Growth, Nutrient Uptake, and Foliar Gas Exchange in Pepper Cultured with Un-composted Fresh Spent Mushroom Residue

Spent mushroom substrate (SMS) can be used as the component of growing medium for the culture of crop plants. Fresh SMS may have the potential as an alternative to peat to raise horticultural plants. In this study, five container media characterized by the proportions of SMS to commercial peat in 0% (control), 25%, 50%, 75%, and 100% were used to raise pepper (Capsicum annum L.) plants. Initial SMS was found to have low available nitrogen (N) content (<20 mg kg-1) but moderate extractable phosphorus (P) content (900 mg kg-1). In the second month photosynthetic rate was found to decline in the 75% treatment. At harvest in the third month, plants in the 100% treatment nearly died out. The 25% treatment resulted in the highest height (19 cm) and diameter growth (0.3 cm), shoot (0.6 g) and root biomass accumulation (0.13 g), fruit weight (3 g), and shoot carbohydrate content (98 mg g-1), but lowest foliar acid phosphatase activity (30 µg NPP g-1 FW min-1). With the increase of SMS proportion in the substrate, the medium pH and electrical conductance (EC) increased with the decrease of foliar size. The available N and P contents in the substrates showed contrasting relationship with N and P contents in pepper plants. Therefore, fresh SMS cannot be directly used as the substrate for the culture of pepper plants. According to our findings fresh SMS was recommended to be mixed in the proportion of 25% with commercial peat for the culture of horticultural plants

HAPI: Hardware-Aware Progressive Inference

Convolutional neural networks (CNNs) have recently become the state-of-the-art in a diversity of AI tasks. Despite their popularity, CNN inference still comes at a high computational cost. A growing body of work aims to alleviate this by exploiting the difference in the classification difficulty among samples and early-exiting at different stages of the network. Nevertheless, existing studies on early exiting have primarily focused on the training scheme, without considering the use-case requirements or the deployment platform. This work presents HAPI, a novel methodology for generating high-performance early-exit networks by co-optimising the placement of intermediate exits together with the early-exit strategy at inference time. Furthermore, we propose an efficient design space exploration algorithm which enables the faster traversal of a large number of alternative architectures and generates the highest-performing design, tailored to the use-case requirements and target hardware. Quantitative evaluation shows that our system consistently outperforms alternative search mechanisms and state-of-the-art early-exit schemes across various latency budgets. Moreover, it pushes further the performance of highly optimised hand-crafted early-exit CNNs, delivering up to 5.11x speedup over lightweight models on imposed latency-driven SLAs for embedded devices.Comment: Accepted at the 39th International Conference on Computer-Aided Design (ICCAD), 202

Endogenous relapse and exogenous reinfection in recurrent pulmonary tuberculosis: A retrospective study revealed by whole genome sequencing

BackgroundTuberculosis may reoccur due to reinfection or relapse after initially successful treatment. Distinguishing the cause of TB recurrence is crucial to guide TB control and treatment. This study aimed to investigate the source of TB recurrence and risk factors related to relapse in Hunan province, a high TB burden region in southern China.MethodsA population-based retrospective study was conducted on all culture-positive TB cases in Hunan province, China from 2013 to 2020. Phenotypic drug susceptibility testing and whole-genome sequencing were used to detect drug resistance and distinguish between relapse and reinfection. Pearson chi-square test and Fisher exact test were applied to compare differences in categorical variables between relapse and reinfection. The Kaplan–Meier curve was generated in R studio (4.0.4) to describe and compare the time to recurrence between different groups. p < 0.05 was considered statistically significant.ResultsOf 36 recurrent events, 27 (75.0%, 27/36) paired isolates were caused by relapse, and reinfection accounted for 25.0% (9/36) of recurrent cases. No significant difference in characteristics was observed between relapse and reinfection (all p > 0.05). In addition, TB relapse occurs earlier in patients of Tu ethnicity compared to patients of Han ethnicity (p < 0.0001), whereas no significant differences in the time interval to relapse were noted in other groups. Moreover, 83.3% (30/36) of TB recurrence occurred within 3 years. Overall, these recurrent TB isolates were predominantly pan-susceptible strains (71.0%, 49/69), followed by DR-TB (17.4%, 12/69) and MDR-TB (11.6%, 8/69), with mutations mainly in codon 450 of the rpoB gene and codon 315 of the katG gene. 11.1% (3/27) of relapse cases had acquired new resistance during treatment, with fluoroquinolone resistance occurring most frequently (7.4%, 2/27), both with mutations in codon 94 of gyrA.ConclusionEndogenous relapse is the main mechanism leading to TB recurrences in Hunan province. Given that TB recurrences can occur more than 4 years after treatment completion, it is necessary to extend the post-treatment follow-up period to achieve better management of TB patients. Moreover, the relatively high frequency of fluoroquinolone resistance in the second episode of relapse suggests that fluoroquinolones should be used with caution when treating TB cases with relapse, preferably guided by DST results

The 2021 WHO catalogue of Mycobacterium tuberculosis complex mutations associated with drug resistance: a genotypic analysis.

Background: Molecular diagnostics are considered the most promising route to achievement of rapid, universal drug susceptibility testing for Mycobacterium tuberculosis complex (MTBC). We aimed to generate a WHO-endorsed catalogue of mutations to serve as a global standard for interpreting molecular information for drug resistance prediction. Methods: In this systematic analysis, we used a candidate gene approach to identify mutations associated with resistance or consistent with susceptibility for 13 WHO-endorsed antituberculosis drugs. We collected existing worldwide MTBC whole-genome sequencing data and phenotypic data from academic groups and consortia, reference laboratories, public health organisations, and published literature. We categorised phenotypes as follows: methods and critical concentrations currently endorsed by WHO (category 1); critical concentrations previously endorsed by WHO for those methods (category 2); methods or critical concentrations not currently endorsed by WHO (category 3). For each mutation, we used a contingency table of binary phenotypes and presence or absence of the mutation to compute positive predictive value, and we used Fisher's exact tests to generate odds ratios and Benjamini-Hochberg corrected p values. Mutations were graded as associated with resistance if present in at least five isolates, if the odds ratio was more than 1 with a statistically significant corrected p value, and if the lower bound of the 95% CI on the positive predictive value for phenotypic resistance was greater than 25%. A series of expert rules were applied for final confidence grading of each mutation. Findings: We analysed 41 137 MTBC isolates with phenotypic and whole-genome sequencing data from 45 countries. 38 215 MTBC isolates passed quality control steps and were included in the final analysis. 15 667 associations were computed for 13 211 unique mutations linked to one or more drugs. 1149 (7·3%) of 15 667 mutations were classified as associated with phenotypic resistance and 107 (0·7%) were deemed consistent with susceptibility. For rifampicin, isoniazid, ethambutol, fluoroquinolones, and streptomycin, the mutations' pooled sensitivity was more than 80%. Specificity was over 95% for all drugs except ethionamide (91·4%), moxifloxacin (91·6%) and ethambutol (93·3%). Only two resistance mutations were identified for bedaquiline, delamanid, clofazimine, and linezolid as prevalence of phenotypic resistance was low for these drugs. Interpretation: We present the first WHO-endorsed catalogue of molecular targets for MTBC drug susceptibility testing, which is intended to provide a global standard for resistance interpretation. The existence of this catalogue should encourage the implementation of molecular diagnostics by national tuberculosis programmes. Funding: Unitaid, Wellcome Trust, UK Medical Research Council, and Bill and Melinda Gates Foundation

Research on Intelligent Design of Geometric Factor Encoding for Aircraft Engine Turbine Structures

In recent years, with the rapid development of computer technology and artificial intelligence design technology, multiple possible design solutions can be quickly generated by transforming the experience and knowledge of structural design into computer executable rules and algorithms. To achieve intelligent design of aircraft engines, this paper proposes an encoding model for the turbine rotor structure of aircraft engines using geometric encoding technology. The turbine rotor structure of aircraft engines is divided into several units according to geometric similarity types, these units continue to be divided into attribute sets according to their functional types, connection relationships, and material properties. These attribute sets can be encoded using geometric encoding technology. The experiment simulated that these codes, for the point cloud modeling of turbine rotor structure, can be quickly achieved and they combine various algorithms to display the point cloud model of the turbine rotor in the Microsoft Visual studio MFC class library. The results show that by creating geometric codes for the turbine rotor of aircraft engines, it is possible to quickly create and display point cloud models of the turbine rotor structure, laying the foundation for subsequent application of machine learning to solve and find the optimal design solution

Predictability of short-term passengers’ origin and destination demands in urban rail transit

Accurate prediction of short-term passengers’ origin and destination (OD) demands is key to efficient operation and management of urban rail transit (URT), especially in the case of congestion or an incident. However, short-term OD demand forecasting is more challenging than passenger flow forecasting, due to its uncertainty and high dimensions. So far, most OD prediction models capture the spatio-temporal dependencies of OD flow by means of training models on historical data, but what characteristics and laws influence the performance of OD prediction are still unknown. In this paper, we propose temporal Pearson correlation coefficients and approximate entropy, as well as spatial correlations, as indicators to reflect the inherent time–space correlations and complexity of the OD flow. Then, by analyzing automatic fare collection data of the Beijing and Shanghai URT system, this paper deeply discusses the relationships between the spatio-temporal correlations and complexity of the OD flow and the predictive performances of different models with regard to different intervals. Finally, this paper proposes the predictable problem of travel demands and points out that the spatial correlations of the OD matrix are more important than the temporal correlations and complexity in the short-term prediction of travel demands. In particular, the number of principal components of the OD flow can be a key indicator to measure the forecasting performance of a model. A reasonable interval is very important for short-term OD forecasting, and in the Beijing URT system, 30 min is a preferable choice for workdays and 50 min for weekends. All these findings are beneficial to guide users to build a suitable model or improve the existing model to obtain better prediction performances

Microstructure, texture evolution and yield strength symmetry improvement of as-extruded ZK60 Mg alloy via multi-directional impact forging

Multi-direction impact forging (MDIF) was applied to the as-extruded ZK60 Mg alloy, and the microstructure, texture evolution and yield strength symmetry were investigated in the current study. The results showed that the average grain size of forged piece was greatly refined to 5.3 µm after 120 forging passes, which was ascribed to the segmenting effect of {10–12} twins and the subsequent multiple rounds of dynamic recrystallization (DRX). A great deal of {10–12} twins were activated at the beginning of MDIF process, which played an important role in grain refinement. With forging proceeding, continuous and discontinuous DRX were successively activated, resulting in the fully DRXed microstructure. Meanwhile, the forged piece exhibited a unique four-peak texture, and the initial //ED fiber texture component gradually evolved into multiple texture components composed of //FFD (first forging direction) and //FFD texture. The special strain path was the key to the formation of the unique four-peak texture. The {10–12} twinning and basal slip were two dominant factors to the evolution of texture during MDIF process. Grain strengthening and dislocation strengthening were two main strengthening mechanisms of the forged piece. Besides, the symmetry of yield strength was greatly improved by MDIF process

Identification and drug susceptibility testing of the subspecies of Mycobacterium avium complex clinical isolates in mainland China

Objectives: The Mycobacterium avium complex (MAC), comprising a series of subspecies, has a worldwide distribution, with differences in drug susceptibility among subspecies. This study aimed to assess the composition of MAC and susceptibility differences among subspecies in mainland China. Methods: A total of 287 MAC clinical strains were included in the study. Multitarget sequences were applied to accurately identify subspecies, and a microdilution method was used to evaluate minimum inhibitory concentrations (MICs) among subspecies using Sensititre SLOMYCO plates. Results: Mycobacterium intracellular (N = 169), Mycobacterium avium (N = 52), Mycobacterium chimaera (N = 22), Mycobacterium marseillense (N = 25), Mycobacterium colombiense (N = 14), Mycobacterium yongonense (N = 4), Mycobacterium vulneris (N = 3) and Mycobacterium timonense (N = 2) were isolated from MAC. Clarithromycin, amikacin and rifabutin showed lower MIC50 and MIC90 values than other drugs, and the resistance rates of clarithromycin, amikacin, linezolid and moxifloxacin were 6.3%, 10.5%, 51.9% and 46.3%, respectively. The resistance rates of clarithromycin and moxifloxacin in the initial treatment group were significantly lower than those in the retreatment group (4.09% vs. 12.94%; 30.41% vs. 75.29%; P < 0.05). Drug susceptibility differences were observed in clarithromycin and moxifloxacin among the five major subspecies (P < 0.05); however, those statistically significant differences disappeared when MACs were divided into two groups according to previous anti-tuberculosis (anti-TB) treatment history. Conclusion: This study revealed that MAC, primarily comprising M. intracellulare, was susceptible to clarithromycin, amikacin and rifabutin. Drug susceptibility among subspecies did not exhibit intrinsic differences in our study. Previous anti-TB treatment patients are more resistant to drugs; thus, attention should be given to those patients in the clinic

A feature‐enhanced hybrid attention network for traffic sign recognition in real scenes

Abstract Currently, traffic sign recognition techniques have been brought into the assistive driving of automobiles. However, small traffic sign recognition in real scenes is still a challenging task due to the class imbalance issue and the size limit of the traffic signs. To address the above issues, a feature‐enhanced hybrid attention network is proposed based on YOLOv5s for a small, fast, and accurate traffic sign detector. First, a series of online data augmentation strategies are designed in the preprocessing module for the model training. Second, the hybrid channel and spatial attention module CSAM are integrated into the backbone for a better feature extraction ability. Third, the channel attention module CAM is used in the detection head for a more efficient feature fusion ability. To validate the approach, extensive experiments are conducted based on the Tsinghua‐Tencent 100K dataset. It is found that the novel method achieves state‐of‐the‐art performance with only negligible increases in the model parameter and computational overhead. Specifically, the [email protected], parameters, and FLOPs are 85.8%, 7.13 M, and 16.1 G, respectively

Table1_A neutrophil extracellular traps-associated lncRNA signature predicts the clinical outcomes in patients with lung adenocarcinoma.docx

Backgrounds: Neutrophil extracellular traps (NETs) play an important role in the occurrence, metastasis, and immune escape of cancers. We aim to investigate Long non-coding RNAs (lncRNAs) that are correlated to NETs to find some potentially useful biomarkers for lung adenocarcinoma (LUAD), and to explore their correlations with immunotherapy and chemotherapy, as well as the tumor microenvironment.Methods: Based on the The Cancer Genome Atlas (TCGA) database, we identified the prognosis-related lncRNAs which are associated with NETs using cox regression. The patients were then separated into two clusters based on the expression of NETs-associated lncRNAs to perform tumor microenvironment analysis and immune-checkpoint analysis. Least absolute shrinkage and selection operator (LASSO) regression was then performed to establish a prognostic signature. Furthermore, nomogram analysis, tumor mutation burden analysis, immune infiltration analysis, as well as drug sensitivity analysis were performed to test the signature.Results: Using univariate cox regression, we found 10 NETs-associated lncRNAs that are associated with the outcomes of LUAD patients. Also, further analysis which separated the patients into 2 clusters showed that the 10 lncRNAs had significant correlations with the tumor microenvironment. Using LASSO regression, we finally constructed a signature to predict the outcomes of the patients based on 4 NETs-associated lncRNAs. The 4 NETs-associated lncRNAs were namely SIRLNT, AL365181.3, FAM83A-AS1, and AJ003147.2. Using Kaplan-Meier (K-M) analysis, we found that the risk model was strongly associated with the survival outcomes of the patients both in the training group and in the validation group 1 and 2 (p Conclusion: We constructed a NETs-associated lncRNA signature to predict the outcome of patients with LUAD, which is associated with immunephenoscores and immune checkpoint-gene expression.</p