Opioid Prescribing Rate for Nonoperative Distal Radius Fractures and Clinician Response to a Clinical Decision Support Alert.

BACKGROUND: Opioids are commonly prescribed for the management of acute orthopedic trauma pain, including nonoperative distal radius fractures. OBJECTIVES: This prospective study aimed to determine if a clinical decision support intervention influenced prescribing decisions for patients with known risk factors. We sought to quantify frequency of opioid prescriptions for acute nonoperative distal radius fractures treated. METHODS: We performed a prospective study at one large health care system. Utilizing umbrella code S52.5, we identified all distal radius fractures treated nonoperatively, and the encounters were merged with the Prescription Reporting with Immediate Medication Mapping (PRIMUM) database to identify encounters with opioid prescriptions and patients with risk factors for opioid use disorder. We used multivariable logistic regression to determine patient characteristics associated with the prescription of an opioid. Among encounters that triggered the PRIMUM alert, we calculated the percentage of encounters where the PRIMUM alert influenced the prescribing decision. RESULTS: Of 2984 encounters, 1244 (41.7%) included an opioid prescription. Age increment is a significant factor to more likely receive opioid prescriptions (p < 0.0001) after adjusting for other factors. Among encounters where the physician received an alert, those that triggered the alert for early refill were more likely to influence physicians' opioid prescribing when compared with other risk factors (p = 0.0088). CONCLUSION: Over 90% of patients (106/118) continued to receive an opioid medication despite having a known risk factor for abuse. Additionally, we found older patients were more likely to be prescribed opioids for nonoperatively managed distal radius fractures.

Petascale pipeline for precise alignment of images from serial section electron microscopy.

The reconstruction of neural circuits from serial section electron microscopy (ssEM) images is being accelerated by automatic image segmentation methods. Segmentation accuracy is often limited by the preceding step of aligning 2D section images to create a 3D image stack. Precise and robust alignment in the presence of image artifacts is challenging, especially as datasets are attaining the petascale. We present a computational pipeline for aligning ssEM images with several key elements. Self-supervised convolutional nets are trained via metric learning to encode and align image pairs, and they are used to initialize iterative fine-tuning of alignment. A procedure called vector voting increases robustness to image artifacts or missing image data. For speedup the series is divided into blocks that are distributed to computational workers for alignment. The blocks are aligned to each other by composing transformations with decay, which achieves a global alignment without resorting to a time-consuming global optimization. We apply our pipeline to a whole fly brain dataset, and show improved accuracy relative to prior state of the art. We also demonstrate that our pipeline scales to a cubic millimeter of mouse visual cortex. Our pipeline is publicly available through two open source Python packages.

Cell-type-specific inhibitory circuitry from a connectomic census of mouse visual cortex.

Mammalian cortex features a vast diversity of neuronal cell types, each with characteristic anatomical, molecular and functional properties. Synaptic connectivity powerfully shapes how each cell type participates in the cortical circuit, but mapping connectivity rules at the resolution of distinct cell types remains difficult. Here, we used millimeter-scale volumetric electron microscopy1 to investigate the connectivity of all inhibitory neurons across a densely-segmented neuronal population of 1352 cells spanning all layers of mouse visual cortex, producing a wiring diagram of inhibitory connections with more than 70,000 synapses. Taking a data-driven approach inspired by classical neuroanatomy, we classified inhibitory neurons based on the relative targeting of dendritic compartments and other inhibitory cells and developed a novel classification of excitatory neurons based on the morphological and synaptic input properties. The synaptic connectivity between inhibitory cells revealed a novel class of disinhibitory specialist targeting basket cells, in addition to familiar subclasses. Analysis of the inhibitory connectivity onto excitatory neurons found widespread specificity, with many interneurons exhibiting differential targeting of certain subpopulations spatially intermingled with other potential targets. Inhibitory targeting was organized into "motif groups," diverse sets of cells that collectively target both perisomatic and dendritic compartments of the same excitatory targets. Collectively, our analysis identified new organizing principles for cortical inhibition and will serve as a foundation for linking modern multimodal neuronal atlases with the cortical wiring diagram.

Advances in In Vitro Blood-Air Barrier Models and the Use of Nanoparticles in COVID-19 Research.

Respiratory infections caused by coronaviruses (CoVs) have become a major public health concern in the past two decades as revealed by the emergence of SARS-CoV in 2002, MERS-CoV in 2012, and SARS-CoV-2 in 2019. The most severe clinical phenotypes commonly arise from exacerbation of immune response following the infection of alveolar epithelial cells localized at the pulmonary blood-air barrier. Preclinical rodent models do not adequately represent the essential genetic properties of the barrier, thus necessitating the use of humanized transgenic models. However, existing monolayer cell culture models have so far been unable to mimic the complex lung microenvironment. In this respect, air-liquid interface models, tissue engineered models, and organ-on-a-chip systems, which aim to better imitate the infection site microenvironment and microphysiology, are being developed to replace the commonly used monolayer cell culture models, and their use is becoming more widespread every day. On the contrary, studies on the development of nanoparticles (NPs) that mimic respiratory viruses, and those NPs used in therapy are progressing rapidly. The first part of this review describes in vitro models that mimic the blood-air barrier, the tissue interface that plays a central role in COVID-19 progression. In the second part of the review, NPs mimicking the virus and/or designed to carry therapeutic agents are explained and exemplified.

Novel Antibacterial and Biocompatible Nanostructured Gels Based on One-step Synthesis as a Potential Disinfectant for Endodontic Infection Control.

AIM: The objective of this study was to develop nanostructured gels as biocompatible intracanal disinfectants by one-step microwave radiation-assisted synthesis. METHODS: Polyvinyl alcohol (PVA) and polyvinyl pyrrolidone (PVP) were used as a support network, and polyethylene glycol (PEG) was used as a reducing agent. The gels were characterized by measuring the swelling ratio (SR) and rheological properties and by scanning electron microscopy (SEM), transmission electron microscopy (TEM) and atomic force microscopy (AFM). The antibacterial effects of each gel were evaluated against the endodontic clinical strain Enterococcus faecalis. Then, the viability of the 21-day mature multispecies bacterial biofilm was assessed using confocal microscopy in an ex vivo model, where the biofilm was exposed to the mix of nanogels. The cell proliferation, viability, and morphology of human periodontal ligament (HPDL) cells were quantified using a real-time IncuCyte® S3 Live-Cell System. Viability was measured by confocal microscopy using an ex vivo model exposing a 21-day mature multispecies bacterial biofilm to the mix of nanogels. RESULTS: The antibacterial activity of the gels coincided with the superficial characterization and the solubility of the gel in the growth medium. Gels with higher viscosity (327.85-980.58 Pa s), higher dissolution (42-70%SR), and lower porosity (no porosity and 611.63 nm) showed excellent antibacterial activity against E. faecalis. Despite their physicochemical characteristics, CuNPs gels showed greater effectiveness against E. faecalis.These nanostructured gels with high PVA concentrations promote HPDL cells proliferation while still exerting antibacterial properties. Mix of nanogels showed an increase non-viable cells biomass from at of application. CONCLUSIONS: The use of biocompatible polymers influences the physicochemical, bactericidal, and cytotoxic response, making these materials potential disinfectant agents against resistant bacteria with good biocompatibility and improved HPDL cells proliferation.

Urban effluents affect the invasive bivalve Limnoperna fortunei (Dunker 1857) fitness in a large Pampasic river (Río Negro, Uruguay).

Invasive alien species (IAS) exert a negative impact on native ecosystems and on various human activities. Limnoperna fortunei (Dunker 1857), a sessile mytilid introduced from Asia in the Río de la Plata, demonstrates a high dispersal capacity, growing over other organisms and artificial structures. Understanding its behavior is crucial for developing appropriate control and mitigating its detrimental effects. This study investigated the population dynamics of this mollusk in response to urban effluents in a large river. Water and population parameters of L. fortunei were measured both upstream and downstream of a small town from July to December 2020. Downstream areas exhibited increased temperature and decreased transparency compared to upstream locations, while pH, conductivity, and dissolved oxygen remained relatively constant. However, downstream, the L. fortunei population comprised fewer and smaller individuals, displaying high width-to-length ratios. Most measured water parameters exhibited significant differences between sampling months. The abundance of L. fortunei remained relatively stable over time due to its nearly continuous reproduction in Uruguay. Nonetheless, shell elongation (15.3-22.8 mm) coincided with an increase in dry weight (0.009-0.038 g) from winter to spring. Allometric relationships, primarily following a power-law model, varied between months, ranging from H = 0.39L1.04 to H = 1.36L0.57. This study contributes to the comprehension of the population dynamics and life history of L. fortunei, thereby aiding in the conservation of invaded aquatic systems. Moreover, it provides valuable insights into the effects of urban effluents on this species, contributing to our understanding of its potential role as a bioindicator.

[Coercion in psychiatric hospitalizations in Chile: madness and suffering in the 21st century]. / Coerción en las hospitalizaciones psiquiátricas en Chile: El sufrimiento de la locura en el siglo XXI.

This article reflects on the problem of coercion in Chilean psychiatric hospitalizations from the perspective of users who participate in mental health treatment programs in the public healthcare system. Qualitative research from a hermeneutical epistemic approach was carried out between 2019 and 2020, which included 25 interviews with individuals of both sexes (15 men and 10 women) who had a psychiatric diagnosis. The purpose of this research was to analyze their narratives and critically reframe intervention practices implemented in psychiatric hospitalizations in Chile, which constitute an important space for public health in the country as well as the rights of people with mental health problems. One of the principal findings of the study was that coercive practices still persist in Chile, despite being detrimental to the recovery of mental health care users, representing a negative impact on the quality of life and citizen freedoms of individuals with mental health problems.

Este artículo aborda el problema de la coerción en las hospitalizaciones psiquiátricas chilenas desde la perspectiva de personas usuarias que participan en un tratamiento en salud mental en la red pública de atención. Entre 2019 y 2020 se realizó un estudio cualitativo con enfoque epistémico hermenéutico, en el que se entrevistaron 25 personas de ambos sexos (15 hombres y 10 mujeres) con diagnóstico psiquiátrico, con el fin de analizar sus relatos y repensar críticamente las prácticas de intervención que se desarrollan al interior de las hospitalizaciones psiquiátricas en Chile, las cuales se constituyen como un espacio de importancia para la salud pública chilena y los derechos de las personas con problemas de salud mental. Uno de los principales hallazgos es que, en desmedro de la recuperación de las personas usuarias, las prácticas coercitivas siguen manteniéndose en Chile, lo que implica un impacto negativo en la calidad de vida y en la libertad ciudadana de las personas con problemas de salud mental.

Adherence to legislation limiting opioid prescription duration following musculoskeletal injury.

OBJECTIVES: North Carolina had implemented legislation (Strengthen Opioid Misuse Prevention (STOP) Act) limiting opioid prescriptions to 5 days for acute pain and 7 days for post-operative pain. This study aimed to identify patient, prescriber, and facility characteristics associated with STOP Act adherence for patients with acute or post-surgical musculoskeletal (MSK) conditions. DESIGN: A three-level hierarchical logistic regression model was used to predict odds of adherence with STOP Act duration limits, accounting for fixed and random effects at the patient, prescriber, and facility levels. SETTING: A large healthcare system in North Carolina. PATIENTS AND PARTICIPANTS: Patients (N = 6,849) presenting from 2018 to 2020 with a diagnosis of an acute MSK injury. INTERVENTIONS: The STOP Act limited the duration of opioid prescriptions in North Carolina. MAIN OUTCOME MEASURE: Prescriptions adhering to the STOP Act duration limits of 5 days (nonoperative) or 7 days (operative) were the primary outcome. RESULTS: Opioids were compliant with STOP Act duration limits in 69.3 percent of encounters, with 33 percent of variation accounted for by clinician and 29 percent by facility. Patients prescribed >1 opioid (odds ratio (OR) 0.46, 95 percent confidence interval (CI): 0.36, 0.58) had reduced odds of a compliant prescription; surgical patients had increased odds of a compliant prescription (outpatient surgery: OR 5.89, 95 percent CI: 2.43-14.29; inpatient surgery: OR 7.71, 95 percent CI: 3.04-19.56). Primary care sports medicine clinicians adhered to legislation less frequently than orthopedic surgeons (OR 0.38, 95 percent CI: 0.15, 0.97). CONCLUSIONS: Most prescriptions adhered to STOP Act legislation. Tailored interventions to improve adherence among targeted groups of prescribers, eg, those treating nonoperative injuries and sport medicine clinicians, could be useful.

Importancia de la acreditación internacional de facultades de medicina

A partir de 2024, egresados de facultades de medicina que deseen hacer estudios de postgrado en los EE.UU o el Canadá, deberán graduarse en escuelas de medicina con programas educativos de calidad avalados por agencias reconocidas capaces de otorgar una acreditación internacional. La World Federation for Medical Education (WFME) es una de estas agencias. La WFME aceptó la nueva política de acreditación del Educational Committee for Foreign Medical Education (ECFMG) por la que médicos que postulen para la certificación del ECFMG del 2024 en adelante, tendrán que haberse graduado en un centro universitario de medicina acreditado por una agencia de aseguramiento de calidad que se encuentre reconocida por la WFME. El COMAEM (Consejo Mexicano para la Acreditación de la Educación Médica) está avalado por la WFME y otros organismos internacionales que aseguran la calidad de la educación superior. La acreditación que concede el COMAEM es un reconocimiento que el programa de medicina cumple con los criterios, indicadores y parámetros de calidad establecidos por este organismo. A partir de 2024, los egresados de un programa acreditado podrán postular para la certificación del ECFMG a través del examen de licencia médica de los Estados Unidos o USMLE (United States Medical Licensing Examination) y así poder hacer una residencia de especialización o trabajar en EE. UU. En el Perú, solo la Facultad de Medicina Alberto Hurtado de la Universidad Peruana Cayetano Heredia ha completado el proceso de acreditación internacional a través de COMAEM y ha recibido dicha acreditación.

As of 2024, medical school graduates who wish to pursue graduate studies in the U.S. or in Canada, they must have graduated from medical schools with quality educational programs endorsed by recognized agencies, capable of granting international accreditation. The World Federation for Medical Education (WFME) is one of these agencies. The WFME accepted the new accreditation policy of the Educational Committee for Foreign Medical Education (ECFMG) whereby physicians applying for ECFMG certification from 2024 onwards, must have graduated from a university medical center accredited by a quality assurance agency that is recognized by the WFME. The COMAEM (Mexican Council for the Accreditation of Medical Education) is endorsed by the WFME and other international organizations that ensure the quality of higher education. The accreditation granted by COMAEM is a recognition that the medical program meets the criteria, indicators and quality parameters established by this organization. Starting in 2024, graduates of an accredited program will be able to apply for ECFMG certification through the United States Medical Licensing Examination (USMLE) and thus be able to do a specialty residency or work in the U.S. In Peru, only the Alberto Hurtado School of Medicine of the Cayetano Heredia Peruvian University has completed the international accreditation process through COMAEM and has received such accreditation.

A Multimodal Scaffold for SDF1 Delivery Improves Cardiac Function in a Rat Subacute Myocardial Infarct Model.

Ischemic heart disease is one of the leading causes of death worldwide. The efficient delivery of therapeutic growth factors could counteract the adverse prognosis of post-myocardial infarction (post-MI). In this study, a collagen hydrogel that is able to load and appropriately deliver pro-angiogenic stromal cell-derived factor 1 (SDF1) was physically coupled with a compact collagen membrane in order to provide the suture strength required for surgical implantation. This bilayer collagen-on-collagen scaffold (bCS) showed the suitable physicochemical properties that are needed for efficient implantation, and the scaffold was able to deliver therapeutic growth factors after MI. In vitro collagen matrix biodegradation led to a sustained SDF1 release and a lack of cytotoxicity in the relevant cell cultures. In vivo intervention in a rat subacute MI model resulted in the full integration of the scaffold into the heart after implantation and biocompatibility with the tissue, with a prevalence of anti-inflammatory and pro-angiogenic macrophages, as well as evidence of revascularization and improved cardiac function after 60 days. Moreover, the beneficial effect of the released SDF1 on heart remodeling was confirmed by a significant reduction in cardiac tissue stiffness. Our findings demonstrate that this multimodal scaffold is a desirable matrix that can be used as a drug delivery system and a scaffolding material to promote functional recovery after MI.

Synaptic architecture of leg and wing motor control networks in Drosophila.

Animal movement is controlled by motor neurons (MNs), which project out of the central nervous system to activate muscles. Because individual muscles may be used in many different behaviors, MN activity must be flexibly coordinated by dedicated premotor circuitry, the organization of which remains largely unknown. Here, we use comprehensive reconstruction of neuron anatomy and synaptic connectivity from volumetric electron microscopy (i.e., connectomics) to analyze the wiring logic of motor circuits controlling the Drosophila leg and wing. We find that both leg and wing premotor networks are organized into modules that link MNs innervating muscles with related functions. However, the connectivity patterns within leg and wing motor modules are distinct. Leg premotor neurons exhibit proportional gradients of synaptic input onto MNs within each module, revealing a novel circuit basis for hierarchical MN recruitment. In comparison, wing premotor neurons lack proportional synaptic connectivity, which may allow muscles to be recruited in different combinations or with different relative timing. By comparing the architecture of distinct limb motor control systems within the same animal, we identify common principles of premotor network organization and specializations that reflect the unique biomechanical constraints and evolutionary origins of leg and wing motor control.

Neuronal wiring diagram of an adult brain.

Connections between neurons can be mapped by acquiring and analyzing electron microscopic (EM) brain images. In recent years, this approach has been applied to chunks of brains to reconstruct local connectivity maps that are highly informative, yet inadequate for understanding brain function more globally. Here, we present the first neuronal wiring diagram of a whole adult brain, containing 5×107 chemical synapses between ~130,000 neurons reconstructed from a female Drosophila melanogaster. The resource also incorporates annotations of cell classes and types, nerves, hemilineages, and predictions of neurotransmitter identities. Data products are available by download, programmatic access, and interactive browsing and made interoperable with other fly data resources. We show how to derive a projectome, a map of projections between regions, from the connectome. We demonstrate the tracing of synaptic pathways and the analysis of information flow from inputs (sensory and ascending neurons) to outputs (motor, endocrine, and descending neurons), across both hemispheres, and between the central brain and the optic lobes. Tracing from a subset of photoreceptors all the way to descending motor pathways illustrates how structure can uncover putative circuit mechanisms underlying sensorimotor behaviors. The technologies and open ecosystem of the FlyWire Consortium set the stage for future large-scale connectome projects in other species.

The continuum of violence against women: Gynecological violence within the Medical Model in Chile.

OBJECTIVE: This study aims to investigate the presence of gynecological violence within the health system in Chile, quantify the magnitude of this problem, define its general contours, and shed light on a phenomenon that has long been silenced. Additionally, we are interested in detecting differences between public and private health services, as well as exploring the role played by variables such as sexual orientation, ethnicity, age, and educational level in contributing to the prevalence of gynecological violence. METHODS: This study employed a cross-sectional and not probabilistic sampling approach. It included a sample of 1503 women from all regions of Chile, who were of legal age and who had attended gynecological services. A questionnaire was applied between January 2021 and April 2022 using the online platform SurveyMonkey®. Data were collected through the second national survey on obstetric and gynecological violence (GinObs 2021). The study adheres to activist research methodologies and was conducted in collaboration with activists and academic researchers. RESULTS: 57.9% of the women participants reported having experienced violence. Such violence appears to occur most frequently in the public health system, although not exclusively, and the victims are often people who belong to native ethnic groups, who identify as of African descent, whose sexual orientation is lesbian, who are elderly, and who have a lower level of education. CONCLUSION: Gynecological violence is an integral part of the continuum of violence against women and is consistently reported in both public and private health services. This form of violence has serious consequences for women's health and constitutes a significant public health problem.

Time series causal relationships discovery through feature importance and ensemble models.

Inferring causal relationships from observational data is a key challenge in understanding the interpretability of Machine Learning models. Given the ever-increasing amount of observational data available in many areas, Machine Learning algorithms used for forecasting have become more complex, leading to a less understandable path of how a decision is made by the model. To address this issue, we propose leveraging ensemble models, e.g., Random Forest, to assess which input features the trained model prioritizes when making a forecast and, in this way, establish causal relationships between the variables. The advantage of these algorithms lies in their ability to provide feature importance, which allows us to build the causal network. We present our methodology to estimate causality in time series from oil field production. As it is difficult to extract causal relations from a real field, we also included a synthetic oil production dataset and a weather dataset, which is also synthetic, to provide the ground truth. We aim to perform causal discovery, i.e., establish the existing connections between the variables in each dataset. Through an iterative process of improving the forecasting of a target's value, we evaluate whether the forecasting improves by adding information from a new potential driver; if so, we state that the driver causally affects the target. On the oil field-related datasets, our causal analysis results agree with the interwell connections already confirmed by tracer information; whenever the tracer data are available, we used it as our ground truth. This consistency between both estimated and confirmed connections provides us the confidence about the effectiveness of our proposed methodology. To our knowledge, this is the first time causal analysis using solely production data is employed to discover interwell connections in an oil field dataset.

Predictores de la identificación de las mujeres con el feminismo en Chile / Predictors of identification of women with feminism in Chile

Resumen El feminismo es un movimiento que busca transformar el orden social imperante, establecido a partir de la jerarquización de la diferencia surgida de una concepción binaria sexo-génerica, y que ha situado a las mujeres en una situación histórica de opresión y explotación. A pesar de la vigencia del feminismo y el aporte de este en las democracias actuales, no todas las personas y, en particular, las mujeres, se identifican con este movimiento social. El objetivo principal de este estudio es identificar los predictores de la identificación feminista en mujeres en Chile. Con base en los resultados de un cuestionario online respondido por 389 mujeres, se realizó un análisis de regresión lineal múltiple (R. = .53, F (3,385) = 107.1, p < .001) en el que se utilizó como variable dependiente la identificación feminista. Las variables con mayor poder predictivo fueron: la predisposición a la participación feminista (. = .54); el sexismo hostil (. = -.20), y la orientación política (. = -.16). Lo anterior indicaría que aquellas mujeres dispuestas a participar en actividades feministas, que rechazan las formas de sexismo hostil y que adhieren a posiciones políticas de izquierda, son quienes mayormente se identifican con el feminismo en Chile. Este modelo relacionaría procesos identitarios que involucran una toma de consciencia de la ilegitimidad que implica que un grupo domine a otro o se encuentre en una situación de privilegio injustificados respecto de otros, y el consecuente reconocimiento de la puesta en acción para la transformación social. Esta relación entre toma de conciencia y acción, a su vez, permitiría explicar la imbricación de las luchas sociales durante el estallido social de octubre 2019 y el movimiento feminista chileno. Finalmente, se discute sobre las limitaciones del presente estudio y lineamientos para futuros.

Abstract The data obtained were subjected to a descriptive analysis (mean and standard deviation) and test score reliability (Cronbach´s Alpha). Then, Pearson´s partial correlations were calculated in order to contrast with the dependent variable feminist identification. From the total sample, 74.8 % self-identified with feminism to various degrees (from a form of private acceptance, but not public; up to activism); while 6 % self-declared as not feminist, and 19 %, well in-line with the objectives, did not self-identify as feminist. Feminism is a movement that looks to transform the current social order, established from the hierarchization based on the difference suggested of a binary sex/gender conception. This order has historically situated women in a position of oppression and exploitation. In spite of the relevance and support for feminism in current democracies, not everyone, in particular women, identify with this social movement. Considering this, the main objective of this study is to identify the predictors of feminist identification in women in Chile. This study, of quantitative type, has a sample size made up of 389 women. The type of sample was not probabilistic, rather self-selected. The participants answered an online survey that explored the variables: feminist identification, political orientation (left-right), support of the movement and feminist objectives, predisposition to feminist action, ambivalent sexism (hostile and benevolent sexism) (Glick & Fiske, 1996), social orientation domination SDO (Prato et al., 1994), right wing authoritarianism RWA (Altemeyer, 1996), and socio-demographic variables as well. Also, the participants signed an informed consent form, endorsed by the corresponding ethics committee. The ages of the participants fluctuated between 18 and 72 (M = 27.4; DT = 11.52). The data obtained were subjected to a descriptive analysis (mean and standard deviation) and test score reliability (Cronbach´s Alpha). Then, Pearson´s partial correlations were calculated in order to contrast with the dependent variable feminist identification. From the total sample, 74.8 % self-identified with feminism to various degrees (from a form of private acceptance, but not public; up to activism); while 6 % self-declared as not feminist, and 19 %, well in-line with the objectives, did not self-identify as feminist. Later on, the variables that showed a significant relationship with the dependent variable were subjected to a multiple linear regression analysis (R2 = .53, F (3,385) = 107.1 p < .001). The variables with the highest predictive power were predisposition to feminist participation (β = .54), hostile sexism (β = -.20), and political orientation (β = -.16). This would indicate that those women willing to participate in feminist activities, who reject hostile forms of sexism and adhere to leftist political positions are those who mostly identify with feminism in Chile. This model would relate identarian processes that involve an awareness of the legitimacy that implies that one group dominates another or finds itself in a position of unjustified privilege with respect to others, and the acknowledgement of the need for transformational social action. This relationship between awareness and action, likewise, would explain the overlapping social struggles during the social outbreak of October 2019 and the feminist movement in Chile. Finally, limitations of the present study are discussed with respect to the difficulty to generalize the results due to the type of sample (self-selected); and the characteristics of the sample, referring to the difficult access to women older than 50. These women presented difficulty in forms of access and in completing the survey online. Furthermore, it is considered that due to the bias of explicit attitude studies with regards to social desirability at the moment of response, it would be of great importance to complete this work with implicit attitude measures. Also, the study proposes that as future line of research a study that investigates what do women themselves understand about feminism in order to make realizations of the plurality of this social movement.

NEURD: automated proofreading and feature extraction for connectomics.

We are now in the era of millimeter-scale electron microscopy (EM) volumes collected at nanometer resolution (Shapson-Coe et al., 2021; Consortium et al., 2021). Dense reconstruction of cellular compartments in these EM volumes has been enabled by recent advances in Machine Learning (ML) (Lee et al., 2017; Wu et al., 2021; Lu et al., 2021; Macrina et al., 2021). Automated segmentation methods can now yield exceptionally accurate reconstructions of cells, but despite this accuracy, laborious post-hoc proofreading is still required to generate large connectomes free of merge and split errors. The elaborate 3-D meshes of neurons produced by these segmentations contain detailed morphological information, from the diameter, shape, and branching patterns of axons and dendrites, down to the fine-scale structure of dendritic spines. However, extracting information about these features can require substantial effort to piece together existing tools into custom workflows. Building on existing open-source software for mesh manipulation, here we present "NEURD", a software package that decomposes each meshed neuron into a compact and extensively-annotated graph representation. With these feature-rich graphs, we implement workflows for state of the art automated post-hoc proofreading of merge errors, cell classification, spine detection, axon-dendritic proximities, and other features that can enable many downstream analyses of neural morphology and connectivity. NEURD can make these new massive and complex datasets more accessible to neuroscience researchers focused on a variety of scientific questions.

Functional connectomics reveals general wiring rule in mouse visual cortex.

To understand how the brain computes, it is important to unravel the relationship between circuit connectivity and function. Previous research has shown that excitatory neurons in layer 2/3 of the primary visual cortex of mice with similar response properties are more likely to form connections. However, technical challenges of combining synaptic connectivity and functional measurements have limited these studies to few, highly local connections. Utilizing the millimeter scale and nanometer resolution of the MICrONS dataset, we studied the connectivity-function relationship in excitatory neurons of the mouse visual cortex across interlaminar and interarea projections, assessing connection selectivity at the coarse axon trajectory and fine synaptic formation levels. A digital twin model of this mouse, that accurately predicted responses to arbitrary video stimuli, enabled a comprehensive characterization of the function of neurons. We found that neurons with highly correlated responses to natural videos tended to be connected with each other, not only within the same cortical area but also across multiple layers and visual areas, including feedforward and feedback connections, whereas we did not find that orientation preference predicted connectivity. The digital twin model separated each neuron's tuning into a feature component (what the neuron responds to) and a spatial component (where the neuron's receptive field is located). We show that the feature, but not the spatial component, predicted which neurons were connected at the fine synaptic scale. Together, our results demonstrate the "like-to-like" connectivity rule generalizes to multiple connection types, and the rich MICrONS dataset is suitable to further refine a mechanistic understanding of circuit structure and function.

Implementation of a motion estimation algorithm for Intel FPGAs using OpenCL.

Motion Estimation is one of the main tasks behind any video encoder. It is a computationally costly task; therefore, it is usually delegated to specific or reconfigurable hardware, such as FPGAs. Over the years, multiple FPGA implementations have been developed, mainly using hardware description languages such as Verilog or VHDL. Since programming using hardware description languages is a complex task, it is desirable to use higher-level languages to develop FPGA applications.The aim of this work is to evaluate OpenCL, in terms of expressiveness, as a tool for developing this kind of FPGA applications. To do so, we present and evaluate a parallel implementation of the Block Matching Motion Estimation process using OpenCL for Intel FPGAs, usable and tested on an Intel Stratix 10 FPGA. The implementation efficiently processes Full HD frames completely inside the FPGA. In this work, we show the resource utilization when synthesizing the code on an Intel Stratix 10 FPGA, as well as a performance comparison with multiple CPU implementations with varying levels of optimization and vectorization capabilities. We also compare the proposed OpenCL implementation, in terms of resource utilization and performance, with estimations obtained from an equivalent VHDL implementation.