Limited Throughput Research Articles

Analysis of vulnerability factors of WEB 3.0 technology

The article is devoted to critical aspects during the implementation of Web-3.0 technology. The task is set: based on the analysis of the implementation of Web-3.0 technology to solve a number of tasks, namely: blockchain-based decentralization; creation of general accessibility; increasing trust in sites; increasing the security of personal information from hackers; ensuring true ownership of authors' information; lack of censorship; improvement of social interaction; use of the Internet of Things is compatible with virtual or augmented reality; application of artificial intelligence – consider a zero-day exploit for Web-3.0. A zero-day exploit indicates that the vendor or developer has just become aware of the vulnerability and has "zero days" to fix it. A zero-day attack occurs as a result of attackers exploiting a vulnerability (critical points) before the developers managed to fix it. To solve this problem in the article: a description of the main differences between the Web-3.0 construction architecture and Web-2.0 is made; the analysis of the tasks of Oprah Winfrey Network of the Web-3.0 architecture was carried out; consider the possibilities of: external interface (design and interface of web applications), server part (based on decentralized technologies, primarily dApp, which uses the advantages of blockchain: transparency, reliability and immutability of data.), database (stores data about users, their messages , tags and comments); an analysis of possible threats and vulnerabilities due to the introduction of Web-3.0 technology before the start of the zero-day exploit was performed. Based on the performed analysis, the following conclusions are drawn: that the vulnerability is related to scalability, limited transaction throughput and computing power, security, complexity, compatibility; that Web-3.0 creates many conditions that can be useful to people, but the introduction of new Web-3.0 capabilities leads to the emergence of new threats or vulnerabilities that can be used by attackers to harm people. This requires considering the possible impact of threats, identifying possible vulnerabilities in Web-3.0 technology before the start of a zero-day exploit, that is, it requires the need to investigate the possibility of the appearance of new vulnerabilities.

Design of a virtual simulation interaction system based on enhanced reality

<abstract><p>Traditional virtual simulated interaction systems experience data fragmentation during the process of converting from two-dimensional to three-dimensional information, resulting in reduced realism and an inability to meet the teaching requirements of computer courses. Therefore, the integration of augmented reality (AR) technology into the educational environment remains an urgent and unresolved issue. To address the aforementioned issues, this paper investigates the data throughput limitations present in virtual simulation interaction systems. In response to this problem, an application solution utilizing AR technology is proposed, specifically a design concept for a virtual simulation interactive system tailored to computer-related courses. This system achieves its objectives through the collaborative interaction of AR hardware and supplementary software algorithms. The AR hardware is subdivided into framework design and functional hardware design, while the software components encompass AR models, virtual interaction models, and fusion methods. Through testing and comparison of the data throughput of this system with two other virtual simulation interaction systems, it was found that the virtual simulation interactive system optimized using AR technology can effectively enhance data throughput and address the issue of reduced realism in virtual interaction scenes caused by data fragmentation. This design system provides a more realistic and efficient mode of interaction for teaching computer-related courses.</p></abstract>

LEAF: Let’s Efficiently Make Adaptive Forwarding in Payment Channel Networks

Blockchain technology is widely applicable to modern payment systems but has inherent throughput limitations. Off-chain networks are proposed to solve scalability issues, which allows parties to efficiently perform micropayments without committing all of the payments to the blockchain. Off-chain payments with security and privacy protection requirements use the smart contract to ensure security and reduce the risk of sensitive information leakage. Although off-chain payments avoid expensive on-chain operations, it raises many concerns, such as the capacity limitation of payment channels and highly dynamic channel status, lowering the throughput of payment channel networks (PCNs). In our work, we explore the path overlap issues in PCNs and propose a decentralized payment routing scheme to improve the network throughput and reduce the redundant traffic overhead of PCNs, thereby guaranteeing efficient payments. The simulation results indicate that the proposed routing algorithm can achieve higher throughput than other routing schemes while guaranteeing short payment times.

Advanced DAG-Based Ranking (ADR) Protocol for Blockchain Scalability

In the past decade, blockchain has evolved as a promising solution to develop secure distributed ledgers and has gained massive attention. However, current blockchain systems face the problems of limited throughput, poor scalability, and high latency. Due to the failure of consensus algorithms in managing nodes’identities, blockchain technology is considered inappropriate for many applications, e.g., in IoT environments, because of poor scalability. This paper proposes a blockchain consensus mechanism called the Advanced DAG-based Ranking (ADR) protocol to improve blockchain scalability and throughput. The ADR protocol uses the directed acyclic graph ledger, where nodes are placed according to their ranking positions in the graph. It allows honest nodes to use the Direct Acyclic Graph (DAG) topology to write blocks and verify transactions instead of a chain of blocks. By using a three-step strategy, this protocol ensures that the system is secured against doublespending attacks and allows for higher throughput and scalability. The first step involves the safe entry of nodes into the system by verifying their private and public keys. The next step involves developing an advanced DAG ledger so nodes can start block production and verify transactions. In the third step, a ranking algorithm is developed to separate the nodes created by attackers. After eliminating attacker nodes, the nodes are ranked according to their performance in the system, and true nodes are arranged in blocks in topological order. As a result, the ADR protocol is suitable for applications in the Internet of Things (IoT). We evaluated ADR on EC2 clusters with more than 100 nodes and achieved better transaction throughput and liveness of the network while adding malicious nodes. Based on the simulation results, this research determined that the transaction’s performance was significantly improved over blockchains like Internet of Things Applications (IOTA) and ByteBall.

Ultrasensitive metal-organic cluster resist for patterning of single exposure high numerical aperture extreme ultraviolet lithography applications

BackgroundAn incredible increase in the integration of electronic chips has pushed the semicon industries to endorse high numerical aperture (h-NA ∼ 0.5), extreme-ultraviolet (EUV) lithography (EUVL) (λ ∼ 13.5 nm) at the commercial scale. Induction of h-NA postulates EUV resists that could outperform the resolution, line pattern roughness, and sensitivity (RLS) trade-off for chip fabricators, which is currently extremely limited.AimThe development of EUV resist to balance RLS trade-off as well as overcome throughput limitations of h-NA EUV system to facilitate high volume semiconductor manufacturing.ApproachHere, we developed indium-methacrylic acid-based metal-organic clusters resist for h-NA, EUVL. To examine the h-NA single exposure patterning potential of the resist, prescreening by sub-10 nm next-generation lithography (NGL) tools such as electron beam lithography (EBL), and helium ion beam lithography (HIBL) were conducted as a prelude to EUV exposure.ResultsDense ∼13 nm, (l/s) patterns at ∼45 and ∼30 μC / cm2 were well resolved by EBL and HIBL, on the top this the line edge roughness (LER) was 2.48 ± 0.04 nm, and etch resistance ∼1.98 and ∼0.34 times lower than Si and SiO2 / Si systems. Also, In-MAA MOCs resist shows ultra-sensitivity of 2.3 mJ / cm2 towards h-NA EUVL for patterning up to 26 nm half-pitch line patterns with LER ∼2.36 ± 0.16 nm.

Streptozotocin-induced Alzheimer's disease investigation by one-dimensional plasmonic grating chip

Recently, there has been significant interest in researching brain insulin resistance as it has been hypothesized that it may play a role in the progression of Alzheimer's disease. Alzheimer’s disease (AD) is brain dementia that contributes to damage to the neuron cells and then patient death. This dementia is ranked as the fifth more dangerous disease in the world. Streptozotocin (STZ) is used to induce Alzheimer’s disease experimentally. STZ is toxic to the pancreatic beta cells and induces insulin resistance. Neuroplasmonin techniques have been used to investigate the ability of STZ on the activity of cultured neuron cells. Neuroplasmonic is a novel technology that combines nanotechnology and biosensor. This technique has been used to record neuron signals in vivo and in vitro. Also, it has many facilities such as label-free detection, real-time analysis, biological compatibility, small sample, high throughput, and low detection limit. In this paper, we introduce a one-dimensional electro-plasmonic nanograting platform that consists of a straight nanorod of gold embedded in a dielectric layer of polycarbonate. The chip is connected with an externally applied voltage to induce tunable PIT and increase the sensor sensitivity. To evaluate the sensing performance of the electro-plasmonic sensor, this chip was cultured with Human Nucleus Pulposus Cells (HNPC). The first step was to measure the neuron cell activity in a healthy case. The next step was to measure the activity of neuron cells injected with different concentrations of STZ (0.5, 1, 2 mM) to induce the formation of Alzheimer’s disease in the cultured neuron cells. The results indicated that the electro-plasmonics sensor had a high sensitivity to the cells' activity and showed good results for the effecting STZ on the neuron cell’s activities.

Spatial-ID: a cell typing method for spatially resolved transcriptomics via transfer learning and spatial embedding.

Spatially resolved transcriptomics provides the opportunity to investigate the gene expression profiles and the spatial context of cells in naive state, but at low transcript detection sensitivity or with limited gene throughput. Comprehensive annotating of cell types in spatially resolved transcriptomics to understand biological processes at the single cell level remains challenging. Here we propose Spatial-ID, a supervision-based cell typing method, that combines the existing knowledge of reference single-cell RNA-seq data and the spatial information of spatially resolved transcriptomics data. We present a series of benchmarking analyses on publicly available spatially resolved transcriptomics datasets, that demonstrate the superiority of Spatial-ID compared with state-of-the-art methods. Besides, we apply Spatial-ID on a self-collected mouse brain hemisphere dataset measured by Stereo-seq, that shows the scalability of Spatial-ID to three-dimensional large field tissues with subcellular spatial resolution.

Modeling bioconcentration factors in fish with explainable deep learning

The Bioconcentration Factor (BCF) is an important parameter in the environmental risk assessment of chemicals, relevant for industrial and academic research as well as required in many regulatory contexts. It represents the potential of a substance to accumulate in organic tissues or whole animals and is most frequently measured in fish. However, animal welfare reasons, throughput limitations, and costs push the need for alternative methods that allow accurate and reliable estimations of BCF in silico. We present a new deep learning model to predict BCF values from chemical structures, that outperforms currently available models (R2 of 0.68 and RMSE of 0.59 log units on an external test set; R2 of 0.70 and RMSE of 0.74 log units in a demanding cluster split validation). The model is based on molecular representations encoded as CDDD descriptors and exploits a large in-house dataset with measured logD values as an auxiliary task.Additionally, we developed a post-hoc explainability method based on SMILES character substitutions to accompany our predictions with atom-level interpretations. These sensitivity scores highlight the most influential moieties in the molecule and can help to understand the predictions better and design new molecules.

Exploiting in-Slot Micro-Synchronism for S-ALOHA

Proliferation of the urban Internet-of-Things (IoTs) for smart cities has fuelled massive amounts of data over wireless cellular networks. Random access (RA) system of wireless cellular networks, e.g., 5G New Radio (NR), based on S-ALOHA system should cope with ever-growing IoT traffic. This work proposes S-ALOHA system with time offsets (TOs), where one slot consists of K TOs and one packet transmission time. The length of the overall TOs is a fraction of a packet transmission time. In the system users (re)transmit to the boundary of a TO randomly selected. This enables the base station (BS) to inform the users of who transmits the first and the last packets in the slot with collision so that the two users can retransmit successfully in the following two slots respectively. Our throughput analysis compared to simulations shows that adopting even with three and four TOs surpasses the throughput limit of S-ALOHA system without TOs. Additionally, we propose two Bayesian-optimized backoff algorithms for S-ALOHA system with TOs, with which users can apply throughput-optimal (re)transmission probability or uniform backoff window even in unsaturated traffic scenarios. Numerical results demonstrate that the proposed backoff algorithms can achieve the throughput close to an ideal system and drastically reduce the access delay compared to S-ALOHA system.

Lectin Analysis of SARS-CoV-2-Positive Nasopharyngeal Samples Using GLYcoPROFILE® Technology Platform.

Nasopharyngeal samples are currently accepted as the standard diagnostic samples for nucleic acid amplification testing and antigenic testing for the SARS-CoV-2 virus. In addition to the diagnostic capacity of SARS-CoV-2-positive crude nasopharyngeal samples, their qualitative potential for direct glycan-specific analysis, in order to uncover unique glycol profiles, was assessed. In this study we provide glycan characterization of SARS-CoV-2-positive and -negative nasopharyngeal samples directly from lectin interactions. Although with limited throughput, this study evaluated the clinical sensitivity and specificity of the GLYcoPROFILE® technology platformon45crude nasopharyngeal samples collected between November 2020 and April 2022. Each GLYcoPROFILE® of 39 SARS-CoV-2-positive samples was compared toglycoprofiling on a panel of 10 selected lectins and the results were paralleled with SARS-CoV-2-negative samples' results. The GLYcoPROFILE® showed a clear distinction between positive and negative samples with WFA, GSL-II, PHA-L (GlcNAc-specific) and BPA (GalNAc-specific) highlighted as relevant lectins in SARS-CoV-2-positive samples. In addition, a significant, positive statistical correlation was found for these lectins (p < 0.01).

The Future of Point-of-Care Nucleic Acid Amplification Diagnostics after COVID-19: Time to Walk the Walk.

Since the onset of the COVID-19 pandemic, over 610 million cases have been diagnosed and it has caused over 6.5 million deaths worldwide. The crisis has forced the scientific community to develop tools for disease control and management at a pace never seen before. The control of the pandemic heavily relies in the use of fast and accurate diagnostics, that allow testing at a large scale. The gold standard diagnosis of viral infections is the RT-qPCR. Although it provides consistent and reliable results, it is hampered by its limited throughput and technical requirements. Here, we discuss the main approaches to rapid and point-of-care diagnostics based on RT-qPCR and isothermal amplification diagnostics. We describe the main COVID-19 molecular diagnostic tests approved for self-testing at home or for point-of-care testing and compare the available options. We define the influence of specimen selection and processing, the clinical validation, result readout improvement strategies, the combination with CRISPR-based detection and the diagnostic challenge posed by SARS-CoV-2 variants for different isothermal amplification techniques, with a particular focus on LAMP and recombinase polymerase amplification (RPA). Finally, we try to shed light on the effect the improvement in molecular diagnostics during the COVID-19 pandemic could have in the future of other infectious diseases.

Analyzing and stabilizing multichannel ALOHA with the use of the preamble-based exploration phase

Introduction: Internet of Things devices are actively used within the framework of Massive Machine-Type Communication scenarios. The interaction of devices is carried out by random multiple-access algorithms with limited throughput. To improve throughput one can use orthogonal preambles in the ALOHA-type class of algorithms. Purpose: To analyze ALOHA-based algorithms using the exploration phase and to calculate the characteristics for the algorithm with and without losses with a finite number of channels. Results: We have described a system model that employs random access for data transmission over a common communication channel with the use of orthogonal preambles and exploration phase. We have obtained a formula for numerical calculation of the throughput of an algorithm channel with losses with an infinite number of preambles and a given finite number of channels. The calculation results for several values of the number of independent channels are presented. A modification of the algorithm using the exploration phase and repeated transmissions is proposed and described. The system in question can work without losses. For this system, we have given the analysis of the maximum input throughput up to which the system operates stably. Also, the average delay values for the algorithm that were obtained by simulation modeling are shown. By reducing the number of available preambles, the results obtained can be used as an upper bound on the system throughput. Practical relevance: The results obtained allow to assess the potential for improving the throughput of random multiple-access systems in 6G networks through the application of the exploration phase.

ScTAM-seq enables targeted high-confidence analysis of DNA methylation in single cells

Single-cell DNA methylation profiling currently suffers from excessive noise and/or limited cellular throughput. We developed scTAM-seq, a targeted bisulfite-free method for profiling up to 650 CpGs in up to 10,000 cells per experiment, with a dropout rate as low as 7%. We demonstrate that scTAM-seq can resolve DNA methylation dynamics across B-cell differentiation in blood and bone marrow, identifying intermediate differentiation states that were previously masked. scTAM-seq additionally queries surface-protein expression, thus enabling integration of single-cell DNA methylation information with cell atlas data. In summary, scTAM-seq is a high-throughput, high-confidence method for analyzing DNA methylation at single-CpG resolution across thousands of single cells.

Virtual coil augmentation for MR coil extrapoltion via deep learning

Magnetic resonance imaging (MRI) is a widely used medical imaging modality. However, due to the limitations in hardware, scan time, and throughput, it is often clinically challenging to obtain high-quality MR images. In this article, we propose a method of using artificial intelligence to expand the coils to achieve the goal of generating the virtual coils. The main characteristic of our work is utilizing dummy variable technology to expand/extrapolate the receive coils in both image and k-space domains. The high-dimensional information formed by coil expansion is used as the prior information to improve the reconstruction performance of parallel imaging. Two main components are incorporated into the network design, namely variable augmentation technology and sum of squares (SOS) objective function. Variable augmentation provides the network with more high-dimensional prior information, which is helpful for the network to extract the deep feature information of the data. The SOS objective function is employed to solve the deficiency of k-space data training while speeding up convergence. Experimental results demonstrated its great potentials in accelerating parallel imaging reconstruction.

Emerging Technologies for Advanced 3D Package Characterization to Enable the More-Than-Moore Era

To continue Moore’s Law, semiconductor packaging is adopting new architectures, materials, and structures. Package dimensions are becoming thinner and smaller while going 3D to enable higher performance in smaller footprints. New package technologies such as chiplets, hybrid bonding, TSV silicon interposers and silicon interconnect bridges enable heterogenous integration of diverse functions, such as RF and logic, into a single package. The advanced electrical systems known as system-in-package (SiP) are comprised of many components and materials, each offering unique failure modes and challenges for diagnosis and analysis.The pace of materials and device development today, combined with the central role of IC packaging to meet the demands of system performance through More-than-Moore innovations, drives an urgent need for improved package characterization of structures and defects to aid fast development of reliable 3D packages. Traditional non-destructive techniques like 2D X-ray and scanning acoustic microscopy are hitting limits for 3D package analysis due to resolution and throughput limits. Focused ion beam scanning electron microscopes (FIB-SEM) enabled decades of technology advances for front-end-of-line (FEOL) and back-end-of-line (BEOL) processes, but their usefulness for packaging analysis is limited by slow FIB milling rates relative to the millimeter volumes of material that must be removed.As the line between packaging and silicon interconnects blurs, it becomes more difficult to localize and image defects and structures throughout the package life cycle, starting from material selection through development and on to high volume production and field failure diagnostics. Characterization and failure analysis instruments must work synergistically to provide fast results for rapid package development of products meeting the required electrical and mechanical specifications with high yield, high quality and high reliability.X-ray Computed Tomography (CT) has become a key enabler to solve 3D packaging challenges, because it can non-destructively image the interior of objects in 3D. X-ray Microscopy (XRM) extends this capability through higher resolution and increased versatility. Although 3D XRM achieves submicron resolution on full-sized intact packages, the trade-off for this performance is throughput. Traditional 3D XRM approaches can take many hours at the extreme end of highest resolution for largest package sizes. Meanwhile, the requirement for artifact-free cross sections of fast-shrinking package structures drives the use of ion beam technologies, such as broad Ar ion beams and Ga+ or Xe+ focused ion beams. Advances in packaging technology are pushing standard ion beam approaches beyond their limits, due to the combined need for precise end-pointing and rapid site-specific removal of millimeter volumes of material. Microbumps in 3D packages are 8,000 times smaller than solder balls, and 124 times smaller than C4 bumps [1], while I/O pitches are approaching 1 µm [Figure 1]. Broad Ar+ ion beams can be applied to large areas but lack the end-pointing specificity required by today’s advanced fine-pitch high density interconnect. While Ga+ FIB (used for semiconductor analysis) and Xe+ PFIB (used for far-BEOL structure analysis) deliver excellent end-pointing on the nanometer scale, both are unable to deliver the required milling rates to enable rapid site-specific cross-sectional analysis of structures deeply buried within 2.5/3D and other SiP packages.We present advanced microscopy innovations for 3D package analysis. 3D XRM data acquisition speeds can be increased by 4X and sometimes more than 10X when using artificial intelligence for reconstruction of 3D XRM data [2,3] [Figure 2]. In addition, the recent integration of a fs-laser onto a FIB-SEM enables site-specific material removal rates that are orders of magnitude faster than a FIB alone [4] [Figure 3]. Enabled by the high-accuracy end-pointing and fast milling achievable with a 100nA Ga+ beam, the new laser-integrated FIB-SEM, or LaserFIB, enables site-specific cross sectioning for SiP and complex package structures with high throughput. We show that when combining the 3D XRM and LaserFIB advances into a package analysis workflow, unprecedented capability emerges for rapid, high-resolution characterization of large structures and deeply buried features. This supports fast development of reliable next-generation package technology by providing the combination of speed, site targeting, and end-pointing accuracy required to address increasingly complex devices with higher throughput and analysis success rates. References ASE Group; accessed April 8, 2022 from https://ase.aseglobal.com/en/technology/advanced_25dic Harris et al., Putting AI to Work: A Practical and Simple Application to Improve 3D X-ray FA; International Reliability Physics Symposium (IRPS); Dallas, TX (2022).Gu et al., Accelerate Your 3D X-ray Failure Analysis by Deep Learning High Resolution Reconstruction. ISTFA (2021). https://doi.org/10.31399/asm.cp.istfa2021p0291 Tordoff et al., The LaserFIB: new application opportunities combining a high-performance FIB-SEM with femtosecond laser processing in an integrated second chamber. Applied Microscopy. 50(1), 24 (2020) Figure 1

(Invited, Digital Presentation) High-Throughput Electrochemical Sensing for Cancer Diagnostics

Analyzing circulating extracellular vesicles (EVs) or exosome promises to improve cancer diagnostics and enable serial patient monitoring during therapy. Establishing clinical EV tests, however, faces practical challenges: complex and lengthy sample workup; limited throughput and sensitivity of conventional assays; high equipment or test costs; and lack of clinically validated markers. In this presentation, we report key advances toward clinical EV analyses: i) development of a high-throughput assay strategy, HiMEX (high-throughput magneto-electrochemical exosome), and ii) assessment of EVs as a biomarker of colorectal cancer (CRC). HiMEX integrates magnetic capture with electrochemical detection within a single platform; we enrich cancer-specific EVs directly from plasma samples and profile their protein expression. This approach renders the assay fast (<1 hour) and scalable; we implemented a compact assay system for parallel detection (96-well format). Applying HiMEX, we profiled circulating EVs in clinical samples EVs were found to carry key protein signatures of tumor tissues, and by using a combination of markers, we achieved high diagnostic accuracies (>95%). Preoperative EV tests further showed this method’s prognostic potential for categorizing patients into high- and low-risk groups for 5-year disease-free survival. These outcomes demonstrate HiMEX’s clinical utility for timely, informed cancer care.

STUDY OF THE INFLUENCE OF THE PASSAGE OF HEAVY TRAINS ON THE THROUGHPUT AND CARRYING CAPACITY OF RAILWAY SECTIONS WITH ELECTRIC TRAIN TRACTION

The criterion for the effectiveness of the organization of heavy traffic on the sections of the Russian railway network is the presence of large correspondences of bulk goods presented by the consignor for transportation. An obstacle to the development of a promising cargo traffic are areas with limited throughput and carrying capacity, the overcoming of which is the organization of heavy traffic. All countries that have railways, at a certain stage of development, face capacity constraints. Summarizing the world experience, we can conclude that the priority in the development of increasing volumes of “heavy cargo” is increasing the weight norms of freight trains. The problem of passing heavy trains for Russian Railways is urgent, because the capacity of some sections has exhausted itself completely. The very timely appearance of heavy traffic becomes one of the options for solving the problem of increasing the carrying capacity of sections and directions and, as a result, creating a reserve of carrying capacity. In this article, a study of the influence of their movement on the carrying capacity and carrying capacity has been carried out.&#x0D; Aim. To consider the effectiveness of the formation and passage of heavy trains. The influence of the organization of the movement of such trains on the throughput and carrying capacity of railway sections.&#x0D; Methods. To solve the problem, graphic and analytical methods of mathematical modeling, scientific methods of collecting and processing statistical data, modern achievements in terms of general principles and methods of risk management was used.&#x0D; Results. The authors have developed a formula for determining the rate of removal of freight trains. The analysis of the influence of the values of the inter-train intervals of freight trains and heavy trains on the removal rate is given.&#x0D; Practical relevance. As the main alternative to increasing the size of traffic on the main cargo-intensive directions, with a shortage of traffic capacity, it is proposed to use the technology of heavy traffic. In modern market conditions, an increase in the weight and length of a freight train is one of the main reserves for increasing throughput and carrying capacity. By passing heavy trains, we get an increase in carrying capacity. In the article, we prove the consistency of these measures. The increase in carrying and carrying capacity is considered in the context of increasing unified weight standards.

Research progress and clinical application of the third- generation sequencing techniques

The Sanger sequencing techniques, also known as the first-generation sequencing techniques and the gold standard of sequencing, have promoted the completion of "working draft" of the human genome, but the disadvantages of low throughput and high cost limit its large-scale application. The second-generation sequencing techniques, also known as the next-generation sequencing techniques, have widely used in basic research and clinical application because of its high throughput and low cost, but the short reads has always been an unavoidable shortcoming. Then, the emergence of the third-generation sequencing techniques, with the long reads, provides new technology selection for the analysis of complex repetitive regions on genome sequences and the assembly of high-quality genomes. In recent years, the third-generation sequencing techniques have been further developed, and have gradually demonstrated the clinical application value. This article reviewed the research progress and clinical application of the third-generation sequencing techniques.

Cooperative self-scheduling routing protocol based IOT communication for improving life time duty cycled energy efficient protocol in SDN controlled embedded network

Internet of Things (IoT) is a developing interconnecting medium in a wireless network through internet of communication in network devices like sensors, smart devices and controllers. This interconnect the devices via the internet to make communication. Due to large sectors IoT devices and Software-Defined Networking (SDN) controllers contains energy constraints in improper routing strategies. Specifically in health care industries, the patient monitoring data's consumes higher energy to reduce the lifetime of network communication leads data losses during the IoT devices collecting the information become loss.The embedded communication are relatively controls through the Software defined network to maintain the energy consumption to improve the lifetime of energy efficient network. But leastpropagation energy losses arise communication losses because of traffic, packet losses, latency beyond with throughput limits. The main problem is Passive Infrared Sensor (PHR) sensor nodes and Relay nodes attain more energy because of non-uniform node displacement in routing in uneven energy drainage. So resolving these problems, in this system propose a cooperative self-scheduling routing protocol based IoT communication for improving energy efficient SDN controlled embedded network.Initially this construct the Neighbor Distance Discovery Protocol (NDDP) to identify the minimum depletion path by identifying Sensor closest route to the relay node. Then construct the Multipath Cooperative Self-Scheduling Protocol (MCSSP) to formalize the non-traffic route. By estimating the energy consumption on each sensor node on routing medium, this considers the prior routing communication rate by calculating average transmission delay, sensor absorption rate, power consumption, node response rate, and packet loss rate. UsingLife Time Duty Cycled Energy Efficient Protocol (LTDCEEP) to calculate the delimit of the feature limits along the network threshold latency energy limits. Based on the transition limit the weights of the nodes are estimated by energy consumption from each duty cycle by consuming more transmission energy, the route be scheduled to optimize the communication delay to reduce the power consumption in the sense of responsive nodes. This improves the power consumption rate, latency, power quality in transmission, throughput ratio in SDN network communication.

High-Throughput Neurite Outgrowth Assay Using GFP-Labeled iPSC-Derived Neurons.

The potential neurotoxicity from an increasing number of drugs and untested environmental chemicals creates a need to develop reliable and efficient in vitro methods for identifying chemicals that may adversely affect the nervous system. An important process in neurodevelopment is neurite outgrowth, which can be affected by developmental neurotoxicity. Currently, neurite outgrowth assays rely mainly on staining, which requires multiple sample processing steps, particularly washing steps, that may introduce variation and limit throughput. Here, we describe a neurite outgrowth assay that uses induced pluripotent stem cell (iPSC)-derived human cortical glutamatergic neurons and/or spinal motor neurons labeled with green fluorescent protein (GFP) to test compounds in a high-content and high-throughput format. This method enables live and time-lapse imaging of GFP-labeled neurons using an assay plate that is continuously imaged at multiple times after chemical treatment. In this article, we describe how to thaw frozen GFP-labeled neurons, culture them, treat them with a compound of interest, and analyze neurite outgrowth using a high-content imaging platform. In this assay, GFP-labeled iPSC-derived human neurons represent a promising tool for identifying and prioritizing compounds with potential developmental neurotoxicity for further hazard characterization. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA. Basic Protocol 1: Thawing and seeding of iPSC-derived neurons Basic Protocol 2: Compound plate preparation and treatment of neurons Basic Protocol 3: High-content imaging and analysis.