A Peer-to-peer Federated Continual Learning Network for Improving CT Imaging from Multiple Institutions

Deep learning techniques have been widely used in computed tomography (CT) but require large data sets to train networks. Moreover, data sharing among multiple institutions is limited due to data privacy constraints, which hinders the development of high-performance DL-based CT imaging models from multi-institutional collaborations. Federated learning (FL) strategy is an alternative way to train the models without centralizing data from multi-institutions. In this work, we propose a novel peer-to-peer federated continual learning strategy to improve low-dose CT imaging performance from multiple institutions. The newly proposed method is called peer-to-peer continual FL with intermediate controllers, i.e., icP2P-FL. Specifically, different from the conventional FL model, the proposed icP2P-FL does not require a central server that coordinates training information for a global model. In the proposed icP2P-FL method, the peer-to-peer federated continual learning is introduced wherein the DL-based model is continually trained one client after another via model transferring and inter institutional parameter sharing due to the common characteristics of CT data among the clients. Furthermore, an intermediate controller is developed to make the overall training more flexible. Numerous experiments were conducted on the AAPM low-dose CT Grand Challenge dataset and local datasets, and the experimental results showed that the proposed icP2P-FL method outperforms the other comparative methods both qualitatively and quantitatively, and reaches an accuracy similar to a model trained with pooling data from all the institutions

Learning to Evaluate Performance of Multi-modal Semantic Localization

Semantic localization (SeLo) refers to the task of obtaining the most relevant locations in large-scale remote sensing (RS) images using semantic information such as text. As an emerging task based on cross-modal retrieval, SeLo achieves semantic-level retrieval with only caption-level annotation, which demonstrates its great potential in unifying downstream tasks. Although SeLo has been carried out successively, but there is currently no work has systematically explores and analyzes this urgent direction. In this paper, we thoroughly study this field and provide a complete benchmark in terms of metrics and testdata to advance the SeLo task. Firstly, based on the characteristics of this task, we propose multiple discriminative evaluation metrics to quantify the performance of the SeLo task. The devised significant area proportion, attention shift distance, and discrete attention distance are utilized to evaluate the generated SeLo map from pixel-level and region-level. Next, to provide standard evaluation data for the SeLo task, we contribute a diverse, multi-semantic, multi-objective Semantic Localization Testset (AIR-SLT). AIR-SLT consists of 22 large-scale RS images and 59 test cases with different semantics, which aims to provide a comprehensive evaluations for retrieval models. Finally, we analyze the SeLo performance of RS cross-modal retrieval models in detail, explore the impact of different variables on this task, and provide a complete benchmark for the SeLo task. We have also established a new paradigm for RS referring expression comprehension, and demonstrated the great advantage of SeLo in semantics through combining it with tasks such as detection and road extraction. The proposed evaluation metrics, semantic localization testsets, and corresponding scripts have been open to access at github.com/xiaoyuan1996/SemanticLocalizationMetrics .Comment: 19 pages, 11 figure

Risk Assessment and Prediction of Heavy Metal Pollution in Groundwater and River Sediment: A Case Study of a Typical Agricultural Irrigation Area in Northeast China

The areas with typical municipal sewage discharge river and irrigation water function were selected as study sites in northeast China. The samples from groundwater and river sediment in this area were collected for the concentrations and forms of heavy metals (Cr(VI), Cd, As, and Pb) analysis. The risk assessment of heavy metal pollution was conducted based on single-factor pollution index (I) and Nemerow pollution index (NI). The results showed that only one groundwater sampling site reached a polluted level of heavy metals. There was a high potential ecological risk of Cd on the N21-2 sampling site in river sediment. The morphological analysis results of heavy metals in sediment showed that the release of heavy metals can be inferred as one of the main pollution sources of groundwater. In addition, the changes in the concentration and migration scope of As were predicted by using the Groundwater Modeling System (GMS). The predicted results showed that As will migrate downstream in the next decade, and the changing trend of As polluted areas was changed with As content districts because of some pump wells downstream to form groundwater depression cone, which made the solute transfer upstream

A Miniature Probe for Ultrasonic Penetration of a Single Cell

Although ultrasound cavitation must be avoided for safe diagnostic applications, the ability of ultrasound to disrupt cell membranes has taken on increasing significance as a method to facilitate drug and gene delivery. A new ultrasonic resonance driving method is introduced to penetrate rigid wall plant cells or oocytes with springy cell membranes. When a reasonable design is created, ultrasound can gather energy and increase the amplitude factor. Ultrasonic penetration enables exogenous materials to enter cells without damaging them by utilizing instant acceleration. This paper seeks to develop a miniature ultrasonic probe experiment system for cell penetration. A miniature ultrasonic probe is designed and optimized using the Precise Four Terminal Network Method and Finite Element Method (FEM) and an ultrasonic generator to drive the probe is designed. The system was able to successfully puncture a single fish cell

Complete Genome Sequences of One Salt-Tolerant and Petroleum Hydrocarbon-Emulsifying Terribacillus saccharophilus Strain ZY-1

Salt tolerance is one of the most important problems in the field of environmental governance and restoration. Among the various sources of factors, except temperature, salinity is a key factor that interrupts bacterial growth significantly. In this regard, constant efforts are made for the development of salt-tolerant strains, but few strains with salt tolerance, such as Terribacillus saccharophilus, were found, and there are still few relevant reports about their salt tolerance from complete genomic analysis. Furthermore, with the development of the economy, environmental pollution caused by oil exploitation has attracted much attention, so it is crucial to find the bacteria from T. saccharophilus which could degrade petroleum hydrocarbon even under high-salt conditions. Herein, one T. saccharophilus strain named ZY-1 with salt tolerance was isolated by increasing the salinity on LB medium step by step with reservoir water as the bacterial source. Its complete genome was sequenced, which was the first report of the complete genome for T. saccharophilus species with petroleum hydrocarbon degradation and emulsifying properties. In addition, its genome sequences were compared with the other five strains that are from the same genus level. The results indicated that there really exist some differences among them. In addition, some characteristics were studied. The salt-tolerant strain ZY-1 developed in this study and its emulsification and degradation performance of petroleum hydrocarbons were studied, which is expected to widely broaden the research scope of petroleum hydrocarbon-degrading bacteria in the oil field environment even in the extreme environment. The experiments verified that ZY-1 could significantly grow not only in the salt field but also in the oil field environment. It also demonstrated that the developed salt-tolerant strain can be applied in the petroleum hydrocarbon pollution field for bioremediation. In addition, we expect that the identified variants which occurred specifically in the high-salt strain will enhance the molecular biological understanding and be broadly applied to the biological engineering field

High Sensitivity Carbon Nanotubes Flow-Rate Sensors and Their Performance Improvement by Coating

A new type of hot-wire flow-rate sensor (HWFS) with a sensing element made of a macro-sized carbon nanotube (CNT) strand is presented in this study. An effective way to improve repeatability of the CNT flow-rate sensor by coating a layer of Al2O3 on the CNT surface is proposed. Experimental results show that due to the large surface-to-volume ratio and thin coated Al2O3 layer, the CNT flow-rate sensor has higher sensitivity and faster response than a conventional platinum (Pt) HWFS. It is also demonstrated that the covered CNT flow-rate sensor has better repeatability than its bare counterpart due to insulation from the surrounding environment. The proposed CNT flow-rate sensor shows application potential for high-sensitivity measurement of flow rate