Research on coal mine longwall face gas state analysis and safety warning strategy based on multi-sensor forecasting models.

Intelligent computing is transforming safety inspection methods and response strategies in coal mines. Due to the significant safety hazards associated with mining excavation, this study proposes a multi-source data based predictive model for assessing gas risk and implementing countermeasures. By examining the patterns of gas dispersion at the longwall face, utilizing both temporal and spatial correlation, a predictive model is crafted that incorporates safety thresholds for gas concentrations, four-level early warning method and response strategy are devised by integrating weighted predictive confidence with these correlations. Initially tested using a public dataset from Poland, this method was later verified in coal mine in China. This paper discusses the validity and correlation of multi-source monitoring data in temporal and spatial correlation and proposes a risk warning mechanism based on it, which can be applied not only for safety warning but also for regulatory management.

Machine learning for identifying tumor stemness genes and developing prognostic model in gastric cancer.

Gastric cancer presents a formidable challenge, marked by its debilitating nature and often dire prognosis. Emerging evidence underscores the pivotal role of tumor stem cells in exacerbating treatment resistance and fueling disease recurrence in gastric cancer. Thus, the identification of genes contributing to tumor stemness assumes paramount importance. Employing a comprehensive approach encompassing ssGSEA, WGCNA, and various machine learning algorithms, this study endeavors to delineate tumor stemness key genes (TSKGs). Subsequently, these genes were harnessed to construct a prognostic model, termed the Tumor Stemness Risk Genes Prognostic Model (TSRGPM). Through PCA, Cox regression analysis and ROC curve analysis, the efficacy of Tumor Stemness Risk Scores (TSRS) in stratifying patient risk profiles was underscored, affirming its ability as an independent prognostic indicator. Notably, the TSRS exhibited a significant correlation with lymph node metastasis in gastric cancer. Furthermore, leveraging algorithms such as CIBERSORT to dissect immune infiltration patterns revealed a notable association between TSRS and monocytes and other cell. Subsequent scrutiny of tumor stemness risk genes (TSRGs) culminated in the identification of CDC25A for detailed investigation. Bioinformatics analyses unveil CDC25A's implication in driving the malignant phenotype of tumors, with a discernible impact on cell proliferation and DNA replication in gastric cancer. Noteworthy validation through in vitro experiments corroborated the bioinformatics findings, elucidating the pivotal role of CDC25A expression in modulating tumor stemness in gastric cancer. In summation, the established and validated TSRGPM holds promise in prognostication and delineation of potential therapeutic targets, thus heralding a pivotal stride towards personalized management of this malignancy.

Acoustic Wave-Induced FeRh Magnetic Phase Transition and Its Application in Antiferromagnetic Pattern Writing and Erasing.

We explore the FeRh magnetic phase transition (MPT) and magnetic phase domain (MPD) with the introduction of surface acoustic waves (SAWs). The effects of the SAW pulses with different pulse widths and powers on resistance-temperature loops are investigated, revealing that the SAW can reduce the thermal hysteresis. Meanwhile, the SAW-induced comb-like antiferromagnetic (AFM) phase domains are observed. By changing the pulse width and SAW frequency, we further realize a writing-erasing process of the different comb-like AFM phase domains in the mixed-phase regime of the cooling transition branch. Resistance measurements also display the repeated SAW writing-erasing and the nonvolatile characteristic clearly. MPT paths are measured to demonstrate that short SAW pulses induce isothermal MPT and write magnetic phase patterns via the dynamic strain, whereas long SAW pulses erase patterns via the acoustothermal effect. The Preisach model is introduced to model the FeRh MPT under the SAW pulses, and the calculated results correspond well with our experiments, which reveals the SAW-induced energy modulation promotes FeRh MPT. COMSOL simulations of the SAW strain field also support our results. Our study not only can be used to reduce the thermal hysteresis but also extends the application of the SAW as a tool to write and erase AFM patterns for spintronics and magnonics.

Case Report: Semi-Ex Vivo Hepatectomy Combined with Autologous Liver Transplantation for Alveolar Echinococcosis in Children.

Hepatic alveolar echinococcosis (AE) is a zoonotic disease caused by the metacestode of Echinococcus multilocularis. Although surgical resection is the optimal treatment for hepatic AE, some patients with hepatic AE located in special introhepatic sites cannot be radically cured by conventional surgery. Here, we report that a 10-year-old female patient was admitted to the hospital with occupying liver lesions for 6 months. Computed tomography examination showed irregular mixed-density masses in the right lobe and caudate lobe of the liver, with partial invasion of the right hepatic artery, right hepatic vein, and right branch of the portal vein. The patient was preoperatively diagnosed with hepatic AE, which cannot be cured by conventional liver lobectomy. The patient underwent semi-ex vivo liver resection with autologous liver transplantation (second hepatic portal reconstruction, posterior hepatic inferior vena cava repair, and hepatic artery repair) and biliary-intestinal anastomosis. After hospital discharge, she has kept living a healthy life without disease recurrence for 13 months until the end of the last follow-up. This case shows that semi-ex vivo hepatectomy with autologous liver transplantation might be a feasible and safe choice for certain patients with AE located in special introhepatic sites, which has provided novel experiences for the surgical treatment of hepatic AE.

Advances in Targeting Drug Biological Carriers for Enhancing Tumor Therapy Efficacy.

Chemotherapy drugs continue to be the main component of oncology treatment research and have been proven to be the main treatment modality in tumor therapy. However, the poor delivery efficiency of cancer therapeutic drugs and their potential off-target toxicity significantly limit their effectiveness and extensive application. The recent integration of biological carriers and functional agents is expected to camouflage synthetic biomimetic nanoparticles for targeted delivery. The promising candidates, including but not limited to red blood cells and their membranes, platelets, tumor cell membrane, bacteria, immune cell membrane, and hybrid membrane are typical representatives of biological carriers because of their excellent biocompatibility and biodegradability. Biological carriers are widely used to deliver chemotherapy drugs to improve the effectiveness of drug delivery and therapeutic efficacy in vivo, and tremendous progress is made in this field. This review summarizes recent developments in biological vectors as targeted drug delivery systems based on microenvironmental stimuli-responsive release, thus highlighting the potential applications of target drug biological carriers. The review also discusses the possibility of clinical translation, as well as the exploitation trend of these target drug biological carriers.

Comparison of diagnostic performance and FNA management of the ACR-TIRADS and Chinese-TIRADS based on surgical histological evidence.

Background: The sonography-based risk stratification of thyroid nodules is based on the different sonographic features of benign and malignant thyroid nodules. The American College of Radiology (ACR) Thyroid Imaging Reporting and Data System (TIRADS) and the Chinese-TIRADS define different risks for malignancy categories and the size criteria for fine-needle aspiration (FNA). Few studies have compared their diagnostic performance and FNA management approaches. Thus, we sought to compare the diagnostic performance and FNA management approaches of the ACR-TIRADS and Chinese-TIRADS based on surgical histological evidence. Methods: This retrospective study included patients with complete thyroid ultrasound images and histologic evidence who were consecutively selected from The Affiliated Hospital of Nanjing University of Chinese Medicine. A total of 333 nodules from 252 patients with definitive surgical histological findings were examined. Ultrasonography categories and FNA management proposals were assigned according to the ACR-TIRADS and Chinese-TIRADS. The thyroid nodules were divided into 2 groups based on a cut-off size of 1 cm. The diagnostic performance and recommended and unnecessary FNA rates for each group were compared for both systems. Results: Overall, 280 malignant thyroid nodules (84.1%) and 53 benign nodules (15.9%) were analyzed. Across all groups, the ACR-TIRADS had higher sensitivity, specificity, positive and negative predictive values, and accuracy, and a higher area under the curve (AUC) than the Chinese-TIRADS. However, there was a significant difference in the negative predictive value between the ACR-TIRADS and Chinese-TIRADS of <1 and ≥1 cm, and the overall AUCs differed significantly (39.5% vs. 35.1%, P=0.007 for <1 cm; 70.0% vs. 62.8%, P=0.014 for ≥1 cm; 0.843 vs. 0.806, P=0.037 for all). The rate for recommending the FNA of the nodules in the ACR-TIRADS was lower than that in the Chinese-TIRADS (25.2% vs. 56.5%, P<0.0001), but there was no significant difference between the ACR-TIRADS and Chinese-TIRADS in terms of the unnecessary FNA rate (14.3% vs. 13.8%, P=0.931). The kappa statistics for the consistency of the ACR-TIRADS and Chinese-TIRADS FNA recommendations for nodules sized ≥1.0 to <1.5, ≥1.5 to <2.5, and ≥2.5 cm were 0.084, 0.635, and 0.909, respectively. Conclusions: The ACR-TIRADS had slightly better diagnostic performance and a lower recommended FNA rate than the Chinese-TIRADS for thyroid nodules. For thyroid nodules ≥1.5 cm in size, the FNA recommendations of the 2 guidelines had good consistency.

Glucose-activatable insulin delivery with charge-conversional polyelectrolyte multilayers for diabetes care.

One of the most effective treatments for diabetes is to design a glucose-regulated insulin (INS) delivery system that could adjust the INS release time and rate to reduce diabetes-related complications. Here, mixed multiple layer-by-layer (mmLbL)-INS microspheres were developed for glucose-mediated INS release and an enhanced hypoglycemic effect for diabetes care. To achieve ultrafast glucose-activated INS release, glucose oxidase (GOx) was assembled with a positively charged polymer and modified on INS LbL. The mmLbL-INS microspheres were constructed with one, two, and four layers of the polyelectrolyte LbL assembly at a ratio of 1:1:1. Under hyperglycemia, GOx converts a change in the hyperglycemic environment to a pH stimulus, thus providing sufficient hydrogen ion. The accumulated hydrogen ion starts LbL charge shifting, and anionic polymers are converted to cationic polymers through hydrolytic cleavage of amine-functionalized side chains. The results of in vitro INS release suggested that glucose can modulate the mmLbL-INS microspheres in a pulsatile profile. In vivo studies validated that this formulation enhanced the hypoglycemic effect in STZ-induced diabetic rats within 2 h of subcutaneous administration and facilitated stabilization of blood glucose levels for up to 2 days. This glucose-activatable LbL microsphere system could serve as a powerful tool for constructing a precisely controlled release system.

Hydrodynamic performance optimization and experimental verification of underwater glider based on parametric method.

In this paper, the wing body fusion method is used to complete the design of underwater glider. On this basis, the traditional optimization algorithm of underwater gliding wing shape is improved. Based on the improved Hicks Henne algorithm and genetic algorithm, the shape optimization of underwater glider is completed. Through the further optimization of the overall performance, the overall shape of the glider is improved and the maximum lift drag ratio is increased. Finally, the physical experiment of the optimized shape is carried out according to the experimental water area of the circulating water tank. Through the comparative analysis of the data, the accuracy of the numerical calculation is verified.

Gas Concentration Prediction Based on IWOA-LSTM-CEEMDAN Residual Correction Model.

In this study, to further improve the prediction accuracy of coal mine gas concentration and thereby preventing gas accidents and improving coal mine safety management, the standard whale optimisation algorithm's (WOA) susceptibility to falling into local optima, slow convergence speed, and low prediction accuracy of the single-factor long short-term memory (LSTM) neural network residual correction model are addressed. A new IWOA-LSTM-CEEMDAN model is constructed based on the improved whale optimisation algorithm (IWOA) to improve the IWOA-LSTM one-factor residual correction model through the use of the complete ensemble empirical model decomposition with adaptive noise (CEEMDAN) method. The population diversity of the WOA is enhanced through multiple strategies and its ability to exit local optima and perform global search is improved. In addition, the optimal weight combination model for subsequence is determined by analysing the prediction error of the intrinsic mode function (IMF) of the residual sequence. The experimental results show that the prediction accuracy of the IWOA-LSTM-CEEMDAN model is higher than that of the BP neural network and the GRU, LSTM, WOA-LSTM, and IWOA-LSTM residual correction models by 47.48%, 36.48%, 30.71%, 27.38%, and 12.96%, respectively. The IWOA-LSTM-CEEMDAN model also achieves the highest prediction accuracy in multi-step prediction.

Vehicle Image Detection Method Using Deep Learning in UAV Video.

Traditional machine learning algorithms are susceptible to objective factors such as video quality and weather environment in the vehicle detection of Unmanned Aerial Vehicle (UAV) videos, resulting in poor detection results. A vehicle image detection method using deep learning in UAV video is proposed. The algorithm in this paper treats surveillance video as many frames of images for vehicle detection in the image. First, perform HSV (Hue-Saturation-Value) spatial brightness translation operation on the original sample to increase the adaptability to different light conditions and sample diversity. Then, the Single Shot MultiBox Detector (SSD) model framework is used as the basis for vehicle detection. In order to obtain a better feature extraction effect, focus loss is added to the basic SSD for optimization. Finally, the trained network model is used to analyze the UAV video, and the detection performance is analyzed experimentally. The results show that the vehicle detection rate of this algorithm has reached 96.49%. It can ensure that the vehicle is accurately detected from the drone video.

Superstrong, Lightweight, and Exceptional Environmentally Stable SiO2@GO/Bamboo Composites.

Development of lightweight structural materials from fast-growing bamboos is of great significance to building a sustainable society. However, previously developed structural bamboos by delignification combined with densification would easily fail under large external loading after exposure to water due to structure collapse, severely limiting their practical applications. Here, we demonstrate an ultrastrong and exceptional environmentally stable bamboo composite consisting of a graphene oxide (GO)/bamboo core and hierarchical SiO2 protection layer. The GO/bamboo composite exhibits ultrahigh tensile strength (641.6 MPa), superb flexural strength (428.4 MPa), and excellent toughness (17.5 MJ/m3), which are increased by about 480, 250, and 360% compared with natural bamboo, respectively. As a result, the specific tensile strength of the GO/bamboo composite is up to 513.3 MPa·cm3/g due to its low density (1.25 g/cm3), outperforming engineering structural materials such as aluminum alloys, steels, and titanium alloys. These large improvements benefit from the well-preserved bamboo scaffold and the strong hydrogen bonds between bamboo fibers and GO nanosheets. On the other hand, the SiO2@GO/bamboo composite shows superhydrophobicity due to the construction of hierarchical SiO2 layers, which endows it with outstanding water resistance. Moreover, the bamboo composite shows an ultralow coefficient of thermal expansion (≈2.3 × 10-6 K-1), indicating its excellent dimensional stability. Considering the ultrahigh mechanical performance and outstanding environmental stability, the developed lightweight SiO2@GO/bamboo composite is hopeful to be a green and sustainable structural material for practical engineering applications.

Adversarial Reciprocal Points Learning for Open Set Recognition.

Open set recognition (OSR), aiming to simultaneously classify the seen classes and identify the unseen classes as 'unknown', is essential for reliable machine learning. The key challenge of OSR is how to reduce the empirical classification risk on the labeled known data and the open space risk on the potential unknown data simultaneously. To handle the challenge, we formulate the open space risk problem from the perspective of multi-class integration, and model the unexploited extra-class space with a novel concept Reciprocal Point. Follow this, a novel learning framework, termed Adversarial Reciprocal Point Learning (ARPL), is proposed to minimize the overlap of known distribution and unknown distributions without loss of known classification accuracy. Specifically, each reciprocal point is learned by the extra-class space with the corresponding known category, and the confrontation among multiple known categories are employed to reduce the empirical classification risk. Then, an adversarial margin constraint is proposed to reduce the open space risk by limiting the latent open space constructed by reciprocal points. To further estimate the unknown distribution from open space, an instantiated adversarial enhancement method is designed to generate diverse and confusing training samples, based on the adversarial mechanism between the reciprocal points and known classes. This can effectively enhance the model distinguishability to the unknown classes. Extensive experimental results on various benchmark datasets indicate that the proposed method is significantly superior to other existing approaches and achieves state-of-the-art performance. The code is released on github.com/iCGY96/ARPL.

Expression of PD-L1 and BRCA1 in Triple-Negative Breast Cancer Patients and Relationship with Clinicopathological Characteristics.

OBJECTIVE: To study the expression of programmed cell death ligand-1 (PD-L1) and breast cancer susceptibility gene 1 (BRCA1) in triple-negative breast cancer (TNBC) patients and analyze their relationship with clinicopathological characteristics. METHODS: 76 TNBC tissues were collected as the research object, while 60 adjacent tissues were used as controls. All patients underwent surgical treatment, and the expression of PD-L1 and BRCA1 in cancer tissues and adjacent tissues was detected by immunohistochemistry. At the same time, the relationship between PD-L1, BRCA1, and clinicopathological characteristics of patients with TNBC (including patient age, menopausal status, tumor size, lymph node metastasis, histological grade, Ki-67 expression, and p53 expression) were analyzed by univariate and logistic multivariate analysis. RESULTS: The positive expression rate of PD-L1 in the TNBC group was 64.47%, which was higher than the control group by 41.67%. The positive expression rate of BRCA1 was 27.63%, which was lower than the control group by 48.33%. PD-L1 expression has no significant relationship with age, menopausal status, and p53 expression in TNBC patients. TNBC patients with tumors ≥2 cm, histological grade III, lymph node metastasis, and Ki-67 expression ≥20% had higher PD-L1 positive expression rates. The tumor size, Ki-67 expression, and PD-L1 expression of TNBC patients have independent effects. The expression of BRCA1 has no significant relationship with menopausal status, tumor size, Ki-67 expression, etc. TNBC patients with age <45 years, histological grade I or II, no lymph node metastasis, and high p53 expression positive rate had higher BRCA1 positive expression rate. The age of TNBC patients, p53 expression, and BRCA1 expression have independent effects. CONCLUSION: In TNBC cancer tissues, there is a high expression of PD-L1 and low expression of BRAC1. The tumor size, Ki-67 expression, and PD-L1 expression of TNBC patients have independent effects. The age of TNBC patients, p53 expression, and BRCA1 expression have independent effects.

High-Performance Bamboo Steel Derived from Natural Bamboo.

It is highly desirable to develop green and renewable structural materials from biomaterials to replace synthetic materials involved from civil engineering to aerospace industries. Herein, we put forward a facile but effective top-down strategy to convert natural bamboo into bamboo steel. The fabrication process of bamboo steel involves the removal of lignin and hemicellulose, freeze-drying followed by epoxy infiltration, and densification combined with in situ solidification. The prepared bamboo steel is a super-strong composite material with a high specific tensile strength (302 MPa g-1 cm3), which is higher than that (227 MPa g-1 cm3) of conventional high specific strength steel. The bamboo steel demonstrates a high tensile strength of 407.6 MPa, a record flexural strength of 513.8 MPa, and a high toughness of 14.08 MJ/m3, which is improved by 360, 290, and 380% over those of natural bamboo, respectively. Particularly, the mechanical properties of the bamboo steel are the highest among the biofiber-reinforced polymer composites reported previously. The well-preserved bamboo scaffolds assure the integrity of bamboo fibers, while the densification under high pressure results in a high-fiber volume fraction with an improved hydrogen bonding among the adjacent bamboo fibers, and the epoxy resin impregnated enhances the stress transfer because of its chemical crosslinking with cellulose molecules. These endow the bamboo steel with superior mechanical performance. Furthermore, the bamboo steel demonstrates an excellent thermal insulating capability with a low thermal conductivity (about 0.29 W/mK). In addition, the bamboo steel shows a low coefficient of thermal expansion (about 6.3 × 10-6 K-1) and a very high-dimensional stability to moisture attack. The strategy of fabricating high-performance bamboo steel with green and abundant natural bamboo as raw materials is highly attractive for the sustainable development of structural engineering materials.

Multi-factorial analysis of the removal of dichloromethane and toluene in an airlift packing bioreactor.

Biotechnology has proven effective in removing a wide variety of VOCs. In this study, the effects of pH (from 3 to 7), operating temperature (20-30 °C), empty bed residence time (EBRT, 10-40 s) and transient inlet concentration (400-4000 mg m-3) on the removal performance of an airlift packing bioreactor (ALPR) was investigated. The removal efficiency (RE) and stability of the ALPR was evaluated and compared with the conventional airlift bioreactor (ALR). The results showed that under the influence of single factor variation, the ALPR showed significant higher RE and better stability than the ALR in removing dichloromethane (DCM) and toluene. Besides, a factorial design was used to analyses the interaction of multiple factors and their influence on the removal of DCM and toluene in the ALPR and ALR. It shows that pH value has the most significant influence, and plays a crucial role in maintaining high RE of DCM and toluene in both of the ALPR and ALR. Temperature has a great effect on the removal of toluene. EBRT has certain effect on the removal of DCM in the ALPR. The transient concentration of a single substrate has a significant negative effect on the RE of this substrate, while it does not significantly affect the removal of another substrate in the ALPR. However, the steep increase of DCM concentration has an adverse effect on the RE of high concentration toluene in the ALR. The overall RE and degradation capacity of both toluene and DCM by the ALPR are much higher than that of the conventional ALR.

High-throughput lipidomics analysis to discover lipid biomarkers and profiles as potential targets for evaluating efficacy of Kai-Xin-San against APP/PS1 transgenic mice based on UPLC-Q/TOF-MS.

Lipid metabolism has a significant function in the central nervous system and Alzheimer's disease (AD) is an age-related senile disease characterized by central nerve degeneration. The pathological development of AD is closely related to lipid metabolism disorders. To reveal the influence of Kai-Xin-San (KXS) on lipid metabolism in APP/PSI transgenic mice and potential therapeutic targets for treating AD, brain tissue samples were collected and analyzed by high-throughput lipidomics based on UPLC-Q/TOF-MS. The collected raw data were processed by multivariate data analysis to discover the potential biomarkers and lipid metabolic profiles. Compared with the control wild-type mouse group, nine potential lipid biomarkers were found in the AD model group, of which seven were up-regulated and two were down-regulated. Orally administrated KXS can reverse the changes in these potential biomarkers. Compared with the model group, a total of six differential metabolites showed a recovery trend and may be potential targets for KXS to treat AD. This study showed that high-throughput lipidomics can be used to discover the perturbed pathways and lipid biomarkers as potential targets to reveal the therapeutic effects of KXS.

Genetic characterization of Echinococcus isolates from various intermediate hosts in the Qinghai-Tibetan Plateau Area, China.

This study examined Echinococcus spp. genotypes and genetic variants isolated from humans as well as domestic and wild animals from the Qinghai-Tibetan Plateau Area using the cox1 gene. All samples except the pika isolates were identified as the Echinococcus granulosus sensu stricto. Sixteen different haplotypes with considerable intraspecific variation were detected and characterized in mitochondrial cox1 sequences. The parsimonious network of cox1 haplotypes showed star-like features, and the neutrality indexes computed via Tajima's D and Fu's Fs tests showed high negative values in E. granulosus s. s., indicating deviations from neutrality; the Fst values were low among the populations, implying that the populations were not genetically differentiated. The pika isolates were identified as E. multilocularis and E. shiquicus. Only one haplotype was recognized in the pika isolates. E. granulosus s. s. was the predominant species found in animals and humans, followed by E. multilocularis and E. shiquicus, with high genetic diversity circulating among the animals and humans in this area. Further studies are needed to cover many sample collection sites and larger numbers of pathogen isolates, which may reveal abundant strains and/or other haplotypes in the hydatid cysts infecting human and animal populations of the QTPA, China.

Ultra-performance liquid chromatography/mass spectrometry technology and high-throughput metabolomics for deciphering the preventive mechanism of mirabilite on colorectal cancer via the modulation of complex metabolic networks.

Colorectal cancer (CRC) is a highly virulent and malignant disease and always accompanied by metabolic disorders. Currently, there are no effective therapeutic drugs for the treatment of CRC. High-throughput metabolomics approaches have been used to unveil the metabolic pathways related to several diseases. In this study, ultra-performance liquid chromatography/mass spectrometry-based high-throughput metabolomics was used for deciphering the potential preventive mechanism of mirabilite on CRC via the modulation of the associated metabolic disorders; a total of 28 differential biomarkers, including indole acetaldehyde, 5-hydroxyindoleacetic acid, hypoxanthine, retinal, retinal ester, linoleic acid, stearic acid, 6-deoxocastasterone, 2-hydroxybutyric acid and LysoPC, were identified in the APCmin/+ mice. These differential biomarkers are involved in the tryptophan metabolism, glycerophospholipid metabolism and biosynthesis of unsaturated fatty acids. Note that these biomarkers and their disturbed metabolic pathways were also regulated by mirabilite. It has been found that the prevention of CRC by mirabilite is mainly associated with tryptophan metabolism; this study shows that high-throughput metabolomics can reveal the perturbed metabolic disorders targeted in the action mechanism of drug treatment.

Evaluation on the removal performance of dichloromethane and toluene from waste gases using an airlift packing reactor.

Biological removal of dichloromethane (DCM) from pharmaceutical industry is limited by its recalcitrance. In this study, an airlift packing reactor (ALPR), which combined the suspended and fixed-film microbial growth system, was set up to remove DCM and co-existed toluene. The removal performance of the ALPR for DCM was greater than traditional airlift reactor (ALR). The maximum elimination capacity (ECmax) of the ALPR for DCM reached 108 g m-3 h-1 with removal efficiency (RE) of 41%, increased by 145% if compared to the ALR. The ECmax for toluene was 172 g m-3 h-1 with RE of 70%, decreased by 25% if compared to the ALR, which was mainly due to the higher liquid-phase biomass in the ALR. The results of high-throughput sequencing showed that the microbial composition on the packings of the ALPR had a large difference from its liquid-phase or the liquid-phase of the ALR. Gemmobacter, Rhizomicrobium, Chitinophaga, Vampirovibrio, and Fodinicurvata were genera with great abundance fixed on the packings and Rhizomicrobium, Chitinophaga, Vampirovibrio, and Fodinicurvata are first to be reported in VOCs biological removal. This study indicated that the ALPR can augment the microbial community and effectively improve the removal of recalcitrant VOCs.

Design and synthesis of benzofuro[3,2-b]pyridin-2(1H)-one derivatives as anti-leukemia agents by inhibiting Btk and PI3Kδ.

Btk inhibitors and PI3Kδ inhibitors play crucial roles in the treatment of leukemia, and studies confirmed that the synergetic inhibition against Btk and PI3Kδ could gain an optimal response. Herein, a series of novel benzofuro[3,2-b]pyridin-2(1H)-one derivatives were designed and synthesized as dual Btk/PI3Kδ kinases inhibitors for the treatment of leukemia. Studies indicated that most compounds could suppress the proliferation of multiple leukemia or lymphoma cells (Raji, HL60 and K562 cells) at low micromolar concentrations in vitro. Further kinase assays identified several compounds could simultaneously inhibit Btk kinase and PI3Kδ kinase. Thereinto, compound 16b exhibited the best inhibitory activity (Btk: IC50 = 139 nM; PI3Kδ: IC50 = 275 nM) and showed some selectivity against PI3Kδ compared to PI3Kß/γ. Finally, the SAR of target compounds was preliminarily discussed combined with docking results. In brief, 16b possessed of the potency for the further optimization as anti-leukemia drugs by inhibiting simultaneously Btk kinase and PI3Kδ kinase.