A Multimodal Dataset for Automatic Edge-AI Cough Detection

Counting the number of times a patient coughs per day is an essential biomarker in determining treatment efficacy for novel antitussive therapies and personalizing patient care. Automatic cough counting tools must provide accurate information, while running on a lightweight, portable device that protects the patient’s privacy. Several devices and algorithms have been developed for cough counting, but many use only error-prone audio signals, rely on offline processing that compromises data privacy, or utilize processing and memory-intensive neural networks that require more hardware resources than can fit on a wearable device. Therefore, there is a need for wearable devices that employ multimodal sensors to perform accurate, privacy-preserving, automatic cough counting algorithms directly on the device in an edge Artificial Intelligence (edge-AI) fashion. To advance this research field, we contribute the first publicly accessible cough counting dataset of multimodal biosignals. The database contains nearly 4 hours of biosignal data, with both acoustic and kinematic modalities, covering 4,300 annotated cough events from 15 subjects. Furthermore, a variety of non-cough sounds and motion scenarios mimicking daily life activities are also present, which the research community can use to accelerate machine learning (ML) algorithm development. A technical validation of the dataset reveals that it represents a wide variety of signal-to- noise ratios, which can be expected in a real-life use case, as well as consistency across experimental trials. Finally, to demonstrate the usability of the dataset, we train a simple cough vs non-cough signal classifier that obtains a 91% sensitivity, 92% specificity, and 80% precision on unseen test subject data. Such edge-friendly AI algorithms have the potential to provide continuous ambulatory monitoring of the numerous chronic cough patients.RYC2021-032853-

Deep Impression: Audiovisual Deep Residual Networks for Multimodal Apparent Personality Trait Recognition

Here, we develop an audiovisual deep residual network for multimodal apparent personality trait recognition. The network is trained end-to-end for predicting the Big Five personality traits of people from their videos. That is, the network does not require any feature engineering or visual analysis such as face detection, face landmark alignment or facial expression recognition. Recently, the network won the third place in the ChaLearn First Impressions Challenge with a test accuracy of 0.9109

Validation of an IGF1 Screening Method for Retinopathy of Pre-maturity

Retinopathy of pre-maturity (ROP) is a retinal disease that causes arrest of vascularization of the retina and can result in retinal detachment and blindness. Current screening protocols may not be sufficiently accurate to identify all at-risk patients. The aim of this study is to validate a method for improved identification of newborns at risk of ROP. We conducted a prospective clinical trial of pre-term newborns <32 weeks of gestation and/or <1,500 g birth weight during a 6-year period in a tertiary care hospital. We applied our new method based on measurement of insulin-like growth factor 1 (IGF1) levels at 3 weeks of age and the presence of sepsis during the first 3 weeks of life. Our screening protocol allowed exclusion of 121 (79.1%) patients for whom American Academy of Pediatrics (AAP) guidelines recommended screening, had a negative predictive value of 100%, and correctly identified all patients with ROP. Following retrospective assessment of our data based on these findings, we propose further restriction of the current AAP indications for screening to <1,100 g and <28 weeks of gestation in order to improve diagnostic efficacy while ensuring optimal use of restriction of human and material resources

A Novel Nanoproteomic Approach for the Identification of Molecular Targets Associated with Thyroid Tumors

A thyroid nodule is the most common presentation of thyroid cancer; thus, it is extremely important to differentiate benign from malignant nodules. Within malignant lesions, classification of a thyroid tumor is the primary step in the assessment of the prognosis and selection of treatment. Currently, fine-needle aspiration biopsy (FNAB) is the preoperative test most commonly used for the initial thyroid nodule diagnosis. However, due to some limitations of FNAB, different high-throughput "omics" approaches have emerged that could further support diagnosis based on histopathological patterns. In the present work, formalin-fixed paraffin-embedded (FFPE) tissue specimens from normal (non-neoplastic) thyroid (normal controls (NCs)), benign tumors (follicular thyroid adenomas (FTAs)), and some common types of well-differentiated thyroid carcinoma (follicular thyroid carcinomas (FTCs), conventional or classical papillary thyroid carcinomas (CV-PTCs), and the follicular variant of papillary thyroid carcinomas (FV-PTCs)) were analyzed. For the first time, FFPE thyroid samples were deparaffinized using an easy, fast, and non-toxic method. Protein extracts from thyroid tissue samples were analyzed using a nanoparticle-assisted proteomics approach combined with shotgun LC-MS/MS. The differentially regulated proteins found to be specific for the FTA, FTC, CV-PTC, and FV-PTC subtypes were analyzed with the bioinformatic tools STRING and PANTHER showing a profile of proteins implicated in the thyroid cancer metabolic reprogramming, cancer progression, and metastasis. These proteins represent a new source of potential molecular targets related to thyroid tumors

Are Farm-Reared Quails for Game Restocking Really Common Quails (Coturnix coturnix)?: A Genetic Approach

The common quail (Coturnix coturnix) is a popular game species for which restocking with farm-reared individuals is a common practice. In some areas, the number of released quails greatly surpasses the number of wild breeding common quail. However, common quail are difficult to raise in captivity and this casts suspicion about a possible hybrid origin of the farmed individuals from crosses with domestic Japanese quail (C. japonica). In this study we used a panel of autosomal microsatellite markers to characterize the genetic origin of quails reared for hunting purposes in game farms in Spain and of quails from an experimental game farm which was founded with hybrids that have been systematically backcrossed with wild common quails. The genotypes of these quail were compared to those of wild common quail and domestic strains of Japanese quail. Our results show that more than 85% of the game farm birds were not common quail but had domestic Japanese quail ancestry. In the experimental farm a larger proportion of individuals could not be clearly separated from pure common quails. We conclude that the majority of quail sold for restocking purposes were not common quail. Genetic monitoring of individuals raised for restocking is indispensable as the massive release of farm-reared hybrids could represent a severe threat for the long term survival of the native species

Functional insight into the reciprocal paracrine crosstalk of stromal fibroblasts in the head and neck cancer microenvironment

Trabajo presentado en el 18th ASEICA International Congress, celebrado en Santiago de Compostela (España) del 16 al 18 de noviembre de 2022

Fine Structure in the β\beta-Delayed Proton Decay of 33^{33}Ar

Low energy beta-delayed protons from $^{33}$Ar have been measured for the first time. The data reveal states, which, despite unfavourable barrier penetrability values, strongly decay to the first excited 2$^+$ state in $^{32}$S. The observation is discussed in terms of the standard shell model. A natural explanation is provided by the large spectroscopic amplitudes, involving $s_{1/2}$ and $d_{3/2}$ orbitals, as well as the $l$=0 barrier penetrability, favouring the decay to the 2$^+$ state

Exome sequencing of early-onset patients supports genetic heterogeneity in colorectal cancer

Colorectal cancer (CRC) is a complex disease that can be caused by a spectrum of genetic variants ranging from low to high penetrance changes, that interact with the environment to determine which individuals will develop the disease. In this study, we sequenced 20 early-onset CRC patients to discover novel genetic variants that could be linked to the prompt disease development. Eight genes, CHAD, CHD1L, ERCC6, IGTB7, PTPN13, SPATA20, TDG and TGS1, were selected and re-sequenced in a further 304 early onset CRC patients to search for rare, high-impact variants. Although we found a recurring truncating variant in the TDG gene shared by two independent patients, the results obtained did not help consolidate any of the candidates as promising CRC predisposing genes. However, we found that potential risk alleles in our extended list of candidate variants have a tendency to appear at higher numbers in younger cases. This supports the idea that CRC onset may be oligogenic in nature and may show molecular heterogeneity. Further, larger and robust studies are thus needed to unravel the genetics behind early-onset CRC development, coupled with novel functional analyses and omic approaches that may offer complementary insight