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Abstract Background Observational studies have suggested an association between birth weight and type 2 diabetes mellitus, but the causality between them has not been established. We aimed to obtain the causal relationship between birth weight with T2DM and quantify the mediating effects of potential modifiable risk factors. Methods Two-step, two-sample Mendelian randomization (MR) techniques were applied using SNPs as genetic instruments for exposure and mediators. Summary data from genome-wide association studies (GWAS) for birth weight, T2DM, and a series of fatty acids traits and their ratios were leveraged. The inverse variance weighted (IVW) method was the main analysis approach. In addition, the heterogeneity test, horizontal pleiotropy test, Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) test, and leave-one-out analysis were carried out to assess the robustness. Results The IVW method showed that lower birth weight raised the risk of T2DM (β: −1.113, 95% CI: −1.573 ∼ −0.652). Two-step MR identified 4 of 17 candidate mediators partially mediating the effect of lower birth weight on T2DM, including ratio of polyunsaturated fatty acids to monounsaturated fatty acids (proportion mediated: 7.9%), ratio of polyunsaturated fatty acids to total fatty acids (7.2%), ratio of omega-6 fatty acids to total fatty acids (8.1%) and ratio of linoleic acid to total fatty acids ratio (6.0%). Conclusions Our findings supported a potentially causal effect of birth weight against T2DM with considerable mediation by modifiable risk factors. Interventions that target these factors have the potential to reduce the burden of T2DM attributable to low birth weight.

The conventional real-time screening in organs-on-chips is limited to optical tracking of pre-tagged cells and biological agents. This work introduces an efficient biofabrication protocol to integrate tunable hydrogel electrodes into 3D bioprinted-on-chips. We established our method of fabricating cell-laden hydrogel-based microfluidic chips through digital light processing-based 3D bioprinting. Our conductive ink includes poly-(3,4-ethylene-dioxythiophene)-polystyrene sulfonate (PEDOT: PSS) microparticles doped in polyethylene glycol diacrylate (PEGDA). We optimized the manufacturing process of PEDOT: PSS microparticles characterized our conductive ink for different 3D bioprinting parameters, geometries, and materials conditions. While the literature is limited to 0.5% w/v for PEDOT: PSS microparticle concentration, we increased their concentration to 5% w/v with superior biological responses. We measured the conductivity in the 3–15 m/m for a range of 0.5%–5% w/v microparticles, and we showed the effectiveness of 3D-printed electrodes for predicting cell responses when encapsulated in gelatin-methacryloyl (GelMA). Interestingly, a higher cellular activity was observed in the case of 5% w/v microparticles compared to 0.5% w/v microparticles. Electrochemical impedance spectroscopy measurements indicated significant differences in cell densities and spheroid sizes embedded in GelMA microtissues.

Abstract Developing high‐efficiency and stable bifunctional electrocatalysts for water splitting remains a great challenge. Herein, NiMoO4 nanowires as sacrificial templates to synthesize Mo‐doped NiFe Prussian blue analogs are employed, which can be easily phosphorized to Mo‐doped Fe2xNi2(1‐x)P nanotubes (Mo‐FeNiP NTs). This synthesis method enables the controlled etching of NiMoO4 nanowires that results in a unique hollow nanotube architecture. As a bifunctional catalyst, the Mo‐FeNiP NTs present lower overpotential and Tafel slope of 151.3 (232.6) mV at 100 mA cm−2 and 76.2 (64.7) mV dec−1 for HER (OER), respectively. Additionally, it only requires an ultralow cell voltage of 1.47 V to achieve 10 mA cm−2 for overall water splitting and can steadily operate for 200 h at 100 mA cm−2. First‐principles calculations demonstrate that Mo doping can effectively adjust the electron redistribution of the Ni hollow sites to optimize the hydrogen adsorption‐free energy for HER. Besides, in situ Raman characterization reveals the dissolving of doped Mo can promote a rapid surface reconstruction on Mo‐FeNiP NTs to dynamically stable (Fe)Ni‐oxyhydroxide layers, serving as the actual active species for OER. The work proposes a rational approach addressed by electron manipulation and surface reconstruction of bimetallic phosphides to regulate both the HER and OER activity.

Abstract Non-dissociative chemisorption solid-state storage of hydrogen molecules in host materials is promising to achieve both high hydrogen capacity and uptake rate, but there is the lack of non-dissociative hydrogen storage theories that can guide the rational design of the materials. Herein, we establish generalized design principle to design such materials via the first-principles calculations, theoretical analysis and focused experimental verifications of a series of heteroatom-doped-graphene-supported Ca single-atom carbon nanomaterials as efficient non-dissociative solid-state hydrogen storage materials. An intrinsic descriptor has been proposed to correlate the inherent properties of dopants with the hydrogen storage capability of the carbon-based host materials. The generalized design principle and the intrinsic descriptor have the predictive ability to screen out the best dual-doped-graphene-supported Ca single-atom hydrogen storage materials. The dual-doped materials have much higher hydrogen storage capability than the sole-doped ones, and exceed the current best carbon-based hydrogen storage materials.

Abstract Background The Begonia species are common shade plants that are mostly found in southwest China. They have not been well studied despite their medicinal and decorative uses because gene penetration, decreased adaptability, and restricted availability are all caused by frequent interspecific hybridization. Result To understand the patterns of mutation in the chloroplast genomes of different species of Begonia, as well as their evolutionary relationships, we collected seven Begonia species in China and sequenced their chloroplast genomes. Begonia species exhibit a quadripartite structure of chloroplast genomes (157,634 − 169,694 bp), consisting of two pairs of inverted repeats (IR: 26,529 − 37,674 bp), a large single copy (LSC: 75,477 − 86,500 bp), and a small single copy (SSC: 17,861 − 18,367 bp). 128–143 genes (comprising 82–93 protein-coding genes, 8 ribosomal RNAs, and 36–43 transfer RNAs) are found in the chloroplast genomes. Based on comparative analyses, this taxon has a relatively similar genome structure. A total of six substantially divergent DNA regions (trnT-UGU-trnL-UAA, atpF-atpH, ycf4-cemA, psbC-trnS-UGA, rpl32-trnL-UAG, and ccsA-ndhD) are found in the seventeen chloroplast genomes. These regions are suitable for species identification and phylogeographic analysis. Phylogenetic analysis shows that Begonia species that were suited to comparable environments grouped in a small clade and that all Begonia species formed one big clade in the phylogenetic tree, supporting the genus’ monophyly. In addition, positive selection sites were discovered in eight genes (rpoC1, rpoB, psbE, psbK, petA, rps12, rpl2, and rpl22), the majority of which are involved in protein production and photosynthesis. Conclusion Using these genome resources, we can resolve deep-level phylogenetic relationships between Begonia species and their families, leading to a better understanding of evolutionary processes. In addition to enhancing species identification and phylogenetic resolution, these results demonstrate the utility of complete chloroplast genomes in phylogenetically and taxonomically challenging plant groupings.

Abstract With high spatio‐temporal resolution geostationary hyperspectral infrared sounder (GeoHIS) observations, monitoring, and predicting rapidly changing weather events are expected to be improved with continuous information of 3D weather cube. However, due to the nature of radiation, clouds prevent an adequate retrieval of atmospheric thermodynamic information under clear‐sky conditions due to large uncertainties in the current radiative transfer model. Removing cloud effects from GeoHIS sub‐footprint is an alternative approach for enhancing clear radiance generation. Such cloud removal can be achieved through the optimal cloud‐clearing (OCC) method, which usually relies on the assumption of atmospheric spatial homogeneity. With high temporal resolution observations from GeoHIS, cloud removal can also be achieved through OCC but using the assumption of temporal homogeneity. This concept was demonstrated using 15‐min Geostationary Interferometric Infrared Sounder (GIIRS) observations. The longwave GIIRS clear radiances under partially cloud cover can be effectively produced with observation errors less than 0.02 ± 0.86 K and 0.04 ± 0.77 K for 11 and 12 μm, respectively.

The most well-known criterion for POC devices is ASSURED, and affordability, i.e., using low-cost instrumentation, is the most challenging one. This manuscript provides a pathway for transitioning ESSENCE, an impedance-based biosensor platform, from using an expensive benchtop analyzer—KeySight 4294A (~$50k)—to using a significantly portable and cheaper USB oscilloscope—Analog Discovery 2 (~$200) —with similar sensitivity (around 100 times price difference). To achieve this, we carried out a fundamental study of the interplay between an electrolyte like potassium chloride (KCl), and an electrolyte buffer like phosphate buffered saline (PBS) in the presence and absence of a redox buffer like ferro/ferricyanide system and ([Ru(bpy)3]2+). Redox molecules in the electrolyte caused a significant change in the Nyquist curve of the impedance depending on the redox molecule type. The redox species and the background electrolyte have their own RC semicircles in the Nyquist curve, whose overlap depends on the redox concentration and electrolyte ionic strength. We found that by increasing the electrolyte ionic strength or the redox concentration, the RC semicircle moves to higher frequencies and vice versa. Importantly, the use of the buffer electrolyte, instead of KCl, led to a lower standard deviation and overall signal (lesser sensitivity). However, to achieve the best results from the biorecognition signal, we chose a buffered electrolyte like PBS with high ionic strength and lowered the redox probe concentrations to minimize the standard deviation and reduce any noise from migrating to the low-cost analyzer. Comparing the two analyzers shows similar results, with a lowered detection limit from the low-cost analyzer.

Abstract Geostationary (GEO) imager Infrared (IR) bands are often intercalibrated with hyperspectral IR sounders onboard low earth orbit (LEO) satellites. Due to viewing geometry and time differences between GEO and LEO, the inter‐calibration is limited to certain viewing angles and times. When a GEO hyperspectral IR sounder and GEO imager are on the same platform, the inter‐calibration can be conducted from all angles and at all times. In this study, the Geostationary Interferometric Infrared Sounder (GIIRS) is used to inter‐calibrate the Advanced Geosynchronous Radiation Imager (AGRI), both onboard FengYun‐4A. Radiance inter‐comparisons between GIIRS and AGRI under homogeneous scenes, including both clear and cloudy situations, indicate that the differences are within 1 K. The differences have obvious dependence on scene radiance, less dependence on the cloudy situation, and the local zenith angle when it is less than 70°. However, they exhibit significant diurnal characteristics.

BackgroundIncreasing studies have found that PM2.5 has large adverse effects on stroke mortality. We want to investigate the long-term trends in the mortality of stroke attributable to ambient particulate matter pollution and household air pollution to provide evidence facilitating the design of policy.MethodsThe deaths data of stroke and its subtypes attributable to PM2.5 were obtained from the Global Burden of Disease (GBD) 2019, analyzed by Joinpoint regression software and the age-period-cohort (APC) method to assess the magnitude of the trends in mortality and the temporal trends in the mortality rate by age, period, and cohort.ResultsFrom 1990 to 2019, the age-standardized mortality rate (ASMR) attributable to PM2.5 exposure trended downwards, but the trends of ambient particulate matter pollution and household air pollution were opposite. The trends varied among subtypes, the AAPC of intracerebral hemorrhage, ischemic stroke, and subarachnoid hemorrhage attributable to PM2.5 were 0.7, 2.5, and−3.3%, respectively. The longitudinal age curve of the APC model showed that the mortality rates due to PM2.5 exposure increased with age. The period RRs of ischemic stroke due to ambient particulate matter pollution increased significantly. The cohort RRs of ambient particulate matter pollution increased among those born from 1905 to 1990. The net drifts of all subtypes attributable to PM2.5 were below 0, but owing to the increase of ambient particulate matter pollution, the range of the decline was small. Males had higher net drift values, compared with females.ConclusionsAmbient particulate matter pollution has become the main type of PM2.5 leading to stroke in China. PM2.5 exposure is more harmful to ischemic stroke, males, and elderly. Chinese government should pay attention to the long-term impact of ambient air pollution on stroke and take effective public health policies and interventions.

Paraboea (Gesneriaceae) distributed in the karst areas of South and Southwest China and Southeast Asia, is an ideal genus to study the phylogeny and adaptive evolution of karst plants. In this study, the complete chloroplast genomes of twelve Paraboea species were sequenced and analyzed. Twelve chloroplast genomes ranged in size from 153166 to 154245 bp. Each chloroplast genome had a typical quartile structure, and relatively conserved type and number of gene components, including 131 genes which are composed of 87 protein coding genes, 36 transfer RNAs and 8 ribosomal RNAs. A total of 600 simple sequence repeats and 389 non-overlapped sequence repeats were obtained from the twelve Paraboea chloroplast genomes. We found ten divergent regions (trnH-GUG-psbA, trnM-CAU, trnC-GCA, atpF-atpH, ycf1, trnK-UUU-rps16, rps15, petL, trnS-GCU-trnR-UCU and psaJ-rpl33) among the 12 Paraboea species to be potential molecular markers. In the phylogenetic tree of 31 Gesneriaceae plants including twelve Paraboea species, all Paraboea species clustered in a clade and confirmed the monophyly of Paraboea. Nine genes with positive selection sites were detected, most of which were related to photosynthesis and protein synthesis, and might played crucial roles in the adaptability of Paraboea to diverse karst environments. These findings are valuable for further study of the phylogeny and karst adaptability of Gesneriaceae plants.

Abstract Background Digestive tract reconstruction in totally laparoscopic total gastrectomy can be divided into two types: instrument anastomosis and hand-sewn anastomosis. This study explored the feasibility and safety of hand-sewn sutures in esophagojejunostomy of totally laparoscopic total gastrectomy, compared with instrument anastomosis using an overlap linear cutter. Methods This retrospective cohort study was conducted from January 2017 to January 2020 at one institution. The clinical data of 50 patients who underwent totally laparoscopic total gastrectomy, with an average follow-up time of 12 months, were collected. The clinicopathologic data, short-term survival prognosis, and results of patients in the hand-sewn anastomosis (n=20) and the overlap anastomosis (n=30) groups were analyzed. Results There were no significant differences between the groups in sex, age, body mass index, American Society of Anesthesiologists score, tumor location, preoperative complications, abdominal operation history, tumor size, pTNM stage, blood loss, first postoperative liquid diet, exhaust time, or postoperative length of hospital stay. The hand-sewn anastomosis group had a significantly prolonged operation time (204±26.72min versus 190±20.90min, p=0.04) and anastomosis time (58±22.0min versus 46±15.97min, p=0.029), and a decreased operation cost (CNY 77,100±1700 versus CNY 71,900±1300, p

Infrared (IR) land surface emissivity (LSE) plays an important role in numerical weather prediction (NWP) models through the satellite radiance assimilation. However, due to the large uncertainties in LSE over the desert, many land-surface sensitive channels of satellite IR sensors are not assimilated. This calls for further assessments of the quality of satellite-retrieved LSE in these desert regions. A set of LSE observations were made from field experiments conducted on 16–18 October 2013 along a south/north desert road in the Taklimakan Desert (TD), China. The observed LSEs (EOBS) are thus used in this study as the reference values to evaluate the quality of Combined ASTER MODIS Emissivity over Land (CAMEL) data. Analysis of these data shows four main results. First, the CAMEL datasets appear to sufficiently capture the spatial variations in LSE from the oasis to the hinterland of the TD (this is especially the case in the quartz reststrahlen band). From site 1 at the southern edge of the Taklimakan Desert to site 10 at the northern edge, the measured LSE and the corresponding CAMEL observation in the quartz reststrahlen band first decrease and reach their minimum around sites 4–6 in the hinterland of the Taklimakan Desert. Then, the LSE increases gradually and finally reaches its maximum at site 10, which has a clay ground surface, showing that the LSE is higher at the edges of the desert and lower in the center. Second, the CAMEL values at 11.3 μm have a zonal distribution characterized by a northeast–southwest strike, though such an artifact might have been introduced by ASTER LSE data during the merging process that created the CAMEL dataset. Third, the unrealistic variation of the original EOBS can be filtered out with useful signals, as identified by the first six principal components of the PCA conducted on the laboratory-measured hyperspectral emissivity spectra (ELAB). Fourth, the CAMEL results correlate well with the measured LSE at the 10 observation sites, with the observed LSE being slightly smaller than the CAMEL values in general.

A storm tracking and nowcasting model was developed for the contiguous US (CONUS) by combining observations from the advanced baseline imager (ABI) and numerical weather prediction (NWP) short-range forecast data, along with the precipitation rate from CMORPH (the Climate Prediction Center morphing technique). A random forest based model was adopted by using the maximum precipitation rate as the benchmark for convection intensity, with the location and time of storms optimized by using optical flow (OF) and continuous tracking. Comparative evaluations showed that the optimized models had higher accuracy for severe storms with areas equal to or larger than 5000 km2 over smaller samples, and loweraccuracy for cases smaller than 1000 km2, while models with sample-balancing applied showed higher possibilities of detection (PODs). A typical convective event from August 2019 was presented to illustrate the application of the nowcasting model on local severe storm (LSS) identification and warnings in the pre-convection stage; the model successfully provided warnings with a lead time of 1–2 h before heavy rainfall. Importance score analysis showed that the overall impact from ABI observations was much higher than that from NWP, with the brightness temperature difference between 6.2 and 10.3 microns ranking at the top in terms of feature importance.

This review paper presents the recent developments in spectroelectrochemical (SEC) technologies. The coupling of spectroscopy and electrochemistry enables SEC to do a detailed and comprehensive study of the electron transfer kinetics and vibrational spectroscopic fingerprint of analytes during electrochemical reactions. Though SEC is a promising technique, the usage of SEC techniques is still limited. Therefore, enough publicity for SEC is required, considering the promising potential in the analysis fields. Unlike previously published review papers primarily focused on the relatively frequently used SEC techniques (ultraviolet-visible SEC and surface-enhanced Raman spectroscopy SEC), the two not-frequently used but promising techniques (nuclear magnetic resonance SEC and dark-field microscopy SEC) have also been studied in detail. This review paper not only focuses on the applications of each SEC method but also details their primary working mechanism. In short, this paper summarizes each SEC technique’s working principles, current applications, challenges encountered, and future development directions. In addition, each SEC technique’s applicative research directions are detailed and compared in this review work. Furthermore, integrating SEC techniques into microfluidics is becoming a trend in minimized analysis devices. Therefore, the usage of SEC techniques in microfluidics is discussed.

In order to evaluate the ultimate pressure bearing capacity of high-grade steel pipeline with pure dent defect, finite element analysis was performed for the deformation of steel pipeline during the formation of dent and the hydraulic test. In addition, full-scale hydrostatic burst test and strain test were also conducted for the pipeline with pure dent defect. Based on the full-scale test results, the results of the finite element analysis were verified in terms of burst pressure, rebound behavior and strain of pipeline with pure dent defect. The results show that the finite element analysis results are consistent with the experimental results in both processes of dent formation and hydraulic test, with an error of less than 10%. It indicates that the simulation of the dented pipe is accurate. Besides, the burst pressure of the dented pipe with a depth of 15.9% D(D refers to the outer diameter of pipeline) is 38 MPa, which is higher than the calculated burst pressure, 31.2 MPa, of the non-defective steel pipe, indicating that the tested dent has no adverse effect on the pressure bearing capacity of pipeline. The research findings provide technical support to the theoretical analysis of high-grade steel pipeline with dent defect.

Abstract Background Cholangiocarcinoma (CCA) is a mortal cancer with high mortality, whereas the function and mechanism of occurrence and progression of CCA are still mysterious. Long non-coding RNAs (lncRNAs) could function as important regulators in carcinogenesis and cancer progression. Growing evidences have indicated that the novel lncRNA linc00473 plays an important role in cancer progression and metastasis. However, its function and molecular mechanism in CCA remain unknown. Methods The linc00473 expression in CCA tissues and cell lines was analyzed using qRT-PCR. Gain- and loss-of-function experiments were conducted to investigate the biological functions of linc00473 both in vitro and in vivo. Insights into the underlying mechanisms of competitive endogenous RNAs (ceRNAs) were determined by bioinformatics analysis, dual-luciferase reporter assays, qRT-PCR arrays, RNA immunoprecipitation (RIP) and rescue experiments. Results Linc00473 was highly expressed in CCA tissues and cell lines. Linc00473 knockdown inhibited CCA growth and metastasis. Furthermore, linc00473 acted as miR-506 sponge and regulated its target gene DDX5 expression. Rescue assays verified that linc00473 modulated the tumorigenesis of CCA by regulating miR-506. Conclusions The data indicated that linc00473 played an oncogenic role in CCA growth and metastasis, and could serve as a novel molecular target for treating CCA.

Meta-heuristic algorithms have successfully solved many real-world problems in recent years. Inspired by different natural phenomena, the algorithms with special search mechanisms can be good at tackling certain problems. However, they may fail to solve other problems. Among the various approaches, hybridizing meta-heuristic algorithms may possibly help to enrich their search behaviors while promoting the search adaptability. Accordingly, an efficient hybrid population-based optimization framework, namely the HYPO, is proposed in this study in which two meta-heuristic algorithms with different search ideas are connected by a dynamic contribution-based state transition scheme. Specifically, the dynamic transition scheme determines the directions of information transitions after considering the current contribution and system state at each iteration so that useful information can be shared and learnt between the concerned meta-heuristic algorithms throughout the search process. To carefully examine the effectiveness of the dynamic transition scheme, the proposed HYPO framework is compared against various well-known meta-heuristic algorithms on a set of large-scale benchmark functions and portfolio management problems of different scales in which the HYPO attains outstanding performances on the problems with complex features. Last but not least, the hybrid framework sheds lights on many possible directions for further improvements and investigations.

With the rapid growth of emerging point-of-use (POU)/point-of-care (POC) detection technologies, miniaturized sensors for the real-time detection of gases and airborne pathogens have become essential to fight pollution, emerging contaminants, and pandemics. However, the low-cost development of miniaturized gas sensors without compromising selectivity, sensitivity, and response time remains challenging. Microfluidics is a promising technology that has been exploited for decades to overcome such limitations, making it an excellent candidate for POU/POC. However, microfluidic-based gas sensors remain a nascent field. In this review, the evolution of microfluidic gas sensors from basic electronic techniques to more advanced optical techniques such as surface-enhanced Raman spectroscopy to detect analytes is documented in detail. This paper focuses on the various detection methodologies used in microfluidic-based devices for detecting gases and airborne pathogens. Non-continuous microfluidic devices such as bubble/droplet-based microfluidics technology that have been employed to detect gases and airborne pathogens are also discussed. The selectivity, sensitivity, advantages/disadvantages vis-a-vis response time, and fabrication costs for all the microfluidic sensors are tabulated. The microfluidic sensors are grouped based on the target moiety, such as air pollutants such as carbon monoxide and nitrogen oxides, and airborne pathogens such as E. coli and SARS-CoV-2. The possible application scenarios for the various microfluidic devices are critically examined.

In this study, an Fe–Cr–Mo–Ni–B coating was prepared using plasma cladding on Cr5 steel substrate. The microstructure, phase evolution and tribological performance of the Fe–Cr–Mo–Ni–B coating were investigated. The microstructure is mainly composed of Mo2FeB2, Fe2B, α-Fe, γ-Fe and MoB. The process of phase evolution in the coating was observed in situ by HT-CLSM. The Mo2FeB2 phase with good thermodynamic stability can exist in the high-temperature liquid phase. It also has a phenomenon of connection and merging and turns into different morphology during the plasma cladding process. The hardness value of coating was much higher than the base metal, and the hardness value of Mo2FeB2 (785.5 HV) was higher than the eutectic matrix (693.2 HV). The wear mechanisms of the cladding under dry sliding were primarily caused by adhesive wear, accompanying slight oxidation wear. The Mo2FeB2 phase has an important effect on the wear resistance property.

Abstract Thermodynamic information from low levels in the atmosphere is crucial for operational weather forecasts and meteorological researchers. The NOAA Unique Combined Atmospheric Processing System (NUCAPS) sounding products have been proven beneficial to fill the data gap between synoptic radiosonde observations (RAOBs). However, compared with the upper troposphere, the accuracy of NUCAPS soundings in the low levels still needs improvement. In this study, a deep neural network (DNN) is applied to fuse multiple data sources to enhance the NUCAPS temperature and moisture profiles in the lower atmosphere. The network is developed by combining satellite observations, including NUCAPS sounding retrievals and high resolution geostationary satellite observations from the Advanced Baseline Imager, and surface analysis from the Real‐Time Mesoscale Analysis (RTMA) as inputs, while collocated soundings from ECMWF re‐analysis version 5 are used as the benchmark for the training. The performance of the model is evaluated by using the independent testing data set, data from a different year, as well as collocated RAOBs, showing improvement to the temperature and moisture profiles by reducing the root‐mean‐squared‐error (RMSE) by more than 30% in the lower atmosphere (from 700 hPa to surface) in both clear sky and partially cloudy conditions. A convective event from June 18, 2017 is presented to illustrate the application of the enhanced low level soundings on high impact weather events. The enhanced soundings from fused data capture the large surface‐based convective available potential energy structures in the preconvection environment, which is very useful for severe storm nowcasting and forecasting applications.

Abstract There are a growing number of advanced imagers for geostationary meteorological satellites, which can provide water vapor radiance observations with high temporal and spatial resolutions. To assess the impact of those imagers, radiance assimilation experiments were conducted with the Advanced Baseline imager (ABI) on board the Geostationary Operational Environmental Satellite‐16. The radiances from the three water vapor absorption bands of Geostationary Operational Environmental Satellite‐16 ABI were assimilated through the National Oceanic and Atmospheric Administration Gridpoint Statistical Interpolation data assimilation system in a regional numerical weather prediction (NWP) model. The forecast impacts for Hurricane Irma (2017) and Hurricane Harvey (2017) have been studied and analyzed in this work. Due to complicated surface situations (emissivity, terrain height, etc.) over land, the infrared (IR) radiance assimilation is still limited; thus, handling surface effects in radiance assimilation needs to be considered. By analyzing the Jacobian function of skin temperature in the ABI radiance assimilation process, it is shown that assimilating water vapor IR radiances over high elevation surfaces or in dry regions is problematic even where the bands are mostly sensitive to the upper level of the atmosphere such as Band 8 (6.19 μm). Additional quality control steps using skin temperature Jacobians to eliminate the contamination from the surface impact are developed and added for ABI radiance assimilation. The results show that ABI radiance assimilation with quality controls is able to improve tropical cyclone forecasts. The methodology used in this study can be applied to the assimilation of IR radiances from other geostationary satellites or polar‐orbiting satellites.

Abstract The Geostationary Operational Environmental Satellite‐R (GOES‐R) series started a new era for the U.S. geostationary satellite observing system. The Advanced Baseline Imager (ABI) onboard the GOES‐R series has fine temporal (30 s to 10 min) and spatial resolutions (0.5–2 km), and 16 spectral bands. However, due to the lack of an infrared sounder, the ABI is used to continue the legacy atmospheric profile (LAP) products that the previous GOES Sounder has, including the legacy atmospheric moisture profile, legacy atmospheric temperature profile, total precipitable water, layered precipitable water, and derived atmospheric stability indices. The ABI LAP retrieval algorithms have been developed under the GOES‐R series Algorithm Working Group (AWG) program funded by the GOES‐R Program Office. The LAP products from GOES‐16 have been operational and validated with a series of reference data sets including radiosonde observations, the Global Positioning System from SuomiNet, the Advanced Microwave Scanning Radiometer 2 total precipitable water measurements, as well as global operational analysis from National Oceanic and Atmospheric Administration and European Centre for Medium‐Range Weather Forecasts models, for almost a year (from 2017 to 2018) to assure the data quality for applications. In addition, the LAP products have been successfully demonstrated at the Hazardous Weather Testbed experiments in the summer of 2017 and the spring of 2018. Both validation results and Hazardous Weather Testbed demonstrations indicate that the GOES‐R series LAP products meet the product requirements and provide added value over NWP short‐range forecasts, especially for middle‐upper tropospheric moisture, in situation awareness and nowcasting.

The advanced infrared (IR) and microwave (MW) sounding systems have been providing atmospheric sounding information critical for nowcasting and improving weather forecasts through data assimilation in numerical weather prediction. In recent years, advanced IR and MW sounder systems are being proposed to be onboard CubeSats that are much more cost efficient than traditional satellite systems. An impact study using a regional Observing System Simulation Experiment on a local severe storm (LSS) was carried out to evaluate the alternative of using advanced MW and IR sounders for high-impact weather forecasting in mitigating the potential data gap of the Advanced Technology Microwave Sounder (ATMS) and the Cross-track Infrared Sounder (CrIS) on the Suomi-NPP (SNPP) or Joint Polar Satellite System (JPSS). It was found that either MicroMAS-2 or the CubeSat Infrared Atmospheric Sounder (CIRAS) on a single CubeSat was able to provide a positive impact on the LSS forecast, and more CubeSats with increased data coverage yielded larger positive impacts. MicroMAS-2 has the potential to mitigate the loss of ATMS, and CIRAS the loss of CrIS, on SNPP or JPSS, especially when multiple CubeSats are launched. There are several approximations and limitations to the present study, but these represent efficiencies appropriate to the principal goal of the study — gauging the relative values of these sensors.

Enhancing the catalytic activity of noble-metal alloys is frequently accompanied by side reactions. Here, the authors describe an approach to break the scaling relationship for propane dehydrogenation, by assembling single atom alloys, to achieve simultaneous enhancement of propylene selectivity and propane conversion.

A physical retrieval algorithm has been developed for deriving the layered precipitable water vapor (LPWs) product from infrared radiances of the Advanced Geosynchronous Radiation Imager (AGRI) onboard FengYun-4A (FY-4A), the first of the new generation of Chinese geostationary weather satellites (FengYun-4, or FY-4 Series). The FY-4A AGRI LPWs are evaluated with different types of reference datasets based on Quality Control System (QCS), including those from Himawari-8 AHI (Advanced Himawari Imager), MODIS (Moderate Resolution Imaging Spectroradiometer), Radiosonde, ERA5 (European Centre for Medium-Range Weather Forecasts Reanalysis v5), NCEP (National Centers for Environmental Prediction) reanalysis and CMA (China Meteorological Administration) forecast product from global medium range numerical weather prediction (NWP) system. QCS is one of the important components of FY-4A ground segment, which mainly focuses on the satellite products’ evaluation and validation. It is found that the AGRI LPW product has a good agreement with different evaluating sources and the quality is favorable and stable. With the capability of frequent (5-min interval) observations over the East Asia and Western Pacific regions, the AGRI LPW products can be used to investigate the atmospheric temporal and spatial variations in the pre-landfall environment for typhoons. The QCS is a useful tool to monitor, evaluate, and validate the AGRI LPW products.

Abstract Background Accumulating evidence has indicated that long non-coding RNAs (lncRNAs) behave as a novel class of transcription products during multiple cancer processes. However, the mechanisms responsible for their alteration in cholangiocarcinoma (CCA) are not fully understood. Methods The expression of SPRY4-IT1 in CCA tissues and cell lines was determined by RT-qPCR, and the association between SPRY4-IT1 transcription and clinicopathologic features was analyzed. Luciferase reporter and chromatin immunoprecipitation (ChIP) assays were performed to explore whether SP1 could bind to the promoter region of SPRY4-IT1 and activate its transcription. The biological function of SPRY4-IT1 in CCA cells was evaluated both in vitro and in vivo. ChIP, RNA binding protein immunoprecipitation (RIP) and luciferase reporter assays were performed to determine the molecular mechanism of SPRY4-IT1 in cell proliferation, apoptosis and invasion. Results SPRY4-IT1 was abnormally upregulated in CCA tissues and cells, and this upregulation was correlated with tumor stage and tumor node metastasis (TNM) stage in CCA patients. SPRY4-IT1 overexpression was also an unfavorable prognostic factor for patients with CCA. Additionally, SP1 could bind directly to the SPRY4-IT1 promoter region and activate its transcription. Furthermore, SPRY4-IT1 silencing caused tumor suppressive effects via reducing cell proliferation, migration and invasion; inducing cell apoptosis and reversing the epithelial-to-mesenchymal transition (EMT) process in CCA cells. Mechanistically, enhancer of zeste homolog 2 (EZH2) along with the lysine specific demethylase 1 (LSD1) or DNA methyltransferase 1 (DNMT1) were recruited by SPRY4-IT1, which functioned as a scaffold. Importantly, SPRY4-IT1 positively regulated the expression of EZH2 through sponging miR-101-3p. Conclusions Our data illustrate how SPRY4-IT1 plays an oncogenic role in CCA and may offer a potential therapeutic target for treating CCA.

Abstract Background Long non-coding RNAs (lncRNAs), which are a portion of non-protein-coding RNAs (ncRNAs), have manifested a paramount role in the pathophysiology of human diseases, particularly in pathogenesis and progression of disease. Main body of the abstract Myocardial infarction associated transcript (MIAT), which was recently found to demonstrate aberrant expression in various diseases, such as myocardial infarction, schizophrenia, ischemic stroke, diabetic complications, age-related cataract and cancers, is a novel disease-related lncRNA. This work summarize current evidence regarding the biological functions and underlying mechanisms of lncRNA MIAT during disease development. Short conclusion LncRNA MIAT likely represents a feasible cancer biomarker or therapeutic target.

Six tropical biomass samples namely: Ogbono wood (Irvingia wombolu), Mango wood (Mangifera indica), Neem wood (Azadiracta indica), Ogbono shell (Irvingia wombolu), Ogirisi wood (Neubouldia laevis) and Tropical Almond wood (Terminalia catappa) were pyrolyzed in a bench scale screw reactor at 450oC. The physicochemical properties of the samples were determined prior to the pyrolysis experiments. The bio-oil and bio-char produced were similarly characterized using standard procedures established by American Standard and Test Methods (ASTM). The highest bio-oil yield of 66 wt% and least bio-oil yield of 53 wt% were obtained from Neem wood and Tropical Almond wood respectively. The characterization results of the products show that even though the moisture content of the bio-oil was quite higher than those of the original feedstock, their higher heating values were higher than those of the parent feedstock. Both characterization results show that the feedstock and their fast pyrolysis products are good materials for bioenergy production. The Gas Chromatography Mass Spectroscopy (GC-MS) analysis of the bio-oil shows the presence of useful chemicals such as phenols and levoglucosan, which could be harnessed for industrial applications.

A near global dataset of homogenized clear-sky 6.5-μm brightness temperatures (BTs) from international geostationary (GEO) weather satellites has recently been generated and validated. In this study, these radiance measurements are used to construct the diurnal variation of upper tropospheric humidity (UTH) and to evaluate these diurnal variations simulated by five reanalysis datasets over the 45° N–45° S region. The features of the diurnal variation described by the new dataset are comparable with previous observational studies that a land–sea contrast in the diurnal variation of UTH is exhibited. Distinct diurnal variations are observed over the deep convective regions where high UTH exists. The evaluation of reanalysis datasets indicates that reanalysis systems still have considerable difficulties in capturing the observed features of the diurnal variation of UTH. All five reanalysis datasets present the largest wet biases in the afternoon when the observed UTH experiences a diurnal minimum. Reanalysis can roughly reproduce the day–night contrast of UTH but with much weaker amplitudes and later peak time over both land and ocean. Comparison of the geographical distribution of the diurnal variation shows that both ERA5 and MERRA-2 could capture the larger diurnal variations over convective regions. However, the diurnal amplitudes are widely underestimated, especially over convective land regions, while the phase biases are relatively larger over open oceans. These results suggest that some deficiencies may exist in convection and cloud parameterization schemes in reanalysis models.

The distribution of tropospheric moisture in the environment is highly associated with storm development. Therefore, it is important to evaluate the uncertainty of moisture fields from numerical weather prediction (NWP) models for better understanding and enhancing storm prediction. With water vapor absorption band radiance measurements from the advanced imagers onboard the new generation of geostationary weather satellites, it is possible to quantitatively evaluate the environmental moisture fields from NWP models. Three NWP models—Global Forecast System (GFS), Unified Model (UM), Weather Research and Forecasting (WRF)—are evaluated with brightness temperature (BT) measurements from the three moisture channels of Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite for Typhoon Linfa (2015) case. It is found that the three NWP models have similar performance for lower tropospheric moisture, and GFS has a smaller bias for middle tropospheric moisture. Besides, there is a close relationship between moisture forecasts in the environment and the tropical cyclone (TC) track forecasts in GFS, while regional WRF does not show this pattern. When the infrared and microwave sounder radiance measurements from polar orbit satellite are assimilated in regional WRF, it is clearly shown that the environment moisture fields are improved compared with that with only conventional data are assimilated.

The observation error covariance (R) matrix is a key component in the data assimilation (DA) process for retrieval of atmospheric state parameters (ASPs), also impacting the subsequent numerical weather forecast. However, one commonly used type of R matrix depends on instrument noise, which contravenes reality because the retrieved ASPs would depend on the instrument used. Other types of R matrix rely on the observation operator (H), analyzed state ( x a ), background error covariance (B) matrix or the background state ( x b ), and the selected forecast ensemble. All these dependences reduce the representativeness of the R matrix, since the correctness of H needs verification and no true values exist for x a or x b . As such, a better method to correctly specify the R matrix is needed. Through the physical mechanism occurring between incident radiation and particles in the atmosphere, which complies with the phenomena of energy absorption and emission, correlations among bands or channels in a detected atmospheric radiance spectrum occur. This paper thus proposes a data-derived R matrix based on a large number (N) of detected atmospheric radiance spectra constructed from N real-time measurements, where N real-time measurements can be acquired by staring at some observation location of interest during a short amount of time. This data-derived R matrix for satellite radiance observations does not rely on any assumed quantities and is unambiguous. Technically, recording N real-time measurements is achievable by modifying the trigger configuration of data recording from ground.

Accurate and prior identification of local severe storm systems in pre-convection environments using geostationary satellite imagery measurements is a challenging task. Methodologies for “convective initiation„ identification have already been developed and explored for operational nowcasting applications; however, warning of such convective systems using the new generation of geostationary satellite imagery measurements in pre-convection environments is still not well studied. In this investigation, the Random Forest (RF) machine learning algorithm is used to develop a predictive statistical model for tracking and identifying three different types of convective storm systems (weak, medium, and severe) over East Asia by combining spatially-temporally collocated Himawari-8 (H08) measurements and Numerical Weather Prediction (NWP) forecast data. The Global Precipitation Measurement (GPM) gridded product is used as a benchmark to train the predictive models based on a sample-balance technique which can adjust or balance the samples of three different convection types to avoid over-fitting any type of dataset. Variables such as brightness temperatures (BTs) from H08 water vapor absorption bands (6.2 μm, 6.9 μm and 7.3 μm) and Total Precipitable Water (TPW) from NWP show relatively high ranks in the predictive model training. These sensitive variables are closely associated with convectively dominated precipitation areas, indicating the importance of predictors from both H08 and NWP data. The final optimal RF model is achieved with an accuracy of 0.79 for classification of all convective storm systems, while the Probability of Detection (POD) of this model for severe and medium convections can reach 0.66 and 0.70, respectively. Two typical sudden convective storm cases in the warm season of 2018 tracked by this algorithm are described, and results indicate that the H08 and NWP based statistical model using the RF algorithm is capable of capturing local burst convective storm systems about 1⁻2 h earlier than the outbreak of heavy rainfall.

Catalytic hot gas filtration (CHGF) is used to precondition biomass derived fast pyrolysis (FP) vapors by physically removing reactive char and alkali particulates and chemically converting reactive oxygenates to species that are more easily upgraded during subsequent catalytic fast pyrolysis (CFP). Carboxylic acids, such as acetic acid and propionic acid, form during biomass fast pyrolysis and are recalcitrant to downstream catalytic vapor upgrading. This work developed and evaluated catalysts that can convert these acids to more upgradeable ketones at the laboratory scale. Selective catalytic conversion of these reactive oxygenates to more easily upgraded compounds can enhance bio-refinery processing economics through catalyst preservation by reduced coking from acid cracking, by preserving carbon efficiency, and through process intensification by coupling particulate removal with partial upgrading. Two metal-doped molybdenum carbide (Mo2C) supported catalyst beads were synthesized and evaluated and their performance compared with an undoped Mo2C control catalyst beads. For laboratory scale acetic acid conversion, calcium doped Mo2C supported catalyst beads produced the highest yield of acetone at ~96% at 450 °C among undoped and Ca or Ni doped catalysts.