Assessing clonal correlation of epidemic Vibrio cholerae isolates during 2011 in 16 provinces of Iran.

A total of 1,187 Vibrio cholerae isolates were received during 2011 cholera outbreaks from 16 provinces in different geographical location to Iranian reference Health laboratory. A random selection was performed, and 61 isolates were subjected to further investigations. Cholera cases were come up from May with nine cases and reached to its maximum rate at August (57 cases) and continued to October after which a fall occurred in September. All of the isolates were susceptible to three antimicrobial agents including ciprofloxacin, cefixime, and ampicillin. The highest rate of resistance was seen to nalidixic acid (96.7 %) and co-trimoxazole (91.8 %). Clonality of isolates was investigated through genotyping by PFGE method. A total of seven pulsotypes were obtained from 61 isolates under study. The pulsotypes were highly related with only 1-3 bands differences. Three pulsotypes (PT5, PT6, and PT7) constituted 93.4 % of total isolates. One environmentally isolated strain showed distinct pattern from clinical specimens. This strain although had no any evidence in identified cholera infections, highlighted selecting more environmental specimens in any future outbreaks as long as human samples. In conclusion, emergence and dominance of Ogawa serotypes after about 7 years in Iran are alarming due to fear of import of new V. cholerae clones from out of the country. Approximately, one third of patients in 2011 cholera outbreak in Iran were of Afghan or Pakistani nationality which makes the hypothesis of import of Ogawa serotype strains from neighboring countries more documented and signifies the need to monitor and protect the boundaries.

Optimal decoding of neural dynamics occurs at mesoscale spatial and temporal resolutions.

Introduction: Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood. Materials and methods: In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown to n = 9 mice in the Allen Institute Visual Coding-NeuroPixels dataset from large-scale MUA recordings. We parametrically varied the spatial and temporal resolutions of the MUA data provided to the model, and compared its resulting decoding accuracy. Results: For almost all subjects, we found 125ms temporal resolution to maximize decoding accuracy for both the spatial location of Gabor patches (81 classes for patches presented over a 9×9 grid) as well as the identity of natural images (118 classes corresponding to 118 images) across the whole brain. This optimal temporal resolution nevertheless varied greatly between different regions, followed a sensory-associate hierarchy, and was significantly modulated by the central frequency of theta-band oscillations across different regions. Spatially, the optimal resolution was at either of two mesoscale levels for almost all mice: the area level, where the spiking activity of all neurons within each brain area are combined, and the population level, where neuronal spikes within each area are combined across fast spiking (putatively inhibitory) and regular spiking (putatively excitatory) neurons, respectively. We also observed an expected interplay between optimal spatial and temporal resolutions, whereby increasing the amount of averaging across one dimension (space or time) decreases the amount of averaging that is optimal across the other dimension, and vice versa. Discussion: Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which the unit of information varies dynamically based on neuronal signal and noise correlations across space and time.

Hyperdimensional computing with holographic and adaptive encoder.

Introduction: Brain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensional Computing (HDC) is centered around the idea of having holographic and high-dimensional representation as the neural activities in our brains. Such representation is the fundamental enabler for the efficiency and robustness of HDC. However, existing HDC-based algorithms suffer from limitations within the encoder. To some extent, they all rely on manually selected encoders, meaning that the resulting representation is never adapted to the tasks at hand. Methods: In this paper, we propose FLASH, a novel hyperdimensional learning method that incorporates an adaptive and learnable encoder design, aiming at better overall learning performance while maintaining good properties of HDC representation. Current HDC encoders leverage Random Fourier Features (RFF) for kernel correspondence and enable locality-preserving encoding. We propose to learn the encoder matrix distribution via gradient descent and effectively adapt the kernel for a more suitable HDC encoding. Results: Our experiments on various regression datasets show that tuning the HDC encoder can significantly boost the accuracy, surpassing the current HDC-based algorithm and providing faster inference than other baselines, including RFF-based kernel ridge regression. Discussion: The results indicate the importance of an adaptive encoder and customized high-dimensional representation in HDC.

Deep random forest with ferroelectric analog content addressable memory.

Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼106×/10× and ∼106×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM.

Memory-inspired spiking hyperdimensional network for robust online learning.

Recently, brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivate their combination. With the help of the classical psychological model on memory, we propose SpikeHD, the first framework that fundamentally combines Spiking neural network and hyperdimensional computing. SpikeHD generates a scalable and strong cognitive learning system that better mimics brain functionality. SpikeHD exploits spiking neural networks to extract low-level features by preserving the spatial and temporal correlation of raw event-based spike data. Then, it utilizes HDC to operate over SNN output by mapping the signal into high-dimensional space, learning the abstract information, and classifying the data. Our extensive evaluation on a set of benchmark classification problems shows that SpikeHD provides the following benefit compared to SNN architecture: (1) significantly enhance learning capability by exploiting two-stage information processing, (2) enables substantial robustness to noise and failure, and (3) reduces the network size and required parameters to learn complex information.

GrapHD: Graph-Based Hyperdimensional Memorization for Brain-Like Cognitive Learning.

Memorization is an essential functionality that enables today's machine learning algorithms to provide a high quality of learning and reasoning for each prediction. Memorization gives algorithms prior knowledge to keep the context and define confidence for their decision. Unfortunately, the existing deep learning algorithms have a weak and nontransparent notion of memorization. Brain-inspired HyperDimensional Computing (HDC) is introduced as a model of human memory. Therefore, it mimics several important functionalities of the brain memory by operating with a vector that is computationally tractable and mathematically rigorous in describing human cognition. In this manuscript, we introduce a brain-inspired system that represents HDC memorization capability over a graph of relations. We propose GrapHD, hyperdimensional memorization that represents graph-based information in high-dimensional space. GrapHD defines an encoding method representing complex graph structure while supporting both weighted and unweighted graphs. Our encoder spreads the information of all nodes and edges across into a full holistic representation so that no component is more responsible for storing any piece of information than another. Then, GrapHD defines several important cognitive functionalities over the encoded memory graph. These operations include memory reconstruction, information retrieval, graph matching, and shortest path. Our extensive evaluation shows that GrapHD: (1) significantly enhances learning capability by giving the notion of short/long term memorization to learning algorithms, (2) enables cognitive computing and reasoning over memorization graph, and (3) enables holographic brain-like computation with substantial robustness to noise and failure.

EventHD: Robust and efficient hyperdimensional learning with neuromorphic sensor.

Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Hyper-Dimensional Computing (HDC) has shown promising results in enabling efficient and robust cognitive learning. In this study, we exploit HDC as an alternative computational model that mimics important brain functionalities toward high-efficiency and noise-tolerant neuromorphic computing. We present EventHD, an end-to-end learning framework based on HDC for robust, efficient learning from neuromorphic sensors. We first introduce a spatial and temporal encoding scheme to map event-based neuromorphic data into high-dimensional space. Then, we leverage HDC mathematics to support learning and cognitive tasks over encoded data, such as information association and memorization. EventHD also provides a notion of confidence for each prediction, thus enabling self-learning from unlabeled data. We evaluate EventHD efficiency over data collected from Dynamic Vision Sensor (DVS) sensors. Our results indicate that EventHD can provide online learning and cognitive support while operating over raw DVS data without using the costly preprocessing step. In terms of efficiency, EventHD provides 14.2× faster and 19.8× higher energy efficiency than state-of-the-art learning algorithms while improving the computational robustness by 5.9×.

Achieving software-equivalent accuracy for hyperdimensional computing with ferroelectric-based in-memory computing.

Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple manipulations of hypervectors and can be incredibly memory-intensive. In-memory computing (IMC) can greatly improve the efficiency of HDC by reducing data movement in the system. Most existing IMC implementations of HDC are limited to binary precision which inhibits the ability to match software-equivalent accuracies. Moreover, memory arrays used in IMC are restricted in size and cannot immediately support the direct associative search of large binary HVs (a ubiquitous operation, often over 10,000+ dimensions) required to achieve acceptable accuracies. We present a multi-bit IMC system for HDC using ferroelectric field-effect transistors (FeFETs) that simultaneously achieves software-equivalent-accuracies, reduces the dimensionality of the HDC system, and improves energy consumption by 826x and latency by 30x when compared to a GPU baseline. Furthermore, for the first time, we experimentally demonstrate multi-bit, array-level content-addressable memory (CAM) operations with FeFETs. We also present a scalable and efficient architecture based on CAMs which supports the associative search of large HVs. Furthermore, we study the effects of device, circuit, and architectural-level non-idealities on application-level accuracy with HDC.

The Impact of MTHFR 1298 A > C and 677 C > T Gene Polymorphisms as Susceptibility Risk Factors in Cervical Intraepithelial Neoplasia Related to HPV and Sexually Transmitted Infections.

BACKGROUND: HPV genotypes are the most common etiological factor for genital neoplasia. It would appear that sexually transmitted infections accompanied with HPV genotypes might have synergistic interactions in cancer progression. The genetic polymorphisms are involved in metabolizing carcinogens which may contribute to the susceptibility of developing genital cancers by less efficient or overly down metabolic pathways and cell signaling. MTHFR polymorphisms are related to several metabolic disorders and human cancers. We investigated the contribution of MTHFR 1298 and MTHFR 677 polymorphisms as potential risk factors for outcomes with HPV genotypes and STIs in Iranian population. MATERIALS AND METHODS: As a case-control study, MTHFR A1298C and C677T were assessed for SNPs analysis using a PCR-RFLP assay in 50 cervical intraepithelial neoplasia (CIN) cases, 98 HPV-positive subjects and 47 non-cancerous/non-HPV patients as healthy controls. RESULTS: Finding suggested a significant association between the MTHFR 1298 CC polymorphisms (OR = 3.5, 95% CI = 1.13-10.82, P ≤ 0.05) in women with CIN as compared to non-cancerous/non-HPV subjects. There was not a significant difference of MTHFR 677 between outcomes. DISCUSSION: It would seem MTHFR 1298 CC is more likely to be a potential risk factor for HPV-cervical cancer progression. Consequences support further attempts to understand the clinical manifestations of neoplasia related to genital infections and gene mutations.

Molecular technique for detection of Leishmania infantum isolates in Iran.

INTRODUCTION: Leishmania infantum is the causative agent of autochthonous cutaneous and visceral cases of leishmaniasis and transmitted by female sandflies. The dogs are considered the main reservoir hosts; however, there are the reports on Leishmania infection in other animals. In this study, occurrence types of L. infantum isolates have been analyzed by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) technique. MATERIALS AND METHODS: In this experimental study, 77 samples were cultured and prepared for microscopic study and examined through PCR-RFLP. The samples were used for both deoxyribonucleic acid (DNA) and smear-slide preparations. The DNAs were amplified by PCR for the detection of Leishmania subgenus and PCR products were restricted with HaeIII for the species differentiation. RESULTS: The visceral Leishmania parasites were genotyped as L. infantum. It was also determined sensitivity in PCR (100%) was higher than microscopic examination. CONCLUSION: PCR-RFLP technique appears to be most sensitive for the detection and differentiation of L. infantum. There exists a relationship between genetic heterogeneousness and clinical manifestation and geographical regions of this disease in human.

Efficacy of multiplex PCR procedure for Iranian Streptococcus pneumoniae isolates.

BACKGROUND: Identifying regional types and evaluating the frequency of pneumococcal strains has become increasingly important especially in vaccination. The purpose of this study was the identification and frequency determination of our regional serotype and evaluation of the performance of recent type specific multiplex PCR for the diagnosis of streptococcus pneumonia serotypes. METHODS: All isolated S. pneumonia from suspected patients in Tehran and Isfahan Hospitals from June to December of 2012 were evaluated. All specimens and their serotypes were identified through a conventional method and specific antisera. Serotype specific multiplex PCR was applied and ran in seven reactions consisting of 34 S. pneumonia primer pairs plus a primer pair as an internal control for this purpose. RESULTS: Overall, 14 genotype specific serotypes (including two subtypes for 19 and 23) were detected and had identical results with stereotyping method except for serotype 28 and one of the identified serotype 23. The serotypes 19, 6 and 23 were dominant with the frequency of 51.8%. A cross reactivity was also observed between genotypes 1 and 9A/9V. CONCLUSION: Applied multiplex PCR format can be suitable and cost effective tool for identification of S. pneumonia serotypes.

Real-Time Assay as A Tool for Detecting lytA Gene in Streptococcus pneumoniae Isolates.

OBJECTIVE: In-time diagnosis of Streptococcus pneumoniae (S. pneumonia) can play a significant role in decreasing morbidity and mortality rate. Applying molecular methods has gained popularity due to the existing limits of routine diagnostic methods. Examining the expression of different genes of this bacterium through different molecular methods suggests that lytA gene has a higher sensitivity and specificity in diagnosis of Streptococcus pneumoniae. The aim of this study was to evalutate lytA gene expression in diagnosis of invasive S. pneumonia in culture positive specimens by real-time polymerase chain reaction (PCR). MATERIALS AND METHODS: IIn this a descriptive study, All received specimens were isolated to identify S. pneumoniae. DNA was then extracted and after optimizing the test and determining the detection limit, samples were tested by real-time PCR using lytA gene primers. RESULTS: Twenty seven isolates were diagnosed as S. pneumoniae. In all, the extracted DNA was positive in real-time method. The electrophoresis of the products also confirmed the presence of single product b along with the 53 base pair fragment. The detection limit of the test was less 6 colony forming unit (CFU). CONCLUSION: Real-Time PCR seems to provide reliable and rapid results. We suggest that this test should be conducted on the preliminary isolated specimens, since applying various biochemical tests need one extra working day.