"Nutrient-fungi-host" tripartite interaction in cancer progression.

The human microbiome exhibits a profound connection with the cancer development, progression, and therapeutic response, with particular emphasis on its components of the mycobiome, which are still in the early stages of research. In this review, we comprehensively summarize cancer-related symbiotic and pathogenic fungal genera. The intricate mechanisms through which fungi impact cancer as an integral member of both gut and tissue-resident microbiomes are further discussed.

Big data-driven carbon emission traceability list and characteristics of ships in maritime transportation-a case study of Tianjin Port.

As Chi na's shipping industry continues to develop, ship emissions have become a significant source of pollutants. Consequently, it has become imperative to comprehend accurately the nature and attributes of ship pollutant emissions and understand their causation and effect as a crucial aspect of pollution control and legislation. This paper employs high-precision automatic identification system (AIS) dynamic and static data, along with pollutant emission parameters, to estimate the pollutant emissions from a ship's main engine, auxiliary engine, and boiler using a dynamic approach.

Noise-Transfer2Clean: denoising cryo-EM images based on noise modeling and transfer.

Motivation: Cryo-electron microscopy (cryo-EM) is a widely used technology for ultrastructure determination, which constructs the 3D structures of protein and macromolecular complex from a set of 2D micrographs. However, limited by the electron beam dose, the micrographs in cryo-EM generally suffer from the extremely low signal-to-noise ratio (SNR), which hampers the efficiency and effectiveness of downstream analysis. Especially, the noise in cryo-EM is not simple additive or multiplicative noise whose statistical characteristics are quite different from the ones in natural image, extremely shackling the performance of conventional denoising methods.

Live-SIMBA: an ImageJ plug-in for the universal and accelerated single molecule-guided Bayesian localization super resolution microscopy (SIMBA) method.

Live-cell super-resolution fluorescence microscopy techniques allow biologists to observe subcellular structures, interactions and dynamics at the nanoscale level. Among of them, single molecule-guided Bayesian localization super resolution microscopy (SIMBA) and its derivatives produce an appropriate 50 nm spatial resolution and a 0.1-2s temporal resolution in living cells with simple off-the-shelf total internal reflection fluorescence (TIRF) equipment.

ADST: Forecasting Metro Flow Using Attention-Based Deep Spatial-Temporal Networks with Multi-Task Learning.

Passenger flow prediction has drawn increasing attention in the deep learning research field due to its great importance in traffic management and public safety. The major challenge of this essential task lies in multiple spatiotemporal correlations that exhibit complex non-linear correlations. Although both the spatial and temporal perspectives have been considered in modeling, most existing works have ignored complex temporal correlations or underlying spatial similarity.

Accurate and Robust Floor Positioning in Complex Indoor Environments.

With the widespread development of location-based services, the demand for accurate indoor positioning is getting more and more urgent. Floor positioning, as a prerequisite for indoor positioning in multi-story buildings, is particularly important. Though lots of work has been done on floor positioning, the existing studies on floor positioning in complex multi-story buildings with large hollow areas through multiple floors still cannot meet the application requirements because of low accuracy and robustness.

A Human Activity Recognition Algorithm Based on Stacking Denoising Autoencoder and LightGBM.

Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation.

Pedestrian Stride-Length Estimation Based on LSTM and Denoising Autoencoders.

Accurate stride-length estimation is a fundamental component in numerous applications, such as pedestrian dead reckoning, gait analysis, and human activity recognition. The existing stride-length estimation algorithms work relatively well in cases of walking a straight line at normal speed, but their error overgrows in complex scenes. Inaccurate walking-distance estimation leads to huge accumulative positioning errors of pedestrian dead reckoning.

A Fast Indoor/Outdoor Transition Detection Algorithm Based on Machine Learning.

The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions.

PIXER: an automated particle-selection method based on segmentation using a deep neural network.

Background: Cryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements.

An Infrastructure-Free Indoor Localization Algorithm for Smartphones.

Accurate indoor positioning technology provides location-based service for a variety of applications. However, most existing indoor localization approaches (e.g.

Constructing an Indoor Floor Plan Using Crowdsourcing Based on Magnetic Fingerprinting.

A large number of indoor positioning systems have recently been developed to cater for various location-based services. Indoor maps are a prerequisite of such indoor positioning systems; however, indoor maps are currently non-existent for most indoor environments. Construction of an indoor map by external experts excludes quick deployment and prevents widespread utilization of indoor localization systems.

Using the NONCODE Database Resource.

NONCODE is a comprehensive database that aims to present the most complete collection and annotation of non-coding RNAs, especially long non-coding RNAs (lncRNA genes), and thus NONCODE is essential to modern biological and medical research. Scientists are producing a flood of new data from which new lncRNA genes and lncRNA-disease relationships are continually being identified. NONCODE assimilates such information from a wide variety of sources including published articles, RNA-seq data, micro-array data and databases on genetic variation (dbSNP) and genome-wide associations (GWAS).

A Cooperative Downloading Method for VANET Using Distributed Fountain Code.

Cooperative downloading is one of the effective methods to improve the amount of downloaded data in vehicular ad hoc networking (VANET). However, the poor channel quality and short encounter time bring about a high packet loss rate, which decreases transmission efficiency and fails to satisfy the requirement of high quality of service (QoS) for some applications. Digital fountain code (DFC) can be utilized in the field of wireless communication to increase transmission efficiency.