STPoseNet: A real-time spatiotemporal network model for robust mouse pose estimation

Animal behavior analysis plays a crucial role in contemporary neuroscience research. However, the performance of the frame-by-frame approach may degrade in scenarios with occlusions or motion blur. In this study, we propose a spatiotemporal network model based on YOLOv8 to enhance the accuracy of key-point detection in mouse behavioral experimental videos. This model integrates a time-domain tracking strategy comprising two components: the first part utilizes key-point detection results from the previous frame to detect potential target locations in the subsequent frame; the second part employs Kalman filtering to analyze key-point changes prior to detection, allowing for the estimation of missing key-points. In the comparison of pose estimation results between our approach, YOLOv8, DeepLabCut and SLEAP on videos of three mouse behavioral experiments, our approach demonstrated significantly superior performance. This suggests that our method offers a new and effective means of accurately tracking and estimating pose in mice through spatiotemporal processing.

Learning interpretable causal networks from very large datasets, application to 400,000 medical records of breast cancer patients

Discovering causal effects is at the core of scientific investigation but remains challenging when only observational data are available. In practice, causal networks are difficult to learn and interpret, and limited to relatively small datasets. We report a more reliable and scalable causal discovery method (iMIIC), based on a general mutual information supremum principle, which greatly improves the precision of inferred causal relations while distinguishing genuine causes from putative and latent causal effects. We showcase iMIIC on synthetic and real-world healthcare data from 396,179 breast cancer patients from the US Surveillance, Epidemiology, and End Results program. More than 90% of predicted causal effects appear correct, while the remaining unexpected direct and indirect causal effects can be interpreted in terms of diagnostic procedures, therapeutic timing, patient preference or socio-economic disparity. iMIIC's unique capabilities open up new avenues to discover reliable and interpretable causal networks across a range of research fields.

W-Net: A boundary-aware cascade network for robust and accurate optic disc segmentation

Accurate optic disc (OD) segmentation has a great significance for computer-aided diagnosis of different types of eye diseases. Due to differences in image acquisition equipment and acquisition methods, the resolution, size, contrast, and clarity of images from different datasets show significant differences, resulting in poor generalization performance of deep learning networks. To solve this problem, this study proposes a multi-level segmentation network. The network includes data quality enhancement module (DQEM), coarse segmentation module (CSM), localization module (OLM), and fine segmentation stage module (FSM). In FSM, W-Net is proposed for the first time, and boundary loss is introduced in the loss function, which effectively improves the performance of OD segmentation. We generalized the model in the REFUGE test dataset, GAMMA dataset, Drishti-GS1 dataset, and IDRiD dataset, respectively. The results show that our method has the best OD segmentation performance in different datasets compared with state-of-the-art networks.

Interhemispheric multisensory perception and Bayesian causal inference

In daily life, our brain needs to eliminate irrelevant signals and integrate relevant signals to facilitate natural interactions with the surrounding. Previous study focused on paradigms without effect of dominant laterality and found that human observers process multisensory signals consistent with Bayesian causal inference (BCI). However, most human activities are of bilateral interaction involved in processing of interhemispheric sensory signals. It remains unclear whether the BCI framework also fits to such activities. Here, we presented a bilateral hand-matching task to understand the causal structure of interhemispheric sensory signals. In this task, participants were asked to match ipsilateral visual or proprioceptive cues with the contralateral hand. Our results suggest that interhemispheric causal inference is most derived from the BCI framework. The interhemispheric perceptual bias may vary strategy models to estimate the contralateral multisensory signals. The findings help to understand how the brain processes the uncertainty information coming from interhemispheric sensory signals.

Integrating extrusion complex-associated pattern to predict cell type-specific long-range chromatin loops

The chromatin loop plays a critical role in the study of gene expression and disease. Supervised learning-based algorithms to predict the chromatin loops require large priori information to satisfy the model construction, while the prediction sensitivity of unsupervised learning-based algorithms is still unsatisfactory. Therefore, we propose an unsupervised algorithm, Ecomap-loop. It takes advantage of extrusion complex-associated patterns, including CTCF, RAD21, and SMC enrichments, as well as the orientation distribution of CTCF motif of loops to build feature matrices; then the eigen decomposition model is employed to obtain the cell type-specific loops. We compare the performance of Ecomap-loop with the state-of-the-art unsupervised algorithm using Hi-C, ChIA-PET, expression quantitative trait locus (eQTL), and CRISPR interference (CRISPRi) screen data; the results show that Ecomap-loop achieves the best in four cell types. In addition, the functional analysis reveals the ability of Ecomap-loop to predict active functionality-related and cell type-specific loops.

Improving Alzheimer's stage categorization with Convolutional Neural Network using transfer learning and different magnetic resonance imaging modalities

BACKGROUND

Alzheimer's Disease (AD) is a neurodegenerative disease characterized by progressive loss of memory and general decline in cognitive functions. Multi-modal imaging such as structural MRI and DTI provide useful information for the classification of patients on the basis of brain biomarkers. Recently, CNN methods have emerged as powerful tools to improve classification using images.

NEW METHOD

In this paper, we propose a transfer learning scheme using Convolutional Neural Networks (CNNs) to automatically classify brain scans focusing only on a small ROI: e.g. a few slices of the hippocampal region. The network's architecture is similar to a LeNet-like CNN upon which models are built and fused for AD stage classification diagnosis. We evaluated various types of transfer learning through the following mechanisms: (i) cross-modal (sMRI and DTI) and (ii) cross-domain transfer learning (using MNIST) (iii) a hybrid transfer learning of both types.

RESULTS

Our method shows good performances even on small datasets and with a limited number of slices of small brain region. It increases accuracy with more than 5 points for the most difficult classification tasks, i.e., AD/MCI and MCI/NC.

COMPARISON WITH EXISTING METHODS

Our methodology provides good accuracy scores for classification over a shallow convolutional network. Besides, we focused only on a small region; i.e., the hippocampal region, where few slices are selected to feed the network. Also, we used cross-modal transfer learning.

CONCLUSIONS

Our proposed method is suitable for working with a shallow CNN network for low-resolution MRI and DTI scans. It yields to significant results even if the model is trained on small datasets, which is often the case in medical image analysis.

Dynamic transparency in design: the revival of environmental sustainability in design elements of Iraqi buildings

Buildings in Iraqi cities such as Baghdad and Mosul suffer from several problems such as the application of new materials in modern buildings that changed not just the identity of architectural heritage but also the quality of thermal comfort in façade design. This, unfortunately, adds to the damage regarding environmental sustainability and cultural values away from adaptable solutions to improve energy efficiency in building performance. One of the measures that must be taken to correctly plan in harmony with the Iraqi cities is to ensure the environmental control as part of the overall performance of building façade to maintain an active, healthy indoor environment while preserving the propriety of facade design elements, screen pattern, order and details. Therefore, there are many sustainable trends that vary in their usefulness such as biomimetics examples inspired from natural models in which form and function dictate one another. This is in order to maintain the integrated design relation between transparency, function, and elegance in the overall performance of façade elements. The research question is, how important is the choice of material in developing a sustainable element that revives environmental control while preserving the identity and values of façade design? The main goal of the research study is to identify the role of advanced technologies and the choice of smart glazing materials to revive the quality of thermal comfort in a way that not just sustains the identity of facade elements socially and culturally, but also to be responsive to the changes of climate conditions. Therefore, this research utilizes more than one technological tool such as Revit as a BIM tool with the application of smart dynamic materials such as Photovoltaics and Electrochromic in order to restore part of the design expression and enhance the building performance through its elements in contemporary façade design and its details. In this work, it can be seen that applying a set of technological tools allows to clearly illustrate the impact of smart dynamic materials to improve the quality of design and comfort while protecting the identity of contemporary façade elements when compared to static or traditional materials, aesthetically, and functionally.

Energy optimization of Multiple Stage Evaporator system using Water Cycle Algorithm

Black liquor, a residual stream from the Kraft recovery process of paper mills is an incipient biomass energy resource which finds prospective biofuel-based industrial applications to ensure process self-sufficiency and sustainability. Black liquor is concentrated using Multiple Stage Evaporator, the utmost energy intensive unit, before using it as biofuel. Pertaining to the contemporary global energy scenario, improvement in energy efficiency of Multiple Stage Evaporator becomes indispensable. The present work investigates the non-linear modeling and simulation-based optimization of Heptads' stage based Multiple Stage Evaporator in backward feed flow configuration integrated with various energy saving strategies. A novel metaheuristic approach, Water Cycle Algorithm has been employed to search the optimum estimates of unknown process variables and therefore, the optimum energy efficiency parameters. The optimization results demonstrate the efficiency of Water Cycle Algorithm in screening the most appropriate operating strategy, i.e., hybrid model of all energy saving strategies (steam-split, feed-split and feed-preheating) with optimum energy efficiency i.e. Steam Economy of 7.092 and Steam Consumption of 1.919 kg/s. Moreover, a comparative analysis of the results with previous literature and real-time plant estimates reveal that the hybrid model offers improvement of 52.84% in Steam Economy and reduction in Steam Consumption by 28.13% when compared to the real plant data.

Regression analysis for thermal properties of Al2O3/H2O nanofluid using machine learning techniques

Nanofluids possess higher thermal properties than the other conventional base fluids. Many investigators suggested that the nanofluids have the potential to apply in various engineering fields. In real time situation it is challenging to determine the thermal conductivity of nanofluids with accuracy as they have many depending factors. Moreover, numerous experimental tests are required to acquire the thermal conductivity of nanofluids accurately. In this research paper, thermal conductivity ratio and dynamic viscosity ratio of Al₂O₃/H₂O nanofluid are predicted accurately by using Gaussian Process Regression (GPR) methods. The input predictor variables used in this model are temperature, volume fraction and size of the nanoparticles. 222 experimental data sets are taken to predict the thermal conductivity ratio (TCR), dynamic viscosity ratio (DVR) and also the effectiveness of the predictor variables in predicting the response variables are extensively studied and found that the temperature is the crucial factor to enhance the thermal conductivity ratio. The proposed modeling is performed by using MATLAB software. The predictions were evaluated by various evaluation criterions. It is observed that an optimized Gaussian process regression (GPR) method with matern kernel function shows an accurate agreement with experimental data with Root Mean Square Error (RMSE) value of 0.000126 for TCR and squared exponential kernel function show good agreement with experimental data with Root Mean Square Error (RMSE) value of 0.000045 for DVR. Regression coefficient value (R²) is 0.99; nearer to one hence the predicted results are reliable.

Theoretical investigation using DFT of quinoxaline derivatives for electronic and photovoltaic effects

Photovoltaic properties of solar cells based on fifteen organic dyes have been studied in this work. B3LYP/6-311G (d,p) methods are realized to obtain geometries and optimize the electronic properties, optical and photovoltaic parameters for some quinoxaline derivatives. The results showed that time dependent DFT investigations using the CAM-B3LYP method with the polarized split-valence 6-311G (d,p) basis sets and the polarizable continuum model PCM model were sensibly able to predict the excitation energies, the spectroscopy of the compounds. HOMO and LUMO energy levels of these molecules can make a positive impact on the process of electron injection and dye regeneration. Gaps energy ΔE_g, short-circuit current density J_sc, light-harvesting efficiency LHE, injection driving force ΔG^inject, total reorganization energy λ_total and open-circuit photovoltage V_oc enable qualitative predictions about the reactivity of these dyes.

Water demand modelling using evolutionary computation techniques: integrating water equity and justice for realization of the sustainable development goals

The purpose of this review is to establish and classify the diverse ways in which evolutionary computation (EC) techniques have been employed in water demand modelling and to identify important research challenges and future directions. This review also investigates the potentials of conventional EC techniques in influencing water demand management policies beyond an advisory role while recommending strategies for their use by policy-makers with the sustainable development goals (SDGs) in perspective. This review ultimately proposes a novel integrated water demand and management modelling framework (IWDMMF) that enables water policy-makers to assess the wider impact of water demand management decisions through the principles of egalitarianism, utilitarianism, libertarianism and sufficientarianism. This is necessary to ensure that water policy decisions incorporate equity and justice.

New approaches for Delaunay triangulation and optimisation

New techniques are presented for Delaunay triangular mesh generation and element optimisation. Sample points for triangulation are generated through mapping (a new approach). These sample points are later triangulated by the conventional Delaunay method. Resulting triangular elements are optimised by addition, removal and relocation of mapped sample points (element nodes). The proposed techniques (generation of sample points through mapping for Delaunay triangulation and mesh optimisation) are demonstrated by using Mathematica software. Simulation results show that the proposed techniques are able to form meshes that consist of triangular elements with aspect ratio of less than 2 and minimum skewness of more than 45°.