Miracle-dz/UniFAR-FTUs huggingface dataset:
Diversity of short linear interaction motifs in SARS-CoV-2 nucleocapsid protein

ABSTRACT Molecular mimicry of short linear interaction motifs has emerged as a key mechanism for viral proteins binding host domains and hijacking host cell processes. Here, we examine the role of RNA virus sequence diversity in the dynamics of the virus-host interface by analyzing the uniquely vast sequence record of viable SARS-CoV-2 species with focus on the multi-functional nucleocapsid protein. We observe the abundant presentation of motifs encoding several essential host protein interactions, alongside a majority of possibly non-functional and randomly occurring motif sequences absent in subsets of viable virus species. A large number of motifs emerge ex nihilo through transient mutations relative to the ancestral consensus sequence. The observed mutational landscape implies an accessible motif space that spans at least 25% of known eukaryotic motifs. This reveals motif mimicry as a highly dynamic process with the capacity to broadly explore host motifs, allowing the virus to rapidly evolve the virus-host interface. IMPORTANCE Short linear motifs (SLiMs) are 3–10 amino acid long binding motifs in intrinsically disordered protein regions (IDRs) that serve as ubiquitous protein-protein interaction modules in eukaryotic cells. Through molecular mimicry, viruses hijack these sequence motifs to control host cellular processes. It is thought that the small size of SLiMs and the high mutation frequencies of viral IDRs allow rapid host adaptation. However, a salient characteristic of RNA viruses, due to high replication errors, is their obligate existence as mutant swarms. Taking advantage of the uniquely large genomic database of SARS-CoV-2, here, we analyze the role of sequence diversity in the presentation of SLiMs, focusing on the highly abundant, multi-functional nucleocapsid protein. We find that motif mimicry is a highly dynamic process that produces an abundance of motifs transiently present in subsets of mutant species. This diversity allows the virus to efficiently explore eukaryotic motifs and evolve the host-virus interface. Short linear motifs (SLiMs) are 3–10 amino acid long binding motifs in intrinsically disordered protein regions (IDRs) that serve as ubiquitous protein-protein interaction modules in eukaryotic cells. Through molecular mimicry, viruses hijack these sequence motifs to control host cellular processes. It is thought that the small size of SLiMs and the high mutation frequencies of viral IDRs allow rapid host adaptation. However, a salient characteristic of RNA viruses, due to high replication errors, is their obligate existence as mutant swarms. Taking advantage of the uniquely large genomic database of SARS-CoV-2, here, we analyze the role of sequence diversity in the presentation of SLiMs, focusing on the highly abundant, multi-functional nucleocapsid protein. We find that motif mimicry is a highly dynamic process that produces an abundance of motifs transiently present in subsets of mutant species. This diversity allows the virus to efficiently explore eukaryotic motifs and evolve the host-virus interface.
EZH2 Inhibition Sensitizes IDH1R132H-Mutant Gliomas to Histone Deacetylase Inhibitor

Isocitrate Dehydrogenase-1 (IDH1) is commonly mutated in lower grade diffuse gliomas. The IDH1R132H mutation is an important diagnostic tool for tumor diagnosis and prognosis, however its role in glioma development, and its impact on response to therapy, is not fully understood. We developed a murine model of proneural IDH1R132H mutated glioma that shows elevated production of 2-Hydroxyglutarate (2-HG) and increased tri-methylation of lysine residue K27 on histone H3 (H3K27me3) compared to IDH1 wild-type tumors. We found that using Tazemetostat to inhibit the methyltransferase for H3K27, Enhancer of Zeste 2 (EZH2), reduced H3K27me3 levels and increased acetylation on H3K27. We also found that, although the histone deacetylase inhibitor (HDACi) Panobinostat was less cytotoxic in IDH1R132H mutated cells (either isolated from murine glioma or oligodendrocyte progenitor cells infected in vitro with a retrovirus expressing IDH1R132H) compared to IDH1-wildtype cells, combination treatment with Tazemetostat is synergistic in both mutant and wildtype models. These findings indicate a novel therapeutic strategy for IDH1-mutated gliomas that targets the specific epigenetic alteration in these tumors. Main Points Murine gliomas initiated by the IDH1R132H mutation (in the presence of additional genetic alterations, such as p53 loss and PDGF overexpression) recapitulate the metabolic and transcriptional features of the proneural subtype, as they are characterized by increased 2HG levels, and are enriched for OPC lineage-restricted genes compared to IDH-wildtype murine gliomas. In murine IDH1-R132H glioma cells, EZH2 inhibition is not cytotoxic as a monotherapy but reduces levels of H3K27me3 and increases levels of H3K27ac. IDH1R132H cells are relatively resistant to Panobinostat cytotoxicity compared to IDH-wildtype cells, but combining treatment with EZH2 inhibition synergistically kills glioma cells and increases H3K27ac.
Increasing biases can be more efficient than increasing weights

We introduce a novel computational unit for neural networks that features multiple biases, challenging the traditional perceptron structure. This unit emphasizes the importance of preserving uncorrupted information as it is passed from one unit to the next, applying activation functions later in the process with specialized biases for each unit. Through both empirical and theoretical analyses, we show that by focusing on increasing biases rather than weights, there is potential for significant enhancement in a neural network model's performance. This approach offers an alternative perspective on optimizing information flow within neural networks. See source code at https://github.com/CuriosAI/dac-dev.
Artiﬁcial Intelligence and Machine Learning in 5G Network Security: Opportunities, advantages, and future research trends

—Recent technological and architectural advancements in 5G networks have proven their worth as the deployment has started over the world. Key performance elevating factor from access to core network are softwareization, cloudiﬁcation and virtualization of key enabling network functions. Along with the rapid evolution comes the risks, threats and vulnerabilities in the system for those who plan to exploit it. Therefore, ensuring fool proof end-to-end (E2E) security becomes a vital concern. Artiﬁcial intelligence (AI) and machine learning (ML) can play vital role in design, modelling and automation of efﬁcient security protocols against diverse and wide range of threats. AI and ML has already proven their effectiveness in different ﬁelds for classiﬁcation, identiﬁcation and automation with higher accuracy. As 5G networks’ primary selling point has been higher data rates and speed, it will be difﬁcult to tackle wide range of threats from different points using typical/traditional protective measures. Therefore, AI and ML can play central role in protecting highly data-driven softwareized and virtualized network components. This article presents AI and ML driven applications for 5G network security, their implications and possible research directions. Also, an overview of key data collection points in 5G architecture for threat classiﬁcation and anomaly detection are discussed.
Sensitization of meningeal afferents to locomotion-related meningeal deformations in a migraine model

Migraine headache is hypothesized to involve the activation and sensitization of trigeminal sensory afferents that innervate the cranial meninges. To better understand migraine pathophysiology and improve clinical translation, we used two-photon calcium imaging via a closed cranial window in awake mice to investigate changes in the responses of meningeal afferent fibers using a preclinical model of migraine involving cortical spreading depolarization (CSD). A single CSD episode caused a seconds-long wave of calcium activation that propagated across afferents and along the length of individual afferents. Surprisingly, unlike previous studies in anesthetized animals with exposed meninges, only a very small afferent population was persistently activated in our awake mouse preparation, questioning the relevance of this neuronal response to the onset of migraine pain. In contrast, we identified a larger subset of meningeal afferents that developed augmented responses to acute three-dimensional meningeal deformations that occur in response to locomotion bouts. We observed increased responsiveness in a subset of afferents that were already somewhat sensitive to meningeal deformation before CSD. Furthermore, another subset of previously insensitive afferents also became sensitive to meningeal deformation following CSD. Our data provides new insights into the mechanisms underlying migraine, including the emergence of enhanced meningeal afferent responses to movement-related meningeal deformations as a potential neural substrate underlying the worsening of migraine headache during physical activity.
Strengthening the AI Operating Environment: Distributed Competence as a Means to Risk Mitigation

In the rapidly evolving discourse on artificial intelligence (AI), the familiar refrain of “maximizing potential while mitigating risks” has become somewhat of a ubiquitous mantra, emphasizing the need for an effective risk mitigation framework. This paper briefly examines the current state of AI-enabled applications and discusses the various risk containment strategies being implemented. Initial efforts focused on establishing high-level principles for responsible AI use. More recent strategies have sought to operationalize these principles through normative instruments, such as industry best practices and legal statutes, that govern AI applications and their creators. While valuable, such a top-down approach is not sufficiently effective; a complementary, bottom-up approach focused on strengthening the environment in which AI is deployed is also necessary. The paperanalyzestwospecificinitiativesaimedatenhancingthehumancomponentofAIdeployment(creating abetter-informed public through AI benchmarks, creating a better-equipped public with resources for local validation) and offers insights on how this environment-focused track can contribute to risk containment. Furthermore, we suggest additional steps for leveraging this approach in tandem with top-down strategies to cultivate a more robust risk mitigation framework.
The ciliary MBO2 complex targets assembly of inner arm dynein b and reveals additional doublet microtubule asymmetries

Ciliary motility requires the spatiotemporal coordination of multiple dynein motors by regulatory complexes located within the 96 nm axoneme repeat. Many organisms can alter ciliary waveforms in response to internal or external stimuli, but little is known about the specific polypeptides and structural organization of complexes that regulate waveforms. In Chlamydomonas, several mutations convert the ciliary waveform from an asymmetric, ciliary-type stroke to a symmetric, flagellar-type stroke. Some of these mutations alter subunits located at the inner junction of the doublet microtubule and others alter interactions between the dynein arms and the radial spokes. These and other axonemal substructures are interconnected by a network of poorly characterized proteins. Here we re-analyze several motility mutants (mbo, fap57, pf12/pacrg) to identify new components in this network. The mbo (move backwards only) mutants are unable to swim forwards with an asymmetric waveform. Proteomics identified more than 19 polypeptides that are missing or reduced in mbo mutants, including one inner dynein arm, IDA b. Several MBO2-associated proteins are also altered in fap57 and pf12/parcg mutants, suggesting overlapping networks. Two subunits are highly conserved, coiled coil proteins found in other species with motile cilia and others contain potential signaling domains. Cryo-electron tomography and epitope tagging revealed that the MBO2 complex is found on specific doublet microtubules and forms a large, L-shaped structure that contacts the base of IDA b that interconnects multiple dynein regulatory complexes and varies in a doublet microtubule specific fashion.
Building Online Public Consultation Knowledge Graphs

Online consultation platforms have improved the possibilities for citizens to have an input on public decision making. However, and especially at large scale, identification of the topics discussed and entities evoked has been identified as difficult for both citizens and platform administrators. In this paper, we leverage topic modeling, Named Entity Recognition and Linking and Semantic Textual Similarity to build a knowledge graph representing the different contributions to the République Numérique online citizen consultation in French language. The generated graph links the different proposals to topics identified in the consultation and to relevant DBpedia resources. The model proposed for representation of citizen consultations as knowledge graphs simplifies the retrieval of proposals focused on specific topics or mentioning a given entity. It also allows us to improve contextualization of important words in proposals by linking them to short definitions extracted from Wikipedia.
Case-based Explanations for Black-Box Time Series and Image Models with Applications in Smart Agriculture

Black-box models are frequently deployed for high stakes prediction tasks in a variety of domains (e.g., disease diagnosis and agricultural prediction). The predictions of these opaque systems are often plagued by a lack of transparency, motivating novel research in eXplainable AI (XAI) aiming to understand why a certain prediction was made. One increasingly promising form of explanation is counterfactual explanation where the aim is to elucidate how a prediction could change, given some change in the input space. While the majority of existing work has focused on producing counterfactual explanations for tabular data, significantly less focus has been placed on generating and evaluating counterfactual explanations for time series and image data. Explaining predictions for these data types, arguably, presents a whole new set of issues for XAI, due to the complex and multi-dimensional nature of the data. In this research, we examine how leveraging case-based reasoning (CBR) techniques such as NearestUnlike-Neighbors (NUNs) can aid the generation and evaluation of explanations in these domains. We also demonstrate the inadequacies of many traditional techniques that are used to evaluate explanations and highlight the promise of CBR and user studies in the evaluation of explanations.
On Transforming Reinforcement Learning by Transformer: The Development Trajectory

Transformer, originally devised for natural language processing, has also attested significant success in computer vision. Thanks to its super expressive power, researchers are investigating ways to deploy transformers to reinforcement learning (RL) and the transformer-based models have manifested their potential in representative RL benchmarks. In this paper, we collect and dissect recent advances on transforming RL by transformer (transformer-based RL or TRL), in order to explore its development trajectory and future trend. We group existing developments in two categories: architecture enhancement and trajectory optimization, and examine the main applications of TRL in robotic manipulation, text-based games, navigation and autonomous driving. For architecture enhancement, these methods consider how to apply the powerful transformer structure to RL problems under the traditional RL framework, which model agents and environments much more precisely than deep RL methods, but they are still limited by the inherent defects of traditional RL algorithms, such as bootstrapping and"deadly triad". For trajectory optimization, these methods treat RL problems as sequence modeling and train a joint state-action model over entire trajectories under the behavior cloning framework, which are able to extract policies from static datasets and fully use the long-sequence modeling capability of the transformer. Given these advancements, extensions and challenges in TRL are reviewed and proposals about future direction are discussed. We hope that this survey can provide a detailed introduction to TRL and motivate future research in this rapidly developing field.
Electron Target Cooling Analyses of the KIPT ADS Using MCNP and Ansys Fluent

Abstract This study presents multiphysics analyses of the electron target cooling system of the accelerator-driven system (ADS) of the Kharkiv Institute of Physics and Technology (KIPT) using MCNP and Fluent computer programs. MCNP has been used to transport electrons, gammas, and neutrons, and to calculate the energy deposition in the target materials. The MCNP mesh-tally data have been imported into Fluent by a C subroutine that has been compiled and linked to Fluent as a user-defined function. The KIPT ADS is located in Ukraine and was in operation until February 2022. The Fluent model is based on the computer-aided design files from the manufacturing process of the target assembly. The Fluent results for the reference case match very well the literature results obtained by STAR-CCM+ during the design phase. Other cases that differ from the reference one have been analyzed; in these cases, it is assumed a malfunction of the electron accelerator or of the water cooling system. The target cooling system operates normally for all the analyzed cases except when the inlet water mass flow rate is decreased. The transient analysis showed that the target cooling system can operate for 180 s with full power when the inlet water mass flow rate is decreased down by 75%.
Improving Natural Product Automatic Extraction with Named Entity Recognition

Knowledge graphs (KGs) play a vital role in providing structured data for various applications, but their creation is time-consuming and prone to errors. To address these challenges, automatic knowledge extraction methods using machine learning (ML) have gained attention. ML algorithms have shown promise in capturing subtle nuances in language data, offering comprehensive and robust solutions. In the field of biochemistry, knowledge extraction is crucial for advancing scientific research, product development, and policy-making. The First International Biochemical Knowledge Extraction Challenge focuses on extracting biochemical knowledge from scientific articles. This paper presents an updated approach that incorporates named entity recognition (NER) using scispaCy models to improve the accuracy and relevance of extracted entities. The evaluation of the approach utilizes the NatUKE benchmark and demonstrates improved performance in extracting bioactivity and isolation type. However, challenges remain in identifying compound names and species. Future research may explore hybrid approaches combining different techniques to address these specific challenges.
Training Restricted Boltzmann Machines on Word Observations

The restricted Bolzmann machine (RBM) has been used in many different modeling contexts, typically characterized by varying types of observation vectors, e.g., binary values, integers and real numbers. One type of observation that has always proven problematic is the high-dimensional multinomial, which would be useful for deep learning of natural language representations with large vocabularies. Conventional learning methods such as contrastive divergence have not succeeded in this setting, as it requires softmax units that can generate hundreds of thousands of discrete outcomes, matching the size of the vocabulary being modeled. Even generating data from such a distribution requires linear time, making it unwieldy to produce negative examples for learning. In this work, we address this issue by using a more general class of Markov chain Monte Carlo moves on these visible units. With the use of appropriately-selected transition kernels, it is possible to sample (correlated) negative observations in constant time, making it viable to learn with contrastive divergence. We confirm the success of this approach by showing that RBMs can be used to learn meaningful word representations on hundreds of millions of n-gram word windows.
Frequency Measurement of Tellurium Lines Near Calcium

We report measurement of frequencies of tellurium lines with respect to atomic calcium employing a 423 nm commercial violet laser diode placed in an extended cavity. Doppler-free saturated absorption spectroscopy of molecular tellurium is performed on three lines, which are lower and higher in energy to the calcium 4s2 1S0–4s4p 1P1 transition. The measured frequency difference of 717(13) MHz between the strong absorption line, #1508 in the tellurium atlas and the calcium transition is in agreement within 10% of the values available from literature. Wavenumbers of two new tellurium lines are derived from the measurement. In addition, spectroscopy and laser cooling and trapping of francium (n = 7) on the ns 2S1/2–(n + 1)p 2P3/2 transition and spectroscopy of Te2 lines near francium could be performed employing the 423 nm diode laser.
Structural Surface Mapping for Shape Analysis

OF THE DISSERTATION STRUCTURAL SURFACE MAPPING FOR SHAPE ANALYSIS by Muhammad Razib Florida International University, 2017 Miami, Florida Professor Wei Zeng, Major Professor Natural surfaces are usually associated with feature graphs, such as the cortical surface with anatomical atlas structure. Such a feature graph subdivides the whole surface into meaningful sub-regions. Existing brain mapping and registration methods did not integrate anatomical atlas structures. As a result, with existing brain mappings, it is difficult to visualize and compare the atlas structures. And also existing brain registration methods can not guarantee the best possible alignment of the cortical regions which can help computing more accurate shape similarity metrics for neurodegenerative disease analysis, e.g., Alzheimer’s disease (AD) classification. Also, not much attention has been paid to tackle surface parameterization and registration with graph constraints in a rigorous way which have many applications in graphics, e.g., surface and image morphing. This dissertation explores structural mappings for shape analysis of surfaces using the feature graphs as constraints. (1) First, we propose structural brain mapping which maps the brain cortical surface onto a planar convex domain using Tutte embedding of a novel atlas graph and harmonic map with atlas graph constraints to facilitate visualization and comparison between the atlas structures. (2) Next, we propose a novel brain registration technique based on an intrinsic atlas-constrained harmonic map which provides the best possible alignment of the cortical regions. (3) After that, the proposed brain registration technique has been applied to compute
Machine Learning for Multimodal Learning Analytics and Feedback

Multimodal measurement of human learning enables a data-driven approach to learning analysis and feedback generation. This position paper discusses the possibility of feedback based on multimodal learning analytics from how machine learning methods can be applied. In particular, we first discuss how (1) behavioral measurements, such as learner browsing logs and eye tracking, and (2) content analysis of learning materials can lead to (3) prediction and modeling of learners’ states (e.g., performance) and (4) feedback generation, such as information presentation and content optimization, through some research examples. We then show future research directions of machine learning-based learners’ state modeling for feedback generation.