E-value: a superior alternative to P-value and its adjustments in DNA methylation studies.

DNA methylation plays a crucial role in transcriptional regulation. Reduced representation bisulfite sequencing (RRBS) is a technique of increasing use for analyzing genome-wide methylation profiles. Many computational tools such as Metilene, MethylKit, BiSeq and DMRfinder have been developed to use RRBS data for the detection of the differentially methylated regions (DMRs) potentially involved in epigenetic regulations of gene expression. For DMR detection tools, as for countless other medical applications, P-values and their adjustments are among the most standard reporting statistics used to assess the statistical significance of biological findings. However, P-values are coming under increasing criticism relating to their questionable accuracy and relatively high levels of false positive or negative indications. Here, we propose a method to calculate E-values, as likelihood ratios falling into the null hypothesis over the entire parameter space, for DMR detection in RRBS data. We also provide the R package 'metevalue' as a user-friendly interface to implement E-value calculations into various DMR detection tools. To evaluate the performance of E-values, we generated various RRBS benchmarking datasets using our simulator 'RRBSsim' with eight samples in each experimental group. Our comprehensive benchmarking analyses showed that using E-values not only significantly improved accuracy, area under ROC curve and power, over that of P-values or adjusted P-values, but also reduced false discovery rates and type I errors. In applications using real RRBS data of CRL rats and a clinical trial on low-salt diet, the use of E-values detected biologically more relevant DMRs and also improved the negative association between DNA methylation and gene expression.

Regio- and Stereoselective Transfer Hydrogenation of Aryloxy Group-Substituted Unsymmetrical 1,2-Diketones: Synthetic Applications and Mechanistic Studies.

Developing a general method that leads to the formation of different classes of chiral bioactive compounds and their stereoisomers is an attractive but challenging research topic in organic synthesis. Furthermore, despite the great value of asymmetric transfer hydrogenation (ATH) in both organic synthesis and the pharmaceutical industry, the monohydrogenation of unsymmetrical 1,2-diketones remains underdeveloped. Here, we report the aryloxy group-assisted highly regio-, diastereo-, and enantioselective ATH of racemic 1,2-diketones. The work produces a myriad of enantioenriched dihydroxy ketones, and further transformations furnish all eight stereoisomers of diaryl triols, polyphenol, emblirol, and glycerol-type natural products. Mechanistic studies and calculations reveal two working modes of the aryloxy group in switching the regioselectivity from a more reactive carbonyl to a less reactive one, and the potential of ATH on 1,2-diketones in solving challenging synthetic issues has been clearly demonstrated.

Circulating exosomal circRNA-miRNA-mRNA network in a familial partial lipodystrophy type 3 family with a novel PPARG frameshift mutation c.418dup.

Familial Partial Lipodystrophy 3 (FPLD3) is a rare genetic disorder caused by loss-of-function mutations in the PPARG gene, characterized by a selective absence of subcutaneous fat and associated metabolic complications. However, the molecular mechanisms of FPLD3 remain unclear. In this study, we recruited a 17-year-old Chinese female with FPLD3 and her family, identifying a novel PPARG frameshift mutation (exon 4: c.418dup: p.R140Kfs*7) that truncates the PPARγ protein at the 7th amino acid, significantly expanding the genetic landscape of FPLD3. By performing next generation sequencing of circular RNAs (circRNAs), microRNAs (miRNAs), and mRNAs in plasma exosomes, we discovered 59 circRNAs, 57 miRNAs, and 299 mRNAs were significantly altered in the mutation carriers in the comparison of healthy controls. Integration analysis highlighted that the circ_0001597-miR-671-5p pair and 18 mRNAs might be incorporated into the metabolic regulatory networks of the FPLD3 induced by the novel PPARG mutation. Functional annotation suggested that these genes were significantly enriched in glucose and lipid metabolism related pathways. Among the circRNA-miRNA-mRNA network, we identified two critical regulators, EGR1, a key transcription factor known for its role in insulin signaling pathways and lipid metabolism, and AGPAT3, which gets involved in the biosynthesis of triglycerides and lipolysis. Circ_0001597 regulates the expression of these genes through miR-671-5p, potentially contributing to the pathophysiology of FPLD3. Overall, this study clarified a circulating exosomal circRNA-miRNA-mRNA network in a FPLD3 family with a novel PPARG mutation, providing evidence for exploring promising biomarkers and developing novel therapeutic strategies for this rare genetic disorder.

In Situ Fast Construction of Ni3S4/FeS Catalysts on 3D Foam Structure Achieving Stable Large-Current-Density Water Oxidation.

By increasing the content of Ni3+, the catalytic activity of nickel-based catalysts for the oxygen evolution reaction (OER), which is still problematic with current synthesis routes, can be increased. Herein, a Ni3+-rich of Ni3S4/FeS on FeNi Foam (Ni3S4/FeS@FNF) via anodic electrodeposition to direct obtain high valence metal ions for OER catalyst is presented. XPS showed that the introduction of Fe not only further increased the Ni3+ concentration in Ni3S4/FeS to 95.02%, but also inhibited the dissolution of NiOOH by up to seven times. Furthermore, the OER kinetics is enhanced by the combination of the inner Ni3S4/FeS heterostructures and the electrochemically induced surface layers of oxides/hydroxides. Ni3S4/FeS@FNF shows the most excellent OER activity with a low Tafel slope of 11.2 mV dec-1 and overpotentials of 196 and 445 mV at current densities of 10 and 1400 mA cm-2, respectively. Furthermore, the Ni3S4/FeS@FNF catalyst can be operated stably at 1500 mA cm-2 for 200 h without significant performance degradation. In conclusion, this work has significantly increased the high activity Ni3+ content in nickel-based OER electrocatalysts through an anodic electrodeposition strategy. The preparation process is time-saving and mature, which is expected to be applied in large-scale industrialization.

Noninvasive liver fibrosis markers are independently associated with carotid atherosclerosis risk in patients with nonalcoholic fatty liver disease.

OBJECTIVE: Nonalcoholic fatty liver disease (NAFLD) is considered an independent risk factor for cardiovascular disease (CVD). The overall morbidity and mortality of CVD increase with higher fibrosis stage in NAFLD. Carotid atherosclerosis (CAS) is an important predictor of cardiovascular events. However, the relationship between liver fibrosis degree and the risk of CAS in NAFLD patients remains uncertain. We aimed to investigate the relationship between noninvasive liver fibrosis markers and CAS risk in patients with NAFLD. MATERIALS AND METHODS: This study included 3,302 participants with NAFLD. Participants were divided into a CAS group and a non-CAS group based on carotid artery ultrasound results. They were then stratified into quartiles using various noninvasive liver fibrosis markers (fibrosis-4 (FIB-4), modified FIB-4 (mFIB-4), aminotransferase to platelet ratio index (APRI), aminotransferase to alanine aminotransferase ratio (AAR), AAR-to-platelet ratio index (AARPRI), and Forns index) to assess the associations between these markers and the risk of CAS. RESULTS: In the NAFLD population, individuals with CAS exhibited elevated levels of blood pressure, glucose, lipids, and noninvasive liver fibrosis markers (p < 0.001). The higher quartiles of noninvasive liver fibrosis markers, including FIB-4, mFIB-4, AAR, AARPRI, and Forns index, were significantly associated with increased risks of CAS, even after adjusting for multiple CVD risk factors. CONCLUSIONS: In individuals with NAFLD, increased noninvasive liver fibrosis markers were independently associated with elevated CAS risk, which may be beneficial in assessing the risk of CVD in individuals with NAFLD.

Evaporation-Induced Transformations in Volatile Chemical Product-Derived Secondary Organic Aerosols: Browning Effects and Alterations in Oxidative Reactivity.

Volatile chemical products (VCPs) are increasingly recognized as significant sources of volatile organic compounds (VOCs) in urban atmospheres, potentially serving as key precursors for secondary organic aerosol (SOA) formation. This study investigates the formation and physicochemical transformations of VCP-derived SOA, produced through ozonolysis of VOCs evaporated from a representative room deodorant air freshener, focusing on the effects of aerosol evaporation on its molecular composition, light absorption properties, and reactive oxygen species (ROS) generation. Following aerosol evaporation, solutes become concentrated, accelerating reactions within the aerosol matrix that lead to a 42% reduction in peroxide content and noticeable browning of the SOA. This process occurs most effectively at moderate relative humidity (∼40%), reaching a maximum solute concentration before aerosol solidification. Molecular characterization reveals that evaporating VCP-derived SOA produces highly conjugated nitrogen-containing products from interactions between existing or transformed carbonyl compounds and reduced nitrogen species, likely acting as chromophores responsible for the observed brownish coloration. Additionally, the reactivity of VCP-derived SOA was elucidated through heterogeneous oxidation of sulfur dioxide (SO2), which revealed enhanced photosensitized sulfate production upon drying. Direct measurements of ROS, including singlet oxygen (1O2), superoxide (O2•-), and hydroxyl radicals (•OH), showed higher abundances in dried versus undried SOA samples under light exposure. Our findings underscore that drying significantly alters the physicochemical properties of VCP-derived SOA, impacting their roles in atmospheric chemistry and radiative balance.

Thickness-Controlled Synthesis of Compact and Uniform MOF Protective Layer for Zinc Anode to Achieve 85% Zinc Utilization.

Zinc anode-based aqueous batteries have attracted considerable interest for large-scale energy storage and wearable devices. Unfortunately, the formation of Zn dendrite, parasitic hydrogen evolution reaction (HER), and irreversible by-products, seriously restrict their practical applications. Herein, a series of compact and uniform metal-organic frameworks (MOFs) films with precisely controlled thickness (150-600 nm) are constructed by a pre-oxide gas deposition (POGD) method on Zn foil. Under the protection of MOF layer with optimum thickness, the corrosion of zinc, the side reaction of hydrogen evolution, and the growth of dendrites on the zinc surface are suppressed. The symmetric cell based on Zn@ZIF-8 anode exhibits exceptional cyclicality for over 1100 h with low voltage hysteresis of≈38 mV at 1 mA cm-2 . Even at current densities of 50 mA cm-2 with an area capacity of 50 mAh cm-2 (85% Zn utilization), the electrode can keep cycling for >100 h. Besides, this Zn@ZIF-8 anode also delivers a high average CE of 99.4% at 1 mA cm-2 . Moreover, a rechargeable Zn ion battery is fabricated based on the Zn@ZIF-8 anode and MnO2 cathode, which presents an exceptionally long lifespan with no capacity attenuation for 1000 cycles.

Sulfate Formation by Photosensitization in Mixed Incense Burning-Sodium Chloride Particles: Effects of RH, Light Intensity, and Aerosol Aging.

Elevated particulate sulfate concentrations have been frequently observed in coastal areas when air masses are influenced by continental emissions, especially combustion sources like biomass burning. We studied the SO2 uptake by laboratory-generated droplets containing incense smoke extracts and sodium chloride (IS-NaCl) under irradiation and found enhanced sulfate production over pure NaCl droplets, attributable to photosensitization induced by constituents in IS. Low relative humidity and high light intensity facilitated sulfate formation and increased the SO2 uptake coefficient by IS-NaCl particles. Aging of the IS particles further enhanced sulfate production, attributable to the enhanced secondary oxidant production promoted by increased proportions of nitrogen-containing CHN and oxygen- and nitrogen-containing CHON species under light and air. Experiments using model compounds of syringaldehyde, pyrazine, and 4-nitroguaiacol verified the enhancements of CHN and CHON species in sulfate formation. This work provides experimental evidence of enhanced sulfate production in laboratory-generated IS-NaCl droplets via enhanced secondary oxidant production triggered by photosensitization in multiphase oxidation processes under light and air. Our results can shed light on the possible interactions between sea salt and biomass burning aerosols in enhancing sulfate production.

Recognition of Ellipsoid-like Herbaceous Tibetan Medicinal Materials Using DenseNet with Attention and ILBP-Encoded Gabor Features.

Tibetan medicinal materials play a significant role in Tibetan culture. However, some types of Tibetan medicinal materials share similar shapes and colors, but possess different medicinal properties and functions. The incorrect use of such medicinal materials may lead to poisoning, delayed treatment, and potentially severe consequences for patients. Historically, the identification of ellipsoid-like herbaceous Tibetan medicinal materials has relied on manual identification methods, including observation, touching, tasting, and nasal smell, which heavily rely on the technicians' accumulated experience and are prone to errors. In this paper, we propose an image-recognition method for ellipsoid-like herbaceous Tibetan medicinal materials that combines texture feature extraction and a deep-learning network. We created an image dataset consisting of 3200 images of 18 types of ellipsoid-like Tibetan medicinal materials. Due to the complex background and high similarity in the shape and color of the ellipsoid-like herbaceous Tibetan medicinal materials in the images, we conducted a multi-feature fusion experiment on the shape, color, and texture features of these materials. To leverage the importance of texture features, we utilized an improved LBP (local binary pattern) algorithm to encode the texture features extracted by the Gabor algorithm. We inputted the final features into the DenseNet network to recognize the images of the ellipsoid-like herbaceous Tibetan medicinal materials. Our approach focuses on extracting important texture information while ignoring irrelevant information such as background clutter to eliminate interference and improve recognition performance. The experimental results show that our proposed method achieved a recognition accuracy of 93.67% on the original dataset and 95.11% on the augmented dataset. In conclusion, our proposed method could aid in the identification and authentication of ellipsoid-like herbaceous Tibetan medicinal materials, reducing errors and ensuring the safe use of Tibetan medicinal materials in healthcare.

Blockade of chloride channel-3 enhances cisplatin sensitivity of cholangiocarcinoma cells though inhibiting autophagy.

Chemotherapy is one of the most important strategies in the treatment of cancer; however, chemoresistance restricts the effect of chemotherapy. Growing reports suggest that chloride channel-3 (ClC-3) is involved in regulating the sensitivity of multiple chemotherapeutic agents in the chemotherapy of various tumours, while its role in the chemotherapy of cholangiocarcinoma (CCA) is still poorly understood. Herein, we observed that ClC-3 was highly expressed in CCA chemoresistant tissues and CCA cisplatin-resistant cells QBC939/DDP, and the sensitivities of QBC939 and QBC939/DDP cells to cisplatin were all increased after inhibition of ClC-3. Further mechanism exploration revealed that ClC-3 knockdown reduced the level of autophagy. Furthermore, in both QBC939 and QBC939/DDP cells, the autophagy agonist rapamycin eliminated the increased cisplatin sensitivity of ClC-3 knockdown without affecting ClC-3 expression. Collectively, all the findings demonstrate that ClC-3 knockdown increases cisplatin-induced cell death in CCA cells though inhibiting autophagy, regardless of the occurrence of cisplatin resistance. In addition, our results also suggest that targeted inhibition of ClC-3 may be a potential strategy for chemosensitization in CCA chemotherapy.

Plasma-derived exosomal miR-15a-5p as a promising diagnostic biomarker for early detection of endometrial carcinoma.

Endometrial cancer (EC) is a major cause of death among gynecologic malignancies. To improve early detection of EC in patients, we carried out a large plasma-derived exosomal microRNA (miRNA) studies for diagnostic biomarker discovery in EC. Small RNA sequencing was performed to identify candidate exosomal miRNAs as diagnostic biomarkers in 56 plasma samples from healthy subjects and EC patients. These miRNA candidates were further validated in 202 independent plasma samples by droplet digital PCR (ddPCR), 32 pairs of endometrial tumors and adjacent normal tissues by quantitative real-time PCR (qRT-PCR), and matched plasma samples of 12 patients before and after surgery by ddPCR. miR-15a-5p, miR-106b-5p, and miR107 were significantly upregulated in exomes isolated from plasma samples of EC patients compared with healthy subjects. Particularly, miR-15a-5p alone yielded an AUC value of 0.813 to distinguish EC patients with stage I from healthy subjects. The integration of miR-15a-5p and serum tumor markers (CEA and CA125) achieved a higher AUC value of 0.899. There was also a close connection between miR-15a-5p and clinical manifestations in EC patients. Its exosomal expression was not only associated with the depth of muscular infiltration and aggressiveness of EC, but also correlated with levels of reproductive hormones such as TTE and DHEAS. Collectively, plasma-derived exosomal miR-15a-5p is a promising and effective diagnostic biomarker for the early detection of endometrial cancer.

MDEHT: a multivariate approach for detecting differential expression of microRNA isoform data in RNA-sequencing studies.

MOTIVATION: miRNA isoforms (isomiRs) are produced from the same arm as the archetype miRNA with a few nucleotides different at 5 and/or 3 termini. These well-conserved isomiRs are functionally important and have contributed to the evolution of miRNA genes. Accurate detection of differential expression of miRNAs can bring new insights into the cellular function of miRNA and a further improvement in miRNA-based diagnostic and prognostic applications. However, very few methods take isomiR variations into account in the analysis of miRNA differential expression. RESULTS: To overcome this challenge, we developed a novel approach to take advantage of the multidimensional structure of isomiR data from the same miRNAs, termed as a multivariate differential expression by Hotelling's T2 test (MDEHT). The utilization of the information hidden in isomiRs enables MDEHT to increase the power of identifying differentially expressed miRNAs that are not marginally detectable in univariate testing methods. We conducted rigorous and unbiased comparisons of MDEHT with seven commonly used tools in simulated and real datasets from The Cancer Genome Atlas. Our comprehensive evaluations demonstrated that the MDEHT method was robust among various datasets and outperformed other commonly used tools in terms of Type I error rate, true positive rate and reproducibility. AVAILABILITY AND IMPLEMENTATION: The source code for identifying and quantifying isomiRs and performing miRNA differential expression analysis is available at https://github.com/amanzju/MDEHT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Concluding remarks: Faraday Discussion on air quality in megacities.

Megacities are metropolitan areas with populations over 10 million, and many of them are facing significant global environmental challenges such as air pollution. Intense economic and human activities in megacities result in air pollution emissions, inducing high levels of air pollutants in the atmosphere that harm human health, cause regional haze and acid deposition, damage crops, influence regional air quality, and contribute to climate change. Since the Great London Smog and the first recognized episode of Los Angeles photochemical smog seventy years ago, substantial progress has been achieved in improving the scientific understanding of air pollution and in developing emissions reduction technologies and control measures. However, much remains to be understood about the complex processes of atmospheric transport and reaction mechanisms; the formation and evolution of secondary particles, especially those containing organic species; and the influence of emerging emissions sources and changing climate on air quality and health. Molina (DOI: ) has provided an excellent overview of the sources of emissions in megacities, atmospheric physicochemical processes, air quality trends and management, and the impacts on health and climate for the introductory lecture of this Faraday Discussion.

Emissions and Secondary Formation of Air Pollutants from Modern Heavy-Duty Trucks in Real-World Traffic-Chemical Characteristics Using On-Line Mass Spectrometry.

Complying with stricter emissions standards, a new generation of heavy-duty trucks (HDTs) has gradually increased its market share and now accounts for a large percentage of on-road mileage. The potential to improve air quality depends on an actual reduction in both emissions and subsequent formation of secondary pollutants. In this study, the emissions in real-world traffic from Euro VI-compliant HDTs were compared to those from older classes, represented by Euro V, using high-resolution time-of-flight chemical ionization mass spectrometry. Gas-phase primary emissions of several hundred species were observed for 70 HDTs. Furthermore, the particle phase and secondary pollutant formation (gas and particle phase) were evaluated for a number of HDTs. The reduction in primary emission factors (EFs) was evident (∼90%) and in line with a reduction of 28-97% for the typical regulated pollutants. Secondary production of most gas- and particle-phase compounds, for example, nitric acid, organic acids, and carbonyls, after photochemical aging in an oxidation flow reactor exceeded the primary emissions (EFAged/EFFresh ratio ≥2). Byproducts from urea-selective catalytic reduction systems had both primary and secondary sources. A non-negative matrix factorization analysis highlighted the issue of vehicle maintenance as a remaining concern. However, the adoption of Euro VI has a significant positive effect on emissions in real-world traffic and should be considered in, for example, urban air quality assessments.

DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies.

Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predicted from these computational tools lack consistency and are prone to false positives. Here, we developed an approach (DriverML) integrating Rao's score test and supervised machine learning to identify cancer driver genes. The weight parameters in the score statistics quantified the functional impacts of mutations on the protein. To obtain optimized weight parameters, the score statistics of prior driver genes were maximized on pan-cancer training data. We conducted rigorous and unbiased benchmark analysis and comparisons of DriverML with 20 other existing tools in 31 independent datasets from The Cancer Genome Atlas (TCGA). Our comprehensive evaluations demonstrated that DriverML was robust and powerful among various datasets and outperformed the other tools with a better balance of precision and sensitivity. In vitro cell-based assays further proved the validity of the DriverML prediction of novel driver genes. In summary, DriverML uses an innovative, machine learning-based approach to prioritize cancer driver genes and provides dramatic improvements over currently existing methods. Its source code is available at https://github.com/HelloYiHan/DriverML.

A comprehensive evaluation of computational tools to identify differential methylation regions using RRBS data.

DNA methylation plays a vital role in transcription regulation. Reduced representation bisulfite sequencing (RRBS) is becoming common for analyzing genome-wide methylation profiles at the single nucleotide level. A major goal of RRBS studies is to detect differentially methylated regions (DMRs) between different biological conditions. The previous tools to predict DMRs lack consistency. Here, we simulated RRBS datasets with significant attributes of real sequencing data under a wide range of scenarios, and systematically evaluated seven DMR detection tools in terms of type I error rate, precision/recall (PR), and area under ROC curve (AUC) using different methylation levels, sequencing coverage depth, length of DMRs, read length, and sample sizes. DMRfinder, methylSig, and methylKit were our preferred tools for RRBS data analysis, in terms of their AUC and PR curves. Our comparison highlights the different applicability of DMR detection tools and provides information to guide researchers towards the advancement of sequence-based DMR analysis.

OncotRF: an online resource for exploration of tRNA-derived fragments in human cancers.

Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs whose biological roles in cancers are not well understood. Emerging evidence suggests that tRFs are involved in gene regulation at multiple levels. In this study, we constructed an integrative database, OncotRF (http://bioinformatics.zju.edu.cn/OncotRF), for in silico exploration of tRF functions, and identification of diagnostic and prognostic biomarkers in cancers. The database contains an analysis pipeline for tRF identification and characterization, analysis results of 11,211 small RNA sequencing samples and 8,776 RNA sequencing samples, and clinicopathologic annotation data from The Cancer Genome Atlas (TCGA). The results include: tRF identification and quantification across 33 cancers, abnormally expressed tRFs and genes, tRF-gene correlations, tRF-gene networks, survival analyses, and tRF-related functional enrichment analyses. Users are also able to identify differentially expressed tRFs, predict their functions, and assess the relevance of the tRF expression levels to the clinical outcome according to user-defined groups. Additionally, an online Kaplan-Meier plotter is available in OncotRF for plotting survival curves according to user-defined groups. OncotRF will be a valuable online database and functional annotation tool for researchers studying the roles, functions, and mechanisms of tRFs in human cancers.

Profiling of the Risk Factors and Designing of a Model to Identify Ischemic Stroke in Patients with Non-Hodgkin Lymphoma: A Multicenter Retrospective Study.

BACKGROUND: The occurrence of ischemic stroke in patients with non-Hodgkin lymphoma (NHL) is not well understood. This study aimed to determine independent risk factors to identity ischemic stroke in non-Hodgkin lymphoma-associated ischemic stroke (NHLAIS) patients. METHODS: This retrospective study was conducted on NHLAIS patients and age- and gender-matched NHL patients. We collected clinical data of patients in both groups and used multiple logistic regression analysis to identify independent risk factors for NHLAIS. A receiver operating characteristic (ROC) analysis was used to establish an identification model based on potential risk factors of NHLAIS. RESULTS: Sixty-three NHLAIS patients and 63 NHL patients were enrolled. Stage III/IV (58/63, 92.1%) and multiple arterial infarcts (44/63, 69.8%) were common among NHLAIS patients. Notably, NHLAIS patients had higher levels of serum fibrinogen (FIB), D-dimer, and ferritin (SF) and prolonged thromboplastin time and prothrombin time (PT) compared with NHL patients (all p < 0.05). Elevated FIB, D-dimer, and SF and prolonged PT were independent risk factors for NHLAIS. The area under the ROC curve of the identification model of NHLAIS patients was largest compared to that of other risk factors (0.838, 95% confidence interval: 0.759-0.899) (p < 0.05). CONCLUSION: This study reveals that elevated serum FIB, D-dimer, and SF and prolonged PT are potential independent risk factors of NHLAIS. The identification model established in this study may help monitor NHL patients who are at high risk of developing NHLAIS.

A comprehensive evaluation of alignment software for reduced representation bisulfite sequencing data.

Motivation: The rapid development of next-generation sequencing technology provides an opportunity to study genome-wide DNA methylation at single-base resolution. However, depletion of unmethylated cytosines brings challenges for aligning bisulfite-converted sequencing reads to a large reference. Software tools for aligning methylation reads have not yet been comprehensively evaluated, especially for the widely used reduced representation bisulfite sequencing (RRBS) that involves enrichment for CpG islands (CGIs). Results: We specially developed a simulator, RRBSsim, for benchmarking analysis of RRBS data. We performed extensive comparison of seven mapping algorithms for methylation analysis in both real and simulated RRBS data. Eighteen lung tumors and matched adjacent tissues were sequenced by the RRBS protocols. Our empirical evaluation found that methylation results were less consistent between software tools for CpG sites with low sequencing depth, medium methylation level, on CGI shores or gene body. These observations were further confirmed by simulations that indicated software tools generally had lower recall of detecting these vulnerable CpG sites and lower precision of estimating methylation levels in these CpG sites. Among the software tools tested, bwa-meth and BS-Seeker2 (bowtie2) are currently our preferred aligners for RRBS data in terms of recall, precision and speed. Existing aligners cannot efficiently handle moderately methylated CpG sites and those CpG sites on CGI shores or gene body. Interpretation of methylation results from these vulnerable CpG sites should be treated with caution. Our study reveals several important features inherent in methylation data, and RRBSsim provides guidance to advance sequence-based methylation data analysis and methodological development. Availability and implementation: RRBSsim is a simulator for benchmarking analysis of RRBS data and its source code is available at https://github.com/xwBio/RRBSsim or https://github.com/xwBio/Docker-RRBSsim. Supplementary information: Supplementary data are available at Bioinformatics online.

Secondary Organic Aerosol Formation from Urban Roadside Air in Hong Kong.

Motor vehicle emissions are an important but poorly constrained source of secondary organic aerosol (SOA). Here, we investigated in situ SOA formation from urban roadside air in Hong Kong during winter time using an oxidation flow reactor (OFR), with equivalent atmospheric oxidation ranging from several hours to several days. The campaign-average mass enhancement of OA, nitrate, sulfate, and ammonium upon OFR aging was 7.0, 7.2, 0.8, and 2.6 µg m-3, respectively. To investigate the sources of SOA formation potential, we performed multilinear regression analysis between measured peak SOA concentrations from OFR and the concentrations of toluene that represent motor vehicle emissions and cooking OA from positive matrix factorization (PMF) analysis of ambient OA. Traffic-related SOA precursors contributed 92.3%, 92.4%, and 83.1% to the total SOA formation potential during morning rush hours, noon and early afternoon, and evening meal time, respectively. The SOA production factor (PF) was approximately 5.2 times of primary OA (POA) emission factor (EF) and the secondary particulate matter (PM) PF was approximately 2.6 times of primary particles EF. This study highlights the potential benefit of reducing secondary PM production from motor vehicle emissions in mitigating PM pollutions.