Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics.

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

scCancer2: data-driven in-depth annotations of the tumor microenvironment at single-level resolution.

SUMMARY: Single-cell RNA-seq (scRNA-seq) is a powerful technique for decoding the complex cellular compositions in the tumor microenvironment (TME). As previous studies have defined many meaningful cell subtypes in several tumor types, there is a great need to computationally transfer these labels to new datasets. Also, different studies used different approaches or criteria to define the cell subtypes for the same major cell lineages. The relationships between the cell subtypes defined in different studies should be carefully evaluated. In this updated package scCancer2, designed for integrative tumor scRNA-seq data analysis, we developed a supervised machine learning framework to annotate TME cells with annotated cell subtypes from 15 scRNA-seq datasets with 594 samples in total. Based on the trained classifiers, we quantitatively constructed the similarity maps between the cell subtypes defined in different references by testing on all the 15 datasets. Secondly, to improve the identification of malignant cells, we designed a classifier by integrating large-scale pan-cancer TCGA bulk gene expression datasets and scRNA-seq datasets (10 cancer types, 175 samples, 663 857 cells). This classifier shows robust performances when no internal confidential reference cells are available. Thirdly, scCancer2 integrated a module to process the spatial transcriptomic data and analyze the spatial features of TME. AVAILABILITY AND IMPLEMENTATION: The package and user documentation are available at http://lifeome.net/software/sccancer2/ and https://doi.org/10.5281/zenodo.10477296.

JEBIN: analyzing gene co-expressions across multiple datasets by joint network embedding.

The inference of gene co-expression associations is one of the fundamental tasks for large-scale transcriptomic data analysis. Due to the high dimensionality and high noises in transcriptomic data, it is difficult to infer stable gene co-expression associations from single dataset. Meta-analysis of multisource data can effectively tackle this problem. We proposed Joint Embedding of multiple BIpartite Networks (JEBIN) to learn the low-dimensional consensus representation for genes by integrating multiple expression datasets. JEBIN infers gene co-expression associations in a nonlinear and global similarity manner and can integrate datasets with different distributions in linear time complexity with the gene and total sample size. The effectiveness and scalability of JEBIN were verified by simulation experiments, and its superiority over the commonly used integration methods was proved by three indexes on real biological datasets. Then, JEBIN was applied to study the gene co-expression patterns of hepatocellular carcinoma (HCC) based on multiple expression datasets of HCC and adjacent normal tissues, and further on latest HCC single-cell RNA-seq data. Results show that gene co-expressions are highly different between bulk and single-cell datasets. Finally, many differentially co-expressed ligand-receptor pairs were discovered by comparing HCC with adjacent normal data, providing candidate HCC targets for abnormal cell-cell communications.

Single-cell and spatially resolved transcriptomics for liver biology.

Single-cell transcriptomics enables the identification of rare cell types and the inference of state transitions, whereas spatially resolved transcriptomics allows the quantification of cells and genes in the context of tissues. The recent progress in these new technologies is improving our understanding of the cell landscape and its roles in diseases. Here, we review key biological insights into liver homeostasis, development, regeneration, chronic liver disease, and cancer obtained from single-cell and spatially resolved transcriptomics. We highlight recent progress in the liver cell atlas that characterizes the comprehensive cellular composition; diversity and function; the spatial architecture such as liver zonation, cell communication, and proximity; the cell identity conversion and cell-specific alterations that are associated with liver pathology; and new therapeutic targets. We further discuss outstanding challenges, advanced experimental technologies, and computational methods that help to address these challenges.

Factors associated with dysmenorrhoea and menorrhagia improvement in patients with adenomyosis after uterine artery embolisation.

BACKGROUND: This study examined the improvement of dysmenorrhoea and menorrhagia after uterine artery embolisation (UAE) in women with symptomatic adenomyosis and identified factors that could predict the improvement of dysmenorrhoea and menorrhagia. METHODS: This retrospective study included women with adenomyosis who underwent bilateral UAE between December 2014 and December 2016. The percentage of the volume of the absence of contrast enhancement on T1-weighted images was evaluated 5-7 days after UAE. A receiver operating characteristic (ROC) analysis was used to determine a cut-off point and predict the improvement of dysmenorrhoea and menorrhagia. RESULTS: Forty-eight patients were included. At 24 and 36 months after UAE, the improvement rates for dysmenorrhoea and menorrhagia were 60.4% (29/48) and 85.7% (30/35), and the recurrence rates were 19.4% (7/36) and 9.1% (3/33), respectively. Only the percentage of the volume of the absence of contrast enhancement on T1-weighted images was associated with the improvement of dysmenorrhoea (p = 0.001, OR = 1.051; 95% CI: 1.02-1.08) and menorrhagia (p = 0.006, OR = 1.077; 95% CI: 1.021-1.136). When the cut-off value of the ROC analysis was 73.1%, sensitivity, specificity, positive predictive value, and negative predictive value for the improvement of dysmenorrhoea were 58.6%, 94.7%, 94.4%, and 60%, while they were 58.9%, 80%, 100%, 100%, and 45.5% for the improvement of dysmenorrhoea. CONCLUSION: Bilateral UAE for symptomatic adenomyosis led to good improvement of dysmenorrhoea and menorrhagia. The percentage of the volume of the absence of contrast enhancement on T1-weighted images of the uterus in postoperative magnetic resonance imaging might be associated with the improvement of dysmenorrhoea and menorrhagia.

This study examined the improvement of dysmenorrhoea and menorrhagia after uterine artery embolisation in women with symptomatic adenomyosis and identified factors that could predict the improvement of dysmenorrhoea and menorrhagia. This retrospective study included women with adenomyosis who underwent uterine artery embolisation. A total of 48 patients were included. Only the percentage of the volume of the absence of contrast enhancement on T1-weighted images was associated with improvement of dysmenorrhoea and menorrhagia. Bilateral uterine artery embolisation for symptomatic adenomyosis led to good improvement. The percentage of the volume of the absence of contrast enhancement on images in postoperative T1-weighted magnetic resonance imaging of the uterus might be associated with the improvement of dysmenorrhoea and menorrhagia.

scCancer: a package for automated processing of single-cell RNA-seq data in cancer.

Molecular heterogeneities and complex microenvironments bring great challenges for cancer diagnosis and treatment. Recent advances in single-cell RNA-sequencing (scRNA-seq) technology make it possible to study cancer cell heterogeneities and microenvironments at single-cell transcriptomic level. Here, we develop an R package named scCancer, which focuses on processing and analyzing scRNA-seq data for cancer research. Except basic data processing steps, this package takes several special considerations for cancer-specific features. Firstly, the package introduced comprehensive quality control metrics. Secondly, it used a data-driven machine learning algorithm to accurately identify major cancer microenvironment cell populations. Thirdly, it estimated a malignancy score to classify malignant (cancerous) and non-malignant cells. Then, it analyzed intra-tumor heterogeneities by key cellular phenotypes (such as cell cycle and stemness), gene signatures and cell-cell interactions. Besides, it provided multi-sample data integration analysis with different batch-effect correction strategies. Finally, user-friendly graphic reports were generated for all the analyses. By testing on 56 samples with 433 405 cells in total, we demonstrated its good performance. The package is available at: http://lifeome.net/software/sccancer/.

Hypovirulence-associated mycovirus epidemics cause pathogenicity degeneration of Beauveria bassiana in the field.

BACKGROUND: The entomogenous fungus Beauveria bassiana is used as a biological insecticide worldwide, wild B. bassiana strains with high pathogenicity in the field play an important role in controlling insect pests via not only screening of highly virulent strains but also natural infection, but the pathogenicity degeneration of wild strains severely affected aforementioned effects. Previous studies have showed that multiple factors contributed to this phenomenon. It has been extensively proved that the mycovirus infection caused hypovirulence of phytopathogenic fungi, which has been used for plant disease biocontrol. However, it remains unknown whether the mycovirus epidemics is a key factor causing hypovirulence of B. bassiana naturally in the field. METHODS: Wild strains of B. bassiana were collected from different geographic locations in Jilin Province, China, to clarify the epidemic and diversity of the mycoviruses. A mycovirus Beauveria bassiana chrysovirus 2 (BbCV2) we have previously identified was employed to clarify its impact on the pathogenicity of host fungi B. bassiana against the larvae of insect pest Ostrinia furnacalis. The serological analysis was conducted by preparing polyclonal antibody against a BbCV2 coat protein, to determine whether it can dissociate outside the host fungal cells and subsequently infect new hosts. Transcriptome analysis was used to reveal the interactions between viruses and hosts. RESULTS: We surprisingly found that the mycovirus BbCV2 was prevalent in the field as a core virus in wild B. bassiana strains, without obvious genetic differentiation, this virus possessed efficient and stable horizontal and vertical transmission capabilities. The serological results showed that the virus could not only replicate within but also dissociate outside the host cells, and the purified virions could infect B. bassiana by co-incubation. The virus infection causes B. bassiana hypovirulence. Transcriptome analysis revealed decreased expression of genes related to insect epidermis penetration, hypha growth and toxin metabolism in B. bassiana caused by mycovirus infection. CONCLUSION: Beauveria bassiana infected by hypovirulence-associated mycovirus can spread the virus to new host strains after infecting insects, and cause the virus epidemics in the field. The findings confirmed that mycovirus infection may be an important factor affecting the pathogenicity degradation of B. bassiana in the field.

Recent advances in the combinations of plant-sourced natural products for the prevention of mycotoxin contamination in food.

Mycotoxins, secondary metabolites produced by mycotoxigenic fungi, are a major problem affecting food safety and security, because of their adverse health effects, their socio-economic impact and the difficulty of degradation or removal by conventional food processing methods. Plant-sourced natural products are a novel and effective control method for fungal infestation and mycotoxin production, with the advantages of biodegradability and acceptability for food use. However, development of resistance, low and inconsistent efficacy, and a limited range of antifungal activities hinder the effective application of single plant natural products for controlling mycotoxin contamination. To overcome these limitations, combinations of plant natural products have been tested extensively and found to increase efficacy, often synergistically. However, this extensive and promising research area has seen little development of practical applications. This review aims to provide up-to-date information on the antifungal, anti-mycotoxigenic and synergistic effects of combinations of plant natural products, as well as their mechanisms of action, to provide a reference source for future research and encourage application development.

Pre-exposure to Fe2O3 or TiO2 Nanoparticles Inhibits Subsequent Biological Uptake of 55Fe-Labeled Fe2O3 Nanoparticles.

Aquatic organisms are frequently exposed to various nanoparticles (NPs) in the natural environment. Thus, studies of NP bioaccumulation should include organisms that have been previously exposed to NPs. Our study investigated the effects of pre-exposure of Tetrahymena thermophila (T. thermophila) to Fe2O3 or TiO2 NPs on the protozoan's subsequent uptake of 55Fe-labeled Fe2O3 (55Fe2O3) NPs. Molecular mechanisms underlying the pre-exposure effects were explored in transcriptomic and metabolomic experiments. Pre-exposure to either NPs inhibited the subsequent uptake of 55Fe2O3 NPs. The results of the transcriptomic experiment indicated that NP pre-exposure influenced the expression of genes related to phagosomes and lysosomes and physiological processes such as glutathione and lipid metabolism, which are closely associated with the endocytosis of 55Fe2O3 NPs. The differentially expressed metabolites obtained from the metabolomic experiments showed an enrichment of energy metabolism and antioxidation pathways in T. thermophila pre-exposed to NPs. Together, these results demonstrate that the pre-exposure of T. thermophila to Fe2O3 or TiO2 NPs inhibited the protozoan's subsequent uptake of 55Fe2O3 NPs, possibly by mechanisms involving the alteration of endocytosis-related organelles, the induction of oxidative stress, and a lowering of the intracellular energy supply. Thus, NP pre-exposure represents a scenario which can inform increasingly realistic estimates of NP bioaccumulation.

An Exploration of the Coherent Effects between METTL3 and NDUFA10 on Alzheimer's Disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized primarily by a decline in cognitive function. However, the etiopathogenesis of AD is unclear. N6-methyladenosine (m6A) is abundant in the brain, and it is interesting to explore the relationship between m6A and AD causes. In this paper, the gene expression of METTL3 and NDUFA10 were found to correlate with the Mini-mental State Examination (MMSE), which is a clinical indicator of the degree of dementia. METTL3 is involved in post-transcriptional methylation and the formation of m6A. NDUFA10 encodes the protein with NADH dehydrogenase activity and oxidoreductase activity in the mitochondrial electron transport chain. The following three characteristics were observed in this paper: 1. The lower the expression level of NDUFA10, the smaller the MMSE, and the higher the degree of dementia. 2. If the expression level of METTL3 dropped below its threshold, the patient would have a risk of AD with a probability close to 100%, suggesting a basic necessity for m6A to protect mRNA. 3. The lower the expression levels of both METTL3 and NDUFA10, the more likely the patient would suffer from AD, implying the coherence between METTL3 and NDUFA10. Regarding the above discovery, the following hypothesis is presented: METTL3 expression level is downregulated, then the m6A modification level of NDUFA10 mRNA is also decreased, thereby reducing the expression level of NDUFA10-encoded protein. Furthermore, the abnormal expression of NDUFA10 contributes to the assembly disorder of mitochondrial complex I and affects the process of the electron respiratory chain, with the consequent development of AD. In addition, to confirm the above conclusions, the AI Ant Colony Algorithm was improved to be more suitable for discovering the characteristics of AD data, and the SVM diagnostic model was applied to mine the coherent effects on AD between METTL3 and NDUFA10. In conclusion, our findings suggest that dysregulated m6A leads to altered expression of its target genes, thereby affecting AD's development.

Time-overlapping structured-light projection: high performance on 3D shape measurement for complex dynamic scenes.

High-speed three-dimensional (3D) shape measurement has been continuously researched due to the demand for analyzing dynamic behavior in transient scenes. In this work, a time-overlapping structured-light 3D shape measuring technique is proposed to realize high-speed and high-performance measurement on complex dynamic scenes. Time-overlapping structured-light projection is presented to maximumly reduce the information redundancy in temporal sequences and improve the measuring efficiency; generalized tripartite phase unwrapping (Tri-PU) is used to ensure the measuring robustness; fringe period extension is achieved by improving overlapping rate to further double the encoding fringe periods for higher measuring accuracy. Based on the proposed measuring technique, one new pixel-to-pixel and unambiguous 3D reconstruction result can be updated with three newly required patterns at a reconstruction rate of 3174 fps. Three transient scenes including collapsing wood blocks struck by a flying arrow, free-falling foam snowflakes and flying water balloon towards metal grids were measured to verify the high performance of the proposed method in various complex dynamic scenes.

Brain tumour segmentation based on an improved U-Net.

BACKGROUND: Automatic segmentation of brain tumours using deep learning algorithms is currently one of the research hotspots in the medical image segmentation field. An improved U-Net network is proposed to segment brain tumours to improve the segmentation effect of brain tumours. METHODS: To solve the problems of other brain tumour segmentation models such as U-Net, including insufficient ability to segment edge details and reuse feature information, poor extraction of location information and the commonly used binary cross-entropy and Dice loss are often ineffective when used as loss functions for brain tumour segmentation models, we propose a serial encoding-decoding structure, which achieves improved segmentation performance by adding hybrid dilated convolution (HDC) modules and concatenation between each module of two serial networks. In addition, we propose a new loss function to focus the model more on samples that are difficult to segment and classify. We compared the results of our proposed model and the commonly used segmentation models under the IOU, PA, Dice, precision, Hausdorf95, and ASD metrics. RESULTS: The performance of the proposed method outperforms other segmentation models in each metric. In addition, the schematic diagram of the segmentation results shows that the segmentation results of our algorithm are closer to the ground truth, showing more brain tumour details, while the segmentation results of other algorithms are smoother. CONCLUSIONS: Our algorithm has better semantic segmentation performance than other commonly used segmentation algorithms. The technology we propose can be used in the brain tumour diagnosis to provide better protection for patients' later treatments.

Electrochemical aptasensor for Staphylococcus aureus by stepwise signal amplification.

An electrochemical aptasensor for ultrasensitive detection of Staphylococcus aureus (SA) has been developed based on stepwise signal amplification. In the sample processing stage, the specific recognition between SA and aptamer triggers the enzyme-assisted cyclic cleavage to produce a large amount of target DNA (tDNA), realizing the first-level signal amplification. In the sensor assembly stage, tDNA induces a catalytic hairpin assembly (CHA) cycle to capture much more hairpin DNA H2 labeled by the electrochemical tag ferrocene, bringing the second-level signal amplification. In the signal detection stage, ferrocene is quasi-adsorbed on the electrode surface, and a high redox peak current linearly increasing with the scan rate up to 1000 V/s has been obtained by fast scan cyclic voltammetry (FSCV), achieving the third-level signal amplification. Under the optimized experimental conditions, the linear range and detection limit are 1 ~ 108 CFU/mL and 0.3 CFU/mL, respectively. The sensor has good reproducibility, stability, and sensitivity, affording practical sample detection. This detection principle is widely applicable to other pathogens, and provides a new path for the establishment of highly sensitive detection strategies.

Real-time motion-induced error compensation for 4-step phase-shifting profilometry.

Phase-shifting profilometry has been widely used in high-accuracy three-dimensional (3D) shape measurement. However, for dynamic scenarios, the object motion will lead to extra phase shift and then motion-induced error. Convenient and efficient motion-induced error compensation is still challenging. Therefore, we proposed a real-time motion-induced error compensation method for 4-step phase-shifting profilometry. The four phase-shifting images are divided into two groups to calculate two corresponding wrapped phases, one from the first three fringes and the other from the last three fringes. As the motion-induced error doubles the frequency of the projected fringes, the average phase can effectively compensate the motion-induced error because there is a π/2 phase shift between the adjacent frames. Furthermore, we designed a time sequence by recycling the projection fringes in a proper order, and the efficiency of 3D reconstruction could be effectively improved. This method performs pixel-wise error compensation, based on which we realized 50 fps real-time 3D measurement by GPU acceleration. Experimental results demonstrate that the proposed method can effectively reduce the motion-induced error.

Generalized phase unwrapping method that avoids jump errors for fringe projection profilometry.

Jump errors easily occur on the discontinuity of the wrapped phase because of the misalignment between wrapped phase and fringe order in fringe projection profilometry (FPP). In this paper, a phase unwrapping method that avoids jump errors is proposed for FPP. By building two other staggered wrapped phases from the original wrapped phase and dividing each period of fringe order into three parts, the proposed generalized tripartite phase unwrapping (Tri-PU) method can be used to avoid rather than compensatorily correct jump errors. It is suitable for the phase unwrapping method assisted by fringe order with a basic wrapped phase and fringe order, no matter which method is used to recover them. The experimental results demonstrate the effectiveness and generality of the proposed method, which is simple to implement and superior to measure complex objects with sharp edges.

Determinants of pain in advanced HCC patients recieving hepatic artery infusion chemotherapy.

Purpose Hepatic arterial infusion chemotherapy (HAIC) is one of the options to treat unresectable hepatocellular carcinoma (HCC). The majority of HCC patients suffer great pain in the course of HAIC treatment. To improve the quality of life and the efficacy of HAIC treatment, the causes of pain, the choice of an analgesic regimen, and the relationship between pain and prognosis of HCC were analyzed. Methods A total of 376 HCC patients under HAIC in our hospital were recriuted between March 2017 and September 2019. Multivariate linear regression analysis (stepwise) was used to calculate the potential factors related to the severe pain in HCC patients under HAIC. Analgesics treatments were carried out based on the results of the visual analogue scale (VAS) score which was used to evaluate the pain. Results The mean value of the VAS score is 3.604, which indicates that the pain in most patients is mild and endurable. Intra-arterial lidocaine injection is an effective method in most patients (96%, 361 of 376), and the total score of VAS is reduced from 1355 to 195 following lidocaine injection. Multivariate analysis suggestes that oxaliplatin (OXA) preparation time, hepatic artery diameter and OXA manufacturers (R2 = 0.859) are influential factors for pain scores. Conclusion This study demonstrates an effective way to systematically assess and ease pain in HCC patients with HAIC treatment. OXA preparation time, hepatic artery diameter, and OXA manufacturers are the potential influencing factors for pain. This work presented here will provide a detailed understanding of the clinical application of HAIC in advanced HCC patients.

Pembrolizumab plus lenvatinib with or without hepatic arterial infusion chemotherapy in selected populations of patients with treatment-naive unresectable hepatocellular carcinoma exhibiting PD-L1 staining: a multicenter retrospective study.

BACKGROUND: Not all patients with unresectable hepatocellular carcinoma (uHCC) benefit from treatment with immune checkpoint inhibitors and molecular-targeted agents. The aim of this retrospective study was to assess the efficacy and safety of pembrolizumab plus lenvatinib plus hepatic arterial infusion chemotherapy (HAIC) versus pembrolizumab plus lenvatinib in selected populations of patients with treatment-naive uHCC exhibiting programmed cell death ligand-1 (PD-L1) staining. METHODS: Consecutive patients with treatment-naive uHCC exhibiting PD-L1 staining who were treated with pembrolizumab plus lenvatinib plus HAIC (PLH) or pembrolizumab plus lenvatinib (PL) were retrospectively identified from our medical centres from 2018 to 2021. HAIC involved oxaliplatin, fluorouracil, and leucovorin (FOLFOX). Follow-up occurred every 3 weeks for 1 year and then every 6 weeks thereafter. The primary endpoints included overall survival (OS) and progression-free survival (PFS). Secondary endpoints were the frequency of key adverse events (AEs). RESULTS: In total, 248 treatment-naive patients were retrospectively reviewed, 78 of whom were ineligible on the basis of the current criteria. Thus, 170 patients (PLH: n = 84, median age 52 years [range, 42-67]; PL: n = 86, 53 years [range, 43-69]) were eligible for the analysis. The median follow-up was 18.6 months (range, 1-26). At the final follow-up, the median OS was 17.7 months (95% confidence interval [CI], 15.2-18.3) in the PLH group versus 12.6 months (95% CI, 11.1-13.7) in the PL group (hazard ratio [HR] 0.52; 95% CI, 0.36-0.75; p = 0.001). A significant difference was also detected in the median PFS (10.9 months [95% CI, 8.7-11.4] for PLH vs. 6.8 months (95% CI, 5.2-7.4) for PL; HR 0.61, 95% CI, 0.43-0.85; p = 0.001). Significant differences in the rate of the key AEs were noted between groups (79.8% for PLH vs. 62.8% for PL, p = 0.015), but these AEs were controllable. CONCLUSIONS: Among selected populations of patients with treatment-naive uHCC exhibiting PD-L1 staining, the PLH regimen may substantially improve the survival benefits compared with the PL regimen with a controllable safety profile.

Burden and trend of ischemic heart disease and colorectal cancer attributable to a diet low in fiber in China, 1990-2017: findings from the Global Burden of Disease Study 2017.

PURPOSE: The burden of non-communicable diseases (NCDs) has increased in China. However, the contribution of dietary risks to the NCD burden has not been evaluated. This study aimed to estimate the burden of ischemic heart disease (IHD) and colorectal cancer (CRC) attributable to a diet low in fiber in China from 1990 to 2017. METHODS: China data from the Global Burden of Disease Study (GBD) 2017 were used to assess the age-, sex-, and province-specific mortality and disability-adjusted life-years (DALYs) of IHD and CRC related to a diet low in fiber. RESULTS: In 2017, a diet low in fiber contributed 170,143 [95% uncertainty interval (UI): 99,623-256,806] IHD deaths and 25,561 (95% UI: 13,726-39,215) CRC deaths, with the population attributable fractions (PAFs) were 9.7 and 13.7%, respectively. Males had higher risk-attributable mortality and DALY rates for IHD and CRC than females. An upward trend with age in rates of mortality and DALY was observed. All-age risk-attributable mortality and DALY rates increased significantly by 111.4 and 53.2% for IHD, and 94.4 and 59.6% for CRC from 1990 to 2017, respectively; however, the corresponding age-standardized rates for IHD and CRC showed relatively stable trends. Heilongjiang, Xinjiang, and Inner Mongolia were ranked as the top three provinces in terms of total risk-attributable NCD burden in 2017. CONCLUSIONS: China has a large and growing NCD burden attributable to a diet low in fiber. Greater priority in disease prevention and control should be given to male and older adults throughout China, particularly in some western provinces.

Data-driven analysis of the electronic-structure factors controlling the work functions of perovskite oxides.

Tuning the work functions of materials is of practical interest for maximizing the performance of microelectronic and (photo)electrochemical devices, as the efficiency of these systems depends on the ability to control electronic levels at surfaces and across interfaces. Perovskites are promising compounds to achieve such control. In this work, we examine the work functions of more than 1000 perovskite oxide surfaces (ABO3) using data-driven (machine-learning) analysis and identify the factors that determine their magnitude. While the work functions of the BO2-terminated surfaces are sensitive to the energy of the hybridized oxygen p bands, the work functions of the AO-terminated surfaces exhibit a much less trivial dependence with respect to the filling of the d bands of the B-site atom and of its electronic affinity. This study shows the utility of interpretable data-driven models in analyzing the work functions of cubic perovskites from a limited number of electronic-structure descriptors.

Examining geographical disparities in the incubation period of the COVID-19 infected cases in Shenzhen and Hefei, China.

BACKGROUND: Current studies on the COVID-19 depicted a general incubation period distribution and did not examine whether the incubation period distribution varies across patients living in different geographical locations with varying environmental attributes. Profiling the incubation distributions geographically help to determine the appropriate quarantine duration for different regions. METHODS: This retrospective study mainly applied big data analytics and methodology, using the publicly accessible clinical report for patients (n = 543) confirmed as infected in Shenzhen and Hefei, China. Based on 217 patients on whom the incubation period could be identified by the epidemiological method. Statistical and econometric methods were employed to investigate how the incubation distributions varied between infected cases reported in Shenzhen and Hefei. RESULTS: The median incubation period of the COVID-19 for all the 217 infected patients was 8 days (95% CI 7 to 9), while median values were 9 days in Shenzhen and 4 days in Hefei. The incubation period probably has an inverse U-shaped association with the meteorological temperature. The warmer condition in the winter of Shenzhen, average environmental temperature between 10 °C to 15 °C, may decrease viral virulence and result in more extended incubation periods. CONCLUSION: Case studies of the COVID-19 outbreak in Shenzhen and Hefei indicated that the incubation period of COVID-19 had exhibited evident geographical disparities, although the pathological causality between meteorological conditions and incubation period deserves further investigation. Methodologies based on big data released by local public health authorities are applicable for identifying incubation period and relevant epidemiological research.