Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic expression of tRNA‐derived small RNAs define cellular states

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) have recently emerged as important regulators of protein translation and shown to have diverse biological functions. However, the underlying cellular and molecular mechanisms of tsRNA function in the context of dynamic cell-state transitions remain unclear. Here we report the identification of a set of tsRNAs upregulated in differentiating mouse embryonic stem cells (mESCs). Mechanistic analyses revealed primary functions of tsRNAs in regulating polysome assembly and translation. Notably, interactome studies with differentially-enriched tsRNAs revealed a switch in associations with ‘effector’ RNPs and ‘target’ mRNAs in different cell-states. We also demonstrate that a specific pool of tsRNAs can interact with Igf2bp1, an RNA-binding protein, to influence the expression of the pluripotency-promoting factor-c-Myc, thereby providing evidence for tsRNAs in modulating stem cell-states in mESCs. Finally, tsRNA expression analyses in distinct, heterologous cell and tissue models of stem/transformed versus differentiated/normal states reveal that tsRNA-mediated regulation of protein translation may represent a global biological phenomenon associated with cell-state transitions. One Sentence Summary Identification and functional characterization of tRNA-derived small RNAs (tsRNAs) in cell state switches.
Synthesis of long-tail nonionic surfactants and their investigation for vesicle formation, drug entrapment, and biocompatibility

Abstract Nonionic surfactants have an extraordinary fascination for the researchers in the field of drug delivery for enhancing drug bioavailability and therapeutic efficacy. Here, we are reporting the synthesis, characterization, drug entrapment efficiency (EE), critical micellar concentration, and biocompatibility evaluation of sulphanilamide based new nonionic surfactants. The surfactants were synthesized in single step reactions and characterized through 1H NMR, FT-IR, and mass spectrometric analysis. The surfactants abilities for niosomal vesicles formation were investigated utilizing Ciprofloxacin as a model drug. The drug loaded niosomal suspension of the synthesized surfactants was screened for shape; size, polydispersity index, and drug EE utilizing AFM, Zetasizer, and UV, respectively. The compatibility of the drug in drug loaded vesicles with excipients was assessed utilizing FT-IR spectroscopy. The biocompatibility of the synthesized surfactants was assessed through blood haemolysis and cell cytotoxicity assays. Results of this study showed that the synthesized surfactants were quite haemocompatible and nontoxic in nature and were able to form spherical vesicles. The size and drug EE of the vesicles were dependant on the length of surfactant aliphatic chain. Surfactant with long aliphatic chain was more efficient in entrapping the drug and could be used as a potential vesicular drug delivery vehicle for improving the lipophilic drug’s bioavailability.
19F-NMR in target-based drug discovery.

Solution NMR spectroscopy plays important roles in understanding protein structures, dynamics and protein-protein/ligand interactions. In a target-based drug discovery project, NMR can serve an important function in the steps of hit identification and lead optimization. Fluorine is a valuable probe for evaluating protein conformational changes and protein-ligand interactions. Accumulated studies demonstrate that 19F-NMR will be playing important roles in fragment-based drug discovery (FBDD) and probing protein-ligand interactions. This review summarizes the application of 19F-NMR in understanding protein-ligand interactions and drug discovery. Several examples are included to show the roles of 19F-NMR in confirming identified hits/leads in the drug discovery process. In addition to identifying hits from fluorine-containing compound libraries, 19F-NMR will play an important role in drug discovery by providing a fast and robust way in novel hit identification. This technique can be used for ranking compounds with different binding affinities and particularly useful for screening competitive compounds when a reference ligand is available.
Culturally Specific Interventions to Support Adolescent Immigrant and Refugee Mental Health

This exploratory qualitative study examines various culturally specific interventions practitioners use to support the mental health needs of adolescent immigrants and refugees. Studies have noted that resilience, family involvement, peer support, art-based interventions, school-based interventions, and accessible community resources are all protective factors that promote positive mental health outcomes for adolescents adjusting to a new host country. The researcher conducted eight semi-structured interviews with three licensed clinical social workers (LICSW), one licensed graduate social worker (LGSW), one psychotherapist with a doctorate in psychology, a school counselor, and two clinical counselors. All of the participants of the study serve the refugee and immigrant adolescent population directly. The author used an analytic induction method, transcribing and coding the interviews with the research Chair for reliability checks. Seven themes emerged in the literature to analyze for this study: 1) Protective Factors 2) Parent/Family Involvement in the Adolescent’s Therapeutic Process 3) Expressive Arts Interventions 4) Multi-Disciplinary Teams 5) Group Work 6) Cultural Brokers 7) Culturally Responsive Clinician. There were four additional themes that emerged from the data collection: 1) Not enough providers 2) Parents misunderstanding of the mental health provider’s role 3) Physical Discipline 4) Mindfulness Interventions. The study found culturally responsive clinicians are using interventions that acknowledge the protective factors and build off of these factors in their treatment of adolescent immigrants and refugees. Clinicians who are immigrants themselves, bilingual, or bicultural were found to be more culturally competent. However, the systemic barriers that immigrant and refugee families face, the cultural stigma, language barriers and parents’ lack of education make it challenging for immigrant and non-immigrant clinicians to implement culturally relevant interventions such as involving parents in the treatment. The INTERVENTIONS FOR ADOLESCENT IMMIGRANTS AND REFUGEES 4 study found the use of multi-disciplinary teams and group work interventions are rare, despite clinicians’ awareness of their efficacy and desire to implement them in practice. Additional practice-based and evidence-based, empirical research pertaining to the efficacy and effectiveness of culturally appropriate, applied treatments specifically for the adolescent immigrants and refugees is greatly needed.
Finite-sample Bounds for Marginal MAP

Marginal MAP is a key task in Bayesian inference and decision-making, and known to be very challenging in general. In this paper, we present an algorithm that blends heuristic search and importance sampling to provide any-time ﬁnite-sample bounds for marginal MAP along with predicted MAP solutions. We convert bounding marginal MAP to a surrogate task of bounding a series of summation problems of an augmented graphical model, and then adapt dynamic importance sampling [Lou et al. , 2017b], a recent advance in bounding the partition function, to provide ﬁnite-sample bounds for the surrogate task. Those bounds are guaranteed to be tight given enough time, and the values of the predicted MAP solutions will converge to the optimum. Our algorithm runs in an anytime/anyspace manner, which gives ﬂexible trade-offs between memory, time, and solution quality. We demonstrate the effectiveness of our approach empirically on multiple challenging benchmarks in comparison with some state-of-the-art search algorithms.
Multi-graph Affinity Embeddings for Multilingual Knowledge Graphs

Multilingual knowledge graph embeddings provide important latent semantic representations for knowledge-driven cross-lingual NLP tasks. Learning such embeddings is currently based on the training of a weakly supervised alignment model in joint with monolingual knowledge models. However, existing techniques for alignment model suffer signiﬁcantly from the multilingual inconsistency of knowledge graphs. In this paper, we propose an improved model by learning a generalized afﬁne-map-based alignment model. We ﬁnd that the proposed approach effectively addresses the limitations of existing approaches, especially in handling the incoherence of embedding spaces on different languages. Experimental results show that the proposed approach offers better performance for both entity and triple-wise knowledge alignment tasks.
Harnessing GPU Tensor Cores for Fast FP16 Arithmetic to Speed up Mixed-Precision Iterative Refinement Solvers

Low-precision floating-point arithmetic is a powerful tool for accelerating scientific computing applications, especially those in artificial intelligence. Here, we present an investigation showing that other high-performance computing (HPC) applications can also harness this power. Specifically, we use the general HPC problem, Ax b, where A is a large dense matrix, and a double precision (FP64) solution is needed for accuracy. Our approach is based on mixed-precision (FP16-FP64) iterative refinement, and we generalize and extend prior advances into a framework, for which we develop architecture-specific algorithms and highly tuned implementations. These new methods show how using half-precision Tensor Cores (FP16-TC) for the arithmetic can provide up to 4× speedup. This is due to the performance boost that the FP16-TC provide as well as to the improved accuracy over the classical FP16 arithmetic that is obtained because the GEMM accumulation occurs in FP32 arithmetic.
The Yeast PCNA Unloader Elg1 RFC-Like Complex Plays a Role in Eliciting the DNA Damage Checkpoint

The Elg1protein forms an RFC-like complex in charge of unloading PCNA from chromatin during DNA replication and repair. Mutations in the ELG1 gene caused genomic instability in all organisms tested and cancer in mammals. Here we show that Elg1 plays a role in the induction of the DNA damage checkpoint, a cellular response to DNA damage. We show that this defect is due to a defect in the signal amplification process during induction. Thus, cells coordinate the cell's response and the PCNA unloading through the activity of Elg1. ABSTRACT The PCNA (proliferating cell nuclear antigen) ring plays central roles during DNA replication and repair. The yeast Elg1 RFC-like complex (RLC) is the principal unloader of chromatin-bound PCNA and thus plays a central role in maintaining genome stability. Here we identify a role for Elg1 in the unloading of PCNA during DNA damage. Using DNA damage checkpoint (DC)-inducible and replication checkpoint (RC)-inducible strains, we show that Elg1 is essential for eliciting the signal in the DC branch. In the absence of Elg1 activity, the Rad9 (53BP1) and Dpb11 (TopBP1) adaptor proteins are recruited but fail to be phosphorylated by Mec1 (ATR), resulting in a lack of checkpoint activation. The chromatin immunoprecipitation of PCNA at the Lac operator sites reveals that accumulated local PCNA influences the checkpoint activation process in elg1 mutants. Our data suggest that Elg1 participates in a mechanism that may coordinate PCNA unloading during DNA repair with DNA damage checkpoint induction. IMPORTANCE The Elg1protein forms an RFC-like complex in charge of unloading PCNA from chromatin during DNA replication and repair. Mutations in the ELG1 gene caused genomic instability in all organisms tested and cancer in mammals. Here we show that Elg1 plays a role in the induction of the DNA damage checkpoint, a cellular response to DNA damage. We show that this defect is due to a defect in the signal amplification process during induction. Thus, cells coordinate the cell's response and the PCNA unloading through the activity of Elg1.
Effect of laterality discrimination on joint position sense and cervical range of motion in patients with chronic neck pain: a randomized single-blind clinical trial

Abstract Purpose: The main objective of the present study was to evaluate the effects of laterality discrimination training on neck joint position sense and cervical range of motion (ROM) in patients with chronic non-specific neck pain (NSCNP). Materials and methods: Forty-eight patients with NSCNP were randomly assigned to the neck group (NG) that observed neck images or the foot group (FG) that observed foot images. Response time, response accuracy, cervical ROM, and joint position error (JPE) were the main variables. The secondary outcome measures included psychosocial variables. Results: Differences between groups in the cervical ROM for flexion (p = .043) were obtained, being NG group the one which obtained greater values. NG showed an improvement in right rotation (p = .018) and a decrease in flexion was found in the FG (p = .039). In JPE, differences between groups were obtained in the left rotation (p = .021) and significant changes were found in the NG for flexion, extension, and left rotation movements (p < .05). Moderate associations were found between left and right accuracy regarding to post-intervention flexion and right rotation (r = 0.46, r = 0.41; p < .05) in NG. Conclusion: Improvements in cervical range of motion and joint position sense are obtained after the performance of the laterality discrimination task of images of the neck but not the feet. Visualization of images of the painful region presents moderate correlations with the accuracy and response time in the movements of flexion and right rotation.
The Importance of Nontechnical Skills in Leading Cardiopulmonary Resuscitation Teams

Cardiopulmonary resuscitation (CPR) is one of the important clinical competencies for medical trainees. Since the introduction of the first guidelines for CPR in 1966, further research has resulted in more recent updates. The latest in the UK was published in 2015 by the resuscitation council. The multidisciplinary nature of the resuscitation team requires a designated leader, capable of directing the team's effort and making decisions. There is evidence that leadership makes a difference in the performance of the resuscitation team and the outcome of patients. Research performed on leadership in emergency medicine highlighted the importance of the nontechnical skills displayed by CPR team leaders. Nontechnical skills refer to cognitive, behavioural and social skills that contribute to efficient team performance, such as task management, assertiveness, situational awareness, communication and decision-making. This review discusses the importance and applicability of nontechnical leadership skills in CPR based on current evidence in the literature and the clinical practice in UK hospitals.
Discovery of O-glycans on atrial natriuretic peptide (ANP) that affect both its proteolytic degradation and potency at its cognate receptor

Atrial natriuretic peptide (ANP) is a peptide hormone that in response to atrial stretch is secreted from atrial myocytes into the circulation, where it stimulates vasodilatation and natriuresis. ANP is an important biomarker of heart failure where low plasma concentrations exclude cardiac dysfunction. ANP is a member of the natriuretic peptide (NP) family, which also includes the B-type natriuretic peptide (BNP) and the C-type natriuretic peptide. The proforms of these hormones undergo processing to mature peptides, and for proBNP, this process has previously been demonstrated to be regulated by O-glycosylation. It has been suggested that proANP also may undergo post-translational modifications. Here, we conducted a targeted O-glycoproteomics approach to characterize O-glycans on NPs and demonstrate that all NP members can carry O-glycans. We identified four O-glycosites in proANP in the porcine heart, and surprisingly, two of these were located on the mature bioactive ANP itself. We found that one of these glycans is located within a conserved sequence motif of the receptor-binding region, suggesting that O-glycans may serve a function beyond intracellular processing and maturation. We also identified an O-glycoform of proANP naturally occurring in human circulation. We demonstrated that site-specific O-glycosylation shields bioactive ANP from proteolytic degradation and modifies potency at its cognate receptor in vitro. Furthermore, we showed that ANP O-glycosylation attenuates acute renal and cardiovascular ANP actions in vivo. The discovery of novel glycosylated ANP proteoforms reported here significantly improves our understanding of cardiac endocrinology and provides important insight into the etiology of heart failure.
Precognition of Students Academic Failure Using Data Mining Techniques

This paper proposes to pre-recognize student's academic failure. Real time data on school or graduating students from an institute is taken and various data mining techniques (classification algorithms), such as induction rules, decision trees and naive bayes are applied on it. The results of these algorithms are being compared and optimized for foretelling which students might fail in future. We first consider all the available attributes of students, then select few best attributes and finally, rebalance the data using classification algorithms. The use of data mining concept in the field of education is called as Educational Data Mining, EDM (2). This paper focuses on designing various methods that will help the teachers and the principal (Administrator) of the school to figure out the weak students and improve their educational standards and environment in which they learn. I propose the use of data mining procedures, because the complexity of the problem is high, data to be handled is very large and often highly unbalanced. The final objective of this paper is to detect the failure of students as early as possible to prevent them from dropping out and improve their academic performance. The outcomes are compared and the best results are shown.
Application of Dually Activated Michael Acceptor to the Rational Design of Reversible Covalent Inhibitor for Enterovirus 71 3C Protease.

Targeted covalent inhibitors (TCIs) have attracted growing attention from the pharmaceutical industry in recent decades because they have potential advantages in terms of efficacy, selectivity and safety. TCIs have recently evolved into a new version with reversibility that can be systematically modulated. This feature may diminish the risk of haptenization and help optimize the drug-target residence time as needed. The enteroviral 3C protease (3Cpro) is a valuable therapeutic target, but the development of 3Cpro inhibitors is far from satisfactory. Therefore, we aimed to apply a reversible TCI approach to the design of novel 3Cpro inhibitors. The introduction of various substituents onto the α-carbon of classical Michael acceptors yielded inhibitors bearing several classes of warheads. Using steady-state kinetics and biomolecular mass spectrometry, we confirmed the mode of reversible covalent inhibition and elucidated the mechanism by which the potency and reversibility were affected by electronic and steric factors. This research produced several potent inhibitors with good selectivity and suitable reversibility; moreover, it validated the reversible TCI approach in the field of viral infection, suggesting broader applications in the design of reversible covalent inhibitors for other proteases.
endo-Hydroxamic Acid Monomers for the Assembly of a Suite of Non-native Dimeric Macrocyclic Siderophores Using Metal-Templated Synthesis.

An expedited synthesis of endo-hydroxamic acid aminocarboxylic acid ( endo-HXA) compounds has been developed. These monomeric ligands are relevant to the synthesis of metal-macrocycle complexes using metal-templated synthesis (MTS), and the downstream production of apomacrocycles. Macrocycles can display useful drug properties and be used as ligands for radiometals in medical imaging applications, which supports methodological advances in accessing this class of molecule. Six endo-HXA ligands were prepared that contained methylene groups, ether atoms, or thioether atoms in different regions of the monomer (1-6). MTS using a 1:2 Fe(III)/ligand ratio furnished six dimeric hydroxamic acid macrocycles complexed with Fe(III) (1a-6a). The corresponding apomacrocycles (1b-6b) were produced upon treatment with diethylenetriaminepentaacetic acid (DTPA). Constitutional isomers of the apomacrocycles that contained one ether oxygen atom in the diamine-containing (2b) or dicarboxylic acid-containing (3b) region were well resolved by reverse-phase high-performance liquid chromatography (RP-HPLC). Density functional theory calculations were used to compute the structures and solvated molecular properties of 1b-6b and showed that the orientation of the amide bonds relative to the pseudo- C2 axis was close to parallel in 1b, 2b, and 4b-6b but tended toward perpendicular in 3b. This conformational constraint in 3b reduced the polarity compared with 2b, consistent with the experimental trend in polarity observed using RP-HPLC. The improved synthesis of endo-HXA ligands allows expanded structural diversity in MTS-derived macrocycles and the ability to modulate macrocycle properties.
A Demonstration of MAGiQ: Matrix Algebra Approach for Solving RDF Graph Queries

Existing RDF engines follow one of two design paradigms: relational or graph-based. Such engines are typically designed for specific hardware architectures, mainly CPUs, and are not easily portable to new architectures. Porting an existing engine to a different architecture (e.g., many-core architectures) entails almost redesign from scratch. We explore sparse matrix algebra as a third paradigm for designing a portable, scalable, and efficient RDF engine. We demonstrate MAGiQ; a matrix algebra approach for evaluating complex SPARQL queries over large RDF datasets. MAGiQ represents an RDF graph as a sparse matrix, and translates SPARQL queries to matrix algebra programs. MAGiQ takes advantage of the existing rich software infrastructure for processing sparse matrices, optimized for many architectures (e.g., CPUs, GPUs, distributed), effortlessly. This demo motivates the adoption of matrix algebra in RDF graph processing by showing MAGiQ's performance with different matrix algebra backend engines. MAGiQ, using a GPU, is orders of magnitude faster in solving complex queries on a billion edge graph than state-of-the-art RDF systems.
Reinforced Variational Inference

Recent years have seen an increase in the complexity and scale of probabilistic models used to understand and analyze data, with a corresponding increase in the difficulty of performing inference. An important enabling factor in this context has been the development of stochastic gradient algorithms for learning variational approximations to posterior distributions. In a separate line of work researchers have been investigating how to use probabilistic inference for the problem of optimal control [5, 6, 12, 17, 25]. By viewing control as an inference problem, they showed that they could ‘borrow’ algorithms from the inference literature (e.g. belief propagation) and turn them into control algorithms. In this work, we do just the opposite: we formally map the problem of learning approximate posterior distributions in variational inference (VI) onto the policy optimization problem in reinforcement learning (RL), explaining this connection at two levels.