Machine Learning for Sequence and Structure-Based Protein-Ligand Interaction Prediction.

Developing new drugs is too expensive and time -consuming. Accurately predicting the interaction between drugs and targets will likely change how the drug is discovered. Machine learning-based protein-ligand interaction prediction has demonstrated significant potential. In this paper, computational methods, focusing on sequence and structure to study protein-ligand interactions, are examined. Therefore, this paper starts by presenting an overview of the data sets applied in this area, as well as the various approaches applied for representing proteins and ligands. Then, sequence-based and structure-based classification criteria are subsequently utilized to categorize and summarize both the classical machine learning models and deep learning models employed in protein-ligand interaction studies. Moreover, the evaluation methods and interpretability of these models are proposed. Furthermore, delving into the diverse applications of protein-ligand interaction models in drug research is presented. Lastly, the current challenges and future directions in this field are addressed.

Pedigree genome data of an early-matured Geng/japonica glutinous rice mega variety Longgeng 57.

By using PacBio HiFi technology, we produced over 700 Gb of long-read sequencing (LRS) raw data; and by using Illumina paired-end whole-genome shotgun (WGS) sequencing technology, we generated more than 70 Gb of short-read sequencing (SRS) data. With LRS data, we assembled one genome and then generate a set of annotation data for an early-matured Geng/japonica glutinous rice mega variety genome, Longgeng 57 (LG57), which carries multiple elite traits including good grain quality and wide adaptability. Together with the SRS data from three parents of LG57, pedigree genome variations were called for three representative types of genes. These data sets can be used for deep variation mining, aid in the discovery of new insights into genome structure, function, and evolution, and help to provide essential support to biological research in general.

Significance of Volatile Organic Compounds to Secondary Pollution Formation and Health Risks Observed during a Summer Campaign in an Industrial Urban Area.

The chemical complexity and toxicity of volatile organic compounds (VOCs) are primarily encountered through intensive anthropogenic emissions in suburban areas. Here, pollution characteristics, impacts on secondary pollution formation, and health risks were investigated through continuous in-field measurements from 1-30 June 2020 in suburban Nanjing, adjacent to national petrochemical industrial parks in China. On average, the total VOCs concentration was 34.47 ± 16.08 ppb, which was comprised mostly by alkanes (41.8%) and halogenated hydrocarbons (29.4%). In contrast, aromatics (17.4%) dominated the ozone formation potential (OFP) and secondary organic aerosol formation potential (SOAFP) with 59.6% and 58.3%, respectively. Approximately 63.5% of VOCs were emitted from the petrochemical industry and from solvent usage based on source apportionment results, followed by biogenic emissions of 22.3% and vehicle emissions of 14.2%. Of the observed 46 VOC species, hexachlorobutadiene, dibromoethane, butadiene, tetrachloroethane, and vinyl chloride contributed as high as 98.8% of total carcinogenic risk, a large fraction of which was ascribed to the high-level emissions during ozone pollution episodes and nighttime. Therefore, the mitigation of VOC emissions from petrochemical industries would be an effective way to reduce secondary pollution and potential health risks in conurbation areas.

The DFT and Machine Learning Method Accelerated the Discovery of DMSCs with High ORR and OER Catalytic Activities.

The oxygen reduction reaction (ORR) and the oxygen evolution reaction (OER) are crucial for the conversion of clean energy. Recently, dual-metal-site catalysts (DMSCs) have gained much attention due to their high atom utilization, stronger stability, and better catalytic performance. An advanced method that combines density functional theory (DFT) and machine learning (ML) has been employed in this study to investigate the adsorption free energies of adsorbates on hundreds of potential catalysts, with the aim of screening for catalysts that are highly active for the ORR and OER. The result of this study is that 30 DMSCs with ORR activity superior to Pt, 10 DMSCs with OER activity superior to RuO2, and 4 bifunctional catalysts for the OER and ORR are identified. This work provides guidance for the rational selection of metals on DMSCs to prepare catalysts with a high electrocatalytic performance for renewable energy applications.

[Characteristics of Light Absorption, Sources, and Seasonal Variation of Atmospheric Brown Carbon in Northern Nanjing].

Brown carbon (BrC) refers to a group of organic compounds in fine atmospheric particles (PM2.5) that are able to absorb light in the ultraviolet and visible range. They have a significant impact on the visibility of air and on the earth's climate. In this study, we used a black carbon analyzer (Model AE33) to conduct field measurements in northern suburban Nanjing from March 2021 to February 2022. We measured the light absorption coefficients of BrC in PM2.5 and quantified the contributions of primary (BrCpri) and secondary brown carbon (BrCsec) in BrC by using the minimum correlation method (MRS), combined with the backward trajectories,potential source contribution function (PSCF) analysis, and diurnal patterns to analyze the seasonal characteristics of BrC. The results showed that the annual average light absorption of BrC was(7.76±7.17)Mm-1 (at 370 nm), and its contribution to the total aerosol light absorption was (22.0±8.8)%. BrC light absorption at different wavelengths all showed a U-shape seasonal variation of high in spring and winter and low in summer and fall. MRS analysis showed that the annual average contributions of BrCpri and BrCsec were (62.9±21.4)% and (37.1±21.4)% (at 370 nm), respectively; however, the contribution of BrCsec increased with the increase in wavelength, and it became dominant in longer wavelengths such as 660 nm. Backward trajectory and PSCF analysis showed that BrC was heavily influenced by air masses from the sea in spring, summer, and fall but was influenced greatly by local and regional continental emissions in winter. Traffic emissions in spring, summer, and fall were more intense to contribute to BrCpri than that in winter, whereas coal and biomass combustion had a greater impact on BrCpri in winter. Detailed analysis revealed that gas-phase photochemistry and aqueous chemistry had different influences on BrCsec formation in different seasons. It was mainly from gas-phase photochemistry in summer but was dominated by aqueous process in winter; both processes, however, were important pathways to BrCsec in spring and fall.

[Clinical and genetic analysis of a child with Cerebral creatine deficiency syndrome due to variant of SLC6A8 gene].

OBJECTIVE: To explore the clinical features and genetic variant in a child with Cerebral creatine deficiency syndrome (CCDS). METHODS: A child who had presented at the Affiliated Children's Hospital of Fudan University on March 5, 2021 was selected as the study subject. Whole exome sequencing (WES) was carried out for the child, and candidate variant was verified by Sanger sequencing. The level of creatine in the brain was determined by magnetic resonance spectroscopy. RESULTS: The patient, a 1-year-and-10-month male, had presented with developmental delay and epilepsy. Both his mother and grandmother had a history of convulsions. MRS showed reduced cerebral creatine in bilateral basal ganglia and thalamus. The child was found to harbor a hemizygous splicing variant of the SLC6A8 gene, namely c.1767+1_1767+2insA, which may lead to protein truncation. The variant was not found in the public databases. Both his mother and grandmother were heterozygous carriers for the same variant. CONCLUSION: The hemizygous c.1767+1_1767+2insA variant of the SLC6A8 gene probably underlay the CCDS in this child. Discovery of the novel variant has also expanded the mutational spectrum of the SLC6A8 gene.

Extracting the Synthetic Route of Pd-Based Catalysts in Methanol Steam Reforming from the Scientific Literature.

The structured material synthesis route is crucial for chemists in performing experiments and modern applications such as machine learning material design. With the exponential growth of the chemical literature in recent years, manual extraction from the published literature is time-consuming and labor-intensive. This study focuses on developing an automated method for extracting Pd-based catalyst synthesis routes from the chemical literature. First, a paragraph classification model based on regular expressions is employed to identify paragraphs that contain material synthesis processes. The identified paragraphs are verified using machine learning techniques. Second, natural language processing techniques are applied to automatically parse the material synthesis routes from the identified paragraphs, generate regularized flowcharts, and output structured data. Lastly, we utilized the structured data of the synthesis routes to train machine learning models and predict the performance of the materials. The extracted material entities include the product, preparation method, precursor, support, loading, synthesis operation, and operation condition. This method avoids extensive manual data annotation and improves the scientific literature information acquisition efficiency. The accuracy of the 11 material entities exceeds 80%, and the accuracy of the method, support, precursor, drying time, and reduction time exceeds 90%.

BNM-CDGNN: Batch Normalization Multilayer Perceptron Crystal Distance Graph Neural Network for Excellent-Performance Crystal Property Prediction.

Recently, in the field of crystal property prediction, the graph neural network (GNN) model has made rapid progress. The GNN model can effectively capture high-dimensional crystal features from the crystal structure, thereby achieving optimal performance in property prediction. However, the existing GNN model faces limitations in handling the hidden layer after the pooling layer, which restricts the training performance of the model. In the present research, we propose a novel GNN model called the batch normalization multilayer perceptron crystal distance graph neural network (BNM-CDGNN). BNM-CDGNN encodes the crystal's geometry structure only based on the distance vector between atoms. The graph convolutional layer utilizes the radial basis function as the attention mask, ensuring the crystal's rotation invariance and adding the geometric information on the crystal. Subsequently, the average pooling layer is connected after the convolutional layer to enhance the model's ability to learn precise information. BNM-CDGNN connects multiple hidden layers after the average pooling layers, and these layers are processed by the batch normalization layer. Finally, the fully connected layer maps the results to the target property. BNM-CDGNN significantly enhances the accuracy of crystal property prediction compared with previous baseline models such as SchNet, MPNN, CGCNN, MEGNet, and GATGNN.

Machine Learning Explains Long-Term Trend and Health Risk of Air Pollution during 2015-2022 in a Coastal City in Eastern China.

Exposure to air pollution is one of the greatest environmental risks for human health. Air pollution level is significantly driven by anthropogenic emissions and meteorological conditions. To protect people from air pollutants, China has implemented clean air actions to reduce anthropogenic emissions, which has led to rapid improvement in air quality over China. Here, we evaluated the impact of anthropogenic emissions and meteorological conditions on trends in air pollutants in a coastal city (Lianyungang) in eastern China from 2015 to 2022 based on a random forest model. The annual mean concentration of observed air pollutants, including fine particles, inhalable particles, sulfur dioxide, nitrogen dioxide, and carbon monoxide, presented significant decreasing trends during 2015-2022, with dominant contributions (55-75%) by anthropogenic emission reduction. An increasing trend in ozone was observed with an important contribution (28%) by anthropogenic emissions. The impact of meteorological conditions on air pollution showed significant seasonality. For instance, the negative impact on aerosol pollution occurred during cold months, while the positive impact was in warm months. Health-risk-based air quality decreased by approximately 40% in 8 years, for which anthropogenic emission made a major contribution (93%).

Machine learning assesses drivers of PM2.5 air pollution trend in the Tibetan Plateau from 2015 to 2022.

The Tibetan Plateau (known as the Earth's Third Pole) has significant impact on climate. Fine particulate matter (PM2.5) is an important air pollutant in this region and has significant impact on health and climate. To mitigate PM2.5 air pollution over China, a series of clean air actions has been implemented. However, interannual trends in particulate air pollution and its response to anthropogenic emissions in the Tibetan Plateau are poorly understood. Here, we applied a random forest (RF) algorithm to quantify drivers of PM2.5 trends in six cities of the Tibetan Plateau from 2015 to 2022. The decreasing trends (-5.31 to -0.73 µg m-3 a-1) in PM2.5 during 2015-2022 were observed in all cities. The RF weather-normalized PM2.5 trends - which were driven by anthropogenic emissions - were -4.19 to -0.56 µg m-3 a-1, resulting in dominant contributions (65 %-83 %) to the observed PM2.5 trends. Relative to 2015, such anthropogenic emission driver was estimated to contribute -27.12 to -3.16 µg m-3 to declines in PM2.5 concentrations in 2022. However, the interannual changes in meteorological conditions only made a small contribution to the trends in PM2.5 concentrations. Potential source analysis suggested biomass burning from local residential sector and/or long-range transports originated from South Asia could significantly promote PM2.5 air pollution in this region. Based on health-risk air quality index (HAQI) assessment, the HAQI value was decreased by 15 %-76 % between 2015 and 2022 in these cities, with significant contributions (47 %-93 %) from anthropogenic emission abatements. Indeed, relative contribution of PM2.5 to the HAQI was decreased from 16 %-30 % to 11 %-18 %, while increasing and significant contribution from ozone was observed, highlighting that further effective mitigation of both PM2.5 and ozone air pollution could obtain more substantial health benefits in the Tibetan Plateau.

Concurrent dominant pathways of multifunctional products formed from nocturnal isoprene oxidation.

Determination of dominant chemical pathways toward the formation of nocturnal secondary organic aerosols (SOA) remains ambiguous by which nitrogen oxides (NOx) always affect oxidation of volatile alkenes. Here, comprehensive chamber simulations on dark isoprene ozonolysis were conducted under different nitrogen dioxides (NO2) mixing ratios to exam multiple functionalized isoprene oxidation products. Aside from that the oxidation processes were concurrently driven by nitrogen radical (NO3) and small hydroxyl radicals (OH), ozone (O3) cycloaddition at isoprene was launched initially regardless of NO2 to rapidly form first-generation oxidation products, i.e., carbonyls and Criegee intermediates (CI) referred to carbonyl oxides. They could further undergo complicated self- and cross-reactions to produce alkylperoxy radicals (RO2). Corresponding to yields of the C5H10O3 tracer, weak OH pathway at night was credited to ozonolysis of isoprene but suppressed by unique NO3 chemistry. Following the ozonolysis of isoprene, NO3 played a crucial supplementary role in nighttime SOA formation. The ensuing production of gas-phase nitrooxy carbonyls (the first-generation nitrates) became dominant in the production of a sizeable pool of organic nitrates (RO2NO2). By contrast, isoprene dihydroxy dinitrates (C5H10N2O8) were outstanding with the elevated NO2, related to typical second-generation nitrates. As such, the yielding number concentrations of dark SOA were promoted to approximately 1.8 × 104 cm-3 but presented a nonlinear relation with excess high-NO2 condition. This study provides valuable insights into importance of multifunctional organic compounds from alkene oxidation to constitute nighttime SOA.

Universal Approach to De Novo Drug Design for Target Proteins Using Deep Reinforcement Learning.

In drug design, the design and manufacture of safe and effective compounds is a long-term, complex, and complicated process. Therefore, developing a new rapid and generalizable drug design method is of great value. This study aimed to propose a general model based on reinforcement learning combined with drug-target interaction, which could be used to design new molecules according to different protein targets. The method adopted recurrent neural network molecular modeling and took the drug-target affinity model as the reward function of optimal molecular generation. It did not need to know the three-dimensional structure and active sites of protein targets but only required the information of a one-dimensional amino acid sequence. This approach was demonstrated to produce drugs highly similar to marketed drugs and design molecules with a better binding energy.

Chemical Characteristics and Source-Specific Health Risks of the Volatile Organic Compounds in Urban Nanjing, China.

This work comprehensively investigated the constituents, sources, and associated health risks of ambient volatile organic compounds (VOCs) sampled during the autumn of 2020 in urban Nanjing, a megacity in the densely populated Yangtze River Delta region in China. The total VOC (TVOC, sum of 108 species) concentration was determined to be 29.04 ± 14.89 ppb, and it was consisted of alkanes (36.9%), oxygenated VOCs (19.9%), halogens (19.1%), aromatics (9.9%), alkenes (8.9%), alkynes (4.9%), and others (0.4%). The mean TVOC/NOx (ppbC/ppbv) ratio was only 3.32, indicating the ozone control is overall VOC-limited. In terms of the ozone formation potential (OFP), however, the largest contributor became aromatics (41.9%), followed by alkenes (27.6%), and alkanes (16.9%); aromatics were also the dominant species in secondary organic aerosol (SOA) formation, indicative of the critical importance of aromatics reduction to the coordinated control of ozone and fine particulate matter (PM2.5). Mass ratios of ethylbenzene/xylene (E/X), isopentane/n--pentane (I/N), and toluene/benzene (T/B) ratios all pointed to the significant influence of traffic on VOCs. Positive matrix factorization (PMF) revealed five sources showing that traffic was the largest contributor (29.2%), particularly in the morning. A biogenic source, however, became the most important source in the afternoon (31.3%). The calculated noncarcinogenic risk (NCR) and lifetime carcinogenic risk (LCR) of the VOCs were low, but four species, acrolein, benzene, 1,2-dichloroethane, and 1,2-dibromoethane, were found to possess risks exceeding the thresholds. Furthermore, we conducted a multilinear regression to apportion the health risks to the PMF-resolved sources. Results show that the biogenic source instead of traffic became the most prominent contributor to the TVOC NCR and its contribution in the afternoon even outpaced the sum of all other sources. In summary, our analysis reveals the priority of controls of aromatics and traffic/industrial emissions to the efficient coreduction of O3 and PM2.5; our analysis also underscores that biogenic emissions should be paid special attention if considering the direct health risks of VOCs.

Disentangling drivers of air pollutant and health risk changes during the COVID-19 lockdown in China.

The COVID-19 restrictions in 2020 have led to distinct variations in NO2 and O3 concentrations in China. Here, the different drivers of anthropogenic emission changes, including the effects of the Chinese New Year (CNY), China's 2018-2020 Clean Air Plan (CAP), and the COVID-19 lockdown and their impact on NO2 and O3 are isolated by using a combined model-measurement approach. In addition, the contribution of prevailing meteorological conditions to the concentration changes was evaluated by applying a machine-learning method. The resulting impact on the multi-pollutant Health-based Air Quality Index (HAQI) is quantified. The results show that the CNY reduces NO2 concentrations on average by 26.7% each year, while the COVID-lockdown measures have led to an additional 11.6% reduction in 2020, and the CAP over 2018-2020 to a reduction in NO2 by 15.7%. On the other hand, meteorological conditions from 23 January to March 7, 2020 led to increase in NO2 of 7.8%. Neglecting the CAP and meteorological drivers thus leads to an overestimate and underestimate of the effect of the COVID-lockdown on NO2 reductions, respectively. For O3 the opposite behavior is found, with changes of +23.3%, +21.0%, +4.9%, and -0.9% for CNY, COVID-lockdown, CAP, and meteorology effects, respectively. The total effects of these drivers show a drastic reduction in multi-air pollutant-related health risk across China, with meteorology affecting particularly the Northeast of China adversely. Importantly, the CAP's contribution highlights the effectiveness of the Chinese government's air-quality regulations on NO2 reduction.

Sources and processes of organic aerosol in non-refractory PM1 and PM2.5 during foggy and haze episodes in an urban environment of the Yangtze River Delta, China.

Organic aerosol (OA) generally accounts for a large fraction of fine particulate matter (PM2.5) in the urban atmosphere. Despite significant advances in the understanding their emission sources, transformation processes and optical properties in the submicron aerosol fraction (PM1), larger size fractions - e.g., PM2.5 - still deserve complementary investigations. In this study, we conducted a comprehensive analysis on sources, formation process and optical properties of OA in PM1 and PM2.5 under haze and foggy environments in the Yangtze River Delta (eastern China), using two aerosol chemical speciation monitors, as well as a photoacoustic extinctiometer at 870 nm. Positive matrix factorization analysis - using multilinear engine (ME2) algorithm - was conducted on PM1 and PM2.5 organic mass spectra. Four OA factors were identified, including three primary OA (POA) factors, i.e., hydrocarbon-like OA (HOA), cooking OA (COA), and biomass burning OA (BBOA), and a secondary OA (SOA) factor, i.e., oxidized oxygenated OA (OOA). An enhanced PM1-2.5 COA concentration was clearly observed during cooking peak hours, suggesting important contribution of fresh cooking emissions on large-sized particles (i.e., PM1-2.5). The oxidation state and concentration of PM2.5 HOA were higher than that in PM1, suggesting that large-sized HOA particles might be linked to oxidized POA. High contribution (44%) of large-sized OOA to non-refractory PM2.5 mass was observed during haze episodes. During foggy episodes, PM1 and PM2.5 OOA concentrations increased as a positive relationship over time, along with an exponential increase in the PM2.5-OOA to PM1-OOA ratio. Meanwhile, OOA loadings increased with the aerosol liquid water content (ALWC) during foggy episodes. Random forest cross-validation analysis also supported the important influence of ALWC on OOA variations, supporting substantial impact of aqueous process on SOA formation during haze and/or foggy episodes. Obtained results also indicated high OOA contributions (21%-36%) and low POA contributions (6%-14%) to the PM2.5 scattering coefficient during haze and foggy episodes, respectively. Finally, we could illustrate that atmospheric vertical diffusion and horizontal transport have important but different effects on the concentrations of different primary and secondary OA factors in different particle size fractions.

Chemical composition, sources and optical properties of nitrated aromatic compounds in fine particulate matter during winter foggy days in Nanjing, China.

Functionalized aromatic compounds are one of the most important light-absorbing organic chromophores - so-called brown carbon (BrC) - in fine particulate matter (PM2.5). In this study, we conducted a wintertime field campaign to measure eight nitrated aromatic compounds (NACs) in PM2.5 with offline analysis techniques, including liquid chromatograph mass spectrometer (LC-MS) and aerodyne high-resolution aerosol mass spectrometer (AMS) measurements, during foggy and nonfoggy days in suburban Nanjing in the Yangtze River Delta region, China. On average, 4-nitrophenol could be one of the most important light absorbing materials in the observed BrC, which accounted for over 40% of the mass concentration of identified chromophores. The mass concentration of 2-methyl-4-nitrophenol and 2,6-dimethyl-4-nitrophenol were evidently increased during foggy days, contribution of which to total NACs were increased by 10% and 5%, respectively. Positive matrix factorization analysis of combining LC-MS and AMS dataset was performed to identify the primary and secondary sources of NACs. Primary sources, e.g., traffic and solid-fuel combustion, accounted for 71% of the sum of 4-nitrophenol, 2,6-dimethyl-4-nitrophenol and 3-nitrosalicylic acid, suggesting important contribution of primary emissions to these NACs. The contribution of secondary sources, associated with two oxygenated organic aerosols, could contribute 66% to 4-nitrophenol, reflecting the link of such nitrated aromatic compounds to secondary organic aerosol source. Together with optical measurements, 4-nitrophenol presented a high contribution (>50%) to the identified BrC absorbance in the light range 250 and 550 nm was observed. This could highlight an important role of such NACs in ambient BrC light absorption, despite its mass contribution to total organic carbon was negligible. Our work could improve the understanding of the links between optical properties and chemical composition of BrC, and the difference between BrC chromophores from nonfoggy days and foggy days under the typical polluted atmospheric conditions.

Secondary organic aerosol formation from photooxidation of C3H6 under the presence of NH3: Effects of seed particles.

Frequently-occurred secondary organic aerosols (SOAs) under low-NOx conditions contribute to the winter haze episodes and remain unclear in the abundant presence of NH3. Here, the effects of CaCl2 seed particles on the photooxidation of low-molecular-weight C3H6 with co-existing NO2 and NH3 were highlighted and investigated through a chamber-simulation study equipped with high-resolution proton-transfer-reaction time-of-flight mass spectrometer (PTR-ToF-MS). The influences of NH3 are often overestimated to exclusively enhance SOA yields under a low-[NO2]0 condition. Instead, the seeds played a central role in the heterogeneous formation of SOAs in this reaction with two orders of magnitudes higher than that in the absence of seeds at relative humidity (RH) of 82%. Interestedly, the O3 production was unchanged whether the seeds existed or not, small changes in the production of O3 were observed whether the seeds existed or not, indicating that the gas-phase conversions of C3H6 and NOx into C1-C3 oxygenated volatile organic compounds (OVOCs) and nitrogen-containing compounds (NOCs) were not affected by seed particles. Given that the ensuing formation of these low-volatile compounds was condensed into nucleation on the seeds, the explosive growth of C3H6 SOAs was then stimulated in the addition of NH3. Besides NO2 photolysis, the producing O3 was related to the formation of secondary carbonyls such as formaldehyde and then was consumed in the ·OH generation of approximately 3.40 × 10-12 molecules cm-3. This study provides a new insight to better understand the new gas-to-particle formation mechanisms when the haze pollution outbreaks in the complex air mixture.

Substantial brown carbon emissions from wintertime residential wood burning over France.

Brown carbon (BrC) is known to absorb light at subvisible wavelengths but its optical properties and sources are still poorly documented, leading to large uncertainties in climate studies. Here, we show its major wintertime contribution to total aerosol absorption at 370 nm (18-42%) at 9 different French sites. Moreover, an excellent correlation with levoglucosan (r2 = 0.9 and slope = 22.2 at 370 nm), suggesting important contribution of wood burning emissions to ambient BrC aerosols in France. At all sites, BrC peaks were mainly observed during late evening, linking to local intense residential wood burning during this time period. Furthermore, the geographic origin analysis also highlighted the high potential contribution of local and/or small-regional emissions to BrC. Focusing on the Paris region, twice higher BrC mass absorption efficiency value was obtained for less oxidized biomass burning organic aerosols (BBOA) compared to more oxidized BBOA (e.g., about 4.9 ± 0.2 vs. 2.0 ± 0.1 m2 g-1, respectively, at 370 nm). Finally, the BBOA direct radiative effect was found to be 40% higher when these two BBOA fractions are treated as light-absorbing species, compared to the non-absorbing BBOA scenario.

Chlamydia trachomatis and mycoplasma infections in tubal pregnancy.

Chlamydia trachomatis (CT) infection is an important factor for tubal pregnancy. However, whether Ureaplasma urealyticum (UU) and Mycoplasma hominis (MH) infections are also involved in tubal pregnancy remains unknown. This study is aimed to detect CT, UU, and MH in cervical secretions from patients with tubal pregnancy and control women in early pregnancy, to explore their prevalence rates and drug susceptibilities. Analysis was performed on patients with tubal pregnancy and those requiring termination of early pregnancy at <12 weeks from July 2013 to March 2014. Cervical secretions were tested for UU/MH with a UU/MH isolation and culture kit and for CT antigen by an immunochromatographic assay. Mycoplasma samples were tested for resistance to 12 antibiotics. There were no cases of CT infection detected. Mycoplasma infection rates (single or mixed) were similar in the tubal pregnancy and control groups, but the total rate of infection was higher for tubal pregnancy. All MH samples were sensitive to tetracyclines as well as josamycin and azithromycin. Josamycin and clarithromycin were effective against all UU cultures. Over 50% of the samples tested were resistant to ciprofloxacin.

A Large-Scale Serological Survey of Toxoplasma gondii Infection Among Persons Participated in Health Screening in Yunnan Province, Southwestern China.

Surveys of Toxoplasma gondii infection in animals have been reported in Yunnan province, southwestern China. However, limited information is available regarding the epidemiology of T. gondii infection among persons participated in health screening in Yunnan. From January 2014 to December 2016, a large-scale and cross-sectional serological survey was conducted to reveal the seroprevalence of T. gondii infection in persons participated in health screening in three major hospitals. A total of 64,533 serum samples were collected and anti-T. gondii antibodies were examined by commercially available enzyme-linked immunosorbent assay kits. The total seroprevalence of T. gondii infection was 6.67% (4306/64,533). Of these, 3721 persons (5.77%, 3721/64,533) were positive for Immunoglobulin G (IgG) only, 473 persons (0.73%, 473/64,533) were positive for Immunoglobulin M (IgM) only, and 112 persons (0.17%, 112/64,533) were positive for both IgG and IgM. Female seroprevalence (6.83%, 3167/46,389) was higher than male (6.28%, 1139/18,144). The highest seroprevalence of T. gondii infection was found in the age range of 41-50 years (10.60%, 228/2150) (p < 0.001). The seroprevalence in 2014, 2015, and 2016 were 6.39% (1083/16,946), 6.24% (1261/20,201), and 7.16% (1962/27,386), respectively. The results showed that T. gondii infection is common in persons participated in health screening in Yunnan province, which has significant public health concern. Thus, improved integrated measures should be executed to prevent and control T. gondii infection humans and animals in Yunnan province.