Impact of primary and secondary air supply intensity in stove on emissions of size-segregated particulate matter and carbonaceous aerosols from apple tree wood burning

In order to assess emission factors (EF) more accurately from household biomass burning, a series of laboratory controlled apple tree wood burning tests were conducted to measure the EFs of size-segregated particulate matter (PM) and carbonaceous aerosols. The controlled burning experiments were conducted with designed primary air (PA) and secondary air (SA) supply intensity. An optimum value of 7 m(3).h(-1) was found for SA, resulting the highest modified combustion efficiency (92.4 +/- 2.5%) as well as the lowest EFs of PM2.5 (0.13 +/- 0.01 g.MJ(-1)), OC (0.04 +/- 0.03 g.MJ(-1)) and EC (0.03 +/- 0.01 g.MJ(-1)). SA values of 7 and 10 m(3).h(-1) resulted the lowest EFs for all the different PM sizes. In a test with PA of 6 m(3.)h(-1) and SA of 7 m(3).h(-1), very low EFs were observed for 0C1 (8.2%), 0C2 (11.2%) and especially OP (Pyrolyzed OC) (0%, not detected), indicating nearly complete combustion under this air supply condition. Besides SA, higher PA was proved to have positive effects on PM and carbonaceous fraction emission reduction. For example, with a fixed SA of 1.5 m(3).h(-1), EFs of PM2.5 decreased from 0.64 to 0.27 g.MJ(-1) when PA increased from 6 to 15 m(3).h(-1) (P < 0.05). Similar reductions were also observed in EFs of OC, EC and size segregated PM

Rapid detection of multiple resistance genes to last-resort antibiotics in Enterobacteriaceae pathogens by recombinase polymerase amplification combined with lateral flow dipstick

The worrying emergence of multiple resistance genes to last-resort antibiotics in food animals and human populations throughout the food chain and relevant environments has been increasingly reported worldwide. Enterobacteriaceae pathogens are considered the most common reservoirs of such antibiotic resistance genes (ARGs). Thus, a rapid, efficient and accurate detection method to simultaneously screen and monitor such ARGs in Enterobacteriaceae pathogens has become an urgent need. Our study developed a recombinase polymerase amplification (RPA) assay combined with a lateral flow dipstick (LFD) for simultaneously detecting predominant resistance genes to last-resort antibiotics of Enterobacteriaceae pathogens, including mcr-1, blaNDM-1 and tet(X4). It is allowed to complete the entire process, including crude DNA extraction, amplification as well as reading, within 40 min at 37°C, and the detection limit is 101 copies/μl for mcr-1, blaNDM-1 and tet(X4). Sensitivity analysis showed obvious association of color signals with the template concentrations of mcr-1, blaNDM-1 and tet(X4) genes in Enterobacteriaceae pathogens using a test strip reader (R2 = 0.9881, R2 = 0.9745, and R2 = 0.9807, respectively), allowing for quantitative detection using multiplex RPA-LFD assays. Therefore, the RPA-LFD assay can suitably help to detect multiple resistance genes to last-resort antibiotics in foodborne pathogens and has potential applications in the field

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

SubMDTA: drug target affinity prediction based on substructure extraction and multi-scale features

Abstract Background Drug–target affinity (DTA) prediction is a critical step in the field of drug discovery. In recent years, deep learning-based methods have emerged for DTA prediction. In order to solve the problem of fusion of substructure information of drug molecular graphs and utilize multi-scale information of protein, a self-supervised pre-training model based on substructure extraction and multi-scale features is proposed in this paper. Results For drug molecules, the model obtains substructure information through the method of probability matrix, and the contrastive learning method is implemented on the graph-level representation and subgraph-level representation to pre-train the graph encoder for downstream tasks. For targets, a BiLSTM method that integrates multi-scale features is used to capture long-distance relationships in the amino acid sequence. The experimental results showed that our model achieved better performance for DTA prediction. Conclusions The proposed model improves the performance of the DTA prediction, which provides a novel strategy based on substructure extraction and multi-scale features

Microstructure features induced by fatigue crack initiation up to very-high-cycle regime for an additively manufactured aluminium alloy

Fatigue failure can still occur beyond 10(7) cycles, i.e. very-high-cycle fatigue (VHCF), in many metallic materials, such as aluminium alloys and high-strength steels. For VHCF of high-strength steels, a fine granular area (FGA) surrounding an inclusion is commonly identified as the characteristic region of crack initiation on the fracture surface. However, no such FGA feature and related crack initiation behaviour were observed in VHCF of conventionally cast or wrought aluminium alloys. Here, we first reported the distinct mechanisms of crack initiation and early growth, namely the microstructure feature and the role of FGA in VHCF performance for an additively manufactured (AM) AlSi10Mg alloy. The AM pores play a key role in fatigue crack initiation similar to that of the inclusions in high-strength steels, resulting in almost identical FGA behaviour for different materials under a range of mean stress with a stress ratio at R 0. The profile microstructure of FGA is identified as a nanograin layer with Si rearrangement and grain boundary transition. This process consumes a large amount of cyclic plastic energy making FGA undertake a vast majority of VHCF life. These results will deepen the understanding of VHCF nature and shed light on crack initiation mechanism of other aluminium and AM alloys. (c) 2023 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology

Values of Magnetic Resonance Imaging and Computed Tomography in the Diagnosis of Patients with Syndromes of Subacromial Impingement

Subacromial impingement syndrome (SIS) is defined as pressurization and impingement between the acromion, the bursa under the acromion, and the rotator cuff during the abduction and elevation of the shoulder joint, resulting in pain and a functional disturbance of elevation. It is the most common disorder of the shoulder, accounting for 44-65% of all complaints of shoulder pain during a physician’s office visit. The study was performed with the aim of valuing the magnetic resonance imaging (MRI) and computed tomography (CT) in diagnosing patients with SIS. A total of 68 patients with SIS were selected as study subjects and subjected to MRI and CT examinations. The diagnostic accuracy and sensitivity of MRI and CT were, respectively, 97.06 and 70.59% (P0.05). In conclusion, the diagnostic accuracy, sensitivity, and detection rate of acromion of MRI were higher compared with those of CT examination, and MRI is more suitable in the clinical diagnosis of SIS

Crack initiation mechanisms under two stress ratios up to very-high-cycle fatigue regime for a selective laser melted Ti-6Al-4V

Crack initiation mechanisms under two stress ratios (R = - 1 and 0.5) and up to very-high-cycle fatigue (VHCF) regime of a selective laser melted (SLMed) Ti-6Al-4V were investigated. Type I lack-of-fusion defects (almost equiaxed) induced crack initiation except for the cases of VHCF under R = 0.5 in which type II defects (large aspect ratio) caused facet mode crack initiation. A nanograin layer formed underneath the crack initiation region of rough area for the cases of VHCF at R = - 1, which was explained by the numerous cyclic pressing (NCP) model. A P-S-N approach was introduced to well describe the fatigue life up to VHCF regime under R = - 1 and 0.5 for the SLMed titanium alloy

Effect of Chemical Corrosion on Rock Fracture Behavior in Coastal Deep Mines: Insights from Mechanical and Acoustic Characteristics

The demand for critical minerals has increased extraction activities in coastal deep mines where challenges such as high stresses, chemical corrosion, and mining disturbance impacts are present. This study investigated the effects of chemical corrosion and confining pressure on the mechanical and fracture behaviors of granite specimens, which are crucial for ensuring the stability of surrounding rock in coastal deep mines. Triaxial compression tests were conducted on uncorroded specimens and corroded specimens immersed in acid and alkali solutions under varying confining pressures, with real-time acoustic emission (AE) monitoring. Based on the test results, the strength and deformation properties, progressive fracture, and failure processes, as well as the AE response characteristics of the specimens under chemical corrosion and confining pressure were analyzed. Additionally, the influence of confining pressure on the chemical damage and brittle–ductile transition behavior of specimens was discussed, and the mechanism of chemical corrosion on the physical and mechanical behavior of specimens was revealed based on mineralogical analysis. These findings underscore the importance of understanding the interplay between chemical corrosion, confining pressure, and rock fracture behavior in coastal deep mining and contribute to evaluating the stability of underground surrounding rocks under corrosion environments

Drug-target binding affinity prediction using message passing neural network and self supervised learning

Abstract Background Drug-target binding affinity (DTA) prediction is important for the rapid development of drug discovery. Compared to traditional methods, deep learning methods provide a new way for DTA prediction to achieve good performance without much knowledge of the biochemical background. However, there are still room for improvement in DTA prediction: (1) only focusing on the information of the atom leads to an incomplete representation of the molecular graph; (2) the self-supervised learning method could be introduced for protein representation. Results In this paper, a DTA prediction model using the deep learning method is proposed, which uses an undirected-CMPNN for molecular embedding and combines CPCProt and MLM models for protein embedding. An attention mechanism is introduced to discover the important part of the protein sequence. The proposed method is evaluated on the datasets Ki and Davis, and the model outperformed other deep learning methods. Conclusions The proposed model improves the performance of the DTA prediction, which provides a novel strategy for deep learning-based virtual screening methods

Effects of inclusion size and stress ratio on the very-high-cycle fatigue behavior of pearlitic steel

The effects of inclusion size and stress ratio (R) on the very-high-cycle fatigue (VHCF) behavior of pearlitic steel were experimentally assessed. The S-N curve under rotating bending loading exhibited a horizontal asymptote shape and a clear fatigue limit. In contrast, the S-N curve under ultrasonic axial loading exhibited a continuously descending shape, and the fatigue limit disappeared at a fatigue life of 10(7) cycles. The fine granular area (FGA) within nanograins formed in the crack initiation region only for the samples subjected to VHCF at R = -1. All instances of interior crack initiation were caused by the MnS inclusions