Miracle-dz/UniFAR-FTUs huggingface dataset:
Safety and effectiveness of RBD-specific polyclonal equine F(ab´)2 fragments for the treatment of hospitalized patients with severe Covid-19 disease: A retrospective cohort study

Passive immunotherapy has been evaluated as a therapeutic alternative for patients with COVID-19 disease. Equine polyclonal immunotherapy for COVID-19 (EPIC) showed adequate safety and potential efficacy in a clinical trial setting and obtained emergency use authorisation in Argentina. We studied its utility in a real world setting with a larger population. Methods: We conducted a retrospective cohort study at "Hospital de Campana Escuela-Hogar" in Corrientes, Argentina, to assess safety and effectiveness of EPIC in hospitalized adults with severe COVID-19 pneumonia. Primary endpoints were 28-days all cause mortality and safety. Mortality and improvement in modified WHO clinical scale at 14 and 21 days were secondary endpoints. Potential confounder adjustment was made by logistic regression weighted by the inverse of the probability of receiving the treatment (IPTW) and doubly robust approach. Results: Clinical records of 395 exposed (EPIC) and 446 non-exposed (Controls) patients admitted between November 2020 and April 2021 were analyzed. Median age was 58 years, 56.8% males. Mortality at 28 days was 15.7% ( EPIC) vs 21.5% (Control). After IPTW adjustment the OR was 0.66 (95 % CI: 0.46 - 0.96) p= 0.03. The effect was more evident in the subgroup who received two EPIC doses (complete treatment, n=379), OR 0.58 (95% CI 0.39 to 0.85) p=0.005. Overall and serious adverse events were not significantly different between groups.
Structured Understanding of Assessment and Plans in Clinical Documentation

Physicians record their detailed thought-processes about diagnoses and treatments as unstructured text in a section of a clinical note called the assessment and plan. This information is more clinically rich than structured billing codes assigned for an encounter but harder to reliably extract given the complexity of clinical language and documentation habits. We describe and release a dataset containing annotations of 579 admission and progress notes from the publicly available and de-identified MIMIC-III ICU dataset with over 30,000 labels identifying active problems, their assessment, and the category of associated action items (e.g. medication, lab test). We also propose deep-learning based models that approach human performance, with a F1 of 0.88. We found that by employing weak supervision and domain specific data-augmentation, we could improve generalization across departments and reduce the number of human labeled notes without sacrificing performance.
The effect of calcium supplementation in people under 35 years old: A systematic review and meta-analysis of randomized controlled trials

Background and objective: The effect of calcium supplementation on bone mineral accretion in people under 35 years old is inconclusive. To comprehensively summarize the evidence for the effect of calcium supplementation on bone mineral accretion in young populations ([≤]35 years). Design: This is a systematic review and meta analysis. Data sources: The Pubmed, Embase, ProQuest and CENTRAL databases were systematically searched from database inception to April 25, 2021. Eligibility: Randomized clinical trials assessing the effects of calcium supplementation on bone mineral density (BMD) or bone mineral content (BMC) in people under 35 years old. Results: This systematic review and meta analysis identified 43 studies involving 7382 subjects. Moderate certainty of evidence showed that calcium supplementation was associated with the accretion of BMD and BMC, especially on femoral neck (SMD 0.627, 95% CI 0.338 to 0.915; SMD 0.364, 95% CI 0.134 to 0.595; respectively) and total body (SMD 0.330, 95% CI 0.163 to 0.496; SMD 0.149, 95% CI 0.00 6 to 0.291), also with a slight improvement effect on lumbar spine BMC (SMD 0.163, 95% CI 0.008 to 0.317), no effects on total hip BMD and BMC and lumbar spine BMD were observed. Very interestingly, subgroup analyses suggested that the improvement of bone at femoral neck was more pronounced in the peri PBM population (20 - 35 years) than the pre PBM population (<20 years). Conclusion: Our findings provided novel insights and evidence in calcium supplementation, which showed that calcium supplementation signif icantly improves bone mass, implying that preventive calcium supplementation before or around achieving PBM may be a shift in the window of intervention for osteoporosis.Funding This work was supported by Wenzhou Medical University grant [89219029].
An Innovative Matrix-Based Approach for Designing Product Variety

New product development (NPD) and innovation are key factors that affect a company’s long-term survival and growth. The design process is an important stage in new product development (NPD). Based on graph theory and the weighting concept , this paper presents a Quantified Design Structure Matrix (QDSM) which is a systematic planning method of optimizing design priorities and product architecture for managing product variety from an informational structure perspective. Focusing on product variety and the design process in concurrent engineering (CE), the planning model is divided into two phases: global planning and local planning. The proposed method helps designers optimize the design planning and plan better design strategies for product variety. A case study is used to illustrate this method. The results verify that designers may concurrently create variant design solutions in a product family that can meet different market needs without extra effort being spent on redundant design loops.
Mitigating Negative Impacts of Monitoring high levels of Automation: the MINIMA Project

In this poster, we present the preliminary study conducted in the framework of the research project Mitigating Negative Impacts of Monitoring high levels of Automation (MINIMA). The main objectives of MINIMA are i) to develop vigilance and attention neuro-physiological indexes, and ii) to implement them in a system that can adapt its behavior and guide the operator’s attention in order to mitigate negative impacts of the foreseen increasing automation on Air Traffic Controller (ATCo) performance in future Air Traffic Control (ATC) scenarios. The first step of research activities consists of better comprehension of Out-Of-The-Loop (OOTL) phenomena and of current methods to measure and compensate such effects. The innovation proposed by MINIMA stands in the exploitation of recent progress in non-intrusive physiological measures devices, such as eye-tracking or ElectroEncephaloGraphy (EEG), combined to gather a unique measure of the level of attention and vigilance of an ATCo. In order to set up a framework for the concept development phase a preliminary study has been conducted on a target attention and basic guidance concept. Our results have shown the validity of the attention guidance concept from a subjective point of view, and have demonstrated to be an adequate starting point for further evaluation through neuro-physiological measurements.
Defective in utilizing glutathione 3, DUG3, is required for conidiation and host infection in the rice blast fungus Magnaporthe oryzae

Glutathione, an important redox buffer of the cell, also functions as a source of sulphur and nitrogen under starvation conditions. The metabolism and maintenance of glutathione homeostasis are vital for the appropriate functioning of the cell. In addition to the γ-glutamyl transpeptidase, the fungus-specific alternative pathway involving DUG1, DUG2 and DUG3 genes also mediate glutathione degradation. Here, we studied the functional significance of DUG3 in the vegetative growth and infection cycle of the cereal blast fungus Magnaporthe oryzae . Cells lacking the DUG3 gene displayed reduced conidiation, delayed appressorium formation, and a decrease in the severity of host infection. Further, we show that the γ-glutamyl transpeptidase inhibitor severely compromises the vegetative growth of the M. oryzae cells lacking the DUG3 gene. Taken together, our results suggest a significant role of glutathione metabolism in the growth and virulence of M. oryzae .
16-element photodiode array for the angular microdeflection detector and for stabilization of a laser radiation direction

In this paper, the design and technology of two types of 16-element photodiode arrays is described. The arrays were developed by the ITE and are to be used in detection of microdeflection of laser radiation at the Institute of Metrology and Biomedical Engineering in the Faculty of Mechatronics of Warsaw University of Technology. The electrical and photoelectrical parameters of the arrays are presented.
The impact of living in an un‐ or under‐furnished house on individuals who transitioned from homelessness

Abstract Very few empirical evaluations have been conducted on the impact of furniture on the lives of those who have transitioned from homelessness into permanent housing, especially within the United States. Our study contributes to this limited body of research by exploring the impact of furniture on the lives of 20 recently housed individuals residing in the Detroit Metropolitan Area. In partnership with the Furniture Bank of Southeast Michigan, we conducted semi‐structured interviews with recently housed individuals that lived for a period of time in an un‐ or under‐furnished house before receiving furniture support. Given the study's exploratory nature, interview questions were purposefully broad to allow themes to naturally emerge and were analyzed using a qualitative data analysis software package, NVivo (release 1.0). We present a conceptual model that outlines our findings and conclude with a discussion of the limitations of our approach, avenues for future research, and policy implications.
INDUSTRIAL MATHEMATICS AS A MEANS OF ENHANCING NATIONAL DEVELOPMENT

National development involves the interplay of people and their environment to enhance the living standards of members of the society. Indices for measuring national development could be material or social. The important thing is that a country develops when the welfare of its citizens are well taken care of. Industrial mathematics is a specialized field of study with various applications to national development. This study consider the role played by Industrial Mathematics in enhancing national development
Low-power, small-area 10 bit D/A converter for cell-based IC

A low-power, small-area, 10-bit D/A converter for cell-based IC and its design method are presented. An analytical design method which minimizes power dissipation and cell area of a current-cell D/A converter taking process variation into account is proposed. A 10-bit, 500-kHz D/A converter was implemented using the proposed method. A low power dissipation of 9.7 mW, a small area of 1.31 mm/sup 2/, and a differential non-linearity of /spl plusmn/1/8 LSB has been achieved using a 0.8-/spl mu/m CMOS process.
End-to-End Sensitivity-Based Filter Pruning

In this paper, we present a novel sensitivity-based filter pruning algorithm (SbF-Pruner) to learn the importance scores of filters of each layer end-to-end. Our method learns the scores from the filter weights, enabling it to account for the correlations between the filters of each layer. Moreover, by training the pruning scores of all layers simultaneously our method can account for layer interdependencies, which is essential to find a performant sparse sub-network. Our proposed method can train and generate a pruned network from scratch in a straightforward, one-stage training process without requiring a pretrained network. Ultimately, we do not need layer-specific hyperparameters and pre-defined layer budgets, since SbF-Pruner can implicitly determine the appropriate number of channels in each layer. Our experimental results on different network architectures suggest that SbF-Pruner outperforms advanced pruning methods. Notably, on CIFAR-10, without requiring a pretrained baseline network, we obtain 1.02% and 1.19% accuracy gain on ResNet56 and ResNet110, compared to the baseline reported for state-of-the-art pruning algorithms. This is while SbF-Pruner reduces parameter-count by 52.3% (for ResNet56) and 54% (for ResNet101), which is better than the state-of-the-art pruning algorithms with a high margin of 9.5% and 6.6%.
An Attachment Style Based Experimental Design to Maximize Dog Adoption Success

Evolution and domestication have brought dogs very close to humans. Research has found numerous behavioral, cognitive, neurological, and physiological similarities between the two species. Additional research has found that humans and dogs can share cross-species attachments that are comparable to mother-infant attachments. Furthermore, attachment styles in dogs are classified the same way they are in children. The statistics on the vast amount of dogs in animal shelters, too many of which are being senselessly killed, are shocking. I propose a two-part study that first assesses which attachment style pairings are most successful and which are unsuccessful based on measurements of satisfaction and oxytocin levels reflecting attachment. The second study is designed to verify these pairings by manipulating adoptions and following pairs. If particular pairings are found more successful than others and are utilized at adoption, I hypothesize an attachment style based program would produce more successful adoptions, lower the amount of dogs returned to shelters, and eventually, lower euthanization rates.
Signal Reception With Generic Three-State Receptors in Synaptic MC

Synaptic communication is studied by communication engineers for two main reasons. One is to enable novel neuroengineering applications that require interfacing with neurons. The other reason is to draw inspiration for the design of synthetic molecular communication systems. Both of these goals require understanding of how the chemical synaptic signal is sensed and transduced at the synaptic receiver (Rx). While signal reception in synaptic molecular communication (SMC) depends heavily on the kinetics of the receptors employed by the synaptic Rxs, existing channel models for SMC either oversimplify the receptor kinetics or employ complex, high-dimensional kinetic schemes limited to specific types of receptors. Both approaches do not facilitate a comparative analysis of different types of natural synapses. In this paper, we propose a novel deterministic channel model for SMC which employs a generic three-state receptor model that captures the characteristics of the most important receptor types in SMC. The model is based on a transfer function expansion of Fick's diffusion equation and accounts for release, diffusion, and degradation of neurotransmitters as well as their reversible binding to finitely many generic postsynaptic receptors. The proposed SMC model is the first that allows studying the impact of the characteristic dynamics of the main postsynaptic receptor types on synaptic signal transmission. Numerical results indicate that the proposed model indeed exhibits a wide range of biologically plausible dynamics when specialized to specific natural receptor types.
Brazilian Court Documents Clustered by Similarity Together Using Natural Language Processing Approaches with Transformers

Recent advances in Artiﬁcial intelligence (AI) have leveraged promising results in solving complex problems in the area of Natural Language Processing (NLP), being an important tool to help in the expeditious resolution of judicial proceedings in the legal area. In this context, this work targets the problem of detecting the degree of similarity between judicial documents that can be achieved in the inference group, by applying six NLP techniques based on transformers, namely BERT, GPT-2 and RoBERTa pre-trained in the Brazilian Portuguese language and the same specialized using 210,000 legal proceedings. Documents were pre-processed and had their content transformed into a vector representation using these NLP techniques. Unsupervised learning was used to cluster the lawsuits, calculating the quality of the model based on the cosine of the distance between the elements of the group to its centroid. We noticed that models based on transformers present better performance when compared to previous research, highlighting the RoBERTa model specialized in the Brazilian Portuguese language, making it possible to advance in the current state of the art in the area of NLP applied to the legal sector.
Deep CardioSound - An Ensembled Deep Learning Model for Heart Sound MultiLabelling

Heart sound diagnosis and classification play an essential role in detecting cardiovascular disorders, especially when the remote diagnosis becomes standard clinical practice. Most of the current work is designed for single category based heard sound classification tasks. To further extend the landscape of the automatic heart sound diagnosis landscape, this work proposes a deep multilabel learning model that can automatically annotate heart sound recordings with labels from different label groups, including murmur's timing, pitch, grading, quality, and shape. Our experiment results show that the proposed method has achieved outstanding performance on the holdout data for the multi-labelling task with sensitivity=0.990, specificity=0.999, F1=0.990 at the segments level, and an overall accuracy=0.969 at the patient's recording level.
Discontinuity analysis for video processing

This thesis is concerned with analysis of digital video through discontinuity detection. A discontinuity here is a sudden change that corresponds to some meaningful event in the video content. The paradigm of discontinuity-based analysis is applied within three distinct contexts. Firstly, the problem of shot-change detection is explored. This amounts to the detection of signal-level discontinuities. An extensive review and bibliography of existing methods proposed for shot change detection are presented. The next area of activity focuses on discontinuities as events, specifically in snooker video. It is described how many of the significant events within televised snooker play can be detected by targeted discontinuity detection, exploiting the characteristics of sports footage. Techniques for detecting ball pots and near misses, for summarising the episodes of play, and for tracking individual balls are described. Finally, the detection of discontinuities in local motion is examined. A number of methods for the description of local motion in video are evaluated. The detection of extreme values in these descriptions is established as a powerful means for edit point detection in dance video. As part of this work, a Bayesian video segmentation algorithm is designed which incorporates new refinements for maintaining temporal coherence in the segmentation over motion discontinuities. Successful edit point detection in dance video is shown to enable a number of novel applications, including the resynchronisation of the dance video to a new music signal. A DVD demonstrating some results of this technique is included.
A Keypoint-based Global Association Network for Lane Detection

Lane detection is a challenging task that requires predicting complex topology shapes of lane lines and distinguishing different types of lanes simultaneously. Earlier works follow a top-down roadmap to regress predefined anchors into various shapes of lane lines, which lacks enough flexibility to fit complex shapes of lanes due to the fixed anchor shapes. Lately, some works propose to formulate lane detection as a keypoint estimation problem to describe the shapes of lane lines more flexibly and gradually group adjacent keypoints belonging to the same lane line in a point-by-point manner, which is inefficient and time-consuming during postprocessing. In this paper, we propose a Global Association Network (GANet) to formulate the lane detection problem from a new perspective, where each keypoint is directly regressed to the starting point of the lane line instead of point-by-point extension. Concretely, the association of keypoints to their belonged lane line is conducted by predicting their offsets to the corresponding starting points of lanes globally without dependence on each other, which could be done in parallel to greatly improve efficiency. In addition, we further propose a Lane-aware Feature Aggregator (LFA), which adaptively captures the local correlations between adjacent keypoints to supplement local information to the global association. Extensive experiments on two popular lane detection benchmarks show that our method outperforms previous methods with F1 score of 79.63% on CULane and 97.71% on Tusimple dataset with high FPS.
Spatio-Temporal Analysis of Transformer based Architecture for Attention Estimation from EEG

For many years now, understanding the brain mechanism has been a great research subject in many different fields. Brain signal processing and especially electroencephalogram (EEG) has recently known a growing interest both in academia and industry. One of the main examples is the increasing number of Brain-Computer Interfaces (BCI) aiming to link brains and computers. In this paper, we present a novel framework allowing us to retrieve the attention state, i.e degree of attention given to a specific task, from EEG signals. While previous methods often consider the spatial relationship in EEG through electrodes and process them in recurrent or convolutional based architecture, we propose here to also exploit the time and frequency information with a transformer-based network that has already shown its supremacy in many machine-learning (ML) related studies, e.g. machine translation. In addition to this novel architecture, an extensive study on the feature extraction methods, frequential bands and temporal windows length has also been carried out. The proposed network has been trained and validated on two public datasets and achieves higher results compared to state-of-the-art models. As well as proposing better results, the framework could be used in real applications, e.g. Attention Deficit Hyperactivity Disorder (ADHD) symptoms or vigilance during a driving assessment.
Deep Learning-based List Sphere Decoding for Faster-than-Nyquist (FTN) Signaling Detection

Faster-than-Nyquist (FTN) signaling is a candidate non-orthonormal transmission technique to improve the spectral efficiency (SE) of future communication systems. However, such improvements of the SE are at the cost of additional computational complexity to remove the intentionally introduced intersymbol interference. In this paper, we investigate the use of deep learning (DL) to reduce the detection complexity of FTN signaling. To eliminate the need of having a noise whitening filter at the receiver, we first present an equivalent FTN signaling model based on using a set of orthonormal basis functions and identify its operation region. Second, we propose a DL-based list sphere decoding (DL-LSD) algorithm that selects and updates the initial radius of the original LSD to guarantee a pre-defined number NL of lattice points inside the hypersphere. This is achieved by training a neural network to output an approximate initial radius that includes NL lattice points. At the testing phase, if the hypersphere has more than NL lattice points, we keep the NL closest points to the point corresponding to the received FTN signal; however, if the hypersphere has less than NL points, we increase the approximate initial radius by a value that depends on the standard deviation of the distribution of the output radii from the training phase. Then, the approximate value of the log-likelihood ratio (LLR) is calculated based on the obtained NL points. Simulation results show that the computational complexity of the proposed DL-LSD is lower than its counterpart of the original LSD by orders of magnitude.