Local spatial and temporal relation discovery model based on attention mechanism for traffic forecasting.

Recognizing the evolution pattern of traffic condition and making accurate prediction play a vital role in intelligent transportation systems (ITS). With the massive increase of available traffic data, deep learning-based models have attracted considerable attention for their impressive performance in traffic forecasting. However, the majority of existing approaches neglect to model of asynchronously dynamic spatio-temporal correlation and fail to consider the impact of historical traffic data on future condition. Additionally, the attribute of deep learning method presents challenges in interpreting the explicit spatiotemporal relationships. In order to enhance the accuracy of traffic prediction as well as extract comprehensive and explainable spatial-temporal relevance in traffic networks, we propose a novel attention-based local spatial and temporal relation discovery (ALSTRD) model. Our model firstly implements feature representation learning to effectively express latent input traffic information. Then, a local attention mechanism structure is established to model asynchronous dependencies of historical input data. Finally, another attention network and the Pearson Correlation Coefficient method are introduced to extract the elaborate influence of the historical traffic condition of neighboring roads on the future condition of the target road. The experiment results on several datasets demonstrate that our model achieves significant improvements in prediction accuracy compared to other baseline methods, which can be attributed to its ability to extract the fine-grained correlation among historical traffic data and capture the dynamic association between past and future data. In addition, the incorporation of attention mechanism and Pearson Correlation Coefficient promotes the model's ability to elucidate spatiotemporal correlations among traffic data, thereby providing a more robust explanation.

pH-responsive interface conversion efficient oral drug delivery platform for alleviating inflammatory bowel disease.

A key challenge for the effective treatment of intestinal diseases, including inflammatory bowel disease (IBD), is to develop an oral drug delivery system that can resist gastric acid erosion and efficiently release drugs after rapid entry into the intestine. In the present work, we developed oral composite nanoparticles (MSZ@PRHS) consisting of a rough mesoporous silica (RHS) loaded with Mesalazine (MSZ) and a CAP polymer membrane for targeted relief of inflammation in colitis. At the pH values of the simulated stomach and small intestine, the release rate of MSZ from MSZ@PRHS was low, while at the pH values of the simulated colon, the release rate of MSZ was high. In dextran sulfate sodium salt (DSS)-induced acute colitis mouse model, compared with oral administration of the drug Mesalazine in the equivalent solution form, oral administration of PRHS loaded with drug-loaded nanoparticles can significantly alleviate the symptoms of inflammatory bowel disease, and improve the therapeutic effect. We propose that the intestinal microenvironment provides an interface for nanocomposites switch and a promising drug delivery platform for the management and treatment of many intestinal diseases, where controlled drug release and prolonged residence time are required.

Selection and Validation of Reference Genes for Gene Expression in Bactericera gobica Loginova under Different Insecticide Stresses.

Insecticide resistance has long been a problem in crop pest control. Bactericera gobica is a major pest on the well-known medicinal plants Lycium barbarum L. Investigating insecticide resistance mechanisms of B. gobica will help to identify pesticide reduction strategies to control the pest. Gene expression normalization by RT-qPCR requires the selection and validation of appropriate reference genes (RGs). Here, 15 candidate RGs were selected from transcriptome data of B. gobica. Their expression stability was evaluated with five algorithms (Delta Ct, GeNorm, Normfinder, BestKeeper and RefFinder) for sample types differing in response to five insecticide stresses and in four other experimental conditions. Our results indicated that the RGs RPL10 + RPS15 for Imidacloprid and Abamectin; RPL10 + AK for Thiamethoxam; RPL32 + RPL10 for λ-cyhalothrin; RPL10 + RPL8 for Matrine; and EF2 + RPL32 under different insecticide stresses were the most suitable RGs for RT-qPCR normalization. EF1α + RPL8, EF1α + ß-actin, ß-actin + EF2 and ß-actin + RPS15 were the optimal combination of RGs under odor stimulation, temperature, developmental stages and both sexes, respectively. Overall, EF2 and RPL8 were the two most stable RGs in all conditions, while α-TUB and RPL32 were the least stable RGs. The corresponding suitable RGs and one unstable RG were used to normalize a target cytochrome P450 CYP6a1 gene between adult and nymph stages and under imidacloprid stress. The results of CYP6a1 expression were consistent with transcriptome data. This study is the first research on the most stable RG selection in B. gobica nymphs exposed to different insecticides, which will contribute to further research on insecticide resistance mechanisms in B. gobica.

Unveiling spatial inequalities: Exploring county-level disaster damages and social vulnerability on public disaster assistance in contiguous US.

Understanding the dynamics between public disaster assistance, disaster damages, and social vulnerability at county-level is crucial for designing effective disaster mitigation strategies. This study utilized the Local Bivariate Moran Index (LBMI) and geographically weighted regression (GWR) models to examine spatial patterns and relationships between disaster damages, social vulnerability, and public disaster assistance in contiguous US counties from 2001 to 2021. LBMI results reveal that public disaster assistance has predominantly been directed towards post-disaster recovery efforts, with a particular focus on coastal communities affected by major declared disasters. However, the distributions of public assistance and individual housing assistance, which are the two primary sources of public disaster assistance, do not adequately cover physically and socially vulnerable communities. The distribution of pre-disaster risk mitigation also falls short of sufficiently covering vulnerable communities. Results further indicate the complex interactions between different categories of natural disasters and public assistances. The GWR model results demonstrate spatial variations in predicting each category of public disaster assistance. These findings indicate the need to address disparities in accessing public disaster assistance in the US, and advocate for more equitable disaster mitigation strategies.

Microfluidic Device-Based Virus Detection and Quantification in Future Diagnostic Research: Lessons from the COVID-19 Pandemic.

The global economic and healthcare crises experienced over the past three years, as a result of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has significantly impacted the commonplace habits of humans around the world. SARS-CoV-2, the virus responsible for the coronavirus 2019 (COVID-19) phenomenon, has contributed to the deaths of millions of people around the world. The potential diagnostic applications of microfluidic devices have previously been demonstrated to effectively detect and quasi-quantify several different well-known viruses such as human immunodeficiency virus (HIV), influenza, and SARS-CoV-2. As a result, microfluidics has been further explored as a potential alternative to our currently available rapid tests for highly virulent diseases to better combat and manage future potential outbreaks. The outbreak management during COVID-19 was initially hindered, in part, by the lack of available quantitative rapid tests capable of confirming a person's active infectiousness status. Therefore, this review will explore the use of microfluidic technology, and more specifically RNA-based virus detection methods, as an integral part of improved diagnostic capabilities and will present methods for carrying the lessons learned from COVID-19 forward, toward improved diagnostic outcomes for future pandemic-level threats. This review will first explore the context of the COVID-19 pandemic and how diagnostic technology was shown to have required even greater advancements to keep pace with the transmission of such a highly infectious virus. Secondly, the historical significance of integrating microfluidic technology in diagnostics and how the different types of genetic-based detection methods may vary in their potential practical applications. Lastly, the review will summarize the past, present, and future potential of RNA-based virus detection/diagnosis and how it might be used to better prepare for a future pandemic.

A Fast Simulation Method for Evaluating the Single-Event Effect in Aerospace Integrated Circuits.

With the continuous progress in integrated circuit technology, single-event effect (SEE) has become a key factor affecting the reliability of aerospace integrated circuits. Simulating fault injection using the computer simulation technique effectively reflects the SEE in aerospace integrated circuits. Due to various masking effects, only a small number of faults will result in errors; the traditional method of injecting one fault in one workload execution is inefficient. The method of injecting multiple faults in one workload execution will make it impossible to judge which fault results in errors because the propagation characteristic of SEE and faults may affect each other. This paper proposes an improved multi-point fault injection method to improve simulation efficiency and solve the problems of the general multi-point fault injection method. If one workload execution does not result in errors, multiple faults can be verified by one workload execution. If one workload execution results in errors, a specific grouping method can be used to determine which faults result in errors. The experimental results show that the proposed method achieves a good acceleration effect and significantly improves the simulation efficiency.

Feeding-induced plant metabolite responses to a phoretic gall mite, its carrier psyllid and both, after detachment.

Phoresy is one of the most distinctive relationships between mites and insects, and the off-host interaction between phoretic mites and their carriers is the most critical factor sustaining the phoretic association. As phoretic associations commonly occur in temporary habitats, little is known about off-host interactions between phoronts and carriers. However, an off-host interaction has been reported, in which the plant-mediated competition between a phoretic gall mite, Aceria pallida, and its psyllid vector, Bactericera gobica, after detachment decreases leaf abscission caused by B. gobica and then directly facilitates their phoretic association. In this obligate phoresy, A. pallida seasonally attaches to B. gobica for overwinter survival and they share the same host plant, Lycium barbarum, during the growing season. It is unknown how the host plant responds to these two herbivores and what plant metabolites are involved in their interspecific interaction. Here, effects of A. pallida and B. gobica on the host plant's transcriptome and metabolome, and on enzymes involved in plant defence, at various infestation stages were studied by inoculating A. pallida and B. gobica either separately or simultaneously on leaves of L. barbarum. Our results showed that (a) A. pallida significantly promoted primary and secondary metabolite accumulation, (b) B. gobica markedly inhibited primary and secondary metabolite accumulation and had little influence on defence enzyme activity, and (c) under simultaneous A. pallida and B. gobica infestation, an intermediate response was predicted. These findings indicate that A. pallida and B. gobica have different effects on host plants, A. pallida inhibits B. gobica mainly by increasing the secondary metabolism of L. barbarum, whereas B. gobica inhibits A. pallida mainly by decreasing the primary metabolism of L. barbarum. In conjunction with our previous research, we speculate that this trade-off in host plant metabolite response between A. pallida and B. gobica after detachment promotes a stable phoretic association.

Epsilon-near-zero gratings for polarization selectivity.

Zero-index materials have emerged as a topic of significant scientific interest in recent years. In this Letter, we investigate the electromagnetic properties of epsilon-near-zero (ENZ) gratings composed of materials with near-zero effective permittivity. Our study reveals that ENZ gratings exhibit a unique polarization selectivity that is opposite to that observed in perfect conductor gratings. Furthermore, we demonstrate that hybrid gratings combining perfect conductors and ENZ materials can block omnidirectional electromagnetic waves of any polarization. In addition, we propose a practical design of the ENZ and hybrid gratings based on dielectric ENZ MMs, exhibiting excellent polarization selectivity and blocking effect. Our research presents a promising approach for the flexible manipulation of polarizations using ENZ gratings.

ALBSNN: ultra-low latency adaptive local binary spiking neural network with accuracy loss estimator.

Spiking neural network (SNN) is a brain-inspired model with more spatio-temporal information processing capacity and computational energy efficiency. However, with the increasing depth of SNNs, the memory problem caused by the weights of SNNs has gradually attracted attention. In this study, we propose an ultra-low latency adaptive local binary spiking neural network (ALBSNN) with accuracy loss estimators, which dynamically selects the network layers to be binarized to ensure a balance between quantization degree and classification accuracy by evaluating the error caused by the binarized weights during the network learning process. At the same time, to accelerate the training speed of the network, the global average pooling (GAP) layer is introduced to replace the fully connected layers by combining convolution and pooling. Finally, to further reduce the error caused by the binary weight, we propose binary weight optimization (BWO), which updates the overall weight by directly adjusting the binary weight. This method further reduces the loss of the network that reaches the training bottleneck. The combination of the above methods balances the network's quantization and recognition ability, enabling the network to maintain the recognition capability equivalent to the full precision network and reduce the storage space by more than 20%. So, SNNs can use a small number of time steps to obtain better recognition accuracy. In the extreme case of using only a one-time step, we still can achieve 93.39, 92.12, and 69.55% testing accuracy on three traditional static datasets, Fashion- MNIST, CIFAR-10, and CIFAR-100, respectively. At the same time, we evaluate our method on neuromorphic N-MNIST, CIFAR10-DVS, and IBM DVS128 Gesture datasets and achieve advanced accuracy in SNN with binary weights. Our network has greater advantages in terms of storage resources and training time.

Hedysarum polybotrys polysaccharide attenuates renal inflammatory infiltration and fibrosis in diabetic mice by inhibiting the HMGB1/RAGE/TLR4 pathway.

Diabetic kidney disease (DKD) is a leading cause of kidney failure. Previous studies demonstrated the therapeutic potential of Astragalus polysaccharide in treating diabetic nephropathy. Astragalus and Hongqi both come from the leguminous plant Astragalus, but their species and genera are different, belonging to the same family and different genera of traditional Chinese medicinal plants. However, the effects of Hedysarum polybotrys polysaccharide (HPS), a polysaccharide compound from Hongqi, on DKD, including its components and efficacy, have remained elusive. The present study utilized db/db mice as a DKD animal model administered with low (30 mg/kg) and high doses (60 mg/kg) of HPS, in addition to glyburide (7.2 mg/kg). Blood and urine samples were collected from mice and blood glucose, serum creatinine, urinary albumin excretion and urinary ß2-microglobulin were measured. In addition, apoptosis and histological changes in kidney tissue were observed using TUNEL and HE staining, respectively, and the secretion and expression of inflammatory factors in kidney tissue were detected using EILSA and reverse transcription-quantitative PCR. Furthermore, we the expression of fibrosis-related proteins and NF-κB signaling pathway proteins was determined using western blot analysis. HPS was found to reduce the blood glucose concentration, serum creatinine levels, urinary albumin excretion rates and urinary ß2-microglobulin in a dose-dependent manner. In addition, HPS treatment mitigated apoptosis and pathological damage in the kidney tissues of DKD mice. The expression levels of fibrosis-related proteins fibronectin, α-smooth muscle actin and TGF-ß1 were observed to be decreased in kidney tissues of DKD mice following HPS treatment. The secretion levels of inflammatory factors (IL-6, TNF-α and IL-1ß) were also reduced in kidney tissues, with high-dose HPS treatment found to be more effective, similar to the effects mediated by the glyburide. Further mechanistic analysis revealed that the therapeutic effects of HPS on DKD mice may be mediated by inhibiting the high mobility group box 1/receptor for advanced glycation end-products/toll-like receptor 4 pathway. In conclusion, the present findings could provide insight for the treatment of DKD.

Numerical Modeling and Simulation of Non-Invasive Acupuncture Therapy Utilizing Near-Infrared Light-Emitting Diode.

Acupuncture is one of the most extensively used complementary and alternative medicine therapies worldwide. In this study, we explore the use of near-infrared light-emitting diodes (LEDs) to provide acupuncture-like physical stimulus to the skin tissue, but in a completely non-invasive way. A computational modeling framework has been developed to investigate the light-tissue interaction within a three-dimensional multi-layer model of skin tissue. Finite element-based analysis has been conducted, to obtain the spatiotemporal temperature distribution within the skin tissue, by solving Pennes' bioheat transfer equation, coupled with the Beer-Lambert law. The irradiation profile of the LED has been experimentally characterized and imposed in the numerical model. The experimental validation of the developed model has been conducted through comparing the numerical model predictions with those obtained experimentally on the agar phantom. The effects of the LED power, treatment duration, LED distance from the skin surface, and usage of multiple LEDs on the temperature distribution attained within the skin tissue have been systematically investigated, highlighting the safe operating power of the selected LEDs. The presented information about the spatiotemporal temperature distribution, and critical factors affecting it, would assist in better optimizing the desired thermal dosage, thereby enabling a safe and effective LED-based photothermal therapy.

[Role of TGF-ß/Smad signaling pathway in diabetic kidney disease and research progress of traditional Chinese medicine intervention].

Diabetic kidney disease is an important microvascular complication of diabetes and the leading cause of end-stage renal disease. Its pathological characteristics mainly include epithelial mesenchymal transition(EMT) in glomerulus, podocyte apoptosis and autophagy, and damage of glomerular filtration barrier. Transforming growth factor-ß(TGF-ß)/Smad signaling pathway is specifically regulated by a variety of mechanisms, and is a classic pathway involved in physiological activities such as apoptosis, proliferation and differentiation. At present, many studies have found that TGF-ß/Smad signaling pathway plays a key role in the pathogenesis of diabetic kidney disease. Traditional Chinese medicine has significant advantages in the treatment of diabetic kidney disease for its multi-component, multi-target and multi-pathway characteristics, and some traditional Chinese medicine extracts, traditional Chinese medicines and traditional Chinese medicine compound prescription improve the renal injury of diabetic kidney disease by regulating TGF-ß/Smad signaling pathway. This study clarified the mechanism of TGF-ß/Smad signaling pathway in diabetic kidney disease by expounding the relationship between the key targets of the pathway and diabetic kidney disease, and summarized the research progress of traditional Chinese medicine in the treatment of diabetic kidney disease by interfering with TGF-ß/Smad signaling pathway in recent years, to provide reference for drug research and clinical treatment of diabetic kidney disease in the future.

Integrated Transcriptome and Metabolome Dynamic Analysis of Galls Induced by the Gall Mite Aceria pallida on Lycium barbarum Reveals the Molecular Mechanism Underlying Gall Formation and Development.

Galls have become the best model for exploring plant-gall inducer relationships, with most studies focusing on gall-inducing insects but few on gall mites. The gall mite Aceria pallida is a major pest of wolfberry, usually inducing galls on its leaves. For a better understanding of gall mite growth and development, the dynamics of the morphological and molecular characteristics and phytohormones of galls induced by A. pallida were studied by histological observation, transcriptomics and metabolomics. The galls developed from cell elongation of the epidermis and cell hyperplasia of mesophylls. The galls grew quickly, within 9 days, and the mite population increased rapidly within 18 days. The genes involved in chlorophyll biosynthesis, photosynthesis and phytohormone synthesis were significantly downregulated in galled tissues, but the genes associated with mitochondrial energy metabolism, transmembrane transport, carbohydrates and amino acid synthesis were distinctly upregulated. The levels of carbohydrates, amino acids and their derivatives, and indole-3-acetic acid (IAA) and cytokinins (CKs), were markedly enhanced in galled tissues. Interestingly, much higher contents of IAA and CKs were detected in gall mites than in plant tissues. These results suggest that galls act as nutrient sinks and favor increased accumulation of nutrients for mites, and that gall mites may contribute IAA and CKs during gall formation.

Secondary Rhinaria Contribute to Major Sexual Dimorphism of Antennae in the Aphid Semiaphis heraclei (Takahashi).

Sexual generation is an important generation in the life cycle of host-alternating aphids, and its population size determines the intensity of the peak in the next spring. Although male trapping techniques based on olfactory stimuli have been successfully established in the field, the biological basis of olfactory perception in males is unclear. In this study, we compared the morphology of antennae and the types, sizes, numbers, and distribution of sensilla between males and sexual females in the host-alternating aphid Semiaphis heraclei (Hemiptera: Aphididae). We found that flagellum length differentiation contributed to the majority of the sexual dimorphism of antennae. Most sensillum types or subtypes, including trichoid sensilla subtype I, campaniform sensilla, and primary rhinaria subtypes I and II, were enlarged in males. In addition, males bore more trichoid sensilla subtype I than sexual females. In particular, secondary rhinaria were present in males only and could not be detected in sexual females. These results revealed the structural basis of male olfactory perception. Our findings provide insight into the mechanism underlying chemical communication between sexual aphids and could thus be useful for pest control.

Label-Free Cyanobacteria Quantification Using a Microflow Cytometry Platform for Early Warning Detection and Characterization of Hazardous Cyanobacteria Blooms.

The eutrophication of aquatic ecosystems caused by rapid human urbanization has led to an increased production of potentially hazardous bacterial populations, known as blooms. One of the most notorious forms of these aquatic blooms are cyanobacteria, which in sufficiently large quantities can pose a hazard to human health through ingestion or prolonged exposure. Currently, one of the greatest difficulties in regulating and monitoring these potential hazards is the early detection of cyanobacterial blooms, in real time. Therefore, this paper presents an integrated microflow cytometry platform for label-free phycocyanin fluorescence detection, which can be used for the rapid quantification of low-level cyanobacteria and provide early warning alerts for potential harmful cyanobacterial blooms. An automated cyanobacterial concentration and recovery system (ACCRS) was developed and optimized to reduce the assay volume, from 1000 mL to 1 mL, to act as a pre-concentrator and subsequently enhance the detection limit. The microflow cytometry platform utilizes an on-chip laser-facilitated detection to measure the in vivo fluorescence emitted from each individual cyanobacterial cell, as opposed to measuring overall fluorescence of the whole sample, potentially decreasing the detection limit. By applying transit time and amplitude thresholds, the proposed cyanobacteria detection method was verified by the traditional cell counting technique using a hemocytometer with an R2 value of 0.993. It was shown that the limit of quantification of this microflow cytometry platform can be as low as 5 cells/mL for Microcystis aeruginosa, 400-fold lower than the Alert Level 1 (2000 cells/mL) set by the World Health Organization (WHO). Furthermore, the decreased detection limit may facilitate the future characterization of cyanobacterial bloom formation to better provide authorities with ample time to take the appropriate actions to mitigate human risk from these potentially hazardous blooms.

Research on Temperature Dependence of Single-Event Burnout in Power MOSFETs.

Power MOSFETs are found to be very vulnerable to single-event burnout (SEB) in space irradiation environments, and the military components generally require that devices could operate reliably as the temperature varies from 218 K to 423 K (-55 °C to 150 °C); thus, the temperature dependence of single-event burnout (SEB) in power MOSFETs should be investigated. Our simulation results showed that the Si power MOSFETs are more tolerant to SEB at a higher temperature at the lower LET (10 MeV∙cm2/mg) due to the decrease of the impact ionization rate, which is in good agreement with the previous research. However, the state of the parasitic BJT plays a primary role in the SEB failure mechanism when the LET value is greater than 40 MeV∙cm2/mg, which exhibits a completely different temperature dependence from that of 10 MeV∙cm2/mg. Results indicate that with the temperature increasing, the lower difficulty to turn on the parasitic BJT and the increasing current gain all make it easier to build up the regenerative feedback process responsible for SEB failure. As a result, the SEB susceptibility of power MOSFETs increases as ambient temperature increases when the LET value is greater than 40 MeV∙cm2/mg.

Combined Metabolome and Transcriptome Analysis Highlights the Host's Influence on Cistanche deserticola Metabolite Accumulation.

The medicinal plant Cistanche deserticola Ma (Orobanchaceae) is a holoparasitic angiosperm that takes life-essential materials from Haloxylon ammodendron (C. A. Mey.) Bunge (Amaranthaceae) roots. Although many experiments have been conducted to improve the quality of C. deserticola, little attention has been paid to the host's influence on metabolite accumulation. In this study, transcriptomic and metabolomic analyses were performed to unveil the host's role in C. deserticola's metabolite accumulation, especially of phenylethanoid glycosides (PhGs). The results indicate that parasitism by C. deserticola causes significant changes in H. ammodendron roots in relation to metabolites and genes linked to phenylalanine metabolism, tryptophan metabolism and phenylpropanoid biosynthesis pathways, which provide precursors for PhGs. Correlation analysis of genes and metabolites further confirms that C. deserticola's parasitism affects PhG biosynthesis in H. ammodendron roots. Then we found specific upregulation of glycosyltransferases in haustoria which connect the parasites and hosts. It was shown that C. deserticola absorbs PhG precursors from the host and that glycosylation takes place in the haustorium. We mainly discuss how the host resists C. deserticola parasitism and how this medicinal parasite exploits its unfavorable position and takes advantage of host-derived metabolites. Our study highlights that the status of the host plant affects not only the production but also the quality of Cistanches Herba, which provides a practical direction for medicinal plant cultivation.

Flexible solar cells based on foldable silicon wafers with blunted edges.

Flexible solar cells have a lot of market potential for application in photovoltaics integrated into buildings and wearable electronics because they are lightweight, shockproof and self-powered. Silicon solar cells have been successfully used in large power plants. However, despite the efforts made for more than 50 years, there has been no notable progress in the development of flexible silicon solar cells because of their rigidity1-4. Here we provide a strategy for fabricating large-scale, foldable silicon wafers and manufacturing flexible solar cells. A textured crystalline silicon wafer always starts to crack at the sharp channels between surface pyramids in the marginal region of the wafer. This fact enabled us to improve the flexibility of silicon wafers by blunting the pyramidal structure in the marginal regions. This edge-blunting technique enables commercial production of large-scale (>240 cm2), high-efficiency (>24%) silicon solar cells that can be rolled similarly to a sheet of paper. The cells retain 100% of their power conversion efficiency after 1,000 side-to-side bending cycles. After being assembled into large (>10,000 cm2) flexible modules, these cells retain 99.62% of their power after thermal cycling between -70 °C and 85 °C for 120 h. Furthermore, they retain 96.03% of their power after 20 min of exposure to air flow when attached to a soft gasbag, which models wind blowing during a violent storm.

Exogenous spermidine alleviates diabetic cardiomyopathy via suppressing reactive oxygen species, endoplasmic reticulum stress, and Pannexin-1-mediated ferroptosis.

Diabetic cardiomyopathy (DCM) is a serious complication and death cause of diabetes mellitus (DM). Recent cardiology studies suggest that spermidine (SPD) has cardioprotective effects. Here, we verified the hypothesis of SPD's protective effects on DCM. Therefore, db/db mice and primary neonatal mouse cardiomyocytes were used to observe the effects of SPD. Immunoblotting showed that ornithine decarboxylase (ODC) and SPD/spermine N1-acetyltransferase (SSAT) were downregulated and upregulated in the myocardium of db/db mice, respectively. We found that diabetic mice showed cardiac dysfunction in 12 weeks. Conversely, exogenous SPD could improve cardiac functions and reduce the deposition of collagens, myocardial damage, reactive oxygen species (ROS) levels, and endoplasmic reticulum stress (ERS) in diabetic mouse hearts. Our results also demonstrated that cardiomyocytes displayed ferroptosis and then activated Pannexin-1 expression, which resulted in the increase of the extracellular adenosine triphosphate (ATP). Subsequently, increased ATP as a paracrine molecule combined to purinergic receptor P2X7 to activate ERK1/2 signaling pathway in cardiomyocytes and activated NCOA4-mediated ferroptinophagy to promote lipid peroxidation and ferroptosis. Interestingly, SPD could reverse these molecular processes. Our findings indicate an important new mechanism for DCM and suggest that SPD has potential applicability to protect against deterioration of cardiac function with DCM.

A Reliability System Evaluation Model of NoC Communication with Crosstalk Analysis from Backend to Frontend.

Network on chip (NoC) is the main solution to the communication bandwidth of a multi-processor system on chip (MPSoC). NoC also brings more route requirements and is highly prone to errors caused by crosstalk. Crosstalk has become a major design problem in deep-submicron NoC communication design. Hence, a crosstalk error model and corresponding reliable system with error correction code (ECC) are required to make NoC communication reliable. In this paper, a reliability system evaluation model (RSE) of NoC communication with analysis from backend to frontend has been proposed. In the backend, a crosstalk error rate model (CER) is established with a three-wire RLC coupling model and timing constraints. The CER is used to establish functional relations between interconnect spacing, length and signal frequency, and test system reliability. In the frontend, a reliability system performance model (RSP) is established with a CER, reliability method cost and bandwidth. The RSE summarizes the frontend and backend model. In order to verify the RSE model, we propose a reliability system with a hybrid automatic repeat request technique (RSHARQ). Simulation demonstrates that the CER model is close to real circuit design. Through the CER and RSP model, the performance of RSHARQ could be simulated.