Mitigating urban heat island and enhancing indoor thermal comfort using terrace garden.

The United Nations advocates for sustainable urban planning and design, emphasizing green infrastructure initiatives to mitigate urban heat island effects and enhance the resilience and livability of cities globally. To address urban heat challenges, a study was conducted in Chennai, India, from April to June 2023. The study focused on assessing temperature dynamics on a building's terrace by comparing a well-maintained garden area with an exposed region. Temperature and humidity sensors were deployed in both the garden and exposed areas of the terrace, as well as within rooms beneath it, to monitor hourly temperature fluctuations. The findings indicate a significant reduction in internal room temperatures in areas with rooftop gardens, ranging from 4 to 11 °C, depending on the time of year and sun's position, compared to rooms with fully exposed roof configurations. Additionally, simulation studies were performed to validate these findings, suggesting that optimizing the distribution of soil beds and plant density across the roof could yield an additional temperature reduction of 3-4 °C, resulting in an overall difference of up to 14-15 °C. The study highlights the efficacy of rooftop gardens in providing cooling effects during daylight hours and maintaining temperature parity post-sunset. Through analysis of sensor data, the research elucidates the intricate relationship between green infrastructure and thermal comfort, offering insights for energy-efficient building design and resilient urban planning. The findings underscore the potential of rooftop gardens in fostering a more comfortable, energy-efficient, and sustainable urban living environment.

Sugarcane leaf dataset: A dataset for disease detection and classification for machine learning applications.

Sugarcane, a vital crop for the global sugar industry, is susceptible to various diseases that significantly impact its yield and quality. Accurate and timely disease detection is crucial for effective management and prevention strategies. We persent the "Sugarcane Leaf Dataset" consisting of 6748 high-resolution leaf images classified into nine disease categories, a healthy leaves category, and a dried leaves category. The dataset covers diseases such as smut, yellow leaf disease, pokkah boeng, mosale, grassy shoot, brown spot, brown rust, banded cholorsis, and sett rot. The dataset's potential for reuse is significant. The provided dataset serves as a valuable resource for researchers and practitioners interested in developing machine learning algorithms for disease detection and classification in sugarcane leaves. By leveraging this dataset, various machine learning techniques can be applied, including deep learning, feature extraction, and pattern recognition, to enhance the accuracy and efficiency of automated sugarcane disease identification systems. The open availability of this dataset encourages collaboration within the scientific community, expediting research on disease control strategies and improving sugarcane production. By leveraging the "Sugarcane Leaf Dataset," we can advance disease detection, monitoring, and management in sugarcane cultivation, leading to enhanced agricultural practices and higher crop yields.

The image dataset of Indian coins: A machine learning approach for Indian currency.

In an increasingly digital world, the significance of creating a Comprehensive Image Dataset of Contemporary Indian Coins (CIDCIC) cannot be overstated. This research presents a dataset comprising 6,672 images of 53 different classes of Indian coins, including denominations of 25 Paisa, 50 Paisa, 1 Rupee, 2 Rupee, 5 Rupee, 10 Rupee, and 20 Rupee. The images of coins with various shapes and sizes are taken from obverse and reverse sides in various environments and different backgrounds. The core significance of this dataset unfolds in its potential to offer invaluable assistance to visually impaired individuals as they navigate their daily financial transactions. The dataset is a significant contribution to the domains of computer vision, artificial intelligence, and machine learning, specifically addressing the challenges related to coin detection, recognition, and monetary system integrity. These technologies can empower visually impaired individuals to independently and accurately recognize and distinguish between various coin denominations, thereby enhancing their participation in the financial realm. The dataset addresses limitations in existing dataset of having limited size, and scope. It addresses the limitations associated to the limited number of coins and the lack of diversity in images, encompassing various angles, environments, backgrounds, and directions of coins. The dataset provides a broader and more up-to-date representation of contemporary Indian coins.

Empowering agricultural research: A comprehensive custard apple (Annona squamosa) disease dataset for precise detection.

The Custard Apple, known as sugar apple or sweetsop, spans diverse regions like India, Portugal, Thailand, Cuba, and the West Indies. This dataset holds 8226 images of Custard Apple (Annona squamosa) fruit and leaf diseases, categorized into six types: Athracnose, Blank Canker, Diplodia Rot, Leaf Spot on fruit, Leaf Spot on leaf, and Mealy Bug. It's a key resource for refining machine learning algorithms focused on detecting and classifying diseases in Custard Apple plants. Utilizing methods like deep learning, feature extraction, and pattern recognition, this dataset sharpens automated disease identification precision. Its extensive range suits testing and training disease identification techniques. Public access fosters collaboration, fast-tracking plant pathology advancements and supporting Custard Apple plant sustainability. This dataset fosters collaborative efforts, aiding disease prevention techniques to boost Custard Apple yield and refine farming. It enhances disease identification, monitoring, and management in Custard Apple production, aiming to elevate agricultural practices and crop yields.

A comprehensive lemongrass (Cymbopogon citratus) leaf dataset for agricultural research and disease prevention.

This article introduces a dataset of 10,042 Lemongrass (Cymbopogon citratus) leaf images, captured with high quality camera of a mobile phone in real-world conditions. The dataset classifies leaves as "Dried," "Healthy," or "Unhealthy," making it useful for machine learning, agriculture research, and plant health analysis. We collected the plant leaves from the Vishwakarma University Pune herbal garden and the captured the images in diverse backgrounds, angles, and lighting conditions. The images underwent pre-processing, involving batch image resizing through FastStone Photo Resizer and subsequent operations for compatibility with pre-trained models using the 'preprocess_input' function in the Keras library. The significance of the Lemongrass Leaves Dataset was demonstrated through experiments using well-known pre-trained models, such as InceptionV3, Xception, and MobileNetV2, showcasing its potential to enhance machine learning model accuracy in Lemongrass leaf identification and disease detection. Our goal is to aid researchers, farmers, and enthusiasts in improving Lemongrass cultivation and disease prevention. Researchers can use this dataset to train machine learning models for leaf condition classification, while farmers can monitor their crop's health. Its authenticity and size make it valuable for projects enhancing Lemongrass cultivation, boosting crop yield, and preventing diseases. This dataset is a significant step toward sustainable agriculture and plant health management.

Plumbago Zeylanica (Chitrak) leaf image dataset: A comprehensive collection for botanical studies, herbal medicine research, and environmental analyses.

The Plumbago Zeylanica (Chitrak) Leaf Image Dataset is a valuable resource for botanical studies, herbal medicine research, and environmental analyses. Comprising a total of 10,660 high-resolution leaf images, the dataset is meticulously categorized into three distinct classes: Unhealthy leaves (3343 images), Healthy leaves (5288 images), and Dried leaves (2029 images). These images were captured from the medicinal plant Chitrak, a species of paramount importance in traditional medicine and environmental contexts. Researchers and practitioners can benefit from this dataset's richness in terms of both quantity and quality, using it to develop and test algorithms for leaf classification and health assessment. The Chitrak leaf image dataset holds the potential to foster innovative investigations and applications within the domains of botany, medicine, and environmental sciences.

A multifaceted approach for identification, validation, and immunogenicity of naturally processed and in silico-predicted highly conserved SARS-CoV-2 peptides.

SARS-CoV-2 remains a major global public health concern. Antibody waning and immune escape variant emergence necessitate the development of next generation vaccines that induce cross-reactive durable immune responses. T cell responses to SARS-CoV-2 demonstrate higher conservation, antigenic breadth, and longevity than antibody responses. Therefore, we sought to identify pathogen-derived T cell epitopes for a potential peptide-based vaccine. We pursued an approach leveraging: 1) liquid chromatography and tandem mass spectrometry (LC-MS/MS)-based identification of peptides from ancestral SARS-CoV-2-infected cell lines, 2) epitope prediction algorithms, and 3) overlapping peptide libraries. From this strategy, we identified 380 unique SARS-CoV-2-derived peptide sequences, including 53 antigenic HLA class I and class II peptides from multiple structural and non-structural/accessory viral proteins. These peptide sequences were highly conserved across variants of concern/interest (VoC/VoIs), and are estimated to achieve coverage of >96% of the world population. Our findings validate this discovery pipeline for peptide identification and immunogenicity testing, and are a crucial step toward the development of a next-generation multi-epitope SARS-CoV-2 peptide vaccine, and a novel vaccine platform methodology.

Face mask wearing image dataset: A comprehensive benchmark for image-based face mask detection models.

The Face Mask Wearing Image Dataset is a comprehensive collection of images aimed at facilitating research in the domain of face mask detection and classification. This dataset consists of 24,916 images, carefully categorized into two main folders: "Correct" and "Incorrect" representing instances of face masks being worn properly and improperly, respectively. Each folder is further divided into four subfolders, each denoting a specific type of face mask - Bandana, Cotton, N95, and Surgical. In the "Correct" folder, images depict individuals correctly wearing their respective face masks, while the "Incorrect" folder contains images of improper face mask usage. To capture variations in face mask application across different demographics, such as age and gender, each subfolder also includes three additional subfolders - Child, Male, and Female. The dataset's diverse content encompasses different face mask types, covering bandana-style, cloth, N95 respirators, and surgical masks, across various age groups and genders. This design ensures a comprehensive representation of real-world scenarios, enabling the evaluation of machine learning algorithms for face mask detection and classification. Researchers can leverage this dataset to develop and assess models that can accurately identify and distinguish between correct and incorrect face mask usage. By contributing to the advancement of face mask detection technologies, this dataset further supports public health initiatives and encourages proper mask-wearing behavior to mitigate the spread of infectious diseases, particularly during times of heightened health concerns such as the COVID-19 pandemic.

Coconut (Cocos nucifera) tree disease dataset: A dataset for disease detection and classification for machine learning applications.

The ``Coconut (Cocos nucifera) Tree Disease Dataset'' comprises 5,798 images across five disease categories: ``Bud Root Dropping,'' ``Bud Rot,'' ``Gray Leaf Spot,'' ``Leaf Rot,'' and ``Stem Bleeding.'' This dataset is intended for machine learning applications, facilitating disease detection and classification in coconut trees. The dataset's diversity and size make it suitable for training and evaluating disease detection models. The availability of this dataset will support advancements in plant pathology and aid in the sustainable management of coconut plantations. By providing a valuable resource for researchers, this dataset contributes to improved disease management and sustainable coconut plantation practices.

Mint leaves: Dried, fresh, and spoiled dataset for condition analysis and machine learning applications.

We present a comprehensive dataset of 5,323 images of mint (pudina) leaves in various conditions, including dried, fresh, and spoiled. The dataset is designed to facilitate research in the domain of condition analysis and machine learning applications for leaf quality assessment. Each category of the dataset contains a diverse range of images captured under controlled conditions, ensuring variations in lighting, background, and leaf orientation. The dataset also includes manual annotations for each image, which categorize them into the respective conditions. This dataset has the potential to be used to train and evaluate machine learning algorithms and computer vision models for accurate discernment of the condition of mint leaves. This could enable rapid quality assessment and decision-making in various industries, such as agriculture, food preservation, and pharmaceuticals. We invite researchers to explore innovative approaches to advance the field of leaf quality assessment and contribute to the development of reliable automated systems using our dataset and its associated annotations.

A comprehensive dataset of damaged banknotes in Indian currency (Rupees) for analysis and classification.

Detecting authentic and quality banknotes presents a significant challenge, particularly for individuals with low vision or visual impairments. Extensive research has been dedicated to achieving accurate banknote detection. It is crucial for clean banknotes to be readily detectable and accepted in daily transactions. However, existing Indian currency datasets suffer from limitations, including insufficient size, a lack of datasets on damaged/spoiled banknotes, and the unavailability of publicly accessible datasets featuring spoiled, torn, or altered banknotes. Recognizing the vital importance of a spoiled banknote dataset for the benefit of low vision and visually impaired individuals, we introduce a comprehensive dataset of spoiled banknotes comprising 5125 Indian currency notes. This dataset encompasses both old and new denominations of 10, 20, 50, and 100 Rupees, aiming to significantly enhance the accessibility and accuracy of banknote detection systems. By making this dataset openly accessible to the researchers, we aim to promote research and development of solutions for detection of spoiled banknote.

An overview of protein-based SARS-CoV-2 vaccines.

SARS-CoV-2 resulted in the COVID-19 pandemic which, to date, has resulted in an estimated loss of over 15 million human lives globally and continues to have negative social, and economic implications worldwide. Vaccine platforms that can be quickly updated to counter newly emerging SARS-CoV-2 variants are critical in combating the COVID-19 pandemic. Messenger RNA-based SARS-CoV-2 vaccines can be easily updated and have shown superior efficacy over other vaccine types, yet their high cost, reactogenicity, and stringent need for ultracold storage limit their accessibility. Global access to economic, safe, and effective SARS-CoV-2 vaccines is a critical step toward reducing COVID-19-associated mortality and ending the pandemic. Several protein-based SARS-CoV-2 vaccines targeting the spike protein (or its receptor-binding domain) have demonstrated safety and efficacy in clinical studies. Moreover, protein-based vaccines can be updated to immunize against new virus variants. Protein-based vaccines do not contain live viruses and are safe to use in immunocompromised and elderly populations, and can be optimized to improve the immune outcome in these poorly immunoresponsive individuals by using adjuvants. SARS-CoV-2 shows high genetic variability, similar to other RNA viruses, and protein-based vaccines are an economically feasible vaccine platform that can be used to design new vaccines with durable protective immunity, in addition to expanding the vaccine coverage.

Sensor based dataset to assess the impact of urban heat island effect mitigation and indoor thermal comfort via terrace gardens.

This dataset contains temperature variations observed on a building terrace that is partially covered with plantations on one side while the other side remains exposed. The study was conducted at a shelter named "Anbagham" in Tamil Nadu, India. Two sets of temperature and humidity sensors were utilized, with one set placed on the external roofs and the other set placed inside the rooms corresponding to these roofs. The analysis spanned over a period of two months, specifically during the hottest period of the year, totaling 66 days, with measurements taken every hour. The provided dataset can be effectively utilized to examine temperature disparities and patterns in the internal environment attributed to the presence or absence of roof gardens. This research and the accompanying dataset have significant implications for various disciplines. They can aid in the planning and design of energy-efficient buildings, assist green building engineers in estimating internal thermal comfort, enable city/urban planners to estimate land surface temperatures, allow botanists to evaluate the impact of foliage on temperature relief, aid civil engineers in proposing green and insulative roof assemblies, and help mechanical engineers estimate reduced cooling loads and corresponding energy savings.

Yoga dataset: A resource for computer vision-based analysis of Yoga asanas.

The practice of yoga has been shown to have numerous benefits for both physical and mental health, and it has gained popularity worldwide as a form of exercise and relaxation. However, yoga postures can be complex and challenging, especially for beginners who may struggle with proper alignment and positioning. To address this issue, there is a need for a dataset of different yoga postures that can be used to develop computer vision algorithms capable of recognizing and analyzing yoga poses. For this we created the image and video datasets of different yoga asana using the mobile device Samsung Galaxy M30s. The dataset contains images and videos of effective (right) and ineffective postures for 10 Yoga asana, with a total of 11,344 images and 80 videos. The image dataset is organized into 10 subfolders, each with "Effective (right) Steps" and "Ineffective (wrong) Steps" folders. The video dataset has 4 videos for each posture, with 40 videos demonstrating effective (right) postures and 40 demonstrating ineffective (wrong) postures. This dataset benefits app developers, machine learning researchers, Yoga instructors, and practitioners, who can use it to develop apps, train computer vision algorithms, and improve their practice. We strongly believe that this type of dataset would provide the foundation for the development of new technologies that can help individuals improve their Yoga practice, such as posture detection and correction tools or personalized recommendations based on individual abilities and needs.

VegNet: Dataset of vegetable quality images for machine learning applications.

The agricultural industry has an unmet requirement for quick and accurate classification or recognition of vegetables according to the quality criteria. This open research problem draws attention to the research scholars every time. The classification and object detection challenges have seen highly encouraging outcomes from machine learning and deep learning techniques. The foundational condition for developing precise and reliable machine learning models for the real-time context is a neat and clean dataset. With this goal in mind, we have developed a picture dataset of four popular vegetables in India that are also highly exported worldwide. In order to generate a dataset, we have taken into account four vegetables: Bell Peppers, Tomatoes, Chili Peppers, and New Mexico Chiles. The dataset is divided into four vegetable folders, including Bell Pepper, Tomato, Chili Pepper, and New Mexico Chile. Further each vegetable folder contains five subfolders namely (1) Unripe, (2) Ripe, (3) Old, and (4) Dried (5) Damaged. The image collection includes a total of 6850 pictures of vegetables in dataset. We firmly feel that the provided dataset is very beneficial for developing, evaluating, and validating a machine learning model for vegetable categorization or reorganization.

MicroRNA-detargeting proves more effective than leader gene deletion for improving safety of oncolytic Mengovirus in a nude mouse model.

A dual microRNA-detargeted oncolytic Mengovirus, vMC24NC, proved highly effective against a murine plasmacytoma in an immunocompetent syngeneic mouse model; however, there remains the concern of escape mutant development and the potential for toxicity in severely immunocompromised cancer patients when it is used as an oncolytic virus. Therefore, we sought to compare the safety and efficacy profiles of an attenuated Mengovirus containing a virulence gene deletion versus vMC24NC in an immunodeficient xenograft mouse model of human glioblastoma. A Mengovirus construct, vMC24ΔL, wherein the gene coding for the leader protein, a virulence factor, was deleted, was used for comparison. The vMC24ΔL induced significant levels of toxicity following treatment of subcutaneous human glioblastoma (U87-MG) xenografts as well as when injected intracranially in athymic nude mice, reducing the overall survival. The in vivo toxicity of vMC24ΔL was associated with viral replication in nervous and cardiac tissue. In contrast, microRNA-detargeted vMC24NC demonstrated excellent efficacy against U87-MG subcutaneous xenografts and improved overall survival significantly compared to that of control mice without toxicity. These results reinforce microRNA-detargeting as an effective strategy for ameliorating unwanted toxicities of oncolytic picornaviruses and substantiate vMC24NC as an ideal candidate for clinical development against certain cancers in both immunocompetent and immunodeficient hosts.

Oncolytic Viruses for Malignant Glioma: On the Verge of Success?

Glioblastoma is one of the most difficult tumor types to treat with conventional therapy options like tumor debulking and chemo- and radiotherapy. Immunotherapeutic agents like oncolytic viruses, immune checkpoint inhibitors, and chimeric antigen receptor T cells have revolutionized cancer therapy, but their success in glioblastoma remains limited and further optimization of immunotherapies is needed. Several oncolytic viruses have demonstrated the ability to infect tumors and trigger anti-tumor immune responses in malignant glioma patients. Leading the pack, oncolytic herpesvirus, first in its class, awaits an approval for treating malignant glioma from MHLW, the federal authority of Japan. Nevertheless, some major hurdles like the blood-brain barrier, the immunosuppressive tumor microenvironment, and tumor heterogeneity can engender suboptimal efficacy in malignant glioma. In this review, we discuss the current status of malignant glioma therapies with a focus on oncolytic viruses in clinical trials. Furthermore, we discuss the obstacles faced by oncolytic viruses in malignant glioma patients and strategies that are being used to overcome these limitations to (1) optimize delivery of oncolytic viruses beyond the blood-brain barrier; (2) trigger inflammatory immune responses in and around tumors; and (3) use multimodal therapies in combination to tackle tumor heterogeneity, with an end goal of optimizing the therapeutic outcome of oncolytic virotherapy.

Tanapoxvirus: From discovery towards oncolytic immunovirotherapy.

Insufficiency of standard cancer therapeutic agents and a high degree of toxicity associated with chemotherapy and radiotherapy have created a dearth of therapeutic options for metastatic cancers. Oncolytic viruses (OVs) are an emerging therapeutic option for the treatment of various human cancers. Several OVs, including poxviruses, are currently in preclinical and clinical studies and have shown to be effective in treating metastatic cancer types. Tanapoxvirus (TANV), a member of the Poxviridae family, is being developed as an OV for different human cancers due to its desirable safety and efficacy features. TANV causes a mild self-limiting febrile disease in humans, does not spread human to human, and its large genome makes it a relatively safer OV for use in humans. TANV is relatively well characterized at both molecular and clinical levels. Some of the TANV-encoded proteins that are a part of the virus' immune evasion strategy are also characterized. TANV replicates considerably slower than vaccinia virus. TANV has been shown to replicate in different human cancer cells in vitro and regresses human tumors in a nude mouse model. TANV is currently being developed as a therapeutic option for several human cancers including breast cancer, ovarian cancer, colorectal cancer, pancreatic cancer, retinoblastoma, and melanoma. This review provides a comprehensive summary from the discovery to the development of TANV as an OV candidate for a wide array of human cancers.

Oncolytic Tanapoxvirus Expressing Interleukin-2 is Capable of Inducing the Regression of Human Melanoma Tumors in the Absence of T Cells.

BACKGROUND: Oncolytic viruses (OVs), which preferentially infect cancer cells and induce host anti-tumor immune responses, have emerged as an effective melanoma therapy. Tanapoxvirus (TANV), which possesses a large genome and causes mild self-limiting disease in humans, is potentially an ideal OV candidate. Interleukin-2 (IL-2), a T-cell growth factor, plays a critical role in activating T cells, natural killer (NK) cells and macrophages in both the innate and adaptive immune system. OBJECTIVE: We aimed to develop a recombinant TANV expressing mouse IL-2 (TANVΔ66R/mIL- 2), replacing the viral thymidine kinase (TK) gene (66R) with the mouse (m) mIL-2 transgene resulting in TANVΔ66R/mIL-2. METHODS: Human melanoma tumors were induced in female athymic nude mice by injecting SKMEL- 3 cells subcutaneously. Mice were treated with an intratumoral injection of viruses when the tumor volumes reached 45 ± 4.5 mm3. RESULTS: In cell culture, expression of IL-2 attenuated virus replication of not only TANVΔ66R/ mIL-2, but also TANVGFP. It was demonstrated that IL-2 inhibited virus replication through intracellular components and without activating the interferon-signaling pathway. Introduction of mIL-2 into TANV remarkably increased its anti-tumor activity, resulting in a more significant regression than with wild-type (wt) TANV and TANVΔ66R. Histopathological studies showed that extensive cell degeneration with a significantly increased peri-tumor accumulation of mononuclear cells in the tumors treated with TANVΔ66R/mIL-2, compared to wtTANV or TANVΔ66R. CONCLUSION: We conclude that TANVΔ66R/mIL-2 is potentially therapeutic for human melanomas in the absence of T cells, and IL-2 expression resulted in an overall increase of therapeutic efficacy.

Nitrogen isotopes provide clues to amino acid metabolism in human colorectal cancer cells.

Glutamic acid and alanine make up more than 60 per cent of the total amino acids in the human body. Glutamine is a significant source of energy for cells and also a prime donor of nitrogen in the biosynthesis of many amino acids. Several studies have advocated the role of glutamic acid in cancer therapy. Identification of metabolic signatures in cancer cells will be crucial for advancement of cancer therapies based on the cell's metabolic state. Stable nitrogen isotope ratios (15N/14N, δ15N) are of particular advantage to understand the metabolic state of cancer cells, since most biochemical reactions involve transfer of nitrogen. In our study, we used the natural abundances of nitrogen isotopes (δ15N values) of individual amino acids from human colorectal cancer cell lines to investigate isotope discrimination among amino acids. Significant effects were noticed in the case of glutamic acid, alanine, aspartic acid and proline between cancer and healthy cells. The data suggest that glutamic acid is a nitrogen acceptor while alanine, aspartic acid and proline are nitrogen donors in cancerous cells. One plausible explanation is the transamination of the three acids to produce glutamic acid in cancerous cells.