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Abstract Background General mental health inpatient units hold a valuable place in the stepped system of care, and for identification and treatment of people with eating disorders (EDs) or disordered eating behaviours (DEBs). This study aimed to pragmatically evaluate an evidence-informed screening and care pathway, alongside a staff education program, implemented to improve identification and treatment access for consumers with EDs and DEBs, with co-occurring psychiatric conditions, on a general mental health ward. Methods A mixed methodology design was mapped to the RE-AIM implementation framework. It encompassed medical record audits across two 3-month time points pre and post implementation of the pathway, and key informant consumer and health professional interviews. Results Process and implementation data were compared for three-month periods pre (2019, n = 348) and post-implementation (2021, n = 284). Post-implementation, intake SCOFF screening occurred in 94.7% of admissions. People with ED/DEBs diagnoses were 35 times more likely to have a SCOFF score ≥ 2 (OR = 35.2, p

Abstract Single nucleotide polymorphism (SNP) interactions are the key to improving polygenic risk scores. Previous studies reported several significant SNP–SNP interaction pairs that shared a common SNP to form a cluster, but some identified pairs might be false positives. This study aims to identify factors associated with the cluster effect of false positivity and develop strategies to enhance the accuracy of SNP–SNP interactions. The results showed the cluster effect is a major cause of false-positive findings of SNP–SNP interactions. This cluster effect is due to high correlations between a causal pair and null pairs in a cluster. The clusters with a hub SNP with a significant main effect and a large minor allele frequency (MAF) tended to have a higher false-positive rate. In addition, peripheral null SNPs in a cluster with a small MAF tended to enhance false positivity. We also demonstrated that using the modified significance criterion based on the 3 p-value rules and the bootstrap approach (3pRule + bootstrap) can reduce false positivity and maintain high true positivity. In addition, our results also showed that a pair without a significant main effect tends to have weak or no interaction. This study identified the cluster effect and suggested using the 3pRule + bootstrap approach to enhance SNP–SNP interaction detection accuracy.

The digital transformation process of power systems towards smart grids is resulting in improved reliability, efficiency and situational awareness at the expense of increased cybersecurity vulnerabilities. Given the availability of large volumes of smart grid data, machine learning-based methods are considered an effective way to improve cybersecurity posture. Despite the unquestionable merits of machine learning approaches for cybersecurity enhancement, they represent a component of the cyberattack surface that is vulnerable, in particular, to adversarial attacks. In this paper, we examine the robustness of autoencoder-based cyberattack detection systems in smart grids to adversarial attacks. A novel iterative-based method is first proposed to craft adversarial attack samples. Then, it is demonstrated that an attacker with white-box access to the autoencoder-based cyberattack detection systems can successfully craft evasive samples using the proposed method. The results indicate that naive initial adversarial seeds cannot be employed to craft successful adversarial attacks shedding insight on the complexity of designing adversarial attacks against autoencoder-based cyberattack detection systems in smart grids.

IntroductionNK cells can mediate tumor cell killing by natural cytotoxicity and by antibody-dependent cell-mediated cytotoxicity (ADCC), an anti-tumor mechanism mediated through the IgG Fc receptor CD16A (FcγRIIIA). CD16A polymorphisms conferring increased affinity for IgG positively correlate with clinical outcomes during monoclonal antibody therapy for lymphoma, linking increased binding affinity with increased therapeutic potential via ADCC. We have previously reported on the FcγR fusion CD64/16A consisting of the extracellular region of CD64 (FcγRI), a high-affinity Fc receptor normally expressed by myeloid cells, and the transmembrane/cytoplasmic regions of CD16A, to create a highly potent and novel activating fusion receptor. Here, we evaluate the therapeutic potential of engineered induced pluripotent stem cell (iPSC)-derived NK (iNK) cells expressing CD64/16A as an “off-the-shelf”, antibody-armed cellular therapy product with multi-antigen targeting potential.MethodsiNK cells were generated from iPSCs engineered to express CD64/16A and an interleukin (IL)-15/IL-15Rα fusion (IL-15RF) protein for cytokine independence. iNK cells and peripheral blood NK cells were expanded using irradiated K562-mbIL21–41BBL feeder cells to examine in in vitro and in vivo assays using the Raji lymphoma cell line. ADCC was evaluated in real-time by IncuCyte assays and using a xenograft mouse model with high circulating levels of human IgG.ResultsOur data show that CD64/16A expressing iNK cells can mediate potent anti-tumor activity against human B cell lymphoma. In particular, (i) under suboptimal conditions, including low antibody concentrations and low effector-to-target ratios, iNK-CD64/16A cells mediate ADCC, (ii) iNK-CD64/16A cells can be pre-loaded with tumor-targeting antibodies (arming) to elicit ADCC, (iii) armed iNK-CD64/16A cells can be repurposed with additional antibodies to target new tumor antigens, and (iv) cryopreserved, armed iNK-CD64/16A are capable of sustained ADCC in a tumor xenograft model under saturating levels of human IgG.DiscussioniNK-CD64/16A cells allow for a flexible use of antibodies (antibody arming and antibody targeting), and an “off-the-shelf” platform for multi-antigen recognition to overcome limitations of adoptive cell therapies expressing fixed antigen receptors leading to cancer relapse due to antigen escape variants.

Abstract The human gastrointestinal tract is populated with a diverse microbial community. The vast genetic and metabolic potential of the gut microbiome underpins its ubiquity in nearly every aspect of human biology, including health maintenance, development, aging, and disease. The advent of new sequencing technologies and culture-independent methods has allowed researchers to move beyond correlative studies toward mechanistic explorations to shed light on microbiome–host interactions. Evidence has unveiled the bidirectional communication between the gut microbiome and the central nervous system, referred to as the “microbiota–gut–brain axis”. The microbiota–gut–brain axis represents an important regulator of glial functions, making it an actionable target to ameliorate the development and progression of neurodegenerative diseases. In this review, we discuss the mechanisms of the microbiota–gut–brain axis in neurodegenerative diseases. As the gut microbiome provides essential cues to microglia, astrocytes, and oligodendrocytes, we examine the communications between gut microbiota and these glial cells during healthy states and neurodegenerative diseases. Subsequently, we discuss the mechanisms of the microbiota–gut–brain axis in neurodegenerative diseases using a metabolite-centric approach, while also examining the role of gut microbiota-related neurotransmitters and gut hormones. Next, we examine the potential of targeting the intestinal barrier, blood–brain barrier, meninges, and peripheral immune system to counteract glial dysfunction in neurodegeneration. Finally, we conclude by assessing the pre-clinical and clinical evidence of probiotics, prebiotics, and fecal microbiota transplantation in neurodegenerative diseases. A thorough comprehension of the microbiota–gut–brain axis will foster the development of effective therapeutic interventions for the management of neurodegenerative diseases.

Machine vision systems with programmed text detection and text recognition features are useful in manufacturing process to automatically locate and read text markings on mounted printed circuit board (PCB) components. To better handle input images with varying image quality, text quality, and text variations, the robustness of deep learning approach for end-to-end text spotting on PCB component images is worth exploring. However, limitations of public PCB component datasets for such research and imbalance of data in actual collected PCB component datasets hinder the training of deep learning text spotting model, and consequently necessitate the generation of synthetic data. In this study, a synthetic PCB component dataset is generated using our synthetic data generator that adds synthetic text with random character sequences on manually edited PCB component images to elevate the realism of the synthetic images. The synthetic dataset covers 66 character classes while providing synthetic text with diverse text variations in font, style, size, and color. We train an existing text spotting model called Text Perceptron using both real and synthetic datasets to detect and recognize arbitrary-shaped text markings on PCB components. Our synthetic PCB component dataset has improved the text spotting performance of Text Perceptron. The trained model achieves promising text detection result and encouraging end-to-end text spotting F-score on real PCB component images. It also meets an acceptable average inference time per image. Still, the text spotting performance of the trained model needs improvement to realize deployment for PCB component inspection.

Abstract Background The longitudinal associations between physical behaviours and lean muscle mass indices need to be better understood to aid healthy ageing intervention development. Methods We assessed physical behaviours (total physical activity, moderate-to-vigorous physical activity (MVPA), light physical activity, total sedentary time and prolonged sedentary bout time) for 7 days using hip-worn accelerometers. We also assessed domain-specific physical behaviours (walking, cycling, gardening and housework time) with self-report questionnaires at baseline (2006–2011) and follow-up (2012–2016) in the European Prospective Investigation into Cancer (EPIC)-Norfolk study. We assessed body composition using dual-energy X-ray absorptiometry (DEXA) at follow-up in 1535 participants (≥ 60 years at baseline). From this, we derived appendicular lean muscle mass (ALM) indices (% relative ALM = (ALM/total body weight)*100), body mass index (BMI)-scaled ALM (ALM/BMI, kg/kg/m2) and height-scaled ALM (ALM/height2, kg/m2)). We evaluated the prospective associations of both baseline and change in physical behaviours with follow-up muscle mass indices using multivariable linear regression. Results Over 5.5 years (SD 14.8) follow-up, higher baseline accelerometer-measured physical activity and lower sedentary time were associated with higher subsequent relative ALM and BMI-scaled ALM, but not height-scaled ALM (e.g. 0.02% higher subsequent relative ALM per minute/day of baseline MVPA for men). Greater increases in physical activity and greater declines in sedentary time variables were associated with higher subsequent relative ALM and BMI-scaled ALM, but not height-scaled ALM (e.g. 0.001 kg/kg/m2 subsequent BMI-scaled ALM and 0.04% subsequent relative ALM per min/day/year increases in LPA over follow-up; 0.001 kg/kg/m2 subsequent BMI-scaled ALM and -0.03% subsequent relative ALM per min/day/year less of total sedentary time over follow-up). Greater increases in women’s cycling and gardening over follow-up were associated with greater subsequent relative ALM (cycling 0.9% per hour/week/year; gardening 0.2% per hour/week/year) and BMI-scaled ALM (cycling 0.03 kg/kg/m2 per hour/week/year; gardening 0.004 kg/kg/m2 per hour/week/year). Conclusion Physical behaviours across all intensities, and in women more specifically cycling and gardening, may help prevent age-related declines in muscle mass.

Ensuring the quality and functionality of printed circuit boards (PCBs) during manufacturing requires precise, automated visual inspection. Detecting integrated circuits (ICs) on PCBs poses a significant challenge due to diverse component sizes, types, and intricate board markings that complicate accurate object detection. This study addresses this challenge by proposing an enhanced EfficientNet-YOLOv4 algorithm tailored explicitly for the IC detection of PCBs. Numerous modifications are integrated into YOLOv4, with the replacement of its original backbone by a robust feature extraction network, EfficientNetv2-L, and meticulous hyperparameter tuning, including variations in loss functions, anchor size configurations, and other training techniques. The methodology further incorporates diverse data augmentation techniques to enrich the training dataset and enhance the model’s generalization ability. Extensive experiments conducted in this study showed the efficacy and robustness of the algorithm in handling complex PCB layouts and varying lighting conditions, outperforming existing PCB inspection models. The proposed method, EfficientNetv2-L-YOLOv4, achieved an impressive F1-score of 99.22 with an inference speed of 0.14 s per image. The proposed method also performed well compared to EfficientNet-B7-FasterRCNN and the original YOLOv4; it attains an F1-score of 98.96 and an inference speed of 0.10 s per image (with a batch size of 4). These results highlight the significance of effective feature extraction networks for object detection. Beyond addressing IC detection challenges, this algorithm advances the fields of computer vision and object detection. The implementation of EfficientNetv2-L-YOLOv4 in real manufacturing scenarios holds promise for automating component inspections and potentially eliminating the need for human intervention.

The effect of different coal bottom ash (CBA) gradings on ground granulated blast furnace slag (GGBS) binary blended cement mortar is not well-documented in current literature. Therefore, previous studies have reported inconsistent and contradictory effects of using various CBA gradings as a fine aggregate replacement in mortar and concrete. Hence, the present work aimed to establish the influence of different coal bottom ash (CBA) gradings, which is crucial for reducing coal power plant waste by utilising larger fractions of CBA in mortar and concrete without compromising its hardened properties. Specifically, this study investigated the optimal grading of coal bottom ash (CBA) for natural sand replacement in ground granulated blast furnace slag (GGBS) binary blended cement mortar. The research examines three CBA gradings: passing 10 mm sieve (R-CBA), passing 4.75 mm sieve (S-CBA), and grading according to BS EN12620 (Gr-CBA) at 20 %, 40 %, and 60 % sand replacement levels for comparison. Resultant mortar specimens were tested at 7, 28, and 91 days for mechanical strength, fluid transport, and drying shrinkage. Results show that all CBA gradings performed better than the control mix (with river sand only). The Gr-CBA mix outperformed the S-CBA mix by up to 29 % in strength due to highly consistent grading. R-CBA mix exhibited optimal performance compared to the other two CBA mix up to 30 % in strength. The coarser grading of the R-CBA mix enhances the interfacial transition zone and aggregate-binder bond and, hence, exhibited a better performance. This study highlights the potential use of R-CBA grading to increase the CBA reuse rate in mortar or concrete while maintaining desirable performance.

Purpose: This study aimed to examine the roles of big data analytics technological capabilities (BDATC) on the performance of private higher education institutions (PHEIs) in Malaysia. This study explored mediating effect of strategic agility (SA) between BDATC and PHEIs' performance. The moderating effect of enterprise risk management (ERM) influencing the relationship between SA and PHEIs performance was also examined. Design/methodology/approach: To evaluate the hypotheses, data were collected from PHEIs in Malaysia using an online survey questionnaire. Structural equation modelling software SmartPLS 3.3.9 was used to analyse the 186 valid responses received. Findings: This empirical research discovered that BDATC and SA have positive and significant impact on PHEIs' performance. BDATC has positive and significant influence on SA of PHEIs. Moreover, SA mediates the influence of BDATC on PHEIs' performance. ERM enhances the impact of SA on performance of PHEIs in Malaysia. Originality/value: The findings contributed to the advancement of the resource-based view (RBV) theory and dynamic capability theory (DCT). Practitioners of PHEIs will understand how to maximise the resources and skills to improve BDATC, SA and ERM that eventually drive the performance of the practitioners' institution.

The microalgae-bacteria interaction in enriched culture media could affect the growth and nutritional properties of Artemia. The addition of carbon sources to the media improves the heterotrophic bacterial population, which in turn interacts with microalgae to improve the health and production of Artemia. The aim of this study was to assess the impacts of the microbial consortium developed with different fermented carbon sources and microalgae on Artemia. Three different microalgae, Chlorella sp./A1, Nannochloropsis oceanica/A2 and Chaetoceros calcitrans/A3 were mixed with three fermented carbon sources (rice bran/C1, tapioca flour/C2 and molasses/C3) to produce nine treatments (A1C1, A1C2, A1C3, A2C1, A2C2, A2C3, A3C1, A3C2, A3C3) and three controls with microalgae alone (A1, A2, A3). Enrichment was carried out for 24 h with Artemia instar I at an initial density of 25 individuals mL-1. Two-way ANOVA revealed that the survival, length, protein and lipid contents of Artemia nauplii were significantly affected (P < 0.05) by carbon source and species of microalgae. The carbon source–microalgae interaction also significantly affects Artemia survival, length, biomass and protein content. This study illustrated that bacteria associated with different carbon sources and microalgae consortia improved Artemia growth, survival, protein and lipid content.

The electrical properties of bismuth magnesium tantalate pyrochlore, Bi3.30Mg1.88Ta2.82O13.88 (BMT) were investigated by both inductor-capacitor-resistor (LCR) and impedance spectroscopy techniques covering a broad temperature range of 10–1073 K and a frequency range of 5 Hz - 1 MHz. At below ∼180 K, BMT pyrochlore exhibited interesting relaxor behaviour that showed high dispersion characteristics in its frequency-temperature dependent dielectric constants, ε′ and dielectric losses, tan δ, respectively. The maximum ε′max of ∼77 was obtained at the temperature maximum, Tm of 154 K. The frequency-independent ε′ data above 154 K at a fixed frequency of 1 MHz can be well fitted with the Curie-Weiss law and the relaxation features of Bi3.30Mg1.88Ta2.82O13.88 obeyed the Vogel-Fulcher equation. The dielectric properties of Bi3.30Mg1.88Ta2.82O13.88 relaxor in the low temperature range of 20–320 K could be satisfactorily modeled with different equivalent circuits. In this perspective, a master circuit consisting of a parallel R-C-CPE element in series with a capacitor was required to accurately fit the low temperature data.

Gas-forming pyogenic liver abscess (GFLPA) carries a high mortality rate. Early identification of the source of infection in sepsis results in better survival. Bedside point of care ultrasound (POCUS) can be used to help localize a source of infection. A 59-year-old man presented with systemic inflammatory response syndrome (SIRS) and was diagnosed with GFLPA on the initial encounter via clinical assessment and POCUS examination. After commencing antibiotics, optimal glucose control, adequate fluid resuscitation, and early infective source control, he achieved full recovery and was followed up in outpatient medical and surgical clinics. This case illustrates the role of POCUS as a diagnostic tool in sepsis and raises awareness among clinicians to recognize the features of GFLPA on POCUS.

Tasks ranging from synthesis to regression are executed within a computational farm environment during chip design. This process is managed by a compute farm scheduler, which handles job scheduling based on the availability of such computational resources as central processing units, memory, and storage. The increasing complexity of chip design over the years, combined with a growing number of cores per chip, has resulted in memory-intensive applications often being executed as compute jobs. Jobs submitted with inaccurate resource-related requests, especially those concerning memory, can overload a compute farm and lead to wasted resources. This study addresses this issue by using a data science-driven, machine learning-based approach to predict the memory required for a compute job at the time of its submission. Improving the accuracy of such predictions can significantly reduce the overall wait times of jobs and enable efficient use of the compute farm to reduce the overall cost because fewer machines are required to complete a set of jobs. We explored the use of the K-nearest neighbor, random forest, and ensemble methods to this end. The proposed approach yielded an accuracy of 80% in experiments, where this demonstrates the success of predicting the memory-related requirements of compute jobs across a diverse suite of applications used in the process of chip design.

he escalating carbon dioxide (CO2) emissions have precipitated climate change, underscoring the urgent need for innovative solutions. Carbon Capture and Storage (CCS) represents a groundbreaking technology to mitigate carbon emissions by capturing CO2 and depositing it deep within reservoirs. This study focuses on the storage aspect of CCS by scrutinising reservoir properties. A pivotal factor influencing the geological storage of CO2 is the wettability of the rock/CO2/brine system, which is intricately linked to the contact angle. However, previous research has indicated that pH can significantly influence the wettability of this system. Acetic acid, a prevalent organic acid in the petroleum industry, is commonly encountered and serves as a means to modulate pH. This experiment investigates pH variations, ranging from 3 to 7, through the introduction of acetic acid, while maintaining a salinity level of 30,000 ppm. Additionally, the type of rock mineral plays a crucial role in these findings. Therefore, this paper emphasises carbonate rocks, with a particular focus on limestone composed primarily of calcium carbonate (CaCO3). The experiment is conducted under elevated supercritical conditions, encompassing pressures of 10 MPa and 15 MPa, along with temperatures of 305 K and 323 K. Equilibrium contact angles are determined by measuring receding and advancing angles using the Vinci IFT 700 instrument under supercritical conditions. The results reveal a consistent trend wherein contact angles decrease as pH, temperature, and pressure increase, indicating a shift towards a water-wet system at higher pH, temperature, and pressure levels.

Abstract The complexity of CT perfusion (CTP) acquisition protocols may limit the availability of target mismatch assessment at resource-limited hospitals. We compared CTP mismatch with a mismatch surrogate generated from a simplified dynamic imaging sequence comprising widely available non-contrast CT (NCCT) and multiphase CT angiography (mCTA). Consecutive patients with anterior circulation acute ischemic stroke who received NCCT, mCTA, and CTP were retrospectively included in this study. An mCTA-perfusion (mCTA-P) dynamic series was formed by co-registering NCCT and mCTA. We simulated an ideal mCTA-P study by down-sampling CTP (dCTP) dynamic images according to mCTA timing. Ischemic core and penumbra volumes were estimated by cerebral blood flow and Tmax thresholding, respectively, on perfusion maps calculated independently for CTP, dCTP, and mCTA-P by deconvolution. Concordance in target mismatch (core