Maize leaf disease recognition using PRF-SVM integration: a breakthrough technique.

The difficulty of collecting maize leaf lesion characteristics in an environment that undergoes frequent changes, suffers varying illumination from lighting sources, and is influenced by a variety of other factors makes detecting diseases in maize leaves difficult. It is critical to monitor and identify plant leaf diseases during the initial growing period to take suitable preventative measures. In this work, we propose an automated maize leaf disease recognition system constructed using the PRF-SVM model. The PRFSVM model was constructed by combining three powerful components: PSPNet, ResNet50, and Fuzzy Support Vector Machine (Fuzzy SVM). The combination of PSPNet and ResNet50 not only assures that the model can capture delicate visual features but also allows for end-to-end training for smooth integration. Fuzzy SVM is included as a final classification layer to accommodate the inherent fuzziness and uncertainty in real-world image data. Five different maize crop diseases (common rust, southern rust, grey leaf spot, maydis leaf blight, and turcicum leaf blight along with healthy leaves) are selected from the Plant Village dataset for the algorithm's evaluation. The average accuracy achieved using the proposed method is approximately 96.67%. The PRFSVM model achieves an average accuracy rating of 96.67% and a mAP value of 0.81, demonstrating the efficacy of our approach for detecting and classifying various forms of maize leaf diseases.

A 3D Finite Element Analysis of biomechanical effects on teeth and bone during true intrusion of posteriors using miniscrews.

OBJECTIVE: The primary objective of this study was to investigate the biomechanical effects and stresses on bone, PDL, cementum and displacement along X-,Y- and Z-axis during true intrusion of molars using mini-implants with finite element analysis; the secondary objective of the study was to find out the best method for posterior intrusion in clinical practice. MATERIAL AND METHODS: A 3D finite element method was used to simulate true molar intrusion using sliding mechanics. Two groups were made, with mini-implants placed on buccal side and palatal side with a cap splint for MODEL1, and a single mini-implant placed buccally with transpalatal arch (TPA) for MODEL2. The material characteristics which include the Young's modulus and Poison's ratio were assigned. von Mises stress, principal stress on PDL and alveolar bone, displacements in all the 3 planes were determined. RESULTS: Bone stress patterns showed compressive stresses on the buccal aspect and tensile stresses on the palatal aspect for both MODELS. Stresses in the PDL and cementum were mainly concentrated in the apex region, with a more uniform distribution of stresses for MODEL 1. Tooth displacement showed true intrusion for both MODELS, i.e. the Z axis, and a more controlled buccal tipping for MODEL 1. CONCLUSION: Of the modalities compared, the best controlled tooth movements for posterior intrusion in the treatment of open bite were obtained with mini-implants placed with a cap splint (MODEL 1).

Developing an SDN security model (EnsureS) based on lightweight service path validation with batch hashing and tag verification.

Software-defined networking (SDN) has significantly transformed the field of network management through the consolidation of control and provision of enhanced adaptability. However, this paradigm shift has concurrently presented novel security concerns. The preservation of service path integrity holds significant importance within SDN environments due to the potential for malevolent entities to exploit network flows, resulting in a range of security breaches. This research paper introduces a model called "EnsureS", which aims to enhance the security of SDN by proposing an efficient and secure service path validation approach. The proposed approach utilizes a Lightweight Service Path Validation using Batch Hashing and Tag Verification, focusing on improving service path validation's efficiency and security in SDN environments. The proposed EnsureS system utilizes two primary techniques in order to validate service pathways efficiently. Firstly, the method utilizes batch hashing in order to minimize computational overhead. The proposed EnsureS algorithm enhances performance by aggregating packets through batches rather than independently; the hashing process takes place on each one in the service pathway. Additionally, the implementation of tag verification enables network devices to efficiently verify the authenticity of packets by leveraging pre-established trust relationships. EnsureS provides a streamlined and effective approach for validating service paths in SDN environments by integrating these methodologies. In order to assess the efficacy of the Proposed EnsureS, a comprehensive series of investigations were conducted within a simulated SDN circumstance. The efficacy of Proposed EnsureS was then compared to that of established methods. The findings of our study indicate that the proposed EnsureS solution effectively minimizes computational overhead without compromising on the established security standards. The implementation successfully reduces the impact of different types of attacks, such as route alteration and packet spoofing, increasing SDN networks' general integrity.

Risk of Sudden Death in Patients With RASopathy Hypertrophic Cardiomyopathy.

BACKGROUND: Genetic defects in the RAS/mitogen-activated protein kinase pathway are an important cause of hypertrophic cardiomyopathy (RAS-HCM). Unlike primary HCM (P-HCM), the risk of sudden cardiac death (SCD) and long-term survival in RAS-HCM are poorly understood. OBJECTIVES: The study's objective was to compare transplant-free survival, incidence of SCD, and implantable cardioverter-defibrillator (ICD) use between RAS-HCM and P-HCM patients. METHODS: In an international, 21-center cohort study, we analyzed phenotype-positive pediatric RAS-HCM (n = 188) and P-HCM (n = 567) patients. The between-group differences in cumulative incidence of all outcomes from first evaluation were compared using Gray's tests, and age-related hazard of all-cause mortality was determined. RESULTS: RAS-HCM patients had a lower median age at diagnosis compared to P-HCM (0.9 years [IQR: 0.2-5.0 years] vs 9.8 years [IQR: 2.0-13.9 years], respectively) (P < 0.001). The 10-year cumulative incidence of SCD from first evaluation was not different between RAS-HCM and P-HCM (4.7% vs 4.2%, respectively; P = 0.59). The 10-year cumulative incidence of nonarrhythmic deaths or transplant was higher in RAS-HCM compared with P-HCM (11.0% vs 5.4%, respectively; P = 0.011). The 10-year cumulative incidence of ICD insertions, however, was 5-fold lower in RAS-HCM compared with P-HCM (6.9% vs 36.6%; P < 0.001). Nonarrhythmic deaths occurred primarily in infancy and SCD primarily in adolescence. CONCLUSIONS: RAS-HCM was associated with a higher incidence of nonarrhythmic death or transplant but similar incidence of SCD as P-HCM. However, ICDs were used less frequently in RAS-HCM compared to P-HCM. In addition to monitoring for heart failure and timely consideration of advanced heart failure therapies, better risk stratification is needed to guide ICD practices in RAS-HCM.

Safety and effectiveness of intranasal esketamine for treatment-resistant depression: a real-world retrospective study.

Aim: There is limited real-world evidence for patients with treatment-resistant depression (TRD) receiving esketamine nasal spray. Methods: This retrospective cohort study used data collected from a psychiatric clinic's EHR system. Results: A total of 171 TRD patients received esketamine July 2019-June 2021. This predominantly female, white population had several mental health comorbidities and high exposure to psychiatric medications. We observed significant reductions (p < 0.001) in average PHQ-9 and GAD-7 scores from baseline (PHQ-9: mean: 16.7; SD: 5.8; GAD-7: mean: 12.0; SD: 5.8) to last available treatment (PHQ-9: mean: 12.0; SD: 6.4; GAD-7: mean: 8.7; SD: 5.6). There were no reports of serious adverse events. Conclusion: This study found a significant disease burden for patients with TRD. Esketamine appears to be well tolerated and effective in improving depression and anxiety.

Real-world depression, anxiety and safety outcomes of intramuscular ketamine treatment: a retrospective descriptive cohort study.

BACKGROUND: Ketamine has emerged as a promising pharmacotherapy for depression and other mental illnesses, and the intramuscular (IM) administration of ketamine is now offered at many North American outpatient psychiatric clinics. However, a characterization of the outpatient population receiving IM ketamine treatment and an evaluation of the real-world depression, anxiety, and safety outcomes of long-term psychiatric IM ketamine treatment has not been reported. This study aimed to evaluate the clinical characteristics, treatment patterns, clinical outcomes, and adverse events of patients receiving IM ketamine treatment. METHODS: Patient data from the electronic health records of a private outpatient psychiatric clinic network in the United States were collected and analyzed retrospectively. Adults with any psychiatric diagnosis who received ketamine treatment only by IM administration from January 2018 to June 2021 were included. A total of 452 patients were included in the cohort. RESULTS: Patients receiving IM ketamine treatment had a mean of 2.8 (SD 1.4) psychiatric diagnoses. 420 (93%) patients had a diagnosis of major depressive disorder, 243 (54%) patients had a diagnosis of generalized anxiety disorder, and 126 (28%) patients had a diagnosis of post-traumatic stress disorder. Patients received a median of 4 (range 1-48) IM ketamine treatments. Median depression scores (PHQ-9) improved 38% from 16.0 (IQR 11.3-21.8) at baseline to 10.0 (IQR 6.0-15.0) at last treatment (p < .001). Median anxiety scores (GAD-7) improved 50% from 14.0 (IQR 8.0-17.0) at baseline to 7.0 (IQR 4.3-11.8) at last treatment (p < .001). With maintenance ketamine treatments, average improvements in depression (PHQ-9) and anxiety (GAD-7) scores of at least 4.7 and 4.9 points were maintained for over 7 months. An adverse event occurred during 59 of 2532 treatments (2.3%). CONCLUSIONS: IM ketamine is being utilized to treat psychiatric outpatients with multiple mental illnesses not limited to depression. Average depression and anxiety levels significantly improve throughout IM ketamine treatment and do not regress to baseline during patients' maintenance treatment phase. Prospective studies are recommended to confirm the long-term effectiveness and safety of IM ketamine.

A Hybrid Intrusion Detection Model Using EGA-PSO and Improved Random Forest Method.

Due to the rapid growth in IT technology, digital data have increased availability, creating novel security threats that need immediate attention. An intrusion detection system (IDS) is the most promising solution for preventing malicious intrusions and tracing suspicious network behavioral patterns. Machine learning (ML) methods are widely used in IDS. Due to a limited training dataset, an ML-based IDS generates a higher false detection ratio and encounters data imbalance issues. To deal with the data-imbalance issue, this research develops an efficient hybrid network-based IDS model (HNIDS), which is utilized using the enhanced genetic algorithm and particle swarm optimization(EGA-PSO) and improved random forest (IRF) methods. In the initial phase, the proposed HNIDS utilizes hybrid EGA-PSO methods to enhance the minor data samples and thus produce a balanced data set to learn the sample attributes of small samples more accurately. In the proposed HNIDS, a PSO method improves the vector. GA is enhanced by adding a multi-objective function, which selects the best features and achieves improved fitness outcomes to explore the essential features and helps minimize dimensions, enhance the true positive rate (TPR), and lower the false positive rate (FPR). In the next phase, an IRF eliminates the less significant attributes, incorporates a list of decision trees across each iterative process, supervises the classifier's performance, and prevents overfitting issues. The performance of the proposed method and existing ML methods are tested using the benchmark datasets NSL-KDD. The experimental findings demonstrated that the proposed HNIDS method achieves an accuracy of 98.979% on BCC and 88.149% on MCC for the NSL-KDD dataset, which is far better than the other ML methods i.e., SVM, RF, LR, NB, LDA, and CART.

A case series of group-based ketamine-assisted psychotherapy for patients in residential treatment for eating disorders with comorbid depression and anxiety disorders.

BACKGROUND: Depression and anxiety outcome measures, safety/tolerability, patient satisfaction, and ease of implementation of group-based ketamine-assisted psychotherapy (G-KAP) delivered to patients in intensive residential eating disorder (ED) treatment were assessed. CASE PRESENTATION: This study reports on five participants with a diagnosis of an ED and comorbid mood and anxiety disorders who received weekly intramuscular ketamine injections in a group setting over 4 weeks. Measures of anxiety (GAD-7) and depression (PHQ-9) were administered pre-dose, 4-h post-dose, and 24-h post dose. Four of the 5 participants experienced clinically significant improvements on the PHQ-9 score (i.e., change greater than 5) while 2 of the 5 participants experienced clinically significant improvements on the GAD-7 score (i.e., change greater than 4) from pre-dose to 24-h post-dose after the last ketamine session. Dosing sessions were well tolerated, and no serious adverse events were reported. Clinical observations and participant reports corroborated improvements in depression and anxiety symptoms, good tolerability of ketamine treatment, and practical implementation of the G-KAP protocol in a residential ED treatment center. CONCLUSIONS: This study suggests the potential utility of G-KAP as an adjunct to intensive, specialized ED treatment. Overall, this novel, cross-diagnostic intervention warrants future research to further explore its appropriateness in a treatment setting.

Ketamine as a Novel Psychopharmacotherapy for Eating Disorders: Evidence and Future Directions.

Eating disorders (EDs) are serious, life-threatening psychiatric conditions associated with physical and psychosocial impairment, as well as high morbidity and mortality. Given the chronic refractory nature of EDs and the paucity of evidence-based treatments, there is a pressing need to identify novel approaches for this population. The noncompetitive N-methyl-D-aspartate receptor (NMDAr) antagonist, ketamine, has recently been approved for treatment-resistant depression, exerting rapid and robust antidepressant effects. It is now being investigated for several new indications, including obsessive-compulsive, post-traumatic, and substance use disorder, and shows transdiagnostic potential for EDs, particularly among clinical nonresponders. Hence, the aim of this review is to examine contemporary findings on the treatment of EDs with ketamine, whether used as a primary, adjunctive, or combination psychopharmacotherapy. Avenues for future research are also discussed. Overall, results are encouraging and point to therapeutic value; however, are limited to case series and reports on anorexia nervosa. Further empirical research is thus needed to explore ketamine efficacy across ED subgroups, establish safety profiles and optimize dosing, and develop theory-driven, targeted treatment strategies at the individual patient level.

An Exploration: Alzheimer's Disease Classification Based on Convolutional Neural Network.

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field.

Classification of Alzheimer's Disease Using Gaussian-Based Bayesian Parameter Optimization for Deep Convolutional LSTM Network.

Alzheimer's disease (AD) is one of the most important causes of mortality in elderly people, and it is often challenging to use traditional manual procedures when diagnosing a disease in the early stages. The successful implementation of machine learning (ML) techniques has also shown their effectiveness and its reliability as one of the better options for an early diagnosis of AD. But the heterogeneous dimensions and composition of the disease data have undoubtedly made diagnostics more difficult, needing a sufficient model choice to overcome the difficulty. Therefore, in this paper, four different 2D and 3D convolutional neural network (CNN) frameworks based on Bayesian search optimization are proposed to develop an optimized deep learning model to predict the early onset of AD binary and ternary classification on magnetic resonance imaging (MRI) scans. Moreover, certain hyperparameters such as learning rate, optimizers, and hidden units are to be set and adjusted for the performance boosting of the deep learning model. Bayesian optimization enables to leverage advantage throughout the experiments: A persistent hyperparameter space testing provides not only the output but also about the nearest conclusions. In this way, the series of experiments needed to explore space can be substantially reduced. Finally, alongside the use of Bayesian approaches, long short-term memory (LSTM) through the process of augmentation has resulted in finding the better settings of the model that too in less iterations with an relative improvement (RI) of 7.03%, 12.19%, 10.80%, and 11.99% over the four systems optimized with manual hyperparameters tuning such that hyperparameters that look more appealing from past data as well as the conventional techniques of manual selection.

Deep learning approach for diabetes prediction using PIMA Indian dataset.

PURPOSE: International Diabetes Federation (IDF) stated that 382 million people are living with diabetes worldwide. Over the last few years, the impact of diabetes has been increased drastically, which makes it a global threat. At present, Diabetes has steadily been listed in the top position as a major cause of death. The number of affected people will reach up to 629 million i.e. 48% increase by 2045. However, diabetes is largely preventable and can be avoided by making lifestyle changes. These changes can also lower the chances of developing heart disease and cancer. So, there is a dire need for a prognosis tool that can help the doctors with early detection of the disease and hence can recommend the lifestyle changes required to stop the progression of the deadly disease. METHOD: Diabetes if untreated may turn into fatal and directly or indirectly invites lot of other diseases such as heart attack, heart failure, brain stroke and many more. Therefore, early detection of diabetes is very significant so that timely action can be taken and the progression of the disease may be prevented to avoid further complications. Healthcare organizations accumulate huge amount of data including Electronic health records, images, omics data, and text but gaining knowledge and insight into the data remains a key challenge. The latest advances in Machine learning technologies can be applied for obtaining hidden patterns, which may diagnose diabetes at an early phase. This research paper presents a methodology for diabetes prediction using a diverse machine learning algorithm using the PIMA dataset. RESULTS: The accuracy achieved by functional classifiers Artificial Neural Network (ANN), Naive Bayes (NB), Decision Tree (DT) and Deep Learning (DL) lies within the range of 90-98%. Among the four of them, DL provides the best results for diabetes onset with an accuracy rate of 98.07% on the PIMA dataset. Hence, this proposed system provides an effective prognostic tool for healthcare officials. The results obtained can be used to develop a novel automatic prognosis tool that can be helpful in early detection of the disease. CONCLUSION: The outcome of the study confirms that DL provides the best results with the most promising extracted features. DL achieves the accuracy of 98.07% which can be used for further development of the automatic prognosis tool. The accuracy of the DL approach can further be enhanced by including the omics data for prediction of the onset of the disease.

Three-dimensional finite element analysis to evaluate biomechanical effects of different alveolar decortication approaches on rate of canine retraction.

INTRODUCTION: The aim of this study was to compare different corticotomy approaches and determine their biomechanical effects on rate of canine displacement when compared to conventional orthodontics. METHOD: Three-dimensional Finite Element Models with conventional non-corticotomy approach (model 1) and three corticotomy approaches ensuing buccal and palatal vertical cuts (model 2), interseptal bone reduction (model 3), buccal vertical cuts (model 4) were fabricated. Displacement of the canine and von Mises stresses in the canine and trabecular bone were calculated and compared under a distal retraction force of 1.5N. RESULTS: The maximum displacement of canine with minimum anchorage loss was seen in model 3 followed by model 2, model 4 and model 1. The maximum equivalent (von Mises) stress was concentrated mainly on the distal side of canine in model 3 and had a uniform distribution of stresses on entire root surface. CONCLUSIONS: Corticotomy approaches effectively accelerated maxillary canine retraction, exhibiting twice the rate of canine movement with minimum anchorage loss when compared to non-corticotomy approach. Corticotomy with interseptal bone reduction was most effective in terms of canine displacement and stress distribution.

Comparison of conventional methods of simultaneous intrusion and retraction of maxillary anterior: a finite element analysis.

OBJECTIVE: To study the biomechanical effects of the three-piece intrusion arch and Kalra simultaneous intrusion and retraction arch (K-SIR) on simultaneous intrusion and retraction of maxillary anterior teeth. DESIGN: Three-dimensional analysis of stresses and displacement of the anterior and posterior teeth with the three-piece intrusion arch and K-SIR arch was done using the finite element method (FEM). SETTING: Department of Orthodontics, Surendera Dental College and Research Institute, India. MATERIAL AND METHODS: For this investigation, the geometric model of the maxilla was constructed using a computed tomography scan. 0.022 × 0.028-inch MBT brackets and molar tubes were modelled, with the specified tip and torque values for all maxillary teeth. The wire components for the three-piece intrusion arch and K-SIR arch were modelled initially as a line diagram and then converted to three dimensional models. The material characteristics which include the Young's modulus and Poisson's ratio were assigned. After defining the boundary conditions, force systems were applied as per design. The analysis was carried out using ANSYS Version 12.1 software. The von Mises stress, principal stress on PDL and alveolar bone, change in the inclination of incisors and initial displacement of the teeth in bucco-palatal, mesio- distal and vertical direction were analysed. RESULTS: Stresses in cortical bone were greater than cancellous. Both modalities showed intrusion of the anterior teeth, although this was slightly more in the three- piece intrusion arch. On studying the principal stresses in the PDL, the three-piece intrusion arch displayed uniform stress distribution compared to K-SIR arch. CONCLUSION: The FEM cannot reflect actual biological responses within the human body to orthodontic forces but based on these findings, the three-piece intrusion arch showed better stress distribution and controlled tooth movement than the K-SIR arch.