Synaptic Resistor Circuits Based on Al Oxide and Ti Silicide for Concurrent Learning and Signal Processing in Artificial Intelligence Systems.

Neurobiological circuits containing synapses can process signals while learning concurrently in real time. Before an artificial neural network (ANN) can execute a signal-processing program, it must first be programmed by humans or trained with respect to a large and defined data set during learning processes, resulting in significant latency, high power consumption, and poor adaptability to unpredictable changing environments. In this work, a crossbar circuit of synaptic resistors (synstors) is reported, each synstor integrating a Si channel with an Al oxide memory layer and Ti silicide Schottky contacts. Individual synstors are characterized and analyzed to understand their concurrent signal-processing and learning abilities. Without any prior training, synstor circuits concurrently execute signal processing and learning in real time to fly drones toward a target position in an aerodynamically changing environment faster than human controllers, and with learning speed, performance, power consumption, and adaptability to the environment significantly superior to an ANN running on computers. The synstor circuit provides a path to establish power-efficient intelligent systems with real-time learning and adaptability in the capriciously mutable real world.

A Case Series of Elizabethkingia meningosepticum Bacteremia in the Cancer Population.

Elizabethkingia meningosepticum (E. meningosepticum) is a ubiquitous microorganism previously known as Chryseobacterium meningosepticum. It is emerging as a pathogen responsible for bacteremia in the immunocompromised population, particularly in cancer patients and those with a history of prolonged hospital stay and frequent instrumentations. A retrospective chart review of all cases over 10 years at the Moffitt Cancer Center showed a total of three patients with E. meningosepticum infection. The first patient (history of multiple myeloma) underwent endoscopy complicated by aspiration pneumonia and positive blood culture for E. meningosepticum infection. The second patient (current acute myelogenous leukemia) had neutropenic fever in the setting of a recent chest port infection. Blood culture from the chest port showed E. meningosepticum. The third patient (history of esophageal adenocarcinoma and acute myelogenous leukemia) had a history of recent pneumonia and cellulitis who presented with recurrent neutropenic fever. Blood culture was positive for E. meningosepticum. E. meningosepticum bacteremia has a high 28-day mortality rate (41%). As these three cases illustrate, early identification of the pathogen along with empiric treatment with a fluoroquinolone and/or minocycline is indicated to reduce morbidity and mortality.

Synaptic Resistors for Concurrent Inference and Learning with High Energy Efficiency.

The fastest supercomputer, Summit, has a speed comparable to the human brain, but is much less energy-efficient (≈1010 FLOPS W-1 , floating point operations per second per watt) than the brain (≈1015 FLOPS W-1 ). The brain processes and learns from "big data" concurrently via trillions of synapses in parallel analog mode. By contrast, computers execute algorithms on physically separated logic and memory transistors in serial digital mode, which fundamentally restrains computers from handling "big data" efficiently. The existing electronic devices can perform inference with high speeds and energy efficiencies, but they still lack the synaptic functions to facilitate concurrent convolutional inference and correlative learning efficiently like the brain. In this work, synaptic resistors are reported to emulate the analog convolutional signal processing, correlative learning, and nonvolatile memory functions of synapses. By circumventing the fundamental limitations of computers, a synaptic resistor circuit performs speech inference and learning concurrently in parallel analog mode with an energy efficiency of ≈1.6 × 1017 FLOPS W-1 , which is about seven orders of magnitudes higher than that of the Summit supercomputer. Scaled-up synstor circuits could circumvent the fundamental limitations in computers, and facilitate real-time inference and learning from "big data" with high efficiency and speed in intelligent systems.

Paragonimiasis in tuberculosis patients in Nagaland, India.

BACKGROUND: One of the infections that mimic tuberculosis (TB) is paragonimiasis (PRG), a foodborne parasitic disease caused by lung flukes of the genus Paragonimus. In the northeastern states of India, TB and PRG are endemic; however, PRG is rarely included in the differential diagnosis of TB. OBJECTIVE: To address limited evidence on the dual burden of TB and PRG in northeastern India, we aimed to document the prevalence of PRG among TB patients using sputum smear, stool examination for children <15 years and ELISA. DESIGN: A cross-sectional study of patients receiving TB treatment in the Médecins Sans Frontières (MSF)-supported TB programme in Mon district, in collaboration with the Regional Medical Research Centre (RMRC), Dibrugarh, Assam, between November 2012 and December 2013. RESULTS: Of 96 patients screened between November 2012 and December 2013, three (3%) had pulmonary PRG and were successfully treated with praziquantel. CONCLUSIONS: PRG should be considered in the TB diagnostic algorithms in PRG-TB dual burden areas. In case of TB-PRG co-infection, it is preferable to treat PRG first followed by anti-TB treatment a few days later.

Self-administered tuberculosis treatment outcomes in a tribal population on the indo-myanmar border, Nagaland, India.

BACKGROUND: Multiple strategies are being adopted by national tuberculosis (TB) programmes to achieve universal coverage of tuberculosis treatment. However, populations living in 'hard-to-reach' areas of north-east India have poor access to health services. Our study aimed to detail treatment outcomes in TB program supported by Médecins Sans Frontières (MSF) and using an alternative model of TB treatment delivery in Mon district, Nagaland, India. METHODS: This was a retrospective cohort study of TB patients, initiated on self-administered therapy (SAT) through Mon District Hospital, Nagaland, India between April 2012 and March 2013. RESULTS: A total of 238 tuberculosis patients had final TB treatment outcomes during the study period, including 82 and 156 from semi-urban and rural areas respectively. The majority of patients (62%, 147/238) were suffering from pulmonary, smear-positive tuberculosis. Overall, 74% of patients (175/238) had successful outcomes, being cured or having completed their treatment. Females (81%), pulmonary TB patients (75%) and those on a Category I regimen (79%) had better treatment success rates than males (67%), extra-pulmonary TB patients (62%) and patients on a Category II regimen (61%). The univariate and bivariate analyses found age, sex and TB treatment regimen significantly associated with unsuccessful TB treatment outcomes (defined as death, loss-to-follow-up and failure). However, only older age showed significance in a multivariate binary logistic regression model. CONCLUSION: Our study suggests that self-administered TB treatment is feasible for patients living in areas with limited or no access to health services. The relatively low number of patients with adverse outcomes suggests that SAT models are safe; other advantages include the need for fewer resources and less frequent movements by patients. National TB programmes should consider allowing SAT strategies for delivery of TB treatment to 'hard-to-reach' populations, which could in turn help to achieve universal coverage and contribute to global TB elimination by 2050.