A Statistical Texture Model of the Liver Based on Generalized N-Dimensional Principal Component Analysis (GND-PCA) and 3D Shape Normalization

We present a method based on generalized N-dimensional principal component analysis (GND-PCA) and a 3D shape normalization technique for statistical texture modeling of the liver. The 3D shape normalization technique is used for normalizing liver shapes in order to remove the liver shape variability and capture pure texture variations. The GND-PCA is used to overcome overfitting problems when the training samples are too much fewer than the dimension of the data. The preliminary results of leave-one-out experiments show that the statistical texture model of the liver built by our method can represent an untrained liver volume well, even though the mode is trained by fewer samples. We also demonstrate its potential application to classification of normal and abnormal (with tumors) livers

News Recommendation with Attention Mechanism

This paper explores the area of news recommendation, a key component of online information sharing. Initially, we provide a clear introduction to news recommendation, defining the core problem and summarizing current methods and notable recent algorithms. We then present our work on implementing the NRAM (News Recommendation with Attention Mechanism), an attention-based approach for news recommendation, and assess its effectiveness. Our evaluation shows that NRAM has the potential to significantly improve how news content is personalized for users on digital news platforms.Comment: 7 pages, Journal of Industrial Engineering and Applied Scienc

Mass flowmeter using a multi-sensor chip

We report here a novel mass flowmeter using a multisensor chip that includes a 1-D array of pressure, temperature and shear stress sensors. This shear stress sensor based flowmeter is capable of high sensitivity and wide measurement range. Our study also shows that the mass flowmeter using shear-stress sensors produces better resolution than that from pressure sensors in the laminar flow regime. Extensive tests have been carried out to evaluate the effects of overheat ratio, channel height and gas properties. We also find the V^2 ∝ τ^(1/3) law for conventional hot film sensors does not hold for our micromachined shear stress sensor

Spatial and Temporal Variation of Soil Salinity During Dry and Wet Seasons in the Southern Coastal Area of Laizhou Bay, China

260-270The southern coastal area of Laizhou Bay is subjected to severe soil salinization due to saline groundwater. The degree of spatial variability is strongly affected by seasonal changes during an annual cycle. In this paper, the spatio-temporal variability of soil salinity in Laizhou Bay, China, was examined to ascertain the current situation of soil salinization in the study area and to reveal the characteristics of seasonal variation of soil salinity. The classical statistical methods and geostatistical methods were applied to soil salinity data collected from four soil layers, i.e., 0-30, 30-60, 60-90, and 0-100 cm, during summer and autumn in 2014. The results indicated that the variation of soil salinity of all the soil layers in summer and autumn was moderate. The soil salinity in the 0-30 cm layer showed a moderate spatial autocorrelation, whereas the spatial autocorrelations of soil salinity in other layers were strong. The overall spatial distribution of soil salinity showed a clear banding distribution and the degree of salinization in the eastern area was lower than that in the western and northern regions.A high ratio of evaporation/precipitation is one of the important reasons for the soil salinity in July is significantly higher than that in November. The rank of soil salinity under different land-use types was: salt pan > orchard > weeds > soybean > woods > cotton > maize > ginger > sweet potato. The research findings can provide theoretical guidance for accurate assessment and soil partition management of regional soil salinization

Miniaturized Head-Mount Doppler Optical Coherence Tomography Scope for Freely Moving Mouse.

This study presents a miniaturized head-mount optical coherence tomography (OCT) system tailored for high-resolution brain imaging in freely moving mice, providing an advanced noninvasive imaging tool in neuroscience research. Leveraging optical coherence tomography technology, the system enables depth-resolved imaging and integrates functional OCT extensions, including angiography and Doppler imaging. Remarkably lightweight at 1.5 g, the device allows for the preservation of natural mouse behavior during imaging sessions. With a maximum 4 × 4 mm field of view and 7.4 μm axial resolution, the system offers reliable imaging capabilities. Noteworthy features include focal adjustability, rotary joint integration for fiber-twist-free operation, and a high-speed swept-source OCT laser at 200 kHz, facilitating real-time imaging. By providing insights into brain mechanisms and neurological disorders without disrupting natural behavior, this innovative system holds promise as a powerful tool in neuroscience research. Its compact design and comprehensive imaging capabilities make it well-suited for studying various brain regions and dynamic processes, contributing significantly to our understanding of brain function and pathology

Enhancing Both Efficiency and Representational Capability of Isomap by Extensive Landmark Selection

The problems of improving computational efficiency and extending representational capability are the two hottest topics in approaches of global manifold learning. In this paper, a new method called extensive landmark Isomap (EL-Isomap) is presented, addressing both topics simultaneously. On one hand, originated from landmark Isomap (L-Isomap), which is known for its high computational efficiency property, EL-Isomap also possesses high computational efficiency through utilizing a small set of landmarks to embed all data points. On the other hand, EL-Isomap significantly extends the representational capability of L-Isomap and other global manifold learning approaches by utilizing only an available subset from the whole landmark set instead of all to embed each point. Particularly, compared with other manifold learning approaches, the data manifolds with intrinsic low-dimensional concave topologies and essential loops can be unwrapped by the new method more successfully, which are shown by simulation results on a series of synthetic and real-world data sets. Moreover, the accuracy, robustness, and computational complexity of EL-Isomap are analyzed in this paper, and the relation between EL-Isomap and L-Isomap is also discussed theoretically

Tetherin inhibits prototypic foamy virus release

Background: Tetherin (also known as BST-2, CD317, and HM1.24) is an interferon- induced protein that blocks the release of a variety of enveloped viruses, such as retroviruses, filoviruses and herpesviruses. However, the relationship between tetherin and foamy viruses has not been clearly demonstrated. Results: In this study, we found that tetherin of human, simian, bovine or canine origin inhibits the production of infectious prototypic foamy virus (PFV). The inhibition of PFV by human tetherin is counteracted by human immunodeficiency virus type 1 (HIV-1) Vpu. Furthermore, we generated human tetherin transmembrane domain deletion mutant (delTM), glycosyl phosphatidylinositol (GPI) anchor deletion mutant (delGPI), and dimerization and glycosylation deficient mutants. Compared with wild type tetherin, the delTM and delGPI mutants only moderately inhibited PFV production. In contrast, the dimerization and glycosylation deficient mutants inhibit PFV production as efficiently as the wild type tetherin. Conclusions: These results demonstrate that tetherin inhibits the release and infectivity of PFV, and this inhibition is antagonized by HIV-1 Vpu. Both the transmembrane domain and the GPI anchor of tetherin are important for the inhibition of PFV, whereas the dimerization and the glycosylation of tetherin are dispensable

Enhanced predictor–corrector Mars entry guidance approach with atmospheric uncertainties

Due to the long-range data communication and complex Mars environment, the Mars lander needs to promote the ability to autonomously adapt uncertain situations ensuring high precision landing in future Mars missions. Based on the analysis of multiple disturbances, this study demonstrates an enhanced predictor–corrector guidance method to deal with the effect of atmospheric uncertainties during the entry phase of the Mars landing. In the proposed method, the predictor–corrector guidance algorithm is designed to autonomously drive the Mars lander to the parachute deployment. Meanwhile, the disturbance observer is designed to onboard estimate the effect of fiercely varying atmospheric uncertainties resulting from rapidly height decreasing. Then, with the estimation of atmospheric uncertainties compensated in the feed-forward channel, the composite guidance method is put forward such that both anti-disturbance and autonomous performance of the Mars lander guidance system are improved. Convergence of the proposed composite method is analysed. Simulations for a Mars lander entry guidance system demonstrates that the proposed method outperforms the baseline method in consideration of the atmospheric uncertainties