Genetic analysis of heat tolerance in hot pepper: insights from comprehensive phenotyping and QTL mapping

High temperatures present a formidable challenge to the cultivation of hot pepper, profoundly impacting not only vegetative growth but also leading to flower and fruit abscission, thereby causing a significant reduction in yield. To unravel the intricate genetic mechanisms governing heat tolerance in hot pepper, an F2 population was developed through the crossing of two distinct genotypes exhibiting contrasting heat tolerance characteristics: DLS-161-1 (heat tolerant) and DChBL-240 (heat susceptible). The F2 population, along with the parental lines, was subjected to comprehensive phenotyping encompassing diverse morphological, physiological, and biochemical heat-related traits under high temperature conditions (with maximum temperature ranging from 31 to 46.5°C and minimum temperature from 15.4 to 30.5°C). Leveraging the Illumina Nova Seq-6000 platform, Double digest restriction-site associated DNA sequencing (ddRAD-seq) was employed to generate 67.215 Gb data, with subsequent alignment of 218.93 million processed reads against the reference genome of Capsicum annuum. Subsequent variant calling and ordering resulted in 5806 polymorphic SNP markers grouped into 12 LGs. Further QTL analysis identified 64 QTLs with LOD values ranging from 2.517 to 11.170 and explained phenotypic variance ranging from 4.05 to 19.39%. Among them, 21 QTLs, explaining more than 10% phenotypic variance, were identified as major QTLs controlling 9 morphological, 3 physiological, and 2 biochemical traits. Interestingly, several QTLs governing distinct parameters were found to be colocalized, suggesting either a profound correlation between the QTLs regulating these traits or their significant genomic proximity. In addition to the QTLs, we also identified 368380 SSR loci within the identified QTL regions, dinucleotides being the most abundant type (211,381). These findings provide valuable insights into the genetics of heat tolerance in hot peppers. The identified QTLs and SSR markers offer opportunities to develop heat-tolerant varieties, ensuring better crop performance under high-temperature conditions

Submasseteric abscess: A rare head and neck abscess

The objective of the article is to highlight and make people aware of a rare abscess which is often missed or misdiagnosed. As only a few cases have been reported, the authors feel that reporting such a case would help in proper management of the disease. We are presenting a 6.5-year-old male child with 3 weeks history of right facial swelling in the parotid region, with low-grade fever and trismus. Submasseteric abscess is a rare abscess which is often misdiagnosed as a parotid abscess or parotitis. Only a few cases have been reported. The cause is mostly dental in origin. Intravenous antibiotics often fail to alleviate the symptoms as this is a closed space and needs prompt drainage. Therefore, awareness of this complication of dental infections is vital for proper diagnosis and timely management

Analysis of the S-ANFIS Algorithm for the Detection of Blood Infections Using Hybrid Computing

Environment and climate change have caused a rise in a wide range of diseases and infections. In countries where overpopulation is a problem, many infections spread severely. The main focus of this paper is the detection and identification of blood diseases. An automated system that examines all potential diseases using patient information and data is needed to deal with unpredictable circumstances. Having an automated and intelligent system that evaluates the reports and counsels doctors in any other area or nation is a demand of the time. The same solutions can be identified by the proposed system. To apply the adaptive neuro-fuzzy inference system (ANFIS) and related techniques to predict chronic diseases early, the authors have gone through various existing models and case studies on diabetics and other patients. The proposed approach, called S-ANFIS which is using the hybrid approach, is based on ANFIS and includes content curation and intelligence analysis in addition to comparison with current models. As a result, the suggested model outperforms other approaches in terms of disease prediction accuracy, with a score of 88.6%

Temple Recommendation Engine for Route Planning Based on TPS Clustering CNN Method

There are no restrictions on religious or cultural practices in India. India’s temples are becoming an ideal platform for Hindu groups to express their ideals in a global context. For the sake of devotees, temples now require widespread accessibility and participation by a wide range of individuals on major holidays. A pilgrim may be unable to determine which site to visit, or where to stay, due to a variety of considerations such as cost, location, and the interests of each individual user. A user’s preferences are taken into consideration when a personalized recommendation list is generated. A large number of systems use Collaborative Filtering to produce user recommendations. In order to generate user-specific recommendations, this system uses a filtering method dubbed the “hybrid approach”. The Proposed OTPS Cluster technique is used to determine TPS (Time, Place, and Service). Users’ interests and TPA recommendations are taken into account. Users can forecast the location of the temple based on the temple’s history. Collaborative Filtering and Material-Based Filtering were used to propose sites based on comparable users and content, respectively. Testing shows that the algorithm is capable of solving difficulties in standard tour routing and providing a temple visit route that is tailored to each individual’s preferences. This study makes use of data from the South Indian city of Temple in the form of temples