Regression of Gastric Cancer by Systemic Injection of RNA Nanoparticles Carrying Both Ligand and siRNA

Gastric cancer is the second leading cause of cancer-related death worldwide. RNA nanotechnology has recently emerged as an important field due to recent finding of its high thermodynamic stability, favorable and distinctive in vivo attributes. Here we reported the use of the thermostable three-way junction (3WJ) of bacteriophage phi29 motor pRNA to escort folic acid, a fluorescent image marker and BRCAA1 siRNA for targeting, imaging, delivery, gene silencing and regression of gastric cancer in animal models. In vitro assay revealed that the RNA nanoparticles specifically bind to gastric cancer cells, and knock-down the BRCAA1 gene. Apoptosis of gastric cancer cells was observed. Animal trials confirmed that these RNA nanoparticles could be used to image gastric cancer in vivo, while showing little accumulation in crucial organs and tissues. The volume of gastric tumors noticeably decreased during the course of treatment. No damage to important organs by RNA nanoparticles was detectible. All the results indicated that this novel RNA nanotechnology can overcome conventional cancer therapeutic limitations and opens new opportunities for specific delivery of therapeutics to stomach cancer without damaging normal cells and tissues, reduce the toxicity and side effect, improve the therapeutic effect, and exhibit great potential in clinical tumor therapy

Simultaneous Detection of Major Drug Resistance Mutations in the Protease and Reverse Transcriptase Genes for HIV-1 Subtype C by Use of a Multiplex Allele-Specific Assay

High-throughput, sensitive, and cost-effective HIV drug resistance (HIVDR) detection assays are needed for large-scale monitoring of the emergence and transmission of HIVDR in resource-limited settings. Using suspension array technology, we have developed a multiplex allele-specific (MAS) assay that can simultaneously detect major HIVDR mutations at 20 loci. Forty-five allele-specific primers tagged with unique 24-base oligonucleotides at the 5′ end were designed to detect wild-type and mutant alleles at the 20 loci of HIV-1 subtype C. The MAS assay was first established and optimized with three plasmid templates (C-wt, C-mut1, and C-mut2) and then evaluated using 148 plasma specimens from HIV-1 subtype C-infected individuals. All the wild-type and mutant alleles were unequivocally distinguished with plasmid templates, and the limits of detection were 1.56% for K219Q and K219E, 3.13% for L76V, 6.25% for K65R, K70R, L74V, L100I, K103N, K103R, Q151M, Y181C, and I47V, and 12.5% for M41L, K101P, K101E, V106A, V106M, Y115F, M184V, Y188L, G190A, V32I, I47A, I84V, and L90M. Analyses of 148 plasma specimens revealed that the MAS assay gave 100% concordance with conventional sequencing at eight loci and >95% (range, 95.21% to 99.32%) concordance at the remaining 12 loci. The differences observed were caused mainly by 24 additional low-abundance alleles detected by the MAS assay. Ultradeep sequencing analysis confirmed 15 of the 16 low-abundance alleles. This multiplex, sensitive, and straightforward result-reporting assay represents a new efficient genotyping tool for HIVDR surveillance and monitoring

Black carbon and organic carbon dataset over the Third Pole

The Tibetan Plateau and its surroundings, also known as the Third Pole, play an important role in the global and regional climate and hydrological cycle. Carbonaceous aerosols (CAs), including black carbon (BC) and organic carbon (OC), can directly or indirectly absorb and scatter solar radiation and change the energy balance on the Earth. CAs, along with the other atmospheric pollutants (e.g., mercury), can be frequently transported over long distances into the inland Tibetan Plateau. During the last decades, a coordinated monitoring network and research program named “Atmospheric Pollution and Cryospheric Changes” (APCC) has been gradually set up and continuously operated within the Third Pole regions to investigate the linkage between atmospheric pollutants and cryospheric changes. This paper presents a systematic dataset of BC, OC, water-soluble organic carbon (WSOC), and water-insoluble organic carbon (WIOC) from aerosols (20 stations), glaciers (17 glaciers, including samples from surface snow and ice, snow pits, and 2 ice cores), snow cover (2 stations continuously observed and 138 locations surveyed once), precipitation (6 stations), and lake sediment cores (7 lakes) collected across the Third Pole, based on the APCC program. These data were created based on online (in situ) and laboratory measurements. High-resolution (daily scale) atmospheric-equivalent BC concentrations were obtained by using an Aethalometer (AE-33) in the Mt. Everest (Qomolangma) region, which can provide new insight into the mechanism of BC transportation over the Himalayas. Spatial distributions of BC, OC, WSOC, and WIOC from aerosols, glaciers, snow cover, and precipitation indicated different features among the different regions of the Third Pole, which were mostly influenced by emission sources, transport pathways, and deposition processes. Historical records of BC from ice cores and lake sediment cores revealed the strength of the impacts of human activity since the Industrial Revolution. BC isotopes from glaciers and aerosols identified the relative contributions of biomass and fossil fuel combustion to BC deposition on the Third Pole. Mass absorption cross sections of BC and WSOC from aerosol, glaciers, snow cover, and precipitation samples were also provided. This updated dataset is released to the scientific communities focusing on atmospheric science, cryospheric science, hydrology, climatology, and environmental science. The related datasets are presented in the form of excel files. BC and OC datasets over the Third Pole are available to download from the National Cryosphere Desert Data Center (10.12072/ncdc.NIEER.db0114.2021; Kang and Zhang, 2021)

Crowdsourced mapping of unexplored target space of kinase inhibitors

Despite decades of intensive search for compounds that modulate the activity of particular protein targets, a large proportion of the human kinome remains as yet undrugged. Effective approaches are therefore required to map the massive space of unexplored compound-kinase interactions for novel and potent activities. Here, we carry out a crowdsourced benchmarking of predictive algorithms for kinase inhibitor potencies across multiple kinase families tested on unpublished bioactivity data. We find the top-performing predictions are based on various models, including kernel learning, gradient boosting and deep learning, and their ensemble leads to a predictive accuracy exceeding that of single-dose kinase activity assays. We design experiments based on the model predictions and identify unexpected activities even for under-studied kinases, thereby accelerating experimental mapping efforts. The open-source prediction algorithms together with the bioactivities between 95 compounds and 295 kinases provide a resource for benchmarking prediction algorithms and for extending the druggable kinome. The IDG-DREAM Challenge carried out crowdsourced benchmarking of predictive algorithms for kinase inhibitor activities on unpublished data. This study provides a resource to compare emerging algorithms and prioritize new kinase activities to accelerate drug discovery and repurposing efforts

A Marketplace Price Anomaly Detection System at Scale

Online marketplaces execute large volume of price updates that are initiated by individual marketplace sellers each day on the platform. This price democratization comes with increasing challenges with data quality. Lack of centralized guardrails that are available for a traditional online retailer causes a higher likelihood for inaccurate prices to get published on the website, leading to poor customer experience and potential for revenue loss. We present MoatPlus (Masked Optimal Anchors using Trees, Proximity-based Labeling and Unsupervised Statistical-features), a scalable price anomaly detection framework for a growing marketplace platform. The goal is to leverage proximity and historical price trends from unsupervised statistical features to generate an upper price bound. We build an ensemble of models to detect irregularities in price-based features, exclude irregular features and use optimized weighting scheme to build a reliable price bound in real-time pricing pipeline. We observed that our approach improves precise anchor coverage by up to 46.6% in high-vulnerability item subsets

Study of Safety Auxiliary Facilities to Prevent the Start-Up Failure of Large Axial Flow Pump Systems under Gate Failure Working Conditions

Large axial flow pump systems are used in coastal pump stations. It is common and very dangerous for large axial flow pump systems to encounter the failure of the fast hydraulic gate during start-up operations. Methods for equipping LAPS with reasonable safety aids for start-up operations in order to deal with the unexpected situation that the quick gate cannot be opened, limiting the safety and stability of LAPS, have become a key focus of research. We aim to investigate the effect of safety aids on the LAPS’s start-up characteristics under gate rejection conditions and to find the best safety aid allocation method to solve the LAPS’s start-up failure problem. Based on the verification of the model test, a numerical simulation of the start-up process of the large axial flow pump system equipped with auxiliary safety features was carried out under the condition of gate rejection. The results show that under the condition of gate rejection, the auxiliary FLVA or OVHO can help LAPS reduce the risk of start-up failure to a certain extent. The FLVA will play the main protective role during the start-up operations of the LAPS if the LAPS is equipped with both the OVHO and FLVA of unrestricted size under the gate rejection condition. LAPS equipped with OVHO (1.27 Hm) and FLVA (49.1% Ag) and LAPS equipped with FLVA (49.1% Ag) have comparable start-up safety. The latter has an His of 1.783 Hr and a Pis of 1.30 Pr. The former has an instantaneous shock head of 1.772 Hr and a Pis of 1.30 Pr, which exhibit a decrease of 0.38% and 0 %, respectively. The research results will provide an important reference value for the prevention of pump station start-up failures under gate rejection conditions

Numerical Study for Flow Loss Characteristic of an Axial-Flow Pump as Turbine via Entropy Production Analysis

Low-head vertical axial-flow pump as turbine (PAT) devices play a vital part in the development of clean energy for hydropower in plain areas. The traditional method of evaluating the flow loss in hydraulic machinery is calculated by the pressure drop method, the limitation of which is that the location of the occurrence of large losses cannot be accurately determined. In this paper, entropy production theory is introduced to evaluate the irreversible losses in the axial-flow PAT from the perspective of the second law of thermodynamics. A three-dimensional model of the axial-flow PAT is established and solved numerically using the Reynolds time-averaged equation, and the turbulence model is adopted as Shear Stress Transport–Curvature Correction (SST-CC) model. The validity of the entropy production theory to evaluate the energy loss distribution of the axial-flow PAT is illustrated by comparing the flow loss calculated by the pressure drop and the entropy production theory, respectively. The entropy production by turbulent dissipative dominates the total entropy production in the whole flow conduit, and the turbulent dissipative entropy accounts for the smallest percentage of the whole conduit entropy production at the optimal working condition Qbep, which is 51%. The impeller and the dustpan-shaped conduit are the essential sources of hydraulic loss in the entire flow conduit of the axial-flow PAT, and most of the energy loss of the impeller occurs at the blade leading edge, the trailing edge, and the flow separation zone near the suction surface. The energy loss of the dustpan-shaped conduit results from the high-speed flow from the impeller outlet to dustpan-shaped conduit to form a vortex, backflow and other chaotic flow patterns. Flow impact, flow separation, vortex and backflow are the main causes of high entropy production and energy loss