Investigation on Vision System: Digital FPGA Implementation in Case of Retina Rod Cells.

The development of prostheses and treatments for illnesses and recovery has recently been centered on hardware modeling for various delicate biological components, including the nervous system, brain, eyes, and heart. The retina, being the thinnest and deepest layer of the eye, is of particular interest. In this study, we employ the Nyquist-Based Approximation of Retina Rod Cell (NBAoRRC) approach, which has been adapted to utilize Look-Up Tables (LUTs) rather than original functions, to implement rod cells in the retina using cost-effective hardware. In modern mathematical models, numerous nonlinear functions are used to represent the activity of these cells. However, these nonlinear functions would require a substantial amount of hardware for direct implementation and may not meet the required speed constraints. The proposed method eliminates the need for multiplication functions and utilizes a fast, cost-effective rod cell device. Simulation results demonstrate the extent to which the proposed model aligns with the behavior of the primary rod cell model, particularly in terms of dynamic behavior. Based on the results of hardware implementation using the Field-Programmable Gate Arrays (FPGA) board Virtex-5, the proposed model is shown to be reliable, consume 30 percent less power than the primary model, and have reduced hardware resource requirements. Based on the results of hardware implementation using the reconfigurable FPGA board Virtex-5, the proposed model is reliable, uses 30% less power consumption than the primary model in the worth state of the set of approximation method, and has a reduced hardware resource requirement. In fact, using the proposed model, this reduction in the power consumption can be achieved. Finally, in this article, by using the LUT which is systematically sampled (Nyquist rate), we were able to remove all costly operators in terms of hardware (digital) realization and achieve very good results in the field of digital implementation in two scales of network and single neuron.

Stability Study of a Compounded Sublingual Buprenorphine Solution for Neonatal Opioid Withdrawal Syndrome.

OBJECTIVE: Sublingual (SL) buprenorphine is a cornerstone of care in the treatment of adult opioid use disorder. Recent studies have demonstrated its advantages in the management of neonatal opioid withdrawal syndrome (NOWS). Commercially available SL tablets and transdermal patches are not amenable to neonatal use, and published compounding formulas of SL solutions contained undesirable excipients, including ethanol, sugars, and preservatives. The objective of this research is to explore the stability of a novel SL buprenorphine formulation free of alcohol, sugars, and preservatives. METHODS: A 0.075 mg/mL buprenorphine solution was prepared by diluting the commercial injectable solution with normal saline and packaged into polyethylene terephthalate amber prescription bottles and polypropylene amber oral syringes and stored in refrigeration. Quality assessments were conducted by visual, pH, and high-performance liquid chromatography (HPLC) analysis immediately after preparation, and at 7 and 14 days of storage. RESULTS: There were neither visual nor pH changes detected through 14 days. HPLC analysis indicated that all samples retained >99% initial buprenorphine concentration. Drug concentration increased slightly in the oral syringe after day 7, probably due to moisture loss. No degradation peaks were observed in chromatograms. CONCLUSIONS: This novel buprenorphine is free of alcohol, sugar, and preservatives, and it may offer a significant safety advantage for NOWS patients. Additional clinical studies are recommended to verify the bioavailability and efficacy of this formulation.

Efficient Digital Realization of Endocrine Pancreatic ß-Cells.

The accurate implementation of biological neural networks, which is one of the important areas of research in the field of neuromorphic, can be studied in the case of diseases, embedded systems, the study of the function of neurons in the nervous system, and so on. The pancreas is one of the main organs of human that performs important and vital functions in the body. One part of the pancreas is an endocrine gland and produces insulin, while another part is an exocrine gland that produces enzymes for digesting fats, proteins and carbohydrates. In this paper, an optimal digital hardware implementation for pancreatic ß-cells, which is the endocrine type, is presented. Since the equations of the original model include nonlinear functions, and the implementation of these functions results in greater use of hardware resources as well as deceleration, to achieve optimal implementation, we have approximated these nonlinear functions using the base-2 functions and LUT. The results of dynamic analysis and simulation show the accuracy of the proposed model compared to the original model. Analysis of the synthesis results of the proposed model on the Spartan-3 XC3S50 (5TQ144) reconfigurable board (FPGA) shows the superiority of the proposed model over the original model. These advantages include using fewer hardware resources, a performance almost twice as fast, and 19% less power consumption, than the original model.

Executive protocol designed for new review study called: systematic review and artificial intelligence network meta-analysis (RAIN) with the first application for COVID-19.

Artificial intelligence (AI) as a suite of technologies can complement systematic review and meta-analysis studies and answer questions that cannot be typically answered using traditional review protocols and reporting methods. The purpose of this protocol is to introduce a new protocol to complete systematic review and meta-analysis studies.In this work, systematic review, meta-analysis, and meta-analysis network based on selected AI technique, and for P < 0.05 are followed, with a view to responding to questions and challenges that the global population is facing in light of the COVID-19 pandemic.Finally, it is expected that conducting reviews by following the proposed protocol can provide suitable answers to some of the research questions raised due to COVID-19.

The silence of the neurons: an application to enhance performance and energy efficiency.

Introduction: Simulation of biological neural networks is a computationally intensive task due to the number of neurons, various communication pathways, and non-linear terms in the differential equations of the neuron. Method: This study proposes an original modification to optimize performance and power consumption in systems, simulating or implementing spiking neural networks. First, the proposed modified models were simulated for validation. Furthermore, digital hardware was designed, and both the original and proposed models were implemented on a Field-Programmable Gate Array (FPGA). Results and discussion: Moreover, the impact of the proposed modification on performance metrics was studied. The implementation results confirmed that the proposed models are considerably faster and require less energy to generate a spike compared with unmodified neurons.

Expression Patterns of miR181a and miR30d in Patients with Breast Cancer.

Background: One of the important molecular pathways in breast cancer is the PTEN-PI3K-AKT pathway. Any change in the activity of the PTEN gene can alter the PI3K-AKT pathway. Moreover, there are subsets of genes and pathways their expression changes by post-transcriptional regulations. For instance, gene regulation alters by non-coding RNAs such as micro-RNAs as post-transcriptional regulators that prevent the expression of the target transcript. Therefore, it is essential to assess the related alterations in micro-RNA expression patterns to find out the possible causes of conversions in related transcripts and pathways such as the PTENPI3K-AKT pathway in breast cancer. Methods: To determine the expression level of miR-181a and miR-30d in 30 breast tumor samples and 30 adjacent normal samples, the RNA extraction, and cDNA synthesis was performed by RiboEx (GeneAll, Korea). Finally, the Real-Time PCR method was used for quantitative analysis of the expression levels of these miRNAs. all the experimental part of the project in done at Islamic Azad University in 2017. Results: After analyzing comparisons in the expression level of miR-181a and miR-30d in tumor and normal tissues, there was a significant increase in the expression level of miR-181a in tumor samples compared with normal samples. Moreover, the expression level of miR-30d in tumor samples reported a significant decrease in comparison with normal samples (P<0.05). Conclusion: Upregulation of miR-181a may affect the transcription of the PTEN gene resulting in the cell progress to cancer. The Downregulation of miR-30d may also lead to cancer cell growth, due to a reduction in the affecting on the CREB gene transcript.

Aflatoxin G1 exposure altered the expression of BDNF and GFAP, histopathological of brain tissue, and oxidative stress factors in male rats.

Background and purpose: Aflatoxins are highly toxic compounds that can cause acute and chronic toxicity in humans and animals. This study aimed to evaluate the expression of BDNF and GFAP, histopathological changes, and oxidative stress factors in brain tissue exposed to aflatoxin G1 (AFG1) in male rats. Experimental approach: Twenty-eight male Wistar rats were used. Animals were randomly divided into 4 groups of 7 each. The control group received 0.2 mL of corn oil and the treatment groups were exposed to AFG1 (2 mg/kg) intra-peritoneally for 15, 28, and 45 days. The tissue was used for histopathological studies, and the level of TAC, SOD, and MDA, and the expression of BDNF and GFAP genes were evaluated. Findings/Results: Real-time PCR results showed that AFG1 increased GFAP expression and decreased BDNF expression in AFG1-treated groups compared to the control group. The tissue level of TAC and SOD over time in the groups receiving AFG1 significantly decreased and the tissue level of MDA increased compared to the control group. Histopathological results showed that AFG1 can cause cell necrosis, a reduction of the normal cells number in the hippocampal region of CA1, cerebral edema, shrinkage of nerve cells, formation of space around neuroglia, and diffusion of gliosis in the cerebral cortex after 45 days. Conclusion and implication: AFG1, by causing pathological complications in cortical tissue, was able to affect the exacerbation of nerve tissue damage and thus pave the way for future neurological diseases.

Non-flipping DNA glycosylase AlkD scans DNA without formation of a stable interrogation complex.

The multi-step base excision repair (BER) pathway is initiated by a set of enzymes, known as DNA glycosylases, able to scan DNA and detect modified bases among a vast number of normal bases. While DNA glycosylases in the BER pathway generally bend the DNA and flip damaged bases into lesion specific pockets, the HEAT-like repeat DNA glycosylase AlkD detects and excises bases without sequestering the base from the DNA helix. We show by single-molecule tracking experiments that AlkD scans DNA without forming a stable interrogation complex. This contrasts with previously studied repair enzymes that need to flip bases into lesion-recognition pockets and form stable interrogation complexes. Moreover, we show by design of a loss-of-function mutant that the bimodality in scanning observed for the structural homologue AlkF is due to a key structural differentiator between AlkD and AlkF; a positively charged ß-hairpin able to protrude into the major groove of DNA.

CORDIC-Astrocyte: Tripartite Glutamate-IP3-Ca 2+ Interaction Dynamics on FPGA.

Real-time, large-scale simulation of biological systems is challenging due to different types of nonlinear functions describing biochemical reactions in the cells. The promise of the high speed, cost effectiveness, and power efficiency in addition to parallel processing has made application-specific hardware an attractive simulation platform. This paper proposes high-speed and low-cost digital hardware to emulate a biological-plausible astrocyte and glutamate-release mechanism. The nonlinear terms of these models were calculated using a high-precision and cost-effective algorithm. Subsequently, the modified models were simulated to study and validate their functions. We developed several hardware versions by setting different constraints to investigate trade-offs and find the best possible design. FPGA implementation results confirmed the ability of the design to emulate biological cell behaviours in detail with high accuracy. As for performance, the proposed design turned out to be faster and more efficient than previously published works that targeted digital hardware for biological-plausible astrocytes.

Additive manufacturing of laminar flow cells for single-molecule experiments.

A microfluidic laminar flow cell (LFC) forms an indispensable component in single-molecule experiments, enabling different substances to be delivered directly to the point under observation and thereby tightly controlling the biochemical environment immediately surrounding single molecules. Despite substantial progress in the production of such components, the process remains relatively inefficient, inaccurate and time-consuming. Here we address challenges and limitations in the routines, materials and the designs that have been commonly employed in the field, and introduce a new generation of LFCs designed for single-molecule experiments and assembled using additive manufacturing. We present single- and multi-channel, as well as reservoir-based LFCs produced by 3D printing to perform single-molecule experiments. Using these flow cells along with optical tweezers, we show compatibility with single-molecule experiments including the isolation and manipulation of single DNA molecules either attached to the surface of a coverslip or as freely movable DNA dumbbells, as well as direct observation of protein-DNA interactions. Using additive manufacturing to produce LFCs with versatility of design and ease of production allow experimentalists to optimize the flow cells to their biological experiments and provide considerable potential for performing multi-component single-molecule experiments.

Automatic Segmentation, Detection, and Diagnosis of Abdominal Aortic Aneurysm (AAA) Using Convolutional Neural Networks and Hough Circles Algorithm.

PURPOSE: An abdominal aortic aneurysm (AAA) is known as a cardiovascular disease involving localized deformation (swelling or enlargement) of aorta occurring between the renal and iliac arteries. AAA would jeopardize patients' lives due to its rupturing risk, so prompt recognition and diagnosis of this disorder is vital. Although computed tomography angiography (CTA) is the preferred imaging modality used by radiologist for diagnosing AAA, computed tomography (CT) images can be used too. In the recent decade, there has been several methods suggested by experts in order to find a precise automated way to diagnose AAA without human intervention base on CT and CTA images. Despite great approaches in some methods, most of them need human intervention and they are not fully automated. Also, the error rate needs to decrease in other methods. Therefore, finding a novel fully automated with lower error rate algorithm using CTA and CT images for Abdominal region segmentation, AAA detection, and disease severity classification is the main goal of this paper. METHODS: The proposed method in this article will be performed in three steps: (1) designing a classifier based on Convolutional Neural Network (CNN) for classifying different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone. (2) After correct aorta detection, defining its edge and measuring its diameter with the use of Hough Circle Algorithm (which is an algorithm for finding an arbitrary shape in images and measuring its diameter in pixel) is the second step. (3) Ultimately, the detected aorta, depending on its diameter, will be categorized in one of these groups: (a) there is no risk of AAA, (b) there is a medium risk of AAA, and (c) there is a high risk of AAA. RESULTS: The designed CNN classifier classifies different parts of abdominal into four different classes such as: abdominal inside region, aorta, body border, and bone with the accuracy, precision, and sensitivity of 97.93, 97.94, and 97.93% respectively. The accuracy of the proposed classifier for aorta region detection is 98.62% and Hough Circles algorithm can classify 120 aorta patches according to their diameter with accuracy of 98.33%. CONCLUSIONS: As a whole, a classifier using Convolutional Neural Network is designed and applied in order to detect AAA region among other abdominal regions. Then Hough Circles algorithm is applied to aorta patches for finding aorta border and measuring its diameter. Ultimately, the detected aortas will be categorized according to their diameters. All steps meet the expected results.

Publisher Correction: Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V.

The original version of this Article was updated shortly after publication to add a link to the Peer Review file, which was inadvertently omitted. The Peer Review file is available to download as a Supplementary File from the HTML version of the Article.

A Novel Nonlinear Function Evaluation Approach for Efficient FPGA Mapping of Neuron and Synaptic Plasticity Models.

Efficient hardware realization of spiking neural networks is of great significance in a wide variety of applications, such as high-speed modeling and simulation of large-scale neural systems. Exploiting the key features of FPGAs, this paper presents a novel nonlinear function evaluation approach, based on an effective uniform piecewise linear segmentation method, to efficiently approximate the nonlinear terms of neuron and synaptic plasticity models targeting low-cost digital implementation. The proposed approach takes advantage of a high-speed and extremely simple segment address encoder unit regardless of the number of segments, and therefore is capable of accurately approximating a given nonlinear function with a large number of straight lines. In addition, this approach can be efficiently mapped into FPGAs with minimal hardware cost. To investigate the application of the proposed nonlinear function evaluation approach in low-cost neuromorphic circuit design, it is applied to four case studies: the Izhikevich and FitzHugh-Nagumo neuron models as 2-dimensional case studies, the Hindmarsh-Rose neuron model as a relatively complex 3-dimensional model containing two nonlinear terms, and a calcium-based synaptic plasticity model capable of producing various STDP curves. Simulation and FPGA synthesis results demonstrate that the hardware proposed for each case study is capable of producing various responses remarkably similar to the original model and significantly outperforms the previously published counterparts in terms of resource utilization and maximum clock frequency.

Breaking the speed limit with multimode fast scanning of DNA by Endonuclease V.

In order to preserve genomic stability, cells rely on various repair pathways for removing DNA damage. The mechanisms how enzymes scan DNA and recognize their target sites are incompletely understood. Here, by using high-localization precision microscopy along with 133 Hz high sampling rate, we have recorded EndoV and OGG1 interacting with 12-kbp elongated λ-DNA in an optical trap. EndoV switches between three distinct scanning modes, each with a clear range of activation energy barriers. These results concur with average diffusion rate and occupancy of states determined by a hidden Markov model, allowing us to infer that EndoV confinement occurs when the intercalating wedge motif is involved in rigorous probing of the DNA, while highly mobile EndoV may disengage from a strictly 1D helical diffusion mode and hop along the DNA. This makes EndoV the first example of a monomeric, single-conformation and single-binding-site protein demonstrating the ability to switch between three scanning modes.

Digital Multiplierless Realization of Coupled Wilson Neuron Model.

The human brain is composed of 1011 neurons with a switching speed of about 1 ms. Studying spiking neural networks, including the modeling, simulation, and implementation of the biological neuron models, helps us to learn about the brain and the related diseases, or to design more efficient bio-mimic processors and smarter robots. Such applications have made this part of neuromorphic research works very popular. In this paper, the Wilson neuron model has been implemented as an approximation of the Hodgkin-Huxley biological model that is adjusted for the efficient digital realization on the platforms. Results show that the proposed model can adequately reproduce neuron dynamical behaviors. The hardware implementation on the field-programmable gate array (FPGA) shows that our modifications on the Wilson original model imitate the biological behavior of neurons, besides using feasibility, targeting a low cost and high efficiency. The modifications raised a 15% speed-up compared with the original model. The mean normalized root-mean-square error, root-mean-square error, and the mean absolute error parameters are 6.43, 0.44, and 0.31, respectively.

Multiplierless Implementation of Noisy Izhikevich Neuron With Low-Cost Digital Design.

Fast speed and a high accuracy implementation of biological plausible neural networks are vital key objectives to achieve new solutions to model, simulate and cure the brain diseases. Efficient hardware implementation of spiking neural networks is a significant approach in biological neural networks. This paper presents a multiplierless noisy Izhikevich neuron (MNIN) model, which is used for the digital implementation of biological neural networks in large scale. Simulation results show that the MNIN model reproduces the same operations of the original noisy Izhikevich neuron. The proposed model has a low-cost hardware implementation property compared with the original neuron model. The field-programmable gate array realization results demonstrated that the MNIN model follows the different spiking patterns appropriately.

Anti-JC virus antibody sera positivity and index value among patients with multiple sclerosis may be correlated with age, sex, and area of residence.

Anti-JC virus (JCV) antibody index is the predictive factor of progressive multifocal leukoencephalopathy (PML) for multiple sclerosis (MS) patients treating with natalizumab. The aim of this study is to evaluate the prevalence of anti-JCV antibody positivity and index among Iranian patients who are the candidate for natalizumab and its correlation with their demographic data and previous therapies. A cross-sectional design was assessed for receiving anti-JCV antibody test results between January 2014 and December 2016. Demographic data and disease characteristics were also obtained. Statistical analysis and logistic regression were done using SPSS. Among 803 MS patients that were observed, the prevalence of anti-JCV antibody positivity was 67.9% (mean of index = 2.23 ± 1.16) and 67.6% of positive patients had an index ≥ 1.5. Males were more antibody positive than females (81.7 and 64% respectively; significance (sig.) < 0.001, OR = 2.51, CI 1.65-3.81). The rate of positivity was lower in patients under the age of 18. Patients who lived in cold regions had significantly more prevalence of positivity (Num. = 403; sig. = 0.043 and OR = 1.86; CI 1.02-3.39) and with higher rate of index ≥ 1.5 (sig. = 0.017; OR = 3.99, CI 1.79-8.88). Disease onset age between 28 and 37 years were more positive compared to 18-27 years (N = 480; sig. = 0.02; OR = 1.85, CI 1.09-3.14). Age, male gender, onset age, and cold area of residency significantly influenced anti-JCV antibody sera positivity. Only age of onset and cold area of residency were related to the index. No significant difference was observed between type, dosage, and duration of previous immunosuppressant drugs and anti-JCV antibody positivity and index value.

Complete Neuron-Astrocyte Interaction Model: Digital Multiplierless Design and Networking Mechanism.

Glial cells, also known as neuroglia or glia, are non-neuronal cells providing support and protection for neurons in the central nervous system (CNS). They also act as supportive cells in the brain. Among a variety of glial cells, the star-shaped glial cells, i.e., astrocytes, are the largest cell population in the brain. The important role of astrocyte such as neuronal synchronization, synaptic information regulation, feedback to neural activity and extracellular regulation make the astrocytes play a vital role in brain disease. This paper presents a modified complete neuron-astrocyte interaction model that is more suitable for efficient and large scale biological neural network realization on digital platforms. Simulation results show that the modified complete interaction model can reproduce biological-like behavior of the original neuron-astrocyte mechanism. The modified interaction model is investigated in terms of digital realization feasibility and cost targeting a low cost hardware implementation. Networking behavior of this interaction is investigated and compared between two cases: i) the neuron spiking mechanism without astrocyte effects, and ii) the effect of astrocyte in regulating the neurons behavior and synaptic transmission via controlling the LTP and LTD processes. Hardware implementation on FPGA shows that the modified model mimics the main mechanism of neuron-astrocyte communication with higher performance and considerably lower hardware overhead cost compared with the original interaction model.

Digital implementation of a biological astrocyte model and its application.

This paper presents a modified astrocyte model that allows a convenient digital implementation. This model is aimed at reproducing relevant biological astrocyte behaviors, which provide appropriate feedback control in regulating neuronal activities in the central nervous system. Accordingly, we investigate the feasibility of a digital implementation for a single astrocyte and a biological neuronal network model constructed by connecting two limit-cycle Hopf oscillators to an implementation of the proposed astrocyte model using oscillator-astrocyte interactions with weak coupling. Hardware synthesis, physical implementation on field-programmable gate array, and theoretical analysis confirm that the proposed astrocyte model, with considerably low hardware overhead, can mimic biological astrocyte model behaviors, resulting in desynchronization of the two coupled limit-cycle oscillators.

A generalized analog implementation of piecewise linear neuron models using CCII building blocks.

This paper presents a set of reconfigurable analog implementations of piecewise linear spiking neuron models using second generation current conveyor (CCII) building blocks. With the same topology and circuit elements, without W/L modification which is impossible after circuit fabrication, these circuits can produce different behaviors, similar to the biological neurons, both for a single neuron as well as a network of neurons just by tuning reference current and voltage sources. The models are investigated, in terms of analog implementation feasibility and costs, targeting large scale hardware implementations. Results show that, in order to gain the best performance, area and accuracy; these models can be compromised. Simulation results are presented for different neuron behaviors with CMOS 350 nm technology.