Search

CRISPR-Cas systems are adaptive immune mechanisms present in most prokaryotes that play an important role in the adaptation of bacteria and archaea to new environments. Shewanella algae is a marine zoonotic pathogen with worldwide distribution, which accounts for the majority of clinical cases of Shewanella infections. However, the characterization of Shewanella algae CRISPR-Cas systems has not been well investigated yet. Through whole genome sequence analysis, we characterized the CRISPR-Cas systems in S. algae. Our results indicate that CRISPR-Cas systems are prevalent in S. algae, with the majority of strains containing the Type I-F system. This study provides new insights into the diversity and function of CRISPR-Cas systems in S. algae and highlights their potential role in the adaptation and survival of these marine pathogens.

In a rural/local energy community (LEC), a PV-battery-based DC microgrid (MG) normally operates at the off-grid/islanded mode. At this off-grid operation mode, the reliability of power converters in both the PV system and the battery energy storage system (BESS) could be reduced by dynamic operation scenarios of the DC microgrid which are frequently repeated, such as the intermittency of PV-system output power, the random fluctuation of load power. Indeed, the dynamic operation of the PV-generating system and the load system in the off-grid DC microgrid could lead to a certain decrease in the reliability of DC–DC bidirectional converters of the BESS, boost converters of the PV system, and buck converters for low-voltage DC loads because of withstanding different power flows to provide a source-load power balance in the DC microgrid. Moreover, transient operation scenarios of the off-grid DC microgrid can significantly impact the reliability of the mentioned power converters. To make the above assumptions more clear, reliability analysis of an aggregate power conversion unit (APCU) in the off-grid PV-battery-based DC microgrid under dynamic and transient operation in the local energy community is presented in this paper. The APCU contains the boost converter of the PV system, the bidirectional converter of the BESS, and the buck converter of the low-voltage DC load system. The main objective of the paper is to shed light on calculations of the dynamic-voltage-dependent failure rate (DVDFR) and fault-current-dependent failure rate (FCDFR) of the APCU from dynamic and transient operation conditions respectively in the off-grid LVDC microgrid. Then, a novel combination of the useful-time-dependent failure rate (UTDFR), the DVDFR, and the FCDFR is proposed to evaluate the system-level and component-level reliability of the APCU in the off-grid DC microgrid. Markov-state transition diagrams are applied for the APCU’s reliability assessment. Experimental results show that the reliability of the bidirectional power converter is more affected by the dynamic and transient operation than that of the boost or buck converters in the APCU. In addition, the DVDFR of the APCU is almost smaller than its FCDFR, but the system-level reliability of the APCU could be significantly reduced by dynamic cases in the islanded DC microgrid. Last but not least, the MTTF and MTBF of the APCU could be dramatically decreased by the dynamic and transient operation of the DC microgrid.

Abstract Dynamic fluctuations in the PV‐source power and different DC‐fault scenarios in the off‐grid PV‐battery‐based DC microgrid could lead to a rapid decrease in the reliability of the PV‐generating system. To make this viewpoint clearer, this paper proposes a novel reliability‐evaluating methodology for PV‐generating systems in the islanded DC microgrid under dynamic and transient operation conditions. Firstly, the dynamic‐voltage‐varying failure rate (DVVFR) and the fault‐current‐varying failure rate (FCVFR) of a PV‐generating system in the off‐grid DC microgrid are formulated. The DVVFR mainly depends on dynamic fluctuations in the PV‐source power and the load power, whereas the FCVFR mostly represents the failure probability due to pole‐to‐pole and pole‐to‐ground faults in the DC microgrid. Then, a possible combination of the used‐time‐varying failure rate (TVFR), the power‐loss and temperature‐dependent failure rate, the DVVFR, and the FCVFR is proposed for evaluating the system‐level and component‐level reliability of PV‐generating sources. Finally, Markov‐state transition diagrams and Chapman–Kolmogorov equations are derived and applied for the PV‐system reliability assessment in the off‐grid PV‐battery‐based DC microgrid. Experimental results are analysed to reveal that the operation of DC‐DC power converters dominates the PV‐system reliability under the dynamic and transient operation. The DVVFR of the PV system is mostly smaller than its FCVFR, however, the system‐level reliability of the PV‐generating source is significantly reduced by dynamic‐voltage‐fluctuating cases due to the more frequent repetition of these dynamic cases. Moreover, the MTTF and MTBF of the PV‐generating system could also be dramatically decreased by the dynamic and transient operation of the DC microgrid.

Deep convolutional neural networks (CNNs) are difficult to be fully deployed to edge devices because of both memory-intensive and computation-intensive workloads. The energy efficiency of CNNs is dominated by convolution computation and off-chip memory (DRAM) accesses, especially for DRAM accesses. In this article, an energy-efficient accelerator is proposed for sparse compressed CNNs by reducing DRAM accesses and eliminating zero-operand computation. Weight compression is utilized for sparse compressed CNNs to reduce the required memory capacity/bandwidth and a large portion of connections. Thus, a tile-based row-independent compression (TRC) method with relative indexing memory is adopted for storing none-zero terms. Additionally, the workloads are distributed based on channels to increase the degree of task parallelism, and all-row-to-all-row non-zero element multiplication is adopted for skipping redundant computation. The simulation results over the dense accelerator show that the proposed accelerator achieves $1.79\times$ speedup and reduces 23.51%, 69.53%, 88.67% on-chip memory size, energy, and DRAM accesses of VGG-16.

Background: The prevalence rate of obstructive sleep apnea (OSA) in the community Down syndrome (DS) children is not clear. Moreover, the impact of OSA and sleep structure on the cognitive function is inconclusive. The present study aimed to investigate 1) the prevalence rate of OSA in the community DS children and 2) the impact of OSA and sleep structure on cognitive performance. Methods: Thirty DS children aged 6–18 years were recruited and evaluated with the performance of the language domain and sensorimotor domain, combining neuropsychological tests and parent-rated behavior. The outcomes were the age-adjusted scores, of which the lower the score was, the better was the patient's ability. The association of score with OSA and sleep structures was determined by linear regression. To diminish the age-related difference, all analyses were conducted separately for all subjects and 6–12-year-old subjects. Results: The median age was 11.3 years and median Full-Scale Intelligence Quotient (FSIQ) was 44. The prevalence of OSA (apnea–hypopnea index ≥ 1/h) was 80% and 62.5% in all subjects and 6–12-year-old subjects, respectively. For 6–12-year-old subjects, after adjustment for age and FSIQ, both %REM and OSA were associated with lower score of the subtest of language domain, WPPSI-R Vocabulary, while %REM was also associated with lower score of VABS-II Communication – Expressive. In contrary, % slow wave sleep was not associated with any subtest. Conclusion: This study identified that OSA may be highly prevalent in community DS children. Among 6–12-year-old DS children, OSA and % REM were associated with their language function. Keywords: Down syndrome, Neuropsychological tests, Polysomnography, Sleep apnea, Slow wave sleep

Summary: The enlarged distinct bulky-ball-like nucleolus matrix assembly is observed in most cancer stem cells (CSCs); however, the underlying mechanism is largely unknown. We show that matrix metalloproteinase-7 (MMP-7) shedding MUC-1 SEA domain releases MUC-1 C-ter, facilitating the nucleolus trafficking of p53 in gefitinib-resistant lung CSCs. The nucleolus colocalizations of p53, MUC-1 C-ter, MMP-7 and nucleolin were observed in the CD34+ CXADR+ CD44v3+ gefitinib-resistant EGFRL858R/T790M CSC colonies. MUC-1 C-ter induced a unique porous bulky-ball-shaped, cagelike nucleolus that functions as a nucleus molecular “garage” for potent tumor suppressor, p53. Nucleolus could also facilitate the novel sub-nucleus compartment for proteolytic processing p53 by MMP-7 to generate a 35 kDa fragment. Moreover, we show that salinomycin, an anti-CSC agent, disrupts nucleolus by inducing nucleoplasm translocation of p53 and sensitizing CSC to chemotherapy drugs. Thus, this study highlights the MMP-7-MUC-1-p53 axis in nucleolus as a potential therapeutic target for anti-CSCs to resolve the chemotherapy-resistance dilemma.

Energy-efficient and reliable channels are provided for on-chip interconnection networks (OCINs) using a self-calibrated voltage scaling technique with self-corrected green (SCG) coding scheme. This self-calibrated low-power coding and voltage scaling technique increases reliability and reduces energy consumption simultaneously. The SCG coding is a joint bus and error correction coding scheme that provides a reliable mechanism for channels. In addition, it achieves a significant reduction in energy consumption via a joint triplication bus power model for crosstalk avoidance. Based on SCG coding scheme, the proposed self-calibrated voltage scaling technique adjusts voltage swing for energy reduction. Furthermore, this technique tolerates timing variations. Based on UMC 65 nm CMOS technology, the proposed channels reduces energy consumption by nearly 28.3% compared with that for uncoded channels at the lowest voltage. This approach makes the channels of OCINs tolerant of transient malfunctions and realizes energy efficiency.

Gold nanoparticles (AuNPs) have become the rising stars in the field of nanotechnology and made a revolution in exploiting the profundity of genomics due to their distinguished properties such as stability, ease in preparation and conjugation, biocompatibility, and unique optical properties. These characteristics have greatly expanded their applications such as sensitive and selective quantitation of nucleic acids and as effective carriers for specifically delivering various important molecules/biomolecules to various targets, which are the cornerstone in treating genetic disorders. This review comprehensively discusses the most recent progress in utilization of AuNPs in quantitation and delivery of nucleic acids. The future prospects and challenges of various methods have also been illustrated. It is believed that researchers will continue to overcome the limitations in previous approaches and AuNPs will still play vital roles in the development of diagnosis and treatment of gene-related diseases. [ABSTRACT FROM AUTHOR]