Machinery condition monitoring by inverse filtering and statistical analysis

Data used for machinery condition monitoring contains mainly the same information as that obtained under normal operation conditions. The traditional practice of feature extraction, which uses such data directly, suffers from low signal-to-noise ratio. This paper presents a method that uses an inverse filter to separate the information contents of the data, so that the feature extraction can be done by statistical analysis algorithms, which would otherwise be difficult. It is shown that the inverse filtering process is equivalent to that of prediction error estimation based on a signal model in the form of an autoregressive moving-average (ARMA) model. The construction of the inverse filter can therefore be carried out by ARMA modeling. An application example of this method for the monitoring of a paper handling system is also given.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30174/1/0000559.pd

Cell surface-specific N-glycan profiling in breast cancer

Aberrant changes in specific glycans have been shown to be associated with immunosurveillance, tumorigenesis, tumor progression and metastasis. In this study, the N-glycan profiling of membrane proteins from human breast cancer cell lines and tissues was detected using modified DNA sequencer-assisted fluorophore-assisted carbohydrate electrophoresis (DSA-FACE). The N-glycan profiles of membrane proteins were analyzed from 7 breast cancer cell lines and MCF 10A, as well as from 100 pairs of breast cancer and corresponding adjacent tissues. The results showed that, compared with the matched adjacent normal tissue samples, two biantennary N-glycans (NA2 and NA2FB) were significantly decreased (p <0.0001) in the breast cancer tissue samples, while the triantennary glycan (NA3FB) and a high-mannose glycan (M8) were dramatically increased (p = 0.001 and p <0.0001, respectively). Moreover, the alterations in these specific N-glycans occurred through the oncogenesis and progression of breast cancer. These results suggested that the modified method based on DSA-FACE is a high-throughput detection technology that is suited for analyzing cell surface N-glycans. These cell surface-specific N-glycans may be helpful in recognizing the mechanisms of tumor cell immunologic escape and could be potential targets for new breast cancer drugs

The Blood Biomarkers of Asthma

Asthma was a chronic inflammatory airway disease which characterized by complex pathogenesis, various clinical manifestations and severity. Blood biomarkers have been used to evaluate the severity of the disease, predict the efficacy and prognosis. Currently, some incredible progress in most of the research on biomarkers for asthma have achieved, including cell, antibodies, cytokines, chemokines, proteins and non-coding RNAs. We reviewed the application of these biomarkers in diagnosis, treatment, prognosis monitoring and phenotypic identification of asthma, in order to improve clinicians’ understanding of asthma biomarkers

Comparison of quality/quantity mNGS and usual mNGS for pathogen detection in suspected pulmonary infections

Improved metagenomic next-generation sequencing (mNGS), for example, quality/quantity mNGS (QmNGS), is being used in the diagnosis of pulmonary pathogens. There are differences between QmNGS and the usual mNGS (UmNGS), but reports that compare their detection performances are rare. In this prospective study of patients enrolled between December 2021 and March 2022, the bronchoalveolar lavage fluid of thirty-six patients with suspected pulmonary infection was assessed using UmNGS and QmNGS. The sensitivity of QmNGS was similar to that of UmNGS. The specificity of QmNGS was higher than that of UmNGS; however, the difference was not statistically significant. The positive likelihood ratios (+LR) of QmNGS and UmNGS were 3.956 and 1.394, respectively, and the negative likelihood ratios (-LR) were 0.342 and 0.527, respectively. For the co-detection of pathogens, the depth and coverage of the QmNGS sequencing were lower than those of UmNGS, while for the detection of pathogens isolated from patients with pulmonary infection, the concordance rate was 77.2%. In the eleven patients with nonpulmonary infection, only viruses were detected using QmNGS, while UmNGS detected not only viruses but also bacteria and fungi. This study provides a basis for the selection of mNGS for the diagnosis of suspected pulmonary infection

Semantic-Enhanced Differentiable Search Index Inspired by Learning Strategies

Recently, a new paradigm called Differentiable Search Index (DSI) has been proposed for document retrieval, wherein a sequence-to-sequence model is learned to directly map queries to relevant document identifiers. The key idea behind DSI is to fully parameterize traditional ``index-retrieve'' pipelines within a single neural model, by encoding all documents in the corpus into the model parameters. In essence, DSI needs to resolve two major questions: (1) how to assign an identifier to each document, and (2) how to learn the associations between a document and its identifier. In this work, we propose a Semantic-Enhanced DSI model (SE-DSI) motivated by Learning Strategies in the area of Cognitive Psychology. Our approach advances original DSI in two ways: (1) For the document identifier, we take inspiration from Elaboration Strategies in human learning. Specifically, we assign each document an Elaborative Description based on the query generation technique, which is more meaningful than a string of integers in the original DSI; and (2) For the associations between a document and its identifier, we take inspiration from Rehearsal Strategies in human learning. Specifically, we select fine-grained semantic features from a document as Rehearsal Contents to improve document memorization. Both the offline and online experiments show improved retrieval performance over prevailing baselines.Comment: Accepted by KDD 202

Repression of Esophageal Neoplasia and Inflammatory Signaling by Anti-miR-31 Delivery In Vivo.

BACKGROUND: Overexpression of microRNA-31 (miR-31) is implicated in the pathogenesis of esophageal squamous cell carcinoma (ESCC), a deadly disease associated with dietary zinc deficiency. Using a rat model that recapitulates features of human ESCC, the mechanism whereby Zn regulates miR-31 expression to promote ESCC is examined. METHODS: To inhibit in vivo esophageal miR-31 overexpression in Zn-deficient rats (n = 12-20 per group), locked nucleic acid-modified anti-miR-31 oligonucleotides were administered over five weeks. miR-31 expression was determined by northern blotting, quantitative polymerase chain reaction, and in situ hybridization. Physiological miR-31 targets were identified by microarray analysis and verified by luciferase reporter assay. Cellular proliferation, apoptosis, and expression of inflammation genes were determined by immunoblotting, caspase assays, and immunohistochemistry. The miR-31 promoter in Zn-deficient esophagus was identified by ChIP-seq using an antibody for histone mark H3K4me3. Data were analyzed with t test and analysis of variance. All statistical tests were two-sided. RESULTS: In vivo, anti-miR-31 reduced miR-31 overexpression (P = .002) and suppressed the esophageal preneoplasia in Zn-deficient rats. At the same time, the miR-31 target Stk40 was derepressed, thereby inhibiting the STK40-NF-κΒ-controlled inflammatory pathway, with resultant decreased cellular proliferation and activated apoptosis (caspase 3/7 activities, fold change = 10.7, P = .005). This same connection between miR-31 overexpression and STK40/NF-κΒ expression was also documented in human ESCC cell lines. In Zn-deficient esophagus, the miR-31 promoter region and NF-κΒ binding site were activated. Zn replenishment restored the regulation of this genomic region and a normal esophageal phenotype. CONCLUSIONS: The data define the in vivo signaling pathway underlying interaction of Zn deficiency and miR-31 overexpression in esophageal neoplasia and provide a mechanistic rationale for miR-31 as a therapeutic target for ESCC

What drives soil degradation after gravel mulching for 6 years in northwest China?

Gravel mulch is an agricultural water conservation practice that has been widely used in the semi-arid region of northwest China, but its effectiveness is now lessening due to soil degradation caused by long-term gravel mulching. In this study, we report on a 6-year-long gravel mulch experiment conducted in the northwestern Loess Plateau to evaluate the impact of gravel mulch on soil physicochemical properties and microbial communities, with the objective of clarifying the causes of long-term gravel mulching-induced land degradation. After 6 years mulching, we found that gravel mulched soil contained significantly higher concentrations of total carbon and total organic carbon than non-mulched soil (control). Long-term gravel mulching significantly changed the soil microbial diversity and abundance distribution of bacterial and fungal communities. Notably, the relative abundance of Acidobacteria was significantly higher under gravel mulching than the control (no mulching), being significantly greater in the AG treatment (small-sized gravel, 2–5 mm) than all other treatments. Conversely, the relative abundance of Actinobacteria was significantly lower under gravel mulching than the control, being the lowest in the BG treatment (large-sized gravel, 40–60 mm). At the same time, the relative abundance of Bacteroidetes was significantly lower in AG yet higher in BG vis-à-vis the other treatments. Of the various factors examined, on a 6-year scale, the capture of dust by gravel mulch and altered carbon and nitrogen components in soil play major contributing roles in the compositional change of soil microorganisms. These results suggest that modified soil material input from gravel mulching may be the key factor leading to soil degradation. More long-term experimental studies at different sites are now needed to elucidate the mechanisms responsible for soil degradation under gravel mulching

Characteristics of Bitcoin Transactions on Cryptomarkets

Cryptomarkets (or darknet markets) are commercial hidden-service websites that operate on The Onion Router (Tor) anonymity network. Cryptomarkets accept primarily bitcoin as payment since bitcoin is pseudonymous. Understanding bitcoin transaction patterns in cryptomarkets is important for analyzing vulnerabilities of privacy protection models in cryptocurrecies. It is also important for law enforcement to track illicit online crime activities in cryptomarkets. In this paper, we discover interesting characteristics of bitcoin transaction patterns in cryptomarkets. The results demonstrate that the privacy protection mechanism in cryptomarkets and bitcoin is vulnerable. Adversaries can easily gain valuable information for analyzing trading activities in cryptomarkets