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Creating datasets for Neuromorphic Vision is a challenging task. A lack of available recordings from Neuromorphic Vision sensors means that data must typically be recorded specifically for dataset creation rather than collecting and labeling existing data. The task is further complicated by a desire to simultaneously provide traditional frame-based recordings to allow for direct comparison with traditional Computer Vision algorithms. Here we propose a method for converting existing Computer Vision static image datasets into Neuromorphic Vision datasets using an actuated pan-tilt camera platform. Moving the sensor rather than the scene or image is a more biologically realistic approach to sensing and eliminates timing artifacts introduced by monitor updates when simulating motion on a computer monitor. We present conversion of two popular image datasets (MNIST and Caltech101) which have played important roles in the development of Computer Vision, and we provide performance metrics on these datasets using spike-based recognition algorithms. This work contributes datasets for future use in the field, as well as results from spike-based algorithms against which future works can compare. Furthermore, by converting datasets already popular in Computer Vision, we enable more direct comparison with frame-based approaches.

Background Peripheral neuropathic desensitization associated with aging, diabetes, alcoholism and HIV/AIDS, affects tens of millions of people worldwide, and there is little or no treatment available to improve sensory function. Recent studies that apply imperceptible continuous vibration or electrical stimulation have shown promise in improving sensitivity in both diseased and healthy participants. This class of interventions only has an effect during application, necessitating the design of a wearable device for everyday use. We present a circuit that allows for a low-power, low-cost and small form factor implementation of a current stimulator for the continuous application of subthreshold currents. Results This circuit acts as a voltage-to-current converter and has been tested to drive + 1 to − 1 mA into a 60 k Ω load from DC to 1 kHz. Driving a 60 k Ω load with a 2 mA peak-to-peak 1 kHz sinusoid, the circuit draws less than 21 mA from a 9 V source. The minimum operating current of the circuit is less than 12 mA. Voltage compliance is ± 60 V with just 1.02 mA drawn by the high voltage current drive circuitry. The circuit was implemented as a compact 46 mm × 21 mm two-layer PCB highlighting its potential for use in a body-worn device. Conclusions No design to the best of our knowledge presents comparably low quiescent power with such high voltage compliance. This makes the design uniquely appropriate for low-power transcutaneous current stimulation in wearable applications. Further development of driving and instrumentation circuitry is recommended.

The growing demands placed upon the field of computer vision have renewed the focus on alternative visual scene representations and processing paradigms. Silicon retinea provide an alternative means of imaging the visual environment, and produce frame-free spatio-temporal data. This paper presents an investigation into event-based digit classification using N-MNIST, a neuromorphic dataset created with a silicon retina, and the Synaptic Kernel Inverse Method (SKIM), a learning method based on principles of dendritic computation. As this work represents the first large-scale and multi-class classification task performed using the SKIM network, it explores different training patterns and output determination methods necessary to extend the original SKIM method to support multi-class problems. Making use of SKIM networks applied to real-world datasets, implementing the largest hidden layer sizes and simultaneously training the largest number of output neurons, the classification system achieved a best-case accuracy of 92.87% for a network containing 10,000 hidden layer neurons. These results represent the highest accuracies achieved against the dataset to date and serve to validate the application of the SKIM method to event-based visual classification tasks. Additionally, the study found that using a square pulse as the supervisory training signal produced the highest accuracy for most output determination methods, but the results also demonstrate that an exponential pattern is better suited to hardware implementations as it makes use of the simplest output determination method based on the maximum value.

We present the biologically inspired Ripple Pond Network (RPN), a simply connected spiking neural network which performs a transformation converting two dimensional images to one dimensional temporal patterns (TP) suitable for recognition by temporal coding learning and memory networks. The RPN has been developed as a hardware solution linking previously implemented neuromorphic vision and memory structures such as frameless vision sensors and neuromorphic temporal coding spiking neural networks. Working together such systems are potentially capable of delivering end-to-end high-speed, low-power and low-resolution recognition for mobile and autonomous applications where slow, highly sophisticated and power hungry signal processing solutions are ineffective. Key aspects in the proposed approach include utilizing the spatial properties of physically embedded neural networks and propagating waves of activity therein for information processing, using dimensional collapse of imagery information into amenable TP and the use of asynchronous frames for information binding.

Depression is a leading cause of disability in the U.S. across all race/ethnicity groups. While non-Hispanic Black and Hispanic persons have worse physical health on most indicators than non-Hispanic White persons, the literature on the association between race/ethnicity and rates of depression is mixed. Given unequal distribution of assets across racial/ethnic groups, it is possible that social and economic differences may explain differential rates of depression across race/ethnicity groups. Using National Health and Nutrition Examination Survey (NHANES) data from 2007-2016, we constructed a nationally representative sample of 26,382 adults over 18 years old (11,072 non-Hispanic White, 5,610 non-Hispanic Black, 6,981 Hispanic, and 2,719 Other race). We measured symptoms of depression using the Patient Health Questionnaire-9 (PHQ-9), with a score of 10 or more indicating probable depression. We identified three kinds of assets: financial assets (income), physical assets (home ownership), and social assets (marital status and education). We estimated the weighted prevalence of probable depression across race/ethnicity groups, odds ratios controlling for assets, and predicted probabilities of probable depression across race/ethnicity and asset groups. Three results contribute to our understanding of the differences in probable depression rates between race/ethnicity groups: 1) Non-Hispanic Black and Hispanic persons had a higher weighted prevalence of probable depression in the U.S. than non-Hispanic White persons. In models unadjusted for assets, non-Hispanic Black and Hispanic persons had 1.3 greater odds of probable depression than non-Hispanic White persons (p<0.01). 2) We found an inverse relation between assets and probable depression across all race-ethnicity groups. Also, non-Hispanic Black and Hispanic persons had fewer assets than non-Hispanic Whites. 3) When we controlled for assets, non-Hispanic Black and Hispanic persons had 0.8 times lower odds of probable depression than non-Hispanic White persons (p<0.05). Thus, when holding assets constant, minorities had better mental health than non-Hispanic White persons in the U.S. These three findings help to reconcile findings in the literature on race/ethnicity and depression. Given vastly unequal distribution of wealth in the U.S., it is not surprising that racial minorities, who hold fewer assets, would have an overall larger prevalence of mental illness, as seen in unadjusted estimates. Once assets are taken into account, Black and Hispanic persons appear to have better mental health than non-Hispanic White persons. Assets may explain much of the relation between race/ethnicity group and depression in the U.S. Future research should consider the role of assets in protecting against mental illness.

The coronavirus disease 2019 (COVID-19) pandemic and the policies to contain it have been a near ubiquitous exposure in the US with unknown effects on depression symptoms. To estimate the prevalence of and risk factors associated with depression symptoms among US adults during vs before the COVID-19 pandemic. This nationally representative survey study used 2 population-based surveys of US adults aged 18 or older. During COVID-19, estimates were derived from the COVID-19 and Life Stressors Impact on Mental Health and Well-being study, conducted from March 31, 2020, to April 13, 2020. Before COVID-19 estimates were derived from the National Health and Nutrition Examination Survey, conducted from 2017 to 2018. Data were analyzed from April 15 to 20, 2020. The COVID-19 pandemic and outcomes associated with the measures to mitigate it. Depression symptoms, defined using the Patient Health Questionnaire-9 cutoff of 10 or higher. Categories of depression symptoms were defined as none (score, 0-4), mild (score, 5-9), moderate (score, 10-14), moderately severe (score, 15-19), and severe (score, ≥20). A total of 1470 participants completed the COVID-19 and Life Stressors Impact on Mental Health and Well-being survey (completion rate, 64.3%), and after removing those with missing data, the final during-COVID-19 sample included 1441 participants (619 participants [43.0%] aged 18-39 years; 723 [50.2%] men; 933 [64.7%] non-Hispanic White). The pre-COVID-19 sample included 5065 participants (1704 participants [37.8%] aged 18-39 years; 2588 [51.4%] women; 1790 [62.9%] non-Hispanic White). Depression symptom prevalence was higher in every category during COVID-19 compared with before (mild: 24.6% [95% CI, 21.8%-27.7%] vs 16.2% [95% CI, 15.1%-17.4%]; moderate: 14.8% [95% CI, 12.6%-17.4%] vs 5.7% [95% CI, 4.8%-6.9%]; moderately severe: 7.9% [95% CI, 6.3%-9.8%] vs 2.1% [95% CI, 1.6%-2.8%]; severe: 5.1% [95% CI, 3.8%-6.9%] vs 0.7% [95% CI, 0.5%-0.9%]). Higher risk of depression symptoms during COVID-19 was associated with having lower income (odds ratio, 2.37 [95% CI, 1.26-4.43]), having less than $5000 in savings (odds ratio, 1.52 [95% CI, 1.02-2.26]), and exposure to more stressors (odds ratio, 3.05 [95% CI, 1.95-4.77]). These findings suggest that prevalence of depression symptoms in the US was more than 3-fold higher during COVID-19 compared with before the COVID-19 pandemic. Individuals with lower social resources, lower economic resources, and greater exposure to stressors (eg, job loss) reported a greater burden of depression symptoms. Post-COVID-19 plans should account for the probable increase in mental illness to come, particularly among at-risk populations.

PfSPZ-CVac combines ‘PfSPZ Challenge’, which consists of infectious Plasmodium falciparum sporozoites (PfSPZ), with concurrent antimalarial chemoprophylaxis. In a previously-published PfSPZ-CVac study, three doses of 5.12x10 4 PfSPZ-CVac given 28 days apart had 100% vaccine efficacy (VE) against controlled human malaria infection (CHMI) 10 weeks after the last immunization, while the same dose given as three injections five days apart had 63% VE. Here, we conducted a dose escalation trial of similarly condensed schedules. Of the groups proceeding to CHMI, the first study group received three direct venous inoculations (DVIs) of a dose of 5.12x10 4 PfSPZ-CVac seven days apart and the next full dose group received three DVIs of a higher dose of 1.024x10 5 PfSPZ-CVac five days apart. CHMI (3.2x10 3 PfSPZ Challenge) was performed by DVI 10 weeks after the last vaccination. In both CHMI groups, transient parasitemia occurred starting seven days after each vaccination. For the seven-day interval group, the second and third vaccinations were therefore administered coincident with parasitemia from the prior vaccination. Parasitemia was associated with systemic symptoms which were severe in 25% of subjects. VE in the seven-day group was 0% (7/7 infected) and in the higher-dose, five-day group was 75% (2/8 infected). Thus, the same dose of PfSPZ-CVac previously associated with 63% VE when given on a five-day schedule in the prior study had zero VE here when given on a seven-day schedule, while a double dose given on a five-day schedule here achieved 75% VE. The relative contributions of the five-day schedule and/or the higher dose to improved VE warrant further investigation. It is notable that administration of PfSPZ-CVac on a schedule where vaccine administration coincided with blood-stage parasitemia was associated with an absence of sterile protective immunity. Clinical trials registration : NCT02773979 .

In acute ischemic stroke, thrombectomy with a stent retriever plus intravenous t-PA was more effective than t-PA in improving functional outcomes. At 90 days, 60% of patients in the intervention group were functionally independent, as compared with 35% in the control group. Intravenous tissue plasminogen activator (t-PA) administered within 4.5 hours after the onset of acute ischemic stroke improves outcomes. 1 – 3 However, intravenous t-PA has multiple constraints, including unresponsiveness of large thrombi to rapid enzymatic digestion, a narrow time window for administration, and the risk of cerebral and systemic hemorrhage. Among patients with occlusions of the intracranial internal carotid artery or the first segment of the middle cerebral artery (or both), intravenous t-PA results in early reperfusion in only 13 to 50%. 4 – 7 Neurovascular thrombectomy is a reperfusion strategy that is distinct from pharmacologic fibrinolysis. Endovascular mechanical treatments can remove large, proximal . . .

BackgroundCOVID-19 and related containment policies have caused or heightened financial stressors for many in the USA. We assessed the relation between assets, financial stressors and probable depression during the COVID-19 pandemic.MethodsBetween 31 March 2020 and 13 April 2020, we surveyed a probability-based, nationally representative sample of US adults ages 18 and older using the COVID-19 and Life stressors Impact on Mental Health and Well-being survey (n=1441). We calculated the prevalence of probable depression using the Patient Health Questionnaire-9 (cut-off ≥10) and exposure to financial stressors by financial, physical and social assets categories (household income, household savings, home ownership, educational attainment and marital status). We estimated adjusted ORs and predicted probabilities of probable depression across assets categories and COVID-19 financial stressor exposure groups.ResultsWe found that (1) 40% of US adults experienced COVID-19-related financial stressors during this time period; (2) low assets (OR: 3.0, 95% CI 2.1 to 4.2) and COVID-19 financial stressor exposure (OR: 2.8, 95% CI 2.1 to 3.9) were each associated with higher odds of probable depression; and (3) among persons with low assets and high COVID-19 financial stressors, 42.7% had probable depression; and among persons with high assets and low COVID-19 financial stressors, 11.1% had probable depression. Persons with high assets and high COVID-19 financial stressors had a similar prevalence of probable depression (33.5%) as persons with low assets and low COVID-19 financial stressors (33.5%). The more assets a person had, the lower the level of probable depression.ConclusionPopulations with low assets are bearing a greater burden of mental illness during the COVID-19 pandemic.