Physical activity in recurrent colon cancer: Cancer and Leukemia Group B/SWOG 80702 (Alliance).

BACKGROUND: One in three patients with stage III colon cancer will experience tumor recurrence. It is uncertain whether physical activity during and after postoperative chemotherapy for stage III colon cancer improves overall survival after tumor recurrence. METHODS: A prospective cohort study nested within a randomized multicenter trial of patients initially diagnosed with stage III colon cancer who experienced tumor recurrence (N = 399) was conducted. Postoperative physical activity before tumor recurrence was measured. Physical activity energy expenditure was quantified via metabolic equivalent task hours per week (MET-h/week). The primary end point was overall survival after tumor recurrence. Multivariable flexible parametric survival models estimated relative and absolute effects with two-sided hypothesis tests. RESULTS: Compared with patients expending <3.0 MET-h/week of physical activity (comparable to <1.0 h/week of brisk walking), patients with ≥18.0 MET-h/week of physical activity (comparable to 6 h/week of brisk walking) had a 33% relative improvement in overall survival time after tumor recurrence (hazard ratio, 0.67; 95% CI, 0.42-0.96). The overall survival rate at 3 years after tumor recurrence was 61.3% (95% CI, 51.8%-69.2%) with <3.0 MET-h/week of physical activity and 72.2% (95% CI, 63.1%-79.6%) with ≥18 MET-h/week of physical activity (risk difference, 10.9 percentage points; 95% CI, 1.2-20.8 percentage points). CONCLUSIONS: Higher postoperative physical activity is associated with improved overall survival after tumor recurrence in patients initially diagnosed with stage III colon cancer. These data may be relevant to patients who, despite optimal postoperative medical therapy, have a high risk of tumor recurrence.

A Systematic Literature Review on Distributed Machine Learning in Edge Computing.

Distributed edge intelligence is a disruptive research area that enables the execution of machine learning and deep learning (ML/DL) algorithms close to where data are generated. Since edge devices are more limited and heterogeneous than typical cloud devices, many hindrances have to be overcome to fully extract the potential benefits of such an approach (such as data-in-motion analytics). In this paper, we investigate the challenges of running ML/DL on edge devices in a distributed way, paying special attention to how techniques are adapted or designed to execute on these restricted devices. The techniques under discussion pervade the processes of caching, training, inference, and offloading on edge devices. We also explore the benefits and drawbacks of these strategies.

A scalable framework for smart COVID surveillance in the workplace using Deep Neural Networks and cloud computing.

A smart and scalable system is required to schedule various machine learning applications to control pandemics like COVID-19 using computing infrastructure provided by cloud and fog computing. This paper proposes a framework that considers the use case of smart office surveillance to monitor workplaces for detecting possible violations of COVID effectively. The proposed framework uses deep neural networks, fog computing and cloud computing to develop a scalable and time-sensitive infrastructure that can detect two major violations: wearing a mask and maintaining a minimum distance of 6 feet between employees in the office environment. The proposed framework is developed with the vision to integrate multiple machine learning applications and handle the computing infrastructures for pandemic applications. The proposed framework can be used by application developers for the rapid development of new applications based on the requirements and do not worry about scheduling. The proposed framework is tested for two independent applications and performed better than the traditional cloud environment in terms of latency and response time. The work done in this paper tries to bridge the gap between machine learning applications and their computing infrastructure for COVID-19.

An intelligent framework using disruptive technologies for COVID-19 analysis.

This paper describes a framework using disruptive technologies for COVID-19 analysis. Disruptive technologies include high-tech and emerging technologies such as AI, industry 4.0, IoT, Internet of Medical Things (IoMT), big data, virtual reality (VR), Drone technology, and Autonomous Robots, 5 G, and blockchain to offer digital transformation, research and development and service delivery. Disruptive technologies are essential for Industry 4.0 development, which can be applied to many disciplines. In this paper, we present a framework that uses disruptive technologies for COVID-19 analysis. The proposed framework restricts the spread of COVID-19 outbreaks, ensures the safety of the healthcare teams and maintains patients' physical and psychological healthcare conditions. The framework is designed to deal with the severe shortage of PPE for the medical team, reduce the massive pressure on hospitals, and track recovered patients to treat COVID-19 patients with plasma. The study provides oversight for governments on how to adopt technologies to reduce the impact of unprecedented outbreaks for COVID-19. Our work illustrates an empirical case study on the analysis of real COVID-19 patients and shows the importance of the proposed intelligent framework to limit the current outbreaks for COVID-19. The aim is to help the healthcare team make rapid decisions to treat COVID-19 patients in hospitals, home quarantine, or identifying and treating patients with typical cold or flu.

Patients' perceptions of teleconsultation during COVID-19: A cross-national study.

In recent months, humanity has had to deal with a worldwide pandemic called COVID-19, which has caused the death of hundreds of thousands of people and paralyzed the global economy. Struggling to cure infected patients while continuing to care for patients with other pathologies, health authorities have faced the lack of medical staff and infrastructure. This study aimed to investigate the acceptance of teleconsultation solutions by patients, which help to avoid the spread of the disease during this pandemic period. The model was built using some constructs of the technology acceptance model UTAUT2, Personal traits, Availability, and Perceived Risks. A new scale on Contamination Avoidance was developed by the authors. The questionnaire was disseminated in several countries in Europe and Asia and a total sample of 386 respondents was collected. The results emphasize the huge impact of Performance Expectancy, the negative influence of Perceived Risk, and the positive influence of Contamination Avoidance on the adoption of teleconsultation solutions. The findings highlight the moderating effects of Age, Gender, and Country.

FSS-2019-nCov: A deep learning architecture for semi-supervised few-shot segmentation of COVID-19 infection.

The newly discovered coronavirus (COVID-19) pneumonia is providing major challenges to research in terms of diagnosis and disease quantification. Deep-learning (DL) techniques allow extremely precise image segmentation; yet, they necessitate huge volumes of manually labeled data to be trained in a supervised manner. Few-Shot Learning (FSL) paradigms tackle this issue by learning a novel category from a small number of annotated instances. We present an innovative semi-supervised few-shot segmentation (FSS) approach for efficient segmentation of 2019-nCov infection (FSS-2019-nCov) from only a few amounts of annotated lung CT scans. The key challenge of this study is to provide accurate segmentation of COVID-19 infection from a limited number of annotated instances. For that purpose, we propose a novel dual-path deep-learning architecture for FSS. Every path contains encoder-decoder (E-D) architecture to extract high-level information while maintaining the channel information of COVID-19 CT slices. The E-D architecture primarily consists of three main modules: a feature encoder module, a context enrichment (CE) module, and a feature decoder module. We utilize the pre-trained ResNet34 as an encoder backbone for feature extraction. The CE module is designated by a newly introduced proposed Smoothed Atrous Convolution (SAC) block and Multi-scale Pyramid Pooling (MPP) block. The conditioner path takes the pairs of CT images and their labels as input and produces a relevant knowledge representation that is transferred to the segmentation path to be used to segment the new images. To enable effective collaboration between both paths, we propose an adaptive recombination and recalibration (RR) module that permits intensive knowledge exchange between paths with a trivial increase in computational complexity. The model is extended to multi-class labeling for various types of lung infections. This contribution overcomes the limitation of the lack of large numbers of COVID-19 CT scans. It also provides a general framework for lung disease diagnosis in limited data situations.

A Novel Automated Visual Acuity Test Using a Portable Head-mounted Display.

SIGNIFICANCE: We developed a head-mounted display (HMD) as an automated way of testing visual acuity (VA) to increase workplace efficiency. This study raises its potential utility and advantages, analyzes reasons for its current limitations, and discusses areas of improvement in the development of this device. PURPOSE: Manual VA testing is important but labor-intensive in ophthalmology and optometry clinics. The purpose of this exploratory study is to assess the performance and identify potential limitations of an automated HMD for VA testing. METHODS: Sixty patients from National University Hospital, Singapore, were enrolled in a prospective observational study. The HMD was constructed based on the Snellen chart, with single optotypes displayed at a time. Each subject underwent VA testing of both eyes with the manual Snellen chart tested at 6 m from the subject and the HMD. RESULTS: Fifty-three subjects were included in the final analysis, with an incompletion rate of 11.7% (n = 7). The mean difference in estimated acuity between the HMD and Snellen chart was 0.05 logMAR. However, 95% limits of agreement were large at ±0.33 logMAR. The HMD overestimated vision in patients with poorer visual acuities. In detecting VA worse than 0.30 logMAR (6/12), sensitivity was 63.6% (95% confidence interval, 0.31 to 0.89%), and specificity was 81.0% (95% confidence interval, 0.66 to 0.91%). No significant correlation existed between mean difference and age (r = -0.15, P = .27) or education level (r = 0.04, P = .76). CONCLUSIONS: Advantages of our novel HMD technology include its fully automated nature and its portability. However, the device in its current form is not ready for widespread clinical use primarily because of its low accuracy, which is limited by both technical and user factors. Future studies are needed to improve its accuracy and completion rate and to evaluate for test-retest reliability in a larger population.

Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud.

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.

HSMA_WOA: A hybrid novel Slime mould algorithm with whale optimization algorithm for tackling the image segmentation problem of chest X-ray images.

Recently, a novel virus called COVID-19 has pervasive worldwide, starting from China and moving to all the world to eliminate a lot of persons. Many attempts have been experimented to identify the infection with COVID-19. The X-ray images were one of the attempts to detect the influence of COVID-19 on the infected persons from involving those experiments. According to the X-ray analysis, bilateral pulmonary parenchymal ground-glass and consolidative pulmonary opacities can be caused by COVID-19 - sometimes with a rounded morphology and a peripheral lung distribution. But unfortunately, the specification or if the person infected with COVID-19 or not is so hard under the X-ray images. X-ray images could be classified using the machine learning techniques to specify if the person infected severely, mild, or not infected. To improve the classification accuracy of the machine learning, the region of interest within the image that contains the features of COVID-19 must be extracted. This problem is called the image segmentation problem (ISP). Many techniques have been proposed to overcome ISP. The most commonly used technique due to its simplicity, speed, and accuracy are threshold-based segmentation. This paper proposes a new hybrid approach based on the thresholding technique to overcome ISP for COVID-19 chest X-ray images by integrating a novel meta-heuristic algorithm known as a slime mold algorithm (SMA) with the whale optimization algorithm to maximize the Kapur's entropy. The performance of integrated SMA has been evaluated on 12 chest X-ray images with threshold levels up to 30 and compared with five algorithms: Lshade algorithm, whale optimization algorithm (WOA), FireFly algorithm (FFA), Harris-hawks algorithm (HHA), salp swarm algorithms (SSA), and the standard SMA. The experimental results demonstrate that the proposed algorithm outperforms SMA under Kapur's entropy for all the metrics used and the standard SMA could perform better than the other algorithms in the comparison under all the metrics.

How Blockchain can impact financial services - The overview, challenges and recommendations from expert interviewees.

FinTech (Financial Technology) and Blockchain are prevalent topics among technology leaders in finance today. This article describes the impact and revolution of FinTech and Blockchain in the financial industry and demonstrates the main characteristics of such technology. Then, we present three critical challenges as well as three ethical issues about using Blockchain technology. Next, we discuss the development of Blockchain for the financial sector. In addition, we describe the real motivations for banks to explore Blockchain, and problems they face. In order to have a good understanding of the industry, a qualitative method was adopted, and sixteen experts were interviewed. It was identified that knowledge hiding in Blockchain was common and the rationale behind was analyzed using the TPB (Theory of Planned Behavior) approach. The analysis results suggested that knowledge hiding was due to affective, behavioral and cognitive evaluations. The interviewees also provided several recommendations and success factors to overcome current issues in Blockchain adoption. Therefore, four important propositions have been developed. Finally, this article suggests how financial services should respond to this new technology and how to manage knowledge sharing in a more structured way. This article contributes to the literature related to the current entrepreneurial finance landscape for Blockchain.

Predicting the Ideal Patient for Ambulatory Cleft Lip Repair.

BACKGROUND: The utilization of ambulatory surgical centers (ASCs) for cleft lip repair is increasing to reduce costs. This study better defines the patient population appropriate for ambulatory cleft repair with uplift modeling, a predictive analytics technique. METHODS: Pediatric patients who underwent cleft lip repair were identified in the 2007 to 2011 California Healthcare Cost and Utilization Project State Inpatient Database and State Ambulatory Surgery and Services Database. The 2-model uplift approach was utilized using multivariate logistic regressions fit to assess the effect of ASCs, age, comorbidities, and procedure type on mortality or 30-day readmission. RESULTS: Of the pediatric cleft lip repairs in California between 2007 and 2011, 2383 (83%) were conducted in inpatient facilities and 498 (17%) in ASCs. The 30-day readmission rates were 2.01% and 1.93% for ASC repairs and inpatient repairs, respectively ( P = .909). Uplift modeling predicts that of the 2881 patients, approximately 40% of patients would have benefit from an ASC repair and an ASC repair would have had no effect on the remaining 60%. Patients likely to benefit from an ASC repair were more likely younger than 1 year old, nonsyndromic, not to have a respiratory or neurologic diagnosis, have less number of procedures, and to have undergone an isolated cleft lip repair. CONCLUSION: Uplift modeling predicts that approximately 40% of patients would benefit from an ASC cleft lip repair. Targeting patients younger than 1 year old, nonsyndromic, with no respiratory or neurologic diagnosis for ASC cleft lip repair, may be a safe and cost-saving endeavor.

Assessment of radiographic and clinical outcomes of an articulating expandable interbody cage in minimally invasive transforaminal lumbar interbody fusion for spondylolisthesis.

OBJECTIVE The inability to significantly improve sagittal parameters has been a limitation of minimally invasive surgery for transforaminal lumbar interbody fusion (MIS TLIF). Traditional cages have a limited capacity to restore lordosis. This study evaluates the use of a crescent-shaped articulating expandable cage (Altera) for MIS TLIF. METHODS This is a retrospective review of 1- and 2-level MIS TLIF. Radiographic outcomes included differences in segmental and lumbar lordosis, disc height, evidence of fusion, and any endplate violations. Clinical outcomes included the numeric rating scale for leg and back pain and the Oswestry Disability Index (ODI) for low-back pain. RESULTS Thirty-nine patients underwent single-level MIS TLIF, and 5 underwent 2-level MIS TLIF. The mean age was 63.1 years, with 64% women. On average, spondylolisthesis was corrected by 4.3 mm (preoperative = 6.69 mm, postoperative = 2.39 mm, p < 0.001), the segmental angle was improved by 4.94° (preoperative = 5.63°, postoperative = 10.58°, p < 0.001), and segmental height increased by 3.1 mm (preoperative = 5.09 mm, postoperative = 8.19 mm, p < 0.001). At 90 days after surgery the authors observed the following: a smaller postoperative sagittal vertical axis was associated with larger changes in back pain at 90 days (r = -0.558, p = 0.013); a larger decrease in spondylolisthesis was associated with greater improvements in ODI and back pain scores (r = -0.425, p = 0.043, and r = -0.43, p = 0.031, respectively); and a larger decrease in pelvic tilt (PT) was associated with greater improvements in back pain (r = -0.548, p = 0.043). For the 1-year PROs, the relationship between the change in PT and changes in ODI and numeric rating scale back pain were significant (r = 0.612, p = 0.009, and r = -0.803, p = 0.001, respectively) with larger decreases in PT associated with larger improvements in ODI and back pain. Overall for this study there was a 96% fusion rate. Fourteen patients were noted to have endplate violation on intraoperative fluoroscopy during placement of the cage. Only 3 of these had progression of their subsidence, with an overall subsidence rate of 6% (3 of 49) visible on postoperative CT. CONCLUSIONS The use of this expandable, articulating, lordotic, or hyperlordotic interbody cage for MIS TLIF provides a significant restoration of segmental height and segmental lordosis, with associated improvements in sagittal balance parameters. Patients treated with this technique had acceptable levels of fusion and significant reductions in pain and disability.

Age at Time of Craniosynostosis Repair Predicts Increased Complication Rate.

OBJECTIVE: This study uses administrative data to assess the optimal timing for surgical repair of craniosynostosis and to identify factors associated with risk of perioperative complications. DESIGN: Statistical analysis of the Healthcare Cost and Utilization Project Kids' Inpatient Database (2006, 2009, 2012). SETTING: KID-participating hospitals in 44 states. PATIENTS: Children 0 to 3 years of age with ICD-9 codes for surgical correction of craniosynostosis (756 and 0124, 0125, 0201, 0203, 0204, or 0206). MAIN OUTCOME MEASURE: Age-based cohorts were assessed for perioperative complications. We performed a multivariable analysis to determine characteristics associated with increased risk of complications. RESULTS: 21 million admissions were screened and 8417 visits met criteria for inclusion. Seventy-five percent of procedures occurred before age 1. Complications occurred in 8.6% of patients: 6.6% of patients at age 0 to 6 months, 10.3% of patients aged 7 to 12 months, and 13.9% of patients 12 to 36 months. Patients with acrocephalosyndactyly or associated congenital anomalies experienced complications in 22.9% of cases (OR = 3.07, 95% CI = 2.33, 4.03). CONCLUSION: Craniosynostosis repair is safe; however, the risk of complications increases with age at intervention. Presence of a syndromic congenital deformity at any age carries the greatest increased risk of perioperative complications. This suggests that optimal timing of intervention is within the first year of life, especially in those cases with additional factors increasing perioperative risk. These data support the importance of counseling patients of the increased risk associated with delaying craniosynostosis repair.

A Proposed Solution and Future Direction for Blockchain-Based Heterogeneous Medicare Data in Cloud Environment.

The healthcare data is an important asset and rich source of healthcare intellect. Medical databases, if created properly, will be large, complex, heterogeneous and time varying. The main challenge nowadays is to store and process this data efficiently so that it can benefit humans. Heterogeneity in the healthcare sector in the form of medical data is also considered to be one of the biggest challenges for researchers. Sometimes, this data is referred to as large-scale data or big data. Blockchain technology and the Cloud environment have proved their usability separately. Though these two technologies can be combined to enhance the exciting applications in healthcare industry. Blockchain is a highly secure and decentralized networking platform of multiple computers called nodes. It is changing the way medical information is being stored and shared. It makes the work easier, keeps an eye on the security and accuracy of the data and also reduces the cost of maintenance. A Blockchain-based platform is proposed that can be used for storing and managing electronic medical records in a Cloud environment.

In-Hospital Mortality and Post-Transplantation Complications in Elderly Multiple Myeloma Patients Undergoing Autologous Hematopoietic Stem Cell Transplantation: A Population-Based Study.

Autologous hematopoietic stem cell transplantation (auto-HSCT) has improved survival in patients with multiple myeloma (MM) and is increasingly used in elderly patients. The aim of this study was to characterize and compare in-hospital complications and mortality after auto-HSCT in younger (< age 65) versus elderly (> age 65) MM patients utilizing the Nationwide Inpatient Sample. Over a 3-year period (2008 to 2010), 2209 patients with MM were admitted to US hospitals for auto-HSCT. The median age was 59 years, with 1650 patients (74.7%) younger than age 65 and 559 patients (25.3%) 65 or older. Overall, in-hospital mortality in MM patients after auto-HSCT was rare (1.5%) and there was no significant difference in mortality between elderly and younger patients. Elderly patients did have a significantly increased mean length of stay (18.6 days + 10.8 days [SD] versus 16.8 days + 7.2 days [SD], P < .001) and mean total hospital charges ($161,117 + $105,008 [SD] versus $151,192 + $78,342 [SD] , P = .018) compared with younger patients. Elderly patients were significantly more likely than younger patients to develop major in-hospital post-transplantation complications such as severe sepsis (odds ratio [OR], 2.70; 95% confidence interval [CI], 1.40 to 5.21; P = .003), septic shock (OR, 3.10; 95% CI, 1.43 to 6.71; P = .004), pneumonia (OR, 1.62; 95% CI, 1.06 to 2.46; P = .024), acute respiratory failure (OR, 3.44; 95% CI, 1.70 to 6.96; P = .001), endotracheal intubation requiring prolonged mechanical ventilation (OR, 2.19; 95% CI, 1.06 to 4.55; P = .035), acute renal failure (OR, 2.14; 95% CI, 1.38 to 3.33; P = .001), and cardiac arrhythmias (OR, 2.06; 95% CI, 1.52 to 2.79; P <.001). These data may help guide informed consent discussions and provide a focus for future studies to reduce treatment-related morbidity in elderly MM patients undergoing auto-HSCT.

Discordance between surgical care improvement project adherence and postoperative outcomes: implications for new Joint Commission standards.

BACKGROUND: Infectious (INF) and venous thromboembolism (VTE) complication rates are targeted by surgical care improvement project (SCIP) INF and SCIP VTE measures. We analyzed how adherence to SCIP INF and SCIP VTE affects targeted postoperative outcomes (wound complication [WC], deep vein thrombosis, and pulmonary embolism [PE]) using all-payer data. MATERIALS AND METHODS: A retrospective review (2007-2011) was conducted using Healthcare Cost and Utilization Project State Inpatient Database Florida and Medicare's Hospital Compare. The association between SCIP adherence rates and outcomes across 355 included surgical procedures was measured using multilevel mixed-effects linear regression models. RESULTS: One hundred sixty acute care hospitals and 779,922 patients were included. Over 5 y, SCIP INF-1, -2, and -3 adherence improved by 12.5%, 8.0%, and 20.9%, respectively, whereas postoperative WC rate decreased by 14.8%. When controlling for time, SCIP INF-1 adherence was associated with improvement of postoperative WC rates (ß = -0.0044, P = 0.005), whereas SCIP INF-2 adherence was associated with increased WCs (ß = 0.0031, P = 0.018). SCIP VTE-1, -2 adherence improved by 14.6% and 20.2%, respectively, whereas postoperative deep vein thrombosis rate increased by 7.1% and postoperative PE rate increased by 3.7%. SCIP VTE-1 and -2 adherence were both associated with increased postoperative PE when controlling for time (SCIP VTE-1: ß = 0.0019, P < 0.001; SCIP VTE-2: ß = 0.0015, P < 0.001). Readmission analysis found SCIP INF-1 adherence to be associated with improved 30-d WC rates when controlling for patient and hospital characteristics (ß = -0.0021, P = 0.032), whereas SCIP INF-3 adherence was associated with increased 30-d WC rates when controlling for time (ß = 0.0007, P = 0.04). CONCLUSIONS: Only SCIP INF-1 adherence was associated with improved outcomes. The Joint Commission has retired SCIP INF-2, -3, and SCIP VTE-2 and made SCIP INF-1 and VTE-1 reporting optional. Our study supports continued reporting of SCIP INF-1.

Neurotransmitter Mechanisms Underlying Sacral Neuromodulation of Bladder Overactivity in Cats.

OBJECTIVE: To determine the role of opioid, ß-adrenergic, and metabotropic glutamate 5 receptors in sacral neuromodulation of bladder overactivity. MATERIAL AND METHODS: In α-chloralose anesthetized cats, intravesical infusion of 0.5% acetic acid (AA) irritated the bladder and induced bladder overactivity. Electric stimulation (5 Hz, 0.2 ms, 0.16-0.7V) of S1 or S2 sacral dorsal roots inhibited the bladder overactivity. Naloxone, propranolol, or MTEP were given intravenously (i.v.) to determine different neurotransmitter mechanisms. RESULTS: AA significantly (p < 0.05) reduced bladder capacity to 7.7 ± 3.3 mL from 12.0 ± 5.0 mL measured during saline infusion. S1 or S2 stimulation at motor threshold intensity significantly (p < 0.05) increased bladder capacity to 179.4 ± 20.0% or 219.1 ± 23.0% of AA control, respectively. Naloxone (1 mg/kg) significantly (p < 0.001) reduced the control capacity to 38.3 ± 7.3% and the bladder capacity measured during S1 stimulation to 106.2 ± 20.8% of AA control, but did not significantly change the bladder capacity measured during S2 stimulation. Propranolol (3 mg/kg) significantly (p < 0.01) reduced bladder capacity from 251.8 ± 32.2% to 210.9 ± 33.3% during S2 stimulation, but had no effect during S1 stimulation. A similar propranolol effect also was observed in naloxone-pretreated cats. In propranolol-pretreated cats during S1 or S2 stimulation, MTEP (3 mg/kg) significantly (p < 0.05) reduced bladder capacity and naloxone (1 mg/kg) following MTEP treatment further reduced bladder capacity. However, a significant inhibition could still be induced by S1 or S2 stimulation after all three drugs were administered. CONCLUSIONS: Neurotransmitter mechanisms in addition to those activating opioid, ß-adrenergic, and metabotropic glutamate 5 receptors also are involved in sacral neuromodulation.

Computational Intelligence for Medical Imaging Simulations.

This paper describes how to simulate medical imaging by computational intelligence to explore areas that cannot be easily achieved by traditional ways, including genes and proteins simulations related to cancer development and immunity. This paper has presented simulations and virtual inspections of BIRC3, BIRC6, CCL4, KLKB1 and CYP2A6 with their outputs and explanations, as well as brain segment intensity due to dancing. Our proposed MapReduce framework with the fusion algorithm can simulate medical imaging. The concept is very similar to the digital surface theories to simulate how biological units can get together to form bigger units, until the formation of the entire unit of biological subject. The M-Fusion and M-Update function by the fusion algorithm can achieve a good performance evaluation which can process and visualize up to 40 GB of data within 600 s. We conclude that computational intelligence can provide effective and efficient healthcare research offered by simulations and visualization.

DNA Cryptography and Deep Learning using Genetic Algorithm with NW algorithm for Key Generation.

Cryptography is not only a science of applying complex mathematics and logic to design strong methods to hide data called as encryption, but also to retrieve the original data back, called decryption. The purpose of cryptography is to transmit a message between a sender and receiver such that an eavesdropper is unable to comprehend it. To accomplish this, not only we need a strong algorithm, but a strong key and a strong concept for encryption and decryption process. We have introduced a concept of DNA Deep Learning Cryptography which is defined as a technique of concealing data in terms of DNA sequence and deep learning. In the cryptographic technique, each alphabet of a letter is converted into a different combination of the four bases, namely; Adenine (A), Cytosine (C), Guanine (G) and Thymine (T), which make up the human deoxyribonucleic acid (DNA). Actual implementations with the DNA don't exceed laboratory level and are expensive. To bring DNA computing on a digital level, easy and effective algorithms are proposed in this paper. In proposed work we have introduced firstly, a method and its implementation for key generation based on the theory of natural selection using Genetic Algorithm with Needleman-Wunsch (NW) algorithm and Secondly, a method for implementation of encryption and decryption based on DNA computing using biological operations Transcription, Translation, DNA Sequencing and Deep Learning.