Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Conformational Diversity on the Bioactivity of µ-Conotoxin PIIIA Disulfide Isomers

Cyclic µ-conotoxin PIIIA, a potent blocker of skeletal muscle voltage-gated sodium channel NaV1.4, is a 22mer peptide stabilized by three disulfide bonds. Combining electrophysiological measurements with molecular docking and dynamic simulations based on NMR solution structures, we investigated the 15 possible 3-disulfide-bonded isomers of µ-PIIIA to relate their blocking activity at NaV1.4 to their disulfide connectivity. In addition, three µ-PIIIA mutants derived from the native disulfide isomer, in which one of the disulfide bonds was omitted (C4-16, C5-C21, C11-C22), were generated using a targeted protecting group strategy and tested using the aforementioned methods. The 3-disulfide-bonded isomers had a range of different conformational stabilities, with highly unstructured, flexible conformations with low or no channel-blocking activity, while more constrained molecules preserved 30% to 50% of the native isomer’s activity. This emphasizes the importance and direct link between correct fold and function. The elimination of one disulfide bond resulted in a significant loss of blocking activity at NaV1.4, highlighting the importance of the 3-disulfide-bonded architecture for µ-PIIIA. µ-PIIIA bioactivity is governed by a subtle interplay between an optimally folded structure resulting from a specific disulfide connectivity and the electrostatic potential of the conformational ensemble.
Iranian guideline for rituximab therapy in pemphigus patients

Pemphigus vulgaris (PV) is an autoimmune blistering disease affecting the skin and/or mucosa. Rituximab (RTX) has been approved recently by US FDA as an effective and safe treatment of PV. The high incidence of PV in Iran encouraged our team to prepare a consensus guideline for RTX administration based on literature review and a decade experience of an expert panel. RTX is recommended for the treatment of new cases of PV as well as patients not responding to conventional therapy. Contraindications include history of anaphylaxis or IgE‐mediated hypersensitivity to murine proteins of RTX, severe active infections, pregnancy, breastfeeding, severe heart failure, and arrhythmia. Prophylactic antiviral therapy is recommended in patients at risk of reactivation of HBV and isoniazid for those at risk of reactivation of tuberculosis. Concomitant use of systemic corticosteroids is recommended as a rule. Except for methotrexate, the combination with other immunosuppressive drugs is discouraged. Intravenous immunoglobulin is recommended for those at risk of infections or with extensive disease. The recommended dosage of RTX for the first cycle is 2 g either 500 mg weekly or 1 g biweekly. There is no general consensus whether the next doses of RTX be administered upon relapse or as maintenance therapy. We strongly recommend RTX sooner in the course of pemphigus.
A Whole-Cell Screen for Adjunctive and Direct Antimicrobials Active against Carbapenem-Resistant Enterobacteriaceae

Carbapenem-resistant Enterobacteriaceae (CRE) are an emerging antimicrobial resistance threat for which few if any therapeutic options remain. Identification of new agents that either inhibit CRE or restore activity of existing antimicrobials is highly desirable. Therefore, a high-throughput screen of 182,427 commercially available compounds was used to identify small molecules which either enhanced activity of meropenem against a carbapenem-resistant Klebsiella pneumoniae ST258 screening strain and/or directly inhibited its growth. The primary screening methodology was a whole-cell screen/counterscreen combination assay that tested for reduction of microbial growth in the presence or absence of meropenem, respectively. Screening hits demonstrating eukaryotic cell toxicity based on an orthogonal screening effort or identified as pan-assay interference compounds (PAINS) by computational methods were triaged. Primary screening hits were then clustered and ranked according to favorable physicochemical properties. Among remaining hits, we found 10 compounds that enhanced activity of carbapenems against a subset of CRE. Direct antimicrobials that passed toxicity and PAINS filters were not, however, identified in this relatively large screening effort. It was previously shown that the same screening strategy was productive for identifying candidates for further development when screening known bioactive libraries inclusive of natural products. Our findings therefore further highlight liabilities of commercially available small-molecule screening libraries in the Gram-negative antimicrobial space. In particular, there was especially low yield in identifying compelling activity against a representative, highly multidrug-resistant, carbapenemase-producing K. pneumoniae strain.
An integrative computational approach for a prioritization of key transcription regulators associated with nanomaterial-induced toxicity.

A rapid increase of new nanomaterial products poses new challenges for their risk assessment. Current traditional methods for estimating potential adverse health effect of nanomaterials (NMs) are complex, time consuming and expensive. In order to develop new prediction tests for nanotoxicity evaluation, a systems biology approach and data from high-throughput omics experiments can be used. We present a computational approach that combines reverse engineering techniques, network analysis and pathway enrichment analysis for inferring the transcriptional regulation landscape and its functional interpretation. To illustrate this approach, we used published transcriptomic data derived from mice lung tissue exposed to carbon nanotubes (NM-401 and NRCWE-26). Because fibrosis is the most common adverse effect of these NMs, we included in our analysis the data for bleomycin (BLM) treatment, which is a well-known fibrosis inducer. We inferred gene regulatory networks for each NM and BLM to capture functional hierarchical regulatory structures between genes and their regulators. Despite the different nature of the lung injury caused by nanoparticles and BLM, we identified several conserved core regulators for all agents. We reason that these regulators can be considered as early predictors of toxic responses after NMs exposure. This integrative approach, which refines traditional methods of transcriptomic analysis, can be useful for prioritization of potential core regulators and generation of new hypothesis about mechanisms of nanoparticles toxicity.
Protein interactions study through proximity-labeling

ABSTRACT Introduction: The proteome is a dynamic system in which protein-protein interactions play a crucial part in shaping the cell phenotype. However, given the current limitations of available technologies to describe the dynamic nature of these interactions, the identification of protein-protein interactions has long been a major challenge in proteomics. In recent years, the development of BioID and APEX, two proximity-tagging technologies, have opened-up new perspectives and have already started to change our conception of protein-protein interactions, and more generally, of the proteome. With a broad range of application encompassing health, these new technologies are currently setting milestones crucial to understand fine cellular mechanisms. Area covered: In this article, we describe both the recent and the more conventional available tools to study protein-protein interactions, compare the advantages and the limitations of these techniques, and discuss the recent advancements led by the proximity tagging techniques to refine our conception of the proteome. Expert opinion: The recent development of proximity labeling techniques emphasizes the growing importance of such technologies to decipher cellular mechanism. Although several challenges still need to be addressed, many fields can benefit from these tools and notably the detection of new therapeutic targets for patient care
Radiation doses to brain substructures associated with cognition in radiotherapy of pediatric brain tumors

Abstract Background: Several brain substructures associated with cognition (BSCs) are located close to typical pediatric brain tumors. Pediatric patients therefore have considerable risks of neurocognitive impairment after brain radiotherapy. In this study, we investigated the radiation doses received by BSCs for three common locations of pediatric brain tumor entities. Material and methods: For ten patients in each group [posterior fossa ependymoma (PFE), craniopharyngioma (CP), and hemispheric ependymoma (HE)], the cumulative fraction of BSCs volumes receiving various dose levels were analyzed. We subsequently explored the differences in dose pattern between the three groups and used available dose response models from the literature to estimate treatment-induced intelligence quotient (IQ) decline. Results: Doses to BSCs were found to differ considerably between the groups, depending on their position relative to the tumor. Large inter-patient variations were observed in the ipsilateral structures of the HE groups, and at low doses for all three groups. IQ decline estimates differed depending on the model applied, presenting larger variations in the HE group. Conclusion: While there were notable differences in the dose patterns between the groups, the extent of estimated IQ decline depended more on the model applied. This inter-model variability should be considered in dose–effect assessments on cognitive outcomes of pediatric patients.
Stainless Steel as A Bi-Functional Electrocatalyst—A Top-Down Approach

For a hydrogen economy to be viable, clean and economical hydrogen production methods are vital. Electrolysis of water is a promising hydrogen production technique with zero emissions, but suffer from relatively high production costs. In order to make electrolysis of water sustainable, abundant, and efficient materials has to replace expensive and scarce noble metals as electrocatalysts in the reaction cells. Herein, we study activated stainless steel as a bi-functional electrocatalyst for the full water splitting reaction by taking advantage of nickel and iron suppressed within the bulk. The final electrocatalyst consists of a stainless steel mesh with a modified surface of layered NiFe nanosheets. By using a top down approach, the nanosheets stay well anchored to the surface and maintain an excellent electrical connection to the bulk structure. At ambient temperature, the activated stainless steel electrodes produce 10 mA/cm2 at a cell voltage of 1.78 V and display an onset for water splitting at 1.68 V in 1M KOH, which is close to benchmarking nanosized catalysts. Furthermore, we use a scalable activation method using no externally added electrocatalyst, which could be a practical and cheap alternative to traditionally catalyst-coated electrodes.
Baicalin modulates apoptosis via RAGE, MAPK, and AP-1 in vascular endothelial cells during Haemophilus parasuis invasion

Glässer’s disease, caused by Haemophilus parasuis, is a chronic disease related to an inflammatory immune response. Baicalin exerts important biological functions. In this study, we explored the protective efficacy of treatment with baicalin and the potential mechanism of activation of the MAPK signaling pathway in porcine aortic vascular endothelial cells (PAVECs) induced by H. parasuis. H. parasuis stimulated expression of receptor for advanced glycation end products, induced a significant increase in the level of protein kinase-α and protein kinase-δ phosphorylation, and significantly up-regulated ERK, c-Jun N-terminal kinase, and p38 phosphorylation in PAVECs. H. parasuis also up-regulated the levels of apoptotic genes (Bax, C-myc, and Fasl) and the expression levels of c-Jun and c-Fos, and induced S-phase arrest in PAVECs. However, treatment with baicalin inhibited expression of RAGE, suppressed H. parasuis-induced protein kinase-α and protein kinase-δ phosphorylation, reduced ERK, c-Jun N-terminal kinase, and p38 phosphorylation, down-regulated apoptotic genes (Bax, C-myc, and Fasl), attenuated phospho-c-Jun production from the extracellular to the nuclei, and reversed S-phase arrest in PAVECs. In conclusion, baicalin treatment inhibited the MAPK signaling pathway, thereby achieving its anti-inflammatory responses, which provides a new strategy to control H. parasuis infection.
Modeling the postmerger gravitational wave signal and extracting binary properties from future binary neutron star detections

Gravitational wave astronomy has established its role in measuring the equation of state governing cold supranuclear matter. To date and in the near future, gravitational wave measurements from neutron star binaries arc likely to be restricted to the inspiral. However, future upgrades and the next generation of gravitational wave detectors will enable us to detect the gravitational wave signatures emitted after the merger of two stars, at times when densities beyond those in single neutron stars are reached. Therefore, the postmerger gravitational wave signal enables studies of supranuclear matter at its extreme limit. To support this line of research, we present new and updated phenomenological relations between the binary properties and characteristic features of the postmerger evolution. Most notably, we derive an updated relation connecting the mass-weighted tidal deformability and the maximum neutron star mass to the dominant emission frequency of the postmerger spectrum. With the help of a configuration-independent Bayesian analysis using simplified Lorentzian model functions, we find that the main emission frequency of the postmerger remnant, for signal-to-noise ratios of 8 and above, can be extracted within a 1-sigma uncertainty of about 100 Hz for Advanced LIGO and Advanced Virgo's design sensitivities. In some cases, even a postmerger signal-to-noise ratio of 4 can be sufficient to determine the main emission frequency. This will enable us to measure binary and equation-of-state properties from the postmerger, to perform a consistency check between different parts of the binary neutron star coalescence, and to put our physical interpretation of neutron star mergers to the test.
Solar Models with Convective Overshoot, Solar-wind Mass Loss, and PMS Disk Accretion: Helioseismic Quantities, Li Depletion, and Neutrino Fluxes

Helioseismic observations have revealed many properties of the Sun: the depth and helium abundance of the convection zone, the sound speed, and the density profiles in the solar interior. Those constraints have been used to judge the stellar evolution theory. With the old solar composition (e.g., GS98), the solar standard model is in reasonable agreement with the helioseismic constraints. However, a solar model with a revised composition (e.g., AGSS09) with a low abundance Z of heavy elements cannot be consistent with those constraints. This is the so-called “solar abundance problem,” standing for more than 10 yr even with the recent upward revised Ne abundance. Many mechanisms have been proposed to mitigate the problem. However, there is still no low-Z solar model satisfying all helioseismic constraints. In this paper, we report a possible solution to the solar abundance problem. With some extra physical processes that are not included in the standard model, solar models can be significantly improved. Our new solar models with convective overshoot, the solar wind, and early mass accretion show consistency with helioseismic constraints, the solar Li abundance, and observations of solar neutrino fluxes.
Reconstructing large-scale structure with neutral hydrogen surveys

Upcoming 21-cm intensity surveys will use the hyperfine transition in emission to map out neutral hydrogen in large volumes of the universe. Unfortunately, large spatial scales are completely contaminated with spectrally smooth astrophysical foregrounds which are orders of magnitude brighter than the signal. This contamination also leaks into smaller radial and angular modes to form a foreground wedge, further limiting the usefulness of 21-cm observations for different science cases, especially cross-correlations with tracers that have wide kernels in the radial direction. In this paper, we investigate reconstructing these modes within a forward modeling framework. Starting with an initial density field, a suitable bias parameterization and non-linear dynamics to model the observed 21-cm field, our reconstruction proceeds by {combining} the likelihood of a forward simulation to match the observations (under given modeling error and a data noise model) {with the Gaussian prior on initial conditions and maximizing the obtained posterior}. For redshifts z=2 and 4, we are able to reconstruct 21cm field with cross correlation, rc > 0.8 on all scales for both our optimistic and pessimistic assumptions about foreground contamination and for different levels of thermal noise. The performance deteriorates slightly at z=6. The large-scale line-of-sight modes are reconstructed almost perfectly. We demonstrate how our method also provides a technique for density field reconstruction for baryon acoustic oscillations, outperforming standard methods on all scales. We also describe how our reconstructed field can provide superb clustering redshift estimation at high redshifts, where it is otherwise extremely difficult to obtain dense spectroscopic samples, as well as open up a wealth of cross-correlation opportunities with projected fields (e.g. lensing) which are restricted to modes transverse to the line of sight.
Lifting Datalog-based analyses to software product lines

Applying program analyses to Software Product Lines (SPLs) has been a fundamental research problem at the intersection of Product Line Engineering and software analysis. Different attempts have been made to ”lift” particular product-level analyses to run on the entire product line. In this paper, we tackle the class of Datalog-based analyses (e.g., pointer and taint analyses), study the theoretical aspects of lifting Datalog inference, and implement a lifted inference algorithm inside the Soufflé Datalog engine. We evaluate our implementation on a set of benchmark product lines. We show significant savings in processing time and fact database size (billions of times faster on one of the benchmarks) compared to brute-force analysis of each product individually.
Nanoscale imaging of the full strain tensor of specific dislocations extracted from a bulk sample

Lattice defects play a key role in determining the properties of crystalline materials. Probing the 3D lattice strains that govern their interactions remains a challenge. Bragg Coherent Diffraction Imaging (BCDI) allows strain to be measured with nano-scale 3D resolution. However, it is currently limited to materials that form micro-crystals. Here we introduce a new technique that allows the manufacture of BCDI samples from bulk materials. Using tungsten as an example, we show that focussed ion beam (FIB) machining can be used to extract, from macroscopic crystals, micron-sized BCDI samples containing specific pre-selected defects. To interpret the experimental data, we develop a new displacement-gradient-based analysis for multi-reflection BCDI. This allows accurate recovery of the full lattice strain tensor from samples containing multiple dislocations. These new capabilities open the door to BCDI as a microscopy tool for studying complex real-world materials.
R-LINS: A Robocentric Lidar-Inertial State Estimator for Robust and Efficient Navigation

We present R-LINS, a lightweight robocentric lidar-inertial state estimator, which estimates robot ego-motion using a 6-axis IMU and a 3D lidar in a tightly-coupled scheme. To achieve robustness and computational efficiency even in challenging environments, an iterated error-state Kalman filter (ESKF) is designed, which recursively corrects the state via repeatedly generating new corresponding feature pairs. Moreover, a novel robocentric formulation is adopted in which we reformulate the state estimator concerning a moving local frame, rather than a fixed global frame as in the standard world-centric lidar-inertial odometry(LIO), in order to prevent filter divergence and lower computational cost. To validate generalizability and long-time practicability, extensive experiments are performed in indoor and outdoor scenarios. The results indicate that R-LINS outperforms lidar-only and loosely-coupled algorithms, and achieve competitive performance as the state-of-the-art LIO with close to an order-of-magnitude improvement in terms of speed.
FusionAccel: A General Re-configurable Deep Learning Inference Accelerator on FPGA for Convolutional Neural Networks

The deep learning accelerator is one of the methods to accelerate deep learning network computations, which is mainly based on convolutional neural network acceleration. To address the fact that concurrent convolutional neural network accelerators are not solely open-source and the exclusiveness of platforms, FusionAccel, a scalable convolutional neural network accelerator hardware architecture with supporting software is proposed. It can adapt to different network structures and can be reconstructed before compilation and reconfigured at runtime. This paper realizes this RTL convolutional neural network accelerator design and functional verifications on a Xilinx Spartan-6 FPGA. The result is identical to that of Caffe-CPU. Since the entire project is based on RTL, it can be migrated to ASIC after replacing some FPGA-specific IPs.
Sim2real transfer learning for 3D pose estimation: motion to the rescue

Simulation is an anonymous, low-bias source of data where annotation can often be done automatically; however, for some tasks, current models trained on synthetic data generalize poorly to real data. The task of 3D human pose estimation is a particularly interesting example of this sim2real problem, because learning-based approaches perform reasonably well given real training data, yet labeled 3D poses are extremely difficult to obtain in the wild, limiting scalability. In this paper, we show that standard neural-network approaches, which perform poorly when trained on synthetic RGB images, can perform well when the data is pre-processed to extract cues about the person's motion, notably as optical flow and the motion of 2D keypoints. Therefore, our results suggest that motion can be a simple way to bridge a sim2real gap when video is available. We evaluate on the 3D Poses in the Wild dataset, the most challenging modern standard of 3D pose estimation, where we show full 3D mesh recovery that is on par with state-of-the-art methods trained on real 3D sequences, despite training only on synthetic humans from the SURREAL dataset.
Safe Approximate Dynamic Programming via Kernelized Lipschitz Estimation

We develop a method for obtaining safe initial policies for reinforcement learning via approximate dynamic programming (ADP) techniques for uncertain systems evolving with discrete-time dynamics. We employ the kernelized Lipschitz estimation to learn multiplier matrices that are used in semidefinite programming frameworks for computing admissible initial control policies with provably high probability. Such admissible controllers enable safe initialization and constraint enforcement while providing exponential stability of the equilibrium of the closed-loop system.
Simulated annealing with hit-and-run for convex optimization: rigorous complexity analysis and practical perspectives for copositive programming

We give a rigorous complexity analysis of the simulated annealing algorithm by Kalai and Vempala [Math of OR 31.2 (2006): 253-266] using the type of temperature update suggested by Abernethy and Hazan [arXiv 1507.02528v2, 2015]. The algorithm only assumes a membership oracle of the feasible set, and we prove that it returns a solution in polynomial time which is near-optimal with high probability. Moreover, we propose a number of modifications to improve the practical performance of this method, and present some numerical results for test problems from copositive programming.