Examining the Role of Cultural and Family Factors in Substance Use Risk Among Indian American Youth.

INTRODUCTION: Although Indian Americans constitute the second-largest immigrant group in the United States, there is a paucity of information about Indian American youth, particularly with respect to substance use risk. We examined the relationship of social factors to permissive substance use beliefs (a proxy for substance use risk since they can lead to adulthood substance use and misuse) and family functioning. METHODS: The study used structural equation modeling to examine the prevalence of permissive substance use beliefs in a sample of Indian American youth ages 12-17 (N = 223) and examined the degree to which discrimination, bicultural identity integration, and endorsement of the model minority stereotype were associated with permissive substance use beliefs. RESULTS: Findings suggest that bicultural identity integration (B = 0.267 [SE = 0.112], p = 0.01) and discrimination (B = 0.294 [SE = 0.087], p = 0.001) are positively associated with permissive substance use beliefs. Bicultural identity integration (B = 0.415 [SE = 0.090], p = 0.0001) was positively associated with family support (B= -0.329 [SE = 0.108], p = 0.002) which, in turn, was associated with less permissive substance use beliefs. In contrast, endorsement of the model minority stereotype (B = 0.351 [SE = 0.090], p = 0.001) was positively associated with family closeness (B = 0.232 [SE = 0.927], p = 0.01) which, in turn, was associated with family support and then with less permissive substance use beliefs. CONCLUSIONS: Discrimination and bicultural identity integration emerged as key constructs related to substance use risk among Indian American youth. These youth could benefit from culturally appropriate prevention programming that addresses the negative impact of discrimination and its effect on permissive substance use beliefs and highlights protective factors.

Event-driven adaptive optical neural network.

We present an adaptive optical neural network based on a large-scale event-driven architecture. In addition to changing the synaptic weights (synaptic plasticity), the optical neural network's structure can also be reconfigured enabling various functionalities (structural plasticity). Key building blocks are wavelength-addressable artificial neurons with embedded phase-change materials that implement nonlinear activation functions and nonvolatile memory. Using multimode focusing, the activation function features both excitatory and inhibitory responses and shows a reversible switching contrast of 3.2 decibels. We train the neural network to distinguish between English and German text samples via an evolutionary algorithm. We investigate both the synaptic and structural plasticity during the training process. On the basis of this concept, we realize a large-scale network consisting of 736 subnetworks with 16 phase-change material neurons each. Overall, 8398 neurons are functional, highlighting the scalability of the photonic architecture.

Placental histopathology, maternal characteristics and neonatal outcome in cases of preterm birth in a high-risk population in South Africa.

BACKGROUND: Preterm birth remains a global health concern and is one of the most common pregnancy complications associated with perinatal morbidity and mortality. OBJECTIVE: To investigate placental pathology and its associations with obstetric, maternal and neonatal outcomes in the Eastern Cape region of South Africa (SA) in order to help understand its associations with preterm birth in that region. METHODS: In this prospective study, placentas were collected consecutively from patients attending a public tertiary referral hospital in SA, delivering preterm (n=100; 28 - 34 weeks gestational age) and term (n=20; >36 weeks gestational age). Placentas were submitted for histopathology, and comparisons with maternal characteristics and neonatal outcomes in preterm birth were undertaken. RESULTS: Histological analysis revealed pathology in all preterm placentas (100%), with maternal vascular malperfusion (47%) and abruptio placentae (41%) most commonly identified. Acute chorioamnionitis (21%) was associated with term births (p=0.002). Maternal characteristics and neonatal outcomes significantly associated with preterm birth included pre-eclampsia (p=0.006), neonatal respiratory distress syndrome (p=0.004) and neonatal jaundice (p=0.003). Intrauterine demise (p=0.004) and alcohol abuse (p≤0.005) were significantly associated with term delivery. The number of mothers delivering preterm who were HIV-positive was high (41%). CONCLUSION: The pathology identified in all preterm placentas supports the need to update institutional policies for submission of placentas from all preterm births for histopathology, particularly in countries with a high burden of preterm birth.

In-memory photonic dot-product engine with electrically programmable weight banks.

Electronically reprogrammable photonic circuits based on phase-change chalcogenides present an avenue to resolve the von-Neumann bottleneck; however, implementation of such hybrid photonic-electronic processing has not achieved computational success. Here, we achieve this milestone by demonstrating an in-memory photonic-electronic dot-product engine, one that decouples electronic programming of phase-change materials (PCMs) and photonic computation. Specifically, we develop non-volatile electronically reprogrammable PCM memory cells with a record-high 4-bit weight encoding, the lowest energy consumption per unit modulation depth (1.7 nJ/dB) for Erase operation (crystallization), and a high switching contrast (158.5%) using non-resonant silicon-on-insulator waveguide microheater devices. This enables us to perform parallel multiplications for image processing with a superior contrast-to-noise ratio (≥87.36) that leads to an enhanced computing accuracy (standard deviation σ ≤ 0.007). An in-memory hybrid computing system is developed in hardware for convolutional processing for recognizing images from the MNIST database with inferencing accuracies of 86% and 87%.

Intensive Blood Pressure Management Preserves Functional Connectivity in Patients with Hypertension from the Systolic Blood Pressure Intervention Randomized Trial.

BACKGROUND AND PURPOSE: The Systolic Blood Pressure Intervention (SPRINT) randomized trial demonstrated that intensive blood pressure management resulted in slower progression of cerebral white matter hyperintensities, compared with standard therapy. We assessed longitudinal changes in brain functional connectivity to determine whether intensive treatment results in less decline in functional connectivity and how changes in brain functional connectivity relate to changes in brain structure. MATERIALS AND METHODS: Five hundred forty-eight participants completed longitudinal brain MR imaging, including resting-state fMRI, during a median follow-up of 3.84 years. Functional brain networks were identified using independent component analysis, and a mean connectivity score was calculated for each network. Longitudinal changes in mean connectivity score were compared between treatment groups using a 2-sample t test, followed by a voxelwise t test. In the full cohort, adjusted linear regression analysis was performed between changes in the mean connectivity score and changes in structural MR imaging metrics. RESULTS: Four hundred six participants had longitudinal imaging that passed quality control. The auditory-salience-language network demonstrated a significantly larger decline in the mean connectivity score in the standard treatment group relative to the intensive treatment group (P = .014), with regions of significant difference between treatment groups in the cingulate and right temporal/insular regions. There was no treatment group difference in other networks. Longitudinal changes in mean connectivity score of the default mode network but not the auditory-salience-language network demonstrated a significant correlation with longitudinal changes in white matter hyperintensities (P = .013). CONCLUSIONS: Intensive treatment was associated with preservation of functional connectivity of the auditory-salience-language network, while mean network connectivity in other networks was not significantly different between intensive and standard therapy. A longitudinal increase in the white matter hyperintensity burden is associated with a decline in mean connectivity of the default mode network.

Evaluation of in silico pathogenicity prediction tools for the classification of small in-frame indels.

BACKGROUND: The use of in silico pathogenicity predictions as evidence when interpreting genetic variants is widely accepted as part of standard variant classification guidelines. Although numerous algorithms have been developed and evaluated for classifying missense variants, in-frame insertions/deletions (indels) have been much less well studied. METHODS: We created a dataset of 3964 small (< 100 bp) indels predicted to result in in-frame amino acid insertions or deletions using data from gnomAD v3.1 (minor allele frequency of 1-5%), ClinVar and the Deciphering Developmental Disorders (DDD) study. We used this dataset to evaluate the performance of nine pathogenicity predictor tools: CADD, CAPICE, FATHMM-indel, MutPred-Indel, MutationTaster2021, PROVEAN, SIFT-indel, VEST-indel and VVP. RESULTS: Our dataset consisted of 2224 benign/likely benign and 1740 pathogenic/likely pathogenic variants from gnomAD (n = 809), ClinVar (n = 2882) and, DDD (n = 273). We were able to generate scores across all tools for 91% of the variants, with areas under the ROC curve (AUC) of 0.81-0.96 based on the published recommended thresholds. To avoid biases caused by inclusion of our dataset in the tools' training data, we also evaluated just DDD variants not present in either gnomAD or ClinVar (70 pathogenic and 81 benign). Using this subset, the AUC of all tools decreased substantially to 0.64-0.87. Several of the tools performed similarly however, VEST-indel had the highest AUCs of 0.93 (full dataset) and 0.87 (DDD subset). CONCLUSIONS: Algorithms designed for predicting the pathogenicity of in-frame indels perform well enough to aid clinical variant classification in a similar manner to missense prediction tools.

The need for a measurement-based care professional practice guideline.

Professional practice guidelines (PPGs) are intended to promote a high level of professional practice and serve as an educational resource, providing pragmatic guidance in a clinical area for psychologists. Measurement-based care (MBC) is an evidence-based psychological practice with accumulating empirical support and alignment with patient-centered care. In connection with the American Psychological Association's Advisory Committee for Measurement-based Care and the Mental and Behavioral Health Registry, this article outlines various lines of support for the development and implementation of an MBC PPG. In addition to research evidence, we address the demonstrated need of this guideline across three domains: public benefit, professional guidance, and legal and regulatory issues. Consistent with the aspirational spirit of a PPG, this article proposes a draft PPG statement and highlights how an MBC PPG would improve service delivery, facilitate implementation of an evidence-based practice associated with symptom reduction, improved retention, and greater patient satisfaction, as well as create a framework that will better align changes in reimbursement models with patients' and providers' treatment goals. We also identify key future directions and critical gaps in MBC science and implementation that require attention. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Technology-based communication among Hurricane Maria survivors in the United States: a trans-territorial lens.

PURPOSE: Rooted in a trans-territorial framework, the present study was designed to provide new evidence regarding the patterns of communication among Hurricane Maria survivors who migrated to the U.S. in the aftermath of the storm. METHODS: A total of 319 Hurricane Maria survivor adults ages 18 and older were recruited into the Adelante Boricua study between August 2020 and October 2021. Most participants had relocated to the U.S. between 2017 and 2018. We used latent profile analysis and multinomial regression to examine the relationship of technology-based communication with depressive symptoms, well-being, cultural connection, and migration stress. RESULTS: We identified a five-class solution, consisting of (1) moderate communication (32%), (2) disengaged (24%), (3) no social media (18%), (4) daily with family in Puerto Rico (6%), and (5) daily trans-territorial (13%) typologies. Participants in the disengaged class were more likely to report elevated depressive symptoms and limited English proficiency, lower prosocial behaviors, lower levels of religiosity, lower attendance at religious services in the U.S., and less engagement in social activities, compared to participants in the Moderate Communication class. CONCLUSION: Roughly one in four individuals in our sample reported very limited technology-based communication with friends/family in their sending and new-receiving communities. As technology and smartphones continue to become integrated into 21st-century life, it is vital that researchers explore how the tremendous potential for connectedness relates to trans-territorial crisis migrants' well-being and adaptation.

Estimating the burden of disease attributable to ambient air pollution (ambient PM2.5 and ambient ozone) in South Africa for 2000, 2006 and 2012.

Background: Globally, a growing body of research has shown that ambient air pollution is one of the most critical environmental issues, especially in relation to human health. Exposure to ambient air pollution leads to serious health conditions such as lower respiratory infections, cancers, diabetes mellitus type 2, ischaemic heart disease, stroke and chronic obstructive pulmonary disease. Objectives: To estimate the burden of disease attributable to ambient air pollution in South Africa (SA) for the years 2000, 2006 and 2012. Methods: Comparative risk assessment method was used to determine the burden of disease due to two pollutants (particulate matter (PM2.5) and ambient ozone). Regionally optimised fully coupled climate chemistry models and surface air pollution observations were used to generate concentrations of PM2.5 and ozone for each SA Census Small Area Level, for the year 2012. For 2000 and 2006, population-weighted PM2.5and ozone were estimated, based on the 2012 results. Following the identification of disease outcomes associated with particulate matter with aerodynamic diameter <2.5 µm (PM2.5) and ozone exposure, the attributable burden of disease was estimated for 2000, 2006 and 2012. Furthermore, for the year 2012, the burden of disease attributable to ambient air pollution exposure was computed at provincial levels. Results: In 2012, approximately 97.6% of people in SA were exposed to PM2.5 at levels above the 2005 World Health Organization guideline: 10 µg/m3 annual mean. From 2000 to 2012, population-weighted annual average PM2.5 increased from 26.6 µg/m3 to 29.7 µg/m3, and ozone 6-month high 8-hour daily maximum increased from 64.4 parts per billion (ppb) to 72.1 ppb. At a national scale, in the year 2000, 15 619 (95% uncertainty interval (UI) 8 958 - 21 849) deaths were attributed to PM2.5 exposure, while 1 326 (95% UI 534 - 1 885) deaths were attributed to ozone. In 2006, an estimated 19 672 deaths (95% UI 11 526 - 27 086) were attributed to PM2.5, and a further 1 591 deaths (95% UI 651 - 2 236) to ozone exposure. In 2012, deaths attributed to PM2.5 were 19 507 (95% UI 11 318 - 27 111), and to ozone 1 734 (95% UI 727 - 2 399). Additionally, population-weighted provincial scale analysis showed that Gauteng Province had the highest number of attributable deaths due to both PM2.5 and ozone in 2012. Conclusion: The study showed that ambient air pollution exposure is an important health risk in SA, requiring both short- and long-term intervention. In the short term, the SA Ambient Air Quality Standards and industrial minimum emissions standards need to be enforced. In the longer term, to reduce air pollution and the associated disease burden, the combustion of fossil fuels as a source of energy for power generation and transportation, as well as industrial and domestic uses, needs to be replaced with clean renewable energy sources. In addition to local measures, when the southern African prevalent anticyclonic air dynamics that transport regionally emitted pollutants into SA (especially from biomass burning) are considered, it is also advisable to establish long-term regional co-operation in reducing air pollution.

Estimating the burden of disease attributable to household air pollution from cooking with solid fuels in South Africa for 2000, 2006 and 2012.

BACKGROUND: Household air pollution (HAP) due to the use of solid fuels for cooking is a global problem with significant impacts on human health, especially in low- and middle-income countries. HAP remains problematic in South Africa (SA). While electrification rates have improved over the past two decades, many people still use solid fuels for cooking owing to energy poverty. OBJECTIVES: To estimate the disease burden attributable to HAP for cooking in SA over three time points: 2000, 2006 and 2012. METHODS: Comparative risk assessment methodology was used. The proportion of South Africans exposed to HAP was assessed and assigned the estimated concentration of particulate matter with a diameter <2.5 µg/m3 (PM2.5) associated with HAP exposure. Health outcomes and relative risks associated with HAP exposure were identified. Population-attributable fractions and the attributable burden of disease due to HAP exposure (deaths, years of life lost, years lived with disability and disability-adjusted life years (DALYs)) for SA were calculated. Attributable burden was estimated for 2000, 2006 and 2012. For the year 2012, we estimated the attributable burden at provincial level. RESULTS: An estimated 17.6% of the SA population was exposed to HAP in 2012. In 2012, HAP exposure was estimated to have caused 8 862 deaths (95% uncertainty interval (UI) 8 413 - 9 251) and 1.7% (95% UI 1.6% - 1.8%) of all deaths in SA, respectively. Loss of healthy life years comprised 208 816 DALYs (95% UI 195 648 - 221 007) and 1.0% of all DALYs (95% UI 0.95% - 1.0%) in 2012, respectively. Lower respiratory infections and cardiovascular disease contributed to the largest proportion of deaths and DALYs. HAP exposure due to cooking varied across provinces, and was highest in Limpopo (50.0%), Mpumalanga (27.4%) and KwaZulu-Natal (26.4%) provinces in 2012. Age standardised burden measures showed that these three provinces had the highest rates of death and DALY burden attributable to HAP. CONCLUSION: The burden of disease from HAP due to cooking in SA is of significant concern. Effective interventions supported by legislation and policy, together with awareness campaigns, are needed to ensure access to clean household fuels and improved cook stoves. Continued and enhanced efforts in this regard are required to ensure the burden of disease from HAP is curbed in SA.

Overview.

BACKGROUND: South Africa (SA) faces multiple health challenges. Quantifying the contribution of modifiable risk factors can be used to identify and prioritise areas of concern for population health and opportunities for health promotion and disease prevention interventions. OBJECTIVE: To estimate the attributable burden of 18 modifiable risk factors for 2000, 2006 and 2012. METHODS: Comparative risk assessment (CRA), a standardised and systematic approach, was used to estimate the attributable burden of 18 risk factors. Risk exposure estimates were sourced from local data, and meta-regressions were used to model the parameters, depending on the availability of data. Risk-outcome pairs meeting the criteria for convincing or probable evidence were assessed using relative risks against a theoretical minimum risk exposure level to calculate either a potential impact fraction or population attributable fraction (PAF). Relative risks were sourced from the Global Burden of Disease, Injuries, and Risk Factors (GBD) study as well as published cohort and intervention studies. Attributable burden was calculated for each risk factor for 2000, 2006 and 2012 by applying the PAF to estimates of deaths and years of life lost from the Second South African National Burden of Disease Study (SANBD2). Uncertainty analyses were performed using Monte Carlo simulation, and age-standardised rates were calculated using the World Health Organization standard population. RESULTS: Unsafe sex was the leading risk factor across all years, accounting for one in four DALYs (26.6%) of the estimated 20.6 million DALYs in 2012. The top five leading risk factors for males and females remained the same between 2000 and 2012. For males, the leading risks were (in order of descending rank): unsafe sex; alcohol consumption; interpersonal violence; tobacco smoking; and high systolic blood pressure; while for females the leading risks were unsafe sex; interpersonal violence; high systolic blood pressure; high body mass index; and high fasting plasma glucose. Since 2000, the attributable age-standardised death rates decreased for most risk factors. The largest decrease was for household air pollution (-41.8%). However, there was a notable increase in the age-standardised death rate for high fasting plasma glucose (44.1%), followed by ambient air pollution (7%). CONCLUSION: This study reflects the continued dominance of unsafe sex and interpersonal violence during the study period, as well as the combined effects of poverty and underdevelopment with the emergence of cardiometabolic-related risk factors and ambient air pollution as key modifiable risk factors in SA. Despite reductions in the attributable burden of many risk factors, the study reveals significant scope for health promotion and disease prevention initiatives and provides an important tool for policy makers to influence policy and programme interventions in the country.

Rigorous dynamic model of a silicon ring resonator with phase change material for a neuromorphic node.

The photonics platform has been considered increasingly promising for neuromorphic computing, due to its potential in providing low latency and energy efficient large-scale parallel connectivity. Phase change materials (PCMs) have been recently employed to introduce all-optical non-volatile memory in integrated photonic circuits, especially finding application as non-volatile weighting element in photonic artificial neural networks. Interestingly, these weighting elements can potentially be used as building blocks for large-scale networks that can autonomously adapt to their input, i.e. presenting the property of plasticity, similarly to the biological brain. In this work, we develop a computationally efficient dynamical model of a silicon ring resonator (RR) enhanced by a phase change material, namely Ge2Sb2Te5 (GST). We do so starting from two existing dynamical models (of a silicon RR and of a GST thin film on a straight silicon waveguide), but extending the optical equations to properly account for the high absorption and asymmetry in the ring due to the phase change material. Our model accounts for silicon nonlinear effects due to free carriers and temperature, as well as for the phase change of GST, whose energy efficiency and optical contrast can be enhanced by the RR resonant behaviour. We also restructure the optical equations so that the model can be efficiently employed in a modular way within a commercial software for system-level photonics simulations. Moreover, exploiting the developed model, we explore several design parameters and show that both speed and energy efficiency of memory operations can be enhanced by factors from six to ten. Also, we show that the achievable optical contrast due to GST phase change can be increased by more than a factor ten by leveraging the resonant properties of the RR, at the expense of higher optical loss. Finally, by exploiting the nonlinear dynamics arising in silicon RR networks, we show that a strong contrast is achievable while preserving energy efficiency.

Optical and Thermal Design and Analysis of Phase-Change Metalenses for Active Numerical Aperture Control.

The control of a lens's numerical aperture has potential applications in areas such as photography and imaging, displays, sensing, laser processing and even laser-implosion fusion. In such fields, the ability to control lens properties dynamically is of much interest, and active meta-lenses of various kinds are under investigation due to their modulation speed and compactness. However, as of yet, meta-lenses that explicitly offer dynamic control of a lens's numerical aperture have received little attention. Here, we design and simulate active meta-lenses (specifically, focusing meta-mirrors) using chalcogenide phase-change materials to provide such control. We show that, operating at a wavelength of 3000 nm, our devices can change the numerical aperture by up to a factor of 1.85 and operate at optical intensities of the order of 1.2 × 109 Wm-2. Furthermore, we show the scalability of our design towards shorter wavelengths (visible spectrum), where we demonstrate a change in NA by a factor of 1.92.

An integrated photonics engine for unsupervised correlation detection.

With more and more aspects of modern life and scientific tools becoming digitized, the amount of data being generated is growing exponentially. Fast and efficient statistical processing, such as identifying correlations in big datasets, is therefore becoming increasingly important, and this, on account of the various compute bottlenecks in modern digital machines, has necessitated new computational paradigms. Here, we demonstrate one such novel paradigm, via the development of an integrated phase-change photonics engine. The computational memory engine exploits the accumulative property of Ge2Sb2Te5 phase-change cells and wavelength division multiplexing property of optics in delivering fully parallelized and colocated temporal correlation detection computations. We investigate this property and present an experimental demonstration of identifying real-time correlations in data streams on the social media platform Twitter and high-traffic computing nodes in data centers. Our results demonstrate the use case of high-speed integrated photonics in accelerating statistical analysis methods.

Polarization-selective reconfigurability in hybridized-active-dielectric nanowires.

Wavelength and polarization are two fundamental properties of light within which information can be encoded and (de)multiplexed. While wavelength-selective systems have widely proliferated, polarization-addressable active photonics has not seen notable progress, primarily because tunable and polarization-selective nanostructures have been elusive. Here, we introduce hybridized-active-dielectric (HAD) nanowires to achieve polarization-selective tunability. We then demonstrate the ability to use polarization as a parameter to selectively modulate the conductance of individual nanowires within a multi-nanowire system. By using polarization as the tunable vector, we show matrix-vector multiplication in a nanowire device configuration. While our HAD nanowires use phase-change materials as the active material, this concept is readily generalized to other active materials hybridized with dielectrics and thus has the potential in a broad range of applications from photonic memories and routing to polarization-multiplexed computing.

Genome-wide Scan of Dental Fear and Anxiety Nominates Novel Genes.

Dental care-related fear and anxiety (DFA) is prevalent, affects oral health care utilization, and is related to poor oral health and decreased quality of life. In addition to learned and cultural factors, genetics is hypothesized to contribute to DFA. Therefore, we performed a genome-wide association study to identify genetic variants contributing to DFA. Adult and adolescent participants were from 4 cohorts (3 from the US-based Center for Oral Health Research in Appalachia, n = 1,144, 1,164, and 535, and the UK-based Avon Longitudinal Study of Parents and Children [ALSPAC], n = 2,078). Two self-report instruments were used to assess DFA: the Dental Fear Survey (US cohorts) and Corah's Dental Anxiety Scale (ALSPAC). Genome-wide scans were performed for the DFA total scores and subscale scores (avoidance, physiological arousal, fear of dental treatment-specific stimuli), adjusting for age, sex, educational attainment, recruitment site, and genetic ancestry. Results across cohorts were combined using meta-analysis. Heritability estimates for DFA total and subscale scores were similar across cohorts and ranged from 23% to 59%. The meta-analysis revealed 3 significant (P < 5E-8) associations between genetic loci and 2 DFA subscales: physiological arousal and avoidance. Nearby genes included NTSR1 (P = 3.05E-8), DMRTA1 (P = 4.40E-8), and FAM84A (P = 7.72E-9). Of these, NTSR1, which was associated with the avoidance subscale, mediates neurotensin function, and its deficiency may lead to altered fear memory in mice. Gene enrichment analyses indicated that loci associated with the DFA total score and physiological arousal subscale score were enriched for genes associated with severe and persistent mental health (e.g., schizophrenia) and neurocognitive (e.g., autism) disorders. Heritability analysis indicated that DFA is partly explained by genetic factors, and our association results suggested shared genetic underpinnings with other psychological conditions.

Chalcogenide optomemristors for multi-factor neuromorphic computation.

Neuromorphic hardware that emulates biological computations is a key driver of progress in AI. For example, memristive technologies, including chalcogenide-based in-memory computing concepts, have been employed to dramatically accelerate and increase the efficiency of basic neural operations. However, powerful mechanisms such as reinforcement learning and dendritic computation require more advanced device operations involving multiple interacting signals. Here we show that nano-scaled films of chalcogenide semiconductors can perform such multi-factor in-memory computation where their tunable electronic and optical properties are jointly exploited. We demonstrate that ultrathin photoactive cavities of Ge-doped Selenide can emulate synapses with three-factor neo-Hebbian plasticity and dendrites with shunting inhibition. We apply these properties to solve a maze game through on-device reinforcement learning, as well as to provide a single-neuron solution to linearly inseparable XOR implementation.