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Clinical gait analysis is a crucial step for identifying foot disorders and planning surgery. Automating this process is essential for efficiently assessing the substantial amount of gait data. In this study, we explored the potential of state-of-the-art machine learning (ML) and explainable artificial intelligence (XAI) algorithms to automate all various steps involved in gait analysis for six specific foot conditions. To address the complexity of gait data, we manually created new features, followed by recursive feature elimination using Support Vector Machines (SVM) and Random Forests (RF) to eliminate low-variance features. SVM, RF, K-nearest Neighbor (KNN), and Logistic Regression (LREGR) were compared for classification, with a Majority Voting (MV) model combining trained models. KNN and MV achieved mean balanced accuracy, recall, precision, and F1 score of 0.87. All models were interpreted using Local Interpretable Model-agnostic Explanation (LIME) method and the five most relevant features were identified for each foot condition. High success scores indicate a strong relationship between selected features and foot conditions, potentially indicating clinical relevance. The proposed ML pipeline, adaptable for other foot conditions, showcases its potential in aiding experts in foot condition identification and planning surgeries., (© 2024. The Author(s).)

In this work we report on the characterization and biological functionalization of 2D MoS 2 flakes, epitaxially grown on sapphire, to develop an optical biosensor for the breast cancer biomarker miRNA21. The MoS 2 flakes were modified with a thiolated DNA probe complementary to the target biomarker. Based on the photoluminescence of MoS 2 , the hybridization events were analyzed for the target (miRNA21c) and the control non-complementary sequence (miRNA21nc). A specific redshift was observed for the hybridization with miRNA21c, but not for the control, demonstrating the biomarker recognition via PL. The homogeneity of these MoS 2 platforms was verified with microscopic maps. The detailed spectroscopic analysis of the spectra reveals changes in the trion to excitation ratio, being the redshift after the hybridization ascribed to both peaks. The results demonstrate the benefits of optical biosensors based on MoS 2 monolayer for future commercial devices.

Forest ecosystems sequester large amounts of atmospheric CO 2 , and the contribution from seasonally dry tropical forests is not negligible. Thus, the objective of this study was to quantify and evaluate the seasonal and annual patterns of CO 2 exchanges in the Caatinga biome, as well as to evaluate the ecosystem condition as carbon sink or source during years. In addition, we analyzed the climatic factors that control the seasonal variability of gross primary production (GPP), ecosystem respiration (R eco ) and net ecosystem CO 2 exchange (NEE). Results showed that the dynamics of the components of the CO 2 fluxes varied depending on the magnitude and distribution of rainfall and, as a consequence, on the variability of the vegetation state. Annual cumulative NEE was significantly higher (p < 0.01) in 2014 (-169.0 g C m -2 ) when compared to 2015 (-145.0 g C m -2 ) and annual NEP/GPP ratio was 0.41 in 2014 and 0.43 in 2015. Global radiation, air and soil temperature were the main factors associated with the diurnal variability of carbon fluxes. Even during the dry season, the NEE was at equilibrium and the Caatinga acted as an atmospheric carbon sink during the years 2014 and 2015.

Begomovirus ssDNA plant virus (family Geminiviridae) replication within the Bemisia tabaci vector is controversial. Transovarial transmission, alteration to whitefly biology, or detection of viral transcripts in the vector are proposed as indirect evidence of replication of tomato yellow leaf curl virus (TYLCV). Recently, contrasting direct evidence has been reported regarding the capacity of TYLCV to replicate within individuals of B. tabaci based on quantitave PCR approaches. Time-course experiments to quantify complementary and virion sense viral nucleic acid accumulation within B. tabaci using a recently implemented two step qPCR procedure revealed that viral DNA quantities did not increase for time points up to 96 hours after acquisition of the virus. Our findings do not support a recent report claiming TYLCV replication in individuals of B. tabaci.

Circular single-stranded DNA (ssDNA) viruses are the smallest viruses known to infect eukaryotes. High recombination and mutation rates have conferred these viruses with an evolutionary potential that has facilitated their emergence. Their damaging effects on livestock (circoviruses) and crops (geminiviruses and nanoviruses), and the ubiquity of anelloviruses in human populations and other mammalian species, have resulted in increased interest in better understanding their epidemiology and infection mechanisms. Circular ssDNA viral replication involves the synthesis of dsDNA intermediates containing complementary-sense (CS) and virion-sense (VS) strands. Precise quantification of VS and CS accumulation during viral infections can provide insights into the molecular mechanisms underlying viral replication and the host invasion process. Although qPCR protocols for quantifying viral molecules exist, none of them discriminate VS and CS strands. Here, using a two-step qPCR protocol we have quantified VS and CS molecule accumulation during the infection process of Tomato yellow leaf curl virus (TYLCV) and Tomato yellow leaf curl Sardinia virus (TYLCSV) (genus Begomovirus, family Geminiviridae). Our results show that the VS/CS strand ratio and overall dsDNA amounts vary throughout the infection process. Moreover, we show that these values depend on the virus-host combination, and that most CS strands are present as double-stranded molecules.