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An extensive experimental analysis for heart disease prediction using artificial intelligence techniques
Abstract The heart is an important organ that plays a crucial role in maintaining life. Unfortunately, heart disease is one of the major causes of mortality globally. Early and accurate detection can significantly improve the situation by enabling preventive measures and personalized healthcare recommendations. Artificial intelligence is emerging as a powerful tool for healthcare applications, particularly in predicting heart diseases. Researchers are actively working on this, but challenges remain in achieving accurate heart disease prediction. Therefore, experimenting with various models to identify the most effective one for heart disease prediction is crucial. In this view, this paper addresses this need by conducting an extensive investigation of various models. The proposed research considered 11 feature selection techniques and 21 classifiers for the experiment. The feature selection techniques considered for the research are Information Gain, Chi-Square Test, Fisher Discriminant Analysis (FDA), Variance Threshold, Mean Absolute Difference (MAD), Dispersion Ratio, Relief, LASSO, Random Forest Importance, Linear Discriminant Analysis (LDA), and Principal Component Analysis (PCA). The classifiers considered for the research are Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), XGBoost, AdaBoost, Stochastic Gradient Descent (SGD), Gradient Boosting Classifier, Extra Tree Classifier, CatBoost, LightGBM, Multilayer Perceptron (MLP), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Bidirectional LSTM (BiLSTM), Bidirectional GRU (BiGRU), Convolutional Neural Network (CNN), and Hybrid Model (CNN, RNN, LSTM, GRU, BiLSTM, BiGRU). Among all the extensive experiments, XGBoost outperformed all others, achieving an accuracy of 0.97, precision of 0.97, sensitivity of 0.98, specificity of 0.98, F1 score of 0.98, and AUC of 0.98.
Structural basis for malate-driven, pore lipid-regulated activation of the Arabidopsis vacuolar anion channel ALMT9
Abstract In plant cells, ALMTs are key plasma and vacuolar membrane-localized anion channels regulating plant responses to the environment. Vacuolar ALMTs control anion accumulation in plant cells and, in guard cells, they regulate stomata aperture. The activation of vacuolar ALMTs depends on voltage and cytosolic malate, but the underlying molecular mechanisms remain elusive. Here we report the cryo-EM structures of ALMT9 from Arabidopsis thaliana (AtALMT9), a malate-activated vacuolar anion channel, in plugged and unplugged lipid-bound states. In all these states, membrane lipids interact with the ion conduction pathway of AtALMT9. We identify two unplugged states presenting two distinct pore width profiles. Combining structural and functional analysis we identified conserved residues involved in ion conduction and in the pore lipid interaction. Molecular dynamics simulations revealed a peculiar anion conduction mechanism in AtALMT9. We propose a voltage-dependent activation mechanism based on the competition between pore lipids and malate at the cytosolic entrance of the channel.
Shedding light on dark taxa: exploring a cryptic diversity of parasitoid wasps affected by artificial light at night
Abstract Artificial light at night (ALAN) contributes to the globally observed insect decline. ALAN attracts nocturnal insects from their native ecosystems and disturbs their functions in the food web. Road lights in this context are ubiquitous and relevant ALAN sources that are often not considered in conservation approaches. In a previous study we showed that shielded LED road lights are suited to be part of conservation measures by effectively reducing the attraction of nocturnal insects. Here we show that this positive effect holds true for parasitoid wasps in an experimental BACI design (Before-After-Control-Impact). Combining morphological with molecular and phylogenetic analyses, we identified 106 individuals (62 morphotypes) of a minimum of 45 genera out of 13 Hymenoptera families. We were able to identify 21 species, 11 of which are newly reported in Southern Germany (Baden-Württemberg). Further combining knowledge on life history and host appearance from our data and the literature, we discuss potential impacts of ALAN ranging from an influence on nocturnal pollination via parasitoid pressure on moth species and biological control of invasive pest species to tritrophic interactions between primary and secondary parasitoids. We conclusively think that shielded LED road lights will reduce the ecological impact of ALAN on parasitoid wasps in a large and undescribed number of taxa with different host associations, likely affecting associated ecosystem functions such as biological control.
Striatal arbitration between choice strategies guides few-shot adaptation
A Dickson polynomial based group key agreement authentication scheme for ensuring conditional privacy preservation and traceability in VANETs
Abstract VANETs exchange data in highly dynamic open wireless access environments which are prone to security and privacy attacks. In order to safeguard the transmitted data, group key agreement authentication (GKAA) technique is used. Utilization of group key allows entities to corroborate a group key for secure VANET communication in an unsecure wireless communication channels. The traditional GKAA consumes a considerable amount of resources, verification delay is very high. Since the group key is computed and administered solely by TTA, it leads to central tendency. Additionally the communication delay soars high. To alleviate the problems of computational cost, communication cost, security, conditional privacy, central tendency, a Dickson polynomial based conditional privacy preservation authentication based on group key authentication (GKA) for VANETs has been proposed. The proposed work involves the use of Dickson polynomial to improve the security strength of TTA while authentication vehicles. Since it is based on chaotic mapping algorithm, wherein the chaotic map provides a one-way hash function; Dickson polynomial is used to corroborate a publicly distributed group key; it alleviates the complex modular or scalar multiplication performed using Elliptic curves. The group key gets computed in a distributed fashion by using the Chinese Remainder Theorem (CRT) and gets updated dynamically without the aid of TTA. Conditional Privacy has been ensured by the tracing back the pseudonyms in case of any illicit behavior exhibited by the vehicles. The proposed scheme is lightweight and lowers the communication, computation cost involved during authentication and verification. Performance analysis has been carried out by using BAN logic and ROR model thereby ensuring the security and efficiency. Thus the proposed authentication technique outperforms the traditional certificate-less and group key authentication schemes in terms of improvement in computation cost of 39%, communication cost of 672 bits for a single message with a less verification delay.
Topologically cloaked magnetic colloidal transport
Abstract Cloaking is a method of making obstacles undetectable. Here we cloak unit cells of a magnetic pattern squeezed into an otherwise periodic pattern from a magnetically driven colloidal flow. We apply a time-periodic external magnetic field loop to an ensemble of paramagnetic colloidal particles on the deformed periodic magnetic pattern. There exist topological loops where the particles avoid to trespass the cloaked regions by robustly traveling around the cloak. Afterwards the ensemble of particles continues with a motion identical to the motion as if the distorted region were nonexistent and the ensemble would have trespassed the undeformed region. We construct the cloak by continuously squeezing new conformally mapped unit cells between those of the originally undeformed and periodic pattern. We find a cloaking/decloaking transition as a function of the size and shape of the newly squeezed-in region. A cloak is scalable to arbitrary size if the biholomorphic map from the undistorted periodic lattice to the region outside the cloak locally rotates by less than an angle of forty five degrees. The work generalizes cloaking from waves toward particles.
A fuzzy rank-based deep ensemble methodology for multi-class skin cancer classification
Predicting immunotherapy response of advanced bladder cancer through a meta-analysis of six independent cohorts
Disease burden of trachea, bronchus and lung cancer 1990–2021 and global trends projected to 2035
Expanding protected area coverage for migratory birds could improve long-term population trends
Abstract Populations of many migratory taxa have been declining over recent decades. Although protected areas are a cornerstone for conservation, their role in protecting migratory species can be incomplete due to the dynamic distributions of these species. Here, we use a pan-European citizen science bird occurrence dataset (EurobirdPortal) with Spatiotemporal Exploratory Modelling to assess how the weekly distributions of 30 passerine and near passerine species overlap with protected areas in Europe and compare this to range adjusted policy protection targets. Thirteen of our 30 species were inadequately covered by protected areas for some, or all, of the European part of their annual cycle under a target based on the 2020 Convention on Biodiversity framework and none were adequately covered under a target based on the 2030 Convention on Biodiversity framework. Species associated with farmland had the lowest percentage of their weekly distribution protected. The percentage of a species’ distribution within protected areas was positively correlated with its long-term population trend, even after accounting for confounding factors, suggesting a positive influence of protected areas on long-term trends. This emphasises the positive contribution that an informed expansion of the European protected area system could play for the future conservation of migratory land birds.
Reinforcement learning based Secure edge enabled multi task scheduling model for internet of everything applications
Olaparib combined to metronomic cyclophosphamide and metformin in women with recurrent advanced/metastatic endometrial cancer: the ENDOLA phase I/II trial
Ancient DNA HLA typing reveals significant shifts in frequency in Europe since the Neolithic
A neural implementation model of feedback-based motor learning
Abstract Animals use feedback to rapidly correct ongoing movements in the presence of a perturbation. Repeated exposure to a predictable perturbation leads to behavioural adaptation that compensates for its effects. Here, we tested the hypothesis that all the processes necessary for motor adaptation may emerge as properties of a controller that adaptively updates its policy. We trained a recurrent neural network to control its own output through an error-based feedback signal, which allowed it to rapidly counteract external perturbations. Implementing a biologically plausible plasticity rule based on this same feedback signal enabled the network to learn to compensate for persistent perturbations through a trial-by-trial process. The network activity changes during learning matched those from populations of neurons from monkey primary motor cortex — known to mediate both movement correction and motor adaptation — during the same task. Furthermore, our model natively reproduced several key aspects of behavioural studies in humans and monkeys. Thus, key features of trial-by-trial motor adaptation can arise from the internal properties of a recurrent neural circuit that adaptively controls its output based on ongoing feedback.
The effect of irrigation quota on straw decomposition, nitrogen release, and maize nitrogen uptake
Emergent patterns of patchiness differ between physical and planktonic properties in the ocean
Abstract While a rich history of patchiness research has explored spatial structure in the ocean, there is no consensus over the controls on biological patchiness and how physical-ecological-biogeochemical processes and patchiness relate. The prevailing thought is that physics structures biology, but this has not been tested at basin scale with consistent in situ measurements. Here we use the slope of the relationship between variance vs spatial scale to quantify patchiness and ~650,000 nearly continuous ( dx ~ 200 m) measurements - representing the Atlantic, Pacific, and Southern Oceans - and find that patchiness of biological parameters and physical parameters are uncorrelated. We show variance slope is an emergent property with unique patterns in biogeochemical properties distinct from physical tracers, yet correlated with other biological tracers. These results provide context for decades of observations with different interpretations, suggest the use of spatial tests of biogeochemical model parameterizations, and open the way for studies into processes regulating the observed patterns.
Gene expression analyses of the calmodulin binding protein 60 family under water stress conditions in rice
STANCE: a unified statistical model to detect cell-type-specific spatially variable genes in spatial transcriptomics
Abstract One of the major challenges in spatial transcriptomics is to detect spatially variable genes (SVGs), whose expression patterns are non-random across tissue locations. Many SVGs correlate with cell type compositions, introducing the concept of cell type-specific SVGs (ctSVGs). Existing ctSVG detection methods treat cell type-specific spatial effects as fixed effects, leading to tissue spatial rotation-dependent results. Moreover, SVGs may exhibit random spatial patterns within cell types, meaning an SVG is not always a ctSVG, and vice versa, further complicating detection. We propose STANCE, a unified statistical model for both SVGs and ctSVGs detection under a linear mixed-effect model framework that integrates gene expression, spatial location, and cell type composition information. STANCE ensures tissue rotation-invariant results, with a two-stage approach: initial SVG/ctSVG detection followed by ctSVG-specific testing. We demonstrate its performance through extensive simulations and analyses of public datasets. Downstream analyses reveal STANCE’s potential in spatial transcriptomics analysis.