CMV and HIV Coinfection in Women from a Region in Eastern Europe.

(1) Background: Human cytomegalovirus (CMV) infection is one of the most frequent opportunistic infections in immunosuppressed patients. Romania has one of the highest incidences of patients living with human immunodeficiency virus (HIV) which determines an immunosuppressive state. The aim of this study was to establish the prevalence of CMV infection among women living with HIV in Southeastern Romania and also to evaluate and correlate antiretroviral therapy (ART) with CD4 level and CMV disease evolution. (2) Methods: Seventy women living with HIV from Southeastern Romania were screened for CMV infection using antigen quantification. Of these, 50 were included in the study. First, the patients filled out a questionnaire regarding social conditions and other associated diseases. Then, we explored the statistical correlations between the data and HIV status, CD4+ cell counts, viral load, and antiretroviral therapy (ART). (3) Results: Median age of the patients was 33 years. Twenty-nine cases were diagnosed with HIV after sexual life beginning and 21 before. Most of the patients had a CD4 level over 200 cells/µL. ART duration in the CD4 under 200 cells/µL group was a bit longer than that in the CD4 over 200 cells/µL group. Forty-one patients had undetectable viremia. CD4 average value in the lot of patients with undetectable viremia was 704.71 cells/µL and in the lot with detectable viremia was 452.44 cells/µL. Viremia values correlated negatively with CD4 level. A positive correlation between IgG CMV values and ART therapy length was identified. A negative significant correlation between values of IgG CMV and values of CD4 was identified. CD4 value correlated negatively with IgG CMV values and with CMV avidity. (4) Conclusions: IgG CMV values had a weak positive correlation with ART therapy length, and a negative statistically significant correlation with values of CD4. CMV avidity has a negative correlation with CD4 value.

Prediction of Continuous Emotional Measures through Physiological and Visual Data.

The affective state of a person can be measured using arousal and valence values. In this article, we contribute to the prediction of arousal and valence values from various data sources. Our goal is to later use such predictive models to adaptively adjust virtual reality (VR) environments and help facilitate cognitive remediation exercises for users with mental health disorders, such as schizophrenia, while avoiding discouragement. Building on our previous work on physiological, electrodermal activity (EDA) and electrocardiogram (ECG) recordings, we propose improving preprocessing and adding novel feature selection and decision fusion processes. We use video recordings as an additional data source for predicting affective states. We implement an innovative solution based on a combination of machine learning models alongside a series of preprocessing steps. We test our approach on RECOLA, a publicly available dataset. The best results are obtained with a concordance correlation coefficient (CCC) of 0.996 for arousal and 0.998 for valence using physiological data. Related work in the literature reported lower CCCs on the same data modality; thus, our approach outperforms the state-of-the-art approaches for RECOLA. Our study underscores the potential of using advanced machine learning techniques with diverse data sources to enhance the personalization of VR environments.

The Influence of Maternal KIR Haplotype on the Reproductive Outcomes after Single Embryo Transfer in IVF Cycles in Patients with Recurrent Pregnancy Loss and Implantation Failure-A Single Center Experience.

(1) Background: Recurrent pregnancy loss (RPL) and recurrent implantation failure (RIF) have in common a deficient maternal adaptation to the semi-allogeneic fetus, in which killer immunoglobulin-like receptor (KIR) family expressed by natural killer (NK) cells play an important role. The aim of this study was to evaluate the influence of maternal KIR haplotype on the reproductive outcomes after single embryo transfer in IVF cycles in patients with RPL and RIF. (2) Methods: Patients with RIF and RPL who presented at Origyn Fertility Center from Iasi, Romania, were prospectively enrolled between January 2020 and December 2022. Clinical and paraclinical data was examined. Descriptive statistics and a conditional logistic regression model were used to analyze our data. (3) Results: Patients with a KIR AA haplotype had significantly more chances of miscarriage if they underwent an IVF procedure (aOR: 4.15, 95% CI: 1.39-6.50, p = 0.032) compared with those who spontaneously achieved a pregnancy. Moreover, it appeared that the same haplotype increased the chances of obtaining a pregnancy for patients who underwent an IVF procedure (aOR: 2.57, 95% CI: 0.85-6.75, p = 0.023). (4) Conclusions: Determination of KIR haplotype could be beneficial for patients with RPL or RIF in order to offer an individualized management.

Prognostic Factors of Low-Grade Gliomas in Adults.

Adult low-grade gliomas are a rare and aggressive pathology of the central nervous system. Some of their characteristics contribute to the patient's life expectancy and to their management. This study aimed to characterize and identify the main prognostic factors of low-grade gliomas. The six-year retrospective study statistically analyzed the demographic, imaging, and morphogenetic characteristics of the patient group through appropriate parameters. Immunohistochemical tests were performed: IDH1, Ki-67, p53, and Nestin, as well as FISH tests on the CDKN2A gene and 1p/19q codeletion. The pathology was prevalent in females, with patients having an average age of 56.31 years. The average tumor volume was 41.61 cm3, producing a midline shift with an average of 7.5 mm. Its displacement had a negative impact on survival. The presence of a residual tumor resulted in decreased survival and is an independent risk factor for mortality. Positivity for p53 identified a low survival rate. CDKN2A mutations were an independent risk factor for mortality. We identified that a negative prognosis is influenced by the association of epilepsy with headache, tumor volume, and immunoreactivity to IDH1 and p53. Independent factors associated with mortality were midline shift, presence of tumor residue, and CDKN2A gene deletions and amplifications.

Aggressiveness of Grade 4 Gliomas of Adults.

Grade 4 adult gliomas are IDH-mutant astrocytomas and IDH-wildtype glioblastomas. They have a very high mortality rate, with survival at 5 years not exceeding 5%. We aimed to conduct a clinical imaging and morphogenetic characterization of them, as well as to identify the main negative prognostic factors that give them such aggressiveness. We conducted a ten-year retrospective study. We followed the clinical, imaging, and morphogenetic aspects of the cases. We analyzed immunohistochemical markers (IDH1, Ki-67, and nestin) and FISH tests based on the CDKN2A gene. The obtained results were analyzed using SPSS Statistics with the appropriate parameters. The clinical aspects representing negative prognostic factors were represented by patients' comorbidities: hypertension (HR = 1.776) and diabetes mellitus/hyperglycemia (HR = 2.159). The lesions were mostly supratentorial, and the temporal lobe was the most affected. The mean volume was 88.05 cm3 and produced a midline shift with an average of 8.52 mm. Subtotal surgical resection was a negative prognostic factor (HR = 1.877). The proliferative index did not influence survival rate, whereas CDKN2A gene mutations were shown to have a major impact on survival. We identified the main negative prognostic factors that support the aggressiveness of grade 4 gliomas: patient comorbidities, type of surgical resection, degree of cell differentiation, and CDKN2A gene mutations.

HPV and HIV Coinfection in Women from a Southeast Region of Romania-PICOPIV Study.

Background and Objectives: Romania faces one of the highest cervical cancer burdens in Europe though it is a preventable cancer through population screening by cytology and human papillomavirus (HPV) detection. Also, it has one of the highest incidences of human immunodeficiency virus (HIV) infection. HPV and HIV coinfection are frequently encountered. The aim of study was to establish the prevalence of HPV infection among HIV-positive women in Southeast Region of Romania, to genotype high risk HPV types -and to correlate the results with clinical data and cytological cervical lesions. Materials and Methods: 40 HIV-positive women were screened for HPV types and for cytological cervical lesions. The findings were evaluated in correlation with CD4 cell counts, HIV viral load, age at first sexual intercourse, number of sexual partners, vaginal candidiasis, and Gardnerella using statistical methods. Results: 19/40 (47.5%) women were positive for HPV types, 63.15% infected with single HPV type and 36.85% with multiple HPV types. The most frequent types were type: 31 (42.1%), 56 (31.57%), 53 (15.78%). On cytology, 34 (85%) women were found with NILM of which 38.23% were HPV-positive. Fifteen percent of women had abnormal cytology (three ASC-US, three LSIL), and all of them were HPV-positive. Through analyzing the value of CD4 count, women with CD4 count ≤ 200 cells/µL were found to be significantly more likely to be infected with HPV; meanwhile there was no correlation between the detection of HPV types and HIV viral load. Candida or Gardnerella were more often associated with HIV-positive women with HPV, than in women without HPV. Conclusions: Infection with HPV types is common among HIV-positive women in the Southeast Region of Romania and it is associated with age at the beginning of sexual life, number of sexual partners, CD4 value, vaginal candidiasis, and Gardnerella infection.

Interaction data are identifiable even across long periods of time.

Fine-grained records of people's interactions, both offline and online, are collected at large scale. These data contain sensitive information about whom we meet, talk to, and when. We demonstrate here how people's interaction behavior is stable over long periods of time and can be used to identify individuals in anonymous datasets. Our attack learns the profile of an individual using geometric deep learning and triplet loss optimization. In a mobile phone metadata dataset of more than 40k people, it correctly identifies 52% of individuals based on their 2-hop interaction graph. We further show that the profiles learned by our method are stable over time and that 24% of people are still identifiable after 20 weeks. Our results suggest that people with well-balanced interaction graphs are more identifiable. Applying our attack to Bluetooth close-proximity networks, we show that even 1-hop interaction graphs are enough to identify people more than 26% of the time. Our results provide strong evidence that disconnected and even re-pseudonymized interaction data can be linked together making them personal data under the European Union's General Data Protection Regulation.

Transfer of Learning from Vision to Touch: A Hybrid Deep Convolutional Neural Network for Visuo-Tactile 3D Object Recognition.

Transfer of learning or leveraging a pre-trained network and fine-tuning it to perform new tasks has been successfully applied in a variety of machine intelligence fields, including computer vision, natural language processing and audio/speech recognition. Drawing inspiration from neuroscience research that suggests that both visual and tactile stimuli rouse similar neural networks in the human brain, in this work, we explore the idea of transferring learning from vision to touch in the context of 3D object recognition. In particular, deep convolutional neural networks (CNN) pre-trained on visual images are adapted and evaluated for the classification of tactile data sets. To do so, we ran experiments with five different pre-trained CNN architectures and on five different datasets acquired with different technologies of tactile sensors including BathTip, Gelsight, force-sensing resistor (FSR) array, a high-resolution virtual FSR sensor, and tactile sensors on the Barrett robotic hand. The results obtained confirm the transferability of learning from vision to touch to interpret 3D models. Due to its higher resolution, tactile data from optical tactile sensors was demonstrated to achieve higher classification rates based on visual features compared to other technologies relying on pressure measurements. Further analysis of the weight updates in the convolutional layer is performed to measure the similarity between visual and tactile features for each technology of tactile sensing. Comparing the weight updates in different convolutional layers suggests that by updating a few convolutional layers of a pre-trained CNN on visual data, it can be efficiently used to classify tactile data. Accordingly, we propose a hybrid architecture performing both visual and tactile 3D object recognition with a MobileNetV2 backbone. MobileNetV2 is chosen due to its smaller size and thus its capability to be implemented on mobile devices, such that the network can classify both visual and tactile data. An accuracy of 100% for visual and 77.63% for tactile data are achieved by the proposed architecture.

An Application of Deep Learning to Tactile Data for Object Recognition under Visual Guidance.

Drawing inspiration from haptic exploration of objects by humans, the current work proposes a novel framework for robotic tactile object recognition, where visual information in the form of a set of visually interesting points is employed to guide the process of tactile data acquisition. Neuroscience research confirms the integration of cutaneous data as a response to surface changes sensed by humans with data from joints, muscles, and bones (kinesthetic cues) for object recognition. On the other hand, psychological studies demonstrate that humans tend to follow object contours to perceive their global shape, which leads to object recognition. In compliance with these findings, a series of contours are determined around a set of 24 virtual objects from which bimodal tactile data (kinesthetic and cutaneous) are obtained sequentially and by adaptively changing the size of the sensor surface according to the object geometry for each object. A virtual Force Sensing Resistor array (FSR) is employed to capture cutaneous cues. Two different methods for sequential data classification are then implemented using Convolutional Neural Networks (CNN) and conventional classifiers, including support vector machines and k-nearest neighbors. In the case of conventional classifiers, we exploit contourlet transformation to extract features from tactile images. In the case of CNN, two networks are trained for cutaneous and kinesthetic data and a novel hybrid decision-making strategy is proposed for object recognition. The proposed framework is tested both for contours determined blindly (randomly determined contours of objects) and contours determined using a model of visual attention. Trained classifiers are tested on 4560 new sequential tactile data and the CNN trained over tactile data from object contours selected by the model of visual attention yields an accuracy of 98.97% which is the highest accuracy among other implemented approaches.

Wearable Sensor Data Classification for Human Activity Recognition Based on an Iterative Learning Framework.

The design of multiple human activity recognition applications in areas such as healthcare, sports and safety relies on wearable sensor technologies. However, when making decisions based on the data acquired by such sensors in practical situations, several factors related to sensor data alignment, data losses, and noise, among other experimental constraints, deteriorate data quality and model accuracy. To tackle these issues, this paper presents a data-driven iterative learning framework to classify human locomotion activities such as walk, stand, lie, and sit, extracted from the Opportunity dataset. Data acquired by twelve 3-axial acceleration sensors and seven inertial measurement units are initially de-noised using a two-stage consecutive filtering approach combining a band-pass Finite Impulse Response (FIR) and a wavelet filter. A series of statistical parameters are extracted from the kinematical features, including the principal components and singular value decomposition of roll, pitch, yaw and the norm of the axial components. The novel interactive learning procedure is then applied in order to minimize the number of samples required to classify human locomotion activities. Only those samples that are most distant from the centroids of data clusters, according to a measure presented in the paper, are selected as candidates for the training dataset. The newly built dataset is then used to train an SVM multi-class classifier. The latter will produce the lowest prediction error. The proposed learning framework ensures a high level of robustness to variations in the quality of input data, while only using a much lower number of training samples and therefore a much shorter training time, which is an important consideration given the large size of the dataset.

Multimodal Bio-Inspired Tactile Sensing Module for Surface Characterization.

Robots are expected to recognize the properties of objects in order to handle them safely and efficiently in a variety of applications, such as health and elder care, manufacturing, or high-risk environments. This paper explores the issue of surface characterization by monitoring the signals acquired by a novel bio-inspired tactile probe in contact with ridged surfaces. The tactile module comprises a nine Degree of Freedom Microelectromechanical Magnetic, Angular Rate, and Gravity system (9-DOF MEMS MARG) and a deep MEMS pressure sensor embedded in a compliant structure that mimics the function and the organization of mechanoreceptors in human skin as well as the hardness of the human skin. When the modules tip slides over a surface, the MARG unit vibrates and the deep pressure sensor captures the overall normal force exerted. The module is evaluated in two experiments. The first experiment compares the frequency content of the data collected in two setups: one when the module is mounted over a linear motion carriage that slides four grating patterns at constant velocities; the second when the module is carried by a robotic finger in contact with the same grating patterns while performing a sliding motion, similar to the exploratory motion employed by humans to detect object roughness. As expected, in the linear setup, the magnitude spectrum of the sensors' output shows that the module can detect the applied stimuli with frequencies ranging from 3.66 Hz to 11.54 Hz with an overall maximum error of ±0.1 Hz. The second experiment shows how localized features extracted from the data collected by the robotic finger setup over seven synthetic shapes can be used to classify them. The classification method consists on applying multiscale principal components analysis prior to the classification with a multilayer neural network. Achieved accuracies from 85.1% to 98.9% for the various sensor types demonstrate the usefulness of traditional MEMS as tactile sensors embedded into flexible substrates.

Acquisition and Neural Network Prediction of 3D Deformable Object Shape Using a Kinect and a Force-Torque Sensor.

The realistic representation of deformations is still an active area of research, especially for deformable objects whose behavior cannot be simply described in terms of elasticity parameters. This paper proposes a data-driven neural-network-based approach for capturing implicitly and predicting the deformations of an object subject to external forces. Visual data, in the form of 3D point clouds gathered by a Kinect sensor, is collected over an object while forces are exerted by means of the probing tip of a force-torque sensor. A novel approach based on neural gas fitting is proposed to describe the particularities of a deformation over the selectively simplified 3D surface of the object, without requiring knowledge of the object material. An alignment procedure, a distance-based clustering, and inspiration from stratified sampling support this process. The resulting representation is denser in the region of the deformation (an average of 96.6% perceptual similarity with the collected data in the deformed area), while still preserving the object's overall shape (86% similarity over the entire surface) and only using on average of 40% of the number of vertices in the mesh. A series of feedforward neural networks is then trained to predict the mapping between the force parameters characterizing the interaction with the object and the change in the object shape, as captured by the fitted neural gas nodes. This series of networks allows for the prediction of the deformation of an object when subject to unknown interactions.

Soft object deformation monitoring and learning for model-based robotic hand manipulation.

This paper discusses the design and implementation of a framework that automatically extracts and monitors the shape deformations of soft objects from a video sequence and maps them with force measurements with the goal of providing the necessary information to the controller of a robotic hand to ensure safe model-based deformable object manipulation. Measurements corresponding to the interaction force at the level of the fingertips and to the position of the fingertips of a three-finger robotic hand are associated with the contours of a deformed object tracked in a series of images using neural-network approaches. The resulting model captures the behavior of the object and is able to predict its behavior for previously unseen interactions without any assumption on the object's material. The availability of such models can contribute to the improvement of a robotic hand controller, therefore allowing more accurate and stable grasp while providing more elaborate manipulation capabilities for deformable objects. Experiments performed for different objects, made of various materials, reveal that the method accurately captures and predicts the object's shape deformation while the object is submitted to external forces applied by the robot fingers. The proposed method is also fast and insensitive to severe contour deformations, as well as to smooth changes in lighting, contrast, and background.