Clinical, Dermoscopic and Histopathological Features of Non-melanoma Skin Cancers in People With Skin of Colour: A Series of Five Cases.

Non-melanoma skin cancers (NMSC) such as basal cell carcinoma (BCC) as well as squamous cell carcinoma (SCC) are the two most common skin malignancies globally. They are observed more frequently among Caucasians than Asians, and their incidence is inversely proportional to the pigmentation levels. Even though the occurrence of skin cancers in India is lower, the absolute quantity of cases may be considerable due to the vast population. Here, we report five cases of NMSC in people having skin of colour.

Pseudoglucagonoma Syndrome Following Frey's Surgery: A Case Report of a Rare Presentation.

Pseudoglucagonoma syndrome is defined as the presence of necrolytic migratory erythema in the absence of a glucagon-secreting tumor. Necrolytic migratory erythema is the hallmark of glucagonoma syndrome but can also occur due to pancreatitis, pancreatic insufficiency, gastrointestinal dysfunction, inflammatory bowel disease, celiac disease, malabsorption disorders, nutritional deficiencies, hepatocellular dysfunction, and hypoalbuminemia. Pseudoglucagonoma syndrome is extremely rare, and the diagnosis is often delayed, resulting in delayed treatment. We report a rare case of pseudoglucagonoma syndrome in a malnourished male patient following Frey's surgery. The patient presented with a skin rash which gradually progressed over 20 days with diffuse hair loss. On cutaneous examination, multiple irregular erythematous and eroded plaques surrounded by a hyperpigmented scaly border were present over the dorsal aspect of the lower limbs, upper limbs, gluteal region, and genitals. Routine investigations showed normocytic normochromic anemia, neutropenia, lymphocytosis, dyslipidemia, and hypoalbuminemia. Rapid resolution of the skin lesions was observed with improved nutrition.

Minimizing Reference Bias with an Impute-First Approach.

Pangenome indexes reduce reference bias in sequencing data analysis. However, a greater reduction in bias can be achieved using a personalized reference, e.g. a diploid human reference constructed to match a donor individual's alleles. We present a novel impute-first alignment framework that combines elements of genotype imputation and pangenome alignment. It begins by genotyping the individual from a subsample of the input reads. It next uses a reference panel and efficient imputation algorithm to impute a personalized diploid reference. Finally, it indexes the personalized reference and applies a read aligner, which could be a linear or graph aligner, to align the full read set to the personalized reference. This framework has higher variant-calling recall (99.54% vs. 99.37%), precision (99.36% vs. 99.18%), and F1 (99.45% vs. 99.28%) compared to a graph-based pangenome. The personalized reference is also smaller and faster to query compared to a pangenome index, making it an overall advantageous choice for whole-genome DNA sequencing experiments.

Pangenomic genotyping with the marker array.

We present a new method and software tool called rowbowt that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while reducing the reference bias that results when aligning to a single linear reference. rowbowt can infer accurate genotypes in less time and memory compared to existing graph-based methods. The method is implemented in the open source software tool rowbowt available at https://github.com/alshai/rowbowt .

Pangenomic Genotyping with the Marker Array.

We present a new method and software tool called rowbowt that applies a pangenome index to the problem of inferring genotypes from short-read sequencing data. The method uses a novel indexing structure called the marker array. Using the marker array, we can genotype variants with respect from large panels like the 1000 Genomes Project while avoiding the reference bias that results when aligning to a single linear reference. rowbowt can infer accurate genotypes in less time and memory compared to existing graph-based methods.

Classification of various nutrient deficiencies in tomato plants through electrophysiological signal decomposition and sample space reduction.

Plants leave testimonies of undergoing physical state by depicting distinct variations in their electrophysiological data. Adequate nutrition of plants signifies their role in the growth and a plentiful harvest. Plant signal data carries enough information to detect and analyse nutrient deficiency. Classification of nutrient deficiencies through signal decomposition and bilevel measurements has not been reported earlier. The proposed work explores tomato plants in four-time cycles (Early Morning, Morning, After Noon, Night) of macronutrients Calcium (Ca), Nitrogen (N) and micronutrients Manganese (Mn), Iron (Fe). Using the Empirical Mode Decomposition method (EMD), signals are decomposed into Intrinsic Mode Functions (IMF) in 10-levels. Further, Intrinsic mode functions are grouped into two clusters to extract descriptive data statistics and bi-level measurements. Then a novel sample selection method is proposed to achieve a better classification rate by reducing sample space. A binary classification model is built to train and test 15 features individually using discriminant analysis and naïve-Bayes classifier variants. The reported results achieved a classification rate up to 98% after 5-fold cross-validation. Attained findings endorse novel pathways for detection and classification of nutrient deficiencies in the early stages, consequently promoting prevention and treatment approaches earliest to the appearance of symptoms, also helping to enhance plant growth.