Miracle-dz/UniFAR-FTUs huggingface dataset:
Compressed text indexes: From theory to practice

A compressed full-text self-index represents a text in a compressed form and still answers queries efficiently. This represents a significant advancement over the (full-)text indexing techniques of the previous decade, whose indexes required several times the size of the text. Although it is relatively new, this algorithmic technology has matured up to a point where theoretical research is giving way to practical developments. Nonetheless this requires significant programming skills, a deep engineering effort, and a strong algorithmic background to dig into the research results. To date only isolated implementations and focused comparisons of compressed indexes have been reported, and they missed a common API, which prevented their re-use or deployment within other applications. The goal of this article is to fill this gap. First, we present the existing implementations of compressed indexes from a practitioner's point of view. Second, we introduce the Pizza&Chili site, which offers tuned implementations and a standardized API for the most successful compressed full-text self-indexes, together with effective test-beds and scripts for their automatic validation and test. Third, we show the results of our extensive experiments on these codes with the aim of demonstrating the practical relevance of this novel algorithmic technology.
Study on the mechanism of SND in the treatment of anxiety disorders based on network pharmacology, molecular docking and experiment validation

Background: Sini decoction (SND) is a classic traditional Chinese medicine (TCM) formulation that can be used to treat anxiety-related disorders, but the active substance and underlying molecular mechanism of its anxiolytic effects are unknown. In this study, network pharmacology, molecular docking research and experimental verification methods were used to preliminarily explore the bioactive compounds and potential target mechanisms of SND anxiolytic. Methods: The active components and corresponding targets of SND were collected by TCMSP. GeneCards, OMIM, PharmGkb, TTD and Drugbank were used to search for the targets of anxiety disorders. The core target of SND in the treatment of anxiety was screened by PPI. R language was used to analyze the intersection targets of SND in the treatment of anxiety disorders by GO and KEGG enrichment analysis. AutoDock Vina was used for molecular docking, and Discovery Studio was used for visual conformation analysis after docking. The anti-anxiety effect and molecular mechanism of SND were studied by in vivo experiment. Results: Based on network pharmacological analysis, we obtained 112 active ingredients and 350 effective targets related to anxiety from SND. In PPI analysis, 26 targets such as STAT3, MAPK3, MAPK1, MAPK14, SRC, HSP90AA1, TP53 and PIK3CA were identified as core targets. GO and KEGG analysis showed that the anxiolytic mechanism of SND may be related to the neuroactive ligand-receptor interaction pathway and inflammatory pathway. Molecular docking showed that quercetin, naringenin, licochalcone A had high affinity with JAK2, MAPK14 and MAPK3. Animal experiments have shown that SND reverses the upregulation of GluN2B (NMDAR) and GluA1 (AMPAR) proteins, and SND improves anxiety disorders by regulating glutamate transmitter levels, which may be related to neuroactive ligand-receptor interaction pathways, particularly glutamate receptors. Conclusion: This study shows that SND can improve FS-induced behavioral changes in mice and can modulate hippocampal synapse-associated protein defects, partially reversing glutamate receptor expression through the neuroactive ligand-receptor interaction pathway, and further improved anxiety disorders. At the same time, combined with network pharmacology and molecular docking, the key components, core targets and related pathways of SND are discussed, which shows that the active components of SND play an effective role in anxiety through multi-targets and multi-pathways, which provides a reference for the material basis and mechanism of SND.
Scanning Electron Microscopy Analysis of Tea’s Embryo Axis Explant Cultured on Murashige and Skoog Medium Containing 2,4-Dichlorophenoxyacetic acid

Camellia sinensis L. is an important crop in Indonesia as healthy beverage that contains several secondary metabolism compounds, such as polyphenols and catechins. Tissue culture including somatic embryogenesis and organogenesis has been used for propagating plant for various needs. In this present short-communication, scanning electron microscopic (SEM) analysis of tea was conducted and discussed. This study aimed to investigate surface ultrastructure of TRI2025 embryo axis tea clone cultured on Murashige and Skoog (MS) medium containing 2,4-Dichlorophenoxyacetic acid (2,4-D). The results revealed two different forms of explant’s development, i.e. somatic embryo and transitional form between somatic embryogenesis and organogenesis; or called by “Globular-like Structure” (GLS). Surface ultrastructure analysis of somatic embryo and GLS revealed respectively many stages of somatic embryo development i.e. globular, torpedo, and cotyledon stage, and leaf development form GLS regeneration.
Mechanism and bottlenecks in strand photodissociation of split green fluorescent proteins (GFPs)

Significance Split GFPs have been used exclusively as imaging tools to study protein–protein interactions, but the irreversible nature of split GFP complementation can be highly perturbative. Dissociating the split GFP into constituent parts with light irradiation not only remedies this problem but also invites the opportunity for noninvasive optical control of biological processes. By elucidating an energetics–function model for split GFP strand photodissociation, we show that photodissociation is achieved by light-activated cis-trans isomerization of the chromophore, a mechanism pervasive among reversibly photoswitchable fluorescent proteins. This observation suggests that these proteins can be engineered to provide active control of in vivo processes as optogenetic tools in addition to their conventional roles as passive imaging reporters. Split GFPs have been widely applied for monitoring protein–protein interactions by expressing GFPs as two or more constituent parts linked to separate proteins that only fluoresce on complementing with one another. Although this complementation is typically irreversible, it has been shown previously that light accelerates dissociation of a noncovalently attached β-strand from a circularly permuted split GFP, allowing the interaction to be reversible. Reversible complementation is desirable, but photodissociation has too low of an efficiency (quantum yield <1%) to be useful as an optogenetic tool. Understanding the physical origins of this low efficiency can provide strategies to improve it. We elucidated the mechanism of strand photodissociation by measuring the dependence of its rate on light intensity and point mutations. The results show that strand photodissociation is a two-step process involving light-activated cis-trans isomerization of the chromophore followed by light-independent strand dissociation. The dependence of the rate on temperature was then used to establish a potential energy surface (PES) diagram along the photodissociation reaction coordinate. The resulting energetics–function model reveals the rate-limiting process to be the transition from the electronic excited-state to the ground-state PES accompanying cis-trans isomerization. Comparisons between split GFPs and other photosensory proteins, like photoactive yellow protein and rhodopsin, provide potential strategies for improving the photodissociation quantum yield.
The Dystroglycan Complex Is Necessary for Stabilization of Acetylcholine Receptor Clusters at Neuromuscular Junctions and Formation of the Synaptic Basement Membrane

The dystrophin-associated protein (DAP) complex spans the sarcolemmal membrane linking the cytoskeleton to the basement membrane surrounding each myofiber. Defects in the DAP complex have been linked previously to a variety of muscular dystrophies. Other evidence points to a role for the DAP complex in formation of nerve–muscle synapses. We show that myotubes differentiated from dystroglycan−/− embryonic stem cells are responsive to agrin, but produce acetylcholine receptor (AChR) clusters which are two to three times larger in area, about half as dense, and significantly less stable than those on dystroglycan+/+ myotubes. AChRs at neuromuscular junctions are similarly affected in dystroglycan-deficient chimeric mice and there is a coordinate increase in nerve terminal size at these junctions. In culture and in vivo the absence of dystroglycan disrupts the localization to AChR clusters of laminin, perlecan, and acetylcholinesterase (AChE), but not rapsyn or agrin. Treatment of myotubes in culture with laminin induces AChR clusters on dystroglycan+/+, but not −/− myotubes. These results suggest that dystroglycan is essential for the assembly of a synaptic basement membrane, most notably by localizing AChE through its binding to perlecan. In addition, they suggest that dystroglycan functions in the organization and stabilization of AChR clusters, which appear to be mediated through its binding of laminin.
An Optimization-aware Pre-Routing Timing Prediction Framework Based on Heterogeneous Graph Learning

Accurate and efficient pre-routing timing estimation is particularly crucial in timing-driven placement, as design iterations caused by timing divergence are time-consuming. However, existing machine learning prediction models overlook the impact of timing optimization techniques during routing stage, such as adjusting gate sizes or swapping threshold voltage types to fix routing-induced timing violations. In this work, an optimization-aware pre-routing timing prediction framework based on heterogeneous graph learning is proposed to calibrate the timing changes introduced by wire parasitic and optimization techniques. The path embedding generated by the proposed framework fuses learned local information from graph neural network and global information from transformer network to perform accurate endpoint arrival time prediction. Experimental results demonstrate that the proposed framework achieves an average accuracy improvement of 0.10 in terms of R2 score on testing designs and brings average runtime acceleration of three orders of magnitude compared with the design flow.
Emerging drugs for coronary artery disease. From past achievements and current needs to clinical promises

Introduction: Coronary artery disease (CAD) is one of the major causes of morbidity and mortality worldwide, exerting a huge economic burden. Although drug treatment in the past decades has made large advances, significant residual risk remains. However, in the coming years, there are still a lot of great advances and major breakthroughs expected. Areas covered: New treatments are expected to provide higher efficacy with favorable safety profiles. In this review article, we provide an almost complete overview of the recent and emerging drug therapies of CAD. This includes: drugs for the treatment of atherogenic dyslipidemia, drugs that stabilize atherosclerotic plaques and halt their progression guided by novel anti-inflammatory concepts in atherosclerosis treatment, anti-anginal treatments, renin-angiotensin-aldosterone system inhibitors, antiplatelet and anticoagulant drugs. Expert opinion: Efforts have been made to improve the clinical effectiveness and safety of established treatment strategies and target new frontiers through developing novel treatment strategies that tackle different mechanisms of action. Better understanding of the different molecular and cellular mechanisms underlying CAD has resulted in more innovations and achievements in CAD drug therapy, and still a lot more is anticipated in the coming years.
A Vector‐based Representation for Image Warping

A method for image analysis, representation and re‐synthesis is introduced. Unlike other schemes it is not pixel based but rather represents a picture as vector data, from which an altered version of the original image can be rendered. Representing an image as vector data allows performing operations such as zooming, retouching or colourising, avoiding common problems associated with pixel image manipulation. This paper brings together methods from the areas of computer vision, image compositing and image based rendering to prove that this type of image representation is a step towards accurate and efficient image manipulation.
City Research Online

Exploring interactive visualizations of data generated within the domain for which new products and services are to be designed can play a useful role in stimulating ideas that are considered highly appropriate to that domain. We describe a study in which participants in four collaborative design workshops used information visualizations representing electricity consumption data to help generate ideas for new products and services that could utilise the data generated by a smart home. Participants in the workshops appeared to use sensemaking behaviour to develop insights about the domain, which were later used in generating new ideas. Ideas arising from workshops where the stimulus was data visualized with less ambiguity in the visual encoding were judged to be significantly more appropriate than those from workshops where ambiguity in the visual encoding of the data used as stimulus was intentionally increased. We discuss the implications of this with regards to designing future workshop activities.
Bootstrapping chord in ad hoc networks not going anywhere for a while

With the growing prevalence of wireless devices, infrastructure-less ad hoc networking is coming closer to reality. Research in this field has mainly been concerned with routing. However, to justify the relevance of ad hoc networks, there have to be applications. Distributed applications require basic services such as naming. In an ad hoc network, these services have to be provided in a decentralized way. We believe that structured peer-to-peer overlays are a good basis for their design. Prior work has been focused on the long-run performance of virtual peer-to-peer overlays over ad hoc networks. In this paper, we consider a vital functionality of any peer-to-peer network: bootstrapping. We formally show that the self-configuration process of a spontaneously deployed chord network has a time complexity linear in the network size. In addition to that, its centralized bootstrapping procedure causes an unfavorable traffic load imbalance
Development of an Indoor Delivery Mobile Robot for a Multi-Floor Environment

In recent years, the demand for delivery services has increased by applying robot technology in various fields such as food services, logistics, hospitals, and hotel business. However, it is still challenging to perform autonomous delivery in multi-floor buildings. Particularly for wheeled robots, the use of elevators is essential for indoor last-mile delivery service in buildings. To tackle the problem, We have developed an indoor delivery mobile robot and present its architecture designed for multi-floor environments. The architecture consists of five modules: map management for utilizing an integrated navigation map, localization, path planning, perception, and task planning. The integrated navigation map is generated by combining multi-floor point cloud maps and topological maps based on node graphs for effective localization and path planning. Additionally, the proposed 3D route planning allows inter-floor movement. A feasible path for boarding the elevator can be generated through the perception module, and delivery services to multiple destinations can be repeatedly performed through task planning. Our architecture’s effectiveness is demonstrated through a month-long field test in an ordinary building during regular business hours. This study’s contributions include a novel architecture for autonomous delivery without human intervention, an integrated map for efficient indoor navigation, and the proven robustness of the system in real-world scenarios.
Regulatory Role of Dendritic Cells in Postinfarction Healing and Left Ventricular Remodeling

Background— Inflammation and immune responses are integral components in the healing process after myocardial infarction. We previously reported dendritic cell (DC) infiltration in the infarcted heart; however, the precise contribution of DC in postinfarction healing is unclear. Methods and Results— Bone marrow cells from CD11c-diphtheria toxin receptor/green fluorescent protein transgenic mice were transplanted into lethally irradiated wild-type recipient mice. After reconstitution of bone marrow–derived cells, the recipient mice were treated with either diphtheria toxin (DC ablation) or vehicle (control), and myocardial infarction was created by left coronary ligation. CD11c+ green fluorescent protein–positive DCs expressing CD11b and major histocompatibility complex class II were recruited into the heart, peaking on day 7 after myocardial infarction in the control group. Mice with DC ablation for 7 days showed deteriorated left ventricular function and remodeling. The DC-ablated group demonstrated enhanced and sustained expression of inflammatory cytokines such as interleukin-1&bgr;, interleukin-18, and tumor necrosis factor-&agr;, prolonged extracellular matrix degradation associated with a high level of matrix metalloproteinase-9 activity, and diminished expression level of interleukin-10 and endothelial cell proliferation after myocardial infarction compared with the control group. In vivo analyses revealed that DC-ablated infarcts had enhanced monocyte/macrophage recruitment. Among these cells, marked infiltration of proinflammatory Ly6Chigh monocytes and F4/80+ CD206− M1 macrophages and, conversely, impaired recruitment of anti-inflammatory Ly6Clow monocytes and F4/80+ CD206+ M2 macrophages in the infarcted myocardium were identified in the DC-ablated group compared with the control group. Conclusions— These results suggest that the DC is a potent immunoprotective regulator during the postinfarction healing process via its control of monocyte/macrophage homeostasis.
How to Trade Off the Quantity and Capacity of Teacher Ensemble: Learning Categorical Distribution to Stochastically Employ a Teacher for Distillation

We observe two phenomenons with respect to quantity and capacity: 1) more teacher is not always better for multi-teacher knowledge distillation, and 2) stronger teacher is not always better for single-teacher knowledge distillation. To trade off the quantity and capacity of teacher ensemble, in this paper, we propose a new distillation paradigm named Dynamic Knowledge Distillation (DynaKD) that learn an adaptive categorical distribution to stochastically employ a teacher from a teacher ensemble in each step, to transfer knowledge from teacher ensemble into student. DynaKD has three advantages: 1) it can preserve diversity of each teacher via one-to-one distillation manner instead of several-for-one, 2) it can make the best of powerful teacher via those multi-level assistant teachers in ensemble, and 3) it can also dynamically determine the importance of each teacher for various tasks. To verify the effectiveness of the proposed approach, we conduct extensive experiments for BERT compression on GLUE benchmark. Experimental results show that the proposed approach achieves state-of-the-art score compared to previous compression approaches on five out of seven downstream tasks, including pushing MRPC F1 and accuracy to 92.2 (1.4 point absolute improvement), RTE accuracy to 76.2 (2.8 point absolute improvement). Moreover, we conduct also extensive experiments for image classification on CIFAR-100. Similarly, DynaKD achieves also state-of-the-art performance.
Generation and decay of the immune response to a progressive fibrosarcoma. I. Ly-1+2- suppressor T cells down-regulate the generation of Ly-1-2+ effector T cells

It was shown that the progressive growth of the immunogenic meth A fibrosarcoma in its semisyngeneic host results in the generation of concomitant immunity to the growth of a tumor implant. The generation of immunity occurred between days 6 and 9 of tumor growth and was associated with the generation of sensitized T cells that were capable, on passive transfer, of causing regression of a 3-d tumor in gamma- irradiated recipients. After day 9 of tumor growth, concomitant immunity and the T cells able to passively transfer it were progressively lost, and this was associated with the generation of splenic suppressor T cells able to suppress the expression of adoptive immunity against an established tumor in T cell-deficient ( TXB ) recipients. The T cells that passively transferred concomitant immunity were shown to be of the Ly-1-2+ phenotype, in contrast to the T cells that transferred suppression, which were shown with the same reagents to be Ly-1+2-. The results are consistent with the hypothesis that the progressive growth of an immunogenic tumor results in the generation of Ly-1-2+-sensitized effector T cells that fail to reach a number sufficient to destroy the tumor because their generation is down- regulated by tumor-induced Ly-1+2- suppressor T cells.
Event-Based User Classification in Weibo Media

Weibo media, known as the real-time microblogging services, has attracted massive attention and support from social network users. Weibo platform offers an opportunity for people to access information and changes the way people acquire and disseminate information significantly. Meanwhile, it enables people to respond to the social events in a more convenient way. Much of the information in Weibo media is related to some events. Users who post different contents, and exert different behavior or attitude may lead to different contribution to the specific event. Therefore, classifying the large amount of uncategorized social circles generated in Weibo media automatically from the perspective of events has been a promising task. Under this circumstance, in order to effectively organize and manage the huge amounts of users, thereby further managing their contents, we address the task of user classification in a more granular, event-based approach in this paper. By analyzing real data collected from Sina Weibo, we investigate the Weibo properties and utilize both content information and social network information to classify the numerous users into four primary groups: celebrities, organizations/media accounts, grassroots stars, and ordinary individuals. The experiments results show that our method identifies the user categories accurately.