Electrophotocatalytic hydrogenation of imines and reductive functionalization of aryl halides.

The open-shell catalytically active species, like radical cations or radical anions, generated by one-electron transfer of precatalysts are widely used in energy-consuming redox reactions, but their excited-state lifetimes are usually short. Here, a closed-shell thioxanthone-hydrogen anion species (3), which can be photochemically converted to a potent and long-lived reductant, is generated under electrochemical conditions, enabling the electrophotocatalytic hydrogenation. Notably, TfOH can regulate the redox potential of the active species in this system. In the presence of TfOH, precatalyst (1) reduction can occur at low potential, so that competitive H2 evolution can be inhibited, thus effectively promoting the hydrogenation of imines. In the absence of TfOH, the reducing ability of the system can reach a potency even comparable to that of Na0 or Li0, thereby allowing the hydrogenation, borylation, stannylation and (hetero)arylation of aryl halides to construct C-H, C-B, C-Sn, and C-C bonds.

A review of neuroimaging-based data-driven approach for Alzheimer's disease heterogeneity analysis.

Alzheimer's disease (AD) is a complex form of dementia and due to its high phenotypic variability, its diagnosis and monitoring can be quite challenging. Biomarkers play a crucial role in AD diagnosis and monitoring, but interpreting these biomarkers can be problematic due to their spatial and temporal heterogeneity. Therefore, researchers are increasingly turning to imaging-based biomarkers that employ data-driven computational approaches to examine the heterogeneity of AD. In this comprehensive review article, we aim to provide health professionals with a comprehensive view of past applications of data-driven computational approaches in studying AD heterogeneity and planning future research directions. We first define and offer basic insights into different categories of heterogeneity analysis, including spatial heterogeneity, temporal heterogeneity, and spatial-temporal heterogeneity. Then, we scrutinize 22 articles relating to spatial heterogeneity, 14 articles relating to temporal heterogeneity, and five articles relating to spatial-temporal heterogeneity, highlighting the strengths and limitations of these strategies. Furthermore, we discuss the importance of understanding spatial heterogeneity in AD subtypes and their clinical manifestations, biomarkers for abnormal orderings and AD stages, the recent advancements in spatial-temporal heterogeneity analysis for AD, and the emerging role of omics data integration in advancing personalized diagnosis and treatment for AD patients. By emphasizing the significance of understanding AD heterogeneity, we hope to stimulate further research in this field to facilitate the development of personalized interventions for AD patients.

Organophotocatalytic Alkene Reduction Using Water as a Hydrogen Donor.

The chemical activation and functionalization of water are considered an ideal method for converting earth-abundant sources into valuable chemicals. Here, we show that a non-activated free water molecule can be applied directly as a hydrogen donor to achieve the carbanion-mediated alkene reduction with 9-HTXTF serving as an organophotocatalyst. Notably, direct syntheses of high-value-added drugs and bioactive molecules are readily achieved by utilizing plentiful energy and an earth-abundant resource, showcasing the usefulness of the protocol in chemical synthesis.

Comparison of Machine Learning Models for Brain Age Prediction Using Six Imaging Modalities on Middle-Aged and Older Adults.

Machine learning (ML) has transformed neuroimaging research by enabling accurate predictions and feature extraction from large datasets. In this study, we investigate the application of six ML algorithms (Lasso, relevance vector regression, support vector regression, extreme gradient boosting, category boost, and multilayer perceptron) to predict brain age for middle-aged and older adults, which is a crucial area of research in neuroimaging. Despite the plethora of proposed ML models, there is no clear consensus on how to achieve better performance in brain age prediction for this population. Our study stands out by evaluating the impact of both ML algorithms and image modalities on brain age prediction performance using a large cohort of cognitively normal adults aged 44.6 to 82.3 years old (N = 27,842) with six image modalities. We found that the predictive performance of brain age is more reliant on the image modalities used than the ML algorithms employed. Specifically, our study highlights the superior performance of T1-weighted MRI and diffusion-weighted imaging and demonstrates that multi-modality-based brain age prediction significantly enhances performance compared to unimodality. Moreover, we identified Lasso as the most accurate ML algorithm for predicting brain age, achieving the lowest mean absolute error in both single-modality and multi-modality predictions. Additionally, Lasso also ranked highest in a comprehensive evaluation of the relationship between BrainAGE and the five frequently mentioned BrainAGE-related factors. Notably, our study also shows that ensemble learning outperforms Lasso when computational efficiency is not a concern. Overall, our study provides valuable insights into the development of accurate and reliable brain age prediction models for middle-aged and older adults, with significant implications for clinical practice and neuroimaging research. Our findings highlight the importance of image modality selection and emphasize Lasso as a promising ML algorithm for brain age prediction.

Three-round learning strategy based on 3D deep convolutional GANs for Alzheimer's disease staging.

Accurately diagnosing of Alzheimer's disease (AD) and its early stages is critical for prompt treatment or potential intervention to delay the the disease's progression. Convolutional neural networks (CNNs) models have shown promising results in structural MRI (sMRI)-based diagnosis, but their performance, particularly for 3D models, is constrained by the lack of labeled training samples. To address the overfitting problem brought on by the insufficient training sample size, we propose a three-round learning strategy that combines transfer learning with generative adversarial learning. In the first round, a 3D Deep Convolutional Generative Adversarial Networks (DCGAN) model was trained with all available sMRI data to learn the common feature of sMRI through unsupervised generative adversarial learning. The second round involved transferring and fine-tuning, and the pre-trained discriminator (D) of the DCGAN learned more specific features for the classification task between AD and cognitively normal (CN). In the final round, the weights learned in the AD versus CN classification task were transferred to the MCI diagnosis. By highlighting brain regions with high prediction weights using 3D Grad-CAM, we further enhanced the model's interpretability. The proposed model achieved accuracies of 92.8%, 78.1%, and 76.4% in the classifications of AD versus CN, AD versus MCI, and MCI versus CN, respectively. The experimental results show that our proposed model avoids overfitting brought on by a paucity of sMRI data and enables the early detection of AD.

A Convolutional Neural Network and Graph Convolutional Network Based Framework for AD Classification.

The neuroscience community has developed many convolutional neural networks (CNNs) for the early detection of Alzheimer's disease (AD). Population graphs are thought of as non-linear structures that capture the relationships between individual subjects represented as nodes, which allows for the simultaneous integration of imaging and non-imaging information as well as individual subjects' features. Graph convolutional networks (GCNs) generalize convolution operations to accommodate non-Euclidean data and aid in the mining of topological information from the population graph for a disease classification task. However, few studies have examined how GCNs' input properties affect AD-staging performance. Therefore, we conducted three experiments in this work. Experiment 1 examined how the inclusion of demographic information in the edge-assigning function affects the classification of AD versus cognitive normal (CN). Experiment 2 was designed to examine the effects of adding various neuropsychological tests to the edge-assigning function on the mild cognitive impairment (MCI) classification. Experiment 3 studied the impact of the edge assignment function. The best result was obtained in Experiment 2 on multi-class classification (AD, MCI, and CN). We applied a novel framework for the diagnosis of AD that integrated CNNs and GCNs into a unified network, taking advantage of the excellent feature extraction capabilities of CNNs and population-graph processing capabilities of GCNs. To learn high-level anatomical features, DenseNet was used; a set of population graphs was represented with nodes defined by imaging features and edge weights determined by different combinations of imaging or/and non-imaging information, and the generated graphs were then fed to the GCNs for classification. Both binary classification and multi-class classification showed improved performance, with an accuracy of 91.6% for AD versus CN, 91.2% for AD versus MCI, 96.8% for MCI versus CN, and 89.4% for multi-class classification. The population graph's imaging features and edge-assigning functions can both significantly affect classification accuracy.

Discovery of a Thioxanthone-TfOH Complex as a Photoredox Catalyst for Hydrogenation of Alkenes Using p-Xylene as both Electron and Hydrogen Sources.

Hydrogenation of alkenes is one of the most fundamental transformations in organic synthesis, and widely used in the petrochemical, pharmaceutical, and food industries. Although numerous hydrogenation methods have been developed, novel types of catalysis with new mechanisms and new hydrogen sources are still desirable. Thioxanthone (TX) is widely used in energy-transfer photoreactions, but rarely in photoredox processes. Herein we show that a catalytic amount of TfOH as a co-catalyst can tune the properties of TX to make it a photoredox catalyst with highly enhanced oxidative capability in the hydrogenation of carbonylated alkenes with the cheap petroleum industrial product p-xylene serving as the hydrogen source. Deuterium can also be introduced by this method by using D2 O as the D source. To the best of our knowledge, this is the first example of using p-xylene as a hydrogen source.

Quantitative and Selective DNA Detection with Portable Personal Glucose Meter Using Loop-Based DNA Competitive Hybridization Strategy.

A portable, quantitative, and selective DNA detection biosensor, based on a loop-based DNA competitive hybridization assay and a personal glucose meter (PGM), is an advanced strategy for one-step target DNA recognition and signal reporter generation. In the presence of target DNA, the invertase-DNA conjugates are released due to the competitive binding of target DNA and collected with the help of a magnet subsequently. The released invertase-DNA could catalyze the hydrolysis of sucrose into glucose with millions of turnovers which is target concentration dependent. In addition, the sensor exhibits excellent anti-interference ability, having almost no effect on the detection performance in serum. The biosensor shown here is easier to operate owning its great potential in point of care testing in environments with limited resources and skilled personnel for rapid and sensitive detection of specific DNA sequence in real biological samples.

Automated Diagnosis and Localization of Melanoma from Skin Histopathology Slides Using Deep Learning: A Multicenter Study.

In traditional hospital systems, diagnosis and localization of melanoma are the critical challenges for pathological analysis, treatment instructions, and prognosis evaluation particularly in skin diseases. In literature, various studies have been reported to address these issues; however, a prominent smart diagnosis system is needed to be developed for the smart healthcare system. In this study, a deep learning-enabled diagnostic system is proposed and implemented that it has the capacity to automatically detect malignant melanoma in whole slide images (WSIs). In this system, the convolutional neural network (CNN), sophisticated statistical method, and image processing algorithms were integrated and implemented to locate benign and malignant lesions which are extremely useful in the diagnoses process of melanoma disease. To verify the exceptional performance of the proposed scheme, it is implemented in a multicenter database, which has 701 WSIs (641 WSIs from Central South University Xiangya Hospital (CSUXH) and 60 WSIs from the Cancer Genome Atlas (TCGA)). Experimental results have verified that the proposed system has achieved an area under the receiver operating characteristic curve (AUROC) of 0.971. Furthermore, the lesion area on the WSIs is represented by its degree of malignancy. These results show that the proposed system has the capacity to fully automate the diagnosis and localization problem of the melanoma in the smart healthcare systems.

Interpretable Diagnosis for Whole-Slide Melanoma Histology Images Using Convolutional Neural Network.

At present, deep learning-based medical image diagnosis had achieved high performance in several diseases. However, the black-box nature of the convolutional neural network (CNN) limits their role in diagnosis. In this study, a novel interpretable diagnosis pipeline using the CNN model was proposed. Furthermore, a sizeable melanoma database that contains 841 digital whole-slide images (WSIs) was built to train and evaluate the model. The model achieved strong melanoma classification ability (0.962 areas under the receiver operating characteristic, 0.887 sensitivity, and 0.925 specificity). Moreover, the proposed model outperformed the existing schemes in terms of accuracy that is 20 pathologists (0.933 vs 0.732 accuracy). Finally, the gradient-weighted class activation mapping (Grad-CAM) method was used to show the inner logic of the proposed model and its feasibility to improve diagnosis process in healthcare. The mechanism of feature heat maps which is visualized through a saliency mapping has demonstrated that features learned or extracted by the proposed model are compatible with the accepted pathological features. Conclusively, the proposed model provides a rapid and accurate diagnosis by locating the distinctive features of melanoma to build doctors' trust in the CNNs' diagnosis results.

Multi-model and multi-slice ensemble learning architecture based on 2D convolutional neural networks for Alzheimer's disease diagnosis.

Alzheimer's Disease (AD) is a chronic neurodegenerative disease without effective medications or supplemental treatments. Thus, predicting AD progression is crucial for clinical practice and medical research. Due to limited neuroimaging data, two-dimensional convolutional neural networks (2D CNNs) have been commonly adopted to differentiate among cognitively normal subjects (CN), people with mild cognitive impairment (MCI), and AD patients. Therefore, this paper proposes an ensemble learning (EL) architecture based on 2D CNNs, using a multi-model and multi-slice ensemble. First, the top 11 coronal slices of grey matter density maps for AD versus CN classifications were selected. Second, the discriminator of a generative adversarial network, VGG16, and ResNet50 were trained with the selected slices, and the majority voting scheme was used to merge the multi-slice decisions of each model. Afterwards, those three classifiers were used to construct an ensemble model. Multi-slice ensemble learning was designed to obtain spatial features, while multi-model integration reduced the prediction error rate. Finally, transfer learning was used in domain adaptation to refine those CNNs, moving them from working solely with AD versus CN classifications to being applicable to other tasks. This ensemble approach achieved accuracy values of 90.36%, 77.19%, and 72.36% when classifying AD versus CN, AD versus MCI, and MCI versus CN, respectively. Compared with other state-of-the-art 2D studies, the proposed approach provides an effective, accurate, automatic diagnosis along the AD continuum. This technique may enhance AD diagnostics when the sample size is limited.

Dynamic Dissection of Dynein and Kinesin-1 Cooperatively Mediated Intercellular Transport of Porcine Epidemic Diarrhea Coronavirus along Microtubule Using Single Virus Tracking.

It is now clear that the intercellular transport on microtubules by dynein and kinesin-1 motors has an important role in the replication and spread of many viruses. Porcine epidemic diarrhea virus (PEDV) is an enveloped, single-stranded RNA virus of the Coronavirus family, which can infect swine of all ages and cause severe economic losses in the swine industry. Elucidating the molecular mechanisms of the intercellular transport of PEDV through microtubule, dynein and kinesin-1 will be crucial for understanding its pathogenesis. Here, we demonstrate that microtubule, dynein, and kinesin-1 are involved in PEDV infection and can influence PEDV fusion and accumulation in the perinuclear region but cannot affect PEDV attachment or internalization. Furthermore, we adopted a single-virus tracking technique to dynamically observe PEDV intracellular transport with five different types: unidirectional movement toward microtubule plus ends; unidirectional movement toward microtubule minus ends; bidirectional movement along the same microtubule; bidirectional movement along different microtubules and motionless state. Among these types, the functions of dynein and kinesin-1 in PEDV intercellular transport were further analyzed by single-virus tracking and found that dynein and kinesin-1 mainly transport PEDV to the minus and plus ends of the microtubules, respectively; meanwhile, they also can transport PEDV to the opposite ends of the microtubules different from their conventional transport directions and also coordinate the bidirectional movement of PEDV along the same or different microtubules through their cooperation. These results provided deep insights and references to understand the pathogenesis of PEDV as well as to develop vaccines and treatments.

Cu(II)-Catalyzed 6π-Photocyclization of Non-6π Substrates.

This research successfully achieved a Cu(II)-catalyzed 6π-photocyclization of non-6π substrates. The photoenolization converts ortho-alkylphenyl alkynl ketones into a triene-type intermediate which undergoes the subsequent 6π-photocyclization to give naphthol as the final product. Cu(II) catalyst facilitates both photoenolization and 6π-photocyclization. This research highlighted the tandem reaction strategy and the importance of metal catalysis in photochemistry.

Quantitative Detection and Real-Time Monitoring of Endogenous mRNA at the Single Live Cell Level Using a Ratiometric Molecular Beacon.

Messenger ribonucleic acid (mRNA) plays an important role in various cellular processes. however, traditional techniques cannot realize mRNA detections in live cells as they rely on mRNA purification or cell fixation. To achieve real-time and quantitative mRNA detections at a single live cell level, a single-strand stem-loop-structured ratiometric molecular beacon (RMB) composed of the phosphorothioate-modified loop domain on the 2'-O-methyl RNA backbone with a reporter dye, quencher, and reference dye is proposed to detect the Hsp27 mRNA as a modeled endogenous mRNA. When the RMB hybridizes with the target, the stem-loop structure opens, causing separation of the reporter dye and the quencher and restores the reporter fluorescent signals; therefore, the Hsp27 mRNA can be quantitatively detected according to the ratio of the reporter fluorescent signal to the reference fluorescent signal. Both the phosphorothioate and 2'-O-methyl RNA modifications obviously reduce the nonspecific opening, and the additional reference dye ensures the detection precision using co-localization analysis. Not only does this remove the false-positive signal caused by the nuclease degradation-generated RMB fragment, but it also corrects variations caused by direct measurement of reporter fluorescence intensities at a single cell level owing to inhomogeneity in probe delivery. The designed RMB could detect the Hsp27 mRNA with high signal-to-noise ratio and sensitivity as well as excellent specificity and antidegradation capability proved in vitro and in live cells. Furthermore, it was successfully adopted in subcellular localization, quantitative copy number measurements, and even real-time monitoring of Hsp27 mRNA in live cells, demonstrating that the proposed RMB can be a potential quantitative endogenous mRNA detection tool, especially at a single live cell level.

On-site cell concentration and viability detections using smartphone based field-portable cell counter.

We designed a smartphone based field-portable cell counter combining the smartphone microscope for bright-field image recording and the smartphone application for automatically cell recognition, counting and analysis. To our best knowledge, it is the first time that a smartphone based cell counter can distinguish and count both live and dead cells simultaneously. Compared to the results obtained by hemocytometer, commercial cell counter and flow cytometer, the proposed device was proved to detect cell concentration and viability accurately within the application range between 105 cells/mL and 107 cells/mL. Though multiple fields of view were measured to increase the sampling amount for error reduction, the whole operations including image recording and processing can still be finished rapidly. Moreover, the proposed device is cost-effective with small size of 170 mm × 113 mm × 168 mm containing a built-in power supply. Considering its advantages as high accuracy, fast speed, low cost, long battery life and compact configuration, it is believed the proposed device is a potential tool applied in on-site cell analysis.

Real-time analysis of quantum dot labeled single porcine epidemic diarrhea virus moving along the microtubules using single particle tracking.

In order to study the infection mechanism of porcine epidemic diarrhea virus (PEDV), which causes porcine epidemic diarrhea, a highly contagious enteric disease, we combined quantum dot labeled method, which could hold intact infectivity of the labeled viruses to the largest extent, with the single particle tracking technique to dynamically and globally visualize the transport behaviors of PEDVs in live Vero cells. Our results were the first time to uncover the dynamic characteristics of PEDVs moving along the microtubules in the host cells. It is found that PEDVs kept restricted motion mode with a relatively stable speed in the cell membrane region; while performed a slow-fast-slow velocity pattern with different motion modes in the cell cytoplasm region and near the microtubule organizing center region. In addition, the return movements of small amount of PEDVs were also observed in the live cells. Collectively, our work is crucial for understanding the movement mechanisms of PEDV in the live cells, and the proposed work also provided important references for further analysis and study on the infection mechanism of PEDVs.

Template-free synthesis of salmon pink tube-shaped structure carbon nitride with enhanced visible light photocatalytic activity.

Designing a highly active and stable photocatalyst to directly solve environmental pollution is desirable for solar energy conversion. Herein, an effective strategy, hydrothermal-calcination, for synthesizing extremely active carbon nitride (salmon pink) from a low-cost precursor melamine, is reported. The salmon pink carbon nitride with tube-shaped structure significantly enhanced response to visible light, improved efficiency of charge separation and remarkably enhanced efficiency of methyl orange (MO) degradation than bulk g-C3N4 (light orange). The M-10-200-24-600 composite possessed the most wonderful ability towards MO degradation irradiated by visible light, which could achieve a highest degradation efficiency of 84% within 120 min. Our findings may provide a promising and facile approach to highly efficient photocatalysis for solar-energy conversion.

Influence of Different Coupling Modes on the Robustness of Smart Grid under Targeted Attack.

Many previous works only focused on the cascading failure of global coupling of one-to-one structures in interdependent networks, but the local coupling of dual coupling structures has rarely been studied due to its complex structure. This will result in a serious consequence that many conclusions of the one-to-one structure may be incorrect in the dual coupling network and do not apply to the smart grid. Therefore, it is very necessary to subdivide the dual coupling link into a top-down coupling link and a bottom-up coupling link in order to study their influence on network robustness by combining with different coupling modes. Additionally, the power flow of the power grid can cause the load of a failed node to be allocated to its neighboring nodes and trigger a new round of load distribution when the load of these nodes exceeds their capacity. This means that the robustness of smart grids may be affected by four factors, i.e., load redistribution, local coupling, dual coupling link and coupling mode; however, the research on the influence of those factors on the network robustness is missing. In this paper, firstly, we construct the smart grid as a two-layer network with a dual coupling link and divide the power grid and communication network into many subnets based on the geographical location of their nodes. Secondly, we define node importance ( N I ) as an evaluation index to access the impact of nodes on the cyber or physical network and propose three types of coupling modes based on N I of nodes in the cyber and physical subnets, i.e., Assortative Coupling in Subnets (ACIS), Disassortative Coupling in Subnets (DCIS), and Random Coupling in Subnets (RCIS). Thirdly, a cascading failure model is proposed for studying the effect of local coupling of dual coupling link in combination with ACIS, DCIS, and RCIS on the robustness of the smart grid against a targeted attack, and the survival rate of functional nodes is used to assess the robustness of the smart grid. Finally, we use the IEEE 118-Bus System and the Italian High-Voltage Electrical Transmission Network to verify our model and obtain the same conclusions: (I) DCIS applied to the top-down coupling link is better able to enhance the robustness of the smart grid against a targeted attack than RCIS or ACIS, (II) ACIS applied to a bottom-up coupling link is better able to enhance the robustness of the smart grid against a targeted attack than RCIS or DCIS, and (III) the robustness of the smart grid can be improved by increasing the tolerance α . This paper provides some guidelines for slowing down the speed of the cascading failures in the design of architecture and optimization of interdependent networks, such as a top-down link with DCIS, a bottom-up link with ACIS, and an increased tolerance α .