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Simple Summary: While melanoma accounts for 4% of skin cancer cases, it causes 75% of skin-cancer-related deaths. The survival rate for melanoma is higher for early-identified cases, so improved access to diagnosis and screening programs is essential for addressing skin cancer deaths. Computer-aided diagnosis utilizing machine learning can be used to differentiate malignant and benign skin lesions. There is significant research into the use of convolutional neural networks to classify skin lesions from dermoscopic images. However, to provide cost-effective and accessible options for early detection of malignant melanoma, smartphone applications capable of accurately classifying skin lesions from images taken on a smartphone would be beneficial. This research investigates a previously underexplored dataset of smartphone images and develops a novel multi-layer deep convolutional neural network model, named SkinLesNet, to classify three types of skin lesions, including melanoma. Further studies to validate the model should be conducted as other image datasets become available. Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet's performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet's effectiveness, as it consistently outperformed both benchmarks across all datasets. [ABSTRACT FROM AUTHOR]

The big data sources of National Statistical Offices (NSOs) are provided to make a superior platform for decision-making. The household income and expenditure survey is one of the economically important surveys especially when the inflation rate varies to assess the changes in households' consumption patterns. In this case, big data can be beneficial and help to accurately measure consumption patterns of urban and rural households at every geographical level. This analysis is an exploratory study for the extraction of the size of injustice and imparity of household income and facilities implemented by classifying and clustering all Iranian households. Through this study, classification and soft clustering (Fuzzy clustering) techniques are employed to characterize the Iranian household types from 2011 to 2021, which are supervised and unsupervised approaches, respectively. Moreover, association rule mining techniques are employed to discover and extract consumption patterns for each cluster. Obtained results showed that there was a significant gap between purchasing power/receiving energy between lowest and highest income households from 2011 to 2021, and this gap is increasing day by day. [ABSTRACT FROM AUTHOR]

Introduction: The increasingly popular microbiopsy is an appealing alternative to the more invasive Bergström biopsy given the challenges associated with harvesting skeletal muscle in older populations. Parameters of muscle fiber morphology and composition derived from the microbiopsy have not been compared between young and older adults. Purpose: The purpose of this study was to examine muscle fiber morphology and composition in young (YM) and older (OM) males using the microbiopsy sampling technique. A secondary aim was to determine if specific strength is associated with serum levels of C-terminal agrin fragment [CAF; an indicator of neuromuscular junction (NMJ) degradation]. Methods: Thirty healthy, YM (n = 15, age = 20.7 ± 2.2 years) and OM (n = 15, age = 71.6 ± 3.9 years) underwent ultrasound imaging to determine whole-muscle cross-sectional area (CSA) of the vastus lateralis and rectus femoris as well as isometric and isokinetic (60°⋅s–1 and 180°⋅s–1) peak torque testing of the knee extensors. Microbiopsy samples of the vastus lateralis were collected from 13 YM and 11 OM, and immunofluorescence was used to calculate CSA and proportion of type I and type II fibers. Results: Peak torque was lower in OM at all velocities (p ≤ 0.001; d = 1.39–1.86) but only lower at 180°⋅s–1 (p = 0.003; d = 1.23) when normalized to whole-muscle CSA. Whole-muscle CSA was smaller in OM (p = 0.001; d = 1.34), but atrophy was not present at the single fiber level (p > 0.05). Per individual, ∼900 fibers were analyzed, and type I fiber CSA was larger (p = 0.05; d = 0.94) in OM which resulted in a smaller type II/I fiber CSA ratio (p = 0.015; d = 0.95). CAF levels were not sensitive to age (p = 0.159; d = 0.53) nor associated with specific strength or whole-muscle CSA in OM. Conclusion: The microbiopsy appears to be a viable alternative to the Bergström biopsy for histological analyses of skeletal muscle in older adults. NMJ integrity was not influential for age-related differences in specific strength in our healthy, non-sarcopenic older sample. [ABSTRACT FROM AUTHOR]

Serum response factor (SRF) has an established role in controlling actin homeostasis in mammalian cells, yet its role in non-vertebrate muscle development has remained enigmatic. Here, we demonstrate that the single Drosophila SRF ortholog, termed Blistered (Bs), is expressed in all adult muscles, but Bs is required for muscle organization only in the adult indirect flight muscles. Bs is a direct activator of the flight muscle actin gene, Act88F, via a conserved promoter-proximal binding site. However, Bs only activates Act88F expression in the context of the flight muscle regulatory program provided by the Pbx and Meis orthologs Extradenticle and Homothorax, and appears to function in a similar manner to mammalian SRF in muscle maturation. These studies place Bs in a regulatory framework where it functions to sustain the flight muscle phenotype in Drosophila. Our studies uncover an evolutionarily-ancient role for SRF in regulating muscle actin expression, and provide a model for how SRF might function to sustain muscle fate downstream of pioneer factors. [ABSTRACT FROM AUTHOR]

In this paper, the Gompertz model is extended to incorporate time-dependent covariates in the presence of interval-, right-, left-censored and uncensored data. Then, its performance at different sample sizes, study periods and attendance probabilities are studied. Following that, the model is compared to a fixed covariate model. Finally, two confidence interval estimation methods, Wald and likelihood ratio (LR), are explored and conclusions are drawn based on the results of the coverage probability study. The results indicate that bias, standard error and root mean square error values of the parameter estimates decrease with the increase in study period, attendance probability and sample size. Also, LR was found to work slightly better than the Wald for parameters of the model. [ABSTRACT FROM PUBLISHER]