Interpretable machine learning for predicting risk of invasive fungal infection in critically ill patients in the intensive care unit: A retrospective cohort study based on MIMIC-IV database.

IMPORTANCE: Invasive fungal infections are characterized by high incidence and high mortality rates characteristics. In this study, we developed a clinical prediction model for invasive fungal infections in critically ill patients based on machine learning algorithms. The results show that the machine learning model based on 20 clinical features has good predictive value.

Monolayer Organic Crystals for Ultrahigh Performance Molecular Diodes.

Molecular diodes are of considerable interest for the increasing technical demands of device miniaturization. However, the molecular diode performance remains contact-limited, which represents a major challenge for the advancement of rectification ratio and conductance. Here, it is demonstrated that high-quality ultrathin organic semiconductors can be grown on several classes of metal substrates via solution-shearing epitaxy, with a well-controlled number of layers and monolayer single crystal over 1 mm. The crystals are atomically smooth and pinhole-free, providing a native interface for high-performance monolayer molecular diodes. As a result, the monolayer molecular diodes show record-high rectification ratio up to 5 × 108 , ideality factor close to unity, aggressive unit conductance over 103 S cm-2 , ultrahigh breakdown electric field, excellent electrical stability, and well-defined contact interface. Large-area monolayer molecular diode arrays with 100% yield and excellent uniformity in the diode metrics are further fabricated. These results suggest that monolayer molecular crystals have great potential to build reliable, high-performance molecular diodes and deeply understand their intrinsic electronic behavior.

Genomic inference of a severe human bottleneck during the Early to Middle Pleistocene transition.

Population size history is essential for studying human evolution. However, ancient population size history during the Pleistocene is notoriously difficult to unravel. In this study, we developed a fast infinitesimal time coalescent process (FitCoal) to circumvent this difficulty and calculated the composite likelihood for present-day human genomic sequences of 3154 individuals. Results showed that human ancestors went through a severe population bottleneck with about 1280 breeding individuals between around 930,000 and 813,000 years ago. The bottleneck lasted for about 117,000 years and brought human ancestors close to extinction. This bottleneck is congruent with a substantial chronological gap in the available African and Eurasian fossil record. Our results provide new insights into our ancestry and suggest a coincident speciation event.

Integration of image preprocessing and recognition functions in an optoelectronic coupling organic ferroelectric retinomorphic neuristor.

The human visual system (HVS) has the advantages of a low power consumption and high efficiency because of the synchronous perception and early preprocessing of external image information in the retina, as well as parallel in-memory computing within the visual cortex. Realizing the biofunction simulation of the retina and visual cortex in a single device structure provides opportunities for performance improvements and machine vision system (MVS) integration. Here, we fabricate organic ferroelectric retinomorphic neuristors that integrate the retina-like preprocessing function and recognition of the visual cortex in a single device architecture. Benefiting from the electrical/optical coupling modulation of ferroelectric polarization, our devices show a bidirectional photoresponse that acts as the basis for mimicking retinal preconditioning and multi-level memory capabilities for recognition. The MVS based on the proposed retinomorphic neuristors achieves a high recognition accuracy of ∼90%, which is 20% higher than that of the incomplete system without the preprocessing function. In addition, we successfully demonstrate image encryption and optical programming logic gate functions. Our work suggests that the proposed retinomorphic neuristors offer great potential for MVS monolithic integration and functional expansion.

Upregulation of ENKD1 disrupts cellular homeostasis to promote lymphoma development.

Diffuse large B cell lymphoma (DLBCL) is a common and aggressive form of B cell lymphoma. Approximately 40% of DLBCL patients are incurable despite modern therapeutic approaches. To explore the molecular mechanisms driving the growth and progression of DLBCL, we analyzed genes with differential expression in DLBCL using the Gene Expression Profiling Interactive Analysis database. Enkurin domain-containing protein 1 (ENKD1), a centrosomal protein-encoding gene, was found to be highly expressed in DLBCL samples compared with normal samples. The phylogenetic analysis revealed that ENKD1 is evolutionarily conserved. Depletion of ENKD1 in cultured DLBCL cells induced apoptosis, suppressed cell proliferation, and blocked cell cycle progression in the G2/M phase. Moreover, ENKD1 expression positively correlates with the expression levels of a number of cellular homeostatic regulators, including Sperm-associated antigen 5, a gene encoding an important mitotic regulator. These findings thus demonstrate a critical function for ENKD1 in regulating the cellular homeostasis and suggest a potential value of targeting ENKD1 for the treatment of DLBCL.

GCNs-Net: A Graph Convolutional Neural Network Approach for Decoding Time-Resolved EEG Motor Imagery Signals.

Toward the development of effective and efficient brain-computer interface (BCI) systems, precise decoding of brain activity measured by an electroencephalogram (EEG) is highly demanded. Traditional works classify EEG signals without considering the topological relationship among electrodes. However, neuroscience research has increasingly emphasized network patterns of brain dynamics. Thus, the Euclidean structure of electrodes might not adequately reflect the interaction between signals. To fill the gap, a novel deep learning (DL) framework based on the graph convolutional neural networks (GCNs) is presented to enhance the decoding performance of raw EEG signals during different types of motor imagery (MI) tasks while cooperating with the functional topological relationship of electrodes. Based on the absolute Pearson's matrix of overall signals, the graph Laplacian of EEG electrodes is built up. The GCNs-Net constructed by graph convolutional layers learns the generalized features. The followed pooling layers reduce dimensionality, and the fully-connected (FC) softmax layer derives the final prediction. The introduced approach has been shown to converge for both personalized and groupwise predictions. It has achieved the highest averaged accuracy, 93.06% and 88.57% (PhysioNet dataset), 96.24% and 80.89% (high gamma dataset), at the subject and group level, respectively, compared with existing studies, which suggests adaptability and robustness to individual variability. Moreover, the performance is stably reproducible among repetitive experiments for cross-validation. The excellent performance of our method has shown that it is an important step toward better BCI approaches. To conclude, the GCNs-Net filters EEG signals based on the functional topological relationship, which manages to decode relevant features for brain MI. A DL library for EEG task classification including the code for this study is open source at https://github.com/SuperBruceJia/ EEG-DL for scientific research.

A Motor Imagery Signals Classification Method via the Difference of EEG Signals Between Left and Right Hemispheric Electrodes.

Brain-computer interface (BCI) based on motor imagery (MI) can help patients with limb movement disorders in their normal life. In order to develop an efficient BCI system, it is necessary to decode high-accuracy motion intention by electroencephalogram (EEG) with low signal-to-noise ratio. In this article, a MI classification approach is proposed, combining the difference in EEG signals between the left and right hemispheric electrodes with a dual convolutional neural network (dual-CNN), which effectively improved the decoding performance of BCI. The positive and inverse problems of EEG were solved by the boundary element method (BEM) and weighted minimum norm estimation (WMNE), and then the scalp signals were mapped to the cortex layer. We created nine pairs of new electrodes on the cortex as the region of interest. The time series of the nine electrodes on the left and right hemispheric are respectively used as the input of the dual-CNN model to classify four MI tasks. The results show that this method has good results in both group-level subjects and individual subjects. On the Physionet database, the averaged accuracy on group-level can reach 96.36%, while the accuracies of four MI tasks reach 98.54, 95.02, 93.66, and 96.19%, respectively. As for the individual subject, the highest accuracy is 98.88%, and its four MI accuracies are 99.62, 99.68, 98.47, and 97.73%, respectively.

Molecular-Layer-Defined Asymmetric Schottky Contacts in Organic Planar Diodes for Self-Powered Optoelectronic Synapses.

Optoelectronic synapses have been utilized as neuromorphic vision sensors for image preprocessing in artificial visual systems. Self-powered optoelectronic synapses, which can directly convert optical power into electrical power, are promising for practical applications. The Schottky junction tends to be a promising candidate as the energy source for electrical operations. However, fully utilizing the potential of Schottky barriers is still challenging. Herein, organic self-powered optoelectronic synapses with planar diode architecture are fabricated, which can simultaneously sense and process ultraviolet (UV) signals. The photovoltaic operations are facilitated by the built-in potential originating from the molecular-layer-defined asymmetric Schottky contacts. Diverse synaptic behaviors under UV light stimulation without external power supplies are facilitated by the interfacial carrier-capturing layer, which emulates the membranes of synapses. Furthermore, retina-inspired image preprocessing functions are demonstrated on the basis of synaptic plasticity. Therefore, our devices provide the potential for the development of power-efficient and advanced artificial visual systems.

Emerging Logic Devices beyond CMOS.

Si-based complementary metal-oxide-semiconductor (CMOS) transistors for logic computing have represented the most essential foundation of digital electronic technologies for decades toward the modern information era. The continuous scaling down of the transistor feature size has promoted significant improvements in the computing performance while gradually tending to its limit. Ubiquitous intelligent technologies have quickly penetrated daily life, yielding a tremendous increase in highly data-centric computing applications. Hence, emerging logic devices extending and even transcending the existing CMOS technology are urgently needed to meet the rapidly growing demand for information processing capability, involving revolutionary innovations from material science and architecture design to device applications. This thus gives us the opportunity to realize logic devices for state-of-the-art computing that are fundamentally far beyond the current devices. In this Perspective, we discuss the recent innovative design strategies of emerging logic devices along with the opportunities and challenges, providing a promising avenue toward high-performance and diversiform logic computing in the post-Moore era.

Fine human genetic map based on UK10K data set.

Recombination is a major force that shapes genetic diversity. Determination of recombination rate is important and can theoretically be improved by increasing the sample size. However, it is nearly impossible to estimate recombination rates using traditional population genetics methods when the sample size is large because these methods are highly computationally demanding. In this study, we used a refined machine learning approach to estimate the recombination rate of the human genome using the UK10K human genomic dataset with 7,562 genomic sequences and its three subsets with 200, 400 and 2,000 genomic sequences. The estimation was performed under the human Out-of-Africa demographic model. We not only obtained an accurate human genetic map, but also found that the fluctuation of estimated recombination rate is reduced along the human genome when the sample size increases. The estimated UK10K recombination rate heterogeneity is less than that estimated from its subsets. Our results demonstrate how the sample size affects the estimated recombination rate, and analyses of a larger number of genomes result in a more precise estimation of recombination rate. The accurate genetic map based on UK10K data set is also expected to benefit other human biology researches.

Retina-Inspired Self-Powered Artificial Optoelectronic Synapses with Selective Detection in Organic Asymmetric Heterojunctions.

The retina, the most crucial unit of the human visual perception system, combines sensing with wavelength selectivity and signal preprocessing. Incorporating energy conversion into these superior neurobiological features to generate core visual signals directly from incoming light under various conditions is essential for artificial optoelectronic synapses to emulate biological processing in the real retina. Herein, self-powered optoelectronic synapses that can selectively detect and preprocess the ultraviolet (UV) light are presented, which benefit from high-quality organic asymmetric heterojunctions with ultrathin molecular semiconducting crystalline films, intrinsic heterogeneous interfaces, and typical photovoltaic properties. These devices exhibit diverse synaptic behaviors, such as excitatory postsynaptic current, paired-pulse facilitation, and high-pass filtering characteristics, which successfully reproduce the unique connectivity among sensory neurons. These zero-power optical-sensing synaptic operations further facilitate a demonstration of image sharpening. Additionally, the charge transfer at the heterojunction interface can be modulated by tuning the gate voltage to achieve multispectral sensing ranging from the UV to near-infrared region. Therefore, this work sheds new light on more advanced retinomorphic visual systems in the post-Moore era.

Coronavirus GenBrowser for monitoring the transmission and evolution of SARS-CoV-2.

Genomic epidemiology is important to study the COVID-19 pandemic, and more than two million severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomic sequences were deposited into public databases. However, the exponential increase of sequences invokes unprecedented bioinformatic challenges. Here, we present the Coronavirus GenBrowser (CGB) based on a highly efficient analysis framework and a node-picking rendering strategy. In total, 1,002,739 high-quality genomic sequences with the transmission-related metadata were analyzed and visualized. The size of the core data file is only 12.20 MB, highly efficient for clean data sharing. Quick visualization modules and rich interactive operations are provided to explore the annotated SARS-CoV-2 evolutionary tree. CGB binary nomenclature is proposed to name each internal lineage. The pre-analyzed data can be filtered out according to the user-defined criteria to explore the transmission of SARS-CoV-2. Different evolutionary analyses can also be easily performed, such as the detection of accelerated evolution and ongoing positive selection. Moreover, the 75 genomic spots conserved in SARS-CoV-2 but non-conserved in other coronaviruses were identified, which may indicate the functional elements specifically important for SARS-CoV-2. The CGB was written in Java and JavaScript. It not only enables users who have no programming skills to analyze millions of genomic sequences, but also offers a panoramic vision of the transmission and evolution of SARS-CoV-2.

Synthesis of 2-methoxybenzamide derivatives and evaluation of their hedgehog signaling pathway inhibition.

Aberrant hedgehog (Hh) signaling is implicated in the development of a variety of cancers. Smoothened (Smo) protein is a bottleneck in the Hh signal transduction. The regulation of the Hh signaling pathway to target the Smo receptor is a practical approach for development of anticancer agents. We report herein the design and synthesis of a series of 2-methoxybenzamide derivatives as Hh signaling pathway inhibitors. The pharmacological data demonstrated that compound 21 possessed potent Hh pathway inhibition with a nanomolar IC50 value, and it prevented Shh-induced Smo from entering the primary cilium. Furthermore, mutant Smo was effectively suppressed via compound 21. The in vitro antiproliferative activity of compound 21 against a drug-resistant cell line gave encouraging results.

Genomic consequences of population decline in critically endangered pangolins and their demographic histories.

Pangolins are among the most critically endangered animals due to heavy poaching and worldwide trafficking. However, their demographic histories and the genomic consequences of their recent population declines remain unknown. We generated high-quality de novo reference genomes for critically endangered Malayan (Manis javanica, MJ) and Chinese (M. pentadactyla, MP) pangolins and re-sequencing population genomic data from 74 MJs and 23 MPs. We recovered the population identities of illegally traded pangolins and previously unrecognized genetic populations that should be protected as evolutionarily distinct conservation units. Demographic reconstruction suggested environmental changes have resulted in a population size fluctuation of pangolins. Additionally, recent population size declines due to human activities have resulted in an increase in inbreeding and genetic load. Deleterious mutations were enriched in genes related to cancer/diseases and cholesterol homeostasis, which may have increased their susceptibility to diseases and decreased their survival potential to adapt to environmental changes and high-cholesterol diets. This comprehensive study provides not only high-quality pangolin reference genomes, but also valuable information concerning the driving factors of long-term population size fluctuations and the genomic impact of recent population size declines due to human activities, which is essential for pangolin conservation management and global action planning.

Convergent genomic signatures of high-altitude adaptation among domestic mammals.

Abundant and diverse domestic mammals living on the Tibetan Plateau provide useful materials for investigating adaptive evolution and genetic convergence. Here, we used 327 genomes from horses, sheep, goats, cattle, pigs and dogs living at both high and low altitudes, including 73 genomes generated for this study, to disentangle the genetic mechanisms underlying local adaptation of domestic mammals. Although molecular convergence is comparatively rare at the DNA sequence level, we found convergent signature of positive selection at the gene level, particularly the EPAS1 gene in these Tibetan domestic mammals. We also reported a potential function in response to hypoxia for the gene C10orf67, which underwent positive selection in three of the domestic mammals. Our data provide an insight into adaptive evolution of high-altitude domestic mammals, and should facilitate the search for additional novel genes involved in the hypoxia response pathway.

Genetic variation in glia-neuron signalling modulates ageing rate.

The rate of behavioural decline in the ageing population is remarkably variable among individuals. Despite the considerable interest in studying natural variation in ageing rate to identify factors that control healthy ageing, no such factor has yet been found. Here we report a genetic basis for variation in ageing rates in Caenorhabditis elegans. We find that C. elegans isolates show diverse lifespan and age-related declines in virility, pharyngeal pumping, and locomotion. DNA polymorphisms in a novel peptide-coding gene, named regulatory-gene-for-behavioural-ageing-1 (rgba-1), and the neuropeptide receptor gene npr-28 influence the rate of age-related decline of worm mating behaviour; these two genes might have been subjected to recent selective sweeps. Glia-derived RGBA-1 activates NPR-28 signalling, which acts in serotonergic and dopaminergic neurons to accelerate behavioural deterioration. This signalling involves the SIR-2.1-dependent activation of the mitochondrial unfolded protein response, a pathway that modulates ageing. Thus, natural variation in neuropeptide-mediated glia-neuron signalling modulates the rate of ageing in C. elegans.