Miracle-dz/UniFAR-FTUs huggingface dataset:
DraftRec: Personalized Draft Recommendation for Winning in Multi-Player Online Battle Arena Games

This paper presents a personalized character recommendation system for Multiplayer Online Battle Arena (MOBA) games which are considered as one of the most popular online video game genres around the world. When playing MOBA games, players go through a draft stage, where they alternately select a virtual character to play. When drafting, players select characters by not only considering their character preferences, but also the synergy and competence of their team’s character combination. However, the complexity of drafting induces difficulties for beginners to choose the appropriate characters based on the characters of their team while considering their own champion preferences. To alleviate this problem, we propose DraftRec, a novel hierarchical model which recommends characters by considering each player’s champion preferences and the interaction between the players. DraftRec consists of two networks: the player network and the match network. The player network captures the individual player’s champion preference, and the match network integrates the complex relationship between the players and their respective champions. We train and evaluate our model from a manually collected 280,000 matches of League of Legends and a publicly available 50,000 matches of Dota2. Empirically, our method achieved state-of-the-art performance in character recommendation and match outcome prediction task. Furthermore, a comprehensive user survey confirms that DraftRec provides convincing and satisfying recommendations to the real-world players. Our code and dataset are available at https://github.com/dojeon-ai/DraftRec .
Significance and Coverage in Group Testing on the Social Web

We tackle the longstanding question of checking hypotheses on the social Web. In particular, we address the challenges that arise in the context of testing an input hypothesis on many data samples, in our case, user groups. This is referred to as Multiple Hypothesis Testing, a method of choice for data-driven discoveries. Ensuring sound discoveries in large datasets poses two challenges: the likelihood of accepting a hypothesis by chance, i.e., returning false discoveries, and the pitfall of not being representative of the input data. We develop GroupTest, a framework for group testing that addresses both challenges. We formulate CoverTest, a generic top-n problem that seeks n user groups satisfying one-sample, two-sample, or multiple-sample tests, and maximizing data coverage. We show the hardness of CoverTest and develop a greedy algorithm with a provable approximation guarantee as well as a faster heuristic-based algorithm based on α-investing. Our extensive experiments on four real-world datasets demonstrate the necessity to optimize coverage for sound data-driven discoveries, and the efficiency of our heuristic-based algorithm.
Heat Transfer Enhancement of Shell and Tube Heat Exchanger

Heat exchanger is a device which provides a flow of thermal energy between two or more fluids at different temperatures. There are many problems created in the segmental heat exchanger during and after the work. The major causes of this problem is the geometry of heat exchanger, type of fluid used, type of material etc., The purpose of this work is to design the shell and tube heat exchanger which is one among the majority type of liquid -to -liquid heat exchanger. Since the important design parameters such as the pitch ratio, tube length, and tube layer as well as baffle spacing has a direct effect on pressure drop and effectiveness, they are considered to be the key parameters in this work. General design consideration and design procedure are also illustrated in this work. The analysis of orifice baffle and convergent divergent tube in a shell and tube heat exchanger are experimentally carried out. The newly designed heat exchanger obtained a maximum heat transfer coefficient and a lower pressure drop. From the numerical experimentation, the result shows that the performance of heat exchanger increases in modified baffle and tube than the segmental baffle and tube arrangement.
Navigating in Patient Space Using Camera Pose Estimation Relative to the External Anatomy

Ultrasound probe localization is essential for volumetric imaging with a 2D ultrasound probe, and for establishing a recorded anatomical context for ultrasound-guided surgery and for longitudinal studies. The existing techniques for probe localization, however, require external tracking devices, making them inconvenient for clinical use. In addition, the probe pose is typically measured with respect to a fixed coordinate system independent of the patient’s anatomy, making it difficult to correlate ultrasound studies across time. This dissertation concerns the development and evaluation of a novel self-contained ultrasound probe tracking system, which navigates the probe in patient space using camera pose estimation relative to the anatomical context. As the probe moves in patient space, a video camera on the probe is used to automatically identify natural skin features and subdermal cues, and match them with a pre-acquiring high-resolution 3D surface map that serves as an atlas of the anatomy. We have addressed the problem of distinguishing rotation from translation by including an inertial navigation system (INS) to accurately measure rotation. Experiments on both a phantom containing an image of human skin (palm) as well as actual human upper extremity (fingers, palm, and wrist) validate the effectiveness of our approach. We have also developed a real-time 3D interactive visualization system that superimposes the ultrasound data within the anatomical context of the exterior of the patient, to permit accurate anatomic localization of ultrasound data. The combination of the proposed tracking approach and the visualization system may have broad implications for ultrasound imaging, permitting the compilation of volumetric ultrasound data as the 2D probe is moved, as well as comparison of real-time ultrasound scans registered with previous scans from the same anatomical location. In a broader sense, tools that self-locate by viewing the patient’s exterior could have broad beneficial impact on clinical medicine.
Detecting intragenic trans-splicing events with hybrid transcriptome sequencing in cancer cells

Trans-splicing can generate non-co-linear (NCL) transcripts that consist of exons in an order topologically inconsistent with the corresponding DNA template. Detecting trans-spliced RNAs (ts-RNAs) may be interfered by false positives from experimental artifacts, circular RNAs (circRNAs), and genetic rearrangements. Particularly, intragenic ts-RNAs, which are derived from separate precursor mRNA molecules of the same genes, are often mistaken for circRNAs through analyses of high-throughput transcriptome sequencing (RNA-seq) data. In addition, the biogenesis and function of ts-RNAs remain elusive. Here we developed a bioinformatics pipeline, NCLscan-hybrid, with the integration of long and short RNA-seq reads to minimize false positives and identify intragenic ts-RNAs. We utilized two features of long reads, out-of-circle and rolling circle, to distinguish intragenic ts-RNAs from circRNAs. We also designed multiple experimental validation steps to examine each type of false positives and successfully confirmed an intragenic ts-RNA (ts-ARFGEF1) in breast cancer cells. On the basis of ectopic expression and CRISPR-based endogenous genome modification experiments, we confirmed that ts-ARFGEF1 formation was significantly dependent on the reverse complementary sequences in the flanking introns of the NCL junction. Subsequent in vitro and in vivo experiments demonstrated that ts-ARFGEF1 silencing can significantly inhibit tumor cell growth. We further showed the regulatory role of ts-ARFGEF1 in p53-mediated apoptosis through affecting the PERK/eIF2a/ATF4/CHOP signaling pathway in breast cancer cells. This study thus described both bioinformatics procedures and experimental validation steps for rigorous characterization of transcriptionally non-co-linear RNAs, expanding the discovery of this important but understudied class of RNAs.
A Quantum Dot Biomimetic for SARS-CoV-2 to Interrogate Dysregulation of the Neurovascular Unit Relevant to Brain Inflammation

Despite limited evidence for competent infection and viral replication of SARS-CoV-2 in the central nervous system (CNS), neurologic dysfunction is a common post-acute medical condition reported in “recovered” COVID-19 patients. To identify a potential noninfectious route for SARS-CoV-2-mediated neurological damage, we constructed colloidal nanocrystal quantum dots linked to micelles decorated with spike protein (COVID-QDs) as a biomimetic to interrogate how blood-brain barrier (BBB) dysregulation may subsequently induce neuroinflammation in the absence of infection. In transwell co-culture of endothelial bEnd.3 monolayers and primary neuroglia, we exposed only the bEnd.3 monolayers to COVID-QDs and examined by fluorescence microscopy whether such treatment led to (i) increased inflammation and leakage across the bEnd.3 monolayers, (ii) permeability of the COVID-QDs across the monolayers, and (iii) induction of neuroinflammation in neuroglial cultures. The results of our study provide evidence of neuroinflammatory hallmarks in cultured neurons and astrocytes without direct exposure to SARS-CoV-2-like nanoparticles. Additionally, we found that pre-treatment of our co-cultures with a small-molecule, broad-spectrum inhibitor of mixed lineage and leucine rich repeat kinases led to reversal of the observed dysregulation in endothelial monolayers and resulted in neuroglial protection. The results reported here may serve to guide future studies into the potential mechanisms by which SARS-CoV-2 mediates neurologic dysfunction.
Systems-level identification of key transcription factors in immune cell specification

Transcription factors (TFs) are crucial for regulating cell differentiation during the development of the immune system. However, the key TFs for orchestrating the specification of distinct immune cells are not fully understood. Here, we integrated the transcriptomic and epigenomic measurements in 73 mouse and 61 human primary cell types, respectively, that span the immune cell differentiation pathways. We constructed the cell-type-specific transcriptional regulatory network and assessed the global importance of TFs based on the Taiji framework, which is a method we have previously developed that can infer the global impact of TFs using integrated transcriptomic and epigenetic data. Integrative analysis across cell types revealed putative driver TFs in cell lineage-specific differentiation in both mouse and human systems. We have also identified TF combinations that play important roles in specific developmental stages. Furthermore, we validated the functions of predicted novel TFs in murine CD8+ T cell differentiation and showed the importance of Elf1 and Prdm9 in the effector versus memory T cell fate specification and Kdm2b and Tet3 in promoting differentiation of CD8+ tissue resident memory (Trm) cells, validating the approach. Thus, we have developed a bioinformatic approach that provides a global picture of the regulatory mechanisms that govern cellular differentiation in the immune system and aids the discovery of novel mechanisms in cell fate decisions.
SMURF: embedding single-cell RNA-seq data with matrix factorization preserving self-consistency

The advance of single-cell RNA-sequencing (scRNA-seq) sheds light on cell-specific transcriptomic studies of cell developments, complex diseases, and cancers. Nevertheless, scRNA-seq techniques suffer from “dropout” events, and imputation tools are proposed to address the sparsity. Here, rather than imputation, we propose a tool, SMURF, to embed cells and genes into their latent space vectors utilizing matrix factorization with a mixture of Poisson-Gamma divergent as objective while preserving self-consistency. As for wet lab dataset evaluation, SMURF exhibited feasible cell subpopulation discovery efficacy with the latent vectors on all the eight-cell line mixtures. Furthermore, SMURF can embed the cell latent vectors into a 1D-oval and recover the time course of the cell cycle. SMURF can also serve as an imputation tool, the in silico data assessment shows that SMURF paraded the most robust gene expression recovery power with low root mean square error and high Pearson correlation. Moreover, SMURF recovers the gene distribution for the WM989 Drop-seq data. SMURF is available at https://github.com/deepomicslab/SMURF.
CAPG: comprehensive allopolyploid genotyper

Motivation Genotyping by sequencing is a powerful tool for investigating genetic variation in plants, but many economically important plants are allopolyploids, where homoeologous similarity obscures the subgenomic origin of reads and confounds allelic and homoeologous SNPs. Recent polyploid genotyping methods use allelic frequencies, rate of heterozygosity, parental cross or other information to resolve read assignment, but good subgenomic references offer the most direct information. The typical strategy aligns reads to the joint reference, performs diploid genotyping within each subgenome, and filters the results, but persistent read misassignment results in an excess of false heterozygous calls. Results We introduce the Comprehensive Allopolyploid Genotyper (CAPG), which formulates an explicit likelihood to weight read alignments against both subgenomic references and genotype individual allopolyploids from whole genome resequencing (WGS) data. We demonstrate CAPG in allotetraploids, where it performs better than GATK’s HaplotypeCaller applied to reads aligned to the combined subgenomic references. Availability Code and tutorials are available at https://github.com/Kkulkarni1/CAPG.git.
Evolutionary history of metazoan TMEM16 family

Transmembrane protein 16 (TMEM16) functions as either a Ca2+-activated Cl- channel (CaCC) or phospholipid scramblase (CaPLSase) and plays diverse physiological roles. It is well conserved in eukaryotes; however, the origin and evolution of different subfamilies in Metazoa are not yet understood. To uncover the evolutionary history of the TMEM16 family, we analyzed 412 proteins from 74 invertebrate species using evolutionary genomics. We found that the TMEM16C–F and J subfamilies are vertebrate-specific, but the TMEM16A/B, G, H, and K subfamilies are ancient and present in many, but not all metazoan species. The most ancient subfamilies in Metazoa, TMEM16L and M, are only maintained in limited species. TMEM16N and O are Cnidaria- and Ecdysozoa-specific subfamilies, respectively, and Ctenophora, Xenacoelomorpha, and Rotifera contain species-specific proteins. We also identified TMEM16 genes that are closely linked together in the genome, suggesting that they have been generated via recent gene duplication. The anoctamin domain structures of invertebrate-specific TMEM16 proteins predicted by AlphaFold2 contain conserved Ca2+-binding motifs and permeation pathways with either narrow or wide inner gates. The inner gate distance of TMEM16 protein may have frequently switched during metazoan evolution, and thus determined the function of the protein as either CaCC or CaPLSase. These results demonstrate that TMEM16 family has evolved by gene gain and loss in metazoans, and the genes have been generally under purifying selection to maintain protein structures and physiological functions.
Impaired immune response drives age-dependent severity of COVID-19

SARS-CoV-2 is a highly contagious respiratory virus and the causative agent for COVID-19. The severity of disease varies from mildly symptomatic to lethal and shows an extraordinary correlation with increasing age, which represents the major risk factor for severe COVID-191. However, the precise pathomechanisms leading to aggravated disease in the elderly are currently unknown. Delayed and insufficient antiviral immune responses early after infection as well as dysregulated and overshooting immunopathological processes late during disease were suggested as possible mechanisms. Here we show that the age-dependent increase of COVID-19 severity is caused by the disruption of a timely and well-coordinated innate and adaptive immune response due to impaired interferon (IFN) responses. To overcome the limitations of mechanistic studies in humans, we generated a mouse model for severe COVID-19 and compared the kinetics of the immune responses in adult and aged mice at different time points after infection. Aggravated disease in aged mice was characterized by a diminished IFN-γ response and excessive virus replication. Accordingly, adult IFN-γ receptor-deficient mice phenocopied the age-related disease severity and supplementation of IFN-γ reversed the increased disease susceptibility of aged mice. Mimicking impaired type I IFN immunity in adult and aged mice, a second major risk factor for severe COVID-192–4, we found that therapeutic treatment with IFN-λ in adult and a combinatorial treatment with IFN-γ and IFN-λ in aged Ifnar1-/-mice was highly efficient in protecting against severe disease. Our findings provide an explanation for the age-dependent disease severity of COVID-19 and clarify the nonredundant antiviral functions of type I, II and III IFNs during SARS-CoV-2 infection in an age-dependent manner. Based on our data, we suggest that highly vulnerable individuals combining both risk factors, advanced age and an impaired type I IFN immunity, may greatly benefit from immunotherapy combining IFN-γ and IFN-λ.
Combining stem cell rejuvenation and senescence targeting to synergistically extend lifespan

Why biological age is a major risk factor for many of the most important human diseases remains mysterious. We know that as organisms age, stem cell pools are exhausted while senescent cells progressively accumulate. Independently, induction of pluripotency via expression of Yamanaka factors (Oct4, Klf4, Sox2, c-Myc; OKSM) and clearance of senescent cells have each been shown to ameliorate cellular and physiological aspects of aging, suggesting that both processes are drivers of organismal aging. However, stem cell exhaustion and cellular senescence likely interact in the etiology and progression of age-dependent diseases because both undermine tissue and organ homeostasis in different if not complementary ways. Here, we combine transient cellular reprogramming (stem cell rejuvenation) with targeted removal of senescent cells to test the hypothesis that simultaneously targeting both cell-fate based aging mechanisms will maximize life and health span benefits. We show that these interventions protect the intestinal stem cell pool, lower inflammation, activate pro-stem cell signaling pathways, and synergistically improve health and lifespan. Our findings suggest that a combination therapy, simultaneously replacing lost stem cells and removing senescent cells, shows synergistic potential for anti-aging treatments. Our finding that transient expression of both is the most effective suggests that drug-based treatments in non-genetically tractable organisms will likely be the most translatable.
Bridging scales from spiking activity to the local field potential through latent dynamics

During wakefulness, cortical networks exhibit diverse states of activity arising from fluctuations in modulatory inputs, any of which may shape the recorded neural activity across a range of scales, from local field potential (LFP) to the spiking of individual neurons. Multiple studies have uncovered state-dependent changes in single neuron activity specific to cell type. These changes are heterogeneous across cell types, suggesting that there is a link between changes in states of wakefulness and the dynamic patterns of spiking activity across the population, yet this is currently not well understood. Here, we use an unsupervised statistical modeling framework to link fluctuations in the state of the LFP to the dynamics of spiking activity of populations of neurons in the whisker area of primary somatosensory cortex of awake male mice. By identifying latent states from population spiking activity, we show that these spiking states are more informative of states of the LFP than a direct readout of the spiking activity of single neurons or of the population. Further, we find that the latent spiking states predict trial-by-trial variability in sensory responses as well as one aspect of behavior, whisking activity. Taken together, our approach captures the latent variables that underlie the dynamics of the spiking activity of cortical neuronal populations, providing a framework to relate cortical activity across different scales. Significance statement During wakefulness, variability in cortical activity can arise from shifts in arousal or behavior. We analyze the dynamics of two measures of cortical activity, the spiking activity of local populations of neurons and the local field potential (LFP), typically used to track overall levels of arousal. We show that the latent states that capture variability in population spiking activity are highly predictive of LFP state changes, even more so than spiking activity directly. By linking latent states of spiking activity to changes in the LFP, our novel approach bridges fine-scale microcircuits ensemble activity and the meso-scale LFP. Our latent-state framework opens new avenues for extracting information from the LFP as well as identification of subject-level sources of variability.
Prefusion-Stabilized Lassa Virus Trimer Identifies Neutralizing Nanobodies and Reveals an Apex-Situated Site of Vulnerability

Lassa virus (LASV) is responsible for 100,000-300,000 zoonotic infections annually and poses a threat to public health. Development of antibody-based therapeutics or vaccines has been challenging because neutralizing antibodies – even among Lassa hemorrhagic fever survivors – are generally of low titer, and the target of neutralizing antibodies, the trimeric glycoprotein complex (GPC), a type 1-fusion machine with GP1 and GP2 subunits, has been difficult to produce. Here, we use structure-based design to obtain a soluble LASV GPC by engineering an inter-protomer disulfide (R207GCGP1-L326CGP2) and appending the T4-fibritin trimerization domain. We verified the antigenicity of this prefusion-stabilized LASV GPC against a panel of human antibodies and used electron microscopy (EM) to confirm its trimeric association. We panned the prefusion-stabilized LASV GPC against single domain ‘nanobody’ libraries and identified one of camel origin, which we named D5, which bound GPC with 27 nM affinity and neutralized the Josiah strain of LASV with an IC50 of 12 µg/ml when formatted into a bivalent IgG2a context. The cryo-EM structure of a ternary complex of the D5 nanobody, the antigen-binding fragment of human antibody 8.11G, and LASV GPC revealed D5 to recognize a site-of-vulnerability at the trimer apex. The recognized site appeared to be specific to GPC lacking cleavage of between GP1 and GP2 subunits. Collectively, our findings suggest that GPC-cleavage intermediates may be targets for LASV neutralization and define an apex-situated site of vulnerability for vaccine development. Significance Lassa virus (LASV) infection is expanding outside its traditionally endemic areas in West Africa, posing a biothreat to the world. LASV-neutralizing antibodies, moreover, have proven difficult to elicit. To gain insight into requirements for antibody-mediated neutralization of LASV, we developed a prefusion-stabilized LASV glycoprotein trimer (GPC), panned it against phage libraries comprised single-domain antibodies or nanobodies from shark and camel, and identified one, D5, which – when placed into bivalent IgG2a context – could neutralize LASV. Cryo-EM analysis revealed D5 to recognize a cleavage-dependent site-of-vulnerability at the trimer apex. We propose this apex-situated site to be an attractive target for LASV vaccine and therapeutic development.