Target For Design Research Articles

Structure Improvement of Two-Cylinder Engine Cooling Water Jacket Based on Flow Field Simulation

The flow and heat transfer of the coolant directly affect the cooling performance, thermal load, and emissions of gasoline engine. The accurate estimation of heat transfer and temperature distribution within engines is crucial for studying thermal stresses and calculating engine performance. This study focuses on the design of a new cooling water jacket structure for a two-cylinder gasoline engine. In the novel structure, the coolant flows from the cylinder block to the cylinder head and then returns to the cylinder block, providing better cooling for the cylinder head. The three-dimensional simulation results show that the overall flow velocity of this structure ranges between 0.1 m/s and 3 m/s, which meets the design requirement of 0.1 m/s to 5 m/s. However, there are still some flow dead zones in this structure, which may lead to insufficient heat transfer. Therefore, the water jacket structure is further optimized. After optimization, the pressure drop between the inlet and outlet is decreased, and the velocity distribution becomes more rational. Both the flow velocity and the heat transfer coefficient meet the design targets. These results can provide theoretical guidance for the structural improvement of the water jacket and approaches to studying the flow characteristics of the coolants.

Characterization of cancer-driving nucleotides (CDNs) across genes, cancer types, and patients

A central goal of cancer genomics is to identify, in each patient, all the cancer-driving mutations. Among them, point mutations are referred to as cancer-driving nucleotides (CDNs), which recur in cancers. The companion study shows that the probability of i recurrent hits in n patients would decrease exponentially with i; hence, any mutation with i ≥ 3 hits in The Cancer Genome Atlas (TCGA) database is a high-probability CDN. This study characterizes the 50–150 CDNs identifiable for each cancer type of TCGA (while anticipating 10 times more undiscovered ones) as follows: (i) CDNs tend to code for amino acids of divergent chemical properties. (ii) At the genic level, far more CDNs (more than fivefold) fall on noncanonical than canonical cancer-driving genes (CDGs). Most undiscovered CDNs are expected to be on unknown CDGs. (iii) CDNs tend to be more widely shared among cancer types than canonical CDGs, mainly because of the higher resolution at the nucleotide than the whole-gene level. (iv) Most important, among the 50–100 coding region mutations carried by a cancer patient, 5–8 CDNs are expected but only 0–2 CDNs have been identified at present. This low level of identification has hampered functional test and gene-targeted therapy. We show that, by expanding the sample size to 105, most CDNs can be identified. Full CDN identification will then facilitate the design of patient-specific targeting against multiple CDN-harboring genes.

Characterization of cancer-driving nucleotides (CDNs) across genes, cancer types, and patients.

A central goal of cancer genomics is to identify, in each patient, all the cancer-driving mutations. Among them, point mutations are referred to as cancer-driving nucleotides (CDNs), which recur in cancers. The companion study shows that the probability of i recurrent hits in n patients would decrease exponentially with i; hence, any mutation with i ≥ 3 hits in The Cancer Genome Atlas (TCGA) database is a high-probability CDN. This study characterizes the 50-150 CDNs identifiable for each cancer type of TCGA (while anticipating 10 times more undiscovered ones) as follows: (i) CDNs tend to code for amino acids of divergent chemical properties. (ii) At the genic level, far more CDNs (more than fivefold) fall on noncanonical than canonical cancer-driving genes (CDGs). Most undiscovered CDNs are expected to be on unknown CDGs. (iii) CDNs tend to be more widely shared among cancer types than canonical CDGs, mainly because of the higher resolution at the nucleotide than the whole-gene level. (iv) Most important, among the 50-100 coding region mutations carried by a cancer patient, 5-8 CDNs are expected but only 0-2 CDNs have been identified at present. This low level of identification has hampered functional test and gene-targeted therapy. We show that, by expanding the sample size to 105, most CDNs can be identified. Full CDN identification will then facilitate the design of patient-specific targeting against multiple CDN-harboring genes.

Preliminary Design and Thermal-Hydraulic Study of a 250-kW LBE Experimental Spallation Target for CiADS

The CiADS (China initiative Accelerator Driven System) project plans to build a 250-kW experimental target before construction of the 2.5-MW spallation target. In this paper, the effect of proton beam distribution on the performance of the 250-kW target is studied. Hence, a hollow beam distribution, formed by a Gaussian beam scanning around the center of the beam window, is proposed. Then, combined with the orthogonal test method and the CRITIC (Criteria Importance through Intercriteria Correlation) method, four key geometric parameters of the target flow channel are analyzed to simultaneously achieve optimization of the target performance from the viewpoints of both the maximum temperature and velocity under steady-state condition. Finally, transient analysis of the initiation of the Gaussian beam scanning is conducted; the calculation time was 15s. The results show that the temperature on the window rose gradually until reaching its maximum value corresponding to the steady-state analysis. Detailed analyses showed that the optimized target design is capable of meeting the thermal-hydraulic and mechanical requirements.

Evaluations of FDA-approved Drugs Targeting 3CLP of SARS-CoV-2 Employing a Repurposing Strategy.

The SARS-CoV-2 coronavirus (COVID-19) has raised innumerable global concerns, and few effective treatment strategies have yet been permitted by the FDA to lighten the disease burden. SARS-CoV-2 3C-like proteinase (3CLP) is a crucial protease and plays a key role in the viral life cycle, as it controls replication, and thus, it is viewed as a target for drug design. In this study, we performed structure-based virtual screening of FDA drugs approved during 2015-2019 (a total of 220 drugs) for interaction with the active site of 3CLP (PDB ID 6LU7) using AutoDock 4.2. We report the top ten drugs that outperform the reported drugs against 3CLP (Elbasvir and Nelfinavir), particularly Cefiderocol, having the highest affinity among the compounds tested, with a binding energy of -9.97 kcal/mol. H-bond (LYS102:HZ2-ligand: O49), hydrophobic (ligand-VAL104), and electrostatic (LYS102:NZ-ligand: O50) interactions were observed in the cefiderocol-3CLP complex. The docked complex was subjected to a 50 ns molecular dynamics study to check its stability, and stable RMSD and RMSF graphs were observed. Accordingly, we suggest cefiderocol might be effective against SARS-CoV-2 and urge that experimental validation be performed to determine the antiviral efficacy of cefiderocol against SARS-CoV-2. Along with these, cefiderocol is effective for treating respiratory tract pathogens and a wide range of gram-negative bacteria for whom there are limited therapeutic alternatives. This article aimed to explore the FDA-approved drugs as a repurposing study against 3CLP for COVID-19 management.

Design, synthesis, biological evaluation and molecular docking study of novel quinolone derivatives as potent HDAC1 (Histone Deacetylase) inhibitors and anticancer agents

Histone deacetylases (HDACs), especially HDAC1, are attractive targets for anticancer drug design because of their significant role in transcriptional repression and chromatin remodeling. Benzamide groups are widely reported as the most effective zinc binding groups (ZBGs) in design of HDAC inhibitors. Quinolone derivatives, exhibiting various therapeutic effects, are attractive scaffolds for developing novel HDAC inhibitors with potentially supposed therapeutic profiles. To explore the potential of 4-quinolones as a novel scaffold for a cap of HDACis, 14 novel structures were rationally designed and synthesized. Four cancer cell lines (HCT116, HT29, MCF7, and A549) and one normal cell (CHO) were applied to investigate the cytotoxicity of synthesized compounds. Furthermore, the inhibitory activity of the synthesized compounds was analyzed against pan-HDAC isozymes (HDAC1, HDAC2, HDAC3 and HDAC6). Five compounds (5a, 5b, 5c, 5e and 5h) showed strong cytotoxicity and HDAC inhibition even superior to entinostat as the reference drug. Collectively, these results suggest that compounds with an electron-donating group at the positions 6 or 8 of the quinolone ring (cap) appear to enhance the anti-proliferative potency. After performing induce-fit docking (on HDAC1, 2, 3, 6) for all the synthesized compounds, a significant correlation was identified between the MTT results, the inhibitory activity of pan-HDAC, and the docking scores of HDAC1. Therefore, compounds 5e and 5a (the most potent compounds) were identified as the most favorable leads for further investigation of HDAC1-specific enzyme inhibition. Compounds 5a and 5e showed impressive HDAC1 inhibitory potency with IC50 values of 0.48 μM and 0.36 μM, respectively, equal or more than that of Entinostat. Compound 5e (3 µM) also exhibited significant induction of apoptosis in the HCT116 cell line (the percentage of apoptotic cells was 81.87%), even stronger than the reference drug Entinostat (the percentage of apoptotic cells was 37.5%) at the same concentration which is consistent with their antiproliferative activities as well. 100 ns molecular dynamic (MD) simulation revealed that compound 5e can form stable interactions with HDAC1 through coordination with zinc ion, strong hydrophobic interactions and formation of hydrogen bonds. These results suggested that compound 5e can be promising lead for further development of potent anticancer and HDAC inhibitor drugs.

Performance-based seismic design of low-rise steel moment-resisting frames targeting collapse fragility curve

The aim of this study is to develop a performance-based seismic design method targeting a desirable collapse fragility curve. This direct fragility-based design method is developed and validated for low-rise steel moment-resisting frames. To this end, a relationship is first defined between target fragility curve and design displacement. To this end, 4-story buildings are designed with the design drift ratios of 1.5 % to 4 % and the seismic fragility analysis is performed via the Monte Carlo simulation. The mentioned relationship is derived by plotting the median collapse spectral acceleration versus the design drift ratio. With regard to have this relationship, a modified version of performance-based plastic design method is then proposed. The direct fragility-based design method has been proposed for the low-rise buildings located in Log Angeles. However, its efficiency is also validated for two other regions of Vancouver, Canada and San Francisco, U.S. It can be found from the results that the fragility curve of designed buildings is very close to the target fragility curves. Therefore, the direct fragility-based design method is capable of achieving a target fragility curve and can be considered as an effective design method for low-rise steel moment-resisting frames.

Synthesis and oil displacement performance evaluation of asymmetric anionic gemini surfactants

In order to further enhance the water flooding performance in low-permeability tight oil reservoirs, a novel gemini surfactant with high interfacial activity, hydrophilic wettability, and good injectivity and mobility control was developed. Using n-alkylamine (C12–C18), 3-chloro-2-hydroxypropylsulfonate sodium, sodium chloroacetate, and succinic anhydride as raw materials, a series of gemini surfactants with asymmetric hydrophilic head groups (GDCS m-4-m; m = 12, 14, 16, 18) were synthesized via substitution and ring-opening reactions. The structures of the synthesized products were characterized by FTIR and 1H NMR, and their surface and interfacial properties as well as their thickening abilities were evaluated. The optimal gemini surfactant was selected for further oil displacement and wettability alteration tests. The results indicate that the synthesized product is consistent with the molecular structure of the target design product. The surface tension and interfacial tension of GDCS m-4-m series surfactant solutions initially decrease and then increase with the increase in the number of carbon atoms in the hydrophobic carbon chain. Its thickening ability increases as the number of carbon atoms in the hydrophobic carbon chain increases. GDCS16-4-16 exhibited the best surface and interfacial activities, with a critical micelle concentration (CMC) of 0.344 mmol/L, surface tension of 24.04 mN/m, and an interfacial tension as low as 2.16 × 10−2 mN/m at 0.5 wt%. The flooding system formulated with GDCS16-4-16 demonstrated excellent mobility control and oil washing capabilities, and could shift the wettability of rock surfaces to highly hydrophilic. In dual-core flooding experiments, with gradient differentials of 3, 5, and 7, the comprehensive oil recovery increased by 12.81 %, 13.88 %, and 11.78 %, respectively, after injecting 0.4 PV of GDCS16-4-16 surfactant solution followed by subsequent water flooding. This indicates a significant potential to enhance recovery in low-permeability tight sandstone reservoirs, presenting promising application prospects in the development of such formations.

Discovery of a Potential Allosteric Site in the SARS-CoV-2 Spike Protein and Targeting Allosteric Inhibitor to Stabilize the RBD Down State using a Computational Approach.

The novel coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has led to a worldwide public health crisis. At present, the development of effective drugs and/or related therapeutics is still the most urgent and important task for combating the virus. The viral entry and associated infectivity mainly rely on its envelope spike protein to recognize and bind to the host cell receptor angiotensin-converting enzyme 2 (ACE2) through a conformational switch of the spike receptor binding domain (RBD) from inactive to active state. Thus, it is of great significance to design an allosteric inhibitor targeting spike to lock it in the inactive and ACE2-inaccessible state. This study aims to discover the potential broad-spectrum allosteric inhibitors capable of binding and stabilizing the diverse spike variants, including the wild type, Delta, and Omicron, in the inactive RBD down state. In this work, we first detected a potential allosteric pocket within the SARS-CoV-2 spike protein. Then, we performed large-scale structure-based virtual screening by targeting the putative allosteric pocket to identify allosteric inhibitors that could stabilize the spike inactive state. Molecular dynamics simulations were further carried out to evaluate the effects of compound binding on the stability of spike RBD. Finally, we identified three potential allosteric inhibitors, CPD3, CPD5, and CPD6, against diverse SARS-CoV-2 variants, including Wild-type, Delta, and Omicron variants. Our simulation results showed that the three compounds could stably bind the predicted allosteric site and effectively stabilize the spike in the inactive state. The three compounds provide novel chemical structures for rational drug design targeting spike protein, which is expected to greatly assist in the development of new drugs against SARS-CoV-2.

Beam dynamics studies on a specific radio-frequency quadrupole injector for superconducting linacs

Radio-frequency quadrupole (RFQ) linear accelerators are widely used at the front end of large-scale hadron accelerator facilities owing to their extraordinary performance of progressive bunching, high-efficiency acceleration and low emittance growth. Meanwhile, it is worth noting that RFQ linacs gradually become the only normal conducting accelerators in modern continuous-wave high-power superconducting hadron linacs. As a result, high-quality beams at the exit of RFQ linacs with respect to more strict beam dynamics requirements are demanded, where minimizing the emittance growth is one of the most critical design targets. Not only a small rms emittance is required for an easy rms match to the following superconducting cavities but also a small total emittance is needed to reduce the beam loss in the superconducting cavities. In this paper, the design parameters of some typical RFQ injectors for superconducting linacs worldwide were compared, and the beam dynamics design of a 162.5 MHz, 2.1 MeV, continuous-wave RFQ linac was performed. Guided by the design approach called MEGLET (Minimizing Emittance Growth via Low Emittance Transfer), the beam dynamics design of the example RFQ injector was completed with high transmission efficiency, low emittance growth, and large error tolerance. The design result could be a reference for similar RFQ linacs, and the beam dynamics design approach is useful and applicable for other linacs intended to improve the figures of merit.

Structure-guided inhibitor design targeting CntL provides the first chemical validation of the staphylopine metallophore system in bacterial metal acquisition

To survive in the metal-scarce environment within the host, pathogens synthesize various small molecular metallophores to facilitate the acquisition of transition metals. The cobalt and nickel transporter (Cnt) system synthesizes and transports staphylopine, a nicotianamine-like metallophore, and serves as a primary transition metal uptake system in Gram-positive bacteria including the human pathogen Staphylococcus aureus. In this study, we report the design of the first inhibitor of the Cnt system by targeting the key aminobutanoyltransferase CntL which is involved in the biosynthesis of staphylopine. Through structure-guided fragment linking and optimization, a class of acceptor-adenosine dual-site inhibitors against S. aureus CntL (SaCntL) were designed and synthesized. The most potent inhibitor, compound 9, demonstrated a ΔTm value of 9.4 °C, a Kd value of 0.021 ± 0.004 μM, and an IC50 value of 0.06 μM against SaCntL. The detailed mechanism by which compound 9 inhibits SaCntL has been elucidated through a high-resolution co-crystal structure. Treatment with compound 9 resulted in a moderate downregulation of intracellular concentrations of iron, nickel, and cobalt ions in the S. aureus cells cultured in the metal-scarce medium, providing the first chemical validation of the important role of Cnt system in bacterial metal acquisition. Our findings pave the way for the development of CntL-based antibacterial agents in future.

Influence of hydrogen content in tokamak scrape-off-layer on performance of lithium divertor

Abstract Self-replenishing liquid metal coatings are considered as a perspective divertor design able to withstand challenging particle and power loads of a fusion tokamak-reactor. Numerical modeling of the scrape-of-layer (SOL) plasma with advanced 2D codes, such as SOLPS, is necessary for developing of the ‘liquid-metal’ divertor. In this work we report on upgraded version of SOLPS 4.3 code liquid metal erosion module implemented earlier in our group and present results of simulations of T-15MD tokamak with Li-covered divertor plates. The erosion model includes all main processes Li erosion, i.e. physical sputtering, thermal sputtering, evaporation, and prompt redeposition. Unlike some other available implementations, Li atoms are considered in kinetic approximation in our version. A detailed analysis of Li erosion and flow in T-15MD configuration for various powers (6–12 MW) and H content in the SOL is presented. It is shown that the most of eroded Li particles are redeposited on the divertor targets, however, in some regimes absolute Li flow from the divertor is still large and might lead to significant main plasma dilution with Li. Vapor shielding effect is pronounced on both divertor targets in the most reasonable regimes providing low peak heat flux values at the target plates, less than 10 MW m−2. The target erosion rate and surface temperatures are within limits of the most target designs. Moreover, in strongly shielded cases the target temperature can be even lower than the Li melting temperature meaning that external heating is required to keep Li flowing. Sensitivity analysis shows that our results are most sensitive to the target heat conduction parameters, i.e. the target thickness, outer surface temperature. It means that controlling the target cooling rate can be a useful tool for controlling the liquid Li divertor regime. Variation of the Li erosion rate parameters has little effect on the divertor performance.

Hybrid solar-wind renewable energy systems with energy storage for net/nearly zero energy buildings: An uncertainty-based robust design method

Net/nearly zero energy buildings (NZEBs) are recognized as pivotal solutions to reduce building energy consumption and advance sustainable development. Existing studies often used traditional methods to facilitate the design of renewable energy systems (RESs) under deterministic conditions, which may fail to achieve the design targets of NZEBs during their service life. Considering uncertainty correlation and equipment degradation, an uncertainty-based approach was proposed in this study to robustly design RESs in NZEBs. Firstly, scenarios were randomly generated considering correlated uncertainties. A novel scenario reduction technique, which considered correlation loss while reducing scenarios, was then introduced to improve the optimization efficiency. Furthermore, the potential of NZEBs in reducing carbon emissions was explored by evaluating the environmental impact of various RESs using hourly marginal emission factors. Lastly, the optimal design solutions were identified and analyzed based on the specified targets of various NZEB definitions. The results indicated that, compared with the first year, the annual energy supply, net energy, and carbon emission reduction decreased by 28.2 %, 52.2 %, and 51.3 %, respectively, after 20 years of operation. The traditional design scheme failed to consistently achieve the predefined NZEB targets throughout the system's service life. In contrast, the RESs designed using the proposed approach exhibited reliable performance, consistently meeting the requirements under various NZEB definitions and achieving an average self-sufficiency ratio of 89.1 %, validating its energy autonomy throughout the system's operational lifespan. The proposed method can effectively result in robust design of RESs in NZEBs while satisfying the design targets of economic-technical-environmental dimensions under uncertainties.

Alkynyl Ligands Templated Assemblies of Silver Nanoclusters with Exceptional Electrochemiluminescence Activity for Pancreatic Cancer Specific tsRNAs Measurement.

Proper manipulation of the ligand complex on the motifs of metal nanoclusters (MNCs) to form an ordered self-assembly is an effective approach to enhance the electrochemiluminescence (ECL) emission of MNCs. We report a facile approach for the preparation of self-assembled AgNCs (AgNCsAssy) induced by alkynyl ligands with enhanced ECL and stability. The formation of these AgNCsAssy was simultaneously driven by the diverse coordination modes of alkynyl ligands with Ag and intercluster interactions, for which it was found that the para-substituted alkynyl ligands exhibited apparently irregular nanoparticles, while the monosubstituted counterparts were present in the form of ribbons. The calculations revealed that the energy gap between the highest occupied molecular orbitals (HOMOs) and the lowest unoccupied molecular orbitals (LUMOs) played a crucial role in their ECL emissions because of the substituent effects, especially, the low-lying LUMO levels could help to enhance the ECL emission. Moreover, mechanistic studies revealed that both the coreactant and alkynyl ligands made significant contributions to the ECL performance. Concurrently, the CRISPR-associated proteins (CRISPR-Cas) 12a system shows great potential in biosensing applications due to the advantages of easy design and precise targeting. As a proof of concept, we integrated the cascade amplification of catalytic hairpin assembly (CHA) circuit and the collateral cleavage activity of CRISPR-Cas12a to construct an ultrasensitive ECL biosensor for pancreatic cancer (PC)-specific tsRNAs, with a detection limit of 3.33 fM. This work is not only instructive for the synthesis of self-assembled MNCs with high ECL activities but also contributes to the understanding of the ECL mechanism of self-assembled MNCs.

How Do Algorithmic Management Practices Affect Workforce Well-Being? A Parallel Moderated Mediation Model

Modern workplaces increasingly use algorithmic management practices (AMPs), which shape task assignment, monitoring, and evaluation. Despite the potential benefits these practices offer, like increased efficiency and objectivity, their impact on workforce well-being (WFW) has raised concerns. Drawing on self-determination theory (SDT) and conservation of resources theory (COR), this study examines the relationship between algorithmic management practices and workforce well-being, incorporating job burnout (JBO) and perceived threat (PT) as parallel mediators and person–job fit (PJF) as a moderator. The research employed a cross-sectional survey design targeting 2450 KOSGEB-registered manufacturing SMEs in Istanbul, Turkey. A sample of 666 respondents participated, and the data were analyzed using Smart PLS 4, employing structural equation modeling to test the proposed model. The results indicated that algorithmic management practices significantly increased job burnout and perceived threat, both of which negatively impacted workforce well-being. However, the direct effect of algorithmic management practices on workforce well-being was non-significant. Person–job fit moderated the relationships between algorithmic management practices and both job burnout and perceived threat, further influencing workforce well-being. The findings underscore the critical need for organizations to balance algorithmic efficiency with human-centric practices. Prioritizing person–job fit and fostering transparency in algorithmic processes can mitigate negative impacts, enhance employee well-being, and drive sustainable organizational success in the digital age.

Validation of a general-use high flux isotope reactor–specific metaheuristic optimization framework for isotope production target design

Currently, advanced optimization methods are limited for isotope production (IP) campaigns at the US Department of Energy’s High Flux Isotope Reactor (HFIR) located at Oak Ridge National Laboratory (ORNL), leading to years of conservative and historical approaches with minimal innovation. Moreover, the growing demand for new and existing isotopes is beginning to challenge the capacity of HFIR. This work explores the development and integration of metaheuristic (MH) optimization techniques for more efficient target design and irradiation strategies. As a test case, the optimization framework was applied to a routinely produced isotope at HFIR, 188W, with the objective of maximizing the specific activity (SA), a key production metric. The framework includes Gnowee, a Python-based MH optimization algorithm, coupled with the Monte Carlo N-Particle version 6 (MCNP6) and Oak Ridge Isotope Generation (ORIGEN) activation/depletion/decay codes to design, simulate, and evaluate thousands of potential target design and irradiation scheme candidates. The framework relies on mock input files, design and irradiation variables for the algorithm to select, as well as a user-defined objective function to score each candidate based on the returned SA. Given the inherent complexities and computational time required when modeling and simulating the full HFIR model, a novel simplified MCNP6 model is presented in this article to increase the computational efficiency of the framework. The variables explored include irradiation location, number of cycles, and the number of W samples. Over 1,000 simplified model candidates were simulated in the same amount of time as a single full HFIR model run. By comparing the simplified model optimization’s top candidate(s) with the full HFIR model results, the framework was verified to accurately explore the design space and converge on the top performing candidates. Lastly, past experimental data was compared to the data generated by the framework/model and both show that fewer W rings return higher SA, as expected. The verified and validated techniques provide a standardized solution to increase IP efficiencies by exploring thousands of unique target designs and irradiation strategies in a similar time as that required to run a single case in the full HFIR MCNP6 model. Both the novel simplified model and the full HFIR model show a more than 30% increase in SA if all presented modifications are applied to the current design and strategy. Thus, the objective of building a general-use, computationally efficient optimization framework for HFIR IP was accomplished, and has the potential to be applied to other IP campaigns.

Displacement-Based Seismic Design of Multi-Story Resistance Capacitance-Coupled Shear Wall Buildings with Energy-Dissipation Dampers

This research aims to apply the displacement-based design method (DBDM) for the seismic design of reinforced concrete-coupled shear wall buildings equipped with energy dissipation dampers. The DBDM offers design simplicity by focusing on structural design based on a target design displacement, where the building converts into a single degree of freedom (SDOF) system. The implementation of dampers aims to reduce repair costs and downtime for buildings following significant seismic events. Two types of dampers are utilized in this study: metallic damper and viscoelastic damper. The DBDM procedure begins with determining the target displacement, which corresponds to the specific story drift ratio of the structural system, using a nonlinear static pushover analysis. For the structural wall system considered in this study, a target drift ratio of 1/250 is selected due to the inherent rigidity of the structure. The effective damping factor is then determined from the average energy absorption, which is based on the ductility factor of each structural member. Additionally, the effective period of the building is obtained from the displacement spectrum of the design-level earthquakes. Finally, the required damper shear capacity for the SDOF system is calculated based on the target deformation and effective stiffness. The design earthquakes are generated from the acceleration response spectrum for Level 2 earthquakes, as specified in the Japanese seismic code, utilizing three different sets of phase information: Kobe, El Centro, and random phase records. The effectiveness of the DBDM is scrutinized through a comparison with results obtained from time history analysis. The results obtained for 6-, 12-, and 18-story RC-coupled shear walls with energy dissipation dampers indicate that the proposed design methodology effectively meets the specified design objectives.

Delta-like ligand 3 (DLL3) landscape in pulmonary and extra-pulmonary neuroendocrine neoplasms.

Delta-like Ligand 3 (DLL3) targeting therapies are promising in small cell lung cancer (SCLC) treatment. However, DLL3 expression in SCLC and other neuroendocrine neoplasms (NEN) is heterogeneous and not well characterized. We describe the landscape of DLL3 at the mRNA and protein levels across SCLC, large cell neuroendocrine carcinoma (LCNEC), and non-small cell lung cancer. Additionally, we explore its expression in extra-pulmonary NEN (EP-NEN) using a standardized DLL3 IHC assay. DLL3 expression is enriched in SCLC, LCNEC along with combined histology lung cancers. Moreover, we find a wide range of DLL3 expression in high-grade EP-NEN. We describe heterogenous DLL3 expression not only in SCLC but also in different NEN types. This comprehensive characterization of DLL3 can help guide future clinical trial design targeting DLL3 in NEN including LCNEC and EP-NEN that are lacking standard of care treatment options.

Exploring the transcriptomic and m6a landscape of human chromosome 17 in breast cancer: A combined RNA-seq and MeRIP-Seq analysis

Abstract. N6-methyladenosine (m6A), recognized as the most prevalent post-transcriptional modification in organisms, has been substantiated to exert a significant influence on the genetic mechanisms underlying breast cancer. To delve deeper into the disparities in expression, modification, and interactions between mRNA and m6A in breast cancer (BC), we conducted RNA-seq and MeRIP-Seq analyses on eight human breast tissue transcriptome samples acquired from the GEO database. combining two single omics and integrated analysis, the study unveiled the mRNA and m6A expression patterns, differential genes, and enriched pathway functional disparities in BC (on Chromosome 17): (1) A confidence list comprised of 53 differential genes was derived by intersecting the 72 identified differentially expressed mRNA genes and the 781 differentially modified m6A genes; (2) The three-way analysis revealed two distinct types of pathways related to BC: ABC transporter activity and Response to epidermal growth factor. Among these, the EGF pathways exhibited the closest association with the differential m6A modifications. Meanwhile, all three BC subtypes enriched in DisGeNET were all linked to EGF; (3) By integrating PPI and enrichment analysis, we selected target genes from the confidence gene list, which included ABCA6, ABCA8, and ABCA10, known to be involved in regulating ABC transporters, and ERBB2, a central hub gene in PPI with a pivotal role in the EGF pathway and Her2-positive BC subtype under m6A differential modification. Subsequent research may uncover RNA-binding proteins for these target genes and offer effective drug design targets for BC.

Assessment of simulated and observed cavitation-induced erosion damage in spallation neutron source target vessels

Cavitation-induced erosion damage in different Spallation Neutron Source (SNS) target designs are simulated using explicit finite element–based techniques and compared with observations of erosion in targets after operation. The efficacy of the previously developed method, called saturation time, was evaluated using erosion-damaged samples from new target designs. A new metric called maximum bubble size was implemented under the rationale that larger cavitation bubbles will collapse more intensely. The maximum cavitation bubble size over 1 ms of simulated time was calculated based on the Rayleigh–Plesset equation for each element integration point and presented as a contour map at the vessel surface for assessing with erosion observations. SNS targets are now operated with helium gas injection to reduce cavitation damage. A simulation method using a material model for the mixture of mercury and gas bubbles was recently developed and used to account for the effect of small gas bubbles on the structural response of the target vessel. This work compares the new method's results with observed cavitation damage. Maps of the calculated maximum bubble size for targets operated with and without gas injection were compared with photographs of erosion damage observed in SNS targets. The patterns in maximum bubble size maps correlated well with observations of erosion patterns in target vessels after service. Advantages and challenges of the maximum bubble size simulation technique are provided, and differences between results from the previous and the newly proposed metric are discussed.