A Phase Model of the Bio-Inspired NbOx Local Active Memristor under Weak Coupling Conditions.

For some so-called computationally difficult problems, using the method of Boolean logic is fundamentally inefficient. For example, the vertex coloring problem looks very simple, but the number of possible solutions increases sharply with the increase of graph vertices. This is the difficulty of the problem. This complexity has been widely studied because of its wide applications in the fields of data science, life science, social science, and engineering technology. Consequently, it has inspired the use of alternative and more effective non-Boolean methods for obtaining solutions to similar problems. In this paper, we explore the research on a new generation of computers that use local active memristors coupling. First, we study the dynamics of the memristor coupling network. Then, the simplified system phase model is obtained. This research not only clarifies a physics-based calculation method but also provides a foundation for the construction of customized analog computers to effectively solve NP-hard problems.

NeRF-NQA: No-Reference Quality Assessment for Scenes Generated by NeRF and Neural View Synthesis Methods.

Neural View Synthesis (NVS) has demonstrated efficacy in generating high-fidelity dense viewpoint videos using a image set with sparse views. However, existing quality assessment methods like PSNR, SSIM, and LPIPS are not tailored for the scenes with dense viewpoints synthesized by NVS and NeRF variants, thus, they often fall short in capturing the perceptual quality, including spatial and angular aspects of NVS-synthesized scenes. Furthermore, the lack of dense ground truth views makes the full reference quality assessment on NVS-synthesized scenes challenging. For instance, datasets such as LLFF provide only sparse images, insufficient for complete full-reference assessments. To address the issues above, we propose NeRF-NQA, the first no-reference quality assessment method for densely-observed scenes synthesized from the NVS and NeRF variants. NeRF-NQA employs a joint quality assessment strategy, integrating both viewwise and pointwise approaches, to evaluate the quality of NVS-generated scenes. The viewwise approach assesses the spatial quality of each individual synthesized view and the overall inter-views consistency, while the pointwise approach focuses on the angular qualities of scene surface points and their compound inter-point quality. Extensive evaluations are conducted to compare NeRF-NQA with 23 mainstream visual quality assessment methods (from fields of image, video, and light-field assessment). The results demonstrate NeRF-NQA outperforms the existing assessment methods significantly and it shows substantial superiority on assessing NVS-synthesized scenes without references. An implementation of this paper are available at https://github.com/VincentQQu/NeRF-NQA.

Swift-Eye: Towards Anti-blink Pupil Tracking for Precise and Robust High-Frequency Near-Eye Movement Analysis with Event Cameras.

Eye tracking has shown great promise in many scientific fields and daily applications, ranging from the early detection of mental health disorders to foveated rendering in virtual reality (VR). These applications all call for a robust system for high-frequency near-eye movement sensing and analysis in high precision, which cannot be guaranteed by the existing eye tracking solutions with CCD/CMOS cameras. To bridge the gap, in this paper, we propose Swift-Eye, an offline precise and robust pupil estimation and tracking framework to support high-frequency near-eye movement analysis, especially when the pupil region is partially occluded. Swift-Eye is built upon the emerging event cameras to capture the high-speed movement of eyes in high temporal resolution. Then, a series of bespoke components are designed to generate high-quality near-eye movement video at a high frame rate over kilohertz and deal with the occlusion over the pupil caused by involuntary eye blinks. According to our extensive evaluations on EV-Eye, a large-scale public dataset for eye tracking using event cameras, Swift-Eye shows high robustness against significant occlusion. It can improve the IoU and F1-score of the pupil estimation by 20% and 12.5% respectively, compared with the second-best competing approach, when over 80% of the pupil region is occluded by the eyelid. Lastly, it provides continuous and smooth traces of pupils in extremely high temporal resolution and can support high-frequency eye movement analysis and a number of potential applications, such as mental health diagnosis, behaviour-brain association, etc. The implementation details and source codes can be found at https://github.com/ztysdu/Swift-Eye.

EV-LFV: Synthesizing Light Field Event Streams from an Event Camera and Multiple RGB Cameras.

Light field videos captured in RGB frames (RGB-LFV) can provide users with a 6 degree-of-freedom immersive video experience by capturing dense multi-subview video. Despite its potential benefits, the processing of dense multi-subview video is extremely resource-intensive, which currently limits the frame rate of RGB-LFV (i.e., lower than 30 fps) and results in blurred frames when capturing fast motion. To address this issue, we propose leveraging event cameras, which provide high temporal resolution for capturing fast motion. However, the cost of current event camera models makes it prohibitive to use multiple event cameras for RGB-LFV platforms. Therefore, we propose EV-LFV, an event synthesis framework that generates full multi-subview event-based RGB-LFV with only one event camera and multiple traditional RGB cameras. EV-LFV utilizes spatial-angular convolution, ConvLSTM, and Transformer to model RGB-LFV's angular features, temporal features, and long-range dependency, respectively, to effectively synthesize event streams for RGB-LFV. To train EV-LFV, we construct the first event-to-LFV dataset consisting of 200 RGB-LFV sequences with ground-truth event streams. Experimental results demonstrate that EV-LFV outperforms state-of-the-art event synthesis methods for generating event-based RGB-LFV, effectively alleviating motion blur in the reconstructed RGB-LFV.

Evidence of a Sjögren's disease-like phenotype following COVID-19 in mice and humans.

Sjögren's Disease (SjD) is a systemic autoimmune disease characterized by lymphocytic inflammation of the lacrimal and salivary glands (SG), dry eyes and mouth, and systemic symptoms. SARS-CoV-2 may trigger the development or progression of autoimmune diseases. To test this, we used a mouse model of SARS-CoV-2 infection and convalescent patients' blood and SG in order to understand the development of SjD-like autoimmunity after infection. First, SARS-CoV-2-infected human angiotensin-converting enzyme 2 (ACE2) transgenic mice exhibited decreased salivation, elevated antinuclear antibodies (ANA), and lymphocytic infiltration in the lacrimal and SG. The sera from patients with COVID-19 sera showed increased ANA (i.e., anti-SSA [Sjögren's-syndrome-related antigen A]/anti-Ro52 and anti-SSB [SS-antigen B]/anti-La). Male patients showed elevated anti-SSA compared with female patients, and female patients exhibited diverse ANA patterns. SG biopsies from convalescent COVID-19 patients were microscopically similar to SjD SG with focal lymphocytic infiltrates in 4 of 6 patients and 2 of 6 patients exhibiting focus scores of at least 2. Lastly, monoclonal antibodies produced in recovered patients blocked ACE2/spike interaction and cross-reacted with nuclear antigens. Our study shows a direct association between SARS-CoV-2 and SjD. Hallmark features of SjD-affected SGs were histologically indistinguishable from convalescent COVID-19 patients. The results implicate that SARS-CoV-2 could be an environmental trigger for SjD.

A current and future perspective on T cell receptor repertoire profiling.

T cell receptors (TCR) play a vital role in the immune system's ability to recognize and respond to foreign antigens, relying on the highly polymorphic rearrangement of TCR genes. The recognition of autologous peptides by adaptive immunity may lead to the development and progression of autoimmune diseases. Understanding the specific TCR involved in this process can provide insights into the autoimmune process. RNA-seq (RNA sequencing) is a valuable tool for studying TCR repertoires by providing a comprehensive and quantitative analysis of the RNA transcripts. With the development of RNA technology, transcriptomic data must provide valuable information to model and predict TCR and antigen interaction and, more importantly, identify or predict neoantigens. This review provides an overview of the application and development of bulk RNA-seq and single-cell (SC) RNA-seq to examine the TCR repertoires. Furthermore, discussed here are bioinformatic tools that can be applied to study the structural biology of peptide/TCR/MHC (major histocompatibility complex) and predict antigenic epitopes using advanced artificial intelligence tools.

A VO2 Neuristor Based on Microstrip Line Coupling.

The neuromorphic network based on artificial neurons and synapses can solve computational difficulties, and its energy efficiency is incomparable to the traditional von Neumann architecture. As a new type of circuit component, nonvolatile memristors are very similar to biological synapses in structure and function. Only one memristor can simulate the function of a synapse. Therefore, memristors provide a new way to build hardware-based artificial neural networks. To build such an artificial neural network, in addition to the artificial synapses, artificial neurons are also needed to realize the distribution of information and the adjustment of synaptic weights. As the VO2 volatile local active memristor is complementary to nonvolatile memristors, it can be used to simulate the function of neurons. However, determining how to better realize the function of neurons with simple circuits is one of the current key problems to be solved in this field. This paper considers the influence of distribution parameters on circuit performance under the action of high-frequency and high-speed signals. Two Mott VO2 memristor units are connected and coupled with microstrip lines to simulate the Hodgkin-Huxley neuron model. It is found that the proposed memristor neuron based on microstrip lines shows the characteristics of neuron action potential: amplification and threshold.

Ancestral origins are associated with SARS-CoV-2 susceptibility and protection in a Florida patient population.

COVID-19 is caused by severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2). The severity of COVID-19 is highly variable and related to known (e.g., age, obesity, immune deficiency) and unknown risk factors. The widespread clinical symptoms encompass a large group of asymptomatic COVID-19 patients, raising a crucial question regarding genetic susceptibility, e.g., whether individual differences in immunity play a role in patient symptomatology and how much human leukocyte antigen (HLA) contributes to this. To reveal genetic determinants of susceptibility to COVID-19 severity in the population and further explore potential immune-related factors, we performed a genome-wide association study on 284 confirmed COVID-19 patients (cases) and 95 healthy individuals (controls). We compared cases and controls of European (EUR) ancestry and African American (AFR) ancestry separately. We identified two loci on chromosomes 5q32 and 11p12, which reach the significance threshold of suggestive association (p<1x10-5 threshold adjusted for multiple trait testing) and are associated with the COVID-19 susceptibility in the European ancestry (index rs17448496: odds ratio[OR] = 0.173; 95% confidence interval[CI], 0.08-0.36 for G allele; p = 5.15× 10-5 and index rs768632395: OR = 0.166; 95% CI, 0.07-0.35 for A allele; p = 4.25×10-6, respectively), which were associated with two genes, PPP2R2B at 5q32, and LRRC4C at 11p12, respectively. To explore the linkage between HLA and COVID-19 severity, we applied fine-mapping analysis to dissect the HLA association with mild and severe cases. Using In-silico binding predictions to map the binding of risk/protective HLA to the viral structural proteins, we found the differential presentation of viral peptides in both ancestries. Lastly, extrapolation of the identified HLA from the cohort to the worldwide population revealed notable correlations. The study uncovers possible differences in susceptibility to COVID-19 in different ancestral origins in the genetic background, which may provide new insights into the pathogenesis and clinical treatment of the disease.

Evidence of a Sjögren's disease-like phenotype following COVID-19.

Objectives: Sjögren's Disease (SjD) is a chronic and systemic autoimmune disease characterized by lymphocytic infiltration and the development of dry eyes and dry mouth resulting from the secretory dysfunction of the exocrine glands. SARS-CoV-2 may trigger the development or progression of autoimmune diseases, as evidenced by increased autoantibodies in patients and the presentation of cardinal symptoms of SjD. The objective of the study was to determine whether SARS-CoV-2 induces the signature clinical symptoms of SjD. Methods: The ACE2-transgenic mice were infected with SARS-CoV-2. SJD profiling was conducted. COVID-19 patients' sera were examined for autoantibodies. Clinical evaluations of convalescent COVID-19 subjects, including minor salivary gland (MSG) biopsies, were collected. Lastly, monoclonal antibodies generated from single B cells of patients were interrogated for ACE2/spike inhibition and nuclear antigens. Results: Mice infected with the virus showed a decreased saliva flow rate, elevated antinuclear antibodies (ANAs) with anti-SSB/La, and lymphocyte infiltration in the lacrimal and salivary glands. Sera of COVID-19 patients showed an increase in ANA, anti-SSA/Ro52, and anti-SSB/La. The male patients showed elevated levels of anti-SSA/Ro52 compared to female patients, and female patients had more diverse ANA patterns. Minor salivary gland biopsies of convalescent COVID-19 subjects showed focal lymphocytic infiltrates in four of six subjects, and 2 of 6 subjects had focus scores >2. Lastly, we found monoclonal antibodies produced in recovered patients can both block ACE2/spike interaction and recognize nuclear antigens. Conclusion: Overall, our study shows a direct association between SARS-CoV-2 and SjD. Hallmark features of SjD salivary glands were histologically indistinguishable from convalescent COVID-19 subjects. The results potentially implicate that SARS-CoV-2 could be an environmental trigger for SjD. Key Messages: What is already known about this subject?SAR-CoV-2 has a tropism for the salivary glands. However, whether the virus can induce clinical phenotypes of Sjögren's disease is unknown.What does this study add?Mice infected with SAR-CoV-2 showed loss of secretory function, elevated autoantibodies, and lymphocyte infiltration in glands.COVID-19 patients showed an increase in autoantibodies. Monoclonal antibodies produced in recovered patients can block ACE2/spike interaction and recognize nuclear antigens.Minor salivary gland biopsies of some convalescent subjects showed focal lymphocytic infiltrates with focus scores.How might this impact on clinical practice or future developments?Our data provide strong evidence for the role of SARS-CoV-2 in inducing Sjögren's disease-like phenotypes.Our work has implications for how patients will be diagnosed and treated effectively.

Naphthoquinones and triterpenoids from Arnebia euchroma (Royle) Johnst and their hypoglycemic and lipid-lowering effects.

A new pentacyclic triterpenoid, 2-hydroxy-1-ene-hydroxyhopanone (19), and a new benzoxepin-5-one, 3-(4-methyl-3-penten-1-yl)-6-hydroxy-9-methoxy-2H-1-benzoxepin-5-one (25), along with 26 known compounds (1-18, 20-24, 26-28), were isolated from the roots of Arnebia euchroma (Royle) Johnst. The structures of the new compounds were elucidated by extensive spectroscopic analyses. The absolute configurations of shikonofurans 9-13 were determined by quantum chemical ECD calculations and CD spectra comparison for the first time. Pharmacological study revealed that naphthoquinones 1-5, 7, and 8 had obvious cytotoxicity toward human lung adenocarcinoma A549 cell line. Meanwhile, the hypoglycemic and lipid-lowering effects of isolated compounds were assessed by checking their inhibitory effects on key enzymes regulating glucose and lipid metabolism. Results showed that compounds 1, 3, 5, 6, 8, 18, and 19 could inhibit the activity of ATP-citrate lyase (ACL); compound 7 could inhibit the activity of acetyl-CoA carboxylase (ACC1); while compounds 8 and 19 showed inhibitory effects on protein tyrosine phosphatase 1B (PTP1B). Among them, the naphthoquinone 6, steroid 18, and triterpenoid 19 showed moderate inhibitory effects on ACL and PTP1B, but didn't exhibit obvious cytotoxicity. This study demonstrated that compounds 6, 18, and 19 show great promising for the development of new agents for the treatment of metabolic diseases.

BPSK Circuit Based on SDC Memristor.

Digital communication based on memristors is a new field. The main principle is to construct a modulation and demodulation circuit by using the resistance variation characteristics of the memristor. Based on the establishment of the Knowm memristor simulation model, firstly, the modulation circuit is designed by using the polarity and symmetry of the memristor and combined with the commercial current feedback amplifier AD844. It is proved that the modulated signal based on the memristor is a strong function of phase, and the demodulation circuit is designed accordingly. All simulation circuits are based on the actual commercial physical device model. The analytical expression of the output signal of the modulation and demodulation circuit is deduced theoretically, and the communication performance of the whole system is simulated by LTSpice. At the same time, the influence of the parasitic capacitance of the memristor on the circuit performance is also considered. After the simulation verification, the hardware circuit experiment of the modulation and demodulation circuit is carried out. The waveforms of the modulated signal and the demodulated signal are measured by an oscilloscope. The experimental results are completely consistent with the simulation and theoretical results.

Bone marrow-derived macrophages from a murine model of Sjögren's syndrome demonstrate an aberrant, inflammatory response to apoptotic cells.

Sjögren's syndrome (SjS) is a female-dominated autoimmune disease involving lymphocytic infiltration of the exocrine glands. We have previously demonstrated cleavage of the TAM (Tyro3, Axl, Mer) receptor Mer is enhanced in SjS, leading to defective efferocytosis. Mer also plays a role in modulating phagocyte inflammatory response to apoptotic cells. Here we investigated the SjS macrophage response to apoptotic cells (AC). Bone marrow-derived macrophages (BMDMs) from SjS-susceptible (SjSs) C57BL/6.NOD-Aec1Aec2 mice and C57BL/6 (B6) controls were treated with either AC or CpG-oligodeoxynucleotides. RNA was collected from macrophages and bulk sequencing was performed to analyze transcripts. Cytokine expression was confirmed by Bio-plex. RT-qPCR was used to determine toll-like receptor (TLR) 7 and 9 involvement in BMDM inflammatory response to apoptotic cells. SjSS BMDMs exhibited a distinct transcriptional profile involving upregulation of a broad array of inflammatory genes that were not elevated in B6 BMDMs by AC. Inhibition of TLR 7 and 9 was found to limit the inflammatory response of SjSS BMDMs to ACs. ACs elicit an inflammatory reaction in SjSS BMDMs distinct from that observed in B6 BMDMs. This discovery of aberrant macrophage behavior in SjS in conjunction with previously described efferocytosis defects suggests an expanded role for macrophages in SjS, where uncleared dead cells stimulate an inflammatory response through macrophage TLRs recruiting lymphocytes, participating in co-stimulation and establishing an environment conducive to autoimmunity.

Event-Stream Representation for Human Gaits Identification Using Deep Neural Networks.

Dynamic vision sensors (event cameras) have recently been introduced to solve a number of different vision tasks such as object recognition, activities recognition, tracking, etc. Compared with the traditional RGB sensors, the event cameras have many unique advantages such as ultra low resources consumption, high temporal resolution and much larger dynamic range. However, these cameras only produce noisy and asynchronous events of intensity changes, i.e., event-streams rather than frames, where conventional computer vision algorithms can't be directly applied. In our opinion the key challenge for improving the performance of event cameras in vision tasks is finding the appropriate representations of the event-streams so that cutting-edge learning approaches can be applied to fully uncover the spatio-temporal information contained in the event-streams. In this paper, we focus on the event-based human gait identification task and investigate the possible representations of the event-streams when deep neural networks are applied as the classifier. We propose new event-based gait recognition approaches basing on two different representations of the event-stream, i.e., graph and image-like representations, and use graph-based convolutional network (GCN) and convolutional neural networks (CNN) respectively to recognize gait from the event-streams. The two approaches are termed as EV-Gait-3DGraph and EV-Gait-IMG. To evaluate the performance of the proposed approaches, we collect two event-based gait datasets, one from real-world experiments and the other by converting the publicly available RGB gait recognition benchmark CASIA-B. Extensive experiments show that EV-Gait-3DGraph achieves significantly higher recognition accuracy than other competing methods when sufficient training samples are available. However, EV-Gait-IMG converges more quickly than graph-based approaches while training and shows good accuracy with only few number of training samples (less than ten). So image-like presentation is preferable when the amount of training data is limited.

Hepatitis Delta Antigen Regulates mRNA and Antigenome RNA Levels during Hepatitis Delta Virus Replication.

Hepatitis delta virus (HDV) is a satellite of hepatitis B virus that increases the severity of acute and chronic liver disease. HDV produces three processed RNAs that accumulate in infected cells: the circular genome; the circular antigenome, which serves as a replication intermediate; and lesser amounts of the mRNA, which encodes the sole viral protein, hepatitis delta antigen (HDAg). The HDV genome and antigenome RNAs form ribonucleoprotein complexes with HDAg. Although HDAg is required for HDV replication, it is not known how the relative amounts of HDAg and HDV RNA affect replication, or whether HDAg synthesis is regulated by the virus. Using a novel transfection system in which HDV replication is initiated using in vitro-synthesized circular HDV RNAs, HDV replication was found to depend strongly on the relative amounts of HDV RNA and HDAg. HDV controls these relative amounts via differential effects of HDAg on the production of HDV mRNA and antigenome RNA, both of which are synthesized from the genome RNA template. mRNA synthesis is favored at low HDAg levels but becomes saturated at high HDAg concentrations. Antigenome RNA accumulation increases linearly with HDAg and dominates at high HDAg levels. These results provide a conceptual model for how HDV antigenome RNA production and mRNA transcription are controlled from the earliest stage of infection onward and also demonstrate that, in this control, HDV behaves similarly to other negative-strand RNA viruses, even though there is no genetic similarity between them.IMPORTANCE Hepatitis delta virus (HDV) is a satellite of hepatitis B virus that increases the severity of liver disease; approximately 15 million people are chronically infected worldwide. There are no licensed therapies available. HDV is not related to any known virus, and few details regarding its replication cycle are known. One key question is whether and how HDV regulates the relative amounts of viral RNA and protein in infected cells. Such regulation might be important because the HDV RNA and protein form complexes that are essential for HDV replication, and the proper stoichiometry of these complexes could be critical for their function. Our results show that the relative amounts of HDV RNA and protein in cells are indeed important for HDV replication and that the virus does control them. These observations indicate that further study of these regulatory mechanisms is required to better understand replication of this serious human pathogen.

CS²-Collector: A New Approach for Data Collection in Wireless Sensor Networks Based on Two-Dimensional Compressive Sensing.

In this paper, we consider the problem of reconstructing the temporal and spatial profile of some physical phenomena monitored by large-scale Wireless Sensor Networks (WSNs) in an energy efficient manner. Compressive sensing is one of the popular choices to reduce the energy consumption of the data collection in WSNs. The existing solutions only consider sparsity of sensors' data from either temporal or spatial dimensions. In this paper, we propose a novel data collection strategy, CS²-collector, for WSNs based on the theory of Two Dimensional Compressive Sensing (2DCS). It exploits both temporal and spatial sparsity, i.e., 2D-sparsity of WSNs and achieves significant gain on the tradeoff between the compression ratio and reconstruction accuracy as the numerical simulations and evaluations on different types of sensors' data. More intuitively, with the same given energy budget, CS²-collector provides significantly more accurate reconstruction of the profile of the physical phenomena that are temporal-spatially sparse.

A compact optofluidic cytometer with integrated liquid-core/PDMS-cladding waveguides.

We developed a simple method to construct liquid-core/PDMS-cladding optical waveguides through pressurized filling of dead-ended micro-channels with optical fluids. The waveguides are in the same layer as microfluidic channels which greatly simplifies device fabrication. With proper contrast between the refractive index of the core and cladding, the transmission loss of the waveguides is less than 5 dB cm(-1). We also developed a method to create flat and optically clear surfaces on the sides of PDMS devices in order to couple light between free-space and the waveguides embedded inside the chip. With these newly developed techniques, we make a compact flow cytometer and demonstrate the fluorescence counting of single cells at a rate of up to ~50 cell s(-1) and total sample requirement of a few microlitres. This method of making liquid-core optical waveguides and flat surfaces has great potential to be integrated into many PDMS-based microsystems.

Electric-field-driven nano-oxidation trimming of silicon microrings and interferometers.

Nanoscale disorder results in severe spectral misalignment of silicon microring resonators and Mach-Zehnder interferometers. We correct for such effects using electric-field-induced waveguide nano-oxidation, demonstrating a tuning wavelength range of several nanometers and 0.002 nm resolution without line shape degradation. Field-induced nano-oxidation is a permanent and precise technique and requires no new materials or high-temperature processing.