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Transcranial focused ultrasound stimulation is a promising therapeutic modality for human brain disorders because of its noninvasiveness, long penetration depth, and versatile spatial control capability through beamforming and beam steering. However, the skull presents a major hurdle for successful applications of ultrasound stimulation. Specifically, skull-induced focal aberration limits the capability for accurate and versatile targeting of brain subregions. In addition, there lacks a fully functional preclinical neuromodulation system suitable to conduct behavioral studies. Here, we report a miniature ultrasound system for neuromodulation applications that is capable of highly accurate multiregion targeting based on acoustic holography. Our work includes the design and implementation of an acoustic lens for targeting brain regions with compensation for skull aberration through time-reversal recording and a phase conjugation mirror. Moreover, we utilize MEMS and 3D-printing technology to implement a 0.75-g lightweight neuromodulation system and present in vivo characterization of the packaged system in freely moving mice. This preclinical system is capable of accurately targeting the desired individual or multitude of brain regions, which will enable versatile and explorative behavior studies using ultrasound neuromodulation to facilitate widespread clinical adoption.

This work intended to formulate bio-nanocomposites of zinc oxide (ZnO), titanium oxide (TiO2), chitosan, and escin, characterize their physical properties, and evaluate their antimicrobial and anticancer properties. X-ray diffractometers (XRD) and scanning and transmission electron microscopes were applied to characterize the morphology and ultrastructure of chemically synthesized bio-nanocomposites. To investigate the functional groups of bio-nanocomposites, we used Perkin–Elmer spectrometers for Fourier transform infrared (FTIR) analysis and photoluminescence (PL) spectroscopy for PL spectrum analysis. Antimicrobial activities against bacterial and fungal strains were tested with agar well diffusion. Bio-nanocomposites were tested for anticancer effects on a MOLT4 blood cancer cell line using morphological analysis, methyl thiazole tetrazolium assay, apoptosis by acridine orange/ethidium bromide, and mitochondrial membrane potential (ΔΨm). In XRD, FTIR, and PL, the active compounds of ZnO–TiO2, chitosan, and escin peaks were observed. Our bio-nanocomposites demonstrated antimicrobial activity against bacterial and fungal pathogens. The bio-nanocomposite was cytotoxic to MOLT4 cells at an IC50 concentration of 33.4 µg·mL−1. Bio-nanocomposites caused cytotoxicity, changes in cell morphology, and mitochondrial membrane potential degradation, all of which resulted in apoptotic cell death. MOLT4 cells were found to be responsive to bio-nanocomposites based on ZnO–TiO2–chitosan–escin.

Genomic researchers are increasingly utilizing commercial cloud platforms (CCPs) to manage their data and analytics needs. Commercial clouds allow researchers to grow their storage and analytics capacity on demand, keeping pace with expanding project data footprints and enabling researchers to avoid large capital expenditures while paying only for IT capacity consumed by their project. Cloud computing also allows researchers to overcome common network and storage bottlenecks encountered when combining or re-analysing large datasets. However, cloud computing presents a new set of challenges. Without adequate security controls, the risk of unauthorised access may be higher for data stored on the cloud. In addition, regulators are increasingly mandating data access patterns and specific security protocols on the storage and use of genomic data to safeguard rights of the study participants. While CCPs provide tools for security and regulatory compliance, utilising these tools to build the necessary controls required for cloud solutions is not trivial as such skill sets are not commonly found in a genomics lab. The Research Assets Provisioning and Tracking Online Repository (RAPTOR) by the Genome Institute of Singapore is a cloud native genomics data repository and analytics platform focusing on security and regulatory compliance. Using a “five-safes” framework (Safe Purpose, Safe People, Safe Settings, Safe Data and Safe Output), RAPTOR provides security and governance controls to data contributors and users leveraging cloud computing for sharing and analysis of large genomic datasets without the risk of security breaches or running afoul of regulations. RAPTOR can also enable data federation with other genomic data repositories using GA4GH community-defined standards, allowing researchers to boost the statistical power of their work and overcome geographic and ancestry limitations of data sets

This article proposes a common-mode (CM) voltage reduction method using an active power filter (APF) and common-mode transformer (CMT) for a grid-connected three-phase three-level neutral-point-clamped (NPC) AC-DC pulsewidth modulation (PWM) converter. The CM voltage can be caused by switching operations of converters and the CM voltage should be reduced because it is one of the main causes of electromagnetic interference (EMI) noises. In this article, the CM voltage of the three-phase converter with the proposed APF is analyzed to explain the CM voltage reduction using the proposed APF. Based on this analysis, the application of a specific PWM method to the three-phase converter for an optimal reduction of the CM voltage is described. Besides, the APF circuit design and its switching method are proposed considering practical problems such as the self-resonance of inductors and the effect of the dead-time on the CM voltage. To prove the effectiveness of the proposed APF on the CM voltage reduction, experimental results for the CM voltage and current are presented in this article. Furthermore, EMI noise measurement results are shown to verify the performance of the noise reduction capability of the proposed APF.

To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets.Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference.In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97).We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.

Pipes applied to marine plants are used in deep-sea environments; therefore, they must be resistant to high pressure and corrosion. Because it is difficult to satisfy both of these factors in a single pipe, studies on a double-layered pipe are continuously being performed. An outer pipe should be made of carbon steel, with high pressure resistance, and an inner pipe should be made of stainless steel, with high corrosion resistance. A pipe formed by combining these two pipes is called a lined pipe. The shear strength of the lined pipe is an important factor because pipe cracking can occur due to stress concentration when two pipes are separated by bending or high pressure. Therefore, various processes have been applied to increase the shear strength. In this paper, we investigate the effect of the surface roughness of the bonding interface on the shear strength. Surface roughness is in units of micrometers, and it cannot be used for finite element method (FEM) analysis. Therefore, surface roughness should be converted into a friction coefficient to perform FEM analysis. The effect of surface roughness on shear strength was studied in the relationship between the results of pressure from FEM analysis and the shear strength test.

Porous metallic materials are materials that have closed cell or open cell pores within their microstructure and have unique properties such as permeability and high stiffness-to-weight ratio depending on the type of the pore. Open cell pores in porous metallic materials allow minuscule particles such as air to pass through, giving it its permeability property. Permeability in porous metallic materials can further serve as air vents in injection moulds allowing the process to have a better surface finish and also dispel heat from the system better. Additive manufacturing can manufacture these materials to save time and cost and provides more intricate designs compared to traditional manufacturing. This study aims to manufacture maraging 300 steel with tailored porosity by controlling the processing parameters in the selective laser melting (SLM) process. The effect of each parameter on relative density was determined using an L9 orthogonal array through Taguchi’s Design of Experiments. It was found that hatch spacing had the largest effect, followed by laser power, layer thickness, and scanning speed. Regression modelling found that the equation can provide a good prediction of the relative density. When the processing parameters were related to energy density function, regression equation utilising energy density has a better performance, but its use is restricted to the range of energy density reported. The materials became permeable when the relative density is less than 95%. Permeability increases linearly with a sharp increase at a relative density below 80% due to the high volume of open pores. In terms of mechanical properties, both compression modulus and yield strength decrease with decreasing relative density. The method shown here can be applied to produce, within the same part, a combination of porous and non-porous sections in the same manufacturing process.

Little is known about how people’s routine body movements when they are at work affect the perceived fit and comfort of their uniform/workwear. This knowledge gap was addressed in this study throug...

We propose a technique to measure position-independent geometric errors (PIGEs) in the rotary and spindle axes of a hybrid parallel kinematic machine (PKM). The PKM investigated here includes one more rotary axis than an Exechon PKM, which is used to improve the productivity of hybrid processes, such as machining and direct-energy-deposition three-dimensional metal printing. Errors in the measured position and orientation of the rotary axis, and the orientation of the spindle axis produce volumetric errors in the processed workpiece. If accuracy is to be improved, the deviation of each axis must be measured and compensated. In our approach, errors are measured using three methodologies that require only control of the rotary axis: in the first, no offset is applied to account for positional deviation of the rotary axis; in the second, an offset is used to correct the orientation of the rotary axis; and in the third, a tool offset is used to correct the orientation of the spindle axis. We developed an algorithm that uses the three measured datasets to identify PIGEs. The proposed method was applied to a hybrid PKM and the PIGEs were measured and compensated. This technique uses simple measurement paths and sequential measurements to correct rotary and spindle axis errors, and could therefore be widely used in industry.

A multistage manufacturing process (MMP) consists of several consecutive process stages, each of which has multiple machines performing the same functions in parallel. A manufacturing path (simply referred to as path) is defined as an ordered set indicating a record of machines assigned to a product at each process stage of an MMP. An MMP usually produces products through various paths. In practice, multiple machines in a process stage have different operational performances, which accumulate during production and affect the quality of products. This study proposes a heuristic approach to derive the golden paths that produce products whose quality exceeds the desired level. The proposed approach consists of the searching phase and the merging phase. The searching phase extracts two types of machine sequence patterns (MSPs) from a path dataset in an MMP. An MSP is a subset of the path that is defined as an ordered set of assigned machines from several process stages. The two extracted types of MSPs are: (1) superior MSP, which affects the production of superior-quality products, and (2) inferior MSP, which affects the production of inferior-quality products, called inferior MSP. The merging phase derives the golden paths by combining superior MSPs and excluding inferior MSPs. The proposed approach is verified by applying it to a hypothetical path dataset and the semiconductor tool fault isolation (SETFI) dataset. This verification shows that the proposed approach derives the golden paths that exceed the predefined product quality level. This outcome demonstrates the practical viability of the proposed approach in an MMP.

Objectives This study explored whether the excess cardiovascular (CV) disease (CVD) risk in RA could be ameliorated by suppression of inflammation using a treat-to-target (T2T) approach. We compared the CV event (CVE) incidence among ERA patients managed by a T2T strategy with a CV risk factor-matched non-RA population and a historical RA cohort (HRA). Methods This was an observational study using the city-wide hospital data and the ERA registry. ERA patients received T2T management while HRA patients received routine care. Each ERA/HRA patient was matched to three non-RA controls according to age, gender and CV risk factors. Patients on antiplatelet/anticoagulant agents, with pre-existing CVD, chronic kidney disease or other autoimmune diseases were excluded. All subjects were followed for up to 5 years. The primary end point was the first occurrence of a CVE. Results The incidence of CVE in the ERA cohort (n = 261) and ERA controls were similar with a hazard ratio of 0.53 (95% CI 0.15, 1.79). In contrast, the incidence of CVE in the HRA cohort (n = 268) was significantly higher than that of the HRA controls with a hazard ratio of 1.9 (95% CI 1.16, 3.13). The incidence of CVE in the ERA cohort was significantly lower than that of the HRA cohort and the difference became insignificant after adjusting for inflammation, the use of methotrexate and traditional CV risk factors. Conclusion ERA patients managed by a T2T strategy did not develop excess CVE compared with CV risk factor-matched controls over 5 years.

Sulfide glasses are emerging as potential electrolytes for solid-state batteries. The mechanical behavior of these materials can significantly impact cell performance, and it is thus imperative to understand their deformation and fracture mechanisms. Previous work mainly reports properties obtained under quasi-static loading conditions, but very little is known about deformation under dynamic conditions. The current investigation shows that the sulfide glass mechanical behavior is dominated by viscoplasticity, differing substantially from polycrystalline oxide and sulfide solid electrolytes. Finite element modeling indicates that the sulfide glass stiffness is high enough to maintain good contact with softer lithium metal electrodes under moderate stack pressures. The observed viscoplasticity also implies that battery operating conditions will play an important role in electro-chemo-mechanical processes that are associated with dendritic lithium penetration. In general, the rate-dependent mechanical behavior of the sulfide glass electrolytes documented here offers a new dimension for designing next-generation all-solid-state batteries. Published version The authors acknowledge financial support from SK Innovation and the National Science Foundation (DMR-2124775).

BA.2.12.1, BA.4 and BA.5 subvariants of SARS-CoV-2 variant-of-concern (VOC) Omicron (B.1.1.529) are spreading globally. They demonstrate higher transmissibility and immune escape.Determine BA.2.12.1, BA.4 and BA.5 virus plaque reduction neutralization test (PRNT) antibody titres in individuals recently vaccinated with BNT162b2 (n = 20) or CoronaVac (n = 20) vaccines or those convalescent from ancestral wild- type (WT) SARS-CoV-2 (n = 20) or BA.2 infections with (n = 17) or without (n = 7) prior vaccination.Relative to neutralization of the WT virus, those vaccinated with BNT162b2 had 4.8, 3.4, 4.6, 11.3 and 15.5-fold reductions of geometric mean antibody titres (GMT) to BA.1, BA.2, BA.2.12.1, BA.4 and BA.5 viruses, respectively. Similarly, those vaccinated with CoronaVac had 8.0, 7.0, 11.8, 12.0 and 12.0 fold GMT reductions and those with two doses of CoronaVac boosted by BNT162b2 had 6.1, 6.7, 6,3, 13.0 and 21.2 fold GMT reductions to these viruses, respectively. Vaccinated individuals with BA.2 breakthrough infections had higher GMT antibody levels vs. BA.4 (36.9) and BA.5 (36.9) than unvaccinated individuals with BA.2 infections (BA.4 GMT 8.2; BA.5 GMT 11.0).BA.4 and BA.5 subvariants were less susceptible to BNT162b2 or CoronaVac vaccine elicited antibody neutralization than subvariants BA.1, BA.2 and BA.2.12.1. Nevertheless, three doses BNT162b2 or booster of BNT162b2 following two doses of CoronaVac elicited detectable BA.4 and BA.5 neutralizing antibody responses while those vaccinated with three doses of CoronaVac largely fail to do so. BA.2 infections in vaccinated individuals led to higher levels of BA.4 or BA.5 neutralizing antibody compared to those who were vaccine-naive.