Machine Learning based Classification of Diseased Mango Leaves

The preponderance of population depends on agriculture to produce crops which would be their primary subsistence for their livelihood. So, agriculture is considered the backbone of any nation. Mango (Mangifera indica Linn), belonging to a family Anacardiaceous, is a conspicuous fruit that captivates all ages because of its meticulous taste, delicious flavor, ampleness variety, and highly lustiness. Mangoes are generally rich in minerals, vitamins, fibers, and negotiable fat. Mango plants are exposed to many micro-organisms. If these are not detected and treated in the initial developing stages, it would affect peculiar parts of the mango plant and result in loss of overall productivity. Several factors like biotic and abiotic always ensue in the decrease in the overall productivity of mango plants. Self-regulated Detection of mango plant disease is imperative, and it must be detected at the preliminary stages of the growing period of the mango plant. This paper discusses the existing methodology to classify diseases in mango plant leaves by implementing ensemble technique (Stack) which includes algorithms like Decision Tree (DT), Support vector machine (SVM), Neural Network (NN), and Logistic Regression (LR). The developmental results validate that the disease classification methodology can successfully classify a higher percentage in predicting whether mango plant leaf is healthy or diseased.&nbsp

Flow through free fibula flap for upper limb reconstruction in sarcoma patients.

INTRODUCTION: SOFT TISSUE SARCOMAS (STS) are rare malignant tumors arising from extraskeletal mesenchymal tissues. They are less than 1% of all newly diagnosed malignant tumors in the United States. Of these only 25% occur at the upper extremity. STS of the extremities used to be treated with amputation in as much as half of the patients. Nowadays limb-sparing surgery , without any compromise in local control or survival rates, is performed. Advances in tumour biology understanding, improved chemotherapy and radiotherapy (neoadjuvant or adjuvant in post op treatment), ability of surgical oncologists to excise the tumour with a good margin (principle of excision without seeing the tumour) and advances in plastic surgical expertise where there is a technique available to reconstruct any composite defect of the axial vessels of the hand compromising its vitality, have all paved the way for limb salvage surgery. Oncological resections, especially in the forearm sarcomas have resulted in complex composite defects with loss of variable amount of bone, muscle, vessels, nerves and skin. AIM OF THE STUDY: To evaluate the free fibula as a flow through flap for reconstruction of upper limb defects after sarcoma excision. To assess the functional and aesthetic outcomes after upper limb reconstruction using free fibula flap. Evaluation of outcomes takes into account the following:- • Overall patient satisfaction. • Functional outcome assessed by the MSTS scoring system. • Cosmetic acceptability of the patient by visual analogue score. • Complications after surgery. • Donor site morbidity. MATERIALS AND METHODS: In our department, between March 2012 and March 2014 we have done 6 cases of upper extremity Sarcoma reconstruction, all involving the forearm region. The following criteria were decided upon for patient selection Inclusion criteria: Patients with soft tissue sarcoma (biopsy proven) of the upper limb. Patients with good IQ who understood the treatment explained to them. Patients who are fit for surgery. Patients willing for long term regular follow up. Patients who received previous chemo/ radiation were also included. Exclusion Criteria: High risk individuals who are medically unfit. Patients not willing for follow up. Patients not willing for long duration of surgery and for the postoperative physiotherapy rigors Elderly patients. OBSERVATIONS AND RESULTS: A total of six patients underwent upper limb reconstruction with flow through free fibula osteomyocutaneou flap. There were a 4 male and 2 female patients. The patients aged between 19 and 40 with a mean of 30 years. Among the 6 patients 2 had spindle cell sarcoma the remaining each 1 patient had Fibro Sarcoma, Synovial Sarcoma, Osteosracoma, Endothelial Sarcoma. CONCLUSION: Upper limb reconstruction after sarcoma excision is a complex challenging task to the reconstructive surgeon. This is because the defect is composite, preoperative radiation makes anastomosis difficult and patient must regain acceptable function post surgery. Sarcoma reconstruction has undergone a radical change towards preservation of the limb with the usage of the vascularised free fibula flap. The major advantage is that there is no compromise in the extent of resection. Our study and review of the literature has shown that vascularised fibula flap harvested in a chimeric pattern along with the peroneus longus provides excellent quality of tissue to reconstruct both bone and soft tissue loss. We recommend the use of the peroneus longus muscle tendon unit based on an independant perforator from the peroneal artery. The use of the peroneus muscle has improved the functional outcome in these patients. Although the number of cases in our study was limited, results were satisfactory. Low donor site morbidity, acceptable functional outcome, limb preservation makes the free fibula osteocutaneous flap as an ideal option for upper limb reconstruction

Linguistic Adaptation and Psychometric Properties of Tamil Version of General Oral Health Assessment Index‑Tml

Background: Oral health has an impact on quality of life hence for research purpose validation of a Tamil version of General Oral Health Assessment Index would enable it to be used as a valuable tool among Tamil speaking population.Aim: In this study, we aimed to assess the psychometric properties of translated Tamil version of General Oral Health Assessment Index (GOHAI‑Tml).Subjects and Methods: Linguistic adaptation involved forward and backward blind translation process. Reliability was analyzed using test‑retest, Cronbach alpha, and split half reliability. Inter‑item and item‑total correlation were evaluated using Spearman rank correlation. Convenience sampling was done, and 265 consecutive patients aged 20–70 years attending the outpatient department were recruited. Subjects were requested to fill a self‑reporting questionnaire along with Tamil GOHAI version. Clinical examination was done on the same visit. Concurrent validity was measured by assessing the relationship between GOHAI scores and self‑perceived oral health and general health status, satisfaction with oral health, need for dental treatment and esthetic satisfaction. Discriminant validity was evaluated by comparing the GOHAI scores with the objectively assessed clinical parameters. Exploratory factor analysis was done to examine the factor structure.Results: Mean GOHAI‑Tml was 52.7 (6.8, range 22–60, median 54). The mean number of negative impacts was 2 (2.4, range 0–11, median 1). The Spearman rank correlation for test‑retest ranged from 0.8 to 0.9 (P < 0.001) for all the 12 items between visits. The Cronbach alpha for 265 samples was 0.8 suggesting good internal consistency and homogeneity between items. Item scale correlation ranged from 0.4 to 0.8 (P < 0.001). Concurrent and discriminant validity was established. Principal component analysis resulted in extraction of four factors which together accounted for 66.4% (7.9/12) variance.Conclusion: GOHAI‑Tml has shown acceptable psychometric properties, so that it can be used as an efficient tool in identifying the impact of oral health on quality of life among the Tamil speaking population.Keywords: General oral health assessment index, Linguistic adaptation, Oral health‑related quality of life, Psychometric properties, Reliability, Validit

Causes of breakage and disruption in a homogeniser

Many authors have written in the past regarding the exact causes of breakage and disruption in a high pressure homogeniser, but there has been little agreement. This paper investigates some of the most likely causes of the rupture of the walls of unicellular organisms and offers suggestions obtained from various papers and work carried out

R-CNN and YOLOV4 based Deep Learning Model for intelligent detection of weaponries in real time video

The security of civilians and high-profile officials is of the utmost importance and is often challenging during continuous surveillance carried out by security professionals. Humans have limitations like attention span, distraction, and memory of events which are vulnerabilities of any security system. An automated model that can perform intelligent real-time weapon detection is essential to ensure that such vulnerabilities are prevented from creeping into the system. This will continuously monitor the specified area and alert the security personnel in case of security breaches like the presence of unauthorized armed people. The objective of the proposed system is to detect the presence of a weapon, identify the type of weapon, and capture the image of the attackers which will be useful for further investigation. A custom weapons dataset has been constructed, consisting of five different weapons, such as an axe, knife, pistol, rifle, and sword. Using this dataset, the proposed system is employed and compared with the faster Region Based Convolution Neural Network (R-CNN) and YOLOv4. The YOLOv4 model provided a 96.04% mAP score and frames per second (FPS) of 19 on GPU (GEFORCE MX250) with an average accuracy of 73%. The R-CNN model provided an average accuracy of 71%. The result of the proposed system shows that the YOLOv4 model achieves a higher mAP score on GPU (GEFORCE MX250) for weapon detection in surveillance video cameras

Comparison of coronally advanced versus semilunar coronally repositioned flap in the management of maxillary gingival recessions

Objectives: Maxillary gingival recessions can be managed by both semilunar coronally repositioned flap (SLCRF) and coronally advanced flap (CAF). The objective of this study was to compare SLRCF and CAF in terms of wound healing and periodontal parameters in the presence of magnification. Materials and Methods: Thirty patients with Miller’s class I gingival recession in maxillary anteriors and premolars were assigned to 2 groups including SLCRF and CAF. All procedures were performed using 2.5X magnifying loupes. Wound healing and periodontal clinical parameters were assessed at baseline and at 2nd, 4th, 8th and 12th week. Results: No significant difference was observed in wound healing and mean percentage root coverage in both the groups at 12th week (p > 0.05). However, SLCRF showed a statistically significant reduction in percentage of root coverage (PRC) at 12th week compared to 2nd week (p < 0.05). A significant gain in Clinical attachment level, width of keratinised tissue and a significant reduction in Recession Depth and Probing Depth were seen in both the groups at 12th week. Conclusion: Within the limitation of this study, both techniques resulted in similar wound healing at 12th week with the use of magnification. CAF provided more root coverage compared to SLCRF technique in the maxillary class I gingival recession defects

High-throughput sequencing: a failure mode analysis

BACKGROUND: Basic manufacturing principles are becoming increasingly important in high-throughput sequencing facilities where there is a constant drive to increase quality, increase efficiency, and decrease operating costs. While high-throughput centres report failure rates typically on the order of 10%, the causes of sporadic sequencing failures are seldom analyzed in detail and have not, in the past, been formally reported. RESULTS: Here we report the results of a failure mode analysis of our production sequencing facility based on detailed evaluation of 9,216 ESTs generated from two cDNA libraries. Two categories of failures are described; process-related failures (failures due to equipment or sample handling) and template-related failures (failures that are revealed by close inspection of electropherograms and are likely due to properties of the template DNA sequence itself). CONCLUSIONS: Preventative action based on a detailed understanding of failure modes is likely to improve the performance of other production sequencing pipelines

Wearable full-body motion tracking of activities of daily living predicts disease trajectory in Duchenne muscular dystrophy

Artificial intelligence has the potential to revolutionize healthcare, yet clinical trials in neurological diseases continue to rely on subjective, semiquantitative and motivation-dependent endpoints for drug development. To overcome this limitation, we collected a digital readout of whole-body movement behavior of patients with Duchenne muscular dystrophy (DMD) (n = 21) and age-matched controls (n = 17). Movement behavior was assessed while the participant engaged in everyday activities using a 17-sensor bodysuit during three clinical visits over the course of 12 months. We first defined new movement behavioral fingerprints capable of distinguishing DMD from controls. Then, we used machine learning algorithms that combined the behavioral fingerprints to make cross-sectional and longitudinal disease course predictions, which outperformed predictions derived from currently used clinical assessments. Finally, using Bayesian optimization, we constructed a behavioral biomarker, termed the KineDMD ethomic biomarker, which is derived from daily-life behavioral data and whose value progresses with age in an S-shaped sigmoid curve form. The biomarker developed in this study, derived from digital readouts of daily-life movement behavior, can predict disease progression in patients with muscular dystrophy and can potentially track the response to therapy

Remarks on the Formulation of Quantum Mechanics on Noncommutative Phase Spaces

We consider the probabilistic description of nonrelativistic, spinless one-particle classical mechanics, and immerse the particle in a deformed noncommutative phase space in which position coordinates do not commute among themselves and also with canonically conjugate momenta. With a postulated normalized distribution function in the quantum domain, the square of the Dirac delta density distribution in the classical case is properly realised in noncommutative phase space and it serves as the quantum condition. With only these inputs, we pull out the entire formalisms of noncommutative quantum mechanics in phase space and in Hilbert space, and elegantly establish the link between classical and quantum formalisms and between Hilbert space and phase space formalisms of noncommutative quantum mechanics. Also, we show that the distribution function in this case possesses 'twisted' Galilean symmetry.Comment: 25 pages, JHEP3 style; minor changes; Published in JHE