Research to date on abiotic stress responses in plants has been largely focused on the plant itself, but current knowledge indicates that microorganisms can interact with and help plants during periods of abiotic stress. In our research, we aim to investigate the interkingdom communication between the plant root and the rhizo-microbiota. Our investigation showed that miRNA plays a pivotal role in this interkingdom communication. Here, we describe a protocol for the analysis of miRNA secreted by the plant root, which includes all of the steps from the isolation of the miRNA to the bioinformatics analysis. Because of their short nucleotide length, Next Generation Sequencing (NGS) library preparation from miRNAs can be challenging due to the presence of dimer adapter contaminants. Therefore, we highlight some strategies we adopt to inhibit the generation of dimer adapters during library preparation. Current screens of miRNA targets mostly focus on the identification of targets present in the same organism expressing the miRNA. Our bioinformatics analysis challenges the barrier of evolutionary divergent organisms to identify candidate sequences of the microbiota targeted by the miRNA of plant roots. This protocol should be of interest to researchers investigating interkingdom RNA-based communication between plants and their associated microorganisms, particularly in the context of holobiont responses to abiotic stresses.Key wordsmicroRNARhizosphereMicroorganismsBacteriaFungiNGS libraryBioinformaticsInterkingdom communicationAbiotic stress
The actinobacterial species Cellulomonas fimi ATCC484 has long been known to secrete mannose-containing proteins, but a closer examination of glycoproteins associated with the cell has never been reported. Using ConA lectin chromatography and mass spectrometry we have surveyed the cell associated glycoproteome from C. fimi and collected detailed information on the glycosylation sites of 19 cell-associated glycoproteins. In addition, we have expressed a previously known C. fimi secreted cellulase, Celf_3184, (formerly CenA), a putative peptide prolyl-isomerase, Celf_2022, and a penicillin binding protein, Celf_0189, in the mannosylation capable host, Corynebacterium glutamicum. We found that the glycosylation machinery in C. glutamicum was able to use the recombinant C. fimi proteins as substrates and that the glycosylation matched closely that found in the native proteins when expressed in C. fimi. We are pursuing this observation as a prelude to dissecting the biosynthetic machinery and biological consequences of this protein mannosylation.
IgE Abs, best known for their role in allergic reactions, have only rarely been used in immunotherapies. Nevertheless, they offer a potential alternative to the more commonly used IgGs. The affinity of IgE Ag binding influences the type of response from mast cells, so any immunotherapies using IgEs must balance Ag affinity with desired therapeutic effect. One potential way to harness differential binding affinities of IgE is in protein aggregation diseases, where low-affinity binding of endogenous proteins is preferred, but enhanced binding of clusters of disease-associated aggregated proteins could target responses to the sites of disease. For this reason, we sought to create a low-affinity IgE against the prion protein (PrP), which exists in an endogenous monomeric state but can misfold into aggregated states during the development of prion disease. First, we determined that mast cell proteases tryptase and cathepsin G were capable of degrading PrP. Then we engineered a recombinant IgE Ab directed against PrP from the V region of a PrP-specific IgG and tested its activation of the human mast cell line LAD2. The αPrP IgE bound LAD2 through Fc receptors. Crosslinking receptor-bound αPrP IgE activated SYK and ERK phosphorylation, caused Fc receptor internalization, and resulted in degranulation. This work shows that a recombinant αPrP IgE can activate LAD2 cells to release enzymes that can degrade PrP, suggesting that IgE may be useful in targeting diseases that involve protein aggregation.
This article proposes a mobile robot localization system developed using Google Indoor Street View (GISV) and Convolutional Neural Network (CNN) based visual place recognition. The proposed localization system consists of two main modules. The first is a place recognition module based on GISV and a net Vector of Locally Aggregated Descriptors (VLAD)-based CNN. The second is a factor graph-based optimization module. In this work, we show that a CNN-based approach can be utilized to overcome the lack of visually distinct features in indoor environments and changes in images that can occur when using different cameras at different points in time for localization. The proposed CNN-based localization system is implemented using reference and query images obtained from two different sources (GISV and a camera attached to a mobile robot). It has been experimentally validated using a custom indoor dataset captured at the Memorial University of Newfoundland (MUN) engineering building basement. The main results of this paper show that GISV-based place recognition reduces the percentage drift by 4 % for the dataset and achieves a Root Mean Square Error (RMSE) of 2 m for position and 2.5° for orientation.
Influenza and Respiratory Syncytial virus (RSV) infections together contribute significantly to the burden of acute lower respiratory tract infections. Despite the disease burden, no approved RSV vaccine is available. While approved vaccines are available for influenza, seasonal vaccination is required to maintain protection. In addition to both being respiratory viruses, they follow a common seasonality, which warrants the necessity for a concerted vaccination approach. Here, we designed bivalent vaccines by utilizing highly conserved sequences, targeting both influenza A and RSV, as either a chimeric antigen or individual antigens separated by a ribosome skipping sequence. These vaccines were found to be effective in protecting the animals from challenge by either virus, with mechanisms of protection being substantially interrogated in this communication.
Research on graphene-related two-dimensional (2D) materials (GR2Ms) in recent years is strongly moving from academia to industrial sectors with many new developed products and devices on the market. Characterization and quality control of the GR2Ms and their properties are critical for growing industrial translation, which requires the development of appropriate and reliable analytical methods. These challenges are recognized by International Organization for Standardization (ISO 229) and International Electrotechnical Commission (IEC 113) committees to facilitate the development of these methods and standards which are currently in progress. Toward these efforts, the aim of this study was to perform an international interlaboratory comparison (ILC), conducted under Versailles Project on Advanced Materials and Standards (VAMAS) Technical Working Area (TWA) 41 "Graphene and Related 2D Materials" to evaluate the performance (reproducibility and confidence) of the thermogravimetric analysis (TGA) method as a potential new method for chemical characterization of GR2Ms. Three different types of representative and industrially manufactured GR2Ms samples, namely, pristine few-layer graphene (FLG), graphene oxide (GO), and reduced graphene oxide (rGO), were used and supplied to ILC participants to complete the study. The TGA method performance was evaluated by a series of measurements of selected parameters of the chemical and physical properties of these GR2Ms including the number of mass loss steps, thermal stability, temperature of maximum mass change rate (Tp) for each decomposition step, and the mass contents (%) of moisture, oxygen groups, carbon, and impurities (organic and non-combustible residue). TGA measurements determining these parameters were performed using the provided optimized TGA protocol on the same GR2Ms by 12 participants across academia, industry stakeholders, and national metrology institutes. This paper presents these results with corresponding statistical analysis showing low standard deviation and statistical conformity across all participants that confirm that the TGA method can be satisfactorily used for characterization of these parameters and the chemical characterization and quality control of GR2Ms. The common measurement uncertainty for each parameter, key contribution factors were identified with explanations and recommendations for their elimination and improvements toward their implementation for the development of the ISO/IEC standard for chemical characterization of GR2Ms.
Drug shortages are a global and complex issue having negative impacts on patients, pharmacists, and the broader health care system. Using sales data from 22 Canadian pharmacies and historical drug shortage data, we built machine learning models predicting shortages for the majority of the drugs in the most-dispensed interchangeable groups in Canada. When breaking drug shortages into four classes (none, low, medium, high), we were able to correctly predict the shortage class with 69% accuracy and a kappa value of 0.44, one month in advance, without access to any inventory data from drug manufacturers and suppliers. We also predicted 59% of the shortages deemed to be most impactful (given the demand for the drugs and the potential lack of interchangeable options). The models consider many variables, including the average days of a drug supply per patient, the total days of a drug supply, previous shortages, and the hierarchy of drugs within different drug groups and therapeutic classes. Once in production, the models will allow pharmacists to optimize their orders and inventories, and ultimately reduce the impact of drug shortages on their patients and operations.
The manganese salt bath is considered a primary standard for determining the absolute emission rate of radionuclide neutron sources. The National Research Council of Canada has recently revived its manganese salt bath and a full description of the system is given here. The physical characteristics of the bath, as well as the methods for determining the efficiency of the bath system and the induced activity in the bath, are described. An in-depth analysis of the fraction of neutrons captured in the manganese and the correction factor for neutron losses is also provided. Finally, the results of emission rate measurements of four different sources, complete with an uncertainty budget, are given. The emission rates of three americium-beryllium neutron sources and one californium-252 neutron source were found to agree with the known values, within a standard uncertainty of 1.7%.
Key message rAMP-seq based genomic selection for agronomic traits has been shown to be a useful tool for winter wheat breeding programs by increasing the rate of genetic gain. Abstract Genomic selection (GS) is an effective strategy to employ in a breeding program that focuses on optimizing quantitative traits, which results in the ability for breeders to select the best genotypes. GS was incorporated into a breeding program to determine the potential for implementation on an annual basis, with emphasis on selecting optimal parents and decreasing the time and costs associated with phenotyping large numbers of genotypes. The design options for applying repeat amplification sequencing (rAMP-seq) in bread wheat were explored, and a low-cost single primer pair strategy was implemented. A total of 1870 winter wheat genotypes were phenotyped and genotyped using rAMP-seq. The optimization of training to testing population size showed that the 70:30 ratio provided the most consistent prediction accuracy. Three GS models were tested, rrBLUP, RKHS and feed-forward neural networks using the University of Guelph Winter Wheat Breeding Program (UGWWBP) and Elite-UGWWBP populations. The models performed equally well for both populations and did not differ in prediction accuracy (r) for most agronomic traits, with the exception of yield, where RKHS performed the best with an r = 0.34 and 0.39 for each population, respectively. The ability to operate a breeding program where multiple selection strategies, including GS, are utilized will lead to higher efficiency in the program and ultimately lead to a higher rate of genetic gain.
We present an experimental study of the coherence properties of a single heavy-hole spin qubit formed in one quantum dot of a gated GaAs/AlGaAs double quantum dot device. We use a modified spin-readout latching technique in which the second quantum dot serves both as an auxiliary element for a fast spin-dependent readout within a 200 ns time window and as a register for storing the spin-state information. To manipulate the single-spin qubit, we apply sequences of microwave bursts of various amplitudes and durations to make Rabi, Ramsey, Hahn-echo, and CPMG measurements. As a result of the qubit manipulation protocols combined with the latching spin readout, we determine and discuss the achieved qubit coherence times: T1, TRabi, T2*, and T2CPMG vs. microwave excitation amplitude, detuning, and additional relevant parameters.
Encouraging disclosure is important for the patent system, yet the technical information in patent applications is often inadequate. We use algorithms from computational linguistics to quantify the effectiveness of disclosure in patent applications. Relying on the expectation that universities have more ability and incentive to disclose their inventions than corporations, we analyze 64 linguistic measures of patent applications, and show that university patents are more readable by 0.4 SD of a synthetic measure of readability. Results are robust to controlling for non-disclosure-related invention heterogeneity. The linguistic metrics are evaluated by a panel of “expert” student engineers and further examined by USPTO 112(a) – lack of disclosure – rejection. The ability to quantify disclosure opens new research paths and potentially facilitates improvement of disclosure.
Annotated data is critical for machine learning models, but producing large amounts of data with high-quality labeling is a time-consuming and labor-intensive process. Natural language processing (NLP) and machine learning models have traditionally relied on the labels given by human annotators with varying degrees of competency, training, and experience. These kinds of labels are incredibly problematic because they are defined and enforced by arbitrary and ambiguous standards. In order to solve these issues of insufficient high-quality labels, researchers are now investigating automated methods for enhancing training and testing data sets. In this paper, we demonstrate how our proposed method improves the quality and quantity of data in two cybersecurity problems (fake news identification & sensitive data leak) by employing the clonal selection algorithm (CLONALG) and abstract meaning representation (AMR) graphs, and how it improves the performance of a classifier by at least 5% on two datasets.
Pea (Pisum sativum) is one of the most abundant and sustainable alternate source of protein. Although pea proteins have good quantities of most of the essential amino acids, they have a limited supply of tryptophan, methionine and cysteine. Moreover, pea proteins have poor techno-functional properties compared to proteins from animal sources, limiting their use in certain food applications. Bioprocessing techniques like solid-state fermentation (SSF) and enzymatic processing have been explored to improve the nutrient profile and functionality of pea proteins. However, there is a lack of information about proteomic changes in the food matrix during fermentation of the pea substrate. In this research, samples during SSF of pea protein isolate with Aspergillus oryzae were used for shotgun mass spectrometry (LC-MS/MS) analysis to identify the underlying functional pathways which play direct or indirect roles in enabling the colonization of the substrate leading to potential improvement of functional and nutritional value of pea protein. Results revealed the identity of A. oryzae proteins involved in different metabolic pathways that differed during various stages of SSF. Among them, methionine synthase was identified as an abundant protein, which catalyzes methionine biosynthesis. This might suggest how fermentation processes could be used to improve the presence of sulfur containing amino acids to rebalance the essential amino acid profile and improve the nutritional quality of pea proteins.
Measurement of blood pressure (BP) through manual auscultation and the observation of Korotkoff sounds (KSs) remains the gold standard in BP methodology. Critical to determining BP levels via auscultation is the determination of KS audibility. While absolute sound level audibility is well researched, the problem has not been approached from the point of view of psychoacoustic masking of the sounds. Here, during manual auscultation of BP, a direct comparison is made between what an observer perceives as audible and the electronic analysis of audibility level determined from masking of sound signal levels. KSs are collected during auscultation with an electronic stethoscope, which allows simultaneously observing sound audibility and recording the sound electronically. By time-segmenting the recorded sound around Korotkoff peaks into a test segment and a masking segment, performing Fourier transforms on the segments, and comparing frequency-band sound energy levels, signal-to-noise ratios of a sound to its masking counterpart can be defined. Comparing these ratios to difference limen in the psychoacoustic masking literature, an approximate threshold for sound audibility is obtained. It is anticipated that this approach could have profound effects on future development of automated auscultation BP measurements.
A record‐high mobility of holes, reaching 4.3 × 106 cm2 V−1 s−1 at 300 mK in an epitaxial strained germanium (s‐Ge) semiconductor, grown on a standard silicon wafer, is reported. This major breakthrough is achieved due to the development of state‐of‐the‐art epitaxial growth technology culminating in superior monocrystalline quality of the s‐Ge material platform with a very low density of background impurities and other imperfections. As a consequence, the hole mobility in s‐Ge appears to be ≈2 times higher than the highest electron mobility in strained silicon. In addition to the record mobility, this material platform reveals a unique combination of properties, which are a very large and tuneable effective g*‐factor (>18), a very low percolation density (5 × 109 cm−2) and a small effective mass (0.054 m 0). This long‐sought combination of parameters in one material system is important for the research and development of low‐temperature electronics with reduced Joule heating and for quantum‐electronics circuits based on spin qubits. A record‐high mobility of holes in germanium semiconductor grown on a standard silicon wafer is reported, which exceeds the previous state‐of‐the‐art value by over 4 times. Moreover, this material platform reveals a unique combination of properties, essential for quantum‐electronics applications, which are very large and tuneable effective g*‐factor, the lowest percolation density, and small effective mass.
Sodium butyrate (NaBu) is a class I histone deacetylase inhibitor (HDACi) that can impede the proliferation of transformed cells. Although some HDACi downregulate the expression of the stem cell factor receptor (KIT/CD117), the effect of NaBu on KIT expression and human mast cell proliferation requires further elucidation. In this study, we examined the effects of NaBu on three transformed human mast cell lines, HMC-1.1, HMC-1.2 and LAD2. NaBu (100 µM) inhibited the proliferation and metabolic activity of all three cell lines without significantly affecting their viability, suggesting that although the cells had ceased to divide, they were not yet undergoing apoptosis. Cell cycle analysis using the cell-permeant dye, propidium iodide, indicated that NaBu significantly blocked the cell cycle progression of HMC-1.1 and HMC-1.2 from G1 to G2/M phases. Furthermore, NaBu downregulated the expression of C-KIT mRNA and KIT protein expression in all three cell lines, but this effect was most significant in the HMC-1.1 and HMC-1.2, both of which harbour activating mutations in KIT, which proliferate more rapidly than LAD2. These data support earlier observations showing that human mast cell lines are sensitive to histone deacetylase inhibition. However, our data presents the novel observation that inhibition of cell proliferation by NaBu was not associated with a loss in cell viability but rather an arrest of the cell cycle. Higher concentrations of NaBu led to modest increases in histamine content, tryptase expression, and granularity. In conclusion, NaBu treatment of human mast cell lines led to a modest enhancement of the hallmarks of mature mast cells.
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