Strand specific RNA-sequencing and membrane lipid profiling reveals growth phase-dependent cold stress response mechanisms in Listeria monocytogenes

The human pathogen Listeria monocytogenes continues to pose a challenge in the food industry, where it is known to contaminate ready-to-eat foods and grow during refrigerated storage. Increased knowledge of the cold-stress response of this pathogen will enhance the ability to control it in the food-supply-chain. This study utilized strand-specific RNA sequencing and whole cell fatty acid (FA) profiling to characterize the bacterium's cold stress response. RNA and FAs were extracted from a cold-tolerant strain at five time points between early lag phase and late stationary-phase, both at 4°C and 20°C. Overall, more genes (1.3×) were suppressed than induced at 4°C. Late stationary-phase cells exhibited the greatest number (n = 1,431) and magnitude (>1,000-fold) of differentially expressed genes (>2-fold, p<0.05) in response to cold. A core set of 22 genes was upregulated at all growth phases, including nine genes required for branched-chain fatty acid (BCFA) synthesis, the osmolyte transporter genes opuCBCD, and the internalin A and D genes. Genes suppressed at 4°C were largely associated with cobalamin (B12) biosynthesis or the production/export of cell wall components. Antisense transcription accounted for up to 1.6% of total mapped reads with higher levels (2.5×) observed at 4°C than 20°C. The greatest number of upregulated antisense transcripts at 4°C occurred in early lag phase, however, at both temperatures, antisense expression levels were highest in late stationary-phase cells. Cold-induced FA membrane changes included a 15% increase in the proportion of BCFAs and a 15% transient increase in unsaturated FAs between lag and exponential phase. These increases probably reduced the membrane phase transition temperature until optimal levels of BCFAs could be produced. Collectively, this research provides new information regarding cold-induced membrane composition changes in L. monocytogenes, the growth-phase dependency of its cold-stress regulon, and the active roles of antisense transcripts in regulating its cold stress response

A fully-mapped and energy-efficient FPGA accelerator for dual-function AI-based analysis of ECG

In this paper, a fully-mapped field programmable gate array (FPGA) accelerator is proposed for artificial intelligence (AI)-based analysis of electrocardiogram (ECG). It consists of a fully-mapped 1-D convolutional neural network (CNN) and a fully-mapped heart rate estimator, which constitute a complementary dual-function analysis. The fully-mapped design projects each layer of the 1-D CNN to a hardware module on an Intel Cyclone V FPGA, and a virtual flatten layer is proposed to effectively bridge the feature extraction layers and fully-connected layer. Also, the fully-mapped design maximizes computational parallelism to accelerate CNN inference. For the fully-mapped heart rate estimator, it performs pipelined transformations, self-adaptive threshold calculation, and heartbeat count on the FPGA, without multiplexed usage of hardware resources. Furthermore, heart rate calculation is elaborately analyzed and optimized to remove division and acceleration, resulting in an efficient method suitable for hardware implementation. According to our experiments on 1-D CNN, the accelerator can achieve 43.08× and 8.38× speedup compared with the software implementations on ARM-Cortex A53 quad-core processor and Intel Core i7-8700 CPU, respectively. For the heart rate estimator, the hardware implementations are 25.48× and 1.55× faster than the software implementations on the two aforementioned platforms. Surprisingly, the accelerator achieves an energy efficiency of 63.48 GOPS/W, which obviously surpasses existing studies. Considering its power consumption is only 67.74 mW, it may be more suitable for resource-limited applications, such as wearable and portable devices for ECG monitoring

Neural network-based model for evaluating inert nodules and volume doubling time in T1 lung adenocarcinoma: a nested case−control study

ObjectiveThe purpose of this study is to establish model for assessing inert nodules predicting nodule volume-doubling.MethodsA total of 201 patients with T1 lung adenocarcinoma were analysed retrospectively pulmonary nodule information was predicted by an AI pulmonary nodule auxiliary diagnosis system. The nodules were classified into two groups: inert nodules (volume-doubling time (VDT)&gt;600 days n=152) noninert nodules (VDT&lt;600 days n=49). Then taking the clinical imaging features obtained at the first examination as predictive variables the inert nodule judgement model &lt;sn&lt;/sn&gt;&gt;(INM) volume-doubling time estimation model (VDTM) were constructed based on a deep learning-based neural network. The performance of the INM was evaluated by the area under the curve (AUC) obtained from receiver operating characteristic (ROC) analysis the performance of the VDTM was evaluated by R2(determination coefficient).ResultsThe accuracy of the INM in the training and testing cohorts was 81.13% and 77.50%, respectively. The AUC of the INM in the training and testing cohorts was 0.7707 (95% CI 0.6779-0.8636) and 0.7700 (95% CI 0.5988-0.9412), respectively. The INM was effective in identifying inert pulmonary nodules; additionally, the R2 of the VDTM in the training cohort was 0.8008, and that in the testing cohort was 0.6268. The VDTM showed moderate performance in estimating the VDT, which can provide some reference during a patients’ first examination and consultationConclusionThe INM and the VDTM based on deep learning can help radiologists and clinicians distinguish among inert nodules and predict the nodule volume-doubling time to accurately treat patients with pulmonary nodules

A semi-quantitative upconversion nanoparticle-based immunochromatographic assay for SARS-CoV-2 antigen detection

The unprecedented public health and economic impact of the coronavirus disease 2019 (COVID-19) pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection has been met with an equally unprecedented scientific response. Sensitive point-of-care methods to detect SARS-CoV-2 antigens in clinical specimens are urgently required for the rapid screening of individuals with viral infection. Here, we developed an upconversion nanoparticle-based lateral flow immunochromatographic assay (UCNP-LFIA) for the high-sensitivity detection of SARS-CoV-2 nucleocapsid (N) protein. A pair of rabbit SARS-CoV-2 N-specific monoclonal antibodies was conjugated to UCNPs, and the prepared UCNPs were then deposited into the LFIA test strips for detecting and capturing the N protein. Under the test conditions, the limit of detection (LOD) of UCNP-LFIA for the N protein was 3.59 pg/mL, with a linear range of 0.01–100 ng/mL. Compared with that of the current colloidal gold-based LFIA strips, the LOD of the UCNP-LFIA-based method was increased by 100-fold. The antigen recovery rate of the developed method in the simulated pharyngeal swab samples ranged from 91.1 to 117.3%. Furthermore, compared with the reverse transcription-polymerase chain reaction, the developed UCNP-LFIA method showed a sensitivity of 94.73% for 19 patients with COVID-19. Thus, the newly established platform could serve as a promising and convenient fluorescent immunological sensing approach for the efficient screening and diagnosis of COVID-19

Genome-wide association study on serum alkaline phosphatase levels in a Chinese population

Background: Serum alkaline phosphatase (ALP) is a complex phenotype influenced by both genetic and environmental factors. Recent Genome-Wide Association Studies (GWAS) have identified several loci affecting ALP levels; however, such studies in Chinese populations are limited. We performed a GWAS analyzing the association between 658,288 autosomal SNPs and serum ALP in 1,461 subjects, and replicated the top SNPs in an additional 8,830 healthy Chinese Han individuals. The interactions between significant locus and environmental factors on serum ALP levels were further investigated. Results: The association between ABO locus and serum ALP levels was replicated (P = 2.50 × 10-21, 1.12 × 10-56 and 2.82 × 10-27 for SNP rs8176720, rs651007 and rs7025162 on ABO locus, respectively). SNP rs651007 accounted for 2.15% of the total variance of serum ALP levels independently of the other 2 SNPs. When comparing our findings with previously published studies, ethnic differences were observed across populations. A significant interaction between ABO rs651007 and overweight and obesity was observed (FDR for interaction was 0.036); for individuals with GG genotype, those with normal weight and those who were overweight or obese have similar serum ALP concentrations; minor allele A of rs651007 remarkably reduced serum ALP levels, but this effect was attenuated in overweight and obese individuals. Conclusions: Our findings indicate that ABO locus is a major determinant for serum ALP levels in Chinese Han population. Overweight and obesity modifies the effect of ABO locus on serum ALP concentrations

Genomic and Phenotypic Analysis of Salmonella enterica Bacteriophages Identifies Two Novel Phage Species

Bacteriophages (phages) are potential alternatives to chemical antimicrobials against pathogens of public health significance. Understanding the diversity and host specificity of phages is important for developing effective phage biocontrol approaches. Here, we assessed the host range, morphology, and genetic diversity of eight Salmonella enterica phages isolated from a wastewater treatment plant. The host range analysis revealed that six out of eight phages lysed more than 81% of the 43 Salmonella enterica isolates tested. The genomic sequences of all phages were determined. Whole-genome sequencing (WGS) data revealed that phage genome sizes ranged from 41 to 114 kb, with GC contents between 39.9 and 50.0%. Two of the phages SB13 and SB28 represent new species, Epseptimavirus SB13 and genera Macdonaldcampvirus, respectively, as designated by the International Committee for the Taxonomy of Viruses (ICTV) using genome-based taxonomic classification. One phage (SB18) belonged to the Myoviridae morphotype while the remaining phages belonged to the Siphoviridae morphotype. The gene content analyses showed that none of the phages possessed virulence, toxin, antibiotic resistance, type I–VI toxin–antitoxin modules, or lysogeny genes. Three (SB3, SB15, and SB18) out of the eight phages possessed tailspike proteins. Whole-genome-based phylogeny of the eight phages with their 113 homologs revealed three clusters A, B, and C and seven subclusters (A1, A2, A3, B1, B2, C1, and C2). While cluster C1 phages were predominantly isolated from animal sources, cluster B contained phages from both wastewater and animal sources. The broad host range of these phages highlights their potential use for controlling the presence of S. enterica in foods

Optimizing Drug Delivery: Characterization of DLin-KC2-DMA/Distearoylphosphatidylserine by 31P and 2H NMR Spectroscopy

Lipid nanoparticles (LNPs) are used to deliver siRNA to hepatocytes via endocytosis and subsequent endosomal release. With 99% of the LNPs taken up by the cell via endocytosis, only 1% of the siRNA is actually released into the cell cytosol. To improve the effectiveness of LNPs association with and disruption of endosomal membranes, the biophysical properties of a model system composed of 1:1 molar ratio of anionic lipid 1,2-distearoyl(d70)-sn-glycero-3-[phospho-L-serine] (DSPS-d70) and the cationic lipid DLin-KC2-DMA are characterized by 2H and 31P NMR spectroscopy. The bilayer to inverted hexagonal (HII) phase transition of the model system was shown to be influenced by temperature, pH and salt concentration. The order parameter profiles are obtained, revealing the extent of acyl chain movement of DSPS-d70 in bilayer and HII phases. The results help provide insights into computational simulation and eventually optimized LNPs design

Addressing uncertainty and modeling error in the design and control of process systems : methods and applications

A process system faces the challenge of uncertainty throughout its lifetime. At the design stage, uncertainty originates from inaccurate knowledge of design parameters and unmeasured or unmeasurable ambient disturbances. Oftentimes, designers choose to increase system size to account for uncertainty and fluctuations; however, this approach has an economic limit, past which the capital expenditure outweighs the potential operational benefits. In the operational stage, uncertainty is manifest, amongst others, in fluctuations in operating conditions, market demand and raw material availability. Another type of uncertainty in (modern) process operations is related to the quality of process models that are used for making control and operational decisions. Of particular importance is the quality of the dynamic models that are used in real-time optimal control computations. The chemical industry has been the pioneer (and is currently the leader) of model predictive control (MPC) implementations, whereby the control moves are computed, over a receding time horizon, by solving an optimal control problem at each time step. While uniquely able to deal with large-scale, non-square constrained systems, MPC is vitally dependent on the predictive abilities of the built-in model. Changes in plant conditions are a a source of uncertainty in this case as-well, leading to a discrepancy (mismatch) between the model predictions and the true plant behavior. In this dissertation, I address the problems of design under uncertainty and plant-model mismatch. For the former, identification-based optimization (IBO) framework is proposed as a new, computationally efficient framework for optimizing the design of dynamic systems under uncertainty problem. The framework uses properly designed pseudo-random multilevel signals (PRMS) to represent time-varying uncertain variables. This allows us to formulate the design under uncertainty problem as a dynamic optimization problem. A solution algorithm is proposed using a sequential approach. Several application examples are discussed, demonstrating the superior computational performance of the IBO approach. Furthermore, an extension of the method that explicitly considers the tradeoff between conservativeness and dynamic performance is introduced. The latter, plant-model mismatch problem, is addressed using a novel autocovariance-based approach. Under appropriate assumptions, an explicit relation is established between the autocovariance of the process output and the plant-model mismatch terms, represented either in a step response model or a transfer function model. It is demonstrated that an asymptotically correct set of estimates of the values of plant-model mismatch for each model parameters is the global minimizer of the discrepancy between the autocovariance predicted using the relation and the autocovariance calculated from a data set collected from closed-loop operating data. Extensions of this approach handle cases where the active set of the MPC is changing over time and there are setpoint change and measurable disturbances occur in the control loop.Chemical Engineerin

Whole-Genome Comparison Reveals Divergent IR Borders and Mutation Hotspots in Chloroplast Genomes of Herbaceous Bamboos (Bambusoideae: Olyreae)

Herbaceous bamboos (Olyreae) are a separate lineage with idiosyncratic traits, e.g., unisexual flowers and annual or seasonal flowering lifestyle, in the grass family. To elucidate the evolution of herbaceous bamboos we produced two complete chloroplast (cp) genomes from two monotypic genera i.e., Froesiochloa and Rehia via the genome-skimming approach. The assembled F. boutelouoides and R. nervata cp genomes were 135,905 and 136,700 base-pair (bp), respectively. Further whole-genome comparative analyses revealed that the cp genes order was perfectly collinear, but the inverted repeats (IRs) borders, i.e., the junctions between IRs and single copy regions, were highly divergent in Olyreae. The IRs expansions/contractions occurred frequently in Olyreae, which have caused gene content and genome size variations, e.g., the copy number reduction of rps19 and trnH(GUG) genes in Froesiochloa. Subsequent nucleotide mutation analyses uncovered a greatly heterogeneous divergence pattern among different cpDNA regions in Olyreae cp genomes. On average, non-coding loci evolved at a rate of circa 1.9 times faster than coding loci, from which 20 rapidly evolving loci were determined as potential genetic markers for further studies on Olyreae. In addition, the phylogenomic analyses from 67 grass plastomes strongly supported the phylogenetic positions of Froesiochloa and Rehia in the Olyreae