Apprenticeship Standard : Non-Destructive Testing Engineer

High-efficiency video compression technology is of primary importance to the storage and transmission of digital medical video in modern medical communication systems. To further improve the compression performance of medical ultrasound video, two innovative technologies based on diagnostic region-of-interest (ROI) extraction using the high efficiency video coding (H.265/HEVC) standard are presented in this paper. First, an effective ROI extraction algorithm based on image textural features is proposed to strengthen the applicability of ROI detection results in the H.265/HEVC quad-tree coding structure. Second, a hierarchical coding method based on transform coefficient adjustment and a quantization parameter (QP) selection process is designed to implement the otherness encoding for ROIs and non-ROIs. Experimental results demonstrate that the proposed optimization strategy significantly improves the coding performance by achieving a BD-BR reduction of 13.52% and a BD-PSNR gain of 1.16 dB on average compared to H.265/HEVC (HM15.0). The proposed medical video coding algorithm is expected to satisfy low bit-rate compression requirements for modern medical communication systems

Comparative Genomics of Bacillus thuringiensis Reveals a Path to Specialized Exploitation of Multiple Invertebrate Hosts

This is the final version of the article. Available from American Society for Microbiology via the DOI in this record.Understanding the genetic basis of host shifts is a key genomic question for pathogen and parasite biology. The Bacillus cereus group, which encompasses Bacillus thuringiensis and Bacillus anthracis, contains pathogens that can infect insects, nematodes, and vertebrates. Since the target range of the essential virulence factors (Cry toxins) and many isolates is well known, this group presents a powerful system for investigating how pathogens can diversify and adapt to phylogenetically distant hosts. Specialization to exploit insects occurs at the level of the major clade and is associated with substantial changes in the core genome, and host switching between insect orders has occurred repeatedly within subclades. The transfer of plasmids with linked cry genes may account for much of the adaptation to particular insect orders, and network analysis implies that host specialization has produced strong associations between key toxin genes with similar targets. Analysis of the distribution of plasmid minireplicons shows that plasmids with orf156 and orf157, which carry genes encoding toxins against Lepidoptera or Diptera, were contained only by B. thuringiensis in the specialized insect clade (clade 2), indicating that tight genome/plasmid associations have been important in adaptation to invertebrate hosts. Moreover, the accumulation of multiple virulence factors on transposable elements suggests that cotransfer of diverse virulence factors is advantageous in terms of expanding the insecticidal spectrum, overcoming insect resistance, or through gains in pathogenicity via synergistic interactions between toxins.IMPORTANCE Population genomics have provided many new insights into the formation, evolution, and dynamics of bacterial pathogens of humans and other higher animals, but these pathogens usually have very narrow host ranges. As a pathogen of insects and nematodes, Bacillus thuringiensis, which produces toxins showing toxicity to many orders of insects and other invertebrates, can be used as a model to study the evolution of pathogens with wide host ranges. Phylogenomic analysis revealed that host specialization and switching occur at the level of the major clade and subclade, respectively. A toxin gene co-occurrence network indicates that multiple toxins with similar targets were accumulated by the same cell in the whole species. This accumulation may be one of the strategies that B. thuringiensis has used to fight against host resistance. This kind of formation and evolution of pathogens represents a different path used against multiple invertebrate hosts from that used against higher animals.This work was supported by the National Key Research and Development Program of China (2017YFD0201201), the China 948 Program of the Ministry of Agriculture (2016-X21), the National Natural Science Foundation of China (NSFC) (31500003 and 31670085), the China Postdoctoral Science Foundation-funded project (2015M580649 and 2016T90700), and Chinese Fundamental Research Funds for the Central Universities (2662016QD039, 2662015PY123, and 2662017PY094)

Precision Cas9 Genome Editing in vivo with All-in-one, Self-targeting AAV Vectors [preprint]

Adeno-associated virus (AAV) vectors are important delivery platforms for therapeutic genome editing but are severely constrained by cargo limits, especially for large effectors like Cas9s. Simultaneous delivery of multiple vectors can limit dose and efficacy and increase safety risks. The use of compact effectors has enabled single-AAV delivery of Cas9s with 1-3 guides for edits that use end-joining repair pathways, but many precise edits that correct disease-causing mutations in vivo require homology-directed repair (HDR) templates. Here, we describe single-vector, ~4.8-kb AAV platforms that express Nme2Cas9 and either two sgRNAs to produce segmental deletions, or a single sgRNA with an HDR template. We also examine the utility of Nme2Cas9 target sites in the vector for self-inactivation. We demonstrate that these platforms can effectively treat two disease models [type I hereditary tyrosinemia (HT-I) and mucopolysaccharidosis type I (MPS-I)] in mice. These results will enable single-vector AAVs to achieve diverse therapeutic genome editing outcomes

SMA1, a homolog of the splicing factor Prp28, has a multifaceted role in miRNA biogenesis in Arabidopsis

MicroRNAs (miRNAs) are a class of small non-coding RNAs that repress gene expression. In plants, the RNase III enzyme Dicer-like (DCL1) processes primary miRNAs (pri-miRNAs) into miRNAs. Here, we show that SMALL1 (SMA1), a homolog of the DEADbox pre-mRNA splicing factor Prp28, plays essential roles in miRNA biogenesis in Arabidopsis. A hypomorphic sma1-1 mutation causes growth defects and reduces miRNA accumulation correlated with increased target transcript levels. SMA1 interacts with the DCL1 complex and positively influences primiRNA processing. Moreover, SMA1 binds the promoter region of genes encoding pri-miRNAs (MIRs) and is required for MIR transcription. Furthermore, SMA1 also enhances the abundance of the DCL1 protein levels through promoting the splicing of the DCL1 pre-mRNAs. Collectively, our data provide new insights into the function of SMA1/Prp28 in regulating miRNA abundance in plants

Genome-wide identification and functional exploration of the legume lectin genes in Brassica napus and their roles in Sclerotinia disease resistance

As one of the largest classes of lectins, legume lectins have a variety of desirable features such as antibacterial and insecticidal activities as well as anti-abiotic stress ability. The Sclerotinia disease (SD) caused by the soil-borne fungus Sclerotinia sclerotiorum is a devastating disease affecting most oil crops such as Brassica napus. Here, we identified 130 legume lectin (LegLu) genes in B. napus, which could be phylogenetically classified into seven clusters. The BnLegLu gene family has been significantly expanded since the whole-genome duplication (WGD) or segmental duplication. Gene structure and conserved motif analysis suggested that the BnLegLu genes were well conserved in each cluster. Moreover, relative to those genes only containing the legume lectin domain in cluster VI–VII, the genes in cluster I–V harbored a transmembrane domain and a kinase domain linked to the legume lectin domain in the C terminus. The expression of most BnLegLu genes was relatively low in various tissues. Thirty-five BnLegLu genes were responsive to abiotic stress, and 40 BnLegLu genes were strongly induced by S. sclerotiorum, with a most significant up-regulation of 715-fold, indicating their functional roles in SD resistance. Four BnLegLu genes were located in the candidate regions of genome-wide association analysis (GWAS) results which resulted from a worldwide rapeseed population consisting of 324 accessions associated with SD. Among them, the positive role of BnLegLus-16 in SD resistance was validated by transient expression in tobacco leaves. This study provides important information on BnLegLu genes, particularly about their roles in SD resistance, which may help targeted functional research and genetic improvement in the breeding of B. napus

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. In 2020, the COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups. This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level. One of these models was a multi-model ensemble that combined all available forecasts each week. The performance of individual models showed high variability across time, geospatial units, and forecast horizons. Half of the models evaluated showed better accuracy than a naïve baseline model. In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model. Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub (https://covid19forecasthub.org/) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multimodel ensemble forecast that combined predictions from dozens of groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-wk horizon three to five times larger than when predicting at a 1-wk horizon. This project underscores the role that collaboration and active coordination between governmental public-health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks