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ADHD is a prevalent childhood behavioral problem. Early ADHD identification is essential towards addressing the disorder and minimizing its negative impact on school, career, relationships, as well as general well-being. The present ADHD diagnosis relies primarily on an emotional assessment which can be readily influenced by clinical expertise and lacks a basis of objective markers. In this paper, an innovative IoT based ADHD detection is proposed using an EEG signal. To the input EEG signal, the min-max normalization technique is processed. Features are extracted as the subsequent step, where improved fuzzy feature, in which the entropy is estimated to increase the effectiveness of recognizing the vector along with, fractal dimension, wavelet transform and non-linear features are extracted. Also, proposes the new hybrid PUDMO algorithm to select the optimal features from the extracted feature set. Subsequently, the selected features are fed to the proposed hybrid detection system that including IDBN and LSTM classifier to detect whether it is ADHD or not. Further, the weights of both classifiers are tuned optimally as per the hybrid PUDMO algorithm to enhance the detection performance. The PUDMO achieved an accuracy of 0.9649 in the best statistical metric, compared to the SLO's 0.8266, SOA's 0.8201, SMA's 0.8060, BRO's 0.8563, DE's 0.8083, POA's 0.8537, and DMOA's 0.8647, respectively. Thus, the assessments and detection help the clinicians to take appropriate decision.

In the realm of cloud computing, ensuring the dependability and robustness of software systems is paramount. The intricate and evolving nature of cloud infrastructures, however, presents substantial obstacles in the pre-emptive identification and rectification of software anomalies. This study introduces an innovative methodology that amalgamates hybrid optimization algorithms with Neural Networks (NN) to refine the prediction of software malfunctions. The core objective is to augment the purity metric of our method across diverse operational conditions. This is accomplished through the utilization of two distinct optimization algorithms: the Yellow Saddle Goat Fish Algorithm (YSGA), which is instrumental in the discernment of pivotal features linked to software failures, and the Grasshopper Optimization Algorithm (GOA), which further polishes the feature compilation. These features are then processed by Neural Networks (NN), capitalizing on their proficiency in deciphering intricate data patterns and interconnections. The NNs are integral to the classification of instances predicated on the ascertained features. Our evaluation, conducted using the Failure-Dataset-OpenStack database and MATLAB Software, demonstrates that the hybrid optimization strategy employed for feature selection significantly curtails complexity and expedites processing., (© 2024. The Author(s).)

Cloud computing (CC) is a future revolution in the Information technology (IT) and Communication field. Security and internet connectivity are the common major factors to slow down the proliferation of CC. Recently, a new kind of denial of service (DDoS) attacks, known as Economic Denial of Sustainability (EDoS) attack, has been emerging. Though EDoS attacks are smaller at a moment, it can be expected to develop in nearer prospective in tandem with progression in the cloud usage. Here, EfficientNet-B3-Attn-2 fused Deep Quantum Neural Network (EfficientNet-DQNN) is presented for EDoS detection. Initially, cloud is simulated and thereafter, considered input log file is fed to perform data pre-processing. Z-Score Normalization ;(ZSN) is employed to carry out pre-processing of data. Afterwards, feature fusion (FF) is accomplished based on Deep Neural Network (DNN) with Kulczynski similarity. Then, data augmentation (DA) is executed by oversampling based upon Synthetic Minority Over-sampling Technique (SMOTE). At last, attack detection is conducted utilizing EfficientNet-DQNN. Furthermore, EfficientNet-DQNN is formed by incorporation of EfficientNet-B3-Attn-2 with DQNN. In addition, EfficientNet-DQNN attained 89.8% of F1-score, 90.4% of accuracy, 91.1% of precision and 91.2% of recall using BOT-IOT dataset at K-Fold is 9.

The most direct challenge to the healthcare system in Taiwan associated with population aging is the chronic and growing shortfall in medical and nursing practitioners. The integration of artificial intelligence, Internet of Things, and advanced 5G communication technologies is making healthcare more streamlined and intuitive to improve healthcare service efficiency, encourage cross-disciplinary cooperation to improve patient health management, and introduce and mainstream innovative service models such as telecare. To this end, the wearable smart cardiac care system 'Vipasyana', jointly developed by the interdisciplinary research team of the Departments of Electrical Engineering and Computer Science at National Yang Ming Chiao Tung University, not only has an innovative fully functional wireless wearable electrocardiograph but also uses machine learning-based detection/prediction methods to detect and monitor for emergencies such as myocardial infarction, ventricular tachycardia/ventricular fibrillation, and heart failure. To handle the real-time streaming sensing data generated during clinical trials, a big data real-time streaming framework based on Apache Storm and microservices has also been integrated. In addition, a mobile phone app and virtual reality 3D ward round system providing a user-friendly management and monitoring tool for medical staffs were developed and integrated into the system. The 'Vipasyana' system is capable of providing precision medical services to individual patients.

Statement of Problem: Computer-aided design and computer-aided manufacturing (CAD-CAM) technology has greatly improved the efficiency of the fabrication of dental prostheses. However, the design process (CAD stage) is still time-consuming and labor intensive., Purpose: The purpose of this feasibility study was to investigate the accuracy of a novel artificial intelligence (AI) system in designing biomimetic single-molar dental prostheses by comparing and matching them to the natural molar teeth., Material and Methods: A total of 169 maxillary casts were obtained from healthy dentate participants. The casts were digitized, duplicated, and processed with the removal of the maxillary right first molar. A total of 159 pairs of original and processed casts were input into the Generative Adversarial Networks (GANs) for training. In validation, 10 sets of processed casts were input into the AI system, and 10 AI-designed teeth were generated through backpropagation. Individual AI-designed teeth were then superimposed onto each of the 10 original teeth, and the morphological differences in mean Hausdorff distance were measured. True reconstruction was defined as correct matching between the AI-designed and original teeth with the smallest mean Hausdorff distance. The ratio of true reconstruction was calculated as the Intersection-over-Union. The reconstruction performance of the AI system was determined by the Hausdorff distance and Intersection-over-Union., Results: Data of validation showed that the mean Hausdorff distance ranged from 0.441 to 0.752 mm and the Intersection-over-Union of the system was 0.600 (60%)., Conclusions: This study demonstrated the feasibility of AI in designing single-molar dental prostheses. With further training and optimization of algorithms, the accuracy of biomimetic AI-designed dental prostheses could be further enhanced., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Wireless sensor networks (WSNs) rely on mobile anchor nodes (MANs) for network connectivity, data aggregation, and location information. However, MANs' mobility can disrupt energy consumption and network performance. Effective path improvisation algorithms are needed for MANs to optimize energy use, reduce data loss, and maintain network connectivity in dynamic WSN environments. To overcome these issues, Topological Information Embedded Convolutional Neural Network based Lotus Effect Optimization for Path Improvisation of the Mobile Anchors in Wireless Sensor Networks (TIECNN-PIMA-OAC-WSN) was proposed. The approach establishes a robust network setup and energy model, employing TIECNN for initial cluster formation and cluster head selection. The chosen cluster head, termed the Mobile Anchor, undergoes optimization using the Lotus effect optimization algorithm to determine the most efficient and shortest path. This work enhances both the topological information processing and energy efficiency of mobile anchor paths. The simulation outcomes prove the proposed technique attains 33.12%, 39.56%, and 42% higher network lifespan for sensor nodes density 40; 38.22%, 29.66%, and 41.33% higher network lifespan for sensor nodes density 60; 37.45%, 35.55%, and 43.67% higher network lifespan for sensor nodes density 80; 32.45%, 29.45%, and 46.66% higher network lifespan for sensor nodes density 100 analysed to the existing methods.

This manuscript proposes a hybrid method for measuring the battery's dynamic electrical response as it is compressed by an external-force. The proposed hybrid technique is the wrapper of the War Strategy Optimization algorithm and Hierarchical Deep Learning Neural Network, commonly called as WSO-HDLNN technique. The main aim of the proposed method is to lessen the battery-voltage error. The War Strategy Optimization method detects the parameters of the battery method. The Hierarchical Deep Learning Neural Network is used to predict the dynamic-electrical-response of the battery when it deforms during external-force. By using the proposed method, the estimated voltage and measured voltage error are reduced, and identifies the parameter effectively. Finally, the proposed method is done in the MATLAB platform and it is compared with different existing approaches. The error of the proposed method is 4 mV, the Jellyfish Search Optimizer method error is 6 mV, the Heap-based Optimizer method error is 12 mV, and the Grey Wolf Optimizer method error is 14 mV. The proposed method time is 0.7 s The proposed method shows better results in all methods, like Jellyfish Search Optimizer, Heap-based Optimizer, and Grey Wolf Optimizer, The proposed method provides less computation time and error than the existing one is proved from the simulation outcome., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 ISA. Published by Elsevier Ltd. All rights reserved.)

There has been a tremendous increase in the popularity of social media such as blogs, Instagram, twitter, online websites etc. The increasing utilization of these platforms have enabled the users to share information on a regular basis and also publicize social events. Nevertheless, most of the multimedia events are filled with social bots which raise concerns on the authenticity of the information shared in these events. With the increasing advancements of social bots, the complexity of detecting and fact-checking is also increasing. This is mainly due to the similarity between authorized users and social bots. Several researchers have introduced different models for detecting social bots and fact checking. However, these models suffer from various challenges. In most of the cases, these bots become indistinguishable from existing users and it is challenging to extract relevant attributes of the bots. In addition, it is also challenging to collect large scale data and label them for training the bot detection models. The performance of existing traditional classifiers used for bot detection processes is not satisfactory. This paper presents:•A machine learning based adaptive fuzzy neuro model integrated with a hist gradient boosting (HGB) classifier for identifying the persisting pattern of social bots for fake news detection.•And Harris Hawk optimization with Bi-LSTM for social bot prediction.•Results validate the efficacy of the HGB classifier which achieves a phenomenal accuracy of 95.64 % for twitter bot and 98.98 % for twitch bot dataset., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier B.V.)

Importance: Handwriting and the fine motor control (hand and fingers) underlying it are key indicators of numerous motor disorders, especially among children. However, current assessment methods are expensive, slow, and subjective, leading to a lack of knowledge about the relationship between handwriting and motor control., Objective: To develop and validate the iPad precision drawing app Standardized Tracing Evaluation and Grapheme Assessment (STEGA) to enable rapid quantitative assessment of fine motor control and handwriting., Design: Cross-sectional, single-arm observational study., Setting: Academic research institution., Participants: Fifty-seven typically developing right-handed children ages 9 to 12 yr with knowledge of cursive., Outcomes and Measures: Predicted quality, measured as the correlation between handwriting letter legibility (Evaluation Tool of Children's Handwriting-Cursive [ETCH-C]) and predicted legibility (calculated from STEGA's 120 Hz, nine-variable data)., Results: STEGA successfully predicted handwriting (r2 = .437, p < .001) using a support vector regression method. Angular error was the most important aspect of STEGA performance. STEGA was much faster to administer than the ETCH-C (M = 6.7 min, SD = 1.3, versus M = 19.7 min, SD = 5.2)., Conclusions and Relevance: Assessment of motor control (and especially pen direction control) may provide a meaningful, objective way to assess handwriting. Future studies are needed to validate STEGA with a wider age range, but the initial results indicate that STEGA can provide the first rapid, quantitative, high-resolution, telehealth-capable assessment of the motor control that underpins handwriting. What This Article Adds: The ability to control pen direction may be the most important motor skill for successful handwriting. STEGA may provide the first criterion standard for the fine motor control skills that underpin handwriting, suitable for rehabilitation research and practice., (Copyright © 2023 by the American Occupational Therapy Association, Inc.)

Brain tumours are produced by the uncontrolled, and unusual tissue growth of brain. Because of the wide range of brain tumour locations, potential shapes, and image intensities, segmentation of the brain tumour by magnetic resonance imaging (MRI) is challenging. In this research, the deep learning (DL)-enabled brain tumour detection is developed by hybrid optimization method. The pre-processing stage used adaptive Wiener filter for minimizing the noise from input image. After that, the abnormal section of the image is segmented using U-Net. Afterwards, the data augmentation is accomplished to recover the random erasing, brightness, and translation characters. The statistical, shape, and texture features are extracted in feature extraction process. In first-level classification, the abnormal section of the image is sensed as brain tumour or not. Here, the Red Deer Tasmanian Devil Optimization (RDTDO) trained DenseNet is hired for brain tumour detection process. If tumour is identified, then second-level classification provides the brain tumour classification, where deep residual network (DRN)-enabled RDTDO is employed. Furthermore, the system performance is assessed by accuracy, true positive rate (TPR), true negative rate (TNR), positive predictive value (PPV), and negative predictive value (NPV) with the maximum values of 0.947, 0.926, 0.950, 0.937, and 0.926 are attained.

Background: The continuous monitoring of body surface temperature has been proven to help detect potential fever events in hospitalized patients. However, the efficacy of using body surface temperature to detect fever in older adults remains unclear due to the relatively low and slower-to-change body surface temperature in this population., Purpose: This study was designed to investigate 1) the relationship between changes in body surface and routine tympanic temperatures, 2) the correlation between body surface temperature measurement frequency and detection of fever, and 3) the factors related to the incidence of fever in hospitalized older adults., Methods: A prospective study was conducted on 33 hospitalized older adults aged 65 years or older who were suspected to have or diagnosed with an infection in an infectious disease and medical ward at a medical center in southern Taiwan from March to November 2020. Demographic, routine tympanic temperature, and heart rate data were collected by reviewing the participants' medical records. Body surface temperatures were monitored continuously using HEARThermo every 10 seconds until one of the following conditions were met: hospital discharge, no fever for three continuous days, and HEARThermo was removed. Descriptive analysis was used to compare the variations in body surface temperature and routine tympanic temperature measurements. Pearson correlation was used to analyze the correlation between different measurement frequencies and fever events. Finally, mixed effects logistic regression was used to analyze the factors significantly related to fever events., Results: Seven hundred and twenty routine body temperature measurements were taken, with 209 (29.0%) fever events detected in 23 (69.7%) of the participants. The body surface temperatures were more closely correlated with tympanic temperatures during fever events than non-fever events (r = .260, p < .001). More fever events were detected using body surface temperature monitoring frequencies of every 10 seconds and every 1 minute. After controlling for demographic factors, the results of the mixed effect model indicate that body surface temperature and heart rate are significant factors related to fever events in hospitalized older adults (odds ratio, OR: 1.74, p < .001; OR: 1.11, p < .001)., Conclusions / Implications for Practice: The continuous monitoring of body surface temperature may improve the detection of fever events in hospitalized older adults. The application of wearable devices and cloud platforms may further facilitate the real-time assessment and care capabilities of nurses, thus reducing their workload and improving care quality.

Uncontrolled cell growth in the two spongy lung organs in the chest is the most prevalent kind of cancer. When cells from the lungs spread to other tissues and organs, this is referred to as metastasis. This work uses image processing, deep learning, and metaheuristics to identify cancer in its early stages. At this point, a new convolutional neural network is constructed. The predator technique has the potential to increase network architecture and accuracy. Deep learning identified lung cancer spinal metastases in as energy consumption increased CT readings for lung cancer bone metastases decreased. Qualified physicians, on the other hand, discovered 71.14 and 74.60 percent of targets with energies of 140 and 60 keV, respectively, whereas the proposed model gives 76.51 and 81.58 percent, respectively. Expert physicians' detection rate was 74.60 percent lower than deep learning's detection rate of 81.58 percent. The proposed method has the highest accuracy, sensitivity, and specificity (93.4, 98.4, and 97.1 percent, respectively), as well as the lowest error rate (1.6 percent). Finally, in lung segmentation, the proposed model outperforms the CNN model. High-intensity energy-spectral CT images are more difficult to segment than low-intensity energy-spectral CT images., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2023 Vinay Kumar et al.)

Objective: This study aimed to evaluate intercellular bridges in the depth of invasion (DOI) measurement area as prognostic factors in oral squamous cell carcinoma (OSCC)., Study Design: The mode of invasion was determined based on the Yamamoto-Kohama classification system by observing the hematoxylin-eosin-stained whole-slide images of specimens obtained from 78 patients with OSCC, and the clinicopathologic features were characterized. The presence of intercellular bridges was analyzed in 46 patients with Yamamoto-Kohama classification grade ≥3 whose DOI was measured by dividing the measurement area into 3 parts: the surface, center, and front of the tumor., Results: Univariate analyses identified lymph node metastasis, loss of intercellular bridges in the DOI measurement area, DOI of ≥4500 µm, and pattern of invasion 4C-4D as negative prognostic factors. Multivariate analyses revealed that lymph node metastasis and the loss of intercellular bridges in the entire area were independent factors, with hazard ratios of 9.34 (95% confidence interval, 2.09-42.03; P = .003) and 3.64 (95% confidence interval, 1.10-11.99; P = .045), respectively., Conclusions: Loss of intercellular bridges in the DOI measurement area is a negative prognostic factor for OSCC and may be useful in selecting treatment., (Copyright © 2022 Elsevier Inc. All rights reserved.)

Introduction: Heart disease is emerging as the single most critical cause of death worldwide and is one of the costliest chronic conditions., Purpose: Stimulated by the increasing heart disease mortality rate incidents, an effective, low-cost, and reliable heart disease risk evaluation model is developed using significant non-invasive risk attributes. The significant non-invasive risk attributes like (age, systolic BP, diastolic BP, BMI, hereditary factor, smoking, alcohol, and physical inactivity) are identified by the help of medical domain experts, and their reliability in heart disease prediction is investigated through different feature selection techniques. Methodology . The enhancements of applying specific investigated techniques like random forest, Naïve Bayes, decision tree, support vector machine, and K nearest neighbor to the risk factors are tested. The heart disease risk assessment model is developed using the Jupyter Notebook web application, and its performance is tested not only through medical domain measures but also through the model performance measures. Findings . To evaluate heart disease risk evaluation model, we calculated measures of discrimination like error rate, AUROC, sensitivity, specificity, accuracy, precision, and so on. Experimental results show that the random forest heart disease risk evaluation model outperforms other existing risk models with admirable predictive accuracy and minimum misclassification rate. Originality. The heart disease risk evaluation model is developed based on novel non-invasive heart disease dataset, which consists of 5776 records. This dataset is collected from different heterogeneous data sources of Kashmir (India) through quantitative data collection methods. Research Implications. The risk model is applicable where people lack the facilities of integrated primary medical care technologies for untimely heart disease risk prediction. Future Work. To investigate deep learning and study the significance of other controlled attributes on different age and sex groups in the risk estimation of heart disease., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Syed Immamul Ansarullah et al.)

Time-continuous dimensional descriptions of emotions (e.g., arousal, valence) allow researchers to characterize short-time changes and to capture long-term trends in emotion expression. However, continuous emotion labels are generally not synchronized with the input speech signal due to delays caused by reaction-time, which is inherent in human evaluations. To deal with this challenge, we introduce a new convolutional neural network ( multi-delay sinc network ) that is able to simultaneously align and predict labels in an end-to-end manner. The proposed network is a stack of convolutional layers followed by an aligner network that aligns the speech signal and emotion labels. This network is implemented using a new convolutional layer that we introduce, the delayed sinc layer . It is a time-shifted low-pass (sinc) filter that uses a gradient-based algorithm to learn a single delay. Multiple delayed sinc layers can be used to compensate for a non-stationary delay that is a function of the acoustic space. We test the efficacy of this system on two common emotion datasets, RECOLA and SEWA, and show that this approach obtains state-of-the-art speech-only results by learning time-varying delays while predicting dimensional descriptors of emotions.

Purpose: To identify the nursing care problems related to the clinical process of disease by COVID-19., Method: The study applied the taxonomic triangulation technique on a clinical management guide to coronavirus disease, COVID-19, from the World Health Organization. The technique is divided into the phases: extraction of knowledge in natural language about assessment, planning and intervention, translation into standard language NOC and NIC, linking to NANDA-I diagnoses, triangulation looking for diagnostic matches in the three sets, and, finally, validation by a panel of experts from a hospital and a university., Findings: The extraction identified 159 terms in natural language that were translated into 173 variables: 34 NOC for assessment, 19 NOC for planning, and 120 NIC for intervention. The relationships to NANDA-I diagnoses recorded 2,182 links and the triangulation returned 109 diagnoses, 54 of them for a critical situation. The panel of experts unanimously validated the 29 diagnoses with the highest number of links., Conclusion: Coronavirus disease, COVID-19, involves a complex situation with multiple associated care problems that can be identified using the taxonomic triangulation technique., Implications for Nursing Practice: The links between taxonomies and the taxonomic triangulation technique are an important tool for generating knowledge. The results of this study may guide the diagnosis and treatment of coronavirus disease, COVID-19, as well as similar processes that occur with acute respiratory distress syndrome., (© 2020 NANDA International, Inc.)

Purpose: The purpose of this study was to explore what motivates family members to visit a relative with dementia who has been transferred to a nursing home in Taiwan., Design and Methods: Data were collected for this qualitative descriptive study using audiotaped, semi-structured, in-depth, face-to-face interviews. A total of 20 family members of elderly nursing home residents participated in the study. Nursing home residents were from four nursing homes in Taiwan and had been diagnosed with probable or possible dementia by a psychiatrist or neurologist. Transcribed audiotaped interviews were analyzed using thematic analysis., Findings: Most family members were the children of the residents (n =17, 85%). The theme describing the core motivation for family members' visits to nursing home residents was "to maintain the unforgotten family affection." This motivation comprised four relevant categories: hoping to slow degeneration, providing a congruous environment, honoring filial and karmic duty, and ensuring the quality of care., Conclusions: Motivations for Taiwanese family members' visits to nursing home residents with dementia were similar to those in Western cultures. However, "hoping to slow degeneration" and "providing a congruous environment" were unique categories., Clinical Relevance: Nurses and policymakers could use these findings to design interventions that might increase holistic care for both family members and nursing home residents with dementia. Providing programming focused on family members' unique priorities could address swallowing difficulties, management of dementia symptoms, nutritional needs, and selection of residents' roommates. These programs could improve the quality of family members' visits as well as the quality of staff-family relationships., (© 2020 Sigma Theta Tau International.)

The aim of the study was to identify the organized ventricular fibrillation (VF) activity in the dog heart and characterize its quantitative parameters during prolonged heart perfusion., Materials and Methods: A total of four experiments on isolated dog's hearts perfused with the blood of a supporting dog were performed. Episodes of VF were recorded in the form of an electrogram followed by a spectral analysis by the fast Fourier transform in the range of 0.5-15 Hz. The frequency, spectral power (amplitude), and relative weight (%) of the 1 st , 2 nd , and 3 rd highest amplitude oscillations were determined (frequency - mode; amplitude, relative weight - M±SEM; n=120)., Results: In the perfused dog heart, VF was characterized by an organized activity, as evidenced by the dominant structure of the oscillation frequencies. Oscillations with a frequency of 9-10 Hz (occurring in 1/10 of the 0.5-15 Hz range) represent 42-44% of the spectral power and dominate the structure of the oscillation frequencies. The frequency and spectral power of the dominating oscillations proved to be stable thus indicating that under perfusion, VF did not cause disturbances in the heart organized activity., Conclusion: Our experimentation with isolated perfused hearts revealed the patterns of VF that could not be revealed in situ under conditions complicated by nerve factors and ischemia in VF. The results of the work are protected with a patent which is applicable for VF diagnosis in implantable defibrillators.

Reduction of the under-5 mortality rate is a target of the Sustainable Development Goals. Therefore, this study aimed to estimate under-5 child mortality rates in 52 low-migration countries using population data. The study utilized population data from the US Census Bureau from 1990 to 2015. The method involved first estimating mortality rates for countries with negligible net migration and then applying these rates to countries with matching mortality profiles, where it is reasonable to assume that migration is negligible for children under the age of 5 years. The highest child mortality was concentrated in the African region, followed by Asia and the Western region. However, steady progress in child mortality trends was concentrated in low-income countries. This simple method demonstrated that child mortality has significantly improved in high-income countries, followed by middle- and low-income countries. To reduce the under-5 mortality rates even further in these 52 countries, there is a need to accelerate equitable plans and policies related to child health to promote children's longevity and survival., Competing Interests: Conflict of interest No existing or potential conflict of interest relevant to this article was reported., (Copyright © 2020 Korean Academy of Child Health Nursing.)

Problem: Technology can transform health care; future physicians need to keep pace to ensure optimal patient care. Because future doctors are poorly prepared in computer literacy, the authors designed a computer programming certificate course. This Innovation Report describes the course and findings from a qualitative study to understand the ways it prepares medical students to use computing science and technology in medicine., Approach: The 14-month Computing for Medicine certificate course (C4M, offered beginning in February 2016), University of Toronto, is comprised of hands-on workshops to introduce programming accompanied by homework exercises, seminars by computer science experts on the application of programming to medicine, and coding projects. Using purposive and maximal variation sampling, 17 students who completed the course were interviewed from April-May 2017. Thematic analysis was performed using an iterative constant comparison approach., Outcomes: Participants praised the C4M as an opportunity to achieve computer literacy-including language, syntax, and fundamental computational ideas (and their application to medicine)-and acquire or strengthen algorithmic and logical thinking skills for approaching problems. They highlighted that the course illustrated linkages between computer science and medicine. Participants acknowledged a sometimes-existent chasm between producers and users of technology in medicine, recommending two-way communication between the disciplines when developing technology for use in medicine., Next Steps: We recommend that medical schools consider computer literacy an essential skill to foster future collaborative computing partnerships for improved technology use by physicians and optimal patient care. We encourage further evaluation of future iterations of the C4M and similar courses.

Background: There is a suicide on the British railways every 36 hours. However, the reasons why people choose to die by train are not well understood.AimsTo explore factors influencing and discouraging the decision to attempt suicide on the railway networks., Method: We conducted an online survey and qualitative interviews with individuals who had contemplated or attempted suicide by train., Results: A total of 353 survey responders had considered and 23 had attempted suicide at rail locations (including railways and metro/underground); a third of these cases were impulsive. The most frequently reported motivations for contemplating or attempting suicide were perceptions of quick and certain lethality (54 and 37%, respectively) and easy access to rail settings (33 and 38%, respectively). The main factor discouraging people from rail suicide was its wider impact, especially on train drivers (19%). In qualitative interviews (N = 34) the desire to avoid intervention from others was also a common motivating factor for attempting suicide on the railway networks., Conclusions: People attempt suicide by train because railway settings are easy to access and because of an inaccurate perception of certain and quick lethality. Tackling exaggerated perceptions of lethality may help reduce suicides by train.Declaration of interestNone.

Second-generation DNA sequencing techniques generate short reads that can result in fragmented genome assemblies. Third-generation sequencing platforms mitigate this limitation by producing longer reads that span across complex and repetitive regions. However, the usefulness of such long reads is limited because of high sequencing error rates. To exploit the full potential of these longer reads, it is imperative to correct the underlying errors. We propose HECIL-Hybrid Error Correction with Iterative Learning-a hybrid error correction framework that determines a correction policy for erroneous long reads, based on optimal combinations of decision weights obtained from short read alignments. We demonstrate that HECIL outperforms state-of-the-art error correction algorithms for an overwhelming majority of evaluation metrics on diverse, real-world data sets including E. coli, S. cerevisiae, and the malaria vector mosquito A. funestus. Additionally, we provide an optional avenue of improving the performance of HECIL's core algorithm by introducing an iterative learning paradigm that enhances the correction policy at each iteration by incorporating knowledge gathered from previous iterations via data-driven confidence metrics assigned to prior corrections.

Objectives: To establish a possible relation of dependency between pulmonary hypertension (PHT) and several factors, with the evaluation of their predictive potential, in Graves' disease., Methods: For identifying the factors implied in producing PHT and for evaluating its reversibility, we made echocardiography exams, sessions of monitoring the blood pressure during 24 hours and biological test in a group of 42 patients with Graves' disease (group H), comparing them with themselves in a euthyroid status (group E, n=25) and with a control group (group C, n=25). In order to analyse the relation of dependency between pulmonary hypertension (PHT) and the factors identified in the H group, we used both the simple linear regression method (polynomial of degree 1) and the non-linear regression method (polynomial of degree 2, 3) for establishing one model of functional dependency. We used the values of the coefficients of correlation r (degree of dependency) and of determination R 2 (the type of dependency). The statistical test (F-test, AIC criterion, test t) was applied by choosing the most appropriate model of determination, with a higher predictive potential., Results: We identified PHT at 47.6% of the patients with Graves' disease. Once the euthyroidism status is obtained, PHT is normalized. While inducing PHT, we identified a strong relationship of dependency on several possible new factors such as: pre-treatment period, age, level of the thyroid stimulating hormone receptor antibody and values of systolic blood pressure, besides the already known ones (high level of thyroids hormones, cardiac output, pulmonary vascular resistance)., Conclusions: The non-linear model best explains the relation of determination between pulmonary pressure and those factors having a better predictive potential (from 51% to 90%), compared with the linear model, the only exception being the age factor and the systolic blood pressure, where both models seems to be appropriate.

Purpose: To provide an overview of lessons learned during the development process of an app for iOS and Android based on national recommendations for providing quality family planning services., Study and Design: After a review of existing apps was conducted to determine whether an app of clinical recommendations for family planning existed, a team of clinicians, training specialists, and app developers created a resource app by first drafting a comprehensive content map. A prototype of the app was then pilot tested using smart tablets by a volunteer convenience sample of women's healthcare professionals. Outcomes measured included usability, acceptability, download analytics, and satisfaction by clinicians as reported through an investigator-developed tool., Results: Sixty-nine professionals tested a prototype of the app, and completed a user satisfaction tool. Overall, user feedback was positive, and a zoom function was added to the final version as a result of the pilot test. Within 3 months of being publicly available, the app was downloaded 677 times, with 97% of downloads occurring on smart phones, 76% downloads occurring on iOS devices, and 24% on Android devices. This trend persisted throughout the following 3 months., Clinical Implications: Clinicians with an interest in developing an app should consider a team approach to development, pilot test the app prior to wider distribution, and develop a web-based version of the app to be used by clinicians who are unable to access smart devices in their practice setting.

Background: Clinical decision support (CDS) tools-with easily understood and actionable information, at the point of care-are needed to help registered nurses (RNs) make evidence-based decisions. Not clear are the optimal formats of CDS tools. Thorough, preclinical testing is desirable to avoid costly errors associated with premature implementation in electronic health records., Objective: The aims of this study were to determine feasibility of the protocol designed to compare multiple CDS formats and evaluate effects of numeracy and graph literacy on RN adoption of best practices and care planning time in a simulated environment., Methods: In this pilot study, 60 RNs were randomly assigned to one of four CDS conditions (control, text, text + graph, and text + table) and asked to adjust the plan of care for two patient scenarios over three shifts. Fourteen best practices were identified for the two patients and sent as suggestions with evidence to the three CDS groups. Best practice adoption rates, care planning time, and their relationship to the RN's numeracy and graph literacy scores were assessed., Results: CDS groups had a higher adoption rate of best practices (p < .001) across all shifts and decreased care planning time in shifts 2 (p = .01) and 3 (p = .02) compared with the control group. Higher numeracy and graph literacy were associated with shorter care planning times under text + table (p = .05) and text + graph (p = .01) conditions. No significant differences were found between the three CDS groups on adoption rate and care planning time., Discussion: This pilot study shows the feasibility of our protocol. Findings show preliminary evidence that CDS improves the efficiency and effectiveness of care planning decisions and that the optimal format may depend on individual RN characteristics. We recommend a study with sufficient power to compare different CDS formats and assess the impact of potential covariates on adoption rates and care planning time.

In bioinformatics, multiple sequence alignment (MSA) is an NP-hard problem. Hence, nature-inspired techniques can better approximate the solution. In the current study, a novel biogeography-based optimization (NBBO) is proposed to solve an MSA problem. The biogeography-based optimization (BBO) is a new paradigm for optimization. But, there exists some deficiencies in solving complicated problems such as low population diversity and slow convergence rate. NBBO is an enhanced version of BBO, in which, a new migration operation is proposed to overcome the limitations of BBO. The new migration adopts more information from other habitats, maintains population diversity, and preserves exploitation ability. In the performance analysis, the proposed and existing techniques such as VDGA, MOMSA, and GAPAM are tested on publicly available benchmark datasets (ie, Bali base). It has been observed that the proposed method shows the superiority/competitiveness with the existing techniques., Competing Interests: Authors disclose no potential conflicts of interest.

Purpose: Prior text analysis of R01 critiques suggested that female applicants may be disadvantaged in National Institutes of Health (NIH) peer review, particularly for renewals. NIH altered its review format in 2009. The authors examined R01 critiques and scoring in the new format for differences due to principal investigator (PI) sex., Method: The authors analyzed 739 critiques-268 from 88 unfunded and 471 from 153 funded applications for grants awarded to 125 PIs (76 males, 49 females) at the University of Wisconsin-Madison between 2010 and 2014. The authors used seven word categories for text analysis: ability, achievement, agentic, negative evaluation, positive evaluation, research, and standout adjectives. The authors used regression models to compare priority and criteria scores, and results from text analysis for differences due to PI sex and whether the application was for a new (Type 1) or renewal (Type 2) R01., Results: Approach scores predicted priority scores for all PIs' applications (P < .001), but scores and critiques differed significantly for male and female PIs' Type 2 applications. Reviewers assigned significantly worse priority, approach, and significance scores to female than male PIs' Type 2 applications, despite using standout adjectives (e.g., "outstanding," "excellent") and making references to ability in more critiques (P < .05 for all comparisons)., Conclusions: The authors' analyses suggest that subtle gender bias may continue to operate in the post-2009 NIH review format in ways that could lead reviewers to implicitly hold male and female applicants to different standards of evaluation, particularly for R01 renewals.

Background: Active research is being conducted to determine the prognosis for breast cancer. However, the uncertainty is a major obstacle in this domain of medical research. In that context, explanation-aware computing has the potential for providing meaningful interactions between complex medical applications and users, which would ensure a significant reduction of uncertainty and risks. This paper presents an explanation-aware agent, supported by Intensive Knowledge-Distributed Case-Based Reasoning Classifier (IK-DCBRC), to reduce the uncertainty and risks associated with the diagnosis of breast cancer., Methods: A meaningful explanation is generated by inferring from a rule-based system according to the level of abstraction and the reasoning traces. The computer-aided detection is conducted by IK-DCBRC, which is a multi-agent system that applies the case-based reasoning paradigm and a fuzzy similarity function for the automatic prognosis by the class of breast tumors, i.e. malignant or benign, from a pattern of cytological images., Results: A meaningful interaction between the physician and the computer-aided diagnosis system, IK-DCBRC, is achieved via an intelligent agent. The physician can observe the trace of reasoning, terms, justifications, and the strategy to be used to decrease the risks and doubts associated with the automatic diagnosis. The capability of the system we have developed was proven by an example in which conflicts were clarified and transparency was ensured., Conclusion: The explanation agent ensures the transparency of the automatic diagnosis of breast cancer supported by IK-DCBRC, which decreases uncertainty and risks and detects some conflicts.

Statement of Problem: It is not clear whether newly introduced cordless displacement systems are better able to manage gingiva than conventional systems., Purpose: The purpose of this in vivo study was to evaluate the gingival management ability of 4 different displacement methods with a standardized subgingival preparation finish line., Material and Methods: The effects of 4 displacement techniques on gingival management and impression quality were evaluated by means of 6 evaluation criteria. A subgingival preparation finish line of between 1 and 2 mm was ensured, and the buccal aspects of 252 (n=63) teeth were clinically assessed for ease of application, time spent, bleeding, remnants, and dilatation. The complete reproduction of the preparation finish line and the bubble and void formations on polyether impressions were also evaluated. The data were statistically analyzed with the χ(2) test (α=.05). The Bonferroni correction was used to control Type I error for the pairwise comparison groups (α=.008)., Results: Statistically significant differences were found for all criteria among the groups (P<.05). The nonimpregnated displacement cord group was the least effective group in terms of bleeding and impression quality (P<.008). The aluminum chloride impregnated cord group and the displacement paste with cap group were found to be comparable in terms of remnants, dilatation, and impression quality (P>.008). The retraction cap with paste group showed better results for ease of application, time spent, and bleeding than the aluminum chloride impregnated cord group (P<.008). Although the group with aluminum chloride impregnated cord, displacement paste, and cap showed better results for dilatation, it was time consuming and difficult (P<.008)., Conclusions: Except for the nonimpregnated cord group, all of the groups were comparable and clinically useful, with perfect or acceptable impression qualities., (Copyright © 2014 Editorial Council for the Journal of Prosthetic Dentistry. Published by Elsevier Inc. All rights reserved.)

The primary aim of this study is to determine maximum bite force in molar and incisor regions of healthy individuals, to evaluate the bite force after open reduction and internal fixation of mandibular fractures using micro plates, for a period of up to 6 weeks and to determine the rate of recovery of maximum bite force in an Indian population.