TBCOV: Two Billion Multilingual COVID-19 Tweets with Sentiment, Entity, Geo, and Gender Labels

As the world struggles with several compounded challenges caused by the COVID-19 pandemic in the health, economic, and social domains, timely access to disaggregated national and sub-national data are important to understand the emergent situation but it is difficult to obtain. The widespread usage of social networking sites, especially during mass convergence events, such as health emergencies, provides instant access to citizen-generated data offering rich information about public opinions, sentiments, and situational updates useful for authorities to gain insights. We offer a large-scale social sensing dataset comprising two billion multilingual tweets posted from 218 countries by 87 million users in 67 languages. We used state-of-the-art machine learning models to enrich the data with sentiment labels and named-entities. Additionally, a gender identification approach is proposed to segregate user gender. Furthermore, a geolocalization approach is devised to geotag tweets at country, state, county, and city granularities, enabling a myriad of data analysis tasks to understand real-world issues at national and sub-national levels. We believe this multilingual data with broader geographical and longer temporal coverage will be a cornerstone for researchers to study impacts of the ongoing global health catastrophe and to manage adverse consequences related to people’s health, livelihood, and social well-being.

An accurate star identification approach based on spectral graph matching for attitude measurement of spacecraft

Star identification is the foundation of star trackers, which are used to precisely determine the attitude of spacecraft. In this paper, we propose a novel star identification approach based on spectral graph matching. In the proposed approach, we construct a feature called the neighbor graph for each main star, transforming the star identification to the problem of finding the most similar neighbor graph. Then the rough search and graph matching are cooperated to form a dynamic search framework to solve the problem. In the rough search stage, the total edge weight in the minimum spanning tree of the neighbor graph is selected as an indicator, then the k-vector range search is applied for reducing the search scale. Spectral graph matching is utilized to achieve global matching, identifying all stars in the neighbor circle with good noise-tolerance ability. Extensive simulation experiments under the position noise, lost-star noise, and fake-star noise show that our approach achieves higher accuracy (mostly over 99%) and better robustness results compared with other baseline algorithms in most cases.

Speedy Detection Module for Abandoned Belongings in Airport Using Improved Image Processing Technique

Recently, in computer vision and video surveillance applications, moving object recognition and tracking have become more popular and are hard research issues. When an item is left unattended in a video surveillance system for an extended period of time, it is considered abandoned. Detecting abandoned or removed things from complex surveillance recordings is challenging owing to various variables, including occlusion, rapid illumination changes, and so forth. Background subtraction used in conjunction with object tracking are often used in an automated abandoned item identification system, to check for certain pre-set patterns of activity that occur when an item is abandoned. An upgraded form of image processing is used in the preprocessing stage to remove foreground items. In subsequent frames with extended duration periods, static items are recognized by utilizing the contour characteristics of foreground objects. The edge-based object identification approach is used to classify the identified static items into human and nonhuman things. An alert is activated at a specific distance from the item, depending on the analysis of the stationary object. There is evidence that the suggested system has a fast reaction time and is useful for monitoring in real time. The aim of this study is to discover abandoned items in public settings in a timely manner.

Paraboeremia yungensis sp. nov., a new fungal species isolated from Las Yungas, South America, with promising tyrosinase production potential

In the context of a bioprospection programme for tyrosinase/L-DOPA- and melanin-producing fungal strains for biotechnological purposes, a hyperproducer isolate was obtained from Las Yungas rainforest, a relevant biodiverse ecoregion in North-Western Argentina. The selected strain was preliminarily identified as Paraboeremia sp. This is, to the best of our knowledge, the first native reported species of this genus in South America. Single-gene and multi-locus analyses of the internal transcribed spacer nuclear ribosomal RNA gene region (ITS), partial large subunit 28S nrDNA region (LSU), RNA polymerase II region (RPB2) and partial β-tubulin gene (TUB2) alignments were carried out to define the phylogenetic identity of this strain. As part of a polyphasic identification approach, these results were combined with morphological studies of active cultures growing on malt extract, oatmeal and potato dextrose agar plates. Incubation was performed under diverse conditions to stimulate sporulation for the subsequent micromorphological analysis. Microphotographs of pycnidia and conidia were taken with a scanning electron microscope. Maximum likelihood and Bayesian Inference analyses supported the location of the strain within the genus Paraboeremia, whilst morphological features allowed distinguishing it from previously described species within this genus. Based on the results herein reported, the new South-American species Paraboeremia yungensis is described and proposed.

Identification of Phytaspase Interactors via the Proximity-Dependent Biotin-Based Identification Approach

Proteolytic enzymes are instrumental in various aspects of plant development, including senescence. This may be due not only to their digestive activity, which enables protein utilization, but also to fulfilling regulatory functions. Indeed, for the largest family of plant serine proteases, subtilisin-like proteases (subtilases), several members of which have been implicated in leaf and plant senescence, both non-specific proteolysis and regulatory protein processing have been documented. Here, we strived to identify the protein partners of phytaspase, a plant subtilase involved in stress-induced programmed cell death that possesses a characteristic aspartate-specific hydrolytic activity and unusual localization dynamics. A proximity-dependent biotin identification approach in Nicotiana benthamiana leaves producing phytaspase fused to a non-specific biotin ligase TurboID was employed. Although the TurboID moiety appeared to be unstable in the apoplast environment, several intracellular candidate protein interactors of phytaspase were identified. These were mainly, though not exclusively, represented by soluble residents of the endoplasmic reticulum, namely endoplasmin, BiP, and calreticulin-3. For calreticultin-3, whose gene is characterized by an enhanced expression in senescing leaves, direct interaction with phytaspase was confirmed in an in vitro binding assay using purified proteins. In addition, an apparent alteration of post-translational modification of calreticultin-3 in phytaspase-overproducing plant cells was observed.

An artificial neural-network approach to identify motor hotspot for upper-limb based on electroencephalography: a proof-of-concept study

Background To apply transcranial electrical stimulation (tES) to the motor cortex, motor hotspots are generally identified using motor evoked potentials by transcranial magnetic stimulation (TMS). The objective of this study is to validate the feasibility of a novel electroencephalography (EEG)-based motor-hotspot-identification approach using a machine learning technique as a potential alternative to TMS. Methods EEG data were measured using 63 channels from thirty subjects as they performed a simple finger tapping task. Power spectral densities of the EEG data were extracted from six frequency bands (delta, theta, alpha, beta, gamma, and full) and were independently used to train and test an artificial neural network for motor hotspot identification. The 3D coordinate information of individual motor hotspots identified by TMS were quantitatively compared with those estimated by our EEG-based motor-hotspot-identification approach to assess its feasibility. Results The minimum mean error distance between the motor hotspot locations identified by TMS and our proposed motor-hotspot-identification approach was 0.22 ± 0.03 cm, demonstrating the proof-of-concept of our proposed EEG-based approach. A mean error distance of 1.32 ± 0.15 cm was measured when using only nine channels attached to the middle of the motor cortex, showing the possibility of practically using the proposed motor-hotspot-identification approach based on a relatively small number of EEG channels. Conclusion We demonstrated the feasibility of our novel EEG-based motor-hotspot-identification method. It is expected that our approach can be used as an alternative to TMS for motor hotspot identification. In particular, its usability would significantly increase when using a recently developed portable tES device integrated with an EEG device.