Multilevel Analysis of Trachomatous Trichiasis and Corneal Opacity in Nigeria : The Role of Environmental and Climatic Risk Factors on the Distribution of Disease.

Funding: Jennifer L Smith was supported by the International Trachoma Initiative through a grant from the Bill and Melinda Gates Foundation. Anthony Solomon is a Wellcome Trust Intermediate Clinical Fellow (098521). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Peer reviewedPublisher PD

IoT-Based Solution for Paraplegic Sufferer to Send Signals to Physician via Internet

We come across hospitals and non-profit organizations that care for people with paralysis who have experienced all or portion of their physique being incapacitated by the paralyzing attack. Due to a lack of motor coordination by their mind, these persons are typically unable to communicate their requirements because they can speak clearly or use sign language. In such a case, we suggest a system that enables a disabled person to move any area of his body capable of moving to broadcast a text on the LCD. This method also addresses the circumstance in which the patient cannot be attended to in person and instead sends an SMS message using GSM. By detecting the user part's tilt direction, our suggested system operates. As a result, patients can communicate with physicians, therapists, or their loved ones at home or work over the web. Case-specific data, such as heart rate, must be continuously reported in health centers. The suggested method tracks the body of the case's pulse rate and other comparable data. For instance, photoplethysmography is used to assess heart rate. The decoded periodic data is transmitted continually via a Microcontroller coupled to a transmitting module. The croaker's cabin contains a receiver device that obtains and deciphers data as well as constantly exhibits it on Graphical interfaces viewable on the laptop. As a result, the croaker can monitor and handle multiple situations at once

Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection

Diabetic Retinopathy (DR) is a significant cause of blindness globally, highlighting the urgent need for early detection and effective treatment. Recent advancements in Machine Learning (ML) techniques have shown promise in DR detection, but the availability of labeled data often limits their performance. This research proposes a novel Semi-Supervised Graph Learning SSGL algorithm tailored for DR detection, which capitalizes on the relationships between labelled and unlabeled data to enhance accuracy. The work begins by investigating data augmentation and preprocessing techniques to address the challenges of image quality and feature variations. Techniques such as image cropping, resizing, contrast adjustment, normalization, and data augmentation are explored to optimize feature extraction and improve the overall quality of retinal images. Moreover, apart from detection and diagnosis, this work delves into applying ML algorithms for predicting the risk of developing DR or the likelihood of disease progression. Personalized risk scores for individual patients are generated using comprehensive patient data encompassing demographic information, medical history, and retinal images. The proposed Semi-Supervised Graph learning algorithm is rigorously evaluated on two publicly available datasets and is benchmarked against existing methods. Results indicate significant improvements in classification accuracy, specificity, and sensitivity while demonstrating robustness against noise and outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced datasets, common in medical image analysis, further enhancing its practical applicability.Comment: 13 pages, 6 figure

Fish/prawn seed resources and hydrography in the surf and backwater at Cochin

The present study deals with the seasonal variations of hydrographic parameters and fish and prawn seed and juvenile resources in the intertidal surf zone of the sea and the ajoining backwater at Cochin based on regular monitoring of monthly data for 1996-2001

Lactotransferrin in Asian Elephant (Elephas maximus) Seminal Plasma Correlates with Semen Quality

Asian elephants (Elephas maximus) have highly variable ejaculate quality within individuals, greatly reducing the efficacy of artificial insemination and making it difficult to devise a sperm cryopreservation protocol for this endangered species. Because seminal plasma influences sperm function and physiology, including sperm motility, the objectives of this study were to characterize the chemistry and protein profiles of Asian elephant seminal plasma and to determine the relationships between seminal plasma components and semen quality. Ejaculates exhibiting good sperm motility (≥65%) expressed higher percentages of spermatozoa with normal morphology (80.3+-13.0 vs. 44.9+-30.8%) and positive Spermac staining (51.9+-14.5 vs. 7.5+-14.4%), in addition to higher total volume (135.1+-89.6 vs. 88.8+-73.1 ml) and lower sperm concentration (473.0+-511.2 vs. 1313.8+-764.7 x 106 cells ml-1) compared to ejaculates exhibiting poor sperm motility (≤10%; P\u3c0.05). Comparison of seminal plasma from ejaculates with good versus poor sperm motility revealed significant differences in concentrations of creatine phosphokinase, alanine aminotransferase, phosphorus, sodium, chloride, magnesium, and glucose. These observations suggest seminal plasma influences semen quality in elephants. One- and two-dimensional (2D) gel electrophoresis revealed largely similar compositional profiles of seminal plasma proteins between good and poor motility ejaculates. However, a protein of ~80 kDa was abundant in 85% of ejaculates with good motility, and was absent in 90% of poor motility ejaculates (P\u3c0.05). We used mass spectrometry to identify this protein as lactotransferrin, and immunoblot analysis to confirm this identification. Together, these findings lay a functional foundation for understanding the contributions of seminal plasma in the regulation of Asian elephant sperm motility, and for improving semen collection and storage in this endangered species

Chitosan – hydrogen iodide salt supported graphite electrode: A simple and novel electrode for the reduction of nitro group under electrochemical condition

The present investigation provides a unique, simple, selective and efficient method for the electrochemical reduction of aromatic nitro groups into amines using chitosan-hydrogen iodide salt supported graphite electrode. 3:1 tetrabutyl ammonium chloride and acetic acid mixture was used as the medium for electrolytic process and a constant voltage of 5 V applied between the modified electrodes. The reaction was found to be selective and further reduction of amines was not observed. The purity of the products was checked with HPLC and characterized using spectroscopic tools. The electrochemical synthesis resulted in moderate to good yields of amino compounds which were higher than the reduction using conventional graphite electrodes. Quaternary ammonium chloride behaved as supporting electrolyte during synthesis and the reaction did not progress in the absence of acetic acid. The redox characteristic of the process was studied by cyclic voltammetry of the reaction mixture

Should shrimp farmers pay paddy farmers? : the challenges of examining salinisation externalities in South India

This study calculates the externality costs of salinization of land by comparing rice paddy yields in two similar villages in southern India. Shrimp farming causes two kinds of externality costs due to salinization: (i) An externality borne by the current generation due to decline in crop yields; (ii) An inter-generational externality borne by future generations because of environmental damage to land and groundwater resources. Findings show that if soil salinity is reduced to safe levels crop gains are estimated in the range of Rs 1,000 to Rs 5,000 per hectare. A regulatory framework for taxing externalities is recommended

BacHbpred: support vector machine methods for the prediction of bacterial hemoglobin-like proteins

The recent upsurge in microbial genome data has revealed that hemoglobin-like (HbL) proteins may be widely distributed among bacteria and that some organisms may carry more than one HbL encoding gene. However, the discovery of HbL proteins has been limited to a small number of bacteria only. This study describes the prediction of HbL proteins and their domain classification using a machine learning approach. Support vector machine (SVM) models were developed for predicting HbL proteins based upon amino acid composition (AC), dipeptide composition (DC), hybrid method (AC + DC), and position specific scoring matrix (PSSM). In addition, we introduce for the first time a new prediction method based on max to min amino acid residue (MM) profiles. The average accuracy, standard deviation (SD), false positive rate (FPR), confusion matrix, and receiver operating characteristic (ROC) were analyzed. We also compared the performance of our proposed models in homology detection databases. The performance of the different approaches was estimated using fivefold cross-validation techniques. Prediction accuracy was further investigated through confusion matrix and ROC curve analysis. All experimental results indicate that the proposed BacHbpred can be a perspective predictor for determination of HbL related proteins. BacHbpred, a web tool, has been developed for HbL prediction