Miracle-dz/UniFAR-FTUs huggingface dataset:
In vivo toxicogenic potential of Salix alba (Salicaceae) bark extract

ABSTRACT Salix alba (white willow) bark extract is widely used for conditions associated with inflammation, fever, microbial infection or pain. Exposure of human cultured leukocytes to S. alba in vitro noted a genotoxic response. However, data regarding the influence of this bark extract on DNA damage in vivo are lacking. The main goal of this study was to examine the potential of S.alba bark extract to induce DNA damage and chromosome aberrations in an in vivo model using cells obtained from male Swiss albino mice administered the compound orally. The extract was administered by oral gavage daily for 7 days at doses of 500, 1000, or 2000 mg/kg b.w. Genotoxicity analysis was performed using the comet assay on peripheral blood leukocytes, as well as liver, bone marrow, heart, and testicular cells collected 4 hr after the last treatment and the micronucleus (MN) test on bone marrow cells. In essence cells were collected 28 hr after the penultimate treatment Data demonstrated that S. alba bark extract did not induce significant DNA damage in any cell types examined, or clastogenic/aneugenic effects as detected by the MN test at the three tested doses. Under these experimental conditions, evidence indicates that S.alba bark extract did not initiate genotoxic or chromosome aberrations in various mouse cells investigated.
Review and Update of the Risk Factors and Prevention of Antiresorptive-Related Osteonecrosis of the Jaw

Antiresorptive-related osteonecrosis of the jaw (ARONJ) is a rare but serious adverse event of bisphosphonate or denosumab administration; it is associated with severe pain and a deteriorated quality of life. Since its first report in 2003, there have been many studies on its definition, epidemiology, pathophysiology, diagnosis, and treatment. Nevertheless, the epidemiology and mechanisms underlying this condition have not yet been fully delineated and several risk factors are known. Moreover, as there is no effective treatment currently available for osteonecrosis of the jaw, prevention is essential. Furthermore, close cooperation between prescribing physicians and dentists is important. The aim of this review was to provide up-to-date information regarding the risk factors and prevention of ARONJ from a physician’s perspective.
The V2475F CPVT1 mutation yields distinct RyR2 channel populations that differ in their responses to cytosolic Ca2+ and Mg2+

Catecholaminergic polymorphic ventricular tachycardia type 1 (CPVT1) is a lethal genetic disease causing arrhythmias and sudden cardiac death in children and young adults and is linked to mutations in the cardiac ryanodine receptor (RyR2). The effects of CPVT1 mutations on RyR2 ion‐channel function are often investigated using purified recombinant RyR2 channels homozygous for the mutation. However, CPVT1 patients are heterozygous for the disease, so this approach does not reveal the true changes to RyR2 function across the entire RyR2 population of channels in the heart. We therefore investigated the native cardiac RyR2 single‐channel abnormalities in mice heterozygous for the CPVT1 mutation, V2475F(+/‐)‐RyR2, and applied molecular modelling techniques to investigate the possible structural changes that could initiate any altered function. We observed that increased sensitivity of cardiac V2475F(+/‐)‐RyR2 channels to both activating and inactivating levels of cytosolic Ca2+, plus attenuation of Mg2+ inhibition, were the most marked changes. Severity of abnormality was not uniform across all channels, giving rise to multiple sub‐populations with differing functional characteristics. For example, 46% of V2475F(+/‐)‐RyR2 channels exhibited reduced Mg2+ inhibition and 23% were actually activated by Mg2+. Using homology modelling, we discovered that V2475 is situated at a hinge between two regions of the RyR2 helical domain 1 (HD1). Our model proposes that detrimental functional changes to RyR2 arise because mutation at this critical site reduces the angle between these regions. Our results demonstrate the necessity of characterising the total heterozygous population of CPVT1‐mutated channels in order to understand CPVT1 phenotypes in patients.
Response to Letter to the Editor from Roy et al: “Impaired Suppression of Glucagon in Obese Subjects Parallels Decline in Insulin Sensitivity and Beta-Cell Function”

https://academic.oup.com/jcem 1 © The Author(s) 2021. Published by Oxford University Press on behalf of the Endocrine Society. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs licence (https://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial reproduction and distribution of the work, in any medium, provided the original work is not altered or transformed in any way, and that the work is properly cited. For commercial re-use, please contact journals.permissions@oup.com Letter to the Editor Response
Analysis of deep learning-based applications in grid device identification

Deep learning is a brand new field in machine learning research which constructs structured models to extract features by simulating the cognitive aspects of the human brain. The whole training process requires only the cooperative work of computers and no human involvement. In this paper, we explore the application and prospect of image recognition in grid equipment identification with respect to the principle of deep learning and the current development status in image recognition.
An efficient transformer algorithm for image recognition based on ensemble learning methodology

Cassava is an important food security crop in Africa because it can withstand harsh environments. However, viral diseases are the main reason for crop failure. Based on photos of cassava provided by local farmers in Africa, it is possible to use deep learning technology to identify some common viral diseases so that they can be treated. This paper introduced an image classification algorithm based on an ensemble learning[4] model, which combined Vision Transformer[2] and EfficientNet [1]. The practice has proved that the model proposed in this paper has a certain improvement in performance compared with traditional image classification methods and can effectively help local farmers.
A task offloading approach based on opportunistic prediction in mobile ad-hoc network

Compared with the traditional task allocation strategy, the proposed model considers the dynamic, limited and diversity resources in the process of offloading policy selection and analyzes the key factors in task offloading scheduling. Thus, the concept of opportunistic computing capacity is introduced in the opportunistic network, and the specific mechanism of task offloading is proposed. Considering the implementation method, the task execution time is studied in detail, and the mathematical model and solving algorithm are given. Finally, the simulation results show that the proposed method has more advantages than the traditional task scheduling method in terms of offloading efficiency and resource utility ratio.
Data augmentation using style transfer in SAR automatic target classification

Artificial intelligence (AI)-based methods for automatic target detection have been a research hotspot in the field of millimeter-wave security. That is, using artificial intelligence to determine if the results of millimeter-wave imaging include dangerous items, and to communicate the results to security personnel. This will not only avoid the leakage of private information, but also reduce the workload of security personnel and improve the efficiency during the security process. Existing deep learning networks require a large number of training dataset to optimize the network parameters. However, there are few datasets in the field of millimeter-wave imaging. In addition, due to local legal restrictions, researchers often do not have access to a large number of dangerous goods samples for the training of millimeter-wave imaging, which greatly limits the performance and applications of automatic classification in millimeter-wave security. In this paper, a method is proposed which uses style transfer techniques to combine a small number of millimeter-wave images with a large number of optical images to generate a library of millimeter-wave-like images. Specifically, the style transfer method combines the style features of a millimeter-wave image with the content features of an optical image to generate a new image. By combining different style images and content images, a large number of new images can be generated. The above generated images are then used to train any deep network for classification. The performance of proposed method is compared with a conventional method of data augmentation. The comparison results show that the method proposed in this paper effectively improves the accuracy of automatic classification in SAR automatic target classification.
Improved model search based on distillation framework

The model search based on the distillation model framework aims to train the candidate models adequately and guide a correct evaluation of the architecture. This NAS method can easily obtain middle-level monitoring identification indicators, thus significantly improving the effect. However, the model search based on distillation framework also has its shortcomings. First, the supervision indicators differ greatly for various teacher-student models, so how to determine a highly adaptable supervision indicator is a very important issue. Second, different teacher models will introduce biases. Based on the above problems, this paper proposes the following measures. Firstly, this paper adopts a more adaptable supervision index, which can effectively solve the problem that various teacher-student models differ greatly. Secondly, in order to reduce the bias introduced by the teacher model, this paper adopts the largest teacher model as the guidance model in the network training. Finally, this study uses a reinforcement learning algorithm to guide the search in the internal network, and introduce more supervision quantity, which makes the supervision effect between layers more obvious. It can be concluded that the above methods can effectively improve the model performance and consistency.
Maintenance and Welfare of Parents and Senior Citizens Act 2007: A Critical Appraisal

Background: The sociocultural changes associated with globalization and development have weakened the traditional values and family support systems for senior citizens (age 60 years and above). There is an increase in the prevalence of elder abuse and difficulties in getting appropriate care and support. This has mandated legal measures to protect the rights of the senior citizens and provide them care and support by the family and other stakeholders. Consequent to being the signatory for the “Madrid International Plan of Action on Ageing, 2002,” several countries, including India, have introduced legislation for the social protection of senior citizens. “The Maintenance and Welfare of Parents and Senior Citizens(MWP) Act, 2007” is an important legislation in India to safeguard the elderly from exploitation and abuse. Methods: This article critically evaluates the implementation of the MWP Act, 2007, and the related challenges in protecting senior citizens from abuse. The article will also highlight the proposed amendments in the Act to strengthen the effective implementation of legal protection for senior citizens and ensure their well-being and dignity. Conclusions: The MWP act is an important legal measure to ensure mainatinence and welfare of senior citizens and protect them from abuse and neglect. There is an immediate need to incorporate the necessary amendments so that the act gets more pragmatic value and becomes and important tool for elderly care and protection and reduce vulnerabilities and ensure holistic care with support in various bio-psycho-social domains.
Thermal conductivity and dynamic viscosity of mono and hybrid organic- and synthetic-based nanofluids: A critical review

Abstract Thermal conductivity and dynamic viscosity are two critical properties of nanofluids that indicate their heat transfer performance and flow. Nanofluids are prepared by dispersing mono or several organic or synthetic nanoparticles in selected base fluids to form mono or hybrid nanofluids. The qualitative and quantitative stability measurement of nanofluids will then be addressed, followed by a detailed discussion on how the dispersion of nanoparticles in water (W), ethylene glycol (EG), and the mixture of W:EG 60:40% by volume affects the thermal conductivity and dynamic viscosity ratio. The data comparison demonstrated that the thermal conductivity ratio increases with increasing normalized concentrations, the bulk temperature of nanofluids, and the smaller nanoparticle size. The dynamic viscosity ratio is multiplied by the normalized concentration increase. Nevertheless, as the bulk temperature climbed from 0 to 80°C, the dynamic viscosity ratio was scattered, and the dynamic viscosity ratio trend dropped with increasing particle size. While the majority of nanofluids enhanced thermal conductivity ratio by 20%, adding carbon-based nanoparticles to synthetic nanofluid increased it by less than 10%. The disadvantage of nanofluids is that they multiply the dynamic viscosity ratio of all nanofluids, which increase power consumption and reduces the efficiency of any mechanical system.
Bronchial Light Microscopy Image Segmentation Based on Boundary Attention

∗ The identification of bronchus is of great significance in assisting the diagnosis of lung diseases. However, identifying the bronchus from tissue light microscopy images is a heavily repetitive task that requires a lot of time and effort. Most of the mainstream segmentation methods pay attention to the overall accuracy of the region, without special consideration for the boundaries. However, bronchi often have flexible shapes, which poses a challenge for accurate segmentation, especially for details at the edges. Therefore, this paper proposes a boundary-attention based bronchi segmentation network. This network is a “predict and refine” architecture. Specifically, a coarse segmentation result is first generated by the prediction network, and then the edge segmentation quality is improved by the refinement network. In addition, by specially designed hybrid loss, our network can focus on patch-level contextual information as well as pixel-level accuracy. At the same time, the global attention module and the local attention module enable our network to extract both multiscale features and focus on error-prone regions. Through our network, not only fine segmentation results can be achieved, but also superior performance at the bronchial boundary. Experiments on the BronSeg dataset show that our method outperforms mainstream methods in all metrics, especially in mIOU, which reaches 88.41%.
Implementing VTA, a tensor accelerator on Flow-in-Cloud

A multi-FPGA system Flow-in-Cloud (FiC) consists of nodes with a mid-class cost-efficient FPGA and Raspberry Pi3B connected with high speed serial links. It aims to implement a large scale AI applications which is difficult to be implemented on a single FPGA by dividing the target into a number of boards. On the other hand, overlay domain specific architecture receives attention for implementing complicated application programs easily on the FPGA. Here, we focus on an open-source AI compiler framework Apache TVM, and its implementation for FPGA, VTA(Versatile Tensor Accelerator). In order to use FiC through the TVM, we implemented it on FiC and executed the ResNet-18 inference benchmark. The preliminary evaluation results showed that VTA on FiC-SW achieved up to 10 times performance compared to the execution on ARM Cortex-A54 software.
Understanding the Interpersonal Space of Online Meetings: An Exploratory Study of “We-ness”

The covid-19 pandemic has severely limited the possibility for people to meet physically, which forced many individuals and organizations to employ online meetings as their predominant mode of communication. A potential problem with the unprecedentedly central role of online meetings in a wide range of everyday activities is the disruption it may cause to intersubjective experiences, an intuitive mutual understanding of the participants and their thinking of themselves as a group, a “we”. To address this problem, about half a year into the pandemic we conducted an exploratory study, in which the informants (N=36) completed a survey, comprising a set of Likert scales and open-ended questions focusing on “team spirit”, moment-to-moment coordination, emotions, and the sense of presence in online and physical meetings. The results indicate that online meetings may present particular challenges regarding the experience of “we-ness”, and different types of online meetings can be experienced differently. Implications of the results for further research are discussed.
Conversational Search and Recommendation: Introduction to the Special Issue

While conversational search and recommendation has roots in early Information Retrieval (IR) research, the recent advances in automatic voice recognition and conversational agents have created increasing interest in this area. This topic was recognized as an emerging research area in the Third Strategic Workshop on Information Retrieval in Lorne (SWIRL 2018) [Culpepper et al. 2018]. Conversational search and recommendation systems consist of multiple components, from user modeling to conversational understanding to query modeling to result presentation. In recent years, the IR and related communities have witnessed a number of major contributions to the field of conversational search and recommendation. They include but are not limited to conversational search conceptualization (e.g., Azzopardi et al. [2018], Deldjoo et al. [2021], and Radlinski and Craswell [2017]), effective conversational query re-writing (e.g., Yu et al. [2020]), generating and selecting clarifying questions (e.g., Zamani et al. [2020a, c]), conversational preference elicitation (e.g., Radlinski et al. [2019] and Zhang et al. [2018]), and understanding user interactions with spoken conversational systems (e.g., Trippas et al. [2018, 2020]). The growing body of work in this area has been supplemented by an increasing number of recent seminars (e.g., Anand et al. [2020]), workshops (e.g., Arguello et al. [2018], Burtsev et al. [2017], Chuklin et al. [2018], and
The Global Labours of AI and Data Intensive Systems

The remarkable capacities of AI-infused and data intensive systems are regularly presented as emblematic of technoscientific innovation and progress. Far less recognised is the human labour required to sift through and sort data, and ultimately train these systems. This is a labour distributed well beyond the presumed centres of technological innovation, and spun into and strewn across geographically distributed regions in the ”Global South”. This workshop brings together scholars and practitioners interested in and conducting research on the hidden labours that lie behind AI and data intensive systems. Participants will share perspectives or results of their research and, through highly exploratory engagements and dialogues, set out a critical mode of inquiry for future directions in research, design and policy.
Multimodal Product Identification: Submission to Watch and Buy 2021 Challenge

This technical report describes the overview of our approach to the "Watch and Buy: Multimodal Product Identification Challenge". Specifically, we tackle this problem with a three-stage framework, i.e., product detection, retrieval and classification. For the product detection, we leverage the performance by Cascade R-CNN and deformable convolution to alleviate the impact of image distortion. For the product retrieval, we enhance the Multiple Granularity Network (MGN) with global and local context through IBN, SE and Non-local blocks. The task of product classification suffers from fashion variation. To this end, we propose to fuse the global feature of the integral images and local feature of products. Experiments demonstrate that our works could achieve competitive performance with the state-of-the-art methods and our overall approach achieves a F1 score of 0.648, ranking the second place in the final challenge.
Bridging from Crisis to Everyday Life – An Analysis of User Reviews of the Warning App NINA and the COVID-19 Regulation Apps CoroBuddy and DarfIchDas

During a dynamic and protracted crisis such as the COVID-19 pandemic, citizens are continuously challenged with making decisions under uncertainty. In addition to evaluating the risk of their behaviors to themselves and others, citizens also have to consider the most current regulation, which often varies federally and locally and by incidence numbers. Few tools help to stay informed about the current rules. The state-run German multi-hazard warning app NINA incorporated a feature for COVID-19, while two apps, DarfIchDas and CoroBuddy, focus only on COVID-19 regulation and are privately run. To investigate users’ expectations, perceived advantages, and gaps as well as the developers’ challenges, we analyze recent app store reviews of the apps and developers’ replies. We show that the warning app and the COVID-19 regulation apps are evaluated on different terms, that the correctness and portrayal of complex rules are the main challenges and that developers and editors are underusing users’ potential for crowdsourcing.
The Future of Research on Online Health Communities: Discussing Membership, Structure, and Support

Online health communities (OHCs) are spaces where people gather for informational and emotional support around specific medical conditions and concerns. Although OHCs are an active and exciting research area that continuously attracts a wide range of approaches and methodologies, the focus has been mostly on a limited selection of OHCs or medical conditions. At the same time, there are novel challenges that OHCs face, including changes to the medical system resulting from the COVID-19 crisis, increased medical misinformation propagating online, and additional focus on personalized medical advice that is less attainable in traditional medical systems. This workshop will bring together researchers to discuss and produce generalizable lessons about membership, structure, and support in OHCs in the context of these novel changes, generating research agendas for future exploration and design of OHCs.
Optimizing Convolutions for an Inference Accelerator: Case Study: Intel’s NNP-I 1000 DL Compute Grid

With Deep Learning (DL) surpassing humans in Image Recognition and Machine Translation related tasks, the demand for specialized hardware has increased in the recent past. DL Accelerators belong to a category of such purpose-built hardware that promise compelling performance for Neural Net computations. But a specialized hardware needs a powerful compiler to unlock its full potential. This paper discusses the Code Generator and Optimizer (CGO) that produces optimized tiling as well as schedule of Convolution operations for the DL Compute Grid in Intel’s NNP-I 1000 platform. This paper also presents some of the key optimization techniques used and the associated performance gains across a rich variety of Deep Learning workloads.