A Deep Belief Network Based Model for Urban Haze Prediction

In order to improve the accuracy of urban haze prediction, a novel deep belief network (DBN)-based model was proposed. Firstly, data pertaining to both air quality and the environment (e.g. meteorology) data was monitored and collected. The primary haze influencing elements were discovered by analyzing the correlations between each of the meteorological factors and haze. Secondly, a DBN combined with multilayer restricted Boltzmann machines and a single-layer back propagation network was applied. Thirdly, the meteorological data predictions were carried out by using a competitive adaptive-reweighed method. A stable model was established by big-data training and its accuracy was verified by experiments. Results demonstrate that the pollution haze occurs in accordance with regular laws, and is greatly affected by wind direction, atmospheric pressure, and seasons. The correlation coefficient (CC) between the actual haze value and the prediction of the proposed model is 0.8, and the mean absolute error (MAE) is 26 μg/m3. Compared with the traditional prediction algorithms, the CC is improved by 18 % on average, while the MAE is reduced by 15.7 μg/m3. The proposed method has a good prospect to predict haze and investigate the main causes of it. This study provides data support for urban haze prevention and governance

Dummy Molecularly Imprinted Polymers-Capped CdTe Quantum Dots for the Fluorescent Sensing of 2,4,6-Trinitrotoluene

Molecularly imprinted polymers (MIPs) with trinitrophenol (TNP) as a dummy template molecule capped with CdTe quantum dots (QDs) were prepared using 3-aminopropyltriethoxy silane (APTES) as the functional monomer and tetraethoxysilane (TEOS) as the cross linker through a seedgrowth method via a sol gel process (i.e., DMIP@QDs) for the sensing of 2,4,6-trinitrotoluene (TNT) on the basis of electron-transfer-induced fluorescence quenching. With the presence and increase of TNT in sample solutions, a Meisenheimer complex was formed between TNT and the primary amino groups on the surface of the QDs. The energy of the QDs was transferred to the complex, resulting in the quenching of the QDs and thus decreasing the fluorescence intensity, which allowed the TNT to be sensed optically. DMIP@QDs generated a significantly reduced fluorescent intensity within less than 10 min upon binding TNT. The fluorescence-quenching fractions of the sensor presented a satisfactory linearity with TNT concentrations in the range of 0.8-30 mu M, and its limit of detection could reach 0.28 mu M. The sensor exhibited distinguished selectivity and a high binding affinity to TNT over its possibly competing molecules of 2,4-dinitrophenol (DNP), 4-nitrophenol (4-NP), phenol, and dinitrotoluene (DNT) because there are more nitro groups in TNT and therefore a stronger electron-withdrawing ability and because it has a high similarity in shape and volume to TNP. The sensor was successfully applied to determine the amount of TNT in soil samples, and the average recoveries of TNT at three spiking levels ranged from 90.3 to 97.8% with relative standard deviations below 5.12%. The results provided an effective way to develop sensors for the rapid recognition and determination of hazardous materials from complex matrices.Molecularly imprinted polymers (MIPs) with trinitrophenol (TNP) as a dummy template molecule capped with CdTe quantum dots (QDs) were prepared using 3-aminopropyltriethoxy silane (APTES) as the functional monomer and tetraethoxysilane (TEOS) as the cross linker through a seedgrowth method via a sol gel process (i.e., DMIP@QDs) for the sensing of 2,4,6-trinitrotoluene (TNT) on the basis of electron-transfer-induced fluorescence quenching. With the presence and increase of TNT in sample solutions, a Meisenheimer complex was formed between TNT and the primary amino groups on the surface of the QDs. The energy of the QDs was transferred to the complex, resulting in the quenching of the QDs and thus decreasing the fluorescence intensity, which allowed the TNT to be sensed optically. DMIP@QDs generated a significantly reduced fluorescent intensity within less than 10 min upon binding TNT. The fluorescence-quenching fractions of the sensor presented a satisfactory linearity with TNT concentrations in the range of 0.8-30 mu M, and its limit of detection could reach 0.28 mu M. The sensor exhibited distinguished selectivity and a high binding affinity to TNT over its possibly competing molecules of 2,4-dinitrophenol (DNP), 4-nitrophenol (4-NP), phenol, and dinitrotoluene (DNT) because there are more nitro groups in TNT and therefore a stronger electron-withdrawing ability and because it has a high similarity in shape and volume to TNP. The sensor was successfully applied to determine the amount of TNT in soil samples, and the average recoveries of TNT at three spiking levels ranged from 90.3 to 97.8% with relative standard deviations below 5.12%. The results provided an effective way to develop sensors for the rapid recognition and determination of hazardous materials from complex matrices

Duet: efficient and scalable hybriD neUral rElation undersTanding

Learned cardinality estimation methods have achieved high precision compared to traditional methods. Among learned methods, query-driven approaches face the data and workload drift problem for a long time. Although both query-driven and hybrid methods are proposed to avoid this problem, even the state-of-the-art of them suffer from high training and estimation costs, limited scalability, instability, and long-tailed distribution problem on high cardinality and high-dimensional tables, which seriously affects the practical application of learned cardinality estimators. In this paper, we prove that most of these problems are directly caused by the widely used progressive sampling. We solve this problem by introducing predicates information into the autoregressive model and propose Duet, a stable, efficient, and scalable hybrid method to estimate cardinality directly without sampling or any non-differentiable process, which can not only reduces the inference complexity from O(n) to O(1) compared to Naru and UAE but also achieve higher accuracy on high cardinality and high-dimensional tables. Experimental results show that Duet can achieve all the design goals above and be much more practical and even has a lower inference cost on CPU than that of most learned methods on GPU

Features and Prognostic Value of Quantitative Electroencephalogram Changes in Critically Ill and Non-critically Ill Anti-NMDAR Encephalitis Patients: A Pilot Study

Anti-N-methyl-D-aspartate receptor (NMDAR) encephalitis is a common cause of encephalitis in intensive care units. Until now, no reliable method has existed for predicting the outcome of anti-NMDAR encephalitis. In this study, we used quantitative electroencephalography (qEEG) to examine the brain function of anti-NMDAR encephalitis patients and assessed its predictive value. Twenty-six patients diagnosed with anti-NMDAR encephalitis were included and grouped according to whether they were treated in intensive care units (14 critically ill vs. 12 non-critically ill). All patients underwent 2-h 10-channel qEEG recordings at the acute stage. Parameters, including amplitude-integrated electroencephalogram (aEEG), spectral edge frequency 95%, total power, power within different frequency bands (δ, θ, α, and β), and percentages of power in specific frequency bands from frontal and parietal areas were calculated with NicoletOne Software and compared between groups. The short-term outcome was death or moderate/severe disability at 3 months after onset, measured with a modified Rankin Scale, and the long-term outcome was death, disability or relapse at 12 months. No differences in qEEG parameters were observed between the critically ill and non-critically ill patients. However, differential anterior-to-posterior alterations in δ and β absolute band power were observed. Logistic regression analysis revealed that a narrower parietal aEEG bandwidth was associated with favorable long-term outcomes (odds ratio, 37.9; P = 0.044), with an optimal cutoff value of 1.7 μV and corresponding sensitivity and specificity of 90.00 and 56.25%, respectively. In a receiver operating characteristic analysis, the area under the curve was 0.7312. In conclusion, the qEEG parameters failed to reflect the clinical severity of anti-NMDAR encephalitis. However, the parietal aEEG bandwidth may separate patients with favorable and poor long-term outcomes in early stages. The underlying mechanisms require further investigation

Study on species identification of Cronobacter spp. in foods

Objective To compare the species identification method of Cronobacter spp. in food, in order to establish rapid, high throughout identification method. Methods A total of 19 Cronobacter spp. in foods were identified with three method including biochemical method using VITEK 2.0 Compact, matrix assisted laser desorption/ionization time of flight mass spectrometry (MALDI-TOF MS) and whole-genome sequencing (WGS). The advantages and disadvantages of the three identification method were analyzed. Results The result indicated that the accuracy of VITEK identification for C. sakazakii and C. malonaticus was 67% and 66% respectively. However, WGS and MALDI-TOF MS obtained the same result for 19 Cronobacter spp.. Three subspecies could not be identified by MALDI-TOF MS due to the limited database. Conclusion Biochemical method could not be used for species identification of Cronobacter spp. MALDI-TOF MS was rapid, high throughout and accurate, but still need to expand the fingerprint database for more Cronobacter spp. isolates

A Brassica napus Reductase Gene Dissected by Associative Transcriptomics Enhances Plant Adaption to Freezing Stress

Cold treatment (vernalization) is required for winter crops such as rapeseed (Brassica napus L.). However, excessive exposure to low temperature (LT) in winter is also a stress for the semi-winter, early-flowering rapeseed varieties widely cultivated in China. Photosynthetic efficiency is one of the key determinants, and thus a good indicator for LT tolerance in plants. So far, the genetic basis underlying photosynthetic efficiency is poorly understood in rapeseed. Here the current study used Associative Transcriptomics to identify genetic loci controlling photosynthetic gas exchange parameters in a diversity panel comprising 123 accessions. A total of 201 significant Single Nucleotide Polymorphisms (SNPs) and 147 Gene Expression Markers (GEMs) were detected, leading to the identification of 22 candidate genes. Of these, Cab026133.1, an ortholog of the Arabidopsis gene AT2G29300.2 encoding a tropinone reductase (BnTR1), was further confirmed to be closely linked to transpiration rate. Ectopic expressing BnTR1 in Arabidopsis plants significantly increased the transpiration rate and enhanced LT tolerance under freezing conditions. Also, a much higher level of alkaloids content was observed in the transgenic Arabidopsis plants, which could help protect against LT stress. Together, the current study showed that AT is an effective approach for dissecting LT tolerance trait in rapeseed and that BnTR1 is a good target gene for the genetic improvement of LT tolerance in plant

THE ANTI-TUMOR EFFECT AND BIOLOGICAL ACTIVITIES OF THE EXTRACT JMM6 FROM THE STEM-BARKS OF THE CHINESE JUGLANS MANDSHURICA MAXIM ON HUMAN HEPATOMA CELL LINE BEL-7402

Juglans mandshurica Maxim is a traditional herbal medicines in China, and its anti-tumor bioactivities are of research interest. Bioassay-guided fractionation method was employed to isolate anti-tumor compounds from the stem barks of the Juglans mandshurica Maxim. The anti-tumor effect and biological activities of the extracted compound JMM6 were studied in BEL-7402 cells by MTT, Cell cycle analysis, Hoechst 33342 staining, Annexin V-FITC/PI assay and Detection of mitochondrial membrane potential (△Ψm). After treatment with the JMM6, the growth of BEL-7402 cells was inhibited and cells displayed typical morphological apoptotic characteristics. Further investigations revealed that treatment with JMM6 mainly caused G2/M cell cycle arrest and induced apoptosis in BEL-7402 cells. To evaluate the alteration of mitochondria in JMM6 induced apoptosis. The data showed that JMM6 decreased significantly the △Ψm, causing the depolarization of the mitochondrial membrane. Our results show that the JMM6 will have a potential advantage of anti-tumor, less harmful to normal cells. This paper not only summarized the JMM6 pick-up technology from Juglans mandshurica Maxim and biological characteristic, but also may provide further evidence to exploit the potential medicine compounds from the stem-barks of the Chinese Juglans mandshurica Maxim

Efficacy and safety of zanubrutinib plus R-CHOP in treatment of non-GCB DLBCL with extranodal involvement

IntroductionTreatment with rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) shows poor response rates in non–germinal center B cell–like (non-GCB) diffuse large B-cell lymphoma (DLBCL) patients with multiple extranodal involvement. This study aims to evaluate anti-tumor activity and safety of zanubrutinib with R-CHOP (ZR-CHOP) in treatment naïve non-GCB DLBCL with extranodal involvement.MethodsIn this single-arm, phase 2, prospective, single-center study, patients with newly diagnosed non-GCB DLBCL with extranodal involvement enrolled between October 2020 to March 2022 received ZR-CHOP for 6 cycles followed by 2 cycles of maintenance treatment with rituximab and zanubrutinib. The primary endpoint included progression-free survival (PFS) in the intent-to-treat (ITT) population whereas the secondary endpoints included overall response rate (ORR), complete response (CR), and duration of response. Further, next-generation sequencing (NGS) was used for detection of different oncogenic mutations closely related to DLBCL pathogenesis.ResultsFrom October 2020 to March 2022, 26 patients were enrolled, and 23 of them were evaluated for efficacy after receiving 3 cycles of ZR-CHOP treatment. 1-year PFS and OS were 80.8% and 88.5% respectively while expected PFS and OS for 2-years are 74.0% and 88.5% respectively with median follow-up of 16.7 months and ORR was 91.3% (CR: 82.61%; PR: 8.70%). Oncogenic mutations closely related to DLBCL pathogenesis were assessed in 20 patients using NGS. B-cell receptor and NF-κB pathway gene mutations were detected in 10 patients, which occurred in MYD88 (7/19), CD79B (4/19), CARD11 (5/19), and TNFAIP3 (2/19). Hematological adverse events (AEs) ≥ grade 3 included neutropenia (50%), thrombocytopenia (23.1%), and anemia (7.7%) whereas non-hematological AEs ≥ grade 3 included pulmonary infection (19.2%).ConclusionZR-CHOP is safe and effective for treating treatment naïve non-GCB DLBCL patients with extranodal involvement.Clinical Trial RegistrationClinicaltrials.gov, NCT0483587