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Abstract Particulate matter (PM) emissions from anthropogenic sources contribute substantially to air pollution. The unequal adverse health effects caused by source-emitted PM emphasize the need to consider the discrepancy of PM-bound chemicals rather than solely focusing on the mass concentration of PM when making air pollution control strategies. Here, we present a dataset about chemical compositions of real-world PM emissions from typical anthropogenic sources in China, including industrial (power, industrial boiler, iron & steel, cement, and other industrial process), residential (coal/biomass burning, and cooking), and transportation sectors (on-road vehicle, ship, and non-exhaust emission). The data was obtained under the same strict quality control condition on field measurements and chemical analysis, minimizing the uncertainty caused by different study approaches. The concentrations of PM-bound chemical components, including toxic elements and PAHs, exhibit substantial discrepancies among different emission sectors. This dataset provides experimental data with informative inputs to emission inventories, air quality simulation models, and health risk estimation. The obtained results can gain insight into understanding on source-specific PMs and tailoring effective control strategies.

The objective of this study was to reveal the effect of rumen degradable starch (RDS) on bile acid metabolism and liver transcription in dairy goats using metabolomics and transcriptomics. Eighteen Guanzhong dairy goats of a similar weight and production level (body weight = 45.8 ± 1.54 kg, milk yield = 1.75 ± 0.08 kg, and second parity) were randomly assigned to 3 treatment groups where they were fed a low RDS (LRDS, RDS = 20.52% DM) diet, medium RDS (MRDS, RDS = 22.15% DM) diet, or high RDS (HRDS, RDS = 24.88% DM) diet, respectively. The goats were fed with the experimental diets for 5 weeks. On the last day of the experiment, all goats were anesthetized, and peripheral blood and liver tissue samples were collected. The peripheral blood samples were used in metabolomic analysis and white blood cell (WBC) count, whereas the liver tissue samples were used in transcriptomic analysis. Based on the metabolomics results, the relative abundances of primary bile acids in the peripheral blood were significantly reduced in the group that was fed the HRDS diet (P

Abstract Background Epigenetic dysregulation is essential to the tumorigenesis of oral squamous cell carcinoma (OSCC). SET and MYND domain-containing protein 3 (SMYD3), a histone lysine methyltransferase, is implicated in gene transcription regulation and tumor development. However, the roles of SMYD3 in OSCC initiation are not fully understood. The present study investigated the biological functions and mechanisms involved in the SMYD3-mediated tumorigenesis of OSCC utilizing bioinformatic approaches and validation assays with the aim of informing the development of targeted therapies for OSCC. Results 429 chromatin regulators were screened by a machine learning approach and aberrant expression of SMYD3 was found to be closely associated with OSCC formation and poor prognosis. Data profiling of single-cell and tissue demonstrated that upregulated SMYD3 significantly correlated with aggressive clinicopathological features of OSCC. Alterations in copy number and DNA methylation patterns may contribute to SMYD3 overexpression. Functional experimental results suggested that SMYD3 enhanced cancer cell stemness and proliferation in vitro and tumor growth in vivo. SMYD3 was observed to bind to the High Mobility Group AT-Hook 2 (HMGA2) promoter and elevated tri-methylation of histone H3 lysine 4 at the corresponding site was responsible for transactivating HMGA2. SMYD3 also was positively linked to HMGA2 expression in OSCC samples. Furthermore, treatment with the SMYD3 chemical inhibitor BCI-121 exerted anti-tumor effects. Conclusions Histone methyltransferase activity and transcription-potentiating function of SMYD3 were found to be essential for tumorigenesis and the SMYD3–HMGA2 is a potential therapeutic target in OSCC.

Abstract KDM6B (Lysine-specific demethylase 6B) is a histone lysine demethyltransferase that plays a key role in many types of cancers. However, its potential role in gastric cancer (GC) remains unclear. Here, we focused on the clinical significance and potential role of KDM6B in GC. We found that the KDM6B expression is upregulated in GC tissues and that its high expression in patients is related to poor prognosis. KDM6B ectopic expression promotes GC cells’ proliferation and metastasis, while its inhibition has opposite effects in vitro and in vivo. Mechanistically, KDM6B promotes GC cells proliferation and metastasis through its enzymatic activity through the induction of H3K27me3 demethylation near the CXCR4 (C-X-C chemokine receptor type 4) promoter region, resulting in the upregulation of CXCR4 expression. Furthermore, H. pylori was found to induce KDM6B expression. In conclusion, our results suggest that KDM6B is aberrantly expressed in GC and plays a key role in gastric carcinogenesis and metastasis through CXCR4 upregulation. Our work also suggests that KDM6B may be a potential oncogenic factor and a therapeutic target for GC.

Abstract The main challenge of Multiple Object Tracking (MOT) is that there is great uncertainty in data association when using the tracked predicted values and tracked trajectories. Meanwhile, the MOT is complex and time‐consuming. When equipment resources are limited or real‐time requirement is high, its application is very limited. Therefore, we propose a Lightweight Deep Appearance Embedding (LDAE) to assist the association of trajectories. Firstly, in addition to motion information in data association, we also introduce more discriminative appearance features to participate in the affinity measure to effectively distinguish similar targets. Secondly, according to the idea of feature mapping, we design a lightweight deep appearance embedding module. It can help extract appearance features with less computation. Finally, we propose a simulated occlusion strategy for the training of the LDAE, which helps improve the ability to recognise different targets in dense scenes. The LDAE dramatically reduces the computational cost and improves the accuracy of data association. Extensive experiments are conducted on the MOT datasets (MOT16, MOT17 and MOT20), which prove that the LDAE outperforms several state‐of‐the‐art trackers in the tracking accuracy and anti‐occlusion performance. Furthermore, we apply the LDAE to escalators, which can achieve fast and stable tracking effect.

Despite the extensive development of peptide-based tumor antigen vaccines, the field still faces up enormous challenges from lacking clinical efficacy. Here, we report the design and development of a therapeutic nanovaccine for cancer immunotherapy by using gold nanoparticle (AuNPs)-conjugated peptides for incorporation with a supramolecular hydrogel, and via the featured host-guest interactions to carry and deliver select antigen peptides in conjunction with unmethylated cytosine-guanine oligodeoxynucleotide (CpG). The injectable nanovaccine shows significant improvement on the spatial draining lymph nodes (dLNs) delivery of the antigen peptides in antigen presenting cells (APCs) in vivo. Administration of the hydrogel nanovaccine results in a remarkable enhancement of the peptide uptake by APCs in the dLNs with a subsequent induction of the antigen-specific cytotoxic T lymphocytes (CTL) in the periphery, which is in association with 4 to 8-fold increases of these CTLs infiltrating in the tumor tissues. Finally, vaccination with the AuNPs-featured OVAp hydrogel presents with significant retardation of tumor progression that improves the survival rates in mice bearing established B16-OVA melanoma, indicating good efficacy in treating malignant cancers. Together, these findings offer a new strategy for development of spatiotemporally efficient antigen-peptide therapeutic nanovaccine for cancer immunotherapy.

ObjectivePrevious studies have revealed that FAT atypical cadherin 1 (FAT1) plays a tumor-suppressive or oncogenic role in a context-dependent manner in various cancers. However, the functions of FAT1 are ambiguous in tumorigenesis owing to inconsistent research in oral squamous cell carcinoma (OSCC). The present study aimed at gaining an insight into the role of FAT1 in the tumor genesis and development.MethodsThe expression, mutant, and survival data analyses were done using data from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database, verified with clinical samples via real-time polymerase chain reaction (qRT-PCR), Western blot (WB), and immunohistochemical (IHC) staining. OSCC cells transfected with siRNA were employed for in vitro assessment in cell proliferation, apoptosis, and migration ability in appropriate ways. The underlying mechanism was explored by RNA sequencing after FAT1 silencing.ResultsOverall, FAT1 significantly increased in OSCC with a poor prognosis outcome. The in vitro experiment showed the promoting effect of FAT1 in the proliferation and migration of OSCC cells. FAT1 can also inhibit both the early and late apoptosis of OSCC cells. RNA-sequencing analysis of FAT1 silencing revealed that the cell cycle, DNA replication, and some core genes (MCM2, MCM5, CCNE1 SPC24, MYBL2, KIF2C) may be the potential mechanism in OSCC.ConclusionsFAT1 may act as an oncogene in OSCC with potential mechanism influencing the cell cycle and DNA repair.

Abstract Background Glucose oxidase (GOD), an aerobic dehydrogenase, has been used as an antibiotic substitute in feed. A study was conducted to evaluate the differential effects of 2 different GODs fermented by Aspergillus niger or Penicillium amagasakiense on caecal microbiota and to further illuminate the potential roles of changes in the gut microbiota in regulating the growth performance and meat quality of broiler chickens. Results A total of 420 one-day-old healthy Arbor Acres broilers were randomly assigned to 4 treatments: the control group, the antibiotic growth promoter (AGP) supplementation group, and the GOD-A and GOD-P (GODs produced by A. niger and P. amagasakiense, respectively) groups. As a result, supplementation with GOD produced by P. amagasakiense could significantly improve the average daily weight gain and average daily feed intake of broilers before 21 days of age by significantly increasing the enzymatic activities of jejunal amylase and those of ileal amylase, chymotrypsin, and lipase in 21-day-old broilers and could increase the enzymatic activities of duodenal amylase, jejunal amylase and lipase, and ileal chymotrypsin and lipase in 42-day-old broilers. Meanwhile, compared with AGP treatment, supplementation with GOD produced by P. amagasakiense significantly decreased the L value of 21-day-old broilers and the ΔpH and L* value of 42-day-old broilers, while supplementation with GOD produced by A. niger significantly increased the pH24 h value of 21-day-old and 42-day-old broilers by reducing plasma malondialdehyde content. By using 16S rRNA sequencing, we found that the beneficial bacteria and microbiota in broilers were not disturbed but were improved by GOD supplementation compared with ADP treatment, including the genera Eubacterium and Christensenella and the species uncultured_Eubacterium_sp, Clostridium_asparagiforme, and uncultured_Christensenella_sp, which were positively related to the improved intestinal digestive enzymatic activities, growth performance, and meat quality of broilers. Conclusion The altered gut microbiota induced by supplementation with glucose oxidase produced by P. amagasakiense mediate better regulatory effects on the meat quality and growth performance of broilers than that induced by supplementation with glucose oxidase produced by A. niger.

Abstract Background Starch is an important substance that supplies energy to ruminants. To provide sufficient energy for high-yielding dairy ruminants, they are typically fed starch-enriched diets. However, starch-enriched diets have been proven to increase the risk of milk fat depression (MFD) in dairy cows. The starch present in ruminant diets could be divided into rumen-degradable starch (RDS) and rumen escaped starch (RES) according to their different degradation sites (rumen or intestine). Goats and cows have different sensitivities to MFD. Data regarding the potential roles of RDS in milk fat synthesis in the mammary tissue of dairy goats and in regulating the occurrence of MFD are limited. Results Eighteen Guanzhong dairy goats (day in milk = 185 ± 12 d) with similar parity, weight, and milk yield were selected and randomly assigned to one of three groups (n = 6), which were fed an LRDS diet (Low RDS = 20.52%), MRDS diet (Medium RDS = 22.15%), or HRDS diet (High RDS = 24.88%) for 5 weeks. Compared with that of the LRDS group, the milk fat contents in the MRDS and HRDS groups significantly decreased. The yields of short-, medium- and long-chain fatty acids decreased in the HRDS group. Furthermore, increased RDS significantly decreased ruminal B. fibrisolvens and Pseudobutyrivibrio abundances and increased the trans-10, cis-12 conjugated linoleic acid (CLA) and trans-10 C18:1 contents in the rumen fluid. A multiomics study revealed that the HRDS diet affected mammary lipid metabolism down-regulation of ACSS2, MVD, AGPS, SCD5, FADS2, CERCAM, SC5D, HSD17B7, HSD17B12, ATM, TP53RK, GDF1 and LOC102177400. Remarkably, the significant decrease of INSIG1, whose expression was depressed by trans-10, cis-12 CLA, could reduce the activity of SREBP and, consequently, downregulate the downstream gene expression of SREBF1. Conclusions HRDS-induced goat MFD resulted from the downregulation of genes involved in lipogenesis, particularly, INSIG1. Specifically, even though the total starch content and the concentrate-to-fiber ratio were the same as those of the high-RDS diet, the low and medium RDS diets did not cause MFD in lactating goats.

Gastric cancer (GC) is the third leading cause of cancer-related death worldwide. Helicobacter pylori infection can induce GC through a serial cascade of events, with emerging evidence suggesting the important role of epigenetic alterations in the development and progression of the disease. Here, we report on mechanisms responsible for Jumonji AT-rich interactive domain1B (JARID1B) upregulation in GC and its role in the malignant transformation induced by H. pylori infection. We found that upregulation of JARID1B was associated with poorer prognosis, greater tumor purity, and less immune cell infiltration into the tumor. Mechanistically, we showed that the upregulation of JARID1B in human GC was attributed to JARID1B amplification and its induction by H. pylori infection. Furthermore, we identified miR-29c as a negative regulator of JARID1B in GC. H. pylori caused downregulation of miR-29c in human GC and thereby contributed to JARID1B upregulation through relieving posttranscriptional regulation. Functionally, we showed that knockdown of JARID1B reduced GC cell proliferation induced by H. pylori infection. Subsequently, cyclinD1 (CCND1), a key molecule in GC, was shown to be a target gene of JARID1B. In conclusion, these results suggest that JARID1B may be an oncogene upregulated in human GC and could represent a novel therapeutic target to prevent malignant transformation induced by H. pylori infection.

High starch diets have been proven to increase the risk of hindgut acidosis in high-yielding dairy animals. As an effective measurement of dietary carbohydrate for ruminants, studies on rumen degradable starch (RDS) and the effects on the gut microbiota diversity of carbohydrate-active enzymes (CAZymes), and Kyoto Encyclopedia of Genes and Genomes (KEGG) Orthology functional categories are helpful to understand the mechanisms between gut microbiota and carbohydrate metabolism in dairy goats. A total of 18 lactating goats (45.8 ± 1.54 kg) were randomly divided equally into three dietary treatments with low dietary RDS concentrations of 20.52% (LRDS), medium RDS of 22.15% (MRDS), and high RDS of 24.88% (HRDS) on a DM basis for 5 weeks. Compared with the LRDS and MRDS groups, HRDS increased acetate molar proportion in the cecum. For the HRDS group, the abundance of family Ruminococcaceae and genus Ruminococcaceae UCG-010 were significantly increased in the cecum. For the LRDS group, the butyrate molar proportion and the abundance of butyrate producer family Bacteroidale_S24-7, family Lachnospiraceae, and genus Bacteroidale_S24-7_group were significantly increased in the cecum. Based on the BugBase phenotypic prediction, the microbial oxidative stress tolerant and decreased potentially pathogenic in the LRDS group were increased in the cecum compared with the HRDS group. A metagenomic study on cecal bacteria revealed that dietary RDS level could affect carbohydrate metabolism by increasing the glycoside hydrolase 95 (GH95) family and cellulase enzyme (EC 3.2.1.4) in the HRDS group; increasing the GH13_20 family and isoamylase enzyme (EC 3.2.1.68) in the LRDS group. PROBIO probiotics database showed the relative gene abundance of cecal probiotics significantly decreased in the HRDS group. Furthermore, goats fed the HRDS diet had a lower protein expression of Muc2, and greater expression RNA of interleukin-1β and secretory immunoglobulin A in cecal mucosa than did goats fed the LRDS diet. Combined with the information from previous results from rumen, dietary RDS level altered the degradation position of carbohydrates in the gastrointestinal (GI) tract and increased the relative abundance of gene encoded enzymes degrading cellulose in the HRDS group in the cecum of dairy goats. This study revealed that the HRDS diet could bring disturbances to the microbial communities network containing taxa of the Lachnospiraceae and Ruminococcaceae and damage the mucus layer and inflammation in the cecum of dairy goats.

Gastric cancer (GC) is one of the leading causes of cancer-related deaths and shows high levels of heterogeneity. The development of a specific prognostic model is important if we are to improve treatment strategies. Pyroptosis can arise in response to H. pylori, a primary carcinogen, and also in response to chemotherapy drugs. However, the prognostic evaluation of GC to pyroptosis is insufficient. Consensus clustering by pyroptosis-related regulators was used to classify 618 patients with GC from four GEO cohorts. Following Cox regression with differentially expressed genes, our prognosis model (PS-score) was built by LASSO-Cox analysis. The TCGA-STAD cohort was used as the validation set. ESTIMATE, CIBERSORTx, and EPIC were used to investigate the tumor microenvironment (TME). Immunotherapy cohorts by blocking PD1/PD-L1 were used to investigate the treatment response. The subtyping of GC based on pyroptosis-related regulators was able to classify patients according to different clinical traits and TME. The difference between the two subtypes identified in this study was used to develop a prognosis model which we named “PS-score.” The PS-score could predict the prognosis of patients with GC and his/her overall survival time. A low PS-score implies greater inflammatory cell infiltration and better response of immunotherapy by PD1/PD-L1 blockers. Our findings provide a foundation for future research targeting pyroptosis and its immune microenvironment to improve prognosis and responses to immunotherapy.

BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

Background: Oral squamous cell carcinoma (OSCC) is one of the most common malignant tumors worldwide. Patients with poorly differentiated OSCC often exhibit a poor prognosis. AUNIP (Aurora Kinase A and Ninein Interacting Protein), also known as AIBp, plays a key role in cell cycle and DNA damage repair. However, the function of AUNIP in OSCC remains elusive. Methods: The differentially expressed genes (DEGs) were obtained using R language. Receiver operating characteristic curve analysis was performed to identify diagnostic markers for OSCC. The effectiveness of AUNIP in diagnosing OSCC was evaluated by machine learning. AUNIP expression was analyzed in publicly available databases and clinical specimens. Bioinformatics analysis and in vitro experiments were conducted to explore biological functions and prognostic value of AUNIP in OSCC. Findings: The gene integration analysis revealed 90 upregulated DEGs. One candidate biomarker, AUNIP, for the diagnosis of OSCC was detected, and its expression gradually increased along with malignant differentiation of OSCC. Bioinformatics analysis demonstrated that AUNIP could be associated with tumor microenvironment, human papillomavirus infection, and cell cycle in OSCC. The suppression of AUNIP inhibited OSCC cell proliferation and resulted in G0/G1 phase arrest in OSCC cells. The survival analysis showed that AUNIP overexpression predicted poor prognosis of OSCC patients.Interpretation: AUNIP could serve as a candidate diagnostic and prognostic biomarker for OSCC and suppression of AUNIP may be a potential approach to preventing and treating OSCC. Fund: Taishan Scholars Project in Shandong Province (ts201511106) and the National Natural Science Foundation of China (Nos. 61603218). Keywords: Oral squamous cell carcinoma, Receiver operating characteristic curve, Biomarker, Weighted gene co-expression network analysis, Survival analysis, AUNIP

Abstract Mobile edge computing is becoming a promising computing architecture to overcome the resource limitation of mobile devices and bandwidth bottleneck of the core networks in mobile cloud computing. Although offloading applications to the cloud can extend the performance for the mobile devices, it may also lead to greater processing latency. Usually, the mobile users have to pay for the cloudlet resource or cloud resource they used. In this paper, we bring a thorough study on the energy consumption, time consumption, and cost of using the resource of cloudlet and cloud for workflow applications in mobile edge computing. Based on theoretical analysis, a multi-objective optimization model is established. In addition, a corresponding multi-objective computation offloading method based on non-dominated sorting genetic algorithm II is proposed to find the optimal offloading strategy for all the workflow applications. Finally, extensive experimental evaluations are performed to show that our proposed method is effective and energy- and cost-aware for workflow applications in MEC.

Abstract Gastric cancer (GC) is one of the most common and malignant pathologies, and a significant portion of GC incidences develops from Helicobacter pylori (Hp)‐induced chronic gastritis. Although the exact mechanisms of GC are complex and poorly understood, gastric carcinogenesis is a good model to investigate how inflammation and infection collaboratively promote tumorigenesis. Yes‐associated protein 1 (YAP1) is the key effector of the Hippo pathway, which is silenced in most human cancers. Herein, we verified the tumor‐promoting effect of YAP1 in vitro, in vivo, and in human specimens. We revealed that YAP1 displays nuclear translocation and works with TEAD to activate transcription of the crucial inflammatory cytokine IL‐1β in gastric cells infected with Hp. As IL‐1ß accounts for inflammation‐associated tumorigenesis, this process can lead to gastric carcinogenesis. Thus, in addition to activating proliferation genes, YAP1 also plays a major role in inflammation amplification by activating inflammatory cytokine genes. Excitingly, our research demonstrates that transfection of mutant plasmid YAP‐5SA/S94A or addition of the drug verteporfin, both of which are thought to disrupt the YAP1‐TEAD interaction, can arrest the carcinogenesis process. These findings can provide new approaches to GC treatment.

Low-resolution medical images can hamper medical diagnosis seriously, especially in the analysis of retina images and specifically for the detection of macula fovea. Therefore, improving the quality of medical images and speeding up their reconstruction is particularly important for expert diagnosis. To deal with this engineering problem, our paper presents a fast medical image super-resolution (FMISR) method whereby the three hidden layers to complete feature extraction is as same as the super resolution convolution neural network. It is important that a well-designed deep learning network processes images in the low resolution instead of the high-resolution space and enables the super-resolution reconstruction to be more efficient. Sub-pixel convolution layer addition and mini-network substitution in the hidden layers are critical for improving the image reconstruction speed. While the hidden layers are proposed for ensuring reconstruction quality, our FMISR framework performs significantly faster and produces a higher resolution images. As such, the technique underlying this framework presents a high potential in retinal macular examination as it provides a good platform for the segmentation of retinal images.

Visual tracking is a challenging problem since it usually faces adverse factors, such as object deformation, fast motion, occlusion, and background clutter in practical applications. Reinforcement learning based Action-Decision Network (ADNet) has shown great potential for object tracking. However, ADNet has some shortcomings in optimal action selection and action reward, and suffers from inefficient tracking. To this end, an improved ADNet is proposed to enhance the tracking accuracy and efficiency. Firstly, the multi-domain training is incorporated into ADNet to further improve the feature extraction ability of its convolution layers. Then, in the reinforcement learning based training phase, both the selection criteria for optimal action and the reward function are redesigned separately to explore more appropriate action and eliminate useless action. Finally, an effective online adaptive update strategy is proposed to adapt to the appearance changes or deformation of the object during actual tracking. Specifically, meta-learning is utilized to pursue the most appropriate parameters for the network so that the parameters are closer to the optimal ones in the subsequent tracking process. Experimental results demonstrate that the proposed tracker has advantages over ADNet in terms of accuracy and efficiency.

Background: Rhythm abnormalities are crucial for diverse diseases. However, their role in disease progression induced by Helicobacter pylori (H. pylori) remains elusive. Methods: H. pylori infection was used in in vivo and in vitro experiments to examine its effect on rhythmic genes. The GEO database was used to screen H. pylori affecting rhythm genes, and the effect of rhythm genes on inflammatory factors. Chromatin immunoprecipitation and dual luciferase assays were used to further find out the regulation between molecules. Animal models were used to confirm the relationship between rhythm genes and H. pylori-induced inflammation. Findings: BMAL1 disorders aggravate inflammation induced by H. pylori. Specifically, H. pylori induce BMAL1 expression in vitro and in vivo through transcriptional activation of LIN28A, breaking the circadian rhythm. Mechanistically, LIN28A binds to the promoter region of BMAL1 and directly activates its transcription under H. pylori infection. BMAL1 in turn functions as a transcription factor and enhances the expression of proinflammatory cytokine TNF-α, thereby promoting inflammation. Of note, BMAL1 dysfunction in the rhythm disorder animal model aggravates inflammatory response induced by H. pylori infection in vivo. Interpretation: These findings in this study imply the pathogenic relationship between BMAL1 and H. pylori. BMAL1 may serve as a potential diagnostic marker and therapeutic target for the early diagnosis and treatment of diseases related to H. pylori infection. Fund: National Natural Science Foundation of China. Keywords: BMAL1, H. pylori, Circadian rhythm, TNF-α, LIN28A

Background: Histones chaperones have been found to play critical roles in tumor development and progression. However, the role of histone chaperone CHAF1A in gastric carcinogenesis and its underlying mechanisms remain elusive. Methods: CHAF1A expression in gastric cancer (GC) was analyzed in GEO datasets and clinical specimens. CHAF1A knockdown and overexpression were used to explore its functions in gastric cancer cells. The regulation and potential molecular mechanism of CHAF1A expression in gastric cancer cells were studied by using cell and molecular biological methods. Findings: CHAF1A was upregulated in GC tissues and its high expression predicted poor prognosis in GC patients. Overexpression of CHAF1A promoted gastric cancer cell proliferation both in vitro and in vivo, whereas CHAF1A suppression exhibited the opposite effects. Mechanistically, CHAF1A acted as a co-activator in the Wnt pathway. CHAF1A directly interacted with TCF4 to enhance the expression of c-MYC and CCND1 through binding to their promoter regions. In addition, the overexpression of CHAF1A was modulated by specificity protein 1 (Sp1) in GC. Sp1 transcriptionally enhanced the expression of CHAF1A in GC. Furthermore, CHAF1A expression induced by Helicobacter pylori was Sp1 dependent. Interpretation: CHAF1A is a potential oncogene in GC, and may serve as a novel therapeutic target for GC treatment. Keywords: Histone chaperone, CHAF1A, Gastric cancer, Carcinogenesis

BackgroundChronic myeloid leukemia (CML) is a malignant clonal proliferative disease. Once it progresses into the phase of blast crisis (CML-BP), the curative effect is poor, and the fatality rate is extremely high. Therefore, it is urgent to explore the molecular mechanisms of blast crisis and identify new therapeutic targets.MethodsThe expression levels of miR-181d, RBP2 and NF-κB p65 were assessed in 42 newly diagnosed CML-CP patients and 15 CML-BP patients. Quantitative real-time PCR, Western blots, and cell proliferation assay were used to characterize the changes induced by overexpression or inhibition of miR-181d, RBP2 or p65. Luciferase reporter assay and ChIP assay was conducted to establish functional association between miR-181d, RBP2 and p65. Inhibition of miR-181d expression and its consequences in tumor growth was demonstrated in vivo models.ResultsWe found that miR-181d was overexpressed in CML-BP, which promoted leukemia cell proliferation. Histone demethylase RBP2 was identified as a direct target of miR-181d which downregulated RBP2 expression. Moreover, RBP2 inhibited transcriptional expression of NF-κB subunit, p65 by binding to its promoter and demethylating the tri/dimethylated H3K4 region in the p65 promoter locus. In turn, p65 directly bound to miR-181d promoter and upregulated its expression. Therefore, RBP2 inhibition resulting from miR-181d overexpression led to p65 upregulation which further forwarded miR-181d expression. This miR-181d/RBP2/p65 feedback regulation caused sustained NF-κB activation, which contributed to the development of CML-BP.ConclusionsTaken together, the miR-181d/RBP2/p65 feedback regulation promoted CML-BP and miR-181d may serve as a potential therapeutic target of CML-BP.

Metastasis and immune suppression account for the poor prognosis of oral squamous cell carcinoma (OSCC). YKT6 is a member of the soluble NSF attachment protein receptor (SNARE) family, and the effect of YKT6 in OSCC remains elusive. The purpose of this study was to explore promising prognostic and immune therapeutic candidate biomarkers for OSCC and to understand the expression pattern, prognostic value, immune effects, and biological functions of YKT6. Genes correlated with tumor metastasis and CD8 + T cell levels were identified by weighted gene coexpression network analysis (WGCNA). Next, YKT6 was analyzed through differential expression, prognostic and machine learning analyses. The molecular and immune characteristics of YKT6 were analyzed in independent cohorts, clinical specimens, and in vitro. In addition, we investigated the role of YKT6 at the pan-cancer level. The results suggested that the red module in WGCNA, as a hub module, was associated with lymph node (LN) metastasis and CD8 + T cell infiltration. Upregulation of YKT6 was found in OSCC and linked to adverse prognosis. A nomogram model containing YKT6 expression and tumor stage was constructed for clinical practice. The aggressive and immune-inhibitory phenotypes showed YKT6 overexpression, and the effect of YKT6 on OSCC cell invasion and metastasis in vitro was observed. Moreover, the low expression of YKT6 was correlated with high CD8 + T cell levels and potential immunotherapy response in OSCC. Similar results were found at the pan-cancer level. In total, YKT6 is a promising candidate biomarker for prognosis, molecular, and immune characteristics in OSCC.

The objective of this study was to investigate the effects of different dietary rumen degradable starch (RDS) on the diversity of carbohydrate-active enzymes (CAZymes) and Kyoto Encyclopedia of Genes and Genomes Orthology functional categories to explore carbohydrate degradation in dairy goats. Eighteen dairy goats (second lactation, 45.8 ± 1.54 kg) were divided in three groups fed low RDS (LRDS), medium RDS (MRDS), and high RDS (HRDS) diets. The results showed that, HRDS treatment group significantly decreased the ruminal pH (P < 0.05), and increased the propionate proportion (P < 0.05), fumarate and succinate concentrations (P < 0.05), trended to increase lactate concentration (P = 0.50) compared with LRDS group. The relative abundance of acetogens, such as family Clostridiaceae and Ruminococcaceae, genera Clostridium and Blautia were higher in HRDS than LRDS feeding goats. The GH9 family (responsible for cellulose degradation) genes were lower in HRDS than MRDS diet samples, and mainly produced by Prevotellaceae, Ruminococcaceae, and Bacteroidaceae. Amylose (EC3.2.1.3) genes under HRDS treatment were more abundant than under LRDS treatment. However, the abundance of GH13_9 and CBM48 (responsible for starch degradation) were reduced in HRDS group indicating the decreased binding activity from catalytic modules to starch. This study revealed that HRDS-fed dairy goats had decreased CAZymes, which encode enzymes degrade cellulose and starch in the dairy goats.

Background: Abnormal expression of the orphan nuclear receptor Nurr1 is a critical factor in the etiology of multiple cancers. However, its potential role in gastric cancer (GC) remains elusive. In this study, we have demonstrated that the expression of Nurr1 was elevated and had an oncogenic function in GC. Methods: Nurr1 expression was analyzed in clinical specimens and the GEO database. Functions of Nurr1 in GC cells were analyzed using Nurr1 knockdown and overexpression. Various cell and molecular biological methods were used to explore the potential mechanisms of Nurr1 upregulation and its role in promoting GC. Findings: Overexpression of Nurr1 was directly related to the poor prognosis of GC patients. What's more, Nurr1 was induced by Helicobacter pylori (H. pylori) via the PI3K/AKT-Sp1 pathway. Sp1 enhanced Nurr1 expression by binding to its promoter to activate the transcription. Upregulated Nurr1 then directly targeted CDK4 by binding to its promoter region to increase its expression, thereby facilitated GC cells proliferation both in vitro and in vivo. Interpretation: We identified Nurr1 as a driving oncogenic factor in GC. In addition, Nurr1 could be used as a potential therapeutic target for the diagnosis and treatment of H. pylori-associated GC. Funding: This work was supported by the National Natural Science Foundation of China (Nos 81801983, 81871620, 81971901, 81772151 and 81571960), and the Department of Science and Technology of Shandong Province (2018CXGC1208). Keywords: Gastric cancer, PI3K/AKT/Sp1, Proliferation, CDK4, Helicobacter pylori

Various fields in medicine require scientific research and computer application. This results in computation time optimization becoming a task that is of increasing importance due to its highly parallel architecture. As is well-known, the graphics processing unit (GPU) is regarded as a powerful engine for application programs that demand fairly high computation capabilities. Our study is based on the deep analysis of the parallelism pertaining to the calculation of the gray level co-occurrence matrix, whereby an algorithm was introduced to optimize the method used to compute the gray-level co-occurrence matrix (GLCM) of an image. Furthermore, strategies (e.g., copying, image partitioning, and so on) were proposed to optimize the parallel algorithm. Our experiments indicate that without losing the computational accuracy, the speed-up ratio of the GLCM computation of images with different resolutions by GPU utilizing compute unified device architecture was at least 50 times faster than that of the GLCM computation by the central processing unit. This manifestation of a significantly improved performance can lead to the development of a very useful computational tool in medical computer vision.

In this paper, a joint feature and similarity deep learning (JFSDL) method for vehicle reidentification is proposed. The proposed JFSDL method applies a siamese deep network to extract deep learning features for an input vehicle image pair simultaneously. The siamese deep network is learned under the joint identification and verification supervision. The joint identification and verification supervision is realized by linearly combining two softmax functions and one hybrid similarity learning function. Moreover, based on the hybrid similarity learning function, the similarity score between the input vehicle image pair is also obtained by simultaneously projecting the element-wise absolute difference and multiplication of the corresponding deep learning feature pair with a group of learned weight coefficients. Extensive experiments show that the proposed JFSDL method is superior to multiple state-of-the-art vehicle re-identification methods on both the VehicleID and VeRi data sets.

The diagnosis of blood-related diseases involves the identification and characterization of a patient's blood sample. As such, automated methods for detecting and classifying the types of blood cells have important medical applications in this field. Although deep convolutional neural network (CNN) and the traditional machine learning methods have shown good results in the classification of blood cell images, they are unable to fully exploit the long-term dependence relationship between certain key features of images and image labels. To resolve this problem, we have introduced the recurrent neural networks (RNNs). Specifically, we combined the CNN and RNN in order to propose the CNN-RNN framework that can deepen the understanding of image content and learn the structured features of images and to begin endto-end training of big data in medical image analysis. In particular, we apply the transfer learning method to transfer the weight parameters that were pre-trained on the ImageNet dataset to the CNN section and adopted a custom loss function to allow our network to train and converge faster and with more accurate weight parameters. Experimental results show that compared with the other CNN models such as ResNet and Inception V3, our proposed network model is more accurate and efficient in classifying blood cell images.

Macrophage differentiation and inflammatory function are controlled by cell metabolism. Here, the authors use a viral hepatitis model and a high-fat diet model of insulin resistance to show how VSIG4 inhibits inflammatory macrophage activation by modulating mitochondrial pyruvate metabolism.

In December 2019, the outbreak of pneumonia caused by a novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has led to a serious pandemic in China and other countries worldwide. So far, more than 460,000 confirmed cases were diagnosed in nearly 190 countries, causing globally over 20,000 deaths. Currently, the epidemic is still spreading and there is no effective means to prevent the infection. Vaccines are proved to be the most effective and economical means to prevent and control infectious diseases. Several countries, companies, and institutions announced their programs and progress on vaccine development against the virus. While most of the vaccines are under design and preparation, there are some that have entered efficacy evaluation in animals and initial clinical trials. This review mainly focused on the progress and our prospects on field of vaccine development against SARS-CoV-2.

While T helper (Th) cells play a crucial role in host defense, an imbalance in Th effector subsets due to dysregulation in their differentiation and expansion contribute to inflammatory disorders. Here, we show that Casz1, whose function is previously unknown in CD4+ T cells, coordinates Th differentiation in vitro and in vivo. Casz1 deficiency in CD4+ T cells lowers susceptibility to experimental autoimmune encephalomyelitis, consistent with the reduced frequency of Th17 cells, despite an increase in Th1 cells in mice. Loss of Casz1 in the context of mucosal Candida infection severely impairs Th17 and Treg responses, and lowers the ability of the mice to clear the secondary infection. Importantly, in both the models, absence of Casz1 causes a significant diminution in IFN-γ+IL-17A+ double-positive inflammatory Th17 cells (Th1* cells) in tissues in vivo. Transcriptome analyses of CD4+ T cells lacking Casz1 show a signature consistent with defective Th17 differentiation. With regards to Th17 differentiation, Casz1 limits repressive histone marks and enables acquisition of permissive histone marks at Rorc, Il17a, Ahr, and Runx1 loci. Taken together, these data identify Casz1 as a new Th plasticity regulator having important clinical implications for autoimmune inflammation and mucosal immunity.

Necroptosis as a molecular program, rather than simply incidental cell death, was established by elucidating the roles of receptor interacting protein (RIP) kinases 1 and 3, along with their downstream partner, mixed lineage kinase-like domain protein (MLKL). Previous studies suggested that phosphoglycerate mutase family member 5 (PGAM5), a mitochondrial protein that associates with RIP1/RIP3/MLKL complex, promotes necroptosis. We have generated mice deficient in the pgam5 gene and surprisingly found PGAM5-deficiency exacerbated rather than reduced necroptosis in response to multiple in vitro and in vivo necroptotic stimuli, including ischemic reperfusion injury (I/R) in the heart and brain. Electron microscopy, biochemical, and confocal analysis revealed that PGAM5 is indispensable for the process of PINK1 dependent mitophagy which antagonizes necroptosis. The loss of PGAM5/PINK1 mediated mitophagy causes the accumulation of abnormal mitochondria, leading to the overproduction of reactive oxygen species (ROS) that worsen necroptosis. Our results revise the former proposal that PGAM5 acts downstream of RIP1/RIP3 to mediate necroptosis. Instead, PGAM5 protects cells from necroptosis by independently promoting mitophagy. PGAM5 promotion of mitophagy may represent a therapeutic target for stroke, myocardial infarction and other diseases caused by oxidative damage and necroptosis.

A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%.

Due to the limitations of the resolution of the imaging system and the influence of scene changes and other factors, sometimes only low-resolution images can be acquired, which cannot satisfy the practical application’s requirements. To improve the quality of low-resolution images, a novel super-resolution algorithm based on an improved sparse autoencoder is proposed. Firstly, in the training set preprocessing stage, the high- and low-resolution image training sets are constructed, respectively, by using high-frequency information of the training samples as the characterization, and then the zero-phase component analysis whitening technique is utilized to decorrelate the formed joint training set to reduce its redundancy. Secondly, a constructed sparse regularization term is added to the cost function of the traditional sparse autoencoder to further strengthen the sparseness constraint on the hidden layer. Finally, in the dictionary learning stage, the improved sparse autoencoder is adopted to achieve unsupervised dictionary learning to improve the accuracy and stability of the dictionary. Experimental results validate that the proposed algorithm outperforms the existing algorithms both in terms of the subjective visual perception and the objective evaluation indices, including the peak signal-to-noise ratio and the structural similarity measure.

Viral fulminant hepatitis (FH) is a severe disease with high mortality resulting from excessive inflammation in the infected liver. Clinical interventions have been inefficient due to the lack of knowledge for inflammatory pathogenesis in the virus-infected liver. We show that wild-type mice infected with murine hepatitis virus strain-3 (MHV-3), a model for viral FH, manifest with severe disease and high mortality in association with a significant elevation in IL-1β expression in the serum and liver. Whereas, the viral infection in IL-1β receptor-I deficient (IL-1R1-/-) or IL-1R antagonist (IL-1Ra) treated mice, show reductions in virus replication, disease progress and mortality. IL-1R1 deficiency appears to debilitate the virus-induced fibrinogen-like protein-2 (FGL2) production in macrophages and CD45+Gr-1high neutrophil infiltration in the liver. The quick release of reactive oxygen species (ROS) by the infected macrophages suggests a plausible viral initiation of NLRP3 inflammasome activation. Further experiments show that mice deficient of p47phox, a nicotinamide adenine dinucleotide phosphate (NADPH) oxidase subunit that controls acute ROS production, present with reductions in NLRP3 inflammasome activation and subsequent IL-1β secretion during viral infection, which appears to be responsible for acquiring resilience to viral FH. Moreover, viral infected animals in deficiencies of NLRP3 and Caspase-1, two essential components of the inflammasome complex, also have reduced IL-1β induction along with ameliorated hepatitis. Our results demonstrate that the ROS/NLRP3/IL-1β axis institutes an essential signaling pathway, which is over activated and directly causes the severe liver disease during viral infection, which sheds light on development of efficient treatments for human viral FH and other severe inflammatory diseases.