Miracle-dz/UniFAR-FTUs huggingface dataset:
Epitope Mapping of Exposed Tegument and Alimentary Tract Proteins Identifies Putative Antigenic Targets of the Attenuated Schistosome Vaccine

The radiation-attenuated cercarial vaccine remains the gold standard for the induction of protective immunity against Schistosoma mansoni. Furthermore, the protection can be passively transferred to naïve recipient mice from multiply vaccinated donors, especially IFNgR KO mice. We have used such sera versus day 28 infection serum, to screen peptide arrays and identify likely epitopes that mediate the protection. The arrays encompassed 55 secreted or exposed proteins from the alimentary tract and tegument, the principal interfaces with the host bloodstream. The proteins were printed onto glass slides as overlapping 15mer peptides, reacted with primary and secondary antibodies, and reactive regions detected using an Agilent array scanner. Pep Slide Analyzer software provided a numerical value above background for each peptide from which an aggregate score could be derived for a putative epitope. The reactive regions of 26 proteins were mapped onto crystal structures using the CCP4 molecular graphics, to aid selection of peptides with the greatest accessibility and reactivity, prioritizing vaccine over infection serum. A further eight MEG proteins were mapped to regions conserved between family members. The result is a list of priority peptides from 44 proteins for further investigation in multiepitope vaccine constructs and as targets of monoclonal antibodies.
A Machine Learning-Based Model to Predict Survival After Transarterial Chemoembolization for BCLC Stage B Hepatocellular Carcinoma

Objective We sought to develop and validate a novel prognostic model for predicting survival of patients with Barcelona Clinic Liver Cancer Stages (BCLC) stage B hepatocellular carcinoma (HCC) using a machine learning approach based on random survival forests (RSF). Methods We retrospectively analyzed overall survival rates of patients with BCLC stage B HCC using a training (n = 602), internal validation (n = 301), and external validation (n = 343) groups. We extracted twenty-one clinical and biochemical parameters with established strategies for preprocessing, then adopted the RSF classifier for variable selection and model development. We evaluated model performance using the concordance index (c-index) and area under the receiver operator characteristic curves (AUROC). Results RSF revealed that five parameters, namely size of the tumor, BCLC-B sub-classification, AFP level, ALB level, and number of lesions, were strong predictors of survival. These were thereafter used for model development. The established model had a c-index of 0.69, whereas AUROC for predicting survival outcomes of the first three years reached 0.72, 0.71, and 0.73, respectively. Additionally, the model had better performance relative to other eight Cox proportional-hazards models, and excellent performance in the subgroup of BCLC-B sub-classification B I and B II stages. Conclusion The RSF-based model, established herein, can effectively predict survival of patients with BCLC stage B HCC, with better performance than previous Cox proportional hazards models.
Robust Anomaly Detection in Time Series through Variational AutoEncoders and a Local Similarity Score

The rise of time series data availability has demanded new techniques for its automated analysis regarding several tasks, including anomaly detection. However, even though the volume of time series data is rapidly increasing, the lack of labeled abnormal samples is still an issue, hindering the performance of most supervised anomaly detection models. In this paper, we present an unsupervised framework comprised of a Variational Autoencoder coupled with a local similarity score, which learns solely on available normal data to detect abnormalities in new data. Nonetheless, we propose two techniques to improve the results if at least some abnormal samples are available. These include a training set cleaning method for removing the influence of corrupted data on detection performance and the optimization of the detection threshold. Tests were performed in two datasets: ECG5000 and MIT-BIH Arrhythmia. Regarding the ECG5000 dataset, our framework has shown to outperform some supervised and unsupervised approaches found in the literature by achieving an AUC score of 98.79%. In the MIT-BIH dataset, the training set cleaning step removed 60% of the original training samples and improved the anomaly detection AUC score from 91.70% to 93.30%.
Physical Burden in Manual Patient Handling: Quantification of Lower Limb EMG Muscle Activation Patterns of Healthy Individuals Lifting Different Loads Ergonomically

Manual patient handling is a challenging part of daily care and leads to high mechanical loads as well as to the development of degenerative diseases, e.g. lower back pain. To prevent musculoskeletal overload effects, the use of ergonomic working techniques is essential as well as improving caregivers’ functional ability. However, most of the studies do not consider these aspects and biomechanical evaluations including dynamic electromyography (EMG) are rarely analyzed. In this work, we focus on the quantification of lower limb EMG muscle activation patterns of healthy caregiver students in an experimental setup. The extent of lifting different loads ergonomically is analyzed and similarities/dissimilarities of dynamic EMG data of three lower limb muscles are investigated via cross-correlation calculation. One of the main findings of our investigation is an indication of a more consistent mean activity of the quadriceps and hamstring musculature, as the load to be lifted increases. Furthermore, we found an intraas well as an interindividual similarity of EMG muscle activation patterns regarding time and shape of the signals generated during all of the conducted lifting tasks with a predominantly high cross-correlation coefficient for the selected muscles of the lower limb.
Boundaries in space and time: Iconic biases across modalities

The idea that the form of a word reflects information about its meaning has its roots in Platonic philosophy, and has been experimentally investigated for concrete, sensory-based properties since the early 20th century. Here, we provide evidence for an abstract property of 'boundedness' that introduces a systematic, iconic bias on the phonological expectations of a novel lexicon. We show that this abstract property is general across events and objects. In Experiment 1, we show that subjects are systematically more likely to associate sign language signs that end with a gestural boundary with telic verbs (denoting events with temporal boundaries, e.g., die, arrive) and with count nouns (denoting objects with spatial boundaries, e.g., ball, coin). In Experiments 2-3, we show that this iconic mapping acts on conceptual representations, not on grammatical features. Specifically, the mapping does not carry over to psychological nouns (e.g. people are not more likely to associate a gestural boundary with idea than with knowledge). Although these psychological nouns are still syntactically encoded as either count or mass, they do not denote objects that are conceived of as having spatial boundaries. The mapping bias thus breaks down. Experiments 4-5 replicate these findings with a new set of stimuli. Finally, in Experiments 6-11, we explore possible extensions to a similar bias for spoken language stimuli, with mixed results. Generally, the results here suggest that 'boundedness' of words' referents (in space or time) has a powerful effect on intuitions regarding the form that the words should take.
Activity-monitoring in Private Households for Emergency Detection: A Survey of Common Methods and Existing Disaggregable Data Sources

Ambient-Assisted Living (AAL) technologies can enable the elderly people to live a self-determined life in their own home environment instead of hospitals and retirement homes for a longer period of time. Hence, AAL systems are not only used for everyday support but also for the detection of potential emergency situations and for triggering notification chains. For this purpose the people are usually continuously monitored within their residents by ambient or wearable sensors to detect deviations in their daily behavior. This work surveys common used technologies for Human Activity Recognition (HAR) / Human Presence Detection (HPD), which is the basis for emergency detection. Furthermore, by examining various home automation software, existing data sources from the residential infrastructure, are identified that would be suitable for detecting personal activities.
Fairer Evaluation of Zero Shot Action Recognition in Videos

Zero-shot learning (ZSL) for human action recognition (HAR) aims to recognise video action classes that have never been seen during model training. This is achieved by building mappings between visual and semantic embeddings. These visual embeddings are typically provided via a pre-trained deep neural network (DNN). The premise of ZSL is that the training and testing classes should be disjoint. In the parallel domain of ZSL for image input, the widespread poor evaluation protocol of pre-training on ZSL test classes has been highlighted. This is akin to providing a sneak preview of the evaluation classes. In this work, we investigate the extent to which this evaluation protocol has been used in ZSL for human action recognition research work. We show that in the field of ZSL for HAR, accuracies for overlapping classes are being boosted by between 5.75% to 51.94% depending on the use of visual and semantic features as a result of this flawed evaluation protocol. To assist other researchers in avoiding this problem in the future, we provide annotated versions of the relevant benchmark ZSL test datasets in the HAR field: UCF101 and HMDB51 datasets highlighting overlaps to pre-training datasets in the field.
Investigating 3D Convolutional Layers as Feature Extractors for Anomaly Detection Systems Applied to Surveillance Videos

Over the last few years, several strategies have been leveraged to detect unusual behavior in surveillance videos. Nonetheless, there are still few studies that compare strategies based on 3D Convolutional Neural Networks to tackle such problem. This research gap has motivated the this work in which we aim at investigating the features from a pre-trained C3D model and the training of fully 3D-convolutional auto-encoders for automated video anomaly detection systems, comparing them with respect to the anomaly detection performance and the processing power demands. Additionally, we present an auto-encoder model to detect anomalous behavior based on the pixel reconstruction error. While C3D features coming from the first layers were shown to be both better descriptors and faster to be computed, the auto-encoder achieved results comparable to the C3D, while requiring less computational effort. When compared to other studies using two benchmark datasets, the proposed methods are comparable to the state-of-the-art for the Ped2 dataset, while inferior when detecting anomalies on the Ped1 dataset. Additionally, our experimental results support the development of future 3DCNN-based anomaly detection methods.
Extraction and Separation of Zr(IV) and Hf(IV) from Nitrate Medium by Some CYANEX Extractants

Abstract A solvent extraction study has been carried out to extract and separate zirconium and hafnium from nitrate medium by using some phosphine oxide extractants (CYANEX 921, CYANEX 923, and CYANEX 925) in kerosene. The influence of the different factors affecting the extraction process was studied in detail. Apparently the rate of extraction of Zr(IV) and Hf(IV) in CYANEX 921, CYANEX 923, and CYANEX 925 is reasonably fast. The extraction increases with increasing temperature, suggesting that the reaction is endothermic. The stripping percent of Zr(IV) and Hf(IV) by 0.5 M HNO3 from the loaded organic phase after two stages reached 97.5% and 10.2%, respectively, which lead to good separation of the two metals. Under the optimum conditions, the extraction of zirconium was about 90, 87.6, and 91.6% and separation factors equal to 17, 21.4, and 40.7 were obtained for CYANEX 921, CYANEX 923, and CYANEX 925, respectively. The results obtained reveal that 2.0 M nitric acid is the optimum acid concentration for the separation of Zr(IV) and Hf(IV) and 0.4 M CYANEX 925 performs more efficient separation compared with other organophosphorus extractants.
OptMap: Using Dense Maps for Visualizing Multidimensional Optimization Problems

Operations Research is a very important discipline in many industries, and although there were many developments since its inception, to our knowledge there are no visualization tools focused on helping users understand the decision variables’ domain space and its constraints for problems with more than two input dimensions. In this paper, we propose OptMap, a technique that enables the visual exploration of optimization problems using a two-dimensional dense map, regardless of the number of variables and constraints in the problem and for any kind of single-valued objective function. We show the technique in action for several optimization problems of different types, such as linear, nonlinear and integer, constrained and unconstrained problems.
Non-coding DNA: A Methodology for Detection and Analysis of Pseudogenes

It is well known that elements lying outside the coding regions of the human genome are involved in many human diseases. Therefore, the efforts to detect and characterize functional elements in the non-coding regions are rapidly increasing. Among many types of non-coding DNA, pseudogenes are sequences that share some similarities with their parental genes but have lost their ability to code for proteins. In this paper, we propose a methodology for detection and analysis of pseudogenes, based on transition probabilities of the nucleotides and their occurrences. The 1000 base pairs length downstream region of each detected pseudogene is analyzed in order to find a polyA tail and a polyadenylation signal. We implemented a Hidden Markov Model with the Viterbi algorithm to decode the upstream regions of the previously detected pseudogenes in order to search for CpG islands. In order to identify motif signals in the selected pseudogenes, we implemented the Gibbs sampling algorithm and we executed it on the flanking regions of some pseudogenes. Results demonstrate that the proposed methodology is an efficacious solution to detect new potential loci, especially when the query coverage of the alignment is shorter than the coding strand. These loci can be classed as pseudogene
Efficient Multi-stream Temporal Learning and Post-fusion Strategy for 3D Skeleton-based Hand Activity Recognition

: Recognizing ﬁrst-person hand activity is a challenging task, especially when not enough data are available. In this paper, we tackle this challenge by proposing a new hybrid learning pipeline for skeleton-based hand activity recognition, which is composed of three blocks. First, for a given sequence of hand’s joint positions, the spatial features are extracted using a dedicated combination of local and global spacial hand-crafted features. Then, the temporal dependencies are learned using a multi-stream learning strategy. Finally, a hand activity sequence classiﬁer is learned, via our Post-fusion strategy, applied to the previously learned temporal dependencies. The experiments, evaluated on two real-world data sets, show that our approach performs better than the state-of-the-art approaches. For more ablation study, we compared our Post-fusion strategy with three traditional fusion baselines and showed an improvement above 2.4% of accuracy.
Communication-Efficient BFT Protocols Using Small Trusted Hardware to Tolerate Minority Corruption

Agreement protocols for partially synchronous or asynchronous networks tolerate fewer than one-third Byzantine faults. If parties are equipped with trusted hardware that prevents equivo-cation, then fault tolerance can be improved to fewer than one-half Byzantine faults, but typically at the cost of increased communication complexity. In this work, we present results that use small trusted hardware without worsening communication complexity assuming the adversary controls a fraction of the network that is less than one-half. Our results include a version of HotStuﬀ that retains linear communication complexity in each view and a version of the VABA protocol with quadratic communication, both leveraging trusted hardware to tolerate a minority of corruptions. Our results use expander graphs to achieve eﬃcient communication in a manner that may be of independent interest.
Psychological Perspectives and Challenges towards Information Technology and Digital Health Interventions

: Psychology is a quite young discipline in the field of medicine. Established from Wilhelm Wundt in the second half of the 19th century it very soon got its first contacts to information technology. This keynote outlines the relationship of psychology and information technology from the very beginning in the fields of clinical and educational applications and human interaction aspects and illustrates the current development towards a field of digital mental health. In particular, the use of Virtual Reality as a an approach in the treatment mental disorders and diseases such as Anxiety disorders or Parkinson’s disease, the therapeutic use of AVATARS in patients with auditory verbal hallucinations, the predictive power of digital drawing applications i.e. to detect Alzheimer’s disease, or the attitude to robots in the health care system are spotlights illustrating the growing interaction of these domains which are addressed in this keynote. But also critical questions on the impact of digitalization on human identity i.e. in child development, working environment or in the increased use of wearables and smart applications in leisure time fostering a need of quantification of life are touched.
Exploring Fractal Dimension Analysis as a Technique to Study the Role of Intensity of Facial Expression and Viewing Angle

Fractal dimension analysis of the images of facial expressions has been reported earlier by Takehara and colleagues We have performed a similar exercise for two Indian databases, the Indian dataset of basic emotions and the Indian Affective Picture Database, to examine the relationship between the geometric properties of the facial expressions vis-à-vis the intensity of expressions and the viewing angle. It is a first of its kind in the Indian context. We analyzed the geometric pattern of three regions of the face, computed pixel difference, and calculated fractal dimensions of the expressions for all the images of these two databases. Thereafter, we analyzed the obtained outcomes of the geometric analyses and the reported unbiased hit rates for these databases, respectively. Results suggest that recognition of facial expressions is independent of the viewing angle. Further, happiness and anger are recognized best irrespective of their intensity followed by more intense surprise and disgust. The Root Mean Square pixel difference shows identical pattern in the expressions of happiness and disgust. Fractal dimensions indicate self-similarity among surprise, happiness,
Toward a Compare and Contrast Framework for COVID-19 Contact Tracing Mobile Applications: A Look at Usability

This paper reports on the progress in the project COVIGILANT, which is aimed at developing an evaluation taxonomy for Contact Tracing Applications (CTAs) for COVID-19 Specifically, this article describes the development of Usability, one pillar of the COVIGILANT taxonomy, discussing the classification and decision-making processes, and the initial model validation The validation process was undertaken in two stages First, we validated how the Usability pillar could be used to evaluate the Irish Health Services Executive (HSE) COVID-19 CTA While this supported many of the attributes that we had within the Usability pillar, it also identified issues We made amendments based on these, and undertook a second study, this time evaluating 4 CTAs used in other countries This has led to the completion of the Usability pillar, which can now be used to evaluate global CTAs Copyright © 2021 by SCITEPRESS – Science and Technology Publications, Lda All rights reserved
The Extension of the Standard Genetic Code via Optimal Codon Blocks Division

: The standard genetic code (SGC) is a crucial biological system, which allows to transmit genetic information from DNA sequences to the protein world. The idea of the optimal extension of the SGC with new information appears especially interesting in the context of successful experimental achievements in reprogramming of this code. The aim of this code engineering is incorporating non-canonical amino acids (ncAAs) into synthesised artiﬁcial proteins with novel functions. Such molecules open new perspectives in medicine, chemistry and biotechnology. Several methods extending the canonical coding system were proposed. Here, we would like to investigate a problem of the optimal genetic code extension using graph theory methodology. We measured the quality of considered coding systems applying the set conductance, which determines the robustness level against point mutations of individual codon blocks encoding the same information. Thanks to that, we were able to ﬁnd several possible optimal extensions of the SGC based on utilization of the codons redundant in the original code. We found codes that could encode up to 16 ncAAs and simultaneously code for 20 canonical amino acids and one stop translation signal. One of these codes was the most balanced, i.e. it consisted of the canonical set and the extended set that were characterized by the same level of robustness against point mutations. The proposed codes could be helpful in experimental construction of artiﬁcial genetic codes, which can encode new amino acid with new useful properties.
Voxelized Breast Phantoms for Dosimetry in Mammography

X-ray breast imaging techniques are an essential part of breast cancer screening programs and their improvements lead to gain in performance and accuracy. Radiation dose estimate and control play an important role in digital mammography and digital breast tomosynthesis investigations, since the risk of radioinduced cancer to the gland must be contained and dose delivered to the gland must be declared in the medical report. The actual dosimetric protocols suggest the assessment of radiation dose by means of Monte Carlo calculation on digital breast phantoms, providing the assumption of the homogeneous mixture of glandular and adipose tissues within the breast organ, leading to a drastic approximation. In line with the trend of other research groups, with the aim of improving the Monte Carlo model, in the current work a new heterogeneous digital breast model is proposed, involving a voxelized approach and disengaging from the concept of homogeneous phantom. The proposed model is based on new findings in the literature and after a validation process, the model is adopted to evaluate mean glandular dose discrepancies with the traditional model which is adopted in clinic for decades.
Approaching the Semantic Segmentation in Medical Problems: A Solution for Pneumothorax Detection

We present a method for detecting and delineating pneumothorax from X-Ray medical images by using a threestep processing pipeline: a deep learning classification module, responsible for detecting the possible existence of a collapsed lung within an image, followed by a segmentation model applied on the positive samples (as detected by the classification module). The last module attempts to eliminate possible artefacts based on their size. We demonstrate how the pipeline employed significantly improves the results, by increasing the mean-Dice coefficient metric by 0.13, in comparison with the performance of a single segmentation module. In addition to this, we demonstrate that using together specific state-of-the-art techniques leads to improved results, without employing techniques such as dataset enrichment from external sources, semi-supervised learning or pretraining on much larger medical datasets.