Research on Realization of Automatic Control in Intelligent Buildings

The intelligent building has become a key direction in architecture field in China. It is the application-efficiency of automatic control technology that improves the value of intelligent building applications. This article firstly introduces the definition and characteristics of automatic control technology, then explains the function of intelligent building automatic control, and finally expounds the realization mode of intelligent building automatic control

RNA sequencing reveals two major classes of gene expression levels in metazoan cells

The expression level of a gene is often used as a proxy for determining whether the protein or RNA product is functional in a cell or tissue. Therefore, it is of fundamental importance to understand the global distribution of gene expression levels, and to be able to interpret it mechanistically and functionally. Here we use RNA sequencing of mouse Th2 cells, coupled with a range of other techniques, to show that all genes can be separated, based on their expression abundance, into two distinct groups: one group comprising of lowly expressed and putatively non-functional mRNAs, and the other of highly expressed mRNAs with active chromatin marks at their promoters

KAT7 is a genetic vulnerability of acute myeloid leukemias driven by MLL rearrangements.

Histone acetyltransferases (HATs) catalyze the transfer of an acetyl group from acetyl-CoA to lysine residues of histones and play a central role in transcriptional regulation in diverse biological processes. Dysregulation of HAT activity can lead to human diseases including developmental disorders and cancer. Through genome-wide CRISPR-Cas9 screens, we identified several HATs of the MYST family as fitness genes for acute myeloid leukemia (AML). Here we investigate the essentiality of lysine acetyltransferase KAT7 in AMLs driven by the MLL-X gene fusions. We found that KAT7 loss leads to a rapid and complete loss of both H3K14ac and H4K12ac marks, in association with reduced proliferation, increased apoptosis, and differentiation of AML cells. Acetyltransferase activity of KAT7 is essential for the proliferation of these cells. Mechanistically, our data propose that acetylated histones provide a platform for the recruitment of MLL-fusion-associated adaptor proteins such as BRD4 and AF4 to gene promoters. Upon KAT7 loss, these factors together with RNA polymerase II rapidly dissociate from several MLL-fusion target genes that are essential for AML cell proliferation, including MEIS1, PBX3, and SENP6. Our findings reveal that KAT7 is a plausible therapeutic target for this poor prognosis AML subtype

KAT7 is a genetic vulnerability of acute myeloid leukemias driven by MLL rearrangements

Histone acetyltransferases (HATs) catalyze the transfer of an acetyl group from acetyl-CoA to lysine residues of histones and play a central role in transcriptional regulation in diverse biological processes. Dysregulation of HAT activity can lead to human diseases including developmental disorders and cancer. Through genome-wide CRISPR-Cas9 screens, we identified several HATs of the MYST family as fitness genes for acute myeloid leukemia (AML). Here we investigate the essentiality of lysine acetyltransferase KAT7 in AMLs driven by the MLL-X gene fusions. We found that KAT7 loss leads to a rapid and complete loss of both H3K14ac and H4K12ac marks, in association with reduced proliferation, increased apoptosis, and differentiation of AML cells. Acetyltransferase activity of KAT7 is essential for the proliferation of these cells. Mechanistically, our data propose that acetylated histones provide a platform for the recruitment of MLL-fusion-associated adaptor proteins such as BRD4 and AF4 to gene promoters. Upon KAT7 loss, these factors together with RNA polymerase II rapidly dissociate from several MLL-fusion target genes that are essential for AML cell proliferation, including MEIS1, PBX3, and SENP6. Our findings reveal that KAT7 is a plausible therapeutic target for this poor prognosis AML subtype

EpiChIP: gene-by-gene quantification of epigenetic modification levels

The combination of chromatin immunoprecipitation with next-generation sequencing technology (ChIP-seq) is a powerful and increasingly popular method for mapping protein–DNA interactions in a genome-wide fashion. The conventional way of analyzing this data is to identify sequencing peaks along the chromosomes that are significantly higher than the read background. For histone modifications and other epigenetic marks, it is often preferable to find a characteristic region of enrichment in sequencing reads relative to gene annotations. For instance, many histone modifications are typically enriched around transcription start sites. Calculating the optimal window that describes this enrichment allows one to quantify modification levels for each individual gene. Using data sets for the H3K9/14ac histone modification in Th cells and an accompanying IgG control, we present an analysis strategy that alternates between single gene and global data distribution levels and allows a clear distinction between experimental background and signal. Curve fitting permits false discovery rate-based classification of genes as modified versus unmodified. We have developed a software package called EpiChIP that carries out this type of analysis, including integration with and visualization of gene expression data

Proximity-Induced Nucleic Acid Degrader (PINAD) approach to targeted RNA degradation using small molecules

Nature has evolved intricate machinery to target and degrade RNA, and some of these molecular mechanisms can be adapted for therapeutic use. Small interfering RNAs and RNase H-inducing oligonucleotides have yielded therapeutic agents against diseases that cannot be tackled using protein-centered approaches. Because these therapeutic agents are nucleic acid-based, they have several inherent drawbacks which include poor cellular uptake and stability. Here we report a new approach to target and degrade RNA using small molecules, proximity-induced nucleic acid degrader (PINAD). We have utilized this strategy to design two families of RNA degraders which target two different RNA structures within the genome of SARS-CoV-2: G-quadruplexes and the betacoronaviral pseudoknot. We demonstrate that these novel molecules degrade their targets using in vitro, in cellulo, and in vivo SARS-CoV-2 infection models. Our strategy allows any RNA binding small molecule to be converted into a degrader, empowering RNA binders that are not potent enough to exert a phenotypic effect on their own. PINAD raises the possibility of targeting and destroying any disease-related RNA species, which can greatly expand the space of druggable targets and diseases.info:eu-repo/semantics/publishedVersio

RNAmut: robust identification of somatic mutations in acute myeloid leukemia using RNA-sequencing.

Acute myeloid leukemia (AML) is an aggressive malignancy of haematopoietic stem cells driven by a well-defined set of somatic mutations.1,2 Identifying the mutations driving individual cases is important for assigning the patient to a recognized World Health Organisation category, establishing prognostic risk and tailoring post-consolidation therapy.3 As a result, AML research and diagnostic laboratories apply diverse methodologies to detect important mutations and many are introducing next-generation sequencing (NGS) approaches to study extended panels of genes in order to refine genomic classification and prognostic category.1 Besides the implications of these developments on costs, expertise and reliance on commercial providers, they also do not capture gene expression data, which have independent prognostic value that cannot be inferred from somatic mutation profiles. The ability to detect AML gene mutations as well as gene expression profiles from a single assay, could provide a holistic tool that accelerates research, simplifies diagnostic work-up and helps develop integrated algorithms to refine individual patient prognosis. Here, we show that AML RNA sequencing (RNA-seq) data can be used to reliably detect all types of clinically important mutations and develop a bespoke fast and easy-to-use software (RNAmut) for this purpose that can be readily used by teams/laboratories without in-house bioinformatic expertise

Semi-Supervised Machine Learning for Automated Species Identification by Collagen Peptide Mass Fingerprinting

Abstract Background Biomolecular methods for species identification are increasingly being utilised in the study of changing environments, both at the microscopic and macroscopic levels. High-throughput peptide mass fingerprinting has been largely applied to bacterial identification, but increasingly used to identify archaeological and palaeontological skeletal material to yield information on past environments and human-animal interaction. However, as applications move away from predominantly domesticate and the more abundant wild fauna to a much wider range of less common taxa that do not yet have genetically-derived sequence information, robust methods of species identification and biomarker selection need to be determined. Results Here we developed a supervised machine learning algorithm for classifying the species of ancient remains based on collagen fingerprinting. The aim was to minimise requirements on prior knowledge of known species while yielding satisfactory sensitivity and specificity. The algorithm uses iterations of a modified random forest classifier with a similarity scoring system to expand its identified samples. We tested it on a set of 6805 spectra and found that a high level of accuracy can be achieved with a training set of five identified specimens per taxon. Conclusions This method consistently achieves higher accuracy than two-dimensional principal component analysis and similar accuracy with hierarchical clustering using optimised parameters, which greatly reduces requirements for human input. Within the vertebrata, we demonstrate that this method was able to achieve the taxonomic resolution of family or sub-family level whereas the genus- or species-level identification may require manual interpretation or further experiments. In addition, it also identifies additional species biomarkers than those previously published

Additional file 5: of Semi-supervised machine learning for automated species identification by collagen peptide mass fingerprinting

Table S4. Similarity scores assigned to each identification. (XLSX 364 kb