Target For Design Research Articles

Investigating Ligand-Mediated Conformational Dynamics of Pre-miR21: A Machine-Learning-Aided Enhanced Sampling Study.

MicroRNAs (miRs) are short, noncoding RNA strands that regulate the activity of mRNAs by affecting the repression of protein translation, and their dysregulation has been implicated in several pathologies. miR21 in particular has been implicated in tumorigenesis and anticancer drug resistance, making it a critical target for drug design. miR21 biogenesis involves precise biochemical pathways, including the cleavage of its precursor, pre-miR21, by the enzyme Dicer. The present work investigates the conformational dynamics of pre-miR21, focusing on the role of adenine29 in switching between Dicer-binding-prone and inactive states. We also investigated the effect of L50, a cyclic peptide binder of pre-miR21 and a weak inhibitor of its processing. Using time series data and our novel collective variable-based enhanced sampling technique, OneOPES, we simulated these conformational changes and assessed the effect of L50 on the conformational plasticity of pre-miR21. Our results provide insight into peptide-induced conformational changes and pave the way for the development of a computational platform for the screening of inhibitors of pre-miR21 processing that considers RNA flexibility, a stepping stone for effective structure-based drug design, with potentially broad applications in drug discovery.

Advancements in p53-Based Anti-Tumor Gene Therapy Research

The p53 gene is one of the genes most closely associated with human tumors and has become a popular target for tumor drug design. Currently, p53-based gene therapy techniques have been developed, but these therapies face challenges such as immaturity, high safety hazards, limited efficacy, and low patient acceptance. However, researchers are no less enthusiastic about the treatment because of its theoretical potential to treat cancer. In this paper, the advances in p53-based gene therapy and related nucleic acid delivery technologies were reviewed and prospected in order to support further development in this field.

Digitalization, Management, and Synthesis of Thermal-Hydraulic Legacy Data Using the Dynamical System Scaling

A unique attribute of the nuclear industry is the extent by which designers, developers, operators, and regulators pay attention to demonstrating the safety case. Nuclear installation resiliency or safety takes overriding priority over function and performance. The preparation of the safety case heavily relies on extensive experimental campaigns that challenge the target design under a spectrum of postulated accident scenarios. For safety reasons, these experiments, typically conducted in subscale test apparatuses intended to reproduce the postulated accident scenarios and event sequences realistically, but subject to proven similarity criteria, are often referred to as scaling analyses. Since the dawn of the nuclear industry, significant resources have been committed to the construction and operation of such test facilities, and a massive amount of data has been generated. Such test data have been and are still used to validate the fundamental assumptions of nuclear power plant phenomena. As we move into the digital world, capturing this multiple decades worth of information in a transparent, easily accessible, and usable manner for the public and industry stakeholders is of paramount importance. Over the last few years, FPoliSolutions has been actively developing an enterprise digital data ontology platform (OGMA) intended to do just that. The goal is to help designers use experimental data to assess their safety case evaluation models. The platform or application programming interface (API) was designed to ontologically organize the experimental data, as well guide the user in the complex analytics associated with the interpretation and use of such test results. The OGMA API supports the user in accessing a library of sophisticated mathematical procedures for performing scaling analyses. For this purpose, the dynamical system scaling (DSS) procedure invented by Dr. Jose’ Reyes was formulated as one of the services in the digital platform. These methods have been demonstrated to provide an excelled mathematical apparatus to identify similarity criteria or quantify distortions between measured data and the target prototypical conditions. Moreover, DSS can provide a level of synthesis across separate effect tests and integral effect tests that has the promise of yielding efficiency in the evaluation code assessment activities, as setting up evaluation models that support the safety case of current and future nuclear power plants is often a daunting and expensive task.

Molecular mechanism underlying effect of D93 and D289 protonation states on inhibitor-BACE1 binding: exploration from multiple independent Gaussian accelerated molecular dynamics and deep learning

ABSTRACT BACE1 has been regarded as an essential drug design target for treating Alzheimer’s disease (AD). Multiple independent Gaussian accelerated molecular dynamics simulations (GaMD), deep learning (DL), and molecular mechanics general Born surface area (MM-GBSA) method are integrated to elucidate the molecular mechanism underlying the effect of D93 and D289 protonation on binding of inhibitors OV6 and 4B2 to BACE1. The GaMD trajectory-based DL successfully identifies significant function domains. Dynamic analysis shows that the protonation of D93 and D289 strongly affects the structural flexibility and dynamic behaviour of BACE1. Free energy landscapes indicate that inhibitor-bound BACE1s have more conformational states in the protonated states than the wild-type (WT) BACE1, and show more binding poses of inhibitors. Binding affinities calculated using the MM-GBSA method indicate that the protonation of D93 and D289 highly disturbs the binding ability of inhibitors to BACE1. In addition, the protonation of two residues significantly affects the hydrogen bonding interactions (HBIs) of OV6 and 4B2 with BACE1, altering their binding activity to BACE1. The binding hot spots of BACE1 recognized by residue-based free energy estimations provide rational targeting sites for drug design towards BACE1. This study is anticipated to provide theoretical aids for drug development towards treatment of AD.

Precision Cancer Therapy Enabled Anti-Epidermal Growth Factor Receptor-Conjugated Manganese Core Phthalocyanine Bismuth Nanocomposite for Dual Imaging-Guided Breast Cancer Treatment.

Cancer remains a formidable global health challenge, demanding the exploration of innovative treatment modalities with minimized side effects. One promising avenue involves the synergistic integration of targeted photothermal/photodynamic therapy (PTT/PDT), utilizing specially designed functional nanomaterials for precise cancer diagnosis and treatment. This study introduces a composite biomaterial, anti-epidermal growth factor receptor-conjugated manganese core phthalocyanine bismuth (anti-EGFR-MPB), synthesized for precise cancer imaging and treatment. The biomaterial, synthesized via a solvothermal process, effectively treats and images breast cancer in mouse models. Its biomimetic design targets cancer cells precisely, with dual imaging for real-time monitoring. The biomimetic design of the composite enables precise targeting of cancer cells, whereas the dual imaging allows for real-time visualization and monitoring of the treatment. Invivo examinations confirm substantial damage to tumor tissues with no recurrence following 808-nm laser irradiation. The composite shows strong fluorescence/photoacoustic imaging (PAI) contrast, aiding malignancy detection. Biological assays and histological analyses confirmed the efficacy of the nanocomposite in inducing apoptosis in cancer cells. The integrated targeted dual image-guided phototherapy offered by this composite substantially enhances the precision and efficacy of cancer therapy, achieving an impressive photothermal efficiency of ~33.8%. Our findings demonstrate the utility of the anti-EGFR-MPB nanocomposite for both invitro and invivo photoacoustic image-guided PTT and PDT. The optimal treatment strategy for triple-negative breast cancer is found to be the use of 250 μg/ml of nanocomposite irradiated with 1.0 W/cm2 808-nm laser for 7 min.

Histopathological characteristics of PRRS and expression profiles of viral receptors in the piglet immune system

Porcine reproductive and respiratory syndrome (PRRS) is a highly contagious viral disease that causes significant economic losses to the swine industry worldwide. PRRS virus (PRRSV) infection is a receptor-mediated endocytosis and replication process. The purpose of this study was to determine the localization and expression of four important PRRSV receptors in immunological organs of piglets. After piglets were infected with PRRSV, Hematoxylin and Eosin staining, immunofluorescence, and Western blot were used to perform histopathological examination and receptors distribution analysis. The results showed that PRRSV caused severe damage to the piglets’ immune organs, including atrophy of the thymus and swelling of lymph node. Histopathological lesions were mainly observed in the lung and lymph node and were characterized by interstitial pneumonia, collapsed follicles, exhaustion of germinal centers, and extensive hemorrhage. Immunofluorescence staining and Western blot results showed that the receptors of CD163 and NMHCII-A were mainly distributed in the thymus, hilar lymph nodes, and mesenteric lymph nodes. However, Sn and vimentin receptors were expressed at low levels in the immune organs of piglets. The distribution of the four receptors in the immune organs was more concentrated in the cortex but was more scattered in the medulla. Compared to the control group, the relative expression of the four receptors increased significantly in most immune organs after viral infection. In conclusion, our study examined the distribution and expression of four PRRSV receptors in immunological organs. We observed a significant increase in the expression of Sn, CD163, and vimentin following viral infection. These findings may provide potential targets for future antiviral reagent design or vaccine development.

Rational drug design targeting g-protein-coupled receptors: a structural biology perspective

G protein-coupled Receptors (G protein-coupled Receptors, GPCRs) play a key role in the transmission of extracellular signals and regulation of many biological processes, which makes these membrane proteins one of the most important classes of targets for pharmacological agents. The significant increase in the number of atomic structures of GPCRs recently has paved the way for Structure Based Drug Design (SBDD). SBDD uses information on the structure of the receptor-ligand complex to search for affinity and selective ligands without the need for high-throughput experimental ligand screening and allows a significant expansion of the chemical ligand search space. In our review we describe the process of GPCR structure obtaining by X-ray diffraction analysis and cryo-electron microscopy (cryo-EM) – an important step in rational drug design targeting GPCRs. Our main goal is to highlight to a wide range of specialists the current aspects and key features of experimental structural biology methods necessary for a detailed understanding of SBDD GPCRs.

A Universal Target Plate Design Scheme for Stellarators: theoretical basis and its application to heat load control

Abstract This study introduces a novel divertor target design scheme for stellarators, grounded in mathematical treatments and tailored to control toroidal heat load distributions. Initially, a differential equation characterizing toroidally uniform heat load distribution has been established in a two-dimensional (2D) slab configuration, and its analytic solution has been obtained. Subsequently, a numerical scheme has been developed to adapt the analytic solution into the 3D surface shape of stellarator target. The effectiveness of this design scheme has been validated through simulations of the Chinese First Quasi-axisymmetric Stellarator (CFQS) using a suite of codes including HINT, FLARE and EMC3-EIRENE, where a toroidally uniform heat load distribution has been achieved with an island configuration. Further, the effects of input parameters on the target shape and heat load distribution have been studied. The robustness of the designed target has been investigated by simulation results with varying magnetic island configurations, confirming that the toroidal uniformity of heat load distribution is insensitive to changes in island configurations. Moreover, the designed target has been assessed with gas puffing of neon, which shows that neon injections effectively reduce the heat loads without altering the toroidal uniformity of heat load distributions. The proposed scheme highlights the importance of theoretical and mathematical foundations of target design, offering an advantageous alternative/complement to the traditional numerical schemes.

Efficient structural optimization under transient impact loads using multilayer perceptron and genetic algorithms

Currently, there is a lack of structural optimization methods when a structure is suffering transient impact load, considering nonlinear factors such as material plasticity and strain rate effect. The utilization of derivative-free algorithms also increases computational expenses. Accordingly, this paper presents a novel structure transient optimization approach based on multilayer perceptron combined with a derivative-free optimization method, which can efficiently solve the transient structure dynamics optimization problem with multiple optimization objects. In this approach, the area to be optimized is cut by several closed B-spline curves whose shapes are controlled by several parameters and can be varied and merged at each optimization iteration step. The structure's transient analysis process is replaced by a machine learning based surrogate model, which is trained by thousands of results from the FEM explicit transient simulation. Additionally, nearly orthogonal Latin hypercube sampling is utilized to simplify parameter dimensionality, reduce the training data set, save calculation time, and give the training data set a more comprehensive range of design parameters. In our optimization process, our design target is to minimize the peak reaction force while with the constrain of ensuring enough stiffness and mass. The results demonstrate our proposed methods could efficiently handle transient optimization problems without sensitivity calculations, exhibiting strong generalization capabilities.

Energy‐Aware Routing in WSN Utilizing Self‐Attention–Based Cycle‐Consistent Generative Adversarial Network With Honey Badger Algorithm

ABSTRACTEnergy efficiency and secure data transmission are considered as the main design targets of wireless sensor network (WSN). Previous energy‐aware cluster head (CH) selection approaches could not provide secured data transmission. To address this problem, a self‐attention–based cycle‐consistent generative adversarial network (SaCyCsGAN) with honey badger algorithm (HyBA)–adopted energy‐aware routing (EgAR) in WSN is proposed. Initially, the proposed system uses a CH to carry out the routing practice. Accordingly, SaCyCsGAN classifier is exploited for CH selection under firm fitness function: delay, distance, energy, cluster density, and traffic rate. Afterward CH selection, a spiteful node may enter the cluster and acts as a CH. Hence, best path selection is needed. For that, HyBA is utilized, it selects the best path depending upon triplet parameter: trust, connectivity, and service degree. Finally, data are conveying into base station (BS) and vice versa under best path. Finally, the proposed EgAR‐WSN‐SaCyCsGAN‐HyBA method attains 24.13%, 28.57%, 5.88%, and 20.37% higher throughput and 18.50%, 21.67%, 13.33%, and 22.22% lesser energy consumption evaluated to the existing methods such as multiple‐objective CH utilizing self‐attention basis progressive generative adversarial network for safe data aggregation (EgAR‐WSN‐SAPrGAN‐ArVOA), reinforcement learning–based energy‐efficient optimized routing protocol in WSN (EgAR‐WSN‐RLGT‐BCSA), data aggregation including clustering protocol in WSN under machine learning (EgAR‐WSN‐AfNN), and optimum cluster along trusted path for routing formation with categorization of intrusion under machine learning categorization (EgAR‐WSN‐RsBPDT‐HoCSOA).

Orbit Design and Optimization for Point Target Revisit in LEO-LEO Occultation

Orbit Design and Optimization for Point Target Revisit in LEO-LEO Occultation

In silico analysis of ventricular action potential with a current-voltage-time representation: Thresholds, membrane resistance, repolarization reserve.

The waveform of ventricular action potential (AP) is a key determinant of the cardiac cycle, a marker of beating pathophysiology, and a target for anti-arrhythmic drug design. The information contained in the waveform, though, is limited to the actual dynamics of the AP under consideration. By measuring quasi-instantaneous current-voltage relationships during repolarization, I propose a three-dimensional representation of the ventricular AP which includes potential dynamic responses that the beat can show when electrically perturbed. This representation is described in the case of a numerically reconstructed ventricular AP, but it can be, at least partially, derived in real cardiomyocytes. Simulation allows to disclose the potentialities and the limitations of the approach, that can be extended to any non-cardiac AP. By reporting, at any AP time, the ion current available within the physiological membrane potential range at that time, the representation makes all together available: (1) refractory period, (2) thresholds for eliciting full or calcium-driven APs, (3) threshold for all-or-none repolarization, (4) membrane resistance during repolarization, (5) the safety of membrane repolarization. It provides further evidence of a negative membrane resistance during the late phase of ventricular AP and a quantitative estimate of repolarization reserve (RR), key determinants of repolarization dynamics.

Intelligent optimization of horizontal wellbore trajectory based on reinforcement learning

Ultra-long horizontal wells are crucial for improving the production and developmental benefits of shale oil and gas wells. Currently, advanced downhole semi-closed-loop steering drilling technology depends on a dual communication mechanism between the surface and downhole, as well as the empirical decisions of human experts. However, it is difficult to acutely control the trajectory of ultra-long horizontal open hole drilling in a reservoir owing to geological uncertainties related to shale oil and gas, the uncertainty of the drilling tool's building capacity, and the lag of measurement information while drilling. This report proposes an adaptive target detection and design method for ultra-long horizontal well trajectories based on reinforcement learning. The developed approach enables dynamic identification of reservoir sequences according to logging-while-drilling data and prediction of horizontal targets in real-time, with a recognition accuracy of 90.1%. Additionally, measurement-while-drilling data are used to accurately characterize the depth, inclination, and azimuth of the target-entering process. The dynamic interaction mechanism between the bit and the target environment is simulated by defining the bit action, reward function, and an update mechanism. The drilling engineering conditions restrict the interactions, such that the bit automatically decides the direction in which to drill the targets, demonstrating 100% accuracy for the wellbore trajectory toward the target. The experimental application results indicate that the developed method can be applied to determine the dynamic target area of a horizontal well in real time and make intelligent downhole decisions regarding ultra-long horizontal section borehole trajectory adjustments while drilling and measuring. The drilling rate of high-quality reservoirs is therefore significantly improved. The results discussed herein provide insights to support further developments of downhole full-closed-loop intelligent autonomous decision-making borehole trajectory control.

Analysing Turbulence Patterns in Nature‐Like Fishways: An Experimental Approach

ABSTRACTNature‐like fishways are an important measure for restoring fish passage, mitigating the impacts of barriers, and providing valuable habitat benefits in a more natural and effective way than traditional technical fishways. Their ecological advantages make them a preferred solution in many river restoration projects. The turbulence at the fishway's pool is a key challenge in the design of fishways. This study evaluated the hydraulic performance of the nature‐like fishway section of the Zongyang Fishway Project in China. This utilized a trapezoidal cross‐section with a 1:242 bottom slope, 1:2 side slopes, and an operating water depth of 1–3 m. The bottom and sides were paved with 0.2–0.5 m diameter pebbles to create suitable habitat for bottom‐dwelling fish. The target design velocity was 0.7–0.9 m s−1. A physical model was constructed to assess the hydraulic characteristics of the nature‐like fishway, including flow velocities within the slots and turbulence levels in the pools. The results showed that the slot velocities were within the target range, the head loss was parallel to the bottom elevation, and the flow field near the bottom mimicked technical fishway conditions, whereas the surface flow resembled open channel flow. Turbulence intensity remained below 60% of the design velocity. These findings provide valuable insights into the hydraulic performance and suitability of this nature‐like fishway design for facilitating fish passage of the target freshwater species along with directly aiding to SDGs 6 and 14.

High throughput edit distance computation on FPGA-based accelerators using HLS

Edit distance is a computational grand challenge problem to quantify the minimum number of editing operations required to modify one string of characters to the other, finding many applications of natural language processing. In recent years, relevant and increasing interest has also emerged from deoxyribonucleic acid (DNA) applications, like Next Generation Sequencing and DNA storage technologies. Both applications share two crucial features: i) the information is coded into the four bases of DNA and ii) the level of operational noise is still high causing errors in the data, requiring inclusion in the workflow of the computation of algorithms such as the edit distance for finding similarities between sequences. To boost this computation many solutions are available in the literature. Among them, the FPGAs are largely used since the data domain of those applications is strings of 4 characters represented as two-bit values, inconveniently fitting the basic data types of ordinary CPUs and GPUs, with additional benefits of providing a high level of parallelism and low processing latency. This contribution presents a computing- and energy-efficient design implementing the edit distance algorithm combining metaprogramming and High-Level Synthesis. We also assess the performance of our design targeting recent FPGA-based accelerators. Our solution uses nearly 90% of FPGA basic-block hardware resources achieving about 90% of computing efficiency delivering a maximum throughput of 16.8 TCUPS and an energy efficiency of 46 Mpair/Joule, enabling the use of FPGAs as a new class of accelerators for High Performance Computing in DNA applications.

H2S scavenger as a broad-spectrum strategy to deplete bacteria-derived H2S for antibacterial sensitization

Bacteria-derived H2S plays multifunctional protective roles against antibiotics insult, and the H2S biogenesis pathway is emerging as a viable target for the antibacterial adjuvant design. However, the development of a pan-inhibitor against H2S-synthesizing enzymes is challenging and underdeveloped. Herein, we propose an alternative strategy to downregulate the H2S levels in H2S-producing bacteria, which depletes the bacteria-derived H2S chemically by H2S scavengers without acting on the synthesizing enzymes. After the screening of chemically diversified scaffolds and a structural optimization campaign, a potent and specific H2S scavenger is successfully identified, which displays efficient H2S depletion in several H2S-producing bacteria, potentiates both bactericidal agents and photodynamic therapy, enhances the bacterial clearance of macrophages and polymorphonuclear neutrophils, disrupts the formation of bacterial biofilm and increases the sensitivity of bacterial persister cells to antibiotics. Most importantly, such an H2S scavenger exhibits sensitizing effects with gentamicin in Pseudomonas aeruginosa -infected pneumonia and skin wound female mouse models. In aggregate, our results not only provide an effective strategy to deplete bacteria-derived H2S and establish the H2S biogenesis pathway as a viable target for persisters and drug-resistant bacteria, but also deliver a promising antibacterial adjuvant for potential clinical translation.

High throughput sequencing reveals alterations in B cell receptor repertoires associated with the progression of hepatic cirrhosis to hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is developed as a consequence of chronic liver cirrhosis, and both diseases are difficult to diagnose and differentiate. Accurate noninvasive biomarkers for HCC and liver cirrhosis are urgently needed. Here we used high-throughput sequencing to characterize the B cell receptor (BCR) repertoires from 36 HCC tumor samples and 10 liver cirrhosis (LC) tissue biopsies to understand the immune alterations during hepatic carcinogenesis. The principal components analysis (PCA) showed that the pattern of BCR in HCC was distinct from that in LC. As measured by Clonality and Shannon indexes, the diversity of BCR repertoire was significantly lower in HCC than in LC (P < 0.01). Our results corroborated that the BCR diversity and composition could be closely correlated with hepatic carcinogenesis. And BCR repertoire may be used to predict the progression of HCC and design targeting immunotherapy in the near future.

New Trends in the Procurement and Use of Unmanned Aerial Vehicles

Based on the presented classification of unmanned aerial vehicles (UAVs), the specifics of their use in modern conflicts are examined. The main trends in the procurement of UAVs are analyzed. It is indicated that the use of UAVs in combat is not limited to the functions of reconnaissance, target designation and strikes, as well as the detection and destruction of terrorist groups, as was the case in Afghanistan and Iraq conflicts. Thе UAVs are increasingly used within the framework of reconnaissance and strike complexes on the battlefield. The RD and production of small and mini-drones, as well as of unmanned aerial vehicles capable of remaining invulnerable to air defense systems, are actively developing. Other modern technologies of unmanned systems are being advanced. Due to the fact that in recent years the trend of massive use of small and mini-UAVs in conflicts has prevailed, supplies to the armed forces of various countries of heavy and a number of medium (tactical) high-altitude and medium-altitude drones are decreasing, while the purchases of small drones, as well as light civilian UAVs with reconfigured software, are increasing.

CRISPR System and Its Applications in Medicine and Stem Cell Engineering

Context: The clustered regularly interspaced short palindromic repeat (CRISPR)/Cas system is a groundbreaking gene-editing tool that shows great promise for modifying genomes. Derived from prokaryotic adaptive immune defense mechanisms, this technique has been used in research on human diseases, demonstrating remarkable therapeutic potential. Through CRISPR, specific genetic mutations in patients can be corrected during gene therapy, offering a solution for treating diseases that were previously untreatable using conventional methods. This review explores the recent progress and future prospects of the CRISPR system, focusing on its applications in medicine and stem cell engineering. Special emphasis is placed on medical applications, the latest target design or analysis tools for genome editing, advancements in stem cell engineering, and associated innovations and challenges. Evidence Acquisition: This study reviewed articles indexed in ISI, SID, PubMed, and PubMed Central from 2007 to 2024. Results: Cas9, a key protein in CRISPR gene editing, is an endonuclease capable of targeting and cutting specific DNA sequences, guided by short RNA sequences. The gene editing process involves homology-directed repair (HDR), non-homologous end joining (NHEJ), and base editing pathways. Base editing, which modifies the epigenome without inducing DNA breaks, is gaining increasing attention. However, CRISPR still faces technical challenges, and the development of more efficient "super" CRISPR technology will likely require time. This article reviews the effectiveness, limitations, and applications of the CRISPR system. Conclusions: CRISPR/Cas9 tools allow for the creation of precise models, leading to more effective treatment options for patients.

Dual COX-2/TNF-α Inhibitors as Promising Anti-inflammatory and Cancer Chemopreventive Agents: A Review

: Cyclooxygenases (COX) play a pivotal role in inflammation and are responsible for the production of prostaglandins (PGs). Two types of COXs have been identified as key biological targets for drug design: Constitutive COX-1 and inducible COX-2. Nonsteroidal anti-inflammatory drugs (NSAIDs) target COX-1, while selective COX-2 inhibitors are designed for COX-2. These COX isoforms are involved in multiple physiological and pathological pathways throughout the body. Overproduction of tumor necrosis factor-alpha (TNF-α) plays a role in COX-2's inflammatory activity. Tumor necrosis factor-alpha can contribute to cardiac fibrosis, heart failure, and various cancers by upregulating the COX-2/PGE2 axis. Therefore, suppressing COX activity has emerged as a potentially effective treatment for chronic inflammatory disorders and cancer. This review explores the mechanisms of TNF-α-induced COX-2/PGE2 expression, a significant pathophysiological feature of cancer development. Furthermore, we summarize chemical compounds with dual COX-2/TNF-α inhibitory actions, providing an overview of their structure-activity relationship. These insights may contribute to the development of new generations of dual-acting COX-2/TNF-α inhibitors with enhanced efficacy.