Miracle-dz/UniFAR-FTUs huggingface dataset:
International Severe Acute Respiratory and emerging Infections Consortium Partners^

ISARIC (International Severe Acute Respiratory and emerging Infections Consortium) partnerships and outbreak preparedness initiatives enabled the rapid launch of standardised clinical data collection on COVID-19 in Jan 2020. Extensive global uptake of this resource has resulted in a large, standardised collection of comprehensive clinical data from hundreds of sites across dozens of countries. Data are analysed regularly and reported publicly to inform patient care and public health response. This report is a part of a series and includes the results of data analysis on 3 September 2020. We thank all of the data contributors for their ongoing support. Report highlights include: ISARIC collaborators recorded symptoms from over 96,000 patients in hospital with COVID-19. Most had fever, cough or shortness of breath. Children and older adults were less likely to display typical symptoms, and over 40% of patients >80 years experienced confusion. The ISARIC international database continues to grow. Data have been entered for 96,074 individuals from 562 sites across 42 countries. The analysis detailed in this report only includes individuals:
Enabling New Computing Paradigms with Emerging Symmetric-Access Memories

This paper first presents a review of the circuit topologies of recently emerged symmetric memories that support 2-dimensional row-wise and column-wise accesses of read and write operations. Supporting technologies include CMOS, ferroelectric FET (FeFET) and two-terminal resistive memory devices. This paper will reveal how the emerging memory feature of access flexibility could enable new computing paradigms by providing fundamental savings of memory access times with column-wise memory access support. This benefit exists in both conventional applications and emerging computing, such as in-memory database and neural networks. In addition, this paper also proposes 2T1R, a new symmetric memory circuit design based on two-terminal resistive devices, such as RRAM and MTJ. This new design exhibits improved leakage power and scaling capability, as compared with the existing RRAM-based crossbar symmetric memory design.
Large Field of View Cooperative Infrared and Visible Spectral Sensor for Visual Odometry

Thermal images produced by the long-wavelength infrared (LWIR) camera are robust and independent from environmental illumination change. They can help the standard visible light camera working under the complicated environmental condition. Breaking through the traditional stereo multi-spectral sensor consisting of a visible-light camera and a LWIR camera, a novel architecture of large field of view (FOV) cooperated infrared and visible spectral sensor for visual odometry is proposed. The novel sensor is equipped with two visible cameras, four infrared cameras covering 120 degrees FOV in horizontal under both bands. Distribution of cameras and related peripheral devices are specifically designed which makes the sensor’s volume less than 100 cm (length) $\times10$ cm (height) $\times10$ cm (width). The sensor’s cameras calibration, distortion correction and measurement principle are elaborated. Feature-based method for visible and multi-windowed optimization-based image alignment for infrared is designed for the visual odometer based on the different imaging mechanism and distribution of cameras in the sensor. The frames and estimated poses management from both bands are proposed. Moreover, all proposed methodologies can be implemented in the sensor’s embedded processor. The electrical power consumption is only 12W. Experiments of the sensor’s evaluation are performed, experimental results show that large FOV cooperated multi-spectral cameras can efficiently improve the robustness of visual odometry. The real-time performance of the sensor is higher than 10fps with disparity map construction under both bands.
Mems Ion Sources For Spectroscopic Identification Of Gaseous And Liquid Samples

This work describes the construction, working principle and properties of two types of ion sources made in MEMS technology, designed for spectroscopic identification of gaseous and liquid samples. The first one is formed as an electro-magnetic trap efficiently ionizing gas particles even in a high vacuum. The second one uses the principle of electro-spray ionization. The application of the same materials and consistent technology allows for their integration on a single silicon-glass chip.
Introgressive Hybridization between Bighead Carp and Silver Carp in the Mississippi and Illinois Rivers

Abstract Asian carps are classified as either bighead carp Hypophthalmichthys nobilis or silver carp H. molitrix by multiple presumptively diagnostic morphological characteristics; however, hybrids pose a dilemma. Fish sharing the morphological characteristics of both species were observed in an Illinois River backwater (Calhoun County, Illinois) approximately 5 mi (8 km) upriver from the confluence with the Mississippi River as well as in two locations in Pool 26 of the Mississippi River (Madison County, Illinois). Biopsied tissues from individuals exhibiting mixed morphological features were analyzed at four diagnostic allozyme loci (ADH-1*, sMDH-A*, CK-A*, and sSOD-1*) via starch gel electrophoresis. This comparison revealed a high percentage of hybridization (22.5%) from an indiscriminate sample of 120 fish. Moreover, an unexpected percentage (12.5%) of individuals identified in the wild as either parental bighead carp or silver carp by gill raker morphology were genetically identified as hybrids. Fin.
An Intelligent Autonomous Robotic System for Precision Farming

The decline in the agricultural sector is a massive problem for Pakistan, as it relies primarily on agriculture. This decline has come about due to the outdated and traditional methods of farming that are still being employed in the country. These methods are ineffective, lack precision, cause wastage of resources, and also lack productivity. In this paper we present an autonomous robotic system which aims to improve the situation of the agricultural sector by employing precision agriculture techniques reducing human involvement resulting in increased accuracy of the tasks and consequently an improvement in the agricultural yield. The system entails a mechanical movable structure of a robot that is capable of navigating a farm field; it creates a map of the farm field by obtaining pH values at various distances. A machine learning module is employed that uses convolutional neural network to detect the presence of weed plants in the field. A spraying mechanism is constructed that is used not only for drip irrigation, saving water, but also to eliminate the detected weeds by spraying herbicides precisely on them. The overall operational support for the system is provided through Robot Operating System.
Development of approaches for identification of putative genes in the primary sequence of the human genome using virtual models of protein families

Bioinformatics combines the expertise from several disciplines such as biology, computing sciences and mathematics. Tremendous amount of primary sequence material has been created through the sequencing of several genomes, including the human one. One of the most important tasks of bioinformatics is to predict which part of the genomic material actually codes for genes. The aim of this project was to develop approaches for identifying coding regions in the human genome using virtual models of protein families or so called Hidden Markov Models (HMMs). The methods were tested using both available software, sequence and HMM databases. Moreover, additional software and databases were also developed to enable this approach. The entire chromosome 18 in the human genome was searched against a large repertoire of models in the Protein families database (Pfam) using the program HMMER. The results indicate that the method is sensitive for finding putative regions in the genome that may contain new genes. A number of parameters were tested out in order to increase specificity and speed of this approach. Several suggestions for optimisation concerning the HMM database and run time parameters are proposed in the discussion .
The Policy Potential of Measuring Internet Outages

Today it is possible to evaluate the reliability of the Internet. Prior approaches to measure network reliability required telecommunications providers reporting the status of their own networks, resulting in limits on the precision, timeliness, and availability of the results. Recent work in Internet measurement has shown that network outages can be observed with active measurements from a few sites, and from passive measurements of network telescopes (large, unused address space) or large network services such as content-delivery networks. We suggest that these kinds of third-party observations of network outages can provide data that is precise and timely. We discuss early results of Trinocular, an out-age detection system using active probing developed at the University of Southern California. Trinocular has been operating continuously since November 2013, and we provide (at no charge) data covering about 4 million network blocks from around the world. This paper describes some results of Trinocular showing outages in a large U.S. Internet Service Provider, and those resulting from the 2017 Hurricane Irma in Florida. Our data shows the impact of the Broadband America policy for always-on networks, and we discuss how it might be used to address future policy questions and assist in disaster planning and recovery.
Improvement of the Laser-Welded Lap Joint of Dissimilar Mg Alloy and Cu by Incorporation of a Zn Interlayer

During pulsed laser welding of AZ 31B magnesium (Mg) alloy and T2 pure copper (Cu), Cu2Mg and Mg2Cu are generated, but the bonding ability of the two compounds is usually weak, resulting in low strength. In order to improve the joint of two dissimilar metals, a zinc interlayer was inserted between the Mg alloy and Cu, and the effects of the thickness of the Zn interlayer on the microstructure and properties of the joint were studied. The fused zone consisted of Cu2Mg and MgZn, and, according to first-principles calculation, in the same energy range, the area enclosed by the density of the state curve of MgZn was larger than that of Cu2Mg. Hence, the bonding ability of MgZn was better than that of Cu2Mg, and MgZn improved the strength of the welded joint. The most advantageous thickness of the Zn interlayer was 0.1 mm, and the shear strength was 48.15 MPa that was 161% higher than that of the directly welded Mg/Cu joint.
Dual Activities of Receptor-Like Kinase OsWAKL21.2 Induce Immune Responses1

A rice receptor-like kinase induces immune responses in rice and Arabidopsis by kinase and guanylate cyclase activities, respectively. Plant pathogens secrete cell wall-degrading enzymes that degrade various components of the plant cell wall. Plants sense this cell wall damage as a mark of infection and induce immune responses. However, the plant functions that are involved in the elaboration of cell wall damage-induced immune responses remain poorly understood. Transcriptome analysis revealed that a rice (Oryza sativa) receptor-like kinase, WALL-ASSOCIATED KINASE-LIKE21 (OsWAKL21.2), is up-regulated following treatment with either Xanthomonas oryzae pv oryzae (a bacterial pathogen) or lipaseA/esterase (LipA; a cell wall-degrading enzyme of X. oryzae pv oryzae). Overexpression of OsWAKL21.2 in rice induces immune responses similar to those activated by LipA treatment. Down-regulation of OsWAKL21.2 attenuates LipA-mediated immune responses. Heterologous expression of OsWAKL21.2 in Arabidopsis (Arabidopsis thaliana) also activates plant immune responses. OsWAKL21.2 is a dual-activity kinase that has in vitro kinase and guanylate cyclase activities. Interestingly, kinase activity of OsWAKL21.2 is necessary to activate rice immune responses, whereas in Arabidopsis, OsWAKL21.2 guanylate cyclase activity activates these responses. Our study reveals a rice receptor kinase that activates immune responses in two different species via two different mechanisms.
The SrrAB two-component system regulates Staphylococcus aureus pathogenicity through redox sensitive cysteines

Significance Bacteria utilize sensory signal transduction systems to constantly survey and respond to changes in their environment. The human pathogen Staphylococcus aureus uses two-component signaling systems to sense its surroundings and adapt to hostile stresses of the host in part by shifting its metabolism. Here, we sought to understand how the S. aureus SrrAB two-component system senses changes in its environment during an infection. Using biophysical and biochemical approaches and in vivo experiments, we identified a mechanism by which the SrrB sensor histidine kinase detects a shift in its metabolism that occurs during an infection. Our findings provide general insight into the mechanisms by which bacterial histidine kinases sense environmental cues to regulate signal transduction and pathogenicity. Staphylococcus aureus infections can lead to diseases that range from localized skin abscess to life-threatening toxic shock syndrome. The SrrAB two-component system (TCS) is a global regulator of S. aureus virulence and critical for survival under environmental conditions such as hypoxic, oxidative, and nitrosative stress found at sites of infection. Despite the critical role of SrrAB in S. aureus pathogenicity, the mechanism by which the SrrAB TCS senses and responds to these environmental signals remains unknown. Bioinformatics analysis showed that the SrrB histidine kinase contains several domains, including an extracellular Cache domain and a cytoplasmic HAMP-PAS-DHp-CA region. Here, we show that the PAS domain regulates both kinase and phosphatase enzyme activity of SrrB and present the structure of the DHp-CA catalytic core. Importantly, this structure shows a unique intramolecular cysteine disulfide bond in the ATP-binding domain that significantly affects autophosphorylation kinetics. In vitro data show that the redox state of the disulfide bond affects S. aureus biofilm formation and toxic shock syndrome toxin-1 production. Moreover, with the use of the rabbit infective endocarditis model, we demonstrate that the disulfide bond is a critical regulatory element of SrrB function during S. aureus infection. Our data support a model whereby the disulfide bond and PAS domain of SrrB sense and respond to the cellular redox environment to regulate S. aureus survival and pathogenesis.
Sampling for Big Data Profiling: A Survey

Due to the development of internet technology and computer science, data is exploding at an exponential rate. Big data brings us new opportunities and challenges. On the one hand, we can analyze and mine big data to discover hidden information and get more potential value. On the other hand, the 5V characteristic of big data, especially Volume which means large amount of data, brings challenges to storage and processing. For some traditional data mining algorithms, machine learning algorithms and data profiling tasks, it is very difficult to handle such a large amount of data. The large amount of data is highly demanding hardware resources and time consuming. Sampling methods can effectively reduce the amount of data and help speed up data processing. Sampling technology has been widely used in big data context. Data profiling is the activity that finds metadata of data set and has many use cases, e.g., performing data profiling tasks on relational data, graph data, and time series data for anomaly detection and data repair. However, data profiling is computationally expensive, especially for large data sets. Hence this article focuses on researching sampling for data profiling tasks in big data context and investigates the application of sampling in different categories of data profiling. From the experimental results of these studies, the results got from the sampled data are close to or even exceed the results of the full amount of data. Therefore, sampling technology plays an important role in the era of big data, and we also have reason to believe that sampling technology will become an indispensable step in big data processing in the future.
Coral Productivity Is Co-Limited by Bicarbonate and Ammonium Availability

The nitrogen environment and nitrogen status of reef-building coral endosymbionts is one of the important factors determining the optimal assimilation of phototrophic carbon and hence the growth of the holobiont. However, the impact of inorganic nutrient availability on the photosynthesis and physiological state of the coral holobiont is partly understood. This study aimed to determine if photosynthesis of the endosymbionts associated with the coral Stylophora pistillata and the overall growth of the holobiont were limited by the availability of dissolved inorganic carbon and nitrogen in seawater. For this purpose, colonies were incubated in absence or presence of 4 µM ammonium and/or 6 mM bicarbonate. Photosynthetic performances, pigments content, endosymbionts density and growth rate of the coral colonies were monitored for 3 weeks. Positive effects were observed on coral physiology with the supplementation of one or the other nutrient, but the most important changes were observed when both nutrients were provided. The increased availability of DIC and NH4+ significantly improved the photosynthetic efficiency and capacity of endosymbionts, in turn enhancing the host calcification rate. Overall, these results suggest that in hospite symbionts are co-limited by nitrogen and carbon availability for an optimal photosynthesis.
RAD-seq-Based High-Density Linkage Map Construction and QTL Mapping of Biomass-Related Traits in Sorghum Using a Japanese Landrace Takakibi NOG.

Sorghum (Sorghum bicolor (L.) Moench) grown locally by Japanese farmers is generically termed Takakibi, although its genetic diversity compared with geographically distant varieties or even within Takakibi lines remains unclear. To explore the genomic diversity and genetic traits controlling biomass and other physiological traits in Takakibi, we focused on a landrace, NOG in this study. Admixture analysis of 460 sorghum accessions revealed that NOG belonged to the subgroup that represented Asian sorghums, and it was only distantly related to American/African accession including BTx623. In an attempt to dissect major traits related to biomass, we generated a recombinant inbred line (RIL) from a cross between BTx623 and NOG, and we constructed a high-density linkage map, based on 3,710 single nucleotide polymorphisms obtained by restriction-site associated DNA sequencing (RAD-seq) of 213 RIL individuals. Consequently, 13 fine QTLs were detected on chromosomes 2, 3, 6, 7, 8, and 9, which included five QTLs for days to heading, three for plant height and total shoot fresh weight; and two for Brix. Furthermore, we identified two dominant loci for plant height as being identical to the previously reported dw1 and dw3. Together, these results corroborate the diversified genome of Japanese Takakibi, and the RIL population and high-density linkage map generated in this study will be useful for dissecting other important traits in sorghum.
A systematic review of neurological impairments in myalgic encephalomyelitis/ chronic fatigue syndrome using neuroimaging techniques

Background Myalgic encephalomyelitis/ Chronic Fatigue Syndrome (ME/CFS) is a multi-system illness characterised by a diverse range of debilitating symptoms including autonomic and cognitive dysfunction. The pathomechanism remains elusive, however, neurological and cognitive aberrations are consistently described. This systematic review is the first to collect and appraise the literature related to the structural and functional neurological changes in ME/CFS patients as measured by neuroimaging techniques and to investigate how these changes may influence onset, symptom presentation and severity of the illness. Methods A systematic search of databases Pubmed, Embase, MEDLINE (via EBSCOhost) and Web of Science (via Clarivate Analytics) was performed for articles dating between December 1994 and August 2019. Included publications report on neurological differences in ME/CFS patients compared with healthy controls identified using neuroimaging techniques such as magnetic resonance imaging, positron emission tomography and electroencephalography. Article selection was further refined based on specific inclusion and exclusion criteria. A quality assessment of included publications was completed using the Joanna Briggs Institute checklist. Results A total of 55 studies were included in this review. All papers assessed neurological or cognitive differences in adult ME/CFS patients compared with healthy controls using neuroimaging techniques. The outcomes from the articles include changes in gray and white matter volumes, cerebral blood flow, brain structure, sleep, EEG activity, functional connectivity and cognitive function. Secondary measures including symptom severity were also reported in most studies. Conclusions The results suggest widespread disruption of the autonomic nervous system network including morphological changes, white matter abnormalities and aberrations in functional connectivity. However, these findings are not consistent across studies and the origins of these anomalies remain unknown. Future studies are required confirm the potential neurological contribution to the pathology of ME/CFS.
Influence of the addition of flavoring on the sodium content of fried potato chips and the fatty acid profile.

The influence of the addition of flavorings on the sodium content and the fatty acid profile of fried potato chips, particularly of trans fatty acids and the centesimal composition, of fried potato chips was investigated. Three brands of two types of flavored fried potato chips (onion plus parsley and barbecue) and the traditional product were assessed. An increase in the sodium content was observed in all flavored products of all brands. Among the flavored products, brand B had the lowest values of sodium (0.40, 0.47 and 0.60 g 100 g -1 of the unflavored product, onion and parsley, and barbecue, respectively). The total lip id (TL) values were h igh, ranging fro m 28 to 35%. All flavors of brand C had the lowest TL content. The fatty acids in the greatest amounts were palmitic acid (16:0), stearic acid (18:0), and oleic acid (18:2n-6). The amounts of trans fatty acids (TFA) were lo w and under 0.2 g per portion, the limit set by the regulation in force. A mong the samples evaluated, brand C had the highest TFA contents, 92, 157, and 95 mg 100 g -1 of food in the traditional, onion and parsley, and
Performance Analysis of Convolutional Neural Network Using Multi-level Memristor Crossbar for Edge Computing

In this paper, the performance analysis of convolutional neural network (CNN) with multi-level memristor crossbar is presented. Multi-level memristor crossbar is used to implement the Vector-Matrix Multiplication (VMM), which is the most computationally intensive step in the CNN algorithm. A procedure to convert a classical CNN model with floating-point accuracy weights to finite-bit weights implemented with multilevel memristor is presented. The impacts of memristor levels, crossbar line resistance and crossbar array size to the VMM calculation accuracy and the CNN classification accuracy are analyzed in details. As an example, one converted CNN is tested with a down sampled 14x14 hand-writing digits dataset. Classification accuracy of 93% and 95% are achieved with 4-bit memristor crossbar and 5-bit memristor, respectively.