SW-Net: A novel few-shot learning approach for disease subtype prediction

Few-shot learning is becoming more and more popular in many fields,especially in the computer vision field.This inspires us to introduce few-shot learning to the genomic field,which faces a typical few-shot problem because some tasks only have a limited number of samples with high-dimensions.The goal of this study was to investigate the few-shot disease sub-type prediction problem and identify patient subgroups through training on small data.Accurate disease subtype classification allows clinicians to efficiently deliver investigations and interventions in clinical practice.We propose the SW-Net,which simulates the clinical process of extracting the shared knowledge from a range of interrelated tasks and generalizes it to unseen data.Our model is built upon a simple baseline,and we modified it for genomic data.Supportbased initialization for the classifier and transductive fine-tuning techniques were applied in our model to improve prediction accuracy,and an Entropy regularization term on the query set was appended to reduce over-fitting.Moreover,to address the high dimension and high noise issue,we future extended a feature selection module to adaptively select important features and a sample weighting module to prioritize high-confidence samples.Experiments on simulated data and The Cancer Genome Atlas meta-dataset show that our new baseline model gets higher prediction accuracy compared to other competing algorithms.

The effect of iron on the preservation of organic carbon in marine sediments and its implications for carbon sequestration

Marine sediments are the most significant reservoir of organic carbon(OC)in Earth′s surface system.Iron,a crucial component of the marine biogeochemical cycle,has a considerable impact on marine ecology and carbon cycling.Understanding the effect of iron on the preservation of OC in marine sediments is essential for comprehending biogeochemical processes of carbon and climate change.This review summarizes the methods for characterizing the content and structure of iron-bound OC and explores the influencing mechanism of iron on OC preservation in marine sediments from two aspects:the selective preservation of OC by reactive iron minerals(iron oxides and iron sulfides)and iron redox processes.The selective preservation of sedimentary OC is influenced by different types of reactive iron minerals,OC reactivity,and functional groups.The iron redox process has dual effects on the preservation and degradation of OC.By considering sedimentary records of iron-bound OC across diverse marine environments,the role of iron in long-term preservation of OC and its significance for carbon sequestration are illustrated.Future research should focus on identifying effective methods for extracting reactive iron,the effect of diverse functional groups and marine sedimentary environments on the selective preservation of OC,and the mediation of microorganisms.Such work will help elucidate the influencing mechanisms of iron on the long-term burial and preservation of OC and explore its potential application in marine carbon sequestration to maximize its role in achieving carbon neutrality.

Agar-derived nitrogen-doped porous carbon as anode for construction of cost-effective lithium-ion batteries

Balancing cost and performance of porous carbon(PC)as anode for lithium-ion battery(LIBs)is the key to effectively promote commercial application.Herein,low-cost N-doped PC(NPC-Ts,T=600,750 and 900°C)were facilely prepared in batches via one-pot pyrolysis of agar with different carbonization temperature.The NPC-750 with specific surface area of 2914 m^(2)/g and N content of 2.84%exhibits an ultrahigh reversible capacity of 1019 mAh/g at 0.1 A/g after 100 cycles and 837 mAh/g at 1 A/g after 500 cycles.Remarkably,the resulting LIBs exhibit an ultrafast charge-discharge feature with a remarkable capacity of 281 mAh/g at 10 A/g and a superlong cycle life with a capacity retention of 87%after 5000 cycles at 10 A/g.Coupling with LiFePO_(4)cathode,the fabricated lithium-ion full cells possess high capacity,excellent rate and cycling performances(125 mAh/g at 100 mA/g,capacity retention of 95%,after 220 cycles),highlighting the practicability of this NPC-750 as the anode materials.

Development of the automatic navigation system for combine harvester based on GNSS

An automatic navigation system was developed to realize automatic driving for combine harvester,including the mechanical design,control method and software design.First of all,for the harvester modified with the automatic navigation system,a dynamic calibration method of the rear wheel center position was proposed.The control part included the navigation controller and the steering controller.A variable universe fuzzy controller was designed to the navigation controller,which used fuzzy control to change the fuzzy universe of input and output dynamically,that means,under the condition that the fuzzy rules remain unchanged,the fuzzy universe changes with the change of input,which is an adaptive fuzzy control method and can modify the control strategy in time.To realize the automatic navigation of the harvester,the decision result of the navigation controller based on the variable universe fuzzy control was input into the steering controller,and then the electric steering wheel was controlled to rotate.To test the performance of the designed automatic navigation system,the field experiment was carried out.When the combine harvester was navigating linearly at a speed of 0.8 m/s,the overall root mean square error(RMSE)of the lateral deviation was 5.87 cm.The test results showed that the system was designed could make the combine track the preset path smoothly and stably,and the tracking accuracy was at the centimeter level.