Experimental study on a solar-powered thermochemical sorption refrigeration system using strontium chloride/EG-ammonia working pair

An experimental intermittent thermochemical refrigeration system using strontium chloride (SrCl2)–ammonia reaction is described, which mainly consists of an adsorption bed and an evaporator/condenser. The strontium chloride is used as solid absorbent and ammonia as refrigerant. A kind of consolidated composite material based on expanded graphite and strontium chloride is developed firstly, then the composite material is filled into the adsorption bed. The process of desorption or regeneration of strontium chloride is driven by solar energy, and the temperature range of the heating fluid is from 90 to 110℃, which can be heated by solar energy collected by low cost solar flat plate collectors. In the evaporation–absorption process, the evaporating temperature is between -10 and -35℃. The theoretical analysis shows that this refrigeration system is technical feasibility

Transthyretin Stimulates Tumor Growth through Regulation of Tumor, Immune, and Endothelial Cells

Early detection of lung cancer offers an important opportunity to decrease mortality while it is still treatable and curable. Thirteen secretory proteins that are Stat3 downstream gene products were identified as a panel of biomarkers for lung cancer detection in human sera. This panel of biomarkers potentially differentiates different types of lung cancer for classification. Among them, the transthyretin (TTR) concentration was highly increased in human serum of lung cancer patients. TTR concentration was also induced in the serum, bronchoalveolar lavage fluid, alveolar type II epithelial cells, and alveolar myeloid cells of the CCSP-rtTA/(tetO)7-Stat3C lung tumor mouse model. Recombinant TTR stimulated lung tumor cell proliferation and growth, which were mediated by activation of mitogenic and oncogenic molecules. TTR possesses cytokine functions to stimulate myeloid cell differentiation, which are known to play roles in tumor environment. Further analyses showed that TTR treatment enhanced the reactive oxygen species production in myeloid cells and enabled them to become functional myeloid-derived suppressive cells. TTR demonstrated a great influence on a wide spectrum of endothelial cell functions to control tumor and immune cell migration and infiltration. TTR-treated endothelial cells suppressed T cell proliferation. Taken together, these 13 Stat3 downstream inducible secretory protein biomarkers potentially can be used for lung cancer diagnosis, classification, and as clinical targets for lung cancer personalized treatment if their expression levels are increased in a given lung cancer patient in the blood

SmartUnit: Empirical Evaluations for Automated Unit Testing of Embedded Software in Industry

In this paper, we aim at the automated unit coverage-based testing for embedded software. To achieve the goal, by analyzing the industrial requirements and our previous work on automated unit testing tool CAUT, we rebuild a new tool, SmartUnit, to solve the engineering requirements that take place in our partner companies. SmartUnit is a dynamic symbolic execution implementation, which supports statement, branch, boundary value and MC/DC coverage. SmartUnit has been used to test more than one million lines of code in real projects. For confidentiality motives, we select three in-house real projects for the empirical evaluations. We also carry out our evaluations on two open source database projects, SQLite and PostgreSQL, to test the scalability of our tool since the scale of the embedded software project is mostly not large, 5K-50K lines of code on average. From our experimental results, in general, more than 90% of functions in commercial embedded software achieve 100% statement, branch, MC/DC coverage, more than 80% of functions in SQLite achieve 100% MC/DC coverage, and more than 60% of functions in PostgreSQL achieve 100% MC/DC coverage. Moreover, SmartUnit is able to find the runtime exceptions at the unit testing level. We also have reported exceptions like array index out of bounds and divided-by-zero in SQLite. Furthermore, we analyze the reasons of low coverage in automated unit testing in our setting and give a survey on the situation of manual unit testing with respect to automated unit testing in industry.Comment: In Proceedings of 40th International Conference on Software Engineering: Software Engineering in Practice Track, Gothenburg, Sweden, May 27-June 3, 2018 (ICSE-SEIP '18), 10 page

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

Deep learning (DL) techniques are highly effective for defect detection from images. Training DL classification models, however, requires vast amounts of labeled data which is often expensive to collect. In many cases, not only the available training data is limited but may also imbalanced. In this paper, we propose a novel domain adaptation (DA) approach to address the problem of labeled training data scarcity for a target learning task by transferring knowledge gained from an existing source dataset used for a similar learning task. Our approach works for scenarios where the source dataset and the dataset available for the target learning task have same or different feature spaces. We combine our DA approach with an autoencoder-based data augmentation approach to address the problem of imbalanced target datasets. We evaluate our combined approach using image data for wafer defect prediction. The experiments show its superior performance against other algorithms when the number of labeled samples in the target dataset is significantly small and the target dataset is imbalanced

Federated Learning with Imbalanced and Agglomerated Data Distribution for Medical Image Classification

Federated learning (FL), training deep models from decentralized data without privacy leakage, has drawn great attention recently. Two common issues in FL, namely data heterogeneity from the local perspective and class imbalance from the global perspective have limited FL's performance. These two coupling problems are under-explored, and existing few studies may not be sufficiently realistic to model data distributions in practical sceneries (e.g. medical sceneries). One common observation is that the overall class distribution across clients is imbalanced (e.g. common vs. rare diseases) and data tend to be agglomerated to those more advanced clients (i.e., the data agglomeration effect), which cannot be modeled by existing settings. Inspired by real medical imaging datasets, we identify and formulate a new and more realistic data distribution denoted as L2 distribution where global class distribution is highly imbalanced and data distributions across clients are imbalanced but forming a certain degree of data agglomeration. To pursue effective FL under this distribution, we propose a novel privacy-preserving framework named FedIIC that calibrates deep models to alleviate bias caused by imbalanced training. To calibrate the feature extractor part, intra-client contrastive learning with a modified similarity measure and inter-client contrastive learning guided by shared global prototypes are introduced to produce a uniform embedding distribution of all classes across clients. To calibrate the classification heads, a softmax cross entropy loss with difficulty-aware logit adjustment is constructed to ensure balanced decision boundaries of all classes. Experimental results on publicly-available datasets demonstrate the superior performance of FedIIC in dealing with both the proposed realistic modeling and the existing modeling of the two coupling problems

Two hAT transposon genes were transferred from Brassicaceae to broomrapes and are actively expressed in some recipients

A growing body of evidence is pointing to an important role of horizontal gene transfer (HGT) in the evolution of higher plants. However, reports of HGTs of transposable elements (TEs) in plants are still scarce, and only one case is known of a class II transposon horizontally transferred between grasses. To investigate possible TE transfers in dicots, we performed transcriptome screening in the obligate root parasite Phelipanche aegyptiaca (Orobanchaceae), data-mining in the draft genome assemblies of four other Orobanchaceae, gene cloning, gene annotation in species with genomic information, and a molecular phylogenetic analysis. We discovered that the broomrape genera Phelipanche and Orobanche acquired two related nuclear genes (christened BO transposase genes), a new group of the hAT superfamily of class II transposons, from Asian Sisymbrieae or a closely related tribe of Brassicaceae, by HGT. The collinearity of the flanking genes, lack of a classic border structure, and low expression levels suggest that BO transposase genes cannot transpose in Brassicaceae, whereas they are highly expressed in P. aegyptiaca