Cannabis exposure during pregnancy and perinatal outcomes: A cohort study.

INTRODUCTION: Cannabis potency and its use during pregnancy have increased in the last decade. The aim of this study was to investigate the impact of antenatal cannabis use on fetal growth, preterm birth and other perinatal outcomes. MATERIAL AND METHODS: A propensity score-matched analysis was performed in women with singleton pregnancies attending a tertiary care site in Barcelona. Women in the cannabis group were selected based on the results of a detection test. Primary outcomes were small for gestational age at birth (SGA), low birthweight and preterm birth. Secondary outcomes were other biometric parameters (neonatal length and head circumference), respiratory distress, admission to the neonatal intensive care unit and breastfeeding at discharge. A second propensity score-matched analysis excluding other confounders (use of other recreational drugs and discontinuation of cannabis use during pregnancy) was performed. RESULTS: Antenatal cannabis was associated with a higher odds ratio of SGA (OR 3.60, 95% CI: 1.68-7.69), low birthweight (OR 3.94, 95% CI: 2.17-7.13), preterm birth at 37 weeks (OR 2.07, 95% CI: 1.12-3.84) and 32 weeks of gestation (OR 4.13, 95% CI: 1.06-16.11), admission to the neonatal intensive care unit (OR 1.95, 95% CI: 1.03-3.71), respiratory distress (OR 2.77, 95% CI: 1.26-6.34), and lower breastfeeding rates at discharge (OR 0.10, 95% CI: 0.05-0.18). When excluding other confounders, no significant association between antenatal cannabis use and SGA was found. CONCLUSIONS: Antenatal cannabis use increases the risk of SGA, low birthweight, preterm birth and other adverse perinatal outcomes. However, when isolating the impact of cannabis use by excluding women who use other recreational drugs and those who discontinue cannabis during pregnancy, no significant association between antenatal cannabis use and SGA birth was found.

Chatbots and messaging platforms in the classroom: An analysis from the teacher's perspective.

Messaging platforms are applications, generally mediated by an app, desktop program or the web, mainly used for synchronous communication among users. As such, they have been widely adopted officially by higher education establishments, after little or no study of their impact and perception by the teachers. We think that the introduction of these new tools and the opportunities and challenges they have needs to be studied carefully in order to adopt the model, as well as the tool, that is the most adequate for all parties involved. We already studied the perception of these tools by students, in this paper we examine the teachers' experiences and perceptions through a survey that we validated with peers, and what they think these tools should make or serve so that it enhances students learning and helps them achieve their learning objectives. The survey has been distributed among tertiary education teachers, both in universitary and other kind of tertiary establishments, based in Spain (mainly) and Spanish-speaking countries. We have focused on collecting teachers' preferences and opinions on the introduction of messaging platforms in their day-to-day work, as well as other services attached to them, such as chatbots. What we intend with this survey is to understand their needs and to gather information about the various educational use cases where these tools could be valuable. In addition, an analysis of how and when teachers' opinions towards the use of these tools varies across gender, experience, and their discipline of specialization is presented. The key findings of this study highlight the factors that can contribute to the advancement of the adoption of messaging platforms and chatbots in higher education institutions to achieve the desired learning outcomes.

Optical flow estimation from event-based cameras and spiking neural networks.

Event-based cameras are raising interest within the computer vision community. These sensors operate with asynchronous pixels, emitting events, or "spikes", when the luminance change at a given pixel since the last event surpasses a certain threshold. Thanks to their inherent qualities, such as their low power consumption, low latency, and high dynamic range, they seem particularly tailored to applications with challenging temporal constraints and safety requirements. Event-based sensors are an excellent fit for Spiking Neural Networks (SNNs), since the coupling of an asynchronous sensor with neuromorphic hardware can yield real-time systems with minimal power requirements. In this work, we seek to develop one such system, using both event sensor data from the DSEC dataset and spiking neural networks to estimate optical flow for driving scenarios. We propose a U-Net-like SNN which, after supervised training, is able to make dense optical flow estimations. To do so, we encourage both minimal norm for the error vector and minimal angle between ground-truth and predicted flow, training our model with back-propagation using a surrogate gradient. In addition, the use of 3d convolutions allows us to capture the dynamic nature of the data by increasing the temporal receptive fields. Upsampling after each decoding stage ensures that each decoder's output contributes to the final estimation. Thanks to separable convolutions, we have been able to develop a light model (when compared to competitors) that can nonetheless yield reasonably accurate optical flow estimates.

Infective endocarditis in pregnant women without intravenous drug use: a multicentre retrospective case series.

OBJECTIVES: To describe the clinical features and outcomes of infective endocarditis (IE) in pregnant women who do not inject drugs. METHODS: A multinational retrospective study was performed at 14 hospitals. All definite IE episodes between January 2000 and April 2021 were included. The main outcomes were maternal mortality and pregnancy-related complications. RESULTS: Twenty-five episodes of IE were included. Median age at IE diagnosis was 33.2 years (IQR 28.3-36.6) and median gestational age was 30 weeks (IQR 16-32). Thirteen (52%) patients had no previously known heart disease. Sixteen (64%) were native IE, 7 (28%) prosthetic and 2 (8%) cardiac implantable electronic device IE. The most common aetiologies were streptococci (n = 10, 40%), staphylococci (n = 5, 20%), HACEK group (n = 3, 12%) and Enterococcus faecalis (n = 3, 12%). Twenty (80%) patients presented at least one IE complication; the most common were heart failure (n = 13, 52%) and symptomatic embolism other than stroke (n = 4, 16%). Twenty-one (84%) patients had surgery indication and surgery was performed when indicated in 19 (90%). There was one maternal death and 16 (64%) patients presented pregnancy-related complications (11 patients ≥1 complication): 3 pregnancy losses, 9 urgent Caesarean sections, 2 emergency Caesarean sections, 1 fetal death, and 11 preterm births. Two patients presented a relapse during a median follow-up of 3.1 years (IQR 0.6-7.4). CONCLUSIONS: Strict medical surveillance of pregnant women with IE is required and must involve a multidisciplinary team including obstetricians and neonatologists. Furthermore, the potential risk of IE during pregnancy should never be underestimated in women with previously known underlying heart disease.

SORL1 Polymorphisms in Mexican Patients with Alzheimer's Disease.

The present study evaluated the risk effect of 12 Single Nucleotide Polymorphisms in the SORL1 gene in the Mexican population using Late-Onset Alzheimer's Disease (LOAD) and control subjects. Considering APOE as the strongest genetic risk factor for LOAD, we conducted interaction analyses between single nucleotide polymorphisms (SNPs) and the APOE genotype. METHODS: Patients were interviewed during their scheduled visits at neurologic and geriatric clinics from different institutions. The LOAD diagnosis included neurological, geriatric, and psychiatric examinations, as well as the medical history and neuroimaging. Polymorphisms in SORL1 were genotyped by real-time PCR in 156 subjects with LOAD and 221 controls. APOE genotype was determined in each study subject. Allelic, genotypic, and haplotypic frequencies were analyzed; an ancestry analysis was also performed. RESULTS: The A/A genotype in rs1784933 might be associated with an increased LOAD risk. Two blocks with high degree linkage disequilibrium (LD) were identified. The first block composed by the genetic variants rs668387, rs689021 and rs641120 showed a positive interaction (mainly the rs689021) with rs1784933 polymorphism. Moreover, we found a significant association between the APOE ε4 allele carriers and the variant rs2070045 located in the second LD block. CONCLUSION: The rs1784933 polymorphism is associated with LOAD in Mexican patients. In addition, the presence of APOE ε4 allele and SORL1 variants could represent a genetic interaction effect that favors LOAD risk in the Mexican population. SNPs have been proposed as genetic markers associated with the development of LOAD that can support the clinical diagnosis. Future molecular studies could help understand sporadic Alzheimer's Disease (AD) among the Mexican population, where currently there is a sub-estimate number in terms of disease frequency and incidence.

A Dataset for Visual Navigation with Neuromorphic Methods.

Standardized benchmarks in Computer Vision have greatly contributed to the advance of approaches to many problems in the field. If we want to enhance the visibility of event-driven vision and increase its impact, we will need benchmarks that allow comparison among different neuromorphic methods as well as comparison to Computer Vision conventional approaches. We present datasets to evaluate the accuracy of frame-free and frame-based approaches for tasks of visual navigation. Similar to conventional Computer Vision datasets, we provide synthetic and real scenes, with the synthetic data created with graphics packages, and the real data recorded using a mobile robotic platform carrying a dynamic and active pixel vision sensor (DAVIS) and an RGB+Depth sensor. For both datasets the cameras move with a rigid motion in a static scene, and the data includes the images, events, optic flow, 3D camera motion, and the depth of the scene, along with calibration procedures. Finally, we also provide simulated event data generated synthetically from well-known frame-based optical flow datasets.

Evaluation of inflammation-related genes polymorphisms in Mexican with Alzheimer's disease: a pilot study.

Amyloid peptide is able to promote the activation of microglia and astrocytes in Alzheimer's disease (AD), and this stimulates the production of pro-inflammatory cytokines. Inflammation contributes to the process of neurodegeneration and therefore is a key factor in the development of AD. Some of the most important proteins involved in AD inflammation are: clusterin (CLU), complement receptor 1 (CR1), C reactive protein (CRP), tumor necrosis factor α (TNF-α), the interleukins 1α (IL-1α), 6 (IL-6), 10 (IL-10) and cyclooxygenase 2 (COX-2). In particular, COX-2 is encoded by the prostaglandin-endoperoxide synthase 2 gene (PTGS2). Since variations in the genes that encode these proteins may modify gene expression or function, it is important to investigate whether these variations may change the developing AD. The aim of this study was to determine whether the presence of polymorphisms in the genes encoding the aforementioned proteins is associated in Mexican patients with AD. Fourteen polymorphisms were genotyped in 96 subjects with AD and 100 controls; the differences in allele, genotype and haplotype frequencies were analyzed. Additionally, an ancestry analysis was conducted to exclude differences in genetic ancestry among groups as a confounding factor in the study. Significant differences in frequencies between AD and controls were found for the single-nucleotide polymorphism (SNP) rs20417 within the PTGS2 gene. Ancestry analysis revealed no significant differences in the ancestry of the compared groups, and the association was significant even after adjustment for ancestry and correction for multiple testing, which strengthens the validity of the results. We conclude that this polymorphism plays an important role in the development of the AD pathology and further studies are required, including their proteins.

Vector disparity sensor with vergence control for active vision systems.

This paper presents an architecture for computing vector disparity for active vision systems as used on robotics applications. The control of the vergence angle of a binocular system allows us to efficiently explore dynamic environments, but requires a generalization of the disparity computation with respect to a static camera setup, where the disparity is strictly 1-D after the image rectification. The interaction between vision and motor control allows us to develop an active sensor that achieves high accuracy of the disparity computation around the fixation point, and fast reaction time for the vergence control. In this contribution, we address the development of a real-time architecture for vector disparity computation using an FPGA device. We implement the disparity unit and the control module for vergence, version, and tilt to determine the fixation point. In addition, two on-chip different alternatives for the vector disparity engines are discussed based on the luminance (gradient-based) and phase information of the binocular images. The multiscale versions of these engines are able to estimate the vector disparity up to 32 fps on VGA resolution images with very good accuracy as shown using benchmark sequences with known ground-truth. The performances in terms of frame-rate, resource utilization, and accuracy of the presented approaches are discussed. On the basis of these results, our study indicates that the gradient-based approach leads to the best trade-off choice for the integration with the active vision system.

Visual system based on artificial retina for motion detection.

We present a bioinspired model for detecting spatiotemporal features based on artificial retina response models. Event-driven processing is implemented using four kinds of cells encoding image contrast and temporal information. We have evaluated how the accuracy of motion processing depends on local contrast by using a multiscale and rank-order coding scheme to select the most important cues from retinal inputs. We have also developed some alternatives by integrating temporal feature results and obtained a new improved bioinspired matching algorithm with high stability, low error and low cost. Finally, we define a dynamic and versatile multimodal attention operator with which the system is driven to focus on different target features such as motion, colors, and textures.