Neutral or Detrimental Effects of TREM2 Agonist Antibodies in Preclinical Models of Alzheimer's Disease and Multiple Sclerosis.

Human genetics and preclinical studies have identified key contributions of TREM2 to several neurodegenerative conditions, inspiring efforts to modulate TREM2 therapeutically. Here, we characterize the activities of three TREM2 agonist antibodies in multiple mixed-sex mouse models of Alzheimer's disease (AD) pathology and remyelination. Receptor activation and downstream signaling are explored in vitro, and active dose ranges are determined in vivo based on pharmacodynamic responses from microglia. For mice bearing amyloid-ß (Aß) pathology (PS2APP) or combined Aß and tau pathology (TauPS2APP), chronic TREM2 agonist antibody treatment had limited impact on microglia engagement with pathology, overall pathology burden, or downstream neuronal damage. For mice with demyelinating injuries triggered acutely with lysolecithin, TREM2 agonist antibodies unexpectedly disrupted injury resolution. Likewise, TREM2 agonist antibodies limited myelin recovery for mice experiencing chronic demyelination from cuprizone. We highlight the contributions of dose timing and frequency across models. These results introduce important considerations for future TREM2-targeting approaches.

Mathematical programming tools for randomization purposes in small two-arm clinical trials: A case study with real data.

Modern randomization methods in clinical trials are invariably adaptive, meaning that the assignment of the next subject to a treatment group uses the accumulated information in the trial. Some of the recent adaptive randomization methods use mathematical programming to construct attractive clinical trials that balance the group features, such as their sizes and covariate distributions of their subjects. We review some of these methods and compare their performance with common covariate-adaptive randomization methods for small clinical trials. We introduce an energy distance measure that compares the discrepancy between the two groups using the joint distribution of the subjects' covariates. This metric is more appealing than evaluating the discrepancy between the groups using their marginal covariate distributions. Using numerical experiments, we demonstrate the advantages of the mathematical programming methods under the new measure. In the supplementary material, we provide R codes to reproduce our study results and facilitate comparisons of different randomization procedures.

Applications of nature-inspired metaheuristic algorithms for tackling optimization problems across disciplines.

Nature-inspired metaheuristic algorithms are important components of artificial intelligence, and are increasingly used across disciplines to tackle various types of challenging optimization problems. This paper demonstrates the usefulness of such algorithms for solving a variety of challenging optimization problems in statistics using a nature-inspired metaheuristic algorithm called competitive swarm optimizer with mutated agents (CSO-MA). This algorithm was proposed by one of the authors and its superior performance relative to many of its competitors had been demonstrated in earlier work and again in this paper. The main goal of this paper is to show a typical nature-inspired metaheuristic algorithmi, like CSO-MA, is efficient for tackling many different types of optimization problems in statistics. Our applications are new and include finding maximum likelihood estimates of parameters in a single cell generalized trend model to study pseudotime in bioinformatics, estimating parameters in the commonly used Rasch model in education research, finding M-estimates for a Cox regression in a Markov renewal model, performing matrix completion tasks to impute missing data for a two compartment model, and selecting variables optimally in an ecology problem in China. To further demonstrate the flexibility of metaheuristics, we also find an optimal design for a car refueling experiment in the auto industry using a logistic model with multiple interacting factors. In addition, we show that metaheuristics can sometimes outperform optimization algorithms commonly used in statistics.

Endoscopic ultrasound avoids diagnostic ERCP among the ASGE high-risk group - Experience in an Asian population.

BACKGROUND: A prediction system for common bile duct (CBD) stones was originally published by the American Society for Gastrointestinal Endoscopy (ASGE) in 2010 and was last revised in 2019. We wanted to investigate its application in an Asian population, who have different etiologies of bile duct stone formation and accessibility to medical service compared to the West. METHODS: This is a single center retrospective study. Patients who received endoscopic ultrasound (EUS) for suspected CBD stones were collected from our endoscopic record system over a 10-year period. The accuracy of the revised ASGE criteria was estimated according to the results of EUS. A minimum follow-up of 6 months was required to detect false negative results. RESULTS: 142 patients were enrolled, 87 (61%) patients had CBD stones. The sensitivity, specificity, positive predictive value, negative predictive value and accuracy of the revised ASGE criteria for high-risk patients were 69%, 38%, 64%, 44%, and 57%. 36% of the ASGE-defined high-risk patients negative for CBD stones on EUS. The two significant predictors for CBD stone were CBD dilatation (adjusted OR 3.06, 95% C.I. 1.31-7.17, p = 0.010) and ascending cholangitis (adjusted OR 2.28, 95% C.I. 1.01-5.15, p = 0.047). CONCLUSION: ASGE recommends that patients defined as high-risk for choledocholithiasis be considered for direct ERCP without prior need for confirmation imaging. However, our findings indicate a high rate (36%) of patients in that group negative for CBD stones on EUS. Hence, EUS is still be suggested first in selective high-risk patients so that diagnostic ERCP can be avoided in our Asian society.

Optimizing the design of a pharmacokinetic trial to evaluate the dosing scheme of a novel tuberculosis drug in children living with or without HIV.

Pharmacokinetic (PK) studies in children are usually small and have ethical constraints due to the medical complexities of drawing blood in this special population. Often, population PK models for the drug(s) of interest are available in adults, and these models can be extended to incorporate the expected deviations seen in children. As a consequence, there is increasing interest in the use of optimal design methodology to design PK sampling schemes in children that maximize information using a small sample size and limited number of sampling times per dosing period. As a case study, we use the novel tuberculosis drug delamanid, and show how applications of optimal design methodology can result in highly efficient and model-robust designs in children for estimating PK parameters using a limited number of sampling measurements. Using developed population PK models based on available data from adults living with and without HIV, and limited data on children without HIV, competing designs for children living with HIV were derived and assessed based on robustness to model uncertainty.

Microwave-assisted upcycling of plastic waste to high-performance carbon anode for lithium-ion batteries.

The increasing demand for high-performance lithium-ion batteries (LIBs) has emphasized the need for affordable and sustainable materials, prompting the exploration of waste upcycling to address global sustainability challenges. In this study, we efficiently converted polypropylene (PP) plastic waste from used centrifuge tubes into activated polypropylene carbon (APC) using microwave-assisted pyrolysis. The synthesis of APC was optimized using response surface methodology/central composite design (RSM/CCD). Based on the RSM results, the optimal conditions for PP plastic conversion into carbon were determined as follows: HNO3 concentration of 3.5 M, microwave temperature of 230 °C, and holding time of 25 min. Under these conditions, the obtained intensity ratio of Id/Ig in PP carbon was 0.681 ± 0.013, with an error of 6.81 ± 0.013 % between predicted and actual values. The physicochemical studies, including FESEM-EDX, XRD, and Raman spectroscopy, confirmed the successful synthesis of APC samples. The APC 800 material exhibited a well-organized three-dimensional structure characterized by large pores and mesopores, enabling fast ion transport in the electrode. As a result, the APC 800 electrode demonstrated an initial discharge capacity of 381.0 mAh/g, an improved initial coulombic efficiency of 85.1%, and excellent cycling stability after 100 cycles. Notably, the APC 800 electrode displayed remarkable rate performance, showing a reversible capacity of 355.1 mAh/g when the current density was reset to 0.2 A/g, highlighting its high electrochemical reversibility. The outstanding characteristics of APC 800 as an anode electrode material for high-performance lithium-ion batteries suggest a promising future for its application in the field.

An interactive tool for designing efficient toxicology experiments.

The design of dose-response experiments is an important part of toxicology research. Efficient design of these experiments requires choosing optimal doses and assigning the correct number of subjects to those doses under a given criterion. Optimal design theory provides the tools to find the most efficient experimental designs in terms of cost and statistical efficiency. However, the mathematical details can be distracting and make these designs inaccessible to many toxicologists. To facilitate use of these designs, we present an easy to use web-app for finding two types of optimal designs for models commonly used in toxicology. We include tools for checking the optimality of a given design and for assessing efficiency of any user-supplied design. Using state-of-the-art nature-inspired metaheuristic algorithms, the web-app allows the user to quickly find optimal designs for estimating model parameters or the benchmark dose.

An Overview of Adaptive Designs and Some of Their Challenges, Benefits, and Innovative Applications.

Adaptive designs are increasingly developed and used to improve all phases of clinical trials and in biomedical studies in various ways to address different statistical issues. We first present an overview of adaptive designs and note their numerous advantages over traditional clinical trials. In particular, we provide a concrete demonstration that shows how recent adaptive design strategies can further improve an adaptive trial implemented 13 years ago. Despite their usefulness, adaptive designs are still not widely implemented in clinical trials. We offer a few possible reasons and propose some ways to use them more broadly in practice, which include greater availability of software tools and interactive websites to generate optimal adaptive trials freely and effectively, including the use of metaheuristics to facilitate the search for an efficient trial design. To this end, we present several web-based tools for finding various adaptive and nonadaptive optimal designs and discuss nature-inspired metaheuristics. Metaheuristics are assumptions-free general purpose optimization algorithms widely used in computer science and engineering to tackle all kinds of challenging optimization problems, and their use in designing clinical trials is just emerging. We describe a few recent such applications and some of their capabilities for designing various complex trials. Particle swarm optimization is an exemplary nature-inspired algorithm, and similar to others, it has a simple definition but many moving parts, making it hard to study its properties analytically. We investigated one of its hitherto unstudied issues on how to bring back out-of-range candidates during the search for the optimum of the search domain and show that different strategies can impact the success and time of the search. We conclude with a few caveats on the use of metaheuristics for a successful search.

Enhanced tribological properties of diesel-based engine oil through synergistic MoS2-graphene nanohybrid additive.

This research explores the potential of microwave-synthesized MoS2-graphene nanohybrid as additives to enhance the tribological properties of diesel-based engine oil. The synthesis method offers significant advantages, reducing both synthesis time and energy consumption by 90-98% compared to conventional approaches. The synthesized nanohybrids are characterized through FESEM, EDX, XRD, and Raman spectroscopy to understand their morphology and functional group interactions. These nanohybrids are incorporated into 20W40 engine oil following synthesis, and a comprehensive assessment of their properties is conducted. This evaluation covers critical parameters like viscosity index, stability, volatility, as well as tribological properties, oxidation resistance, and thermal conductivity of the oil-nanohybrid system. Results demonstrate that adding just 0.05 wt% of MoS2-graphene nanohybrid leads to a remarkable 58.82% reduction in friction coefficient and a significant 36.26% decrease in the average wear scar diameter. Additionally, oxidation resistance improves by 19.21%, while thermal conductivity increases notably by 19.83% (at 100 °C). The study demonstrates the synergistic effects of these nanohybrids in reducing friction and wear, enhancing oxidation resistance, and improving thermal conductivity. In conclusion, this research highlights the potential of microwave-synthesized MoS2-graphene nanohybrid as promising tribological additives for diesel engine oils. Their successful integration could significantly enhance the performance and durability of critical mechanical components in diesel engines, representing a significant advancement in lubrication technology.

Optimal Designs for Nonlinear Mixed-effects Models Using Competitive Swarm Optimizer with Mutated Agents.

Nature-inspired meta-heuristic algorithms are increasingly used in many disciplines to tackle challenging optimization problems. Our focus is to apply a newly proposed nature-inspired meta-heuristics algorithm called CSO-MA to solve challenging design problems in biosciences and demonstrate its flexibility to find various types of optimal approximate or exact designs for nonlinear mixed models with one or several interacting factors and with or without random effects. We show that CSO-MA is efficient and can frequently outperform other algorithms either in terms of speed or accuracy. The algorithm, like other meta-heuristic algorithms, is free of technical assumptions and flexible in that it can incorporate cost structure or multiple user-specified constraints, such as, a fixed number of measurements per subject in a longitudinal study. When possible, we confirm some of the CSO-MA generated designs are optimal with theory by developing theory-based innovative plots. Our applications include searching optimal designs to estimate (i) parameters in mixed nonlinear models with correlated random effects, (ii) a function of parameters for a count model in a dose combination study, and (iii) parameters in a HIV dynamic model. In each case, we show the advantages of using a meta-heuristic approach to solve the optimization problem, and the added benefits of the generated designs.

Disparities in ethnicity and metabolic disease burden in referrals to nephrology.

Introduction: The profile of patients referred from primary to tertiary nephrology care is unclear. Ethnic Malay patients have the highest incidence and prevalence of kidney failure in Singapore. We hypothesised that there is a Malay predominance among patients referred to nephrology due to a higher burden of metabolic disease in this ethnic group. Methods: This is a retrospective observational cohort study. From 2014 to 2018, a coordinator and physician triaged patients referred from primary care, and determined co-management and assignment to nephrology clinics. Key disease parameters were collated on triage and analysed. Results: A total of 6,017 patients were studied. The mean age of patients was 64 ± 16 years. They comprised 57% men; 67% were Chinese and 22% were Malay. The proportion of Malay patients is higher than the proportion of Malays in the general population (13.4%) and they were more likely than other ethnicities to have ≥3 comorbidities, including diabetes mellitus, hypertension, hyperlipidaemia, coronary artery disease and stroke (70% vs. 57%, P < 0.001). Malay and Indian patients had poorer control of diabetes mellitus compared to other ethnicities (glycated haemoglobin 7.8% vs. 7.4%, P < 0.001). Higher proportion of Malay patients compared to other ethnicities had worse kidney function with estimated glomerular filtration rate (eGFR) <30 mL/min/1.73 m2 on presentation (28% vs. 24%, P = 0.003). More ethnic Malay, Indian and younger patients missed appointments. Conclusion: A disproportionately large number of Malay patients are referred for kidney disease. These patients have higher metabolic disease burden, tend to miss appointments and are referred at lower eGFR. Reasons underpinning these associations should be identified to facilitate efforts for targeting this at-risk population, ensuring kidney health for all.

Randomized Controlled Clinical Trial of the Effect of Treatment with Vitamin K2 on Vascular Calcification in Hemodialysis Patients (Trevasc-HDK).

Introduction: Vitamin K deficiency among patients on hemodialysis (HD) affects the function of matrix GLA protein (MGP), a potent vitamin K-dependent inhibitor of vascular calcification (VC). Methods: We conducted a single-center randomized controlled trial (RCT) on maintenance HD patients to examine if vitamin K2 supplementation can reduce progression of coronary artery calcification (CAC) over an 18-month study period. Patients were randomized to vitamin K2 group receiving menaquinone-7360 µg 3 times/wk or control group. The primary outcome was CAC scores at the end of the study period. The secondary outcomes were aortic valve calcification (AVC), carotid-femoral pulse wave velocity (cfPWV), aortic augmentation index (AIx), dephosphorylated undercarboxylated MGP (dp-ucMGP) levels, major adverse cardiac events (MACE), and vascular access events. Results: Of the 178 patients randomized, follow-up was completed for 138 patients. The CAC scores between the 2 groups were not statistically different at the end of 18 months (relative mean difference [RMD] 0.85, 95% CI 0.55-1.31). The secondary outcomes did not differ significantly in AVC (RMD 0.82, 95% CI 0.34-1.98), cfPWV (absolute mean difference [AMD] 0.55, 95% CI -0.50 to 1.60), and AIx (AMD 0.13, 95% CI -3.55 to 3.80). Supplementation with vitamin K2 did reduce dp-ucMGP levels (AMD -86, 95% CI -854 to -117). The composite outcome of MACE and mortality was not statistically different between the 2 groups (Hazard ratio = 0.98, 95% CI 0.50-1.94). Conclusion: Our study did not demonstrate a beneficial effect of vitamin K2 in reducing progression of VC in this population at the studied dose and duration.

Hyperspectral signature-band extraction and learning: an example of sugar content prediction of Syzygium samarangense.

This study proposes a method to extract the signature bands from the deep learning models of multispectral data converted from the hyperspectral data. The signature bands with two deep-learning models were further used to predict the sugar content of the Syzygium samarangense. Firstly, the hyperspectral data with the bandwidths lower than 2.5 nm were converted to the spectral data with multiple bandwidths higher than 2.5 nm to simulate the multispectral data. The convolution neural network (CNN) and the feedforward neural network (FNN) used these spectral data to predict the sugar content of the Syzygium samarangense and obtained the lowest mean absolute error (MAE) of 0.400° Brix and 0.408° Brix, respectively. Secondly, the absolute mean of the integrated gradient method was used to extract multiple signature bands from the CNN and FNN models for sugariness prediction. A total of thirty sets of six signature bands were selected from the CNN and FNN models, which were trained by using the spectral data with five bandwidths in the visible (VIS), visible to near-infrared (VISNIR), and visible to short-waved infrared (VISWIR) wavelengths ranging from 400 to 700 nm, 400 to 1000 nm, and 400 to 1700 nm. Lastly, these signature-band data were used to train the CNN and FNN models for sugar content prediction. The FNN model using VISWIR signature bands with a bandwidth of ± 12.5 nm had a minimum MAE of 0.390°Brix compared to the others. The CNN model using VISWIR signature bands with a bandwidth of ± 10 nm had the lowest MAE of 0.549° Brix compared to the other CNN models. The MAEs of the models with only six spectral bands were even better than those with tens or hundreds of spectral bands. These results reveal that six signature bands have the potential to be used in a small and compact multispectral device to predict the sugar content of the Syzygium samarangense.

Synergistic performance evaluation of MoS2-hBN hybrid nanoparticles as a tribological additive in diesel-based engine oil.

In this study, MoS2-hBN hybrid nanoparticles were synthesized using an advanced microwave platform for new nanolubricant formulations. The synthesized nanoparticles were characterized by field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, X-ray diffraction, and Raman spectroscopy. The hybrid nanoparticles were then introduced into a 20W40 diesel-based engine oil to produce a nanolubricant. The physical and chemical properties of the nanolubricant were investigated, including the viscosity index, stability, volatility, tribological properties, oxidation properties, and thermal conductivity. The results showed that the inclusion of 0.05 wt% MoS2-hBN hybrid nanoparticles in the oil significantly reduced the coefficient of friction and wear scar diameter by 68.48% and 35.54%, respectively. Moreover, it exhibited substantial oxidation and thermal conductivity improvement of 38.76% and 28.30%, respectively, at 100 °C. These findings demonstrate the potential of MoS2-hBN hybrid nanoparticles as an effective additive to enhance the properties of nanolubricant significantly. Furthermore, this study offers valuable insights into the underlying mechanisms responsible for the observed enhancements. The promising outcomes of this investigation contribute to the advancement of nanotechnology-based lubricants, showcasing their potential for improving engine efficiency and prolonging the lifespan of machinery.

The Future Glioblastoma Clinical Trials Landscape: Early Phase 0, Window of Opportunity, and Adaptive Phase I-III Studies.

PURPOSE OF REVIEW: Innovative clinical trial designs for glioblastoma (GBM) are needed to expedite drug discovery. Phase 0, window of opportunity, and adaptive designs have been proposed, but their advanced methodologies and underlying biostatistics are not widely known. This review summarizes phase 0, window of opportunity, and adaptive phase I-III clinical trial designs in GBM tailored to physicians. RECENT FINDINGS: Phase 0, window of opportunity, and adaptive trials are now being implemented for GBM. These trials can remove ineffective therapies earlier during drug development and improve trial efficiency. There are two ongoing adaptive platform trials: GBM Adaptive Global Innovative Learning Environment (GBM AGILE) and the INdividualized Screening trial of Innovative GBM Therapy (INSIGhT). The future clinical trials landscape in GBM will increasingly involve phase 0, window of opportunity, and adaptive phase I-III studies. Continued collaboration between physicians and biostatisticians will be critical for implementing these trial designs.

CQ + Training: Minimizing Accuracy Loss in Conversion From Convolutional Neural Networks to Spiking Neural Networks.

Spiking neural networks (SNNs) are attractive for energy-constrained use-cases due to their binarized activation, eliminating the need for weight multiplication. However, its lag in accuracy compared to traditional convolutional network networks (CNNs) has limited its deployment. In this paper, we propose CQ+ training (extended "clamped" and "quantized" training), an SNN-compatible CNN training algorithm that achieves state-of-the-art accuracy for both CIFAR-10 and CIFAR-100 datasets. Using a 7-layer modified VGG model (VGG-*), we achieved 95.06% accuracy on the CIFAR-10 dataset for equivalent SNNs. The accuracy drop from converting the CNN solution to an SNN is only 0.09% when using a time step of 600. To reduce the latency, we propose a parameterized input encoding method and a threshold training method, which further reduces the time window size to 64 while still achieving an accuracy of 94.09%. For the CIFAR-100 dataset, we achieved an accuracy of 77.27% using the same VGG-* structure and a time window of 500. We also demonstrate the transformation of popular CNNs, including ResNet (basic, bottleneck, and shortcut block), MobileNet v1/2, and Densenet, to SNNs with near-zero conversion accuracy loss and a time window size smaller than 60. The framework was developed in PyTorch and is publicly available.

Optimizing for In-Memory Deep Learning With Emerging Memory Technology.

In-memory deep learning executes neural network models where they are stored, thus avoiding long-distance communication between memory and computation units, resulting in considerable savings in energy and time. In-memory deep learning has already demonstrated orders of magnitude higher performance density and energy efficiency. The use of emerging memory technology (EMT) promises to increase density, energy, and performance even further. However, EMT is intrinsically unstable, resulting in random data read fluctuations. This can translate to nonnegligible accuracy loss, potentially nullifying the gains. In this article, we propose three optimization techniques that can mathematically overcome the instability problem of EMT. They can improve the accuracy of the in-memory deep learning model while maximizing its energy efficiency. Experiments show that our solution can fully recover most models' state-of-the-art (SOTA) accuracy and achieves at least an order of magnitude higher energy efficiency than the SOTA.

Statistical tests for homogeneity of variance for clinical trials and recommendations.

In most clinical trials, the main interest is to test whether there are differences in the mean outcomes among the treatment groups. When the outcome is continuous, a common statistical test is a usual t-test for a two-group comparison. For more than 2 groups, an ANOVA setup is used and the test for equality for all groups is based on the F-distribution. A key assumption for these parametric tests is that data are normally, independently distributed and the response variances are equal. The robustness of these tests to the first two assumptions is quite well investigated, but the issues arising from heteroscedasticity are less studied. This paper reviews different methods for ascertaining homogeneity of variance across groups and investigates the consequences of heteroscedasticity on the tests. Simulations based on normal, heavy-tailed, and skewed normal data demonstrate that some of the less known methods, such as the Jackknife or Cochran's test, are quite effective in detecting differences in the variances.