Miracle-dz/UniFAR-FTUs huggingface dataset:
Invoking Deep Learning for Joint Estimation of Indoor LiFi User Position and Orientation

Light-fidelity (LiFi) is a fully-networked bidirectional optical wireless communication (OWC) technology that is considered as a promising solution for high-speed indoor connectivity. In this paper, the joint estimation of user 3D position and user equipment (UE) orientation in indoor LiFi systems with unknown emission power is investigated. Existing solutions for this problem assume either ideal LiFi system settings or perfect knowledge of the UE states, rendering them unsuitable for realistic LiFi systems. In addition, these solutions consider the non-line-of-sight (NLOS) links of the LiFi channel gain as a source of deterioration for the estimation performance instead of harnessing these components in improving the position and the orientation estimation performance. This is mainly due to the lack of appropriate estimation techniques that can extract the position and orientation information hidden in these components. In this paper, and against the above limitations, the UE is assumed to be connected with at least one access point (AP), i.e., at least one active LiFi link. Fingerprinting is employed as an estimation technique and the received signal-to-noise ratio (SNR) is used as an estimation metric, where both the line-of-sight (LOS) and NLOS components of the LiFi channel are considered. Motivated by the success of deep learning techniques in solving several complex estimation and prediction problems, we employ two deep artificial neural network (ANN) models, one based on the multilayer perceptron (MLP) and the second on the convolutional neural network (CNN), that can map efficiently the instantaneous received SNR with the user 3D position and the UE orientation. Through numerous examples, we investigate the performance of the proposed schemes in terms of the average estimation error, precision, computational time, and the bit error rate. We also compare this performance to that of the k-nearest neighbours (KNN) scheme, which is widely used in solving wireless localization problems. It is demonstrated that the proposed schemes achieve significant gains and are superior to the KNN scheme.
A Hybrid Neuromorphic Object Tracking and Classification Framework for Real-time Systems

Deep learning inference that needs to largely take place on the "edge" is a highly computational and memory intensive workload, making it intractable for low-power, embedded platforms such as mobile nodes and remote security applications. To address this challenge, this article proposes a real-time, hybrid neuromorphic framework for object tracking and classification using event-based cameras that possess desirable properties such as low-power consumption (5-14 mW) and high dynamic range (120 dB). Nonetheless, unlike traditional approaches of using event-by-event processing, this work uses a mixed frame and event approach to get energy savings with high performance. Using a frame-based region proposal method based on the density of foreground events, a hardware-friendly object tracking scheme is implemented using the apparent object velocity while tackling occlusion scenarios. The frame-based object track input is converted back to spikes for TrueNorth (TN) classification via the energy-efficient deep network (EEDN) pipeline. Using originally collected datasets, we train the TN model on the hardware track outputs, instead of using ground truth object locations as commonly done, and demonstrate the ability of our system to handle practical surveillance scenarios. As an alternative tracker paradigm, we also propose a continuous-time tracker with C ++ implementation where each event is processed individually, which better exploits the low latency and asynchronous nature of neuromorphic vision sensors. Subsequently, we extensively compare the proposed methodologies to state-of-the-art event-based and frame-based methods for object tracking and classification, and demonstrate the use case of our neuromorphic approach for real-time and embedded applications without sacrificing performance. Finally, we also showcase the efficacy of the proposed neuromorphic system to a standard RGB camera setup when simultaneously evaluated over several hours of traffic recordings.
Mechanochemical green synthesis of hyper-crosslinked cyclodextrin polymers

Cyclodextrin nanosponges (CD-NS) are nanostructured crosslinked polymers made up of cyclodextrins. The reactive hydroxy groups of CDs allow them to act as multifunctional monomers capable of crosslinking to bi- or multifunctional chemicals. The most common NS synthetic pathway consists in dissolving the chosen CD and an appropriate crosslinker in organic polar aprotic liquids (e.g., N,N-dimethylformamide or dimethyl sulfoxide), which affect the final result, especially for potential biomedical applications. This article describes a new, green synthetic pathway through mechanochemistry, in particular via ball milling and using 1,1-carbonyldiimidazole as the crosslinker. The polymer obtained exhibited the same characteristics as a CD-based carbonate NS synthesized in a solvent. Moreover, after the synthesis, the polymer was easily functionalized through the reaction of the nucleophilic carboxylic group with three different organic dyes (fluorescein, methyl red, and rhodamine B) and the still reactive imidazoyl carbonyl group of the NS.
Predictability of the toxicity of a multiple mixture of dissimilarly acting chemicals to Vibrio fischeri

A promising tool for the risk assessment of chemical mixtures is the prediction of their toxicities from the effects of the individual components. For that purpose, concentration addition is uniformly regarded as valid for mixtures of similarly acting chemicals. Whether this concept or the competing notion of independent action is more appropriate for mixtures of dissimilarly acting chemicals is still in dispute. Therefore, the presented study analyzed and compared the predictive capabilities of both concepts for a multiple mixture designed of strictly dissimilarly acting compounds. Experimental investigations were conducted using a long‐term bioluminescence inhibition assay with Vibrio fischeri. Results show an excellent predictive power of independent action, while concentration addition overestimates the mixture toxicity. Thus, the precise prediction of mixture toxicities depends on a valid assessment of the similarity/dissimilarity of the mixture components. However, concentration addition underestimates the EC50 of the mixture only by a factor of less than three. As the similarity of components is often unknown for mixtures found in the environment, it is concluded that concentration addition may give a realistic worst case estimation of mixture toxicities for risk assessment procedures.
New insights into the mechanism of dynein motor regulation by lissencephaly-1

Lissencephaly (‘smooth brain’) is a severe brain disease associated with numerous symptoms, including cognitive impairment, and shortened lifespan. The main causative gene of this disease – lissencephaly-1 (LIS1) – has been a focus of intense scrutiny since its first identification almost 30 years ago. LIS1 is a critical regulator of the microtubule motor cytoplasmic dynein, which transports numerous cargoes throughout the cell, and is a key effector of nuclear and neuronal transport during brain development. Here, we review the role of LIS1 in cellular dynein function and discuss recent key findings that have revealed a new mechanism by which this molecule influences dynein-mediated transport. In addition to reconciling prior observations with this new model for LIS1 function, we also discuss phylogenetic data that suggest that LIS1 may have coevolved with an autoinhibitory mode of cytoplasmic dynein regulation.
Frequency of depression, anxiety and stress among university students

Objective: The main objective of the present study was to explore the frequency of Depression, anxiety and stress among university students in Sialkot, Pakistan. Method: Survey research method was used to collect data from three universities of Sialkot by using simple random sampling technique from 500 university students. The study was conducted at GC Women University, Sialkot in total duration of five months from February 2019 to June 2019. A demographic sheet and DASS-21 (Depression, Anxiety Stress Scale) were used to measure the level of depression, anxiety and stress. Data was scored according to the standard scoring procedure for each subscale and for further analysis frequency distribution method was applied through statistical package for social sciences (SPSS. 21). Results: The means of Depression, Anxiety and stress are M=15.08, M=18.24 and M=19.02 respectively. The frequency of depression, anxiety and stress among university students was found 75%, 88.4% and 84.4% respectively. The findings of the study showed the prevalence of Depression within the range of normal (25%), mild (16%), moderate (35.8%), severe (14.6%) and extremely severe (8.6%). The prevalence of anxiety was found to be in the range of normal (11.6%), mild (4.4%), moderate (19.4%), severe (17.8%) and extremely severe (46.8%). Stress was normal (15.6%), mild (33.8%), moderate (35.4%), severe (13.2%) and extremely severe (2.8%). Conclusion: It is concluded that symptoms of anxiety and stress are more prevalent with moderate to extremely severe range than depression in the current sample. These findings suggest urgent need of some preventive measures and interventions to improve the mental health of students.
Epiploic Appendagitis Causing Small Bowel Obstruction: A Pleasant Surprise

Introduction Epiploic appendagitis is a rare cause of acute abdominal pain, mimicking acute diverticulitis or appendicitis. Epiploic appendagitis causing small bowel obstruction is highly unusual, and only a handful of such cases have been reported so far. Case Report. A 69-year-old man presented with diffuse abdominal pain and vomiting over the last 12 hours. Clinical examination, laboratory, and imaging investigations showed small bowel obstruction, and after 12 hours of conservative management, surgical exploration was decided. During surgery, a dilated terminal ileum bowel loop was found densely adhered to the sigmoid colon, attached to an inflamed epiploic appendix. The small bowel was mobilized and freed, and the inflamed epiploic appendix was resected. The postoperative course was uneventful, and the patient was discharged on the 5th postoperative day. Conclusion Epiploic appendagitis, although a rare clinical entity, may explain small bowel obstruction symptoms.
Predicting the effect of variants on splicing using Convolutional Neural Networks

Mutations that cause an error in the splicing of a messenger RNA (mRNA) can lead to diseases in humans. Various computational models have been developed to recognize the sequence pattern of the splice sites. In recent studies, Convolutional Neural Network (CNN) architectures were shown to outperform other existing models in predicting the splice sites. However, an insufficient effort has been put into extending the CNN model to predict the effect of the genomic variants on the splicing of mRNAs. This study proposes a framework to elaborate on the utility of CNNs to assess the effect of splice variants on the identification of potential disease-causing variants that disrupt the RNA splicing process. Five models, including three CNN-based and two non-CNN machine learning based, were trained and compared using two existing splice site datasets, Genome Wide Human splice sites (GWH) and a dataset provided at the Deep Learning and Artificial Intelligence winter school 2018 (DLAI). The donor sites were also used to test on the HSplice tool to evaluate the predictive models. To improve the effectiveness of predictive models, two datasets were combined. The CNN model with four convolutional layers showed the best splice site prediction performance with an AUPRC of 93.4% and 88.8% for donor and acceptor sites, respectively. The effects of variants on splicing were estimated by applying the best model on variant data from the ClinVar database. Based on the estimation, the framework could effectively differentiate pathogenic variants from the benign variants (p = 5.9 × 10−7). These promising results support that the proposed framework could be applied in future genetic studies to identify disease causing loci involving the splicing mechanism. The datasets and Python scripts used in this study are available on the GitHub repository at https://github.com/smiile8888/rna-splice-sites-recognition.
Signalisation apototique induite par les LDL oxydées Implication dans l’athérosclérose

The balance between vascular cell proliferation vs apoptosis plays a key role in vessel wall remodeling. Intimal migration and proliferation of smooth muscle cells (SMC), and secretion of extracellular matrix are involved in fibrous cap formation and plaque stability, whereas apoptosis of vascular cells may contribute to the erosion and instability of the plaque leading to its rupture and subsequent thrombus formation. LDL become atherogenic after undergoing oxidation within the vascular wall. Oxidized LDL (oxLDL) and oxidized lipids exhibit complex biological properties involved in endothelial dysfunction, SMC migration and proliferation, inflammation, and apoptosis. Oxidized LDL-induced apoptosis involves the extrinsic propapoptotic pathway (linked to Fas/Fas ligand) in lymphocytes, and the intrinsic mitochondrial apoptotic pathway, involving bcl-2 family members, cytochrome C release, and the terminal executive caspase-3 pathway, as well as the mitochondrial apoptotic factor AIF, in vascular cells. The apoptotic signaling of oxLDL is mediated in part by an intense and sustained rise of cytosolic calcium. The mechanisms regulating the balance between proliferation and apoptosis triggered by oxLDL and their role in vivo in atherosclerotic plaque progression remains to be clarified.
Future Generation Computer Systems

The Internet of Things (IoT) comes along with new challenges for experimenting, testing, and operating decentralized socio-technical systems at large-scale. In such systems, autonomous agents interact locally with their users, and remotely with other agents to make intelligent collective choices. Via these interactions they self-regulate the consumption and production of distributed (common) resources, e.g., self-management of traffic flows and power demand in Smart Cities. While such complex systems are often deployed and operated using centralized computing infrastructures, the socio-technical nature of these decentralized systems requires new value-sensitive design paradigms; empowering trust, transparency, and alignment with citizens’ social values, such as privacy preservation, autonomy, and fairness among citizens’ choices. Currently, instruments and tools to study such systems and guide the prototyping process from simulation, to live deployment, and ultimately to a robust operation of a high Technology Readiness Level (TRL) are missing, or not practical in this distributed socio-technical context. This paper bridges this gap by introducing a novel testbed architecture for decentralized socio-technical systems running on IoT. This new architecture is designed for a seamless reusability of (i) application-independent decentralized services by an IoT application, and (ii) different IoT applications by the same decentralized service. This dual self-integration promises IoT applications that are simpler to prototype, and can interoperate with decentralized services during runtime to self-integrate more complex functionality, e.g., data analytics, distributed artificial intelligence. Additionally, such integration provides stronger validation of IoT applications, and improves resource utilization, as computational resources are shared, thus cutting down deployment and operational costs. Pressure and crash tests during continuous operations of several weeks, with more than 80K network joining and leaving of agents, 2.4M parameter changes, and 100M communicated messages, confirm the robustness and practicality of the testbed architecture. This work promises new pathways for managing the prototyping and deployment complexity of decentralized socio-technical systems running on IoT, whose complexity has so far hindered the adoption of value-sensitive self-management approaches in Smart Cities. © 2020TheAuthors.PublishedbyElsevierB. V.ThisisanopenaccessarticleundertheCCBYlicense (http://creativecommons.org/licenses/by/4.0/).
Effect of Pulmonary Rehabilitation in Patients with Asthma COPD Overlap Syndrome: A Randomized Control Trial

Objectives We sought to evaluate the effectiveness of six weeks pulmonary rehabilitation (PR) in patients with asthma-chronic obstructive pulmonary disease (COPD) overlap syndrome (ACOS). Methods We enrolled 28 patients with ACOS. Fourteen patients were randomly allocated to the PR group, which comprised of supervised endurance training, supervised resistance training, breathing exercises, self management, and education. The other 14 patients were allocated to the control group, who were asked to continue their usual routine strategies for six weeks. All patients were assessed at baseline and after six weeks using the six minute walk test (6MWT), St George Respiratory Questionnaire (SGRQ), pulmonary function test (PFT), and Bode index (BI). Results We saw a significant improvement in 6MWT (p = 0.001), SGRQ (p = 0.007), and BI (p < 0.001) in the PR group after six weeks compared to the control group. There was no significant difference between the groups for PFT (p = 0.182) after six weeks. Conclusions Use of a short-term PR program in ACOS patients results in favorable changes in functional capacity, health-related quality of life, and BI. However, short-term PR was not sufficient to register changes in pulmonary function in these patients.
Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RAs) for the management of nonalcoholic fatty liver disease (NAFLD): A systematic review

There are no licensed drugs for nonalcoholic fatty liver disease (NAFLD), and there is a lack of consensus on the best outcome measures for controlled trials. This systematic review aimed to evaluate the efficacy of GLP‐1 RAs in the management of NAFLD, the degree of heterogeneity in trial design and the robustness of conclusions drawn from these clinical trials. We searched publication databases and clinical trial registries through 2 November 2019 for clinical trials with NAFLD. We evaluated improvements in histological findings, noninvasive markers of hepatic steatosis, inflammation, and fibrosis, insulin resistance and anthropometric measures. Our final analysis included 24 clinical trials, comprising 6313 participants with a mean duration of 37 weeks. Four clinical trials, including RCT (n = 1), single‐arm studies (n = 2) and case series studies (n = 1), used biopsy‐confirmed liver histological change as their end‐points. The remaining studies (n = 20) used surrogate end‐points. GLP‐1 RAs were effective for the improvement in hepatic inflammation, hepatic steatosis and fibrosis. More importantly, GLP‐1 RAs showed promise in improving the histological features of NASH. In addition, 8 ongoing trials were identified. In this systematic review of published and ongoing clinical trials of the efficacy of GLP‐1RAs for NAFLD, we found that GLP‐1 RAs are effective for hepatic steatosis and inflammation, with the potential to reverse fibrosis. Further prospective studies of sufficient duration using histological end‐points are needed to fully assess the efficacy of GLP‐1 RAs in the management of NAFLD.
The social value of anonymity on campus: a study of the decline of Yik Yak

ABSTRACT This paper considers the social value of anonymity in online university student communities, through the presentation of research which tracked the final year of life of the social media application Yik Yak. Yik Yak was an anonymous, geosocial mobile application launched in 2013 which, at its peak in 2014, was used by around two million students in the US and UK. The research we report here is significant as a mixed method study tracing the final year of the life of this app in a large UK university between 2016 and 2017. The paper uses computational and ethnographic methods to understand what might be at stake in the loss of anonymity within university student communities in a datafied society. Countering the most common argument made against online anonymity – its association with hate speech and victimisation – the paper draws on recent conceptual work on the social value of anonymity to argue that anonymity online in this context had significant value for the communities that use it. This study of a now-lost social network constitutes a valuable portrait by which we might better understand our current predicament in relation to anonymity, its perceived value and its growing impossibility. GRAPHICAL ABSTRACT
Using application layer banner data to automatically identify IoT devices

In this paper, we re-implement a recent work published in Usenix Security 2018: "Acquistional Rule Based Engine for Discovering Internet-of-Things Devices". The paper introduced an NLP-based engine for automatically identifying the type, vendor, and product of IoT devices given banner data as input. We report on our efforts to reproduce the original implementation of the engine, documenting ambiguities around implementation and evaluation details that we encountered, as well as how we addressed them in our work. We evaluate our implementation on two ground truth datasets, finding that it fails to achieve the accuracy reported by the original authors. Our findings highlight the importance of recent community efforts towards a culture of reproducibility by presenting an example of how ambiguities in a research paper combined with lack of access to the original datasets can significantly affect a faithful re-implementation and evaluation.
Statistical Measurement of Ultrasound Placenta Images Complicated by Gestational Diabetes Mellitus Using Segmentation Approach

Medical diagnosis is the major challenge faced by the medical experts. Highly specialized tools are necessary to assist the experts in diagnosing the diseases. Gestational Diabetes Mellitus is a condition in pregnant women which increases the blood sugar levels. It complicates the pregnancy by affecting the placental growth. The ultrasound screening of placenta in the initial stages of gestation helps to identify the complication induced by GDM on the placental development which accounts for the fetal growth. This work focus on the classification of ultrasound placenta images into normal and abnormal images based on statistical measurements. The ultrasound images are usually low in resolution which may lead to loss of characteristic features of the ultrasound images. The placenta images obtained in an ultrasound examination is stereo mapped to reconstruct the placenta structure from the ultrasound images. The dimensionality reduction is done on stereo mapped placenta images using wavelet decomposition. The ultrasound placenta image is segmented using watershed approach to obtain the statistical measurements of the stereo mapped placenta images. Using the statistical measurements, the ultrasound placenta images are then classified as normal and abnormal using Back Propagation neural networks.