Miracle-dz/UniFAR-FTUs huggingface dataset:
Long Non Coding RNA in Triple Negative Breast Cancer: A Promising Biomarker in Tumorigenesis

Globally, Triple-negative breast cancer (TNBC) is an unsurpassed variant of breast cancer (BC) with a very high fatality rate, and disease burden. Nevertheless, the deficit of diagnostic markers and focused treatment are major hurdles for potent therapeutics. They are also the reason for bad outcomes and causes of a worse prognosis and a high rate of flare up in patients with TNBC diagnosis. Long non-coding RNAs (lncRNA) are a new class of molecules that have recently gained interest in healthcare management due to their potential as biomarkers for human diseases especially cancers. The growing interest in lncRNA in clinical practice has created an unmet need for developing assays to test lncRNA quickly and accurately for early diagnostics. These lncRNA modulate multiple stages of tumor development, including growth, proliferation, invasion, angiogenesis, and metastases, by controlling several genes and changing metabolic networks. Highly invasive phenotype and chemo resistance are prominent characteristics of TNBC subtypes that require accurate diagnostic and prognostic instruments involving lncRNA. This review focusses on the evolving purpose and coalition of lncRNAs in TNBC and accentuates their capable effects in diagnosis and treatment of cancer. Moreover, the extensive literature analysis of our review creates an opportunity in the translational application concerning the TNBC lncRNAs described until now. The depiction of lncRNAs enrolled in TNBC is comprehensive, and sufficient substantiation studies are the need of the hour to authenticate the current outcomes and create imminent upcoming of elemental research setting into clinical practice.
Highlights in TMPRSS2 inhibition mechanism with guanidine derivatives approved drugs for COVID-19 treatment

Abstract Transmembrane protease serine 2 (TMPRSS2) has been identified as a critical key for the entry of coronaviruses into human cells by cleaving and activating the spike protein of SARS-CoV-2. To block the TMPRSS2 function, 18 approved drugs, containing the guanidine group were tested against TMPRSS2’s ectodomain (7MEQ). Among these drugs, Famotidine, Argatroban, Guanadrel and Guanethidine strongly binds with TMPRSS2 S1 pocket with estimated Fullfitness energies of −1847.12, −1630.87, −1605.81 and −1600.52 kcal/mol, respectively. A significant number of non-covalent interactions such as hydrogen bonding, hydrophobic and electrostatic interactions were detected in protein-ligand complexes. In addition, the ADMET analysis revealed a perfect concurrence with the aptitude of these drugs to be developed as an anti-SARS-CoV-2 therapeutics. Further, MD simulation and binding free energy calculations were performed to evaluate the dynamic behavior and stability of protein-ligand complexes. The results obtained herein highlight the enhanced stability and good binding affinities of the Argatroban and Famotidine towards the target protein, hence might act as new scaffolds for TMPRSS2 inhibition. Communicated by Ramaswamy H. Sarma
Mitochondrial protein import machinery conveys stress signals to the cytosol and beyond

Mitochondria hold diverse and pivotal roles in fundamental processes that govern cell survival, differentiation, and death, in addition to organismal growth, maintenance, and aging. The mitochondrial protein import system is a major contributor to mitochondrial biogenesis and lies at the crossroads between mitochondrial and cellular homeostasis. Recent findings highlight the mitochondrial protein import system as a signaling hub, receiving inputs from other cellular compartments and adjusting its function accordingly. Impairment of protein import, in a physiological, or disease context, elicits adaptive responses inside and outside mitochondria. In this review, we discuss recent developments, relevant to the mechanisms of mitochondrial protein import regulation, with a particular focus on quality control, proteostatic and metabolic cellular responses, triggered upon impairment of mitochondrial protein import.
Neutrophil-to-Lymphocyte Ratio (NLR) as a Poor Predictive Biomarker for Pathological Response to Neoadjuvant Chemoradiation in Locally Advanced Rectal Cancer: A Prospective Study

Background: The literature is inconsistent for the role of neutrophil-to-lymphocyte ratio (NLR) obtained before neoadjuvant therapy (pre-NLR) in predicting pathological response to neoadjuvant chemoradiation (neoCRT) in patients with locally advanced rectal cancer (LARC). In the present cohort study, we explored the predictive role of pre-NLR in this setting. Methods: We prospectively included patients with LARC who were candidates for neoCRT at the Shohada-e-Hafte Tir Hospital (Tehran, Iran) between Mar 2018 and Feb 2020. The pre-NLR was obtained through a peripheral blood smear before CRT. We used the AJCC system for evaluating tumor regression grade (TRG). The TRGs were categorized into: response-group 1 (TRG 0-1 vs. 2-3), response-group 2 (TRG 0 vs. 1-3), and response-group 3 (TRG 0-2 vs. 3). We applied receiver operating characteristic (ROC) analysis to assess the predictive value of pre-NLR. Results: Of the 86 screened patients with rectal cancer, 30 patients who fulfilled the inclusion criteria were included in the study. In total, 63.3% were responsive, and 23.3% had complete pathologic response. Pre-NLR could not predict the pathologic response in response-group 1 (area under the ROC curve [AUC]: 0.45, 95%CI 0.23-0.66) and response-group 2 (AUC: 0.36, 95%CI 0.13-0.59). Nevertheless, it had a poor predictive value in response-group 3 (AUC: 0.55, CI%95 0.33-0.75) with an optimal NLR cutoff value of 2.94. Conclusions: Pre-NLR could not predict the pathological response to neoCRT in our cohort of patients with LARC.
In silico approach to identify novel allosteric intracellular antagonist for blocking the interleukin-8/CXCR2 receptor signaling pathway

Abstract The role of interleukin-8 (IL-8) and its receptor CXCR2 in inflammatory responses and tumor development and progression has been well documented. Our study aims to discover novel compounds as CXCR2 antagonists to block the IL-8 signaling pathway using an in silico drug design. Herein, a structure-based pharmacophore model was developed based on the crystal structure of inactive CXCR2 in a complex with an allosteric inhibitor. This model was validated and refined, followed by virtual screening with the ZINC15 database. Subsequent molecular docking allows for predicting the best pose of a ligand inside a receptor binding site. We found that the 35 top-ranked hits exhibited docking scores from −30.81 to −25.28 kJ/mol and better interaction potential comparing the reference inhibitor. Analysis of ADME and toxicity properties revealed the efficacy and safety of the selected seven compounds. To validate the stability of the protein-ligand complex structure MD simulations approach has also been performed and confirmed via the critical parameters. The MD results explained that the CXCR2 receptor bound with two best-proposed molecules, including ZINC77105530 and ZINC93176465, was quite stable states as observed from low RMSD, RMSF, Rg, SASA values, and high occupancy of the interaction types. Finally, our data identified that these compounds play as potential inhibitors of IL-8 signaling pathways with the MM/GBSA binding free energies of −41.77 ± 6.45 kcal/mol and −38.84 ± 6.16 kcal/mol, respectively. Communicated by Ramaswamy H. Sarma
Noncontact Haptic Rendering of Static Contact with Convex Surface Using Circular Movement of Ultrasound Focus on a Finger Pad

A noncontact tactile stimulus can be presented by focusing airborne ultrasound on the human skin. Focused ultrasound has recently been reported to produce not only vibration but also static pressure sensation on the palm by modulating the sound pressure distribution at a low frequency. This finding expands the potential for tactile rendering in ultrasound haptics as static pressure sensation is perceived with a high spatial resolution. In this study, we verified that focused ultrasound can render a static pressure sensation associated with contact with a small convex surface on a finger pad. This static contact rendering enables noncontact tactile reproduction of a fine uneven surface using ultrasound. In the experiments, four ultrasound foci were simultaneously and circularly rotated on a finger pad at 5 Hz. When the orbit radius was 3 mm, vibration and focal movements were barely perceptible, and the stimulus was perceived as static pressure. Moreover, under the condition, the pressure sensation rendered a contact with a small convex surface with a radius of 2 mm. The perceived intensity of the static contact sensation was equivalent to a physical contact force of 0.24 N on average, 10.9 times the radiation force physically applied to the skin.
A unifying view on some experimental effects in tapping-mode atomic force microscopy

Several experimental effects that occur in tapping-mode atomic force microscopy are examined: apparent hysteresis effects in force probes and frequency sweeps and distortions in imaging. It is found through examinations of the phase space that they can all be reduced to one common cause: the existence of more than one stable state of the tip vibration for certain parameters. It is shown that the experimental effects can be explained considering only the phase space under the assumption that measurement noise exists. Numerical simulations confirm the theoretical and experimental findings.
Multiscale and multidirectional very long baseline interferometry imaging with CLEAN

Very long baseline interferometry (VLBI) is a radio-astronomical technique in which the correlated signal from various baselines is combined into an image of highest angular resolution. Due to sparsity of the measurements, this imaging procedure constitutes an ill-posed inverse problem. For decades the CLEAN algorithm was the standard choice in VLBI studies, although having some serious disadvantages and pathologies that are challenged by the requirements of modern frontline VLBI applications. We develop a novel multi-scale CLEAN deconvolution method (DoB-CLEAN) based on continuous wavelet transforms that address several pathologies in CLEAN imaging. We benchmark this novel algorithm against CLEAN reconstructions on synthetic data and reanalyze BL Lac observations of RadioAstron with DoB-CLEAN. DoB-CLEAN approaches the image by multi-scalar and multi-directional wavelet dictionaries. Two different dictionaries are used. Firstly, a difference of elliptical spherical Bessel functions dictionary fitted to the uv-coverage of the observation that is used to sparsely represent the features in the dirty image. Secondly, a difference of elliptical Gaussian wavelet dictionary that is well suited to represent relevant image features cleanly. The deconvolution is performed by switching between the dictionaries. DoB-CLEAN achieves super-resolution compared to CLEAN and remedies the spurious regularization properties of CLEAN. In contrast to CLEAN, the representation by basis functions has a physical meaning. Hence, the computed deconvolved image still fits the observed visibilities, opposed to CLEAN. State-of-the-art multi-scalar imaging approaches seem to outperform single-scalar standard approaches in VLBI and are well suited to maximize the extraction of information in ongoing frontline VLBI applications.
Matching Linear Algebra and Tensor Code to Specialized Hardware Accelerators

Dedicated tensor accelerators demonstrate the importance of linear algebra in modern applications. Such accelerators have the potential for impressive performance gains, but require programmers to rewrite code using vendor APIs - a barrier to wider scale adoption. Recent work overcomes this by matching and replacing patterns within code, but such approaches are fragile and fail to cope with the diversity of real-world codes. We develop ATC, a compiler that uses program synthesis to map regions of code to specific APIs. The mapping space that ATC explores is combinatorially large, requiring the development of program classification, dynamic analysis, variable constraint generation and lexical distance matching techniques to make it tractable. We apply ATC to real-world tensor and linear algebra codes and evaluate them against four state-of-the-art approaches. We accelerate between 2.6x and 7x more programs, leading to over an order of magnitude performance improvement.
Inter-View Depth Consistency Testing in Depth Difference Subspace

Multiview depth imagery will play a critical role in free-viewpoint television. This technology requires high quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery at different viewpoints is used to synthesize an arbitrary number of novel views. Usually, depth images at multiple viewpoints are estimated individually by stereo-matching algorithms, and hence, show lack of interview consistency. This inconsistency affects the quality of view synthesis negatively. This paper proposes a method for depth consistency testing in depth difference subspace to enhance the depth representation of a scene across multiple viewpoints. Furthermore, we propose a view synthesis algorithm that uses the obtained consistency information to improve the visual quality of virtual views at arbitrary viewpoints. Our method helps us to find a linear subspace for our depth difference measurements in which we can test the inter-view consistency efficiently. With this, our approach is able to enhance the depth information for real world scenes. In combination with our consistency-adaptive view synthesis, we improve the visual experience of the free-viewpoint user. The experiments show that our approach enhances the objective quality of virtual views by up to 1.4 dB. The advantage for the subjective quality is also demonstrated.
Towards Personalized Review Summarization by Modeling Historical Reviews from Customer and Product Separately

Review summarization is a non-trivial task that aims to summarize the main idea of the product review in the E-commerce website. Different from the document summary which only needs to focus on the main facts described in the document, review summarization should not only summarize the main aspects mentioned in the review but also reflect the personal style of the review author. Although existing review summarization methods have incorporated the historical reviews of both customer and product, they usually simply concatenate and indiscriminately model this two heterogeneous information into a long sequence. Moreover, the rating information can also provide a high-level abstraction of customer preference, it has not been used by the majority of methods. In this paper, we propose the Heterogeneous Historical Review aware Review Summarization Model (HHRRS) which separately models the two types of historical reviews with the rating information by a graph reasoning module with a contrastive loss. We employ a multi-task framework that conducts the review sentiment classification and summarization jointly. Extensive experiments on four benchmark datasets demonstrate the superiority of HHRRS on both tasks.
Outcome-directed Reinforcement Learning by Uncertainty & Temporal Distance-Aware Curriculum Goal Generation

Current reinforcement learning (RL) often suffers when solving a challenging exploration problem where the desired outcomes or high rewards are rarely observed. Even though curriculum RL, a framework that solves complex tasks by proposing a sequence of surrogate tasks, shows reasonable results, most of the previous works still have difficulty in proposing curriculum due to the absence of a mechanism for obtaining calibrated guidance to the desired outcome state without any prior domain knowledge. To alleviate it, we propose an uncertainty&temporal distance-aware curriculum goal generation method for the outcome-directed RL via solving a bipartite matching problem. It could not only provide precisely calibrated guidance of the curriculum to the desired outcome states but also bring much better sample efficiency and geometry-agnostic curriculum goal proposal capability compared to previous curriculum RL methods. We demonstrate that our algorithm significantly outperforms these prior methods in a variety of challenging navigation tasks and robotic manipulation tasks in a quantitative and qualitative way.
Khaos: The Impact of Inter-procedural Code Obfuscation on Binary Diffing Techniques

Software obfuscation techniques can prevent binary diffing techniques from locating vulnerable code by obfuscating the third-party code, to achieve the purpose of protecting embedded device software. With the rapid development of binary diffing techniques, they can achieve more and more accurate function matching and identification by extracting the features within the function. This makes existing software obfuscation techniques, which mainly focus on the intra-procedural code obfuscation, no longer effective. In this paper, we propose a new inter-procedural code obfuscation mechanism Khaos, which moves the code across functions to obfuscate the function by using compilation optimizations. Two obfuscation primitives are proposed to separate and aggregate the function, which are called fission and fusion respectively. A prototype of Khaos is implemented based on the LLVM compiler and evaluated on a large number of real-world programs including SPEC CPU 2006 & 2017, CoreUtils, JavaScript engines, etc. Experimental results show that Khaos outperforms existing code obfuscations and can significantly reduce the accuracy rates of five state-of-the-art binary diffing techniques (less than 19%) with lower runtime overhead (less than 7%).
Solving Constrained Reinforcement Learning through Augmented State and Reward Penalties

Constrained Reinforcement Learning has been employed to enforce safety constraints on policy through the use of expected cost constraints. The key challenge is in handling expected cost accumulated using the policy and not just in a single step. Existing methods have developed innovative ways of converting this cost constraint over entire policy to constraints over local decisions (at each time step). While such approaches have provided good solutions with regards to objective, they can either be overly aggressive or conservative with respect to costs. This is owing to use of estimates for ”future” or ”backward” costs in local cost constraints. To that end, we provide an equivalent unconstrained formulation to constrained RL that has an augmented state space and reward penalties. This intuitive formulation is general and has interesting theoretical properties. More importantly, this provides a new paradigm for solving constrained RL problems effectively. As we show in our experimental results, we are able to outperform leading approaches on multiple benchmark problems from literature.
PhysGraph: Physics-Based Integration Using Graph Neural Networks

Physics-based simulation of mesh based domains remains a challenging task. State-of-the-art techniques can produce realistic results but require expert knowledge. A major bottleneck in many approaches is the step of integrating a potential energy in order to compute velocities or displacements. Recently, learning based method for physics-based simulation have sparked interest with graph based approaches being a promising research direction. One of the challenges for these methods is to generate models that are mesh independent and generalize to different material properties. Moreover, the model should also be able to react to unforeseen external forces like ubiquitous collisions. Our contribution is based on a simple observation: evaluating forces is computationally relatively cheap for traditional simulation methods and can be computed in parallel in contrast to their integration. If we learn how a system reacts to forces in general, irrespective of their origin, we can learn an integrator that can predict state changes due to the total forces with high generalization power. We effectively factor out the physical model behind resulting forces by relying on an opaque force module. We demonstrate that this idea leads to a learnable module that can be trained on basic internal forces of small mesh patches and generalizes to different mesh typologies, resolutions, material parameters and unseen forces like collisions at inference time. Our proposed paradigm is general and can be used to model a variety of physical phenomena. We focus our exposition on the detail enhancement of coarse clothing geometry which has many applications including computer games, virtual reality and virtual try-on.
Bounding Box-based Multi-objective Bayesian Optimization of Risk Measures under Input Uncertainty

In this study, we propose a novel multi-objective Bayesian optimization (MOBO) method to efficiently identify the Pareto front (PF) defined by risk measures for black-box functions under the presence of input uncertainty (IU). Existing BO methods for Pareto optimization in the presence of IU are risk-specific or without theoretical guarantees, whereas our proposed method addresses general risk measures and has theoretical guarantees. The basic idea of the proposed method is to assume a Gaussian process (GP) model for the black-box function and to construct high-probability bounding boxes for the risk measures using the GP model. Furthermore, in order to reduce the uncertainty of non-dominated bounding boxes, we propose a method of selecting the next evaluation point using a maximin distance defined by the maximum value of a quasi distance based on bounding boxes. As theoretical analysis, we prove that the algorithm can return an arbitrary-accurate solution in a finite number of iterations with high probability, for various risk measures such as Bayes risk, worst-case risk, and value-at-risk. We also give a theoretical analysis that takes into account approximation errors because there exist non-negligible approximation errors (e.g., finite approximation of PFs and sampling-based approximation of bounding boxes) in practice. We confirm that the proposed method outperforms compared with existing methods not only in the setting with IU but also in the setting of ordinary MOBO through numerical experiments.
A Strongly Polynomial-Time Algorithm for Weighted General Factors with Three Feasible Degrees

General factors are a generalization of matchings. Given a graph $G$ with a set $\pi(v)$ of feasible degrees, called a degree constraint, for each vertex $v$ of $G$, the general factor problem is to find a (spanning) subgraph $F$ of $G$ such that $\text{deg}_F(x) \in \pi(v)$ for every $v$ of $G$. When all degree constraints are symmetric $\Delta$-matroids, the problem is solvable in polynomial time. The weighted general factor problem is to find a general factor of the maximum total weight in an edge-weighted graph. Strongly polynomial-time algorithms are only known for weighted general factor problems that are reducible to the weighted matching problem by gadget constructions. In this paper, we present the first strongly polynomial-time algorithm for a type of weighted general factor problems with real-valued edge weights that is provably not reducible to the weighted matching problem by gadget constructions.