Miracle-dz/UniFAR-FTUs huggingface dataset:
Microsoft Word-2839778-file00.docx

12 The NMDA receptor (NMDAR) plays important roles in excitatory neurotransmission and in the 13 regulation of reproduction in mammals. NMDAR in insects comprises two subunits, NR1 and 14 NR2. In this study, we identified two NR1 paralogs and eleven NR2 alternative spliced variants 15 in D. punctata. This is the first report of NR1 paralogs in insects. The tissue distributions and 16 expression profiles of DpNR1A, DpNR1B and DpNR2 in different tissues were also investigated. 17 Previous studies have demonstrated NMDA-stimulated JH biosynthesis in the corpora allata 18 (CA) through the influx of extracellular Ca in Diploptera punctata (Chiang A. et al, 2002). 19 However, our data show that the transcript levels of DpNR1A, DpNR1B and DpNR2 were low in 20 the CA. MK-801, a high-affinity antagonist of NMDAR, did not show any effect on JH 21 biosynthesis in vitro. In addition, neither partial knockdown of DpNR2 nor in vivo treatment with 22 a physiologically relevant dose of MK-801 resulted in any significant change in JH biosynthesis 23 by CA or basal oocyte growth. Injection of animals with a high dose of MK-801 (30 24 μg/animal/injection), which paralyzed the animals for 4-5 h, resulted in a significant decrease in 25 JH biosynthesis on days 4 and 5. However, the reproductive events during the first 26 gonadotrophic cycle in female D. punctata were unaffected. Thus, NMDAR does not appear to 27 http://jeb.biologists. org/lookup/doi/10.1242/jeb.115154 Access the most recent version at J Exp Biol Advance Online Articles. First posted online on 5 February 2015 as doi:10.1242/jeb.115154 http://jeb.biologists. org/lookup/doi/10.1242/jeb.115154 Access the most recent version at First posted online on 5 February 2015 as 10.1242/jeb.115154
BPLight-CNN: A Photonics-based Backpropagation Accelerator for Deep Learning

Training deep learning networks involves continuous weight updates across the various layers of the deep network while using a backpropagation (BP) algorithm. This results in expensive computation overheads during training. Consequently, most deep learning accelerators today employ pretrained weights and focus only on improving the design of the inference phase. The recent trend is to build a complete deep learning accelerator by incorporating the training module. Such efforts require an ultra-fast chip architecture for executing the BP algorithm. In this article, we propose a novel photonics-based backpropagation accelerator for high-performance deep learning training. We present the design for a convolutional neural network (CNN), BPLight-CNN , which incorporates the silicon photonics-based backpropagation accelerator. BPLight-CNN is a first-of-its-kind photonic and memristor-based CNN architecture for end-to-end training and prediction. We evaluate BPLight-CNN using a photonic CAD framework (IPKISS) on deep learning benchmark models, including LeNet and VGG-Net. The proposed design achieves (i) at least 34× speedup, 34× improvement in computational efficiency, and 38.5× energy savings during training; and (ii) 29× speedup, 31× improvement in computational efficiency, and 38.7× improvement in energy savings during inference compared with the state-of-the-art designs. All of these comparisons are done at a 16-bit resolution, and BPLight-CNN achieves these improvements at a cost of approximately 6% lower accuracy compared with the state-of-the-art.
Association of MTHFR C677T and A1298C Polymorphisms with Susceptibility to Chronic Lymphocytic Leukemia: A Systematic Review and Meta-Analysis

Background: The relation between methylenetetrahydrofolate reductase)MTHFR(polymorphisms and the risk of developing Chronic lymphocytic leukemia (CLL) is not still clear, while there are reports about the association of MTHFR C677T and A1298C polymorphisms with developing CLL, there are other reports that rolled out the association of MTHFR polymorphisms with developing CLL. Therefore herein we carried out this meta-analysis to clear the association of MTHFR polymorphisms with the risk of CLL, Methods: A comprehensive search was performed through PubMed, Scopus and Embase from inception to Aug 2020. Odds ratios (OR) with their corresponding 95% confidence intervals (CI) for five possible genetic models were calculated. Heterogeneity was evaluated using the Cochran Q test and the I2 statistic. Results: Totals of 1290 cases and 1887 controls for the C677T polymorphism and 1117 cases and 1256 controls for the A1298C polymorphism were included in our analysis. Analyzing the MTHFR C677T and A1298C polymorphisms genotypes showed an association between MTHFR polymorphism at A1298C under Allelic model and the risk of CLL (OR = 1.12, 95% CI = 1.01–1.25), however there was no association between MTHFR polymorphism at MTHFR C677T and risk of CLL. Conclusion: The risk of developing CLL might be associated with MTHFR polymorphism at A1298C under allelic model and not associated with MTHFR polymorphisms at C677T, However, further studies considering other factors such as age, gender, ethnicity, gene-gene interaction and environmental condition are needed to clear the true association of MTHFR polymorphisms with CLL.
Retrieval of Flower Videos Based on a Query With Multiple Species of Flowers

Searching, recognizing and retrieving a video of interest from a large collection of a video data is an instantaneous requirement. This requirement has been recognized as an active area of research in computer vision, machine learning and pattern recognition. Flower video recognition and retrieval is vital in the field of floriculture and horticulture. In this paper we propose a model for the retrieval of videos of flowers. Initially, videos are represented with keyframes and flowers in keyframes are segmented from their background. Then, the model is analysed by features extracted from flower regions of the keyframe. A Linear Discriminant Analysis (LDA) is adapted for the extraction of discriminating features. Multiclass Support Vector Machine (MSVM) classifier is applied to identify the class of the query video. Experiments have been conducted on relatively large dataset of our own, consisting of 7788 videos of 30 different species of flowers captured from three different devices. Generally, retrieval of flower videos is addressed by the use of a query video consisting of a flower of a single species. In this work we made an attempt to develop a system consisting of retrieval of similar videos for a query video consisting of flowers of different species.
180-189 Siraj A Khan.pmd

Japanese encephalitis (JE) is a viral neurologic disease of global public health importance. JE is caused by an RNA virus – the Japanese encephalitis virus (JEV) belonging to genus Flavivirus in the family Flaviviridae. JE is endemic in many parts of Asia and western pacific. Annually, approximately 50,000 JE cases are reported with case fatality rates as high as 30-35% resulting in ~15,000 deaths. Presently, there are no successful drugs against JE. Docking of JEV NS3 helicase/NTPase helicase domain with 10 compounds was performed in iGEMDOCK v2.1. Integrated docking, screening, postanalysis and visualization were performed using RasMol software. The drug susceptibility was evaluated by virus yield reduction assay. Three ligands out of 10 antibiotic compounds studied showed highest binding affinity with receptor protein. Kanamycin, Rolitetracycline and Doxycycline showed better binding energy compared to two study standardsRibavirin and Minocycline. Interacting bonds were formed in all three domains of NS3 helicase/NTPase. The interactions in motifs I, II and VI of helicase are important; these would have possibly inhibited viral replication. Biological assay showed that Kanamycin (Inhibitory concentration50, IC50 – 70 μg/ml), Rolitetracycline (IC50 – 76 μg/ml) and Doxycycline (IC50 – 22 μg/ml) inhibited plaque formation.
52-58 Kanbutra P.pmd

Cystoisospora suis (syn. Isospora suis) is an apicomplexan parasite which causes an important piglet coccidiosis resulting in commercial losses worldwide. Successful coccidiosis control using chemoprevention and chemotherapy as the recommended approach, nonetheless, conduces the extensive use of anticoccidial drugs that may lead to the emergence of drug-resistant parasites. Consequently, it is necessary to continuously engage in drug discovery for alternative anticoccidials. To achieve the drug discovery aspect, an appropriate in vitro model for the endogenous development of the coccidium has to be established. Moreover, the in vitro model can reduce the cost and experimental animal use necessitated by in vivo systems. The aim of this study was to investigate the development of C. suis throughout its endogenous life cycle using swine kidney cell line, SK–6 for the in vitro system. The results showed that SK–6 cells seemed to be an excellent support system for the complete endogenous life cycle of C. suis. For ability to infect, sporozoites were able to penetrate into the host cells, first observed by 2 hours post inoculation. The intracellular penetrated sporozoites underwent asexual multiplications producing presumed type I meronts and merozoites. The meronts each formed 2–8 zoites by 3 days post infection (dpi) which were prominently seen on 3–5 dpi. Developing oocysts were initially observed on 5 dpi and went on sequential sexual development resulting in intracellular mature oocysts from 7–14 dpi. These became prominent from 9–12 dpi. Recycling of merozoites to form more meronts and merozoites also appeared by 10 dpi, indicated by merozoites burst from initially infected cells observed re-entering host cells. In conclusion, the presented in vitro system was able to support complete endogenous development of C. suis that was comparable to its in vivo life cycle. Furthermore, this provides an appropriate model for the study of a vast array of aspects relating to the coccidium, especially in applications with regard to the sexual development of the parasite.
Supplementary material to ” Preselection Bandits ” A . Proofs of Theorems 4

For the proofs of Theorem 4.1 and Theorem 4.2 we need the following result on the Kullback-Leibler divergence of categorical probability distributions, which is Lemma 3 in Chen & Wang (2018). Throughout the proofs we let γ ∈ (0, ∞) be some arbitrary degree of preciseness. Lemma A.1. Let P ∼ Cat(p1, . . . , pm), i.e. P (i) = pi Cm for i = 1, . , m and = 1, as well as Q ∼ i=1 pi Cat(q1, . . . , qm), such that qi = pi + εi and |εi| < 1 for any i = 1, . , m. Then,
Meditation-induced cognitive-control states regulate working memory task performance

Single-bout focused-attention meditation (FAM) and open-monitoring meditation (OMM) are assumed to bias metacontrol states towards more persistent versus more flexible processing, respectively. In Experiment 1, we tested whether monitoring and updating of working memory (WM) representations in an N-back task with high (3-back), medium (2-back), and low (1-back) WM demands (varied within participants) is affected by preceding single-bout FAM or OMM meditation (varied between participants and compared with a control group). The results showed that FAM promotes WM performance in the medium (2-back), but not in the high (3-back) or low (1-back) demand condition, whereas OMM did not affect WM performance. A replication of the 2-back condition only (Experiment 2) showed no meditation effect, but a replication of the 3-back condition only (Experiment 3) produced a similar pattern as the 2-back condition in Experiment 1, with FAM promoting performance compared with OMM and the control condition. Taken together, these findings suggest that the single-bout FAM does promote WM performance but only if the capacity demands are neither too high nor too low.
PROJECT-BASED LEARNING TOOLS FOR LIGHT VEHICLE ENGINE MAINTENANCE SUBJECTS AT VOCATIONAL HIGH SCHOOL

This study aims to develop learning tools for project-based light vehicle engine maintenance and describe the teacher's response to the application of project-based learning tools in light vehicle engine maintenance subjects. This research is a research and development (R&D) model with ADDIE. The ADDIE development model consists of analysis, design, development, implementation, and evaluation. The results showed that the product developed was valid in terms of validity, very practical in terms of practicality, and very high in the aspect of effectiveness. Based on the aspects of validity, it can be specified that the syllabus gets a percentage score of 90.25%, 88.78% learning implementation plan, 86.56% module, 88.93% job sheet, 87.5% achievement test instrument, and project test instrument 87.08%. Based on aspects of practicality assessed from the teacher's response, it can be specified that the syllabus gets a percentage score of 88.67%, 86.88% learning implementation plan, 88.75% module, 88.77% job sheet, 85.42% test instrument for learning outcomes, and 83.33% project test instruments. Overall, teachers find it helpful to develop projectbased learning tools. Based on the effectiveness, the results of students' knowledge tests with the percentage of mastery learning in the knowledge area amounted to 86.67%, and the skill area amounted to 86.67% with very high criteria. Researchers can conclude that project-based learning tools on light vehicle maintenance subjects are in accordance with valid, practical, and effective criteria.
Using Community Structure Information to Improve Complex Networks Robustness

—This paper discusses the relation between two emergent features of most complex networks: community structure and high sensitivity to attacks. More speciﬁcally, we consider how the former can support mechanisms to mitigate the latter. The main point stressed here is that information about the community structure can be useful to detect and mitigate vulnerable topological conﬁgurations w.r. t network connectivity. We demonstrate this through an attack and failure protocol that considers the importance of central nodes regarding their roles connecting nodes, either inside or outside communities. We also propose local mechanisms for evaluating topological conﬁgurations based on community information. The strategy for minimizing the impact of central node failures to network connectivity relies on the creation of redundant paths between communities. The networks evaluated exhibited a signiﬁcant improvement in their robustness regarding connectivity maintenance, being almost unaffected by failures of central nodes. The experimental benchmark encompasses both real complex network datasets and networks generated by well-established construction methods.
Development of a multiplexed and automated serotyping assay for Streptococcus 1 pneumoniae 2 3 Running Title : Automated and complete pneumococcal serotyping assay 4 5

Title: Development of a multiplexed and automated serotyping assay for Streptococcus 1 pneumoniae 2 3 Running Title: Automated and complete pneumococcal serotyping assay 4 5 Jigui Yu, Jisheng Lin, Kyung-Hyo Kim, William H. Benjamin, Jr. , and Moon H. Nahm 6 7 1 Department of Pathology, The University of Alabama at Birmingham, Birmingham, Alabama, 2 8 Department of Pediatrics, School of Medicine, Ewha Womans University, Seoul, Korea. 9 10 Corresponding Author: Moon H. Nahm, MD 11 Department of Pathology 12 University of Alabama at Birmingham 13 BBRB 614 14 1530 Third Avenue South 15 Birmingham, AL 35294-2170 16 Tel: 205-934-0163 17 Fax: 205-975-2149 18 nahm@uab.edu 19 20 21 22 Copyright © 2011, American Society for Microbiology and/or the Listed Authors/Institutions. All Rights Reserved. Clin. Vaccine Immunol. doi:10.1128/CVI.05312-11 CVI Accepts, published online ahead of print on 7 September 2011
Synthesis, characterization and applications of layered double hydroxides containing organic guests

The use of layered inorganic solids as host materials for the creation of inorganic–organic host-guest supramolecular structures is of increasing interest. In this review we outline the preparation, characterization and uses of layered double hydroxides (LDHs) containing organic guests. LDHs consist of stacks of positively charged mixed metal hydroxide layers that require the presence of interlayer anions to maintain overall charge neutrality. A range of organic guests may be incorporated, including aliphatic and aromatic carboxylates, sulfonates and phosphonates as well as porphyrin and phthalocyanine derivatives. Potential applications of organo-LDHs in the areas of catalysis, sorption, photochemistry and electrochemistry are outlined. Overall, it is demonstrated that with LDHs it is possible to create, in a systematic manner, novel inorganic–organic supramolecular structures.
Proof Artifact Co-training for Theorem Proving with Language Models

Labeled data for imitation learning of theorem proving in large libraries of formalized mathematics is scarce as such libraries require years of concentrated effort by human specialists to be built. This is particularly challenging when applying large Transformer language models to tactic prediction, because the scaling of performance with respect to model size is quickly disrupted in the data-scarce, easily-overfitted regime. We propose PACT ({\bf P}roof {\bf A}rtifact {\bf C}o-{\bf T}raining), a general methodology for extracting abundant self-supervised data from kernel-level proof terms for co-training alongside the usual tactic prediction objective. We apply this methodology to Lean, an interactive proof assistant which hosts some of the most sophisticated formalized mathematics to date. We instrument Lean with a neural theorem prover driven by a Transformer language model and show that PACT improves theorem proving success rate on a held-out suite of test theorems from 32\% to 48\%.
BlockHammer: Preventing RowHammer at Low Cost by Blacklisting Rapidly-Accessed DRAM Rows

Aggressive memory density scaling causes modern DRAM devices to suffer from RowHammer, a phenomenon where rapidly activating (i.e., hammering) a DRAM row can cause bit-flips in physically-nearby rows. Recent studies demonstrate that modern DDR4/LPDDR4 DRAM chips, including chips previously marketed as RowHammer-safe, are even more vulnerable to RowHammer than older DDR3 DRAM chips. Many works show that attackers can exploit RowHammer bit-flips to reliably mount system-level attacks to escalate privilege and leak private data. Therefore, it is critical to ensure RowHammersafe operation on all DRAM-based systems as they become increasingly more vulnerable to RowHammer. Unfortunately, state-of-the-art RowHammer mitigation mechanisms face two major challenges. First, they incur increasingly higher performance and/or area overheads when applied to more vulnerable DRAM chips. Second, they require either closely-guarded proprietary information about the DRAM chips’ physical circuit layouts or modifications to the DRAM chip design.In this paper, we show that it is possible to efficiently and scalably prevent RowHammer bit-flips without knowledge of or modification to DRAM internals. To this end, we introduce BlockHammer, a low-cost, effective, and easy-to-adopt RowHammer mitigation mechanism that prevents all RowHammer bit-flips while overcoming the two key challenges. BlockHammer selectively throttles memory accesses that could otherwise potentially cause RowHammer bit-flips. The key idea of BlockHammer is to (1) track row activation rates using area-efficient Bloom filters, and (2) use the tracking data to ensure that no row is ever activated rapidly enough to induce RowHammer bit-flips. By guaranteeing that no DRAM row ever experiences a RowHammer-unsafe activation rate, BlockHammer (1) makes it impossible for a RowHammer bit-flip to occur and (2) greatly reduces a RowHammer attack’s impact on the performance of co-running benign applications. Our evaluations across a comprehensive range of 280 workloads show that, compared to the best of six state-of-the-art RowHammer mitigation mechanisms (all of which require knowledge of or modification to DRAM internals), BlockHammer provides (1) competitive performance and energy when the system is not under a RowHammer attack and (2) significantly better performance and energy when the system is under a RowHammer attack.
A Survey on Synchronous Augmented, Virtual, andMixed Reality Remote Collaboration Systems

Remote collaboration systems have become increasingly important in today’s society, especially during times when physical distancing is advised. Industry, research, and individuals face the challenging task of collaborating and networking over long distances. While video and teleconferencing are already widespread, collaboration systems in augmented, virtual, and mixed reality are still a niche technology. We provide an overview of recent developments of synchronous remote collaboration systems and create a taxonomy by dividing them into three main components that form such systems: Environment, Avatars, and Interaction. A thorough overview of existing systems is given, categorising their main contributions to help researchers working in different fields by providing concise information about specific topics such as avatars, virtual environment, visualisation styles, and interaction. The focus of this work is clearly on synchronised collaboration from a distance. A total of 87 unique systems for remote collaboration are discussed, including more than 100 publications and 25 commercial systems.
Shelf-Supervised Mesh Prediction in the Wild

We aim to infer 3D shape and pose of object from a single image and propose a learning-based approach that can train from unstructured image collections, supervised by only segmentation outputs from off-the-shelf recognition systems (i.e. ‘shelf-supervised’). We first infer a volumetric representation in a canonical frame, along with the camera pose. We enforce the representation geometrically consistent with both appearance and masks, and also that the synthesized novel views are indistinguishable from image collections. The coarse volumetric prediction is then converted to a mesh-based representation, which is further refined in the predicted camera frame. These two steps allow both shape-pose factorization from image collections and per-instance reconstruction in finer details. We examine the method on both synthetic and the real-world datasets and demonstrate its scalability on 50 categories in the wild, an order of magnitude more classes than existing works.
Portfolio Performance Attribution via Shapley Value

We consider an investment process that includes a number of features, each of which can be active or inactive. Our goal is to attribute or decompose an achieved performance to each of these features, plus a baseline value. There are many ways to do this, which lead to potentially different attributions in any specific case. We argue that a specific attribution method due to Shapley is the preferred method, and discuss methods that can be used to compute this attribution exactly, or when that is not practical, approximately.
Contribution of collapsars, supernovae, and neutron star mergers to the evolution of r-process elements in the Galaxy

Yuta Yamazaki, Toshitaka Kajino, Grant J. Mathews, Xiaodong Tang, Jianrong Shi, and Michael A. Famiano Graduate School of Science, The University of Tokyo, Hongo, Bunkyo-ku, Tokyo 11-0033, Japan National Astronomical Observatory of Japan, 2-21-1 Osawa, Mitaka, Tokyo 181-8588, Japan School of Physics, and International Research Center for Big-Bang Cosmology and Element Genesis, Beihang University, Beijing 100083, P. R. China 4 Department of Physics and Center for Astrophysics, University of Notre Dame, Notre Dame, IN 46556, USA Institute of Modern Physics, Chinese Academy of Science, Lanzhou, Gansu 730000, P. R. China Key Laboratory of Optical Astronomy, National Astronomical Observatories, Chinese Academy of Science, Beijing 100012, P. R. China and Physics Department, Western Michigan University, Kalamazoo, MI 49008-5252 USA (Dated: February 12, 2021)