Anomalous Structural and Spin Transition Behaviours in Stimuli-Responsive Metal-Organic Frameworks

The design of artificial stimuli-responsive metal–organic frameworks (SR-MOFs) that are able to dynamically respond to their environment has attracted considerable interest. Spin crossover (SCO) MOF materials are one subset of SR-MOFs. One key challenge in the design of SCO MOFs is to achieve targeted and controllable properties under different stimuli. Thus, uncovering the interplay between external stimuli, intrinsic structure, and spin state properties is crucial for creating desirable materials. This thesis discusses the syntheses and characterisations of a family of new SCO MOFs and their electronic and structural responsivity to temperature, pressure, and guest molecule encapsulation as stimuli. 3D Hofmann-like MOFs [Fe(Tz)Au(CN)2)2] (Tz = 3,6-bis(4-pyridyl)-1,2,4,5-tetrazine) and [Fe(Dz)Au(CN)2)2] (Dz = 3,6-bis(4-pyridyl)-1,2-diazine) were synthesised as mother frameworks to investigate the structural properties and SCO behaviours with single- and binary-component of xylene adsorption. Not only can SCO behaviours and structures be tuned by altering the guest molecules, but also by the building units, which is by altering the component and ratio of cyanidometallate linkers and pillared ligands. The study on manipulating the ratio of dicyanoaurate(I) or dicyanoargenate(I) linkers uncovers the importance of metallophilic interactions on the extent of lattice motion and magnetic properties. The thesis also discusses the precise control of spin transition temperatures, negative thermal expansion and lattice flexing via tuning pillared ligand components. Pressure-induced SCO was investigated, and the materials present anomalous negative linear compressibility and scissor motions in Hofmann layers. The insights gained into these SR-MOFs reveal the complex interplay between the various stimuli, reversible SCO behaviours, and structural distortions. This work should pave the way towards the rational design of controllable intelligent materials

Leveraging Prototype Patient Representations with Feature-Missing-Aware Calibration to Mitigate EHR Data Sparsity

Electronic Health Record (EHR) data frequently exhibits sparse characteristics, posing challenges for predictive modeling. Current direct imputation such as matrix imputation approaches hinge on referencing analogous rows or columns to complete raw missing data and do not differentiate between imputed and actual values. As a result, models may inadvertently incorporate irrelevant or deceptive information with respect to the prediction objective, thereby compromising the efficacy of downstream performance. While some methods strive to recalibrate or augment EHR embeddings after direct imputation, they often mistakenly prioritize imputed features. This misprioritization can introduce biases or inaccuracies into the model. To tackle these issues, our work resorts to indirect imputation, where we leverage prototype representations from similar patients to obtain a denser embedding. Recognizing the limitation that missing features are typically treated the same as present ones when measuring similar patients, our approach designs a feature confidence learner module. This module is sensitive to the missing feature status, enabling the model to better judge the reliability of each feature. Moreover, we propose a novel patient similarity metric that takes feature confidence into account, ensuring that evaluations are not based merely on potentially inaccurate imputed values. Consequently, our work captures dense prototype patient representations with feature-missing-aware calibration process. Comprehensive experiments demonstrate that designed model surpasses established EHR-focused models with a statistically significant improvement on MIMIC-III and MIMIC-IV datasets in-hospital mortality outcome prediction task. The code is publicly available at \url{https://github.com/yhzhu99/SparseEHR} to assure the reproducibility

A quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism

Ancient landslide has strong concealment and disturbance sensitivity due to its special geotechnical mechanical characteristics, and it is the potential hazard that cannot be ignored in human activities and major engineering planning. The quantitative assessment of ancient landslide reactivation risk has become more necessary for pre-disaster scientific warning. However, because the mechanisms of deformation and damage during the evolution of ancient landslides are quite complex, traditional landslide risk assessment methods only select the single-time scale and relatively stable environmental factors for analysis, lacking consideration of dynamic triggering factors such as rainfall. Focusing on the complexity, a quantitative enhanced assessment for ancient landslide reactivation risk considering cross-time scale joint response mechanism is proposed. First, on the basis of systematic analysis of the implicit genesis mechanism and explicit characterization, an evaluation system of the cross-time scale joint characteristics of ancient landslide reactivation is constructed. Then, XGBoost algorithm and SBAS-InSAR are used to establish the long-time scale developmental evolution mechanism model and the short-time scale dynamical trigger model, respectively. Subsequently, we propose a cross-time scale joint response mechanism. The information entropy weight method is applied to calculate the contribution degree of long-short time scale assessment models for ancient landslide reactivation based on the constraints of quantitative interval thresholds, and the assessment processes of different time scales are dynamically and quantitatively correlated. Finally, the updated optimization of the assessment of ancient landslide reactivation risk is achieved. In this research, experimental analysis was carried out for ancient landslide groups in a geological hazard-prone area in Fengjie County, Chongqing, a typical mountainous region of China. The results of the comparative analysis validate the superiority of the method in this paper. It helps to accurately assess the ancient landslide potential hazard in advance, providing scientific basis and technical support for the risk assessment of mountainous watershed geological hazards and major engineering projects

Integration of Multiple Spectral Indices and a Neural Network for Burned Area Mapping Based on MODIS Data

Since wildfires have occurred frequently in recent years, accurate burned area mapping is required for wildfire severity assessment and burned land reconstruction. Satellite remote sensing is an effective technology that can provide valuable information for wildfire assessment. However, the common approaches based on using a single satellite image to promptly detect the burned areas have low accuracy and limited applicability. This paper develops a new burned area mapping method that surpasses the detection accuracy of previous methods, while still using a single Moderate Resolution Imaging Spectroradiometer (MODIS) sensor image. The key innovation is integrating optimal spectral indices and a neural network algorithm. We used the traditional empirical formula method, multi-threshold method and visual interpretation method to extract the sample sets of five typical types (burned area, vegetation, cloud, bare soil, and cloud shadow) from the MODIS data of several wildfires in the American states of Nevada, Washington and California in 2016. Afterward, the separability index M was adopted to assess the capacity of seven spectral bands and 13 spectral indices to distinguish the burned area from four unburned land cover types. Based on the separability analysis between the burned area and unburned areas, the spectral indices with an M value higher than 1.0 were employed to generate the training sample sets that were assessed to have an overall accuracy of 98.68% and Kappa coefficient of 97.46%. Finally, we utilized a back-propagation neural network (BPNN) to learn the spectral differences of different types from the training sample sets and obtain the output burned area map. The proposed method was applied to three wildfire cases in the American states of Idaho, Nevada and Oregon in 2017. A comparison of detection results between the new MODIS-based burned area map and the reference burned area map compiled from Landsat-8 Operational Land Imager (OLI) data indicates that the proposed method can effectively exploit the spectral characteristics of various land cover types. Also, this new method can achieve higher accuracy with the reduction of commission error (CE, >10%) and omission error (OE, >6%) compared to the traditional empirical formula method. The new burned area mapping method could help managers and the public perform more effective wildfire assessments and emergency management

DRHNet: A Deep Residual Network Based on Heterogeneous Kernel for Steganalysis

Convolutional neural networks as steganalysis have problems such as poor versatility, long training time, and limited image size. For these problems, we present a heterogeneous kernel residual learning framework called DRHNet—Dual Residual Heterogeneous Network—to save time on the networks during the training phase. Instead of using the image as an input of the network, we extract and merge the images into a feature matrix using the rich model and use the generated feature matrix as the real input of the network. The architecture we proposed has good versatility and can reduce the computation and the number of parameters while still getting higher accuracy. On BOSSbase 1.01, we evaluate the performance of DRHNet in the setting of the spatial domain and frequency domain. The preliminary experimental results show that DRHNet shows excellent steganalysis performance against the state-of-the-art steganographic algorithms

Influence of Potassium-Based Alkaline Electrolyzed Water on Hydration Process and the Properties of Cement-Based Materials with Fly Ash

Alkaline electrolyzed water, a kind of clean green water with excellent characteristics such as high activity, strong alkalinity, high ion penetrating ability, electrical charge, and good molecule adsorption, was significant to the resource utilization of industrial fly ash waste. This paper studies highly active potassium-based alkaline electrolyzed water’s impact, compared with ordinary water, on the cement hydration process using microstructural methods such as a hydration heat test, differential thermal analysis, X-ray diffraction (XRD) pattern, and Scanning electron microscope (SEM) image analysis. Fly ash cement-based materials were first prepared with alkaline electrolyzed water as the mixing water. The alkaline electrolyzed water’s influence on fly ash paste workability and the mechanical properties of fly ash mortar for varying fly ash proportions were ratified. Then alkaline electrolyzed water with the best pH value was selected to prepare fly ash concrete, and its durability was studied. The test results showed that it is feasible to increase the utilization rate of fly ash by using alkaline electrolyzed water. Furthermore, it promoted the process of cement hydration, increased the rate of the hydration reaction, and the promotion effect increased with the increase in pH value of the alkaline electrolyzed water, and also promoted the effective decomposition of the vitreous shell of fly ash to stimulate its early activity. Concurrent tests with ordinary water paste showed that the water requirement for normal consistency and setting time with alkaline electrolyzed water paste were significantly less. Alkaline electrolyzed water also solved the problem related to the low early strength of fly ash mortar. Furthermore, using alkaline electrolyzed water with an optimum pH value of 11.5 to prepare fly ash concrete effectively reduced concrete’s carbonation depth and carbonation rate and lessened the chloride ion migration coefficient

Amelioration of Diabetic Mouse Nephropathy by Catalpol Correlates with Down-Regulation of Grb10 Expression and Activation of Insulin-Like Growth Factor 1 / Insulin-Like Growth Factor 1 Receptor Signaling.

Growth factor receptor-bound protein 10 (Grb10) is an adaptor protein that can negatively regulate the insulin-like growth factor 1 receptor (IGF-1R). The IGF1-1R pathway is critical for cell growth and apoptosis and has been implicated in kidney diseases; however, it is still unknown whether Grb10 expression is up-regulated and plays a role in diabetic nephropathy. Catalpol, a major active ingredient of a traditional Chinese medicine, Rehmannia, has been reported to possess anti-inflammatory and anti-aging activities and then used to treat diabetes. Herein, we aimed to assess the therapeutic effect of catalpol on a mouse model diabetic nephropathy and the potential role of Grb10 in the pathogenesis of this diabetes-associated complication. Our results showed that catalpol treatment improved diabetes-associated impaired renal functions and ameliorated pathological changes in kidneys of diabetic mice. We also found that Grb10 expression was significantly elevated in kidneys of diabetic mice as compared with that in non-diabetic mice, while treatment with catalpol significantly abrogated the elevated Grb10 expression in diabetic kidneys. On the contrary, IGF-1 mRNA levels and IGF-1R phosphorylation were significantly higher in kidneys of catalpol-treated diabetic mice than those in non-treated diabetic mice. Our results suggest that elevated Grb10 expression may play an important role in the pathogenesis of diabetic nephropathy through suppressing IGF-1/IGF-1R signaling pathway, which might be a potential molecular target of catalpol for the treatment of this diabetic complication

Development of machine learning-based predictors for early diagnosis of hepatocellular carcinoma

Abstract Hepatocellular carcinoma (HCC) remains a formidable malignancy that significantly impacts human health, and the early diagnosis of HCC holds paramount importance. Therefore, it is imperative to develop an efficacious signature for the early diagnosis of HCC. In this study, we aimed to develop early HCC predictors (eHCC-pred) using machine learning-based methods and compare their performance with existing methods. The enhancements and advancements of eHCC-pred encompassed the following: (i) utilization of a substantial number of samples, including an increased representation of cirrhosis tissues without HCC (CwoHCC) samples for model training and augmented numbers of HCC and CwoHCC samples for model validation; (ii) incorporation of two feature selection methods, namely minimum redundancy maximum relevance and maximum relevance maximum distance, along with the inclusion of eight machine learning-based methods; (iii) improvement in the accuracy of early HCC identification, elevating it from 78.15 to 97% using identical independent datasets; and (iv) establishment of a user-friendly web server. The eHCC-pred is freely accessible at http://www.dulab.com.cn/eHCC-pred/ . Our approach, eHCC-pred, is anticipated to be robustly employed at the individual level for facilitating early HCC diagnosis in clinical practice, surpassing currently available state-of-the-art techniques

The diversity and paleoenvironmental significance of <i>Calophyllum</i> (Clusiaceae) from the Miocene of southeastern China

<p>Three species within the genus <i>Calophyllum</i> collected from middle Miocene Fotan Group sediments in Zhangpu County, Fujian, southeastern China are described in this paper. These fossils include <i>Calophyllum zhangpuensis</i> sp. nov., <i>Calophyllum striatum</i>, and <i>Calophyllum suraikholaensis</i>. The new fossil species <i>C. zhangpuensis</i> sp. nov. is oval, possesses entire leaves with closely spaced parallel secondary veins and has a round, or slightly retuse, apex. These specimens represent the first known fossil records of this relative wide leaf-type form of <i>Calophyllum</i> from China and have a length:width (L:W) ratio less than 3:1. In combination with the known modern geographic distribution and habitats of this wide leaf-type <i>Calophyllum</i> and other plants, data suggest that the middle Miocene Fotan flora is indicative of a warm climate. Thus, based on available fossil data, we speculate that this genus probably originated in India during the Paleocene before spreading from India to Bangladesh and into China, Sumatra, Malaysia, Indonesia, and Java during the Neogene, leading to its modern distribution. At least, the 3 fossil species in this region can explain floristic exchange between India, Fujian, and South China, which is consistent with previous studies; the occurrence of these 3 species indicates that <i>Calophyllum</i> began to diversity in China no later than the Miocene.</p