole cross test. In anxiolytic study, extracts displayed increased percentage of entry into open arm at the dose of 200 and 400mg/kg. Extracts produced a significant (P<0.05, P<0.01) increase in sleeping duration and reduction of onset of sleep compared to sodium thiopental at both doses (200 and 400mg/kg). Conclusion This study demonstrates that the treated extracts have significant central nervous system depressant effect. Further studies on the active constituent of the extract can provide approaches for therapeutic intervention.
Separable influences of reward on visual processing and choice

Primate vision is characterized by constant, sequential processing and selection of visual targets to fixate. Although expected reward is known to influence both processing and selection of visual targets, similarities and differences between these effects remains unclear mainly because they have been measured in separate tasks. Using a novel paradigm, we simultaneously measured the effects of reward outcomes and expected reward on target selection and sensitivity to visual motion in monkeys. Monkeys freely chose between two visual targets and received a juice reward with varying probability for eye movements made to either of them. Targets were stationary apertures of drifting gratings, causing the endpoints of eye movements to these targets to be systematically biased in the direction of motion. We used this motion-induced bias as a measure of sensitivity to visual motion on each trial. We then performed different analyses to explore effects of objective and subjective reward values on choice and sensitivity to visual motion in order to find similarities and differences between reward effects on these two processes. Specifically, we used different reinforcement learning models to fit choice behavior and estimate subjective reward values based on the integration of reward outcomes over multiple trials. Moreover, to compare the effects of subjective reward value on choice and sensitivity to motion directly, we considered correlations between each of these variables and integrated reward outcomes on a wide range of timescales. We found that in addition to choice, sensitivity to visual motion was also influenced by subjective reward value, even though the motion was irrelevant for receiving reward. Unlike choice, however, sensitivity to visual motion was not affected by objective measures of reward value. Moreover, choice was determined by the difference in subjective reward values of the two options whereas sensitivity to motion was influenced by the sum of values. Finally, models that best predicted visual processing and choice used sets of estimated reward values based on different types of reward integration and timescales. Together, our results demonstrate separable influences of reward on visual processing and choice, and point to the presence of multiple brain circuits for integration of reward outcomes.
Associations Between Olfactory Impairment and Cognitive Functions in Patients with Parkinson Disease.

Introduction Olfactory impairment and cognitive impairment are common non-motor symptoms in Parkinson's disease (PD). Olfactory impairment may be present even many years before the main symptoms of the disease develop. The associations between olfactory loss and cognition in PD are evaluated in this study. Methods 31 patients with PD and 31 healthy subjects were included in this study. The Unified Parkinson's Disease Rating Scale (UPDRS) and Hoehn and Yahr Scale (H&Y Scale) were administered to all subjects. Butanol threshold test and Sniffin'Sticks test were used to assess olfaction. The Mini Mental State Examination (MMSE) and Clock Drawing Test (CDT) and Stroop Color Word Test (SCWT) were used to assess cognition. Results The Sniffin'Sticks test scores were significantly lower in the Parkinson group in comparison to the control group (p<0.001). The rate of anosmia was 90% in the PD group while this rate was found to be 54.8% in control group (p=0.005). A significant correlation was found between butanol test scores and stoop 5 and 5 errors. Significant correlations were found between the Sniffin'Sticks scores and MMSE scores (p=0.047) and orientation (p=0.041) and language (p=0.003) functions of the MMSE test. Worse olfaction was associated with worse memory. Conclusions In PD, olfactory impairment correlates with cognitive impairment and olfactory tests may be used to predict the likelihood of developing dementia in this patient population.
Atlas Shrugged: Device-agnostic Radiance Megatextures

This paper proposes a novel distributed rendering pipeline for highly responsive high-fidelity graphics based on the concept of device-agnostic radiance megatextures (DARM), a network-based out-of-core algorithm that circumvents VRAM limitations without sacrificing texture variety. After an automatic precomputation stage generates the sparse virtual texture layout for rigid bodies in the scene, the server end of the pipeline populates and updates surface radiance in the texture. On demand, connected clients receive geometry and texture information selectively, completing the pipeline by asynchronously reconstituting these data into a frame using GPUs with minimal functionality. A client-side caching system makes DARM robust to network fluctuations. Furthermore, users can immediately start consuming the service without the need for lengthy downloads or installation processes. DARM was evaluated on its effectiveness as a vehicle for bringing hardware-accelerated ray tracing to various device classes, including smartphones and single board computers. Results show that DARM is effective at allowing these devices to visualise high quality ray traced output at high frame rates and
Mask-guided Image Classification with Siamese Networks

: This paper deals with a CNN-based image classiﬁcation task where the class of each image depends on a small detail in the image. Our original idea consists in providing a binary mask to the network so that it knows where is located the important information. This mask as well as the color image are provided as inputs to a siamese network. A contrastive loss function controls the projection of the network outputs in an embedding space enforcing the extraction of image features at the location proposed by the mask. This solution is tested on a real application whose aim is to secure the boarding on ski chairlifts by checking if the safety bar of the carrier is open or closed. Each chairlift has its own safety bar masks (open and close) and we propose to exploit this additional data to help the image classiﬁcation between close or open safety bar. We show that the use of a siamese network allows to learn a single model that performs very well on 20 different skilifts.
Player Tracking using Multi-viewpoint Images in Basketball Analysis

: In this study, we aim to realize the automatic tracking of basketball players by avoiding occlusion of players, which is an important issue in basketball video analysis, using multi-viewpoint images. Images taken with a hand-held camera are used, to expand the scope of application to uses such as school club activities. By integrating the player tracking results from each camera image with a 2-D map viewed from above the court, using projective transformation, the occlusion caused by one camera is stably solved using the information from other cameras. In addition, using OpenPose for player detection reduces the occlusion that occurs in each camera image before all camera images are integrated. We conﬁrm the effectiveness of our method by experiments with real image sequences.
Multimodal Dance Recognition

: Video content analysis is still an emerging technology, and the majority of work in this area extends from the still image domain. Dance videos are especially difﬁcult to analyse and recognise as the performed human actions are highly dynamic. In this work, we introduce a multimodal approach for dance video recognition. Our proposed method combines visual and audio information, by fusing their representations, to improve classiﬁ-cation accuracy. For the visual part, we focus on motion representation, as it is the key factor in distinguishing dance styles. For audio representation, we put the emphasis on capturing long-term dependencies, such as tempo, which is a crucial dance discriminator. Finally, we fuse two distinct modalities using a late fusion approach. We compare our model with corresponding unimodal approaches, by giving exhaustive evaluation on the Let’s Dance dataset. Our method yields signiﬁcantly better results than each single-modality approach. Results presented in this work not only demonstrate the strength of integrating complementary sources of information in the recognition task, but also indicate the potential of applying multimodal approaches within speciﬁc research areas.
Pedestrian Tracking with Occlusion State Estimation

Visual tracking of multiple pedestrians in video sequences is an important procedure for many computer vision applications. The tracking-by-detection approach is widely used for visual pedestrian tracking. This approach extracts pedestrian regions from each video frame and associates the extracted regions across frames as the same pedestrian according to the similarities of region features (e.g., position, appearance, and movement). When a pedestrian is temporarily occluded by a still obstacle in the scene, he/she disappears at one side of the obstacle in a certain frame and then reappears at the other side of it a few frames later. The occlusion state of the pedestrian, that is the space-time interval where the pedestrian is missing, varies with obstacle areas and pedestrian movements. Such an unknown occlusion state complicates the region association process for the same pedestrian and makes the pedestrian tracking difficult. To solve this difficulty and improve pedestrian tracking robustness, we propose a novel method for tracking pedestrians while estimating their occlusion states. Our method acquires obstacle areas by the pedestrian regions extracted from each frame, estimates the occlusion states from the acquired obstacle areas and pedestrian movements, and reflects the estimated occlusion states in the region association process.
Improving Age Estimation in Minors and Young Adults with Occluded Faces to Fight Against Child Sexual Exploitation

Accurate and fast age estimation is crucial in systems for detecting possible victims in Child Sexual Exploitation Materials. Age estimation obtains state of the art results with deep learning. However, these models tend to perform poorly in minors and young adults, because they are trained with unbalanced data and few examples. Furthermore, some Child Sexual Exploitation images present eye occlusion to hide the identity of the victims, which may also affect the performance of age estimators. In this work, we evaluate the performance of Soft Stagewise Regression Network (SSR-Net), a compact size age estimator model, with non-occluded and occluded face images. We propose an approach to improve the age estimation in minors and young adults by using both types of facial images to create SSR-Net models. The proposed strategy builds robust age estimators that improve SSR-Net pre-trained models on IMBD and MORPH datasets, and a Deep EXpectation model, reducing the Mean Absolute Error (MAE) from 7.26, 6.81 and 6.5 respectively, to 4.07 with our
A Robust Serial FBG Sensor Network with CDM Interrogation Allowing Overlapping Spectra

Massive optical sensor networks gained a lot of attention in recent years. They offer new advances in the fields of smart structures and health monitoring. All serial optic al sensor networks rely on multiplexing techniques that provide huge amounts of sensors in a single optical fiber . Wavelength-division multiplex (WDM) which has been established in many applications, is restricted to the spectral width of the used light source that needs to be shared by several non-overlapping fiber-Bragg-g ratin (FBG) spectra. Time-division multiplex (TDM) uses short impulses and relies on different sensor rou nd trip delays to distinguish each single FBG. These short impulses and long round trip times lead to a low si gnal-to-noise ratio (SNR). Optical frequencydomain reflectometry (OFDR) offers a high spatial resolutio n of FBGs but only within a short fiber length. This contribution deals with a code-division multiplex (CD M) interrogation technique that provides numerous sensors in a single optical fiber, a better SNR, and a long rang e of distributed sensing points. It requires codes with good autocorrelation behavior which is characte ized by certain criteria. The detectable criteria are limited which narrows significantly a search for best pos sible codes for the interrogation system. In this contribution, practical implementation limits such as the rigger timing and the achievable SNR are studied. Based on the introduced SNR definitions for CDM and WDM system , a direct comparison is possible and it shows the superiority of the proposed CDM scheme. A networ k with 25 sensors operating at the same wavelength can provide a 2 . 67 dB improvement compared to WDM
Towards Abstract Test Execution in Early Stages of Model-driven Software Development

Over the last decades, systems immanent complexity has significantly increased. In order to cope with the emerging challenges during the development of such systems, modeling approaches become an indispensable part. While many process steps are applicable to the model-level, there are no sufficient realizations for test execution yet. As a result, we present a semi-formal approach enabling developers to perform abstract test execution straight on the modeled artifacts to support the overarching objective of a shift left of verification and validation tasks. Our concept challenges an abstract test case (derived from test model) against a system model utilizing an integrated set of domain-specific models, i.e. the omni model. Driven by an optimistic dataflow analysis based on a combined view of an abstract test case and its triggered system behavior, possible test verdicts are assigned. Based on a prototypical implementation of the concept, the proof of concept is demonstrated and further on put in the context of related research.
A Taxonomy of Treemap Visualization Techniques

A treemap is a visualization that has been specifically designed to facilitate the exploration of tree-structured data and, more general, hierarchically structured data. The family of visualization techniques that use a visual metaphor for parent-child relationships based “on the property of containment” (Johnson, 1993) is commonly referred to as treemaps. However, as the number of variations of treemaps grows, it becomes increasingly important to distinguish clearly between techniques and their specific characteristics. This paper proposes to discern between Space-filling Treemap TS, Containment Treemap TC, Implicit Edge Representation Tree TIE, and Mapped Tree TMT for classification of hierarchy visualization techniques and highlights their respective properties. This taxonomy is created as a hyponymy, i.e., its classes have an is-a relationship to one another: TS⊂ TC⊂ TIE⊂ TMT. With this proposal, we intend to stimulate a discussion on a more unambiguous classification of treemaps and, furthermore, broaden what is understood by the concept of treemap itself.