Tobacco crop rotation enhances the stability and complexity of microbial networks.

Introduction: The effects of continuous cropping and rotation cropping, two important tobacco cultivation practices, on soil microbial communities at different stages remain unclear. Different planting patterns have been shown to influence soil physical and chemical properties, which in turn can affect the composition and diversity of soil microbial communities. Methods: In order to investigate the impact of different planting methods on soil microbial community structure, we selected two representative planting methods: continuous cropping (tobacco) and rotational cropping (tobacco-maize). These methods were chosen as the focal points of our research to explore the potential effects on soil microbial communities. High-throughput sequencing technology was employed to investigate the structure of soil microbial communities, as well as their relationships with soil environmental factors, by utilizing the 16S rRNA, ITS, and 18S genes. Furthermore, the interaction among microorganisms was explored through the application of the Random Matrix Theory (RMT) molecular ecological network approach. Results: There was no significant difference in α diversity, but significant difference in ß diversity based on Jaccard distance test. Compared to continuous cropping, crop rotation significantly increased the abundance of beneficial prokaryotes Verrucomicrobia and Rhodanobacter. These findings indicate that crop rotation promotes the enrichment of Verrucomicrobia and Rhodanobacter in the soil microbial community. AP and NH4-N had a greater effect on the community structure of prokaryotes and fungi in tobacco soil, while only AP had a greater effect on the community structure of protist. Molecular ecological network analysis showed that the network robustness and Cohesion of rotation were significantly higher than that of continuous cropping, indicating that the complexity and stability of molecular ecological networks were higher in the rotational, and the microbial communities cooperated more effectively, and the community structure was more stable. Discussion: From this point of view, rotational cropping is more conducive to changing the composition of soil microbial community, enhancing the stability of microbial network structure, and enhancing the potential ecological functions in soil.

Unraveling the Structure-Activity-Stability Relationship over Gallium-Promoted HZSM-5 Nanocrystalline Aggregates for Propane Aromatization.

The aromatization of light alkane is an important process for increasing the aromatic production and utilization efficiency of light alkane resources simultaneously. Herein, Ga-modified HZSM-5 catalysts were prepared and investigated by a series of characterization techniques such as X-ray diffraction, nuclear magnetic resonance spectroscopy, transmission electron microscopy, N2 adsorption-desorption, and NH3 temperature-programmed desorption to study their physicochemical properties. The catalytic performance in propane aromatization was also tested. Importantly, the structure-activity relationship, reaction pathway, and coke formation mechanism in propane aromatization were systematically explored. It was found that different Ga introduction methods would affect the amounts of Brønsted and Lewis acid sites, and Ga-HZSM-5 prepared by the hydrothermal method exhibited higher amounts of Brønsted and Lewis acid sites but a lower B/L ratio. As a result, Ga-HZSM-5 showed higher propane conversion and benzene, toluene, and xylene yield compared with that of Ga2O3/HZSM-5. The propane aromatization reaction pathway indicated that propane dehydrogenation to propene was a crucial step for aromatic formation. The increase of the Lewis acid density in Ga-HZSM-5 can effectively improve the dehydrogenation rate and promote the aromatization reaction. Furthermore, the formation of coke species was studied by thermogravimetry-mass spectrometry and Raman approaches, the results of which indicated that the graphitization degree of coke formed over spent Ga-HZSM-5 is lower, resulting in enhanced anticoking stability.

Mechanistic Studies on Aluminum-Catalyzed Ring-Opening Alternating Copolymerization of Maleic Anhydride with Epoxides: Ligand Effects and Quantitative Structure-Activity Relationship Model.

Previous work has indicated that aluminum (Al) complexes supported by a bipyridine bisphenolate (BpyBph) ligand exhibit higher activity in the ring-opening copolymerization (ROCOP) of maleic anhydride (MAH) and propylene oxide (PO) than their salen counterparts. Such a ligand effect in Al-catalyzed MAH-PO copolymerization reactions has yet to be clarified. Herein, the origin and applicability of the ligand effect have been explored by density functional theory, based on the mechanistic analysis for chain initiation and propagation. We found that the lower LUMO energy of the (BpyBph)AlCl complex accounts for its higher activity than the (salen)AlCl counterpart in MAH/epoxide copolymerizations. Inspired by the ligand effect, a structure-energy model was further established for catalytic activity (TOF value) predictions. It is found that the LUMO energies of aluminum chloride complexes and their average NBO charges of coordinating oxygen atoms correlate with the catalytic activity (TOF value) of Al complexes (R2 value of 0.98 and '3-fold' cross-validation Q2 value of 0.88). This verified that such a ligand effect is generally applicable in anhydride/epoxide ROCOP catalyzed by aluminum complex and provides hints for future catalyst design.

Longitudinal prediction of lung nodule invasiveness by sequential modelling with common clinical computed tomography (CT) measurements: a prediction accuracy study.

Background: Accurate preoperative prediction of the invasiveness of lung nodules on computed tomography (CT) can avoid unnecessary invasive procedures and costs for low-risk patients. While previous studies approached this task using cross-sectional data, this study aimed to utilize the commonly available longitudinal data of lung nodules through sequential modelling based on long short-term memory (LSTM) networks. Methods: We retrospectively included 171 patients with lung nodules that were followed-up at least once and pathologically diagnosed with adenocarcinoma for model development. Pathological diagnosis was the gold standard for deciding lung nodule invasiveness. For each nodule, a handful of semantic features, including size intensity and interval since first discovery, were obtained from an arbitrary number of CT scans available to individual patients and used as input variables to pre-operatively predict nodule invasiveness. The LSTM-based classifier was optimized by extensive experiments and compared to logistic regression (LR) as baseline with five-fold cross-validation. Results: The best LSTM-based classifier, capable of receiving data from an arbitrary number of time points, achieved better preoperative prediction of lung nodule invasiveness [area under the curve (AUC), 0.982; accuracy, 0.924; sensitivity, 0.946; specificity, 0.881] than the best LR (AUC, 0.947; accuracy, 0.906; sensitivity, 0.938; specificity, 0.847) classifier. Conclusions: The longitudinal data of lung nodules, though unevenly spaced and varying in length, can be well modeled by the LSTM, allowing for the accurate prediction of nodule invasiveness. Given that the input variables of the sequential modelling consist of a few semantic features that are easily obtained and interpreted by clinicians, our approach is worthy further investigation for the optimal management of lung nodules.

Clinical Applicable AI System Based on Deep Learning Algorithm for Differentiation of Pulmonary Infectious Disease.

Objective: To assess the performance of a novel deep learning (DL)-based artificial intelligence (AI) system in classifying computed tomography (CT) scans of pneumonia patients into different groups, as well as to present an effective clinically relevant machine learning (ML) system based on medical image identification and clinical feature interpretation to assist radiologists in triage and diagnosis. Methods: The 3,463 CT images of pneumonia used in this multi-center retrospective study were divided into four categories: bacterial pneumonia (n = 507), fungal pneumonia (n = 126), common viral pneumonia (n = 777), and COVID-19 (n = 2,053). We used DL methods based on images to distinguish pulmonary infections. A machine learning (ML) model for risk interpretation was developed using key imaging (learned from the DL methods) and clinical features. The algorithms were evaluated using the areas under the receiver operating characteristic curves (AUCs). Results: The median AUC of DL models for differentiating pulmonary infection was 99.5% (COVID-19), 98.6% (viral pneumonia), 98.4% (bacterial pneumonia), 99.1% (fungal pneumonia), respectively. By combining chest CT results and clinical symptoms, the ML model performed well, with an AUC of 99.7% for SARS-CoV-2, 99.4% for common virus, 98.9% for bacteria, and 99.6% for fungus. Regarding clinical features interpreting, the model revealed distinctive CT characteristics associated with specific pneumonia: in COVID-19, ground-glass opacity (GGO) [92.5%; odds ratio (OR), 1.76; 95% confidence interval (CI): 1.71-1.86]; larger lesions in the right upper lung (75.0%; OR, 1.12; 95% CI: 1.03-1.25) with viral pneumonia; older age (57.0 years ± 14.2, OR, 1.84; 95% CI: 1.73-1.99) with bacterial pneumonia; and consolidation (95.8%, OR, 1.29; 95% CI: 1.05-1.40) with fungal pneumonia. Conclusion: For classifying common types of pneumonia and assessing the influential factors for triage, our AI system has shown promising results. Our ultimate goal is to assist clinicians in making quick and accurate diagnoses, resulting in the potential for early therapeutic intervention.

Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.

PURPOSE: To develop and evaluate the effectiveness of a deep learning framework (3D-ResNet) based on CT images to distinguish nontuberculous mycobacterium lung disease (NTM-LD) from Mycobacterium tuberculosis lung disease (MTB-LD). METHOD: Chest CT images of 301 with NTM-LD and 804 with MTB-LD confirmed by pathogenic microbiological examination were retrospectively collected. The differences between the clinical manifestations of the two diseases were analysed. 3D-ResNet was developed to randomly extract data in an 8:1:1 ratio for training, validating, and testing. We also collected external test data (40 with NTM-LD and 40 with MTB-LD) for external validation of the model. The activated region of interest was evaluated using a class activation map. The model was compared with three radiologists in the test set. RESULT: Patients with NTM-LD were older than those with MTB-LD, patients with MTB-LD had more cough, and those with NTM-LD had more dyspnoea, and the results were statistically significant (p < 0.05). The AUCs of our model on training, validating, and testing datasets were 0.90, 0.88, and 0.86, respectively, while the AUC on the external test set was 0.78. Additionally, the performance of the model was higher than that of the radiologist, and without manual labelling, the model automatically identified lung areas with abnormalities on CT > 1000 times more effectively than the radiologists. CONCLUSION: This study shows the efficacy of 3D-ResNet as a rapid auxiliary diagnostic tool for NTB-LD and MTB-LD. Its use can help provide timely and accurate treatment strategies to patients with these diseases.

Dependence of radiomic features on pixel size affects the diagnostic performance of radiomic signature for the invasiveness of pulmonary ground-glass nodule.

OBJECTIVE: To investigate the effect of reducing pixel size on the consistency of radiomic features and the diagnostic performance of the downstream radiomic signatures for the invasiveness for pulmonary ground-glass nodules (GGNs) on CTs. METHODS: We retrospectively collected the clinical data of 182 patients with GGNs on high resolution CT (HRCT). The CT images of different pixel sizes (0.8mm, 0.4mm, 0.18 mm) were obtained by reconstructing the single HRCT scan using three combinations of field of view and matrix size. For each pixel size setting, radiomic features were extracted for all GGNs and radiomic signatures for the invasiveness of GGNs were built through two modeling pipelines for comparison. RESULTS: The study finally extracted 788 radiomic features. 87% radiomic features demonstrated inter pixel size variation. By either modeling pipeline, the radiomic signature under small pixel size performed significantly better than those under middle or large pixel sizes in predicting the invasiveness of GGNs (p's value <0.05 by Delong test). With the independent modeling pipeline, the three pixel size bounded radiomic signatures shared almost no common features. CONCLUSIONS: Reducing pixel size could cause inconsistency in most radiomic features and improve the diagnostic performance of the downstream radiomic signatures. Particularly, super HRCTs with small pixel size resulted in more accurate radiomic signatures for the invasiveness of GGNs. ADVANCES IN KNOWLEDGE: The dependence of radiomic features on pixel size will affect the performance of the downstream radiomic signatures. The future radiomic studies should consider this effect of pixel size.

Quadrupole detection FT-ICR mass spectrometry offers deep profiling of residue oil: A systematic comparison of 2ω 7 Tesla versus 15 Tesla instruments.

Mass resolving power is one of the key features of Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR MS), which enables the molecular characterization of complex mixtures. Quadrupole (2ω) detection provides a significant step forward in FT-ICR MS performance, as it doubles the resolving power for a given signal acquisition time. Whether this 2ω detection technique truly substitutes for a higher magnetic field remains unknown however. In this study, a residue oil sample was characterized using both a 2ω 7 Tesla FT-ICR and a 15 Tesla FT-ICR instrument, and analytical figures of merit were systematically compared. It was shown that 2ω 7T FT-ICR MS provided comparable performance in the deep profiling of the complex oil sample, with better signal intensities and reproducibilities for absorption-mode processing. The 15T FT-ICR MS gave more precise measurements with better estimates of the sample's elemental compositions. To the best of our knowledge, this is the first published study, which thoroughly compared the performance of 2ω detection on a low magnetic field instrument with that of a high magnetic field FT-ICR-MS.

Automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

OBJECTIVE: Osteoporosis is a prevalent and treatable condition, but it remains underdiagnosed. In this study, a deep learning-based system was developed to automatically measure bone mineral density (BMD) for opportunistic osteoporosis screening using low-dose chest computed tomography (LDCT) scans obtained for lung cancer screening. METHODS: First, a deep learning model was trained and tested with 200 annotated LDCT scans to segment and label all vertebral bodies (VBs). Then, the mean CT numbers of the trabecular area of target VBs were obtained based on the segmentation mask through geometric operations. Finally, a linear function was built to map the trabecular CT numbers of target VBs to their BMDs collected from approved software used for osteoporosis diagnosis. The diagnostic performance of the developed system was evaluated using an independent dataset of 374 LDCT scans with standard BMDs and osteoporosis diagnosis. RESULTS: Our deep learning model achieved a mean Dice coefficient of 86.6% for VB segmentation and 97.5% accuracy for VB labeling. Line regression and Bland-Altman analyses showed good agreement between the predicted BMD and the ground truth, with correlation coefficients of 0.964-0.968 and mean errors of 2.2-4.0 mg/cm3. The area under the curve (AUC) was 0.927 for detecting osteoporosis and 0.942 for distinguishing low BMD. CONCLUSION: The proposed deep learning-based system demonstrated the potential to automatically perform opportunistic osteoporosis screening using LDCT scans obtained for lung cancer screening. KEY POINTS: • Osteoporosis is a prevalent but underdiagnosed condition that can increase the risk of fracture. • A deep learning-based system was developed to fully automate bone mineral density measurement in low-dose chest computed tomography scans. • The developed system achieved high accuracy for automatic opportunistic osteoporosis screening using low-dose chest computed tomography scans obtained for lung cancer screening.

Use of cell free DNA as a prognostic biomarker in non-small cell lung cancer patients with bone metastasis.

BACKGROUND: Non-small cell lung cancer (NSCLC) is difficult to treat when metastasis has occurred. This study explores the use of cell-free DNA in the clinical management of NSCLC patients who have Kirsten rat sarcoma viral oncogene homolog (KRAS)-positive mutations and as a marker for prognosis. METHODS: Peripheral blood collected from advanced NSCLC patients was examined with digital droplet polymerase chain reaction and ultraviolet spectrometry. KRAS mutations were analyzed and quantitated. The specificity and sensitivity of the proposed assay was computed by associating the results with tumor tissue specimens. Comparison against different sub-groups of patients with different metastatic sites and healthy volunteers were made. Patients were subsequently followed up and survival analysis was conducted. RESULTS: Among the 186 patients recruited, 150 had concordant KRAS mutational profiles using cell-free DNA with tumor tissues. The assay sensitivity and specificity were 80.6% and 100%, respectively. For the 150 patients with concordant results, the range of cell-free DNA quantities in peripheral blood was 5.3 to 115 ng. Among the patient groups with different metastatic sites, we observed that patients with bone metastasis had higher concentrations of cell-free DNA. Survival analysis showed that these patients had worse survival outcome. Patients with higher KRAS counts in peripheral blood also had worse outcome. CONCLUSION: The use of cell-free DNA presents opportunities for risk stratification of patients and possibly aids in the clinical management of the disease. In the current study for NSCLC, patients with bone metastases showed higher cell-free DNA concentrations. Quantitating the concentrations of cell-free DNA presents a noninvasive biomarker capable of prognostic utility.

Children with Autism Spectrum Disorder Prefer Looking at Repetitive Movements in a Preferential Looking Paradigm.

The present study aimed to investigate the visual preference for repetitive movements in children with autism spectrum disorder (ASD). Young children with ASD and typically-developing (TD) children were presented simultaneously with cartoons depicting repetitive and random movements respectively, while their eye-movements were recorded. We found that: (1) the children with ASD spent more time fixating on the repetitive movements than the random movements, whereas the TD children showed no preference for either type of movements; (2) the children's preference for the repetitive movements was correlated with the parent reports of their repetitive behaviors. Our findings show a promise in using the preferential looking as a potential indicator for the repetitive behaviors and aiding early screening of ASD in future investigations.

[Treatment of Sanders II calcaneus fractures via minimally invasive sinus tarsi approach].

OBJECTIVE: To study the feasibility and clinical efficacy of a minimally invasive sinus tarsi approach in the treatment of Sanders II calcaneus fractures. METHODS: From August of 2015 to July of 2016, 13 patients(totally 13 feet) with Sanders II intra-articular calcaneus fractures were treated via the minimally invasive sinus tarsi approach. The Böhler angle, Gissane angle and the length, width and height of calcaneus were compared between pre-operation and post-operation. The AOFAS ankle and foot scoring system of the orthopaedic ankle foot Association was used to evaluate the efficacy. RESULTS: All the patients were followed up, and the duration ranged from 6 to 15 months, with an average of 9.5 months. No incision complications occurred. The Böhler angle was increased from preoperative (18.82±5.11)° to postoperative(26.63±4.45)°(t=-4.16, P=0.000). The Gissane angle was increased from preoperative(111.07±15.36)° to postoperative (124.56±8.71)° (t=-2.75, P=0.011). The length, width, height of calcaneus were absolutely improved from preoperative(69.82±5.95) mm, (42.07±3.68) mm, (41.20±3.90) mm to preoperatively(72.61±5.46) mm, (39.10±4.02) mm, (44.03±3.33) mm. According to the AOFAS, 8 patients got an excellent result, 4 good and 1 poor, and the postoperative mean score was 88.2±5.9. CONCLUSIONS: The limited open sinus tarsi approach could be used successfully to treat displaced Sanders II fractures with less injury and effectively restored the surface of subtalar joint, however the method is not fit for the patients with comminuted fracture in lateral wall and great change in the length, width, height, varus and valgus of calcaneus.