Discrimination of ground-glass nodular lung adenocarcinoma pathological subtypes via transfer learning: A multicenter study.

BACKGROUND: The surgical approach and prognosis for invasive adenocarcinoma (IAC) and minimally invasive adenocarcinoma (MIA) of the lung differ. However, they both manifest as identical ground-glass nodules (GGNs) in computed tomography images, and no effective method exists to discriminate them. METHODS: We developed and validated a three-dimensional (3D) deep transfer learning model to discriminate IAC from MIA based on CT images of GGNs. This model uses a 3D medical image pre-training model (MedicalNet) and a fusion model to build a classification network. Transfer learning was utilized for end-to-end predictive modeling of the cohort data of the first center, and the cohort data of the other two centers were used as independent external validation data. This study included 999 lung GGN images of 921 patients pathologically diagnosed with IAC or MIA at three cohort centers. RESULTS: The predictive performance of the model was assessed using the area under the receiver operating characteristic curve (AUC). The model had high diagnostic efficacy for the training and validation groups (accuracy: 89%, sensitivity: 95%, specificity: 84%, and AUC: 95% in the training group; accuracy: 88%, sensitivity: 84%, specificity: 93%, and AUC: 92% in the internal validation group; accuracy: 83%, sensitivity: 83%, specificity: 83%, and AUC: 89% in one external validation group; accuracy: 78%, sensitivity: 80%, specificity: 77%, and AUC: 82% in the other external validation group). CONCLUSIONS: Our 3D deep transfer learning model provides a noninvasive, low-cost, rapid, and reproducible method for preoperative prediction of IAC and MIA in lung cancer patients with GGNs. It can help clinicians to choose the optimal surgical strategy and improve the prognosis of patients.

An archetypes approach to malaria intervention impact mapping: a new framework and example application.

BACKGROUND: As both mechanistic and geospatial malaria modeling methods become more integrated into malaria policy decisions, there is increasing demand for strategies that combine these two methods. This paper introduces a novel archetypes-based methodology for generating high-resolution intervention impact maps based on mechanistic model simulations. An example configuration of the framework is described and explored. METHODS: First, dimensionality reduction and clustering techniques were applied to rasterized geospatial environmental and mosquito covariates to find archetypal malaria transmission patterns. Next, mechanistic models were run on a representative site from each archetype to assess intervention impact. Finally, these mechanistic results were reprojected onto each pixel to generate full maps of intervention impact. The example configuration used ERA5 and Malaria Atlas Project covariates, singular value decomposition, k-means clustering, and the Institute for Disease Modeling's EMOD model to explore a range of three-year malaria interventions primarily focused on vector control and case management. RESULTS: Rainfall, temperature, and mosquito abundance layers were clustered into ten transmission archetypes with distinct properties. Example intervention impact curves and maps highlighted archetype-specific variation in efficacy of vector control interventions. A sensitivity analysis showed that the procedure for selecting representative sites to simulate worked well in all but one archetype. CONCLUSION: This paper introduces a novel methodology which combines the richness of spatiotemporal mapping with the rigor of mechanistic modeling to create a multi-purpose infrastructure for answering a broad range of important questions in the malaria policy space. It is flexible and adaptable to a range of input covariates, mechanistic models, and mapping strategies and can be adapted to the modelers' setting of choice.

Identification, characteristics and rice growth promotion of a highly efficient cellulolytic bacterial strain, Cellulomonas iranensis ZJW-6, isolated from paddy soil in central China.

The microbial degradation of lignocellulose is the best way to treat straw, which has a broad application prospect. It is consistent with the idea of agricultural sustainable development and has an important impact on the utilization of biomass resources. To explore and utilize the microbial resources of lignocellulose degradation, 27 lignocellulose degrading strains were screened from 13 regions in China. ZJW-6 was selected because of its 49.6% lignocellulose weight loss rate. According to the theoretical analysis of the experimental results, the following straw degradation conditions were obtained by ZJW-6: nitrogen source input of 8.45 g/L, a pH of 8.57, and a temperature of 31.63°C, the maximum weight loss rate of rice straw could reach 54.8%. It was concluded that ZJW-6 belonged to Cellulomonas iranensis according to 16S rRNA-encoding gene sequence comparison and identification. ZJW-6 is a Gram-positive bacterium that grows slowly and has a small yellowish green colony. To explain the degradation mechanism of lignocellulose, the experiment of enzymatic properties of the strain was prepared and carried out. It was discovered that ZJW-6 has an excellent ability to degrade cellulose, hemicellulose, and lignin, with cellulose and hemicellulose loss rates reaching almost 50% in 4 days and lignin loss rates reaching nearly 30%. Furthermore, ZJW-6 demonstrated lignocellulose degradation under aerobic and anaerobic conditions, indicating the strain's broad application potential. ZJW-6 was found to be more effective than ordinary humic acid in improving rice soil (available phosphorus, available nitrogen, organic matter) and promoting rice growth in a rice pot experiment (increasing root-shoot ratio, root activity, chlorophyll content and net photosynthetic rate). ZJW-6 plays an important role in promoting the development and utilization of straw resources. It has important significance for the advancement of green agriculture.

Preoperative prediction of tumor deposits in rectal cancer with clinical-magnetic resonance deep learning-based radiomic models.

Background: This study aimed to establish an effective model for preoperative prediction of tumor deposits (TDs) in patients with rectal cancer (RC). Methods: In 500 patients, radiomic features were extracted from magnetic resonance imaging (MRI) using modalities such as high-resolution T2-weighted (HRT2) imaging and diffusion-weighted imaging (DWI). Machine learning (ML)-based and deep learning (DL)-based radiomic models were developed and integrated with clinical characteristics for TD prediction. The performance of the models was assessed using the area under the curve (AUC) over five-fold cross-validation. Results: A total of 564 radiomic features that quantified the intensity, shape, orientation, and texture of the tumor were extracted for each patient. The HRT2-ML, DWI-ML, Merged-ML, HRT2-DL, DWI-DL, and Merged-DL models demonstrated AUCs of 0.62 ± 0.02, 0.64 ± 0.08, 0.69 ± 0.04, 0.57 ± 0.06, 0.68 ± 0.03, and 0.59 ± 0.04, respectively. The clinical-ML, clinical-HRT2-ML, clinical-DWI-ML, clinical-Merged-ML, clinical-DL, clinical-HRT2-DL, clinical-DWI-DL, and clinical-Merged-DL models demonstrated AUCs of 0.81 ± 0.06, 0.79 ± 0.02, 0.81 ± 0.02, 0.83 ± 0.01, 0.81 ± 0.04, 0.83 ± 0.04, 0.90 ± 0.04, and 0.83 ± 0.05, respectively. The clinical-DWI-DL model achieved the best predictive performance (accuracy 0.84 ± 0.05, sensitivity 0.94 ± 0. 13, specificity 0.79 ± 0.04). Conclusions: A comprehensive model combining MRI radiomic features and clinical characteristics achieved promising performance in TD prediction for RC patients. This approach has the potential to assist clinicians in preoperative stage evaluation and personalized treatment of RC patients.

Effects of Continuous Straw Returning on Soil Functional Microorganisms and Microbial Communities.

This study examined the changes in soil enzymatic activity, microbial carbon source metabolic diversity, and straw decomposition rates in paddy fields treated with 1, 2, or 3 years of straw returning (SR1-SR3). The soil's ability to decompose straw and cellulolytic bacteria increased with the number of treatment years (1: 31.9% vs. 2: 43.9% vs. 3: 51.9%, P < 0.05). The numbers of Azotobacter, Nitrobacteria, cellulolytic bacteria, and inorganic phosphate bacteria increased progressively with the numbers of straw returning years. Cellulolytic bacteria and inorganic phosphate bacteria were significantly positively correlated with the decomposition rate (r = 0.783 and r = 0.375, P < 0.05). Based on 16S sequencing results, straw returning improved the microbial diversity of paddy soils by increasing unclassified bacteria and keeping dominant soil microorganism populations unchanged. The relative importance of individual microbial taxa was compared using random forest models. Proteobacteria, ammoniating bacteria, and potassium dissolving bacteria contributed to peroxidase activity. The significant contributors to phosphate monoesterase were Acidobacteriota, Desulfobacterota, ammoniating bacteria, cellulolytic bacteria, and potassium-dissolving bacteria. Proteobacteria, ammoniating bacteria, cellulolytic bacteria, and potassium-dissolving bacteria contributed to urease activity. Desulfobacterota, ammoniating bacteria, cellulolytic bacteria, and potassium-dissolving bacteria contributed to the neutral invertase activity. In conclusion, soil microbial community structure and function were affected within 2 years of straw returning, which was driven by the combined effects of soil organic carbon, available nitrogen, available potassium, and pH. With elapsing straw returning years, soil properties interacted with soil microbial communities, and a healthier soil micro-ecological environment would form.

Predictive Efficacy of a Radiomics Random Forest Model for Identifying Pathological Subtypes of Lung Adenocarcinoma Presenting as Ground-Glass Nodules.

Purpose: To establish and verify the ability of a radiomics prediction model to distinguish invasive adenocarcinoma (IAC) and minimal invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs). Methods: We retrospectively analyzed 118 lung GGN images and clinical data from 106 patients in our hospital from March 2016 to April 2019. All pathological classifications of lung GGN were confirmed as IAC or MIA by two pathologists. R language software (version 3.5.1) was used for the statistical analysis of the general clinical data. ITK-SNAP (version 3.6) and A.K. software (Analysis Kit, American GE Company) were used to manually outline the regions of interest of lung GGNs and collect three-dimensional radiomics features. Patients were randomly divided into training and verification groups (ratio, 7:3). Random forest combined with hyperparameter tuning was used for feature selection and prediction modeling. The receiver operating characteristic curve and the area under the curve (AUC) were used to evaluate model prediction efficacy. The calibration curve was used to evaluate the calibration effect. Results: There was no significant difference between IAC and MIA in terms of age, gender, smoking history, tumor history, and lung GGN location in both the training and verification groups (P>0.05). For each lung GGN, the collected data included 396 three-dimensional radiomics features in six categories. Based on the training cohort, nine optimal radiomics features in three categories were finally screened out, and a prediction model was established. We found that the training group had a high diagnostic efficacy [accuracy, sensitivity, specificity, and AUC of the training group were 0.89 (95%CI, 0.73 - 0.99), 0.98 (95%CI, 0.78 - 1.00), 0.81 (95%CI, 0.59 - 1.00), and 0.97 (95%CI, 0.92-1.00), respectively; those of the validation group were 0.80 (95%CI, 0.58 - 0.93), 0.82 (95%CI, 0.55 - 1.00), 0.78 (95%CI, 0.57 - 1.00), and 0.92 (95%CI, 0.83 - 1.00), respectively]. The model calibration curve showed good consistency between the predicted and actual probabilities. Conclusions: The radiomics prediction model established by combining random forest with hyperparameter tuning effectively distinguished IAC from MIA presenting as GGNs and represents a noninvasive, low-cost, rapid, and reproducible preoperative prediction method for clinical application.

Construction of dominant rice population under dry cultivation by seeding rate and nitrogen rate interaction.

This study used the rice cultivar Suijing 18 to investigate the effects of morphological characteristics, photosynthetic changes, yield, as well as nitrogen absorption and utilization. The interaction between seeding rate and nitrogen rate was also assessed to identify the most suitable values of the dominant population for both factors under dry cultivation. Furthermore, the photosynthetic physiological characteristics of the upper three leaves in the dominant population were also explored. The results showed that a combination of 195 kg/ha seeding rate and 140 kg/ha nitrogen rate achieved high yield, high nitrogen utilization, and moderate morphological characteristics. This was achieved by a coordination of the combined advantages of population panicle number and spikelets per panicle. The photosynthetic potential of the population was improved by coordinating the reasonable distribution of light energy in the upper three leaves, which led to the emergence of a dominant rice population under dry cultivation.