Guiding the retraining of convolutional neural networks against adversarial inputs

Background: When using deep learning models, one of the most critical vulnerabilities is their exposure to adversarial inputs, which can cause wrong decisions (e.g., incorrect classification of an image) with minor perturbations. To address this vulnerability, it becomes necessary to retrain the affected model against adversarial inputs as part of the software testing process. In order to make this process energy efficient, data scientists need support on which are the best guidance metrics for reducing the adversarial inputs to create and use during testing, as well as optimal dataset configurations. Aim: We examined six guidance metrics for retraining deep learning models, specifically with convolutional neural network architecture, and three retraining configurations. Our goal is to improve the convolutional neural networks against the attack of adversarial inputs with regard to the accuracy, resource utilization and execution time from the point of view of a data scientist in the context of image classification. Method: We conducted an empirical study using five datasets for image classification. We explore: (a) the accuracy, resource utilization, and execution time of retraining convolutional neural networks with the guidance of six different guidance metrics (neuron coverage, likelihood-based surprise adequacy, distance-based surprise adequacy, DeepGini, softmax entropy and random), (b) the accuracy and resource utilization of retraining convolutional neural networks with three different configurations (one-step adversarial retraining, adversarial retraining and adversarial fine-tuning). Results: We reveal that adversarial retraining from original model weights, and by ordering with uncertainty metrics, gives the best model w.r.t. accuracy, resource utilization, and execution time. Conclusions: Although more studies are necessary, we recommend data scientists use the above configuration and metrics to deal with the vulnerability to adversarial inputs of deep learning models, as they can improve their models against adversarial inputs without using many inputs and without creating numerous adversarial inputs. We also show that dataset size has an important impact on the results.This work was supported by the GAISSA Spanish research project (ref. TED2021-130923B-I00; MCIN/AEI/10.13039/501100011033), the “UNAM-DGECI: Iniciación a la Investigación (verano otoño 2021)” scholarship provided by Universidad Nacional Autónoma de México (UNAM), the “Beatriz Galindo” Spanish Program BEAGAL18/00064, the Austrian Science Fund (FWF): I 4701-N and the project Continuous Testing in Production (ConTest) funded by the Austrian Research Promotion Agency (FFG): 888127.Peer ReviewedObjectius de Desenvolupament Sostenible::7 - Energia Assequible i No ContaminantObjectius de Desenvolupament Sostenible::13 - Acció per al ClimaPostprint (published version

An Automated Fall Detection System Using Recurrent Neural Networks

Falls are the most common cause of fatal injuries in elderly people, causing even death if there is no immediate assistance. Fall detection systems can be used to alert and request help when this type of accident happens. Certain types of these systems include wearable devices that analyze bio-medical signals from the person carrying it in real time. In this way, Deep Learning algorithms could automate and improve the detection of unintentional falls by analyzing these signals. These algorithms have proven to achieve high effectiveness with competitive performances in many classification problems. This work aims to study 16 Recurrent Neural Networks architectures (using Long Short-Term Memory and Gated Recurrent Units) for falls detection based on accelerometer data, reducing computational requirements of previous research. The architectures have been tested on a labeled version of the publicly available SisFall dataset, achieving a mean F1-score above 0.73 and improving state-of-the-art solutions in terms of network complexity.Ministerio de Economía y Competitivida TEC2016-77785-

Breast Cancer Automatic Diagnosis System using Faster Regional Convolutional Neural Networks

Breast cancer is one of the most frequent causes of mortality in women. For the early detection of breast cancer, the mammography is used as the most efficient technique to identify abnormalities such as tumors. Automatic detection of tumors in mammograms has become a big challenge and can play a crucial role to assist doctors in order to achieve an accurate diagnosis. State-of-the-art Deep Learning algorithms such as Faster Regional Convolutional Neural Networks are able to determine the presence of an object and also its position inside the image in a reduced computation time. In this work, we evaluate these algorithms to detect tumors in mammogram images and propose a detection system that contains: (1) a preprocessing step performed on mammograms taken from the Digital Database for Screening Mammography (DDSM) and (2) the Neural Network model, which performs feature extraction over the mammograms in order to locate tumors within each image and classify them as malignant or benign. The results obtained show that the proposed algorithm has an accuracy of 97.375%. These results show that the system could be very useful for aiding physicians when detecting tumors from mammogram images.Ministerio de Economía y Competitividad TEC2016-77785-

Multi-dataset Training for Medical Image Segmentation as a Service

Deep Learning tools are widely used for medical image segmentation. The results produced by these techniques depend to a great extent on the data sets used to train the used network. Nowadays many cloud service providers offer the required resources to train networks and deploy deep learning networks. This makes the idea of segmentation as a cloud-based service attractive. In this paper we study the possibility of training, a generalized configurable, Keras U-Net to test the feasibility of training with images acquired, with specific instruments, to perform predictions on data from other instruments. We use, as our application example, the segmentation of Optic Disc and Cup which can be applied to glaucoma detection. We use two publicly available data sets (RIM-One V3 and DRISHTI) to train either independently or combining their data.Ministerio de Economía y Competitividad TEC2016-77785-

Preliminary data on cold-water corals and large sponges by-catch from Spanish/EU bottom trawl groundfish surveys in NAFO Regulatory Area (Divs. 3LMNO) and Canadian EEZ (Div. 3L): 2005-2007 period

Since 2005, by-catch of vulnerable invertebrates, such as cold-water corals and large sponges, has been studied with special attention in the Spanish/EU bottom trawl groundfish surveys in Northwest Atlantic (NAFO Divs. 3LMNO). Based on this research, twenty-nine different taxa of cold-water corals have been preliminarily identified in the study area: five alcyonaceans, ten gorgonaceans, ten pennatulaceans, three solitary scleractinians and one antipatharian. No colonial scleractinians were recorded during these surveys and reef structures are unlikely to occur in the study area. The main large sponges found belong to the family Geodiidae. The volume of cold-water corals and large sponges in the by-catches was generally low in the regularly-used fishing grounds studied. Most of the by-catches were recorded in hauls carried out in areas outside of regular fishing grounds for the bottom trawlers. By-catches of large gorgonians were recorded in three small areas located in Divs. 3LM (two in Div. 3L and one in Div. 3M), indicating that Vulnerable Marine Ecosystems (VMEs) could occur there. Pennatulaceans, solitary scleractinians, alcyonaceans and antipatharians were also observed as part of bycatch in some hauls carried out in Divs. 3LMO, but it is not clear if these by-catches indicate presence of VMEs in the area sampled. Highest diversity of coral species was found in Div. 3M. Large sponges occurred in deep waters, in a narrow band along Northern slope of the Grand Banks (Div. 3N) and Southern Flemish Pass (Div. 3L) as well as in several patches located in North-eastern and Eastern Flemish Cap. The preliminary information presented here, derived solely from bottom trawl survey by-catch records, it is not enough for identification of VMEs accurately, but it is very valuable to give a general view of where VMEs like to occur or not occur. Previous experience from other North Atlantic high-seas fishing grounds (e.g. NEAFC Regulatory Area) suggests that additional geohabitat mapping and information on fishery footprint will be needed for the accurate delineation of VMEs and for the subsequent adoption of suitable habitat conservation measures such as Marine Protected Areas (MPAs) to preserve cold-water corals and large sponges in NAFO Area

Glioma Diagnosis Aid through CNNs and Fuzzy-C Means for MRI

Glioma is a type of brain tumor that causes mortality in many cases. Early diagnosis is an important factor. Typically, it is detected through MRI and then either a treatment is applied, or it is removed through surgery. Deep-learning techniques are becoming popular in medical applications and image-based diagnosis. Convolutional Neural Networks are the preferred architecture for object detection and classification in images. In this paper, we present a study to evaluate the efficiency of using CNNs for diagnosis aids in glioma detection and the improvement of the method when using a clustering method (Fuzzy C-means) for preprocessing the input MRI dataset. Results offered an accuracy improvement from 0.77 to 0.81 when using Fuzzy C-Means.Ministerio de Economía y Competitividad TEC2016-77785-

Sampling Frequency Evaluation on Recurrent Neural Networks Architectures for IoT Real-time Fall Detection Devices

Falls are one of the most frequent causes of injuries in elderly people. Wearable Fall Detection Systems provided a ubiquitous tool for monitoring and alert when these events happen. Recurrent Neural Networks (RNN) are algorithms that demonstrates a great accuracy in some problems analyzing sequential inputs, such as temporal signal values. However, their computational complexity are an obstacle for the implementation in IoT devices. This work shows a performance analysis of a set of RNN architectures when trained with data obtained using different sampling frequencies. These architectures were trained to detect both fall and fall hazards by using accelerometers and were tested with 10-fold cross validation, using the F1-score metric. The results obtained show that sampling with a frequency of 25Hz does not affect the effectiveness, based on the F1-score, which implies a substantial increase in the performance in terms of computational cost. The architectures with two RNN layers and without a first dense layer had slightly better results than the smallest architectures. In future works, the best architectures obtained will be integrated in an IoT solution to determine the effectiveness empirically.Ministerio de Economía y Competitividad TEC2016-77785-

Embryology of the abnormally high origin of a coronary artery (High Take-Off) in a mouse model.

High take-off (HTO) is a rare congenital coronary artery anomaly associated with sudden cardiac death. The coronary ostium is located in the ascending aorta above the sinotubular junction. The morphogenetic defect leading to HTO is currently unknown. Our group has shown occurrence of HTO in different strains of laboratory mice, including C57Bl/6 strain with 58% incidence of HTO and Balb/c strain with null incidence. Our aim is to investigate the aetiology of HTO, using C57BL/6 and Balb/c mice strains as experimental models. The process of coronariogenesis was examined in E13.5 and E14.5 mouse embryos of C57Bl/6 (n=27) and Balb/c (n=23) strains. We used histochemistry and immunohistochemistry with specific markers for the vascular plexuses involved in the formation of coronary arteries (PROX1 and ERG 1/2/3). In the mouse embryo, coronary ostia develop at approximately stage E14.5. The location of the ostia is determined by the confluence of two vascular plexuses. The transient lymphatic subepicardial aortic plexus migrates from the pharyngeal region, invading the subepicardial space of the intrapericardial thoracic arteries at around E13.5. The primary or ventricular plexus, which constitutes the future coronary vascular network, forms in situ, reaching the embryonic cardiac outflow tract at around E14. Eighteen of the 27 (66.7%) C57Bl/6 embryos showed an exacerbated subepicardial aortic plexus compared to the 23 Balb/c embryos, in which the subepicardial aortic plexus exhibited a normal size. These results suggest that the embryonic origin of HTO could be due to a defect in the growth of the subepicardial aortic plexus, resulting in an exacerbated vessel network. This overgrowth seems to alter the invasion and connection of the primary plexus to the aortic root for the establishment of the ostia and coronary trunks. From a biomedical viewpoint, it would be of great interest to investigate the molecular mechanisms underlying the overgrowth of the aortic plexus.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech; PI-0530-2019 (Consejería de Salud y Familias, Junta de Andalucía); UMA20-FEDERJA-041 (Junta de Andalucía y Fondos FEDER); PROYEXCEL_01009 (PAIDI, Junta de Andalucía); PRE2018-083176 (Ministerio de Ciencia, Innovación y Universidades), CONT-FIMABIS-0610/2023 (Junta de Andalucía)

STIM1 deficiency is linked to Alzheimer’s disease and triggers cell death in SH-SY5Y cells by upregulation of L-type voltage-operated Ca2+ entry

La STIM1 es una proteína del retículo endoplásmico con un papel en la movilización y señalización del Ca2+. Como sensor de los niveles intraluminales de Ca2+, STIM1 modula los canales de Ca2+ de la membrana plasmática para regular la entrada de Ca2+. En las células de neuroblastoma SH-SY5Y y en los fibroblastos cutáneos familiares de pacientes con la enfermedad de Alzheimer, STIM1 se divide en el dominio transmembrana por la presenilina-1-asociada a γ-secretase, lo que lleva a una desregulación de la homeostasis del Ca2+. En este informe, investigamos los niveles de expresión de STIM1 en los tejidos cerebrales (giro frontal medio) de pacientes con enfermedad de Alzheimer confirmada patológicamente, y observamos que el nivel de expresión de la proteína STIM1 disminuyó con la progresión de la neurodegeneración. Para estudiar el papel de STIM1 en la neurodegeneración, se diseñó una estrategia para eliminar la expresión del gen STIM1 en la línea de células de neuroblastoma SH-SY5Y mediante la edición del genoma mediado por CRISPR/Cas9, como un modelo in vitro para examinar el fenotipo de las células neuronales deficientes de STIM1. Se demostró que, si bien la STIM1 no es necesaria para la diferenciación de las células SH-SY5Y, es absolutamente esencial para la supervivencia de las células en la diferenciación. Las células STIM1-KO diferenciadas mostraron una disminución significativa de la actividad del complejo I de la cadena respiratoria mitocondrial, la despolarización de la membrana interna de la mitocondria, la reducción de la concentración de Ca2+ libre en la mitocondria y mayores niveles de senescencia en comparación con las células de tipo salvaje. En paralelo, las células STIM1-KO mostraron una entrada de Ca2+ potenciada en respuesta a la despolarización, que era sensible a la nifedipina, apuntando a los canales de Ca2+ operados por voltaje de tipo L como mediadores de la entrada de Ca2+ aumentada. El derribo estable de las transcripciones de CACNA1C restauró la función mitocondrial, aumentó los niveles mitocondriales de Ca2+ y redujo la senescencia a los niveles basales, demostrando el papel esencial de la regulación de la entrada de Ca2+ operada por voltaje a través de los canales Cav1.2 en la muerte celular deficiente de STIM1 SHSY5Y.STIM1 is an endoplasmic reticulum protein with a role in Ca2+ mobilization and signaling. As a sensor of intraluminal Ca2+ levels, STIM1 modulates plasma membrane Ca2+ channels to regulate Ca2+ entry. In neuroblastoma SH-SY5Y cells and in familial Alzheimer’s disease patient skin fibroblasts, STIM1 is cleaved at the transmembrane domain by the presenilin-1-associated γ-secretase, leading to dysregulation of Ca2+ homeostasis. In this report, we investigated expression levels of STIM1 in brain tissues (medium frontal gyrus) of pathologically confirmed Alzheimer’s disease patients, and observed that STIM1 protein expression level decreased with the progression of neurodegeneration. To study the role of STIM1 in neurodegeneration, a strategy was designed to knock-out the expression of STIM1 gene in the SH-SY5Y neuroblastoma cell line by CRISPR/Cas9-mediated genome editing, as an in vitro model to examine the phenotype of STIM1-deficient neuronal cells. It was proved that, while STIM1 is not required for the differentiation of SH-SY5Y cells, it is absolutely essential for cell survival in differentiating cells. Differentiated STIM1-KO cells showed a significant decrease of mitochondrial respiratory chain complex I activity, mitochondrial inner membrane depolarization, reduced mitochondrial free Ca2+ concentration, and higher levels of senescence as compared with wild-type cells. In parallel, STIM1-KO cells showed a potentiated Ca2+ entry in response to depolarization, which was sensitive to nifedipine, pointing to L-type voltage-operated Ca2+ channels as mediators of the upregulated Ca2+ entry. The stable knocking-down of CACNA1C transcripts restored mitocondrial function, increased mitochondrial Ca2+ levels, and dropped senescence to basal levels, emonstrating the essential role of the upregulation of voltage-operated Ca2+ entry through Cav1.2 channels in STIM1-deficient SHSY5Y cell death.• Ministerio de Educación, Cultura y Deporte. Beca FPU13/03430 • The Company of Biologists. Ayuda JCSTF-170507 • Ministerio de Economía, y Competitividad. Proyectos BFU2014-52401-P y BFU2017-82716, para Francisco Javier Martín Romero • Ministerio de Economía, y Competitividad. Proyectos BFU2014-53641-P y BFU2017-85723-P, para Ana María Mata Durán y Carlos Gutiérrez Merino • Junta de Extremadura. Ayudas GRU15077 e IB16088, para Francisco Javier Martín Romero • Junta de Extremadura. Ayuda GRU15139, para Ana María Mata DuránpeerReviewe

Experimental evidence of the genetic hypothesis on the etiology of bicuspid aortic valve aortopathy in the hamster model.

Bicuspid aortopathy occurs in approximately 50% of patients with bicuspid aortic valve (BAV), the most prevalent congenital cardiac malformation. Although different molecular players and etiological factors (genetic and hemodynamic) have been suggested to be involved in aortopathy predisposition and progression, clear etiophysiopathological mechanisms of disease are still missing. The isogenic (genetically uniform) hamster (T) strain shows 40% incidence of BAV, but aortic dilatations have not been detected in this model. We have performed comparative anatomical, histological and molecular analyses of the ascending aorta of animals with tricuspid aortic valve (TAV) and BAV from the T strain (TTAV and TBAV, respectively) and with TAV from a control strain (HTAV). Aortic diameter, smooth muscle apoptosis, elastic waviness, and Tgf-β and Fbn-2 expression were significantly increased in T strain animals, regardless of the valve morphology. Strain and aortic valve morphology did not affect Mmp-9 expression, whereas Mmp-2 transcripts were reduced in BAV animals. eNOS protein amount decreased in both TBAV and TTAV compared to HTAV animals. Thus, histomorphological and molecular alterations of the ascending aorta appear in a genetically uniform spontaneous hamster model irrespective of the aortic valve morphology. This is a direct experimental evidence supporting the genetic association between BAV and aortic dilatation. This model may represent a population of patients with predisposition to BAV aortopathy, in which increased expression of Tgf-β and Fbn-2 alters elastic lamellae structure and induces cell apoptosis mediated by eNOS. Patients either with TAV or BAV with the same genetic defect may show the same risk to develop bicuspid aortopathy.This work was supported by Consejería de Salud y Familias, Junta de Andalucía (PI-0530-2019), Consejería de Economía y Conocimiento, Junta de Andalucía (UMA20-FEDERJA-041), Ministerio de Ciencia e Innovación (grants CGL2017-85090- P and PT20/00101, fellowship PRE2018-083176 to MS-N), and FEDER funds.S