Conclusion
To address the UDA problem for point clouds, we propose a novel learnable self-supervised task that helps the adapted neural network extract transferable features. Specifically, we propose ...a learnable point cloud transformation and use it in a point cloud destruction-reconstruction self-supervised auxiliary task. We train the main task network and the auxiliary task network, which share an encoder, so that the encoder extracts features that are highly transferable to the target domain. We further propose a multi-region transformation strategy to make the network focus on local features, which are more transferable. New state-of-the-art performance is achieved on the point cloud classification and segmentation UDA benchmarks.
In this study, novel slags with a high basicity index were used to refine silicon melts with carbon dioxide injection to effectively remove Ti from metallurgical-grade silicon. Different compositions ...of the initial slag were used, and silicon samples were obtained during the refining. The results indicate that the Ti-removal rate initially increased with an increase in the basicity index, and it decreased after the basicity index exceeded 1.4. During the refining, silicon emulsification was observed at the slag–silicon interface, which was restricted by the increased basicity index of slags. Impurities that were concentrated in silicon droplets near the slag–silicon interfaces were oxidized, wetted by slags, and transferred to the slag phase. After 15 min of refining, up to 59 wt% of Ti in silicon could be removed and the Ti-concentrating phase in the slag contained 2.05 wt% of Ti. The results of this study provide a reference for low-cost Ti removal from metallurgical-grade silicon using a refining method.
Whether or not treatment with antibiotics or probiotics for bacterial vaginosis (BV) is associated with a change in the diversity of vaginal microbiota in women was investigated. One hundred fifteen ...women, consisting of 30 healthy subjects, 30 BV-positive control subjects, 30 subjects with BV treated with a 7-day metronidazole regimen, and 25 subjects with BV treated with a 10-day probiotics regimen, were analyzed to determine the efficacy and disparity of diversity and richness of vaginal microbiota using 454 pyrosequencing. Follow-up visits at days 5 and 30 showed a greater BV cure rate in the probiotics-treated subjects (88.0 and 96 %, respectively) compared to the metronidazole-treated subjects (83.3 and 70 %, respectively p=0.625 at day 5 and p=0.013 at day 30). Treatment with metronidazole reduced the taxa diversity and eradicated most of the BV-associated phylotypes, while probiotics only suppressed the overgrowth and re-established vaginal homeostasis gradually and steadily. Despite significant interindividual variation, the microbiota of the actively treated groups or participants constituted a unique profile. Along with the decrease in pathogenic bacteria, such as Gardnerella, Atopobium, Prevotella, Megasphaera, Coriobacteriaceae, Lachnospiraceae, Mycoplasma, and Sneathia, a Lactobacillus-dominated vaginal microbiota was recovered. Acting as vaginal sentinels and biomarkers, the relative abundance of Lactobacillus and pathogenic bacteria determined the consistency of the BV clinical and microbiologic cure rates, as well as recurrent BV. Both 7-day intravaginal metronidazole and 10-day intravaginal probiotics have good efficacy against BV, while probiotics maintained normal vaginal microbiota longer due to effective and steady vaginal microbiota restoration, which provide new insights into BV treatment.
In this study, two typical commercially used CaO-SiOsub.2-CaFsub.2-based mold fluxes with different basicities were adopted. Solid slag films of the two mold fluxes were obtained by immersing an ...improved water-cooled copper probe in the molten fluxes for different probe immersion times and molten slag temperatures. The film thickness, closed porosity, and roughness of the film surfaces in contact with the copper probe were measured. The heat flux through the solidified films and the comprehensive thermal conductivity of the films were both calculated. The results indicated that compared with the heat flux through high-basicity films, the heat flux through low-basicity films exhibited high fluctuation due to the evolution of fusion cracks within the glass layer. High-basicity mold fluxes resulted in higher thickness, growth velocity, surface roughness, and devitrification velocity of the films. With the growth and crystallization of the slag films, the comprehensive thermal conductivity of the high-basicity films increased significantly. For the low-basicity films, their comprehensive thermal conductivity first decreased and then increased after the solidification time exceeded 30 s. The comprehensive thermal conductivity of the high- and low-basicity films ranged from 0.63 to 0.91 and 0.62 to 0.81 W/(m·K), respectively. The results provide a novel method for analyzing the potential effect of the structural factors of slag films on heat transfer control and controlling the heat transfer behavior of slag films.
It is challenging to diagnose and manage incidentally detected pulmonary subsolid nodules due to their indolent nature and heterogeneity. The objective of this study is to construct a decision ...tree-based model to predict malignancy of a subsolid nodule based on radiomics features and evolution over time.
We derived a training set (2947 subsolid nodules), a test set (280 subsolid nodules) from a cohort of outpatient CT scans, and a second test set (5171 subsolid nodules) from the National Lung Cancer Screening Trial (NLST). A Computer-Aided Diagnosis system (CADs) automatically extracted 28 preselected radiomics features, and we calculated the feature change rates as the change of the quantitative measure per time unit between the prior and current CT scans. We built classification models based on XGBoost and employed 5-fold cross validation to optimize the parameters.
The model that combined radiomics features with their change rates performed the best. The Areas Under Curve (AUCs) on the outpatient test set and on the NLST test set were 0.977 (95% CI, 0.958-0.996) and 0.955 (95% CI, 0.930-0.980), respectively. The model performed consistently well on subgroups stratified by nodule diameters, solid components, and CT scan intervals.
This decision tree-based model trained with the outpatient dataset gives promising predictive performance on the malignancy of pulmonary subsolid nodules. Additionally, it can assist clinicians to deliver more accurate diagnoses and formulate more in-depth follow-up strategies.
The 2015/16 El Ni?o displayed a distinct feature in the SST anomalies over the far eastern Pacific (FEP) compared to the 1997/98 extreme case. In contrast to the strong warm SST anomalies in the FEP ...in the 1997/98 event, the FEP warm SST anomalies in the 2015/16 El Ni?o were modest and accompanied by strong southeasterly wind anomalies in the southeastern Pacific. Exploring possible underlying causes of this distinct difference in the FEP may improve understanding of the diversity of extreme El Ni?os. Here, we employ observational analyses and numerical model experiments to tackle this issue. Mixed-layer heat budget analysis suggests that compared to the 1997/98 event, the modest FEP SST warming in the 2015/16 event was closely related to strong vertical upwelling, strong westward current, and enhanced surface evaporation, which were caused by the strong southeasterly wind anomalies in the southeastern Pacific. The strong southeasterly wind anomalies were initially triggered by the combined effects of warm SST anomalies in the equatorial central and eastern Pacific (CEP) and cold SST anomalies in the southeastern subtropical Pacific in the antecedent winter, and then sustained by the warm SST anomalies over the northeastern subtropical Pacific and CEP. In contrast, southeasterly wind anomalies in the 1997/98 El Ni?o were partly restrained by strong anomalously negative sea level pressure and northwesterlies in the northeast flank of the related anomalous cyclone in the subtropical South Pacific. In addition, the strong southeasterly wind and modest SST anomalies in the 2015/16 El Ni?o may also have been partly related to decadal climate variability.
Background:Recently, machine learning (ML) has become attractive in genomic prediction, but its superiority in genomic prediction over conventional (ss) GBLUP methods and the choice of optimal ML ...methods need to be investigated. Results:In this study, 2566 Chinese Yorkshire pigs with reproduction trait records were genotyped with the GenoBaits Porcine SNP 50 K and PorcineSNP50 panels. Four ML methods, including support vector regression (SVR), kernel ridge regression (KRR), random forest (RF) and Adaboost.R2 were implemented. Through 20 replicates of fivefold cross-validation (CV) and one prediction for younger individuals, the utility of ML methods in genomic prediction was explored. In CV, compared with genomic BLUP (GBLUP), single-step GBLUP (ssGBLUP) and the Bayesian method BayesHE, ML methods significantly outperformed these conventional methods. ML methods improved the genomic prediction accuracy of GBLUP, ssGBLUP, and BayesHE by 19.3％, 15.0％and 20.8％, respectively. In addition, ML methods yielded smaller mean squared error (MSE) and mean absolute error (MAE) in all scenarios. ssGBLUP yielded an improvement of 3.8％on average in accuracy compared to that of GBLUP, and the accuracy of BayesHE was close to that of GBLUP. In genomic prediction of younger individuals, RF and Adaboost.R2_KRR performed better than GBLUP and BayesHE, while ssGBLUP performed comparably with RF, and ssGBLUP yielded slightly higher accuracy and lower MSE than Adaboost.R2_KRR in the prediction of total number of piglets born, while for number of piglets born alive, Adaboost.R2_KRR performed significantly better than ssGBLUP. Among ML methods, Adaboost.R2_KRR consistently performed well in our study. Our findings also demonstrated that optimal hyperparameters are useful for ML methods. After tuning hyperparameters in CV and in predicting genomic outcomes of younger individuals, the average improvement was 14.3％and 21.8％over those using default hyperparameters, respectively. Conclusion:Our findings demonstrated that ML methods had better overall prediction performance than conventional genomic selection methods, and could be new options for genomic prediction. Among ML methods, Adaboost.R2_KRR consistently performed well in our study, and tuning hyperparameters is necessary for ML methods. The optimal hyperparameters depend on the character of traits, datasets etc.
The characterization of atherosclerotic plaques to predict their vulnerability to rupture remains a diagnostic challenge. Despite existing imaging modalities, none have proven their abilities to ...identify metabolically active oxidized low‐density lipoprotein (oxLDL), a marker of plaque vulnerability. To this end, we developed a machine learning‐directed electrochemical impedance spectroscopy (EIS) platform to analyze oxLDL‐rich plaques, with immunohistology serving as the ground truth. We fabricated the EIS sensor by affixing a six‐point microelectrode configuration onto a silicone balloon catheter and electroplating the surface with platinum black (PtB) to improve the charge transfer efficiency at the electrochemical interface. To demonstrate clinical translation, we deployed the EIS sensor to the coronary arteries of an explanted human heart from a patient undergoing heart transplant and interrogated the atherosclerotic lesions to reconstruct the 3D EIS profiles of oxLDL‐rich atherosclerotic plaques in both right coronary and left descending coronary arteries. To establish effective generalization of our methods, we repeated the reconstruction and training process on the common carotid arteries of an unembalmed human cadaver specimen. Our findings indicated that our DenseNet model achieves the most reliable predictions for metabolically vulnerable plaque, yielding an accuracy of 92.59% after 100 epochs of training.
