Miracle-dz/UniFAR-FTUs huggingface dataset:
Importance of the Features of Event-Related Potentials Used for a Machine Learning-Based Model Applied to Single-Trial Data during Oddball Task

In this study, a method for assessing the human state and brain-machine interface (BMI) has been developed using event-related potentials (ERPs). Most of these algorithms are classified based on the ERP characteristics. To observe the characteristics of ERPs, an averaging method using electroencephalography (EEG) signals cut out by time-locking to the event for each condition is required. To date, several classification methods using only single-trial EEG signals have been studied. In some cases, the machine learning models were used for the classifications; however, the relationship between the constructed model and the characteristics of ERPs remains unclear. In this study, the LightGBM model was constructed for each individual to classify a single-trial waveform and visualize the relationship between these features and the characteristics of ERPs. The features used in the model were the average values and standard deviation of the EEG amplitude with a time width of 10 ms. The best area under the curve (AUC) score was 0.92, but, in some cases, the AUC scores were low. Large individual differences in AUC scores were observed. In each case, on checking the importance of the features, high importance was shown at the 10-ms time width section, where a large difference was observed in ERP waveforms between the target and the non-target. Since the model constructed in this study was found to reflect the characteristics of ERP, as the next step, we would like to try to improve the discrimination performance by using stimuli that the participants can concentrate on with interest.
R&D and Employment Composition: Evidence from UK Local Labour Markets∗

This paper provides a comprehensive account of the local employment impact of firms’ investment in R&D in UK local labour markets. We focus on the composition effects across industries and employment types. We distinguish the impact of R&D across areas with different initial shares of workers in routinised occupations and industry shares. Drawing on two instrumenting strategies, our results consistently suggest that R&D change, on average, exerts a small negative effect on local employment, mainly through changes in its composition. Results differ significantly for local labour markets with different initial shares of workers in routinised occupations. Areas with below median shares of workers in routinised occupations experience a relative reduction in low educated employment in non tradeable services and self-employment. In areas with above median shares of workers in routinised occupations, low education employment also increase, in tandem with an increase in non tradeable services; all the positive employment change occurs in self-employment. We qualify the positive effect of R&D on self-employment in highly routinised areas and find no evidence to distinguish if it is driven by opportunities related to R&D investment or necessity.
PCA AND PROJECTION BASED HAND GESTURE RECOGNITION

Developing hand gesture recognition algorithms, and more generally, pattern recognition algorithms is a very active area of research in computer vision. There are various approaches and techniques to the recognition problem among researchers. In this manuscript, our objective is to develop a novel Principal Component Analysis based hand gesture recognition algorithm, and compare its performance against k-Nearest Neighbor classifier and Sparse Representation based Classifier. The proposed algorithm makes use of linear triplet loss embedding and projections onto subspaces. An open source HandReader dataset consisting of 500 labeled images with 10 signs from American Sign Language is split into a training set with 100 images and a test set with 400 images. The proposed algorithm outperforms with 95% accuracy. This shows that the proposal methodology might be effective in computer vision when there is relatively small amount of data is available. It is expected that approaches similar to the current one will contribute the emergence of machine learning algorithms with Principal Component Analysis based techniques.
Team Opscidia at BioCreative VII LitCovid Track: BERT-based bagging-stacking for multi-topic classification

— This paper describes our submission on the COVID-19 literature annotation task at Biocreative VII. We proposed an approach that exploits the knowledge of the globally non-optimal weights, usually rejected, to build a rich representation of each label. Our proposed approach consists of two stages: (1) A bagging of various initializations of the training data that features weakly trained weights, (2) A stacking of heterogeneous vocabulary models based on BERT and RoBERTa Embeddings. The aggregation of these weak insights performs better than a classical globally efficient model. The purpose is the distillation of the richness of knowledge to a simpler and lighter model. Our system obtains an Instance-based F1 of 92.96 and a Label-based micro-F1 of 91.35.
A 3D nano driving system with large-stroke

To satisfy the need of precision displacement, a 3D Nano driving system, which including metrological vertical scanning system and coplanar 2D precision stage with large-stroke and nanopositioning resolution, is developed in this paper. The coarse-fine dual-stage actuators have been applied in the 3D Nano driving system in X, Y and Z directions. The servomotors combining with precision lead screws and linear guides are used to actuate the coarse driving, and piezoelectric actuators combining with parallel board flexure hinges are used to actuate the fine driving. Three laser interferometers are used to measure the displacement in X, Y and Z directions respectively. The structure and principle of the system is introduced and the displacement range and displacement resolution of the system are also analyzed in details. The 3D Nano driving system has been successfully applied in the white light interference based atomic force probe scanning measurement system for ultra-precision surface topography developed in our laboratory. The experimental results show that the nano-driving in X, Y, Z directions can be realized by the 3D Nano driving system.
Using Knowledge-Based Pretrained Language Model for Mining Drug and Chemical-Protein Interactions

—In this paper, we describe our systems for the DrugProt task of BioCreative VII. This task is to automatically detect in relations between chemical compounds/drug and genes/proteins. First, we use KeBioLM pretrained language model as text encoders and replace the cross-entropy function with focal loss to alleviate the imbalance in relation samples. Then we run five times with different seeds to obtain our ensemble model. Experimental results on the test set demonstrate our ensemble model achieves the F1-score of 0.7419, which outperforms the mean results of this track by 0.1222.
RSS Localization Using an Optimized Fusion of Two Deep Neural Networks

The problem of received signal strength (RSS) source localization in wireless sensor networks has raised attention in recent years due to its low complexity. However, the performance of the RSS localization algorithms is degraded due to malicious phenomena such as multipath and fading. Hence, machine learning and especially deep neural networks (DNNs) are applied to solve this problem. So far, convolutional the neural network (CNN), the recurrent neural network (RNN), and multilayer perceptron (MLP) are used to this end. In this letter, neural networks such as MLP, CNN, and RNN are suggested to be combined pairwise. In this regard, we propose to use a fusion of two general neural network localization estimators. To design the combination coefficients, a heuristic approach based on the errors of the training step of two neural networks are suggested. Moreover, two optimized approaches for selecting the combination coefficients are provided based on mean and variances and also based on the mean square error of the combined estimator. The simulation results show improvement of the localization accuracy by fusion of two neural networks.
Design of Tropical Fruit Quality Safety Traceability System Based on 2-Dimensional Bar Code in Manufacturing Environment

In order to realize the tropical fruit quality safety traceability, the paper proposes that using two-dimensional bar code (QR Code) technology constructs the tropical fruit quality safety traceability system. On the basis of the study of the two-dimensional bar code (QR Code) technology in Manufacturing Environment, author analyzes the elements involved of tropical fruit production and circulation, uses HACCP, FMECA and other technical methods, puts forward the key elements of the quality and safety of tropical fruit, uses country and industry relevant coding standard, and constructs tropical fruit quality safety traceability system based on two dimensional bar code (QR Code). Application shows that the tropical fruit companies or production bases by using the system improve the tropical fruit production safety management level, realize the tropical fruit quality safety traceability, promote tropical fruit industrialization and standardization management.
Antioxidant, Anti-Inflammatory, and Immunomodulatory Properties of Tea—The Positive Impact of Tea Consumption on Patients with Autoimmune Diabetes

The physiological markers of autoimmune diabetes include functional disorders of the antioxidative system as well as progressing inflammation and the presence of autoantibodies. Even though people with type 1 diabetes show genetic predispositions facilitating the onset of the disease, it is believed that dietary factors can stimulate the initiation and progression of the disease. This paper analyses the possibility of using tea as an element of diet therapy in the treatment of type 1 diabetes. Based on information available in literature covering the last 10 years, the impact of regular tea consumption or diet supplements containing tea polyphenols on the oxidative status as well as inflammatory and autoimmune response of the organism was analyzed. Studies conducted on laboratory animals, human patients, and in vitro revealed positive effects of the consumption of tea or polyphenols isolated therefrom on the diabetic body. Few reports available in the literature pertain to the impact of tea on organisms affected by type 1 diabetes as most (over 85%) have focused on cases of type 2 diabetes. It has been concluded that by introducing tea into the diet, it is possible to alleviate some of the consequences of oxidative stress and inflammation, thus limiting their destructive impact on the patients’ organisms, consequently improving their quality of life, regardless of the type of diabetes. Furthermore, elimination of inflammation should reduce the incidence of immune response. One should consider more widespread promotion of tea consumption by individuals genetically predisposed to diabetes, especially considering the drink’s low price, easy availability, overall benefits to human health, and above all, the fact that it can be safely used over extended periods of time, regardless of the patient’s age.
CLaC at BioCreative VII LitCovid Track: Independent modules for multi-label classiﬁcation of Covid articles

—For multi-label classiﬁcation of Covid articles we present a modular system where the representation learning for each class is performed by separate, independent modules that occasionally interact. To further improve the performance of the system, we also leverage specialized gazetteer lists using an additional module. All of our submitted runs outperform the competition mean and our best run performs well in terms of marco-averaged F1 score.
Overview of DrugProt BioCreative VII track: quality evaluation and large scale text mining of drug-gene/protein relations

Considering recent progress in NLP, deep learning techniques and biomedical language models there is a pressing need to generate annotated resources and comparable evaluation scenarios that enable the development of advanced biomedical relation extraction systems that extract interactions between drugs/chemical entities and genes, proteins or miRNAs. Building on the results and experience of the CHEMDNER, CHEMDNER patents and ChemProt tracks, we have posed the DrugProt track at BioCreative VII. The DrugProt track focused on the evaluation of automatic systems able to extract 13 different types of drug-genes/protein relations of importance to understand gene regulatory and pharmacological mechanisms. The DrugProt track addressed regulatory associations (direct/indirect, activator/inhibitor relations), certain types of binding associations (antagonist and agonist relations) as well as metabolic associations (substrate or product relations). To promote development of novel tools and offer a comparative evaluation scenario we have released 61,775 manually annotated gene mentions, 65,561 chemical and drug mentions and a total of 24,526 relationships manually labeled by domain experts. A total of 30 teams submitted results for the DrugProt main track, while 9 teams submitted results for the large-scale text mining sub-track that required processing of over 2,3 million records. Teams obtained very competitive results, with predictions reaching f-measures of over 0.92 for some relation types (antagonist) and f-measures across all relation types close to 0.8.
Evaluation of Applied Force During Nasopharyngeal Swab Sampling Using Handheld Sensorized Instrument

Nasopharyngeal swab is the most widely used diagnostic test for COVID-19 detection. However, enormous tests have posed a high risk of infection to medical professionals due to close contact with patients and substantial health burden. While automation of the nasopharyngeal swab is regarded as a potential solution to address these problems, the quantitative study of force for safe and effective control has not been widely performed yet. Hence, this study presents applied force during the standard nasopharyngeal swab sampling procedure using a handheld sensorized instrument. The sensorized instrument can simultaneously measure multi-axis forces and 6-DOF hand motion while allowing natural hand motion as is used in the standard swab sampling. To accurately measure force from the handheld instrument, the compensation of gravity bias is accomplished online while estimating the orientation of the hand with an embedded IMU sensor. As a result, the instrument can measure all three-axes forces by an error below 5 mN. A simulated test on a phantom model using the sensorized instrument shows that how the forces vary during the sampling sequences.
Exploring Nociceptive Detection Thresholds Combined with Evoked Potentials in Patients with Diabetes Mellitus

There is a lack of diagnostic tools that can objectively measure small fiber neuropathy (SFN) in patients with diabetes mellitus (DM). Recently, nociceptive nerve function was observed by nociceptive detection thresholds (NDTs) and brain evoked potentials (EPs) during intra-epidermal electrical stimulation (IES) targeting Aδ-fibers. In this proof of principle, we studied whether it is possible to measure NDTs combined with EPs in DM patients with and without neuropathic pain. Furthermore, we explored the sensitivity of NDTs and EPs for polyneuropathy in these patients. Five DM patients diagnosed with painful neuropathy (DMp), five DM patients without painful neuropathy (DM), and five healthy controls (HC) were analyzed. These preliminary results showed that we can accurately measure NDTs and EPs in patients with diabetes. Strikingly, increased NDTs were found in DM and DMp compared to HC, of which the DMp showed the largest NDTs. This suggests that NDTs during IES could be a powerful biomarker for monitoring peripheral dysfunctions. Current EEG data of patients did not show any significant differences. The population needs to be enlarged before we can investigate the sensitivity of these NDTs and EPs to diabetic polyneuropathy and associated changes in nociceptive processing in more detail.Clinical Relevance— This proof of principle in a small group of patients with diabetes mellitus potentially treats the observation of the loss of nociceptive function occurring with small fiber neuropathy. That helps the development of a diagnostic measure to monitor future (early-stage) nociceptive dysfunctions in a clinical environment.
Biodiversity of Zygosaccharomyces species in food systems

Zygosaccharomyces species are among the most problematic food spoilage yeasts. The two most infamous species are Zygosaccharomyces balii and Zygosaccharomyces rouxii, although they may also take a positive role during the production of some fermented foods. DNA sequence based yeast identification aided by freely available reference databases of barcoding DNA sequences has boosted the description rate of novel yeast species in the last two decades. The genus Zygosaccharomyces has been considerably expanded as well. Especially the number of the extremely osmotolerant Zygosaccharomyces species, related to Z. rouxii and regularly found in high-sugar foods, has enlarged. A brief account of recent developments in the taxonomy and biodiversity of this important food associated genus is given in this review.
EEG-based Emotion Recognition Using Similarity Measure of Brain Rhythm Sequencing

The similarity is a fundamental measure from the homology theory in bioinformatics, and the biological sequence can be classified based on it. However, such an approach has not been utilized for electroencephalography (EEG)-based emotion recognition. To this end, the sequence generated by choosing the dominant brain rhythm owning maximum instantaneous power at each 0.2 s timestamp of the EEG signal has been proposed. Then, to recognize emotional arousal and valence, the similarity measures between pairwise sequences have been performed by dynamic time warping (DTW). After evaluations, the sequence that provides the highest accuracy has been obtained. Thus, the representative channel has been found. Besides, the appropriate time segment for emotion recognition has been estimated. Those findings helpfully exclude redundant data for assessing emotion. Results from the DEAP dataset displayed that the classification accuracies between 72%–75% can be realized by applying the single-channel data with a 5 s length, which is impressive when considering fewer data sources as the primary concern. Hence, the proposed idea would open a new way that uses the similarity measures of sequences for EEG-based emotion recognition.
Contactless Heart Rate Variability (HRV) Estimation Using a Smartphone During Respiratory Maneuvers and Body Movement

Heart rate variability (HRV) has been extensively investigated as a noninvasive marker to evaluate the functionality of the autonomic nervous system (ANS). Many studies have provided photoplethysmography (PPG) as a surrogate for electrocardiogram (ECG) signal HRV measurements. Remote PPG (rPPG) has been also investigated for pulse rate variability (PRV) estimation but in controlled conditions. We remotely extracted PRV using a smartphone camera for subjects in static and lateral motion while their respiratory rate was set to three breathing rates in an indoor illumination environment. PRV was compared with ECG-based HRV as a gold standard. We tested our algorithms on five healthy subjects. The results showed high correlation for rPPG-based HRV by presenting means of standard deviation of normal-to-normal intervals (SDNN) and root mean square of successive heartbeat interval differences (RMSSD) correlation coefficient greater than 0.95 in rest and greater than 0.87 in motion. The error of mean low frequency over high frequency (LF/HF) ratio estimated from PRV was 0.13 in rest and 0.25 in lateral motion. Moreover, a statistically significant correlation was obtained between HRV and PRV power spectra and temporal signals for all performed tasks. The obtained results contributed to confirm that remote imaging measurement of cardiac parameters is a promising, convenient, and low-cost alternative to specialized biomedical sensors in a diversity of relevant experimental maneuver.
Diffraction efficiency considerations and experimental realization for adaptive phase gratings with liquid crystal panels

We report on diffraction efficiency considerations and experimental implementation of diffraction gratings by means of a phase-only spatial light modulator, parallel aligned (PAL) liquid crystal on silicon (LCoS) display. We present results of the implementation of continuous phase profiles for optimal efficiency, and their application for blazed gratings and diffractive lenses onto displays with reduced phase modulation range and also onto displays with large phase modulation range.
Evaluation of amino acids capped silver nanoconjugates for the altered oxidative stress and antioxidant potential in albino mice

Recent data suggest that there is a continuous need to develop safer and less toxic nanomaterials (NMs). For this, the surface chemistry of commonly used NMs may be modified using several biomolecules/conjugates. The current study has been designed to develop silver nanoconjugates (AgNCs) capped with different amino acids. Characterization of the same was performed using different techniques. During in vitro analysis, l-tyrosine and l-cystine capped AgNCs exhibited antioxidant activity, while l-glycine capped AgNCs showed prooxidant activity. l-cystine and l-tyrosine capped AgNCs also demonstrated good in vivo results. The level of catalase (CAT), superoxide dismutase (SOD), reduced glutathione (GSH), and metallothioneins (MTs) in different organs was evaluated. It is assumed that newly developed nanoconjugates can be utilized as nano-tools for different applications. However, more detailed studies with resolution techniques for safety, the risk associated, and their accuracy are needed for the utilization of these nanoconjugates for specific biomedical uses.
Energy Harvesting from Ambient Vibrations and Heat

Increasing demand in mobile, autonomous devices has made the issue of energy harvesting a particular point of interest. Systems that can be powered up by a few hundreds of microwatts can feature their own energy extraction module, making them truly self-powered. This energy can be harvested from the close environment of the device. Particularly, piezoelectric conversion is one of the most investigated fields for ambient energy harvesting. Moreover, the extraction process can be optimized by proper treatment of the piezomaterial output voltage. This article proposes a detailed explanation of the real energy flow that lies behind several energy conversion techniques for piezoelectric energy scavenging. As well, the principles of energy harvesting using piezoelectric effect is extended to the pyroelectric effect, therefore allowing harvesting energy from temperature variation, which is one of the most common energy sources.