Whole-genome scanning reveals environmental selection mechanisms that shape diversity in populations of the epipelagic diatom Chaetoceros.

Diatoms form a diverse and abundant group of photosynthetic protists that are essential players in marine ecosystems. However, the microevolutionary structure of their populations remains poorly understood, particularly in polar regions. Exploring how closely related diatoms adapt to different environments is essential given their short generation times, which may allow rapid adaptations, and their prevalence in marine regions dramatically impacted by climate change, such as the Arctic and Southern Oceans. Here, we address genetic diversity patterns in Chaetoceros, the most abundant diatom genus and one of the most diverse, using 11 metagenome-assembled genomes (MAGs) reconstructed from Tara Oceans metagenomes. Genome-resolved metagenomics on these MAGs confirmed a prevalent distribution of Chaetoceros in the Arctic Ocean with lower dispersal in the Pacific and Southern Oceans as well as in the Mediterranean Sea. Single-nucleotide variants identified within the different MAG populations allowed us to draw a landscape of Chaetoceros genetic diversity and revealed an elevated genetic structure in some Arctic Ocean populations. Gene flow patterns of closely related Chaetoceros populations seemed to correlate with distinct abiotic factors rather than with geographic distance. We found clear positive selection of genes involved in nutrient availability responses, in particular for iron (e.g., ISIP2a, flavodoxin), silicate, and phosphate (e.g., polyamine synthase), that were further supported by analysis of Chaetoceros transcriptomes. Altogether, these results highlight the importance of environmental selection in shaping diatom diversity patterns and provide new insights into their metapopulation genomics through the integration of metagenomic and environmental data.

Anomaly Detection IDS for Detecting DoS Attacks in IoT Networks Based on Machine Learning Algorithms.

Widespread and ever-increasing cybersecurity attacks against Internet of Things (IoT) systems are causing a wide range of problems for individuals and organizations. The IoT is self-configuring and open, making it vulnerable to insider and outsider attacks. In the IoT, devices are designed to self-configure, enabling them to connect to networks autonomously without extensive manual configuration. By using various protocols, technologies, and automated processes, self-configuring IoT devices are able to seamlessly connect to networks, discover services, and adapt their configurations without requiring manual intervention or setup. Users' security and privacy may be compromised by attackers seeking to obtain access to their personal information, create monetary losses, and spy on them. A Denial of Service (DoS) attack is one of the most devastating attacks against IoT systems because it prevents legitimate users from accessing services. A cyberattack of this type can significantly damage IoT services and smart environment applications in an IoT network. As a result, securing IoT systems has become an increasingly significant concern. Therefore, in this study, we propose an IDS defense mechanism to improve the security of IoT networks against DoS attacks using anomaly detection and machine learning (ML). Anomaly detection is used in the proposed IDS to continuously monitor network traffic for deviations from normal profiles. For that purpose, we used four types of supervised classifier algorithms, namely, Decision Tree (DT), Random Forest (RF), K Nearest Neighbor (kNN), and Support Vector Machine (SVM). In addition, we utilized two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA) and compared their performances. We also utilized the IoTID20 dataset, one of the most recent for detecting anomalous activity in IoT networks, to train our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by GA. However, other metrics, such as training and testing times, showed that DT was superior.

Risk evaluation and mitigation against flood danger in an arid environment. A case study (El Bayadh region, Algeria).

Floods are among the most serious and devastating phenomena of natural disasters. Cities adjacent to flood-prone areas in the last decades have played a major role in increasing the potential adverse effects of flood damage. This research study aims to evaluate and mitigate the risks of flood events in the El Bayadh region, which suffers from poor infrastructure and drained networks. To achieve this, it is necessary to evaluate rainfall intensities and their limits for durations from 0.167 to 24 h with return periods from 2 to 1000 years. Eight different frequency analysis distributions were fit to the historical rainfall data series over 43 years (1970-2012) using hypothesis-based goodness tests and information-based criteria. The most appropriate distributions were used to develop the rainfall intensity-duration-frequency (IDF) and flood risk-duration-frequency (RDF) curves for the study area. The results show that high-intensity rainfall values last for short durations, while high flood risk values last for intermediate durations. The results of the flood RDF curves can provide useful information for policy makers to make the right decisions regarding the effectiveness of the region's protection structures against future flood risks.

Genomic differentiation of three pico-phytoplankton species in the Mediterranean Sea.

For more than a decade, high-throughput sequencing has transformed the study of marine planktonic communities and has highlighted the extent of protist diversity in these ecosystems. Nevertheless, little is known relative to their genomic diversity at the species-scale as well as their major speciation mechanisms. An increasing number of data obtained from global scale sampling campaigns is becoming publicly available, and we postulate that metagenomic data could contribute to deciphering the processes shaping protist genomic differentiation in the marine realm. As a proof of concept, we developed a findable, accessible, interoperable and reusable (FAIR) pipeline and focused on the Mediterranean Sea to study three a priori abundant protist species: Bathycoccus prasinos, Pelagomonas calceolata and Phaeocystis cordata. We compared the genomic differentiation of each species in light of geographic, environmental and oceanographic distances. We highlighted that isolation-by-environment shapes the genomic differentiation of B. prasinos, whereas P. cordata is impacted by geographic distance (i.e. isolation-by-distance). At present time, the use of metagenomics to accurately estimate the genomic differentiation of protists remains challenging since coverages are lower compared to traditional population surveys. However, our approach sheds light on ecological and evolutionary processes occurring within natural marine populations and paves the way for future protist population metagenomic studies.

Long-Term Clinical Outcome in Elderly Patients Undergoing Mitral Valve Repair.

BACKGROUND: Long-term data on patients over 75 years undergoing mitral valve (MV) repair are scarce. At our high-volume institution, we, therefore, aimed to evaluate mortality, stroke risk, and reoperation rates in these patients. METHODS: We investigated clinical outcomes in 372 patients undergoing MV repair with (n = 115) or without (n = 257) tricuspid valve repair. The primary endpoint was the probability of survival up to a maximum follow-up of 9 years. Secondary clinical endpoints were stroke and reoperation of the MV during follow-up. Univariate and multivariable Cox regression analysis was performed to assess independent predictors of mortality. Mortality was also compared with the age- and sex-adjusted general population. RESULTS: During a median follow-up period of 37 months (range: 0.1-108 months), 90 patients died. The following parameters were independently associated with mortality: double valve repair (hazard ratio, confidence interval [HR, 95% CI]: 2.15, 1.37-3.36), advanced age (HR: 1.07, CI: 1.01-1.14 per year), diabetes (HR: 1.97, CI: 1.13-3.43), preoperative New York Heart Association (NYHA) functional class (HR: 1.41, CI: 1.01-1.97 per class), and operative creatininemax levels (HR: 1.32, CI: 1.13-1.55 per mg/dL). The risk of stroke in the isolated MV and double valve repair groups at postoperative year 5 was 5.0 and 4.1%, respectively (p = 0.65). The corresponding values for the risk of reoperation were 4.0 and 7.0%, respectively (p = 0.36). Nine-year survival was comparable with the general population (53.2 vs. 53.1%). CONCLUSION: Various independent risk factors for mortality in elderly MV repair patients could be identified, but overall survival rates were similar to those of the general population. Consequently, our data indicates that repairing the MV in elderly patients represents a suitable and safe surgical approach.

A Novel Hybrid Trustworthy Decentralized Authentication and Data Preservation Model for Digital Healthcare IoT Based CPS.

Digital healthcare is a composite infrastructure of networking entities that includes the Internet of Medical Things (IoMT)-based Cyber-Physical Systems (CPS), base stations, services provider, and other concerned components. In the recent decade, it has been noted that the demand for this emerging technology is gradually increased with cost-effective results. Although this technology offers extraordinary results, but at the same time, it also offers multifarious security perils that need to be handled effectively to preserve the trust among all engaged stakeholders. For this, the literature proposes several authentications and data preservation schemes, but somehow they fail to tackle this issue with effectual results. Keeping in view, these constraints, in this paper, we proposed a lightweight authentication and data preservation scheme for IoT based-CPS utilizing deep learning (DL) to facilitate decentralized authentication among legal devices. With decentralized authentication, we have depreciated the validation latency among pairing devices followed by improved communication statistics. Moreover, the experimental results were compared with the benchmark models to acknowledge the significance of our model. During the evaluation phase, the proposed model reveals incredible advancement in terms of comparative parameters in comparison with benchmark models.

A Lightweight Hybrid Deep Learning Privacy Preserving Model for FC-Based Industrial Internet of Medical Things.

The Industrial Internet of Things (IIoT) is gaining importance as most technologies and applications are integrated with the IIoT. Moreover, it consists of several tiny sensors to sense the environment and gather the information. These devices continuously monitor, collect, exchange, analyze, and transfer the captured data to nearby devices or servers using an open channel, i.e., internet. However, such centralized system based on IIoT provides more vulnerabilities to security and privacy in IIoT networks. In order to resolve these issues, we present a blockchain-based deep-learning framework that provides two levels of security and privacy. First a blockchain scheme is designed where each participating entities are registered, verified, and thereafter validated using smart contract based enhanced Proof of Work, to achieve the target of security and privacy. Second, a deep-learning scheme with a Variational AutoEncoder (VAE) technique for privacy and Bidirectional Long Short-Term Memory (BiLSTM) for intrusion detection is designed. The experimental results are based on the IoT-Botnet and ToN-IoT datasets that are publicly available. The proposed simulations results are compared with the benchmark models and it is validated that the proposed framework outperforms the existing system.

An Industrial IoT-Based Blockchain-Enabled Secure Searchable Encryption Approach for Healthcare Systems Using Neural Network.

The IoT refers to the interconnection of things to the physical network that is embedded with software, sensors, and other devices to exchange information from one device to the other. The interconnection of devices means there is the possibility of challenges such as security, trustworthiness, reliability, confidentiality, and so on. To address these issues, we have proposed a novel group theory (GT)-based binary spring search (BSS) algorithm which consists of a hybrid deep neural network approach. The proposed approach effectively detects the intrusion within the IoT network. Initially, the privacy-preserving technology was implemented using a blockchain-based methodology. Security of patient health records (PHR) is the most critical aspect of cryptography over the Internet due to its value and importance, preferably in the Internet of Medical Things (IoMT). Search keywords access mechanism is one of the typical approaches used to access PHR from a database, but it is susceptible to various security vulnerabilities. Although blockchain-enabled healthcare systems provide security, it may lead to some loopholes in the existing state of the art. In literature, blockchain-enabled frameworks have been presented to resolve those issues. However, these methods have primarily focused on data storage and blockchain is used as a database. In this paper, blockchain as a distributed database is proposed with a homomorphic encryption technique to ensure a secure search and keywords-based access to the database. Additionally, the proposed approach provides a secure key revocation mechanism and updates various policies accordingly. As a result, a secure patient healthcare data access scheme is devised, which integrates blockchain and trust chain to fulfill the efficiency and security issues in the current schemes for sharing both types of digital healthcare data. Hence, our proposed approach provides more security, efficiency, and transparency with cost-effectiveness. We performed our simulations based on the blockchain-based tool Hyperledger Fabric and OrigionLab for analysis and evaluation. We compared our proposed results with the benchmark models, respectively. Our comparative analysis justifies that our proposed framework provides better security and searchable mechanism for the healthcare system.

Path planning optimization in unmanned aerial vehicles using meta-heuristic algorithms: a systematic review.

Unmanned aerial vehicles (UAVs) have recently been increasingly popular in various areas, fields, and applications. Military, disaster management, rescue operations, public services, agriculture, and various other areas are examples. As a result, UAV path planning is concerned with determining the optimal path from the source to the destination while avoiding collisions with lowering the cost of time, energy, and other resources. This review aims to assort academic studies on the path planning optimization in UAV using meta-heuristic algorithms, summarize the results of each optimization algorithm, and extend the understanding of the current state of the path planning in UAV in the meta-heuristic optimization field. For this purpose, we implemented a broad, automated search using Boolean and snowballing searching methods to find academic works on path planning in UAVs. Studies and papers have been distinguished, and the following information was obtained and aggregated from each article: authors, publication's year, the journal name or the conference name, proposed algorithms, the aim of the study, the outcome, and the quality of each study. According to the findings, the meta-heuristic algorithm is a standard optimization method for tackling single and multi-objective problems. Besides, the findings show that meta-heuristic algorithms have a great compact on the path planning optimization in UAVs, and there is good progress in this field. However, the problem still exists mainly in complex and dynamic environments, on battlefields, in rescue missions, mobile obstacles, and with multiple UAVs.

Predictive accuracy of enhanced versions of the on-admission National Early Warning Score in estimating the risk of COVID-19 for unplanned admission to hospital: a retrospective development and validation study.

BACKGROUND: The novel coronavirus SARS-19 produces 'COVID-19' in patients with symptoms. COVID-19 patients admitted to the hospital require early assessment and care including isolation. The National Early Warning Score (NEWS) and its updated version NEWS2 is a simple physiological scoring system used in hospitals, which may be useful in the early identification of COVID-19 patients. We investigate the performance of multiple enhanced NEWS2 models in predicting the risk of COVID-19. METHODS: Our cohort included unplanned adult medical admissions discharged over 3 months (11 March 2020 to 13 June 2020 ) from two hospitals (YH for model development; SH for external model validation). We used logistic regression to build multiple prediction models for the risk of COVID-19 using the first electronically recorded NEWS2 within ± 24 hours of admission. Model M0' included NEWS2; model M1' included NEWS2 + age + sex, and model M2' extends model M1' with subcomponents of NEWS2 (including diastolic blood pressure + oxygen flow rate + oxygen scale). Model performance was evaluated according to discrimination (c statistic), calibration (graphically), and clinical usefulness at NEWS2 ≥ 5. RESULTS: The prevalence of COVID-19 was higher in SH (11.0 %=277/2520) than YH (8.7 %=343/3924) with a higher first NEWS2 scores ( SH 3.2 vs YH 2.8) but similar in-hospital mortality (SH 8.4 % vs YH 8.2 %). The c-statistics for predicting the risk of COVID-19 for models M0',M1',M2' in the development dataset were: M0': 0.71 (95 %CI 0.68-0.74); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.78 (95 %CI 0.75-0.80)). For the validation datasets the c-statistics were: M0' 0.65 (95 %CI 0.61-0.68); M1': 0.67 (95 %CI 0.64-0.70) and M2': 0.72 (95 %CI 0.69-0.75) ). The calibration slope was similar across all models but Model M2' had the highest sensitivity (M0' 44 % (95 %CI 38-50 %); M1' 53 % (95 %CI 47-59 %) and M2': 57 % (95 %CI 51-63 %)) and specificity (M0' 75 % (95 %CI 73-77 %); M1' 72 % (95 %CI 70-74 %) and M2': 76 % (95 %CI 74-78 %)) for the validation dataset at NEWS2 ≥ 5. CONCLUSIONS: Model M2' appears to be reasonably accurate for predicting the risk of COVID-19. It may be clinically useful as an early warning system at the time of admission especially to triage large numbers of unplanned hospital admissions.

Application Layer ARQ Algorithm for Real-Time Multi-Source Data Streaming in UAV Networks.

Because of the specific characteristics of Unmanned Aerial Vehicle (UAV) networks and real-time applications, the trade-off between delay and reliability imposes problems for streaming video. Buffer management and drop packets policies play a critical role in the final quality of the video received by the end station. In this paper, we present a reactive buffer management algorithm, called Multi-Source Application Layer Automatic Repeat Request (MS-AL-ARQ), for a real-time non-interactive video streaming system installed on a standalone UAV network. This algorithm implements a selective-repeat ARQ model for a multi-source download scenario using a shared buffer for packet reordering, packet recovery, and measurement of Quality of Service (QoS) metrics (packet loss rate, delay and, delay jitter). The proposed algorithm MS-AL-ARQ will be injected on the application layer to alleviate packet loss due to wireless interference and collision while the destination node (base station) receives video data in real-time from different transmitters at the same time. Moreover, it will identify and detect packet loss events for each data flow and send Negative-Acknowledgments (NACKs) if packets were lost. Additionally, the one-way packet delay, jitter, and packet loss ratio will be calculated for each data flow to investigate the performances of the algorithm for different numbers of nodes under different network conditions. We show that the presented algorithm improves the QoS of the video data received under the worst network connection conditions. Furthermore, some congestion issues during deep analyses of the algorithm's performances have been identified and explained.

Arabic Version of Learning Organization Survey Short-Form: Hospital Pharmacy Settings.

Objective: The objective of this study was to assess the psychometric properties of a translated Arabic version of the Learning Organization Survey (LOS-27) and to use this to evaluate staff perceptions about the organizational learning process in Kuwaiti hospital pharmacies. Setting: This study adopted a cross-sectional survey of the pharmacy employees in 6 hospital pharmacies in Kuwait. Results: The results indicated that the internal consistency of all composites was more than 0.7, except for one. All item loadings for the construct measurements were above 0.7. The standardized root mean square residual (SRMR) score showed a good fit with a value of 0.08. The intercorrelation among composites ranged from 0.34 to 0.68. Conclusions: The results indicate that the Arabic translation of the LOS-27 questionnaire has adequate levels of reliability and validity in comparison with the original US survey results. The overall average positive rate of composites was 64%. Therefore, the findings suggest that the hospital pharmacy staff surveyed in Kuwait were moderately positive in their perceptions about organizational learning in their organizations.

An Anonymous Channel Categorization Scheme of Edge Nodes to Detect Jamming Attacks in Wireless Sensor Networks.

Wireless Sensor Networks (WSNs) are vulnerable to various security threats. One of the most common types of vulnerability threat is the jamming attack, where the attacker uses the same frequency signals to jam the network transmission. In this paper, an edge node scheme is proposed to address the issue of jamming attack in WSNs. Three edge nodes are used in the deployed area of WSN, which have different transmission frequencies in the same bandwidth. The different transmission frequencies and Round Trip Time (RTT) of transmitting signal makes it possible to identify the jamming attack channel in WSNs transmission media. If an attacker jams one of the transmission channels, then the other two edge nodes verify the media serviceability by means of transmitting information from the same deployed WSNs. Furthermore, the RTT of the adjacent channel is also disturbed from its defined interval of time, due to high frequency interference in the adjacent channels, which is the indication of a jamming attack in the network. The simulation result was found to be quite consistent during analysis by jamming the frequency channel of each edge node in a step-wise process. The detection rate of jamming attacks was about 94% for our proposed model, which was far better than existing schemes. Moreover, statistical analyses were undertaken for field-proven schemes, and were found to be quite convincing compared with the existing schemes, with an average of 6% improvement.

Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic.

The provision and usage of online and e-learning system is becoming the main challenge for many universities during COVID-19 pandemic. E-learning system such as Blackboard has several fantastic features that would be valuable for use during this COVID-19 pandemic. However, the successful usage of e-learning system relies on understanding the adoption factors as well as the main challenges that face the current e-learning systems. There is lack of agreement about the critical challenges and factors that shape the successful usage of e-learning system during COVID-19 pandemic; hence, a clear gap has been identified in the knowledge on the critical challenges and factors of e-learning usage during this pandemic. Therefore, this study aims to explore the critical challenges that face the current e-learning systems and investigate the main factors that support the usage of e-learning system during COVID-19 pandemic. This study employed the interview method using thematic analysis through NVivo software. The interview was conducted with 30 students and 31 experts in e-learning systems at six universities from Jordan and Saudi Arabia. The findings of this study offer useful suggestions for policy-makers, designers, developers and researchers, which will enable them to get better acquainted with the key aspects of the e-learning system usage successfully during COVID-19 pandemic.

Burly1 is a mouse QTL for lean body mass that maps to a 0.8-Mb region of chromosome 2.

To fine map a mouse QTL for lean body mass (Burly1), we used information from intercross, backcross, consomic, and congenic mice derived from the C57BL/6ByJ (host) and 129P3/J (donor) strains. The results from these mapping populations were concordant and showed that Burly1 is located between 151.9 and 152.7 Mb (rs33197365 to rs3700604) on mouse chromosome 2. The congenic region harboring Burly1 contains 26 protein-coding genes, 11 noncoding RNA elements (e.g., lncRNA), and 4 pseudogenes, with 1949 predicted functional variants. Of the protein-coding genes, 7 have missense variants, including genes that may contribute to lean body weight, such as Angpt41, Slc52c3, and Rem1. Lean body mass was increased by the B6-derived variant relative to the 129-derived allele. Burly1 influenced lean body weight at all ages but not food intake or locomotor activity. However, congenic mice with the B6 allele produced more heat per kilogram of lean body weight than did controls, pointing to a genotype effect on lean mass metabolism. These results show the value of integrating information from several mapping populations to refine the map location of body composition QTLs and to identify a short list of candidate genes.

New insights into global biogeography, population structure and natural selection from the genome of the epipelagic copepod Oithona.

In the epipelagic ocean, the genus Oithona is considered as one of the most abundant and widespread copepods and plays an important role in the trophic food web. Despite its ecological importance, little is known about Oithona and cyclopoid copepods genomics. Therefore, we sequenced, assembled and annotated the genome of Oithona nana. The comparative genomic analysis integrating available copepod genomes highlighted the expansions of genes related to stress response, cell differentiation and development, including genes coding Lin12-Notch-repeat (LNR) domain proteins. The Oithona biogeography based on 28S sequences and metagenomic reads from the Tara Oceans expedition showed the presence of O. nana mostly in the Mediterranean Sea (MS) and confirmed the amphitropical distribution of Oithona similis. The population genomics analyses of O. nana in the Northern MS, integrating the Tara Oceans metagenomic data and the O. nana genome, led to the identification of genetic structure between populations from the MS basins. Furthermore, 20 loci were found to be under positive selection including four missense and eight synonymous variants, harbouring soft or hard selective sweep patterns. One of the missense variants was localized in the LNR domain of the coding region of a male-specific gene. The variation in the B-allele frequency with respect to the MS circulation pattern showed the presence of genomic clines between O. nana and another undefined Oithona species possibly imported through Atlantic waters. This study provides new approaches and results in zooplankton population genomics through the integration of metagenomic and oceanographic data.

MaGuS: a tool for quality assessment and scaffolding of genome assemblies with Whole Genome Profiling™ Data.

BACKGROUND: Scaffolding is an essential step in the genome assembly process. Current methods based on large fragment paired-end reads or long reads allow an increase in contiguity but often lack consistency in repetitive regions, resulting in fragmented assemblies. Here, we describe a novel tool to link assemblies to a genome map to aid complex genome reconstruction by detecting assembly errors and allowing scaffold ordering and anchoring. RESULTS: We present MaGuS (map-guided scaffolding), a modular tool that uses a draft genome assembly, a Whole Genome Profiling™ (WGP) map, and high-throughput paired-end sequencing data to estimate the quality and to enhance the contiguity of an assembly. We generated several assemblies of the Arabidopsis genome using different scaffolding programs and applied MaGuS to select the best assembly using quality metrics. Then, we used MaGuS to perform map-guided scaffolding to increase contiguity by creating new scaffold links in low-covered and highly repetitive regions where other commonly used scaffolding methods lack consistency. CONCLUSIONS: MaGuS is a powerful reference-free evaluator of assembly quality and a WGP map-guided scaffolder that is freely available at https://github.com/institut-de-genomique/MaGuS. Its use can be extended to other high-throughput sequencing data (e.g., long-read data) and also to other map data (e.g., genetic maps) to improve the quality and the contiguity of large and complex genome assemblies.

The Effects of Ambulatory Accelerations on the Stability of a Magnetically Suspended Impeller for an Implantable Blood Pump.

This article describes the effects of ambulatory accelerations on the stability of a magnetically suspended impeller for use in implantable blood pumps. A magnetic suspension system is developed to control the radial position of a magnetic impeller using coils in the pump casing. The magnitude and periodicity of ambulatory accelerations at the torso are measured. A test rig is then designed to apply appropriate accelerations to the suspension system. Accelerations from 0 to 1 g are applied to the suspended impeller with ambulatory periodicity while the radial position of the impeller and power consumption of the suspension system are monitored. The test is carried out with the impeller suspended in air, water, and a glycerol solution to simulate the viscosity of blood. A model is developed to investigate the effects of the radial magnetic suspension system and fluid damping during ambulatory accelerations. The suspension system reduces the average displacement of the impeller suspended in aqueous solutions within its casing to 100 µm with a power consumption of below 2 W during higher magnitude ambulatory accelerations (RMS magnitude 0.3 g). The damping effect of the fluid is also examined and it is shown that buoyancy, rather than drag, is the primary cause of the damping at the low displacement oscillations that occur during the application of ambulatory accelerations to such a suspension system.

Genome assembly using Nanopore-guided long and error-free DNA reads.

BACKGROUND: Long-read sequencing technologies were launched a few years ago, and in contrast with short-read sequencing technologies, they offered a promise of solving assembly problems for large and complex genomes. Moreover by providing long-range information, it could also solve haplotype phasing. However, existing long-read technologies still have several limitations that complicate their use for most research laboratories, as well as in large and/or complex genome projects. In 2014, Oxford Nanopore released the MinION® device, a small and low-cost single-molecule nanopore sequencer, which offers the possibility of sequencing long DNA fragments. RESULTS: The assembly of long reads generated using the Oxford Nanopore MinION® instrument is challenging as existing assemblers were not implemented to deal with long reads exhibiting close to 30% of errors. Here, we presented a hybrid approach developed to take advantage of data generated using MinION® device. We sequenced a well-known bacterium, Acinetobacter baylyi ADP1 and applied our method to obtain a highly contiguous (one single contig) and accurate genome assembly even in repetitive regions, in contrast to an Illumina-only assembly. Our hybrid strategy was able to generate NaS (Nanopore Synthetic-long) reads up to 60 kb that aligned entirely and with no error to the reference genome and that spanned highly conserved repetitive regions. The average accuracy of NaS reads reached 99.99% without losing the initial size of the input MinION® reads. CONCLUSIONS: We described NaS tool, a hybrid approach allowing the sequencing of microbial genomes using the MinION® device. Our method, based ideally on 20x and 50x of NaS and Illumina reads respectively, provides an efficient and cost-effective way of sequencing microbial or small eukaryotic genomes in a very short time even in small facilities. Moreover, we demonstrated that although the Oxford Nanopore technology is a relatively new sequencing technology, currently with a high error rate, it is already useful in the generation of high-quality genome assemblies.

Hydrogen bonding and anticancer properties of water-soluble chiral p-cymene Ru(II) compounds with amino-oxime ligands.

The investigation of the hydrogen-bonding effect on the aggregation tendency of ruthenium compounds [(η6-p-cymene)Ru(κNHR,κNOH)Cl]Cl (R = Ph (1a), Bn (1b)) and [(η6-p-cymene)Ru(κ2NH(2-pic),κNOH)][PF6]2 (1c), [(η6-p-cymene)Ru(κNHBn,κNO)Cl] (2b) and [(η6-p-cymene)Ru(κNBn,κ2NO)] (3b), has been performed by means of concentration dependence 1H NMR chemical shifts and DOSY experiments. The synthesis and full characterization of new compounds 1c, [(η6-p-cymene)Ru(κNPh,κ2NO)] (3a) and 3b are also reported. The effect of the water soluble ruthenium complexes 1a-1c on cytotoxicity, cell adhesion and cell migration of the androgen-independent prostate cancer PC3 cells have been assessed by MTT, adhesion to type-I-collagen and recovery of monolayer wounds assays, respectively. Interactions of 1a-1c with DNA and human serum albumin have also been studied. Altogether, the properties reported herein suggest that ruthenium compounds 1a-1c have considerable potential as anticancer agents against advanced prostate cancer.