Biomimetic nanocluster photoreceptors for adaptative circular polarization vision.

Nanoclusters with atomically precise structures and discrete energy levels are considered as nanoscale semiconductors for artificial intelligence. However, nanocluster electronic engineering and optoelectronic behavior have remained obscure and unexplored. Hence, we create nanocluster photoreceptors inspired by mantis shrimp visual systems to satisfy the needs of compact but multi-task vision hardware and explore the photo-induced electronic transport. Wafer-scale arrayed photoreceptors are constructed by a nanocluster-conjugated molecule heterostructure. Nanoclusters perform as an in-sensor charge reservoir to tune the conductance levels of artificial photoreceptors by a light valve mechanism. A ligand-assisted charge transfer process takes place at nanocluster interface and it features an integration of spectral-dependent visual adaptation and circular polarization recognition. This approach is further employed for developing concisely structured, multi-task, and compact artificial visual systems and provides valuable guidelines for nanocluster neuromorphic devices.

MyoPS: A benchmark of myocardial pathology segmentation combining three-sequence cardiac magnetic resonance images.

Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on the myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining three-sequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. Note that MyoPS refers to both myocardial pathology segmentation and the challenge in this paper. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore the potential of solutions, as well as to provide a benchmark for future research. The average Dice scores of submitted algorithms were 0.614±0.231 and 0.644±0.153 for myocardial scars and edema, respectively. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

Bionic Tactile-Gustatory Receptor for Object Identification Based on All-Polymer Electrochemical Transistor.

Human sensory receptors enable the real world to be perceived effortlessly. Hence, massive efforts have been devoted to the development of bionic receptors capable of identifying objects. Unfortunately, most of the existing devices are limited to single sensory emulation and are established on solid-state electronic technologies, which are incompatible with the biological reactions occurring in electrolyte media. Here, an iontronic tactile-gustatory receptor using an all-polymer electrochemical transistor (AECT) is presented. The sensor is biocompatible with the operation voltage of 0.1 V, which is 1 to 2 orders lower than those of reported values. By this study, one receptor is able to accurately recognize various objects perceived by the human tactile and gustatory system without complex circuitry. Additionally, to promote its further application, flexible AECT arrays with channel length of 2 µm and density of 104 167 transistors cm-2 (yield of 97%) are fabricated, 1 to 5 orders higher than those of related works. Finally, a flexible integrated network for electrocardiogram recording is successfully constructed. This study moves a step forward toward state-of-the-art bionic sensors.

An artificial-intelligence-based age-specific template construction framework for brain structural analysis using magnetic resonance images.

It is an essential task to construct brain templates and analyze their anatomical structures in neurological and cognitive science. Generally, templates constructed from magnetic resonance imaging (MRI) of a group of subjects can provide a standard reference space for analyzing the structural and functional characteristics of the group. With recent development of artificial intelligence (AI) techniques, it is desirable to explore AI registration methods for quantifying age-specific brain variations and tendencies across different ages. In this article, we present an AI-based age-specific template construction (called ASTC) framework for longitudinal structural brain analysis using T1-weighted MRIs of 646 subjects from 18 to 82 years old collected from four medical centers. Altogether, 13 longitudinal templates were constructed at a 5-year age interval using ASTC, and tissue segmentation and substructure parcellation were performed for analysis across different age groups. The results indicated consistent changes in brain structures along with aging and demonstrated the capability of ASTC for longitudinal neuroimaging study.

Low-Voltage Intrinsically Stretchable Organic Transistor Amplifiers for Ultrasensitive Electrophysiological Signal Detection.

Stretchability is a prerequisite for electronic skin devices. However, state-of-the-art stretchable thin-film transistors do not possess sufficiently low operating voltages and good stability, significantly limiting their use in real-world biomedical applications. Herein, a van der Waals-controlling elastomer/carbon quantum dot interfacial polarization methodology is proposed to form a hybrid polymer dielectric with 620% tensile strain and large-area film uniformity (>A4 paper size). Using the hybrid polymer dielectrics, the prepared intrinsically stretchable organic thin-film transistors demonstrate a low operating voltage below 5 V, 100% strain tolerance, and excellent operational stability, as well as a high on-current/off-current ratio of 105 and a steep subthreshold slope of 500 mV dec-1 . Based on this device technology, an amplifier with a high gain of 90 V V-1 among the highest values of reported stretchable transistors is realized. This amplifier is at the first time applied to detect human electrophysical signals with an output signal amplitude of over 0.2 V, which even outperforms other types of the state-of-the-art organic amplifiers for human electrophysiology monitoring. This stretchable device technology sufficiently meets the safety and portability requirements of wearable biomedical applications, opening a new opportunity to e-skin with signal control and amplification capabilities.

Van der Waals Multilayer Heterojunction for Low-Voltage Organic RGB Area-Emitting Transistor Array.

Organic light-emitting transistors (OLETs) have garnered considerable attention from academy and industry due to their potential applications in next-generation display technologies, multifunctional devices, and organic electrically pumped lasers. However, overcoming the trade-offs among power consumption, external quantum efficiency (EQE), and uniform area emission remains a long-standing issue for OLETs. Herein, a van der Waals multilayer heterojunction methodology is proposed to enhance the layer-to-layer interfacial interaction and contact, resulting in better dipole shielding, carrier transport, exciton recombination, and current density distribution. The prepared multilayer heterojunction OLET (MLH-OLET) array shows uniform and bright RGB area emission and low operating voltage (<30 V among the lowest applied voltage of reported lateral LETs). Additionally, a high brightness under area emission of 1060 cd m-2 , a high EQE value of 0.85%, and a high loop stability (over 380 cycles, outperforming state-of-the-art OLETs) indicate that the proposed multilayer heterojunction is obviously superior to the reported lateral device configuration. The van der Waals multilayer heterojunction developed for the preparation of OLET arrays sufficiently meets the low-voltage, high-performance, and low-cost requirements of future display technologies.

Visualization of ligand-induced c-MYC duplex-quadruplex transition and direct exploration of the altered c-MYC DNA-protein interactions in cells.

Ligand-Induced duplex-quadruplex transition within the c-MYC promoter region is one of the most studied and advanced ideas for c-MYC regulation. Despite its importance, there is a lack of methods for monitoring such process in cells, hindering a better understanding of the essence of c-MYC G-quadruplex as a drug target. Here we developed a new fluorescent probe ISCH-MYC for specific c-MYC G-quadruplex recognition based on GTFH (G-quadruplex-Triggered Fluorogenic Hybridization) strategy. We validated that ISCH-MYC displayed distinct fluorescence enhancement upon binding to c-MYC G-quadruplex, which allowed the duplex-quadruplex transition detection of c-MYC G-rich DNA in cells. Using ISCH-MYC, we successfully characterized the induction of duplex to G-quadruplex transition in the presence of G-quadruplex stabilizing ligand PDS and further monitored and evaluated the altered interactions of relevant transcription factors Sp1 and CNBP with c-MYC G-rich DNA. Thus, our study provides a visualization strategy to explore the mechanism of G-quadruplex stabilizing ligand action on c-MYC G-rich DNA and relevant proteins, thereby empowering future drug discovery efforts targeting G-quadruplexes.

Ultrahigh-Performance Optoelectronic Skin Based on Intrinsically Stretchable Perovskite-Polymer Heterojunction Transistors.

The optoelectronic skin is acknowledged as the world's current cutting-edge technology in the fields of wearable healthcare monitoring, soft robotics, artificial retinas, and so on. However, the difficulty in preparing stretchable photosensitive polymers and the high-crystallization nature of most reported photosensitive materials (such as perovskites) severely restrict the development of skin-like optoelectronic devices. Herein, a surface energy-induced self-assembly methodology is proposed to form easily transferrable and flexible perovskite quantum dot (PQD) films with a worm-like morphology. Furthermore, intrinsically stretchable phototransistors (ISTPTs) are fabricated based on a stretchable photosensitive layer heterojunction consisting of worm-like PQD films and hybrid polymer semiconductors. The obtained ISTPTs display highly sensitive response to high-energy photons of X-ray (with a detection limit of 79 nGy s-1 , that is 560 times lower than commercial medical chest X-ray diagnosis) and ultraviolet (with photosensitivity of 5 × 106 and detectable light intensity of 50 nW cm-2 among the highest performance of reported photodetectors). In addition, these ISTPTs demonstrate desirable e-skin characteristics with high strain tolerance, high sensing specificity, high optical transparency, and good skin conformability. The surface energy-induced self-assembly methodology for the preparation of ISTPTs is a critical demonstration to enable low-cost and high-performance optoelectronic skins.

Two-Dimensional Metal-Organic Framework Film for Realizing Optoelectronic Synaptic Plasticity.

2D metal-organic framework (MOF) film as the active layer show promising application prospects in various fields including sensors, catalysis, and electronic devices. However, exploring the application of 2D MOF film in the field of artificial synapses has not been implemented yet. In this work, we fabricated a novel 2D MOF film (Cu-THPP, THPP=5,10,15,20-Tetrakis(4-hydroxyphenyl)-21H,23H-porphine), and further used it as an active layer to explore the application in the simulation of human brain synapses. It shows excellent light-stimulated synaptic plasticity properties, and exhibits the foundation function of synapses such as long-term plasticity (LTP), short-term plasticity (STP), and the conversion of STP to LTP. Most critically, the MOF based artificial synaptic device exhibits an excellent stability in atmosphere. This work opens the door for the application of 2D MOF film in the simulation of human brain synapses.

A consistent deep registration network with group data modeling.

Medical image registration is a critical process for automated image computing, and ideally, the deformation field from one image to another should be smooth and inverse-consistent in order to bidirectionally align anatomical structures and to preserve their topology. Consistent registration can reduce bias caused by the order of input images, increase robustness, and improve reliability of subsequent quantitative analysis. Rigorous differential geometry constraints have been used in traditional methods to enforce the topological consistency but require comprehensive optimization and are time consuming. Recent studies show that deep learning-based registration methods can achieve comparable accuracy and are much faster than traditional registration. However, the estimated deformation fields do not necessarily possess inverse consistency when the order of two input images is swapped. To tackle this problem, we propose a new deep registration algorithm by employing the inverse consistency training strategy, so the forward and backward deformations of a pair of images can consistently align anatomical structures. In addition, since fine-tuned deformations among the training images reflect variability of shapes and appearances in a high-dimensional space, we formulate a group prior data modeling framework so that such statistics can be used to improve accuracy and consistency for registering new input image pairs. Specifically, we implement the wavelet principle component analysis (w-PCA) model of deformation fields and incorporate such prior constraints into the inverse-consistent deep registration network. We refer the proposed algorithm as consistent deep registration with group data modeling. Experiments on 3D brain magnetic resonance (MR) images showed that the unsupervised consistent deep registration and data modeling strategy yield consistent deformations after switching the input images and tolerated image variations well.

Computing infection distributions and longitudinal evolution patterns in lung CT images.

BACKGROUND: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. METHODS: A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. We compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across the time course of the disease. RESULTS: For the performance of infection segmentation, comparing the segmentation results with manually annotated ground-truth, the average Dice is 91.6% ± 10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1% ± 3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four subsequent patterns (progression, absorption, enlargement, and further absorption) in our collected dataset, with remarkable concurrent HU patterns for GGO and consolidations. CONCLUSIONS: By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four subsequent disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

An artificial-intelligence lung imaging analysis system (ALIAS) for population-based nodule computing in CT scans.

Computed tomography (CT) screening is essential for early lung cancer detection. With the development of artificial intelligence techniques, it is particularly desirable to explore the ability of current state-of-the-art methods and to analyze nodule features in terms of a large population. In this paper, we present an artificial-intelligence lung image analysis system (ALIAS) for nodule detection and segmentation. And after segmenting the nodules, the locations, sizes, as well as imaging features are computed at the population level for studying the differences between benign and malignant nodules. The results provide better understanding of the underlying imaging features and their ability for early lung cancer diagnosis.

CT based automatic clinical target volume delineation using a dense-fully connected convolution network for cervical Cancer radiation therapy.

BACKGROUND: It is very important to accurately delineate the CTV on the patient's three-dimensional CT image in the radiotherapy process. Limited to the scarcity of clinical samples and the difficulty of automatic delineation, the research of automatic delineation of cervical cancer CTV based on CT images for new patients is slow. This study aimed to assess the value of Dense-Fully Connected Convolution Network (Dense V-Net) in predicting Clinical Target Volume (CTV) pre-delineation in cervical cancer patients for radiotherapy. METHODS: In this study, we used Dense V-Net, a dense and fully connected convolutional network with suitable feature learning in small samples to automatically pre-delineate the CTV of cervical cancer patients based on computed tomography (CT) images and then we assessed the outcome. The CT data of 133 patients with stage IB and IIA postoperative cervical cancer with a comparable delineation scope was enrolled in this study. One hundred and thirteen patients were randomly designated as the training set to adjust the model parameters. Twenty cases were used as the test set to assess the network performance. The 8 most representative parameters were also used to assess the pre-sketching accuracy from 3 aspects: sketching similarity, sketching offset, and sketching volume difference. RESULTS: The results presented that the DSC, DC/mm, HD/cm, MAD/mm, ∆V, SI, IncI and JD of CTV were 0.82 ± 0.03, 4.28 ± 2.35, 1.86 ± 0.48, 2.52 ± 0.40, 0.09 ± 0.05, 0.84 ± 0.04, 0.80 ± 0.05, and 0.30 ± 0.04, respectively, and the results were greater than those with a single network. CONCLUSIONS: Dense V-Net can correctly predict CTV pre-delineation of cervical cancer patients and can be applied in clinical practice after completing simple modifications.

On the performance of lung nodule detection, segmentation and classification.

Computed tomography (CT) screening is an effective way for early detection of lung cancer in order to improve the survival rate of such a deadly disease. For more than two decades, image processing techniques such as nodule detection, segmentation, and classification have been extensively studied to assist physicians in identifying nodules from hundreds of CT slices to measure shapes and HU distributions of nodules automatically and to distinguish their malignancy. Thanks to new parallel computation, multi-layer convolution, nonlinear pooling operation, and the big data learning strategy, recent development of deep-learning algorithms has shown great progress in lung nodule screening and computer-assisted diagnosis (CADx) applications due to their high sensitivity and low false positive rates. This paper presents a survey of state-of-the-art deep-learning-based lung nodule screening and analysis techniques focusing on their performance and clinical applications, aiming to help better understand the current performance, the limitation, and the future trends of lung nodule analysis.

Valley Polarization and Valleyresistance in a Monolayer Transition Metal Dichalcogenide Superlattice.

A significant, fundamental challenge in the field of valleytronics is how to generate and regulate valley-polarized currents in practical ways. Here, we discover a new mechanism for producing valley polarization in a monolayer transition metal dichalcogenide superlattice, in which valley-resolved gaps are formed at the supercell Brillouin zone boundaries and centers due to intervalley scattering. When the incident energy of the electron lies in the gaps, the available states are valley polarized, thus providing a valley-polarized current from the superlattice. We show that the direction and strength of the valley polarization may be further tuned by varying the potential applied to the superlattice. The transmission can have a net valley polarization of 55% for a four-period heterostructure. Moreover, two such valley filters in series may function as an electrostatically controlled giant valleyresistance device, representing a zero-magnetic field counterpart to the familiar giant magnetoresistance device.

Inorganic photovoltaic cells based on BiFeO3: spontaneous polarization, lattice matching, light polarization and their relationship with photovoltaic performance.

Inorganic ferroelectric perovskite oxides are more stable than hybrid perovskites. However, their solar energy harvesting efficiency is not so good. Here, by constructing a series of BiFeO3-based devices (solar cells), we investigated three factors that influence the photovoltaic performance, namely, spontaneous polarization, terminated ion species in the interface between BiFeO3 and the electrode, and polarized light irradiation. This work was carried out under the framework of the density functional theory combined with the non-equilibrium Green's function theory under a built-in electric field or finite bias. The results showed that (1) the photocurrent is larger only under a suitable electronic band gap rather than larger spontaneous polarization; (2) the photocurrent reaches the largest value in the Bi3+ ion-terminated interface than in the case of Fe3+ or O2- with the SrTiO3 electrode; (3) the photocurrent can be largely enhanced if the polarized direction of the monochromatic light is perpendicular to the spontaneous polarization direction. These results would deepen the understanding of some experimental results of BiFeO3-based solar cells.

Agreement Between the JCDCG, Revised NCEP-ATPIII, and IDF Definitions of Metabolic Syndrome in a Northwestern Chinese Population.

INTRODUCTION: The Joint Committee for Developing Chinese Guidelines (JCDCG) introduced the Chinese definition for metabolic syndrome (MS), which has been verified in southern Chinese people but not in northwestern Chinese people. We evaluated the MS definition proposed by the JCDCG in a northwestern Chinese population, in comparison with those of the revised National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATPIII) and the International Diabetes Federation (IDF). METHODS: This population-based cross-sectional study was a part of the China National Diabetes and Metabolic Disorders Study conducted in Shaanxi province. We included 3243 participants aged ≥ 20 years. The age-adjusted MS prevalence was assessed per the 2007 Chinese population structure. The agreement between different definitions was assessed by the kappa statistic. RESULTS: The standardized prevalence of JCDCG-MS, revised ATPIII-MS, and IDF-MS was 22.4%, 29.4%, and 24.9%, respectively. Among women, the agreement of the JCDCG definition with the revised ATPIII and the IDF definition was not good (κ = 0.599 and 0.601, respectively); 54.6% of the revised ATPIII-MS and 56% of the IDF-MS were defined as MS according to the JCDCG definition. Among men, the agreement of JCDCG definition with the revised ATPIII and IDF definitions was very good (κ = 0.863) and substantial (κ = 0.741), respectively. CONCLUSION: The agreement of the JCDCG definition with the revised ATPIII and IDF definitions was insufficient in women. Compared with the other two definitions, the JCDCG definition underestimates MS prevalence in northwestern women.

Role of glycoprotein 78 and cidec in hepatic steatosis.

Hepatic glycoprotein (gp78), a membrane-anchored E3 ubiquitin ligase, has been reported to be involved in regulating lipid and energy metabolism in animals, and cell death­inducing DFFA­like effector c (cidec) has emerged as an important regulator of metabolism, which has been implicated in the process of fat differentiation. Nonalcoholic fatty liver disease is a metabolic disorder associated with hepatic steatosis. In the present study, to investigate the role of gp78 and cidec in hepatic steatosis, an in vitro cell culture model of hepatic steatosis was established, using the AML12 mouse hepatocyte cell line to assess the protein expression of gp78. The results of Oil Red O staining, phase contrast microscopy and triglyceride content detection experiments indicated that the overexpression of gp78 induced lipid accumulation, whereas gp78­knockdown led to a reduction in lipid accumulation in the AML12 cells. The increased expression of gp78 was associated with steatosis. The expression of cidec was consistent with gp78, and the colocalization of gp78 and cidec was observed on the surface of lipid droplets using immunofluorescence analysis. Furthermore, an interaction between gp78 and cidec was detected using coimmunoprecipitation analysis, and this interaction promoted lipid accumulation. Based on these data, it was hypothesized that gp78 is a regulator of hepatic steatosis, and that it may be a putative molecular mediator in metabolic diseases.

Visualization of NRAS RNA G-Quadruplex Structures in Cells with an Engineered Fluorogenic Hybridization Probe.

The RNA G-quadruplex is an important secondary structure formed by guanine-rich RNA sequences. However, its folding studies have mainly been studied in vitro. Accurate identification of RNA G-quadruplex formation within a sequence of interest remains difficult in cells. Herein, and based on the guanine-rich sequence in the 5'-UTR of NRAS mRNA, we designed and synthesized the first G-quadruplex-triggered fluorogenic hybridization (GTFH) probe, ISCH-nras1, for the unique visualization of the G-quadruplexes that form in this region. ISCH-nras1 is made up of two parts: The first is a fluorescent light-up moiety specific to G-quadruplex structures, and the second is a DNA molecule that can hybridize with a sequence that is adjacent to the guanine-rich sequence in the NRAS mRNA 5'-UTR. Further evaluation studies indicated that ISCH-nras1 could directly and precisely detect the targeted NRAS RNA G-quadruplex structures, both in vitro and in cells. Thus, this GTFH probe was a useful tool for directly investigating the folding of G-quadruplex structures within an RNA of interest and represents a new direction for the design of smart RNA G-quadruplex probes.

Non-alcoholic fatty liver disease predicts type 2 diabetes mellitus, but not prediabetes, in Xi'an, China: a five-year cohort study.

BACKGROUND & AIMS: Emerging studies have focused the association between non-alcoholic fatty liver disease (NAFLD) and the risk of type 2 diabetes mellitus (T2DM) but the results were inconsistent. In addition, few studies have put focus on the association between NAFLD and the risk of prediabetes. We aimed to investigate whether NAFLD diagnosed by ultrasonography could predict the risk of future T2DM and prediabetes in Chinese population. METHODS: The population-based cohort study held in Xi'an, Northwestern China, was based on China National Diabetes and Metabolic Disorders Survey. During a follow-up of 5 years, 508 healthy subjects were included as study sample. NAFLD was determined by abdominal ultrasonography. T2DM and prediabetes were diagnosed based on oral glucose tolerance test. RESULTS: Of 508 subjects, 97 (19.1%) were diagnosed as NAFLD and 411 (80.9%) were as non-NAFLD; 20 (3.9%) developed diabetes and 85 (16.7%) developed prediabetes during follow-up. The incidence of diabetes and prediabetes in the NAFLD group was 20.6 and 51.6 per 1000 person-years, respectively, whereas that in non-NAFLD group was 4.9 and 29.2 per 1000 person-years respectively. Cox proportional hazard regression showed that the multivariable-adjusted relative risk (RR) of T2DM and prediabetes in the NAFLD group was 4.462 [95% confidence interval (CI): 1.855-10.734, P < 0.001] and 1.642 (95% CI: 0.965-2.793, P = 0.067), respectively, compared with non-NAFLD group. CONCLUSIONS: Non-alcoholic fatty liver disease was a significant predictor for future diabetes, but not for prediabetes, in Xi'an, China. More cohort studies are needed to confirm our findings.