To optimize the antidepressant efficacy of repetitive transcranial magnetic stimulation (rTMS), it is important to examine the impact of brain state during therapeutic rTMS. Evidence suggests that brain state can modulate the brain’s response to stimulation, potentially diminishing antidepressant efficacy if left uncontrolled or enhancing it with inexpensive psychological or other non-pharmacological methods. Thus, we conducted a PRISMA-ScR-based scoping review to pool studies administering rTMS with psychological and other non-pharmacological methods. PubMed and Web of Science databases were searched from inception to 10 July 2024. Inclusion criteria: neuropsychiatric patients underwent rTMS; studies assessed depressive symptom severity; non-pharmacological tasks or interventions were administered during rTMS, or did not include a wash-out period. Of 8,442 studies, 20 combined rTMS with aerobic exercise, bright light therapy, cognitive training or reactivation, psychotherapy, sleep deprivation, or a psychophysical task. Meta-analyses using random effects models were conducted based on change scores on standardized scales. The effect size was large and therapeutic for uncontrolled pretest-posttest comparisons (17 studies, Hedges’ g = −1.91, (standard error) SE = 0.45, 95% (confidence interval) CI = −2.80 to −1.03, p < 0.01); medium when studies compared active combinations with sham rTMS plus active non-pharmacological methods (8 studies, g = −0.55, SE = 0.14, 95% CI = −0.82 to −0.28, p < 0.01); and non-significant when active combinations were compared with active rTMS plus sham psychological methods (4 studies, p = 0.96). Attempts to administer rTMS with non-pharmacological methods show promise but have not yet outperformed rTMS alone.

Many psychological concepts are unobserved and usually represented as latent factors apprehended through multiple observed indicators. When multiple-subject multivariate time series data are available, dynamic factor analysis models with random effects offer one way of modeling patterns of within- and between-person variations by combining factor analysis and time series analysis at the factor level. Using the Dirichlet process (DP) as a nonparametric prior for individual-specific time series parameters further allows the distributional forms of these parameters to deviate from commonly imposed (e.g., normal or other symmetric) functional forms, arising as a result of these parameters’ restricted ranges. Given the complexity of such models, a thorough sensitivity analysis is critical but computationally prohibitive. We propose a Bayesian local influence method that allows for simultaneous sensitivity analysis of multiple modeling components within a single fitting of the model of choice. Five illustrations and an empirical example are provided to demonstrate the utility of the proposed approach in facilitating the detection of outlying cases and common sources of misspecification in dynamic factor analysis models, as well as identification of modeling components that are sensitive to changes in the DP prior specification.

As avionics systems become increasingly complex, traditional fault prediction methods are no longer sufficient to meet modern demands. This paper introduces four advanced fault prediction methods for avionics components, utilising a multi-step prediction strategy combined with a stacking regressor. By selecting various standard regression models as base regressors, these base regressors are first trained on the original data, and their predictions are subsequently used as input features for training a meta-regressor. Additionally, the Tree-structured Parzen Estimator (TPE) algorithm is employed for hyperparameter optimisation. The experimental results demonstrate that the proposed stacking regression methods exhibit superior accuracy in fault prediction compared to traditional single-model approaches.

Contact binaries challenge contemporary stellar astrophysics with respect to their incidence, structure, and evolution. We explore these issues through a detailed study of two bright examples: S Ant and $\varepsilon$ CrA, that permit high-resolution spectroscopy at a relatively good S/N ratio. The availability of high-quality photometry, including data from the TESS satellite as well as Gaia parallaxes, allows us to apply the Russell paradigm to produce reliable up-to-date information on the physical properties of these binaries. As a result, models of their interactive evolution, such as the thermal relaxation oscillator scenario, can be examined. Mass transfer between the components is clearly evidenced, but the variability of the O’Connell effect over relatively short-time scales points to irregularities in the mass transfer or accretion processes. Our findings indicate that S Ant may evolve into an R CMa type Algol, while the low mass ratio of $\varepsilon$ CrA suggests a likely merger of its components in the not-too-distant future.

Foods consumed at lower eating rates (ER) lead to reductions in energy intake. Previous research has shown that texture-based differences in eating rateER can reduce meal size. The effect size and consistency of these effects across a wide range of composite and complex meals differing considerably in texture and varying in meal occasion have not been reported. We determined how consistently texture-based differences in ER can influence food and energy intake across a wide variety of meals. In a crossover design, healthy participants consumed twelve breakfast and twelve lunch meals that differed in texture to produce a fast or slow ER. A breakfast group (n = 15) and lunch group (n = 15) completed twelve ad libitum meal sessions each (six ‘fast’ and six ‘slow’ meals), where intake was measured and behavioural video annotation was used to characterise eating behaviour. Liking did not differ significantly between fast and slow breakfasts (P = 0·44) or lunches (P = 0·76). The slow meals were consumed on average 39 % ± 9 % (breakfast) and 45 % ± 7 % (lunch) slower than the fast meals (both P < 0·001). Participants consumed on average 22 % ± 5 % less food (84 g) and 13 % ± 6 % less energy (71 kcal) from slow compared with fast meals (mean ± SE; P < 0·001). Consuming meals with a slower ER led to a reduction in food intake, where an average decrease of 20 % in ER produced an 11 % ± 1 % decrease in food intake (mean ± SE). These findings add to the growing body of evidence showing that ER can be manipulated using food texture and that this has aits consistent effect on food and energy intake across a wide variety of Hedonically equivalent meals.

The attitude-tracking problem of hypersonic morphing vehicles (HMVs) is investigated in this research. After introducing variable-span wings, the optimal aerodynamic shape is available throughout the entire flight mission. However, the morphing wings cause significant changes in aerodynamic coefficients and mass distribution, challenging the attitude control. Therefore, a complete design procedure for the flight control system is proposed to address the issue. Firstly, the original model and the control-oriented model of HMVs are built. Secondly, in order to eliminate the influence caused by the multisource uncertainties, an adaptive fixed-time disturbance observer combined with fuzzy control theory is established. Thirdly, the fixed-time control method is developed to stabilise hypersonic morphing vehicles based on a multivariable sliding mode manifold. The control input can be obtained directly. Finally, the effectiveness of the proposed method is proved with the help of the Lyapunov theory and simulation results.

Bentonites are readily available clays used in the livestock industry as feed additives to reduce aflatoxin (AF) exposure; their potential interaction with nutrients is the main concern limiting their use, however. The objective of the present study was to determine the safety of a dietary sodium-bentonite (Na-bentonite) supplement as a potential AF adsorbent, using juvenile Sprague Dawley (SD) rats as a research model. Animals were fed either a control diet or a diet containing Na-bentonite at 0.25% and 2% (w/w) inclusion rate. Growth, serum, and blood biochemical parameters, including selected serum vitamins (A and E) and elements such as calcium (Ca), potassium (K), iron (Fe), and zinc (Zn) were measured. The mineral characteristics and the aflatoxin B1 sorption capacity of Na-bentonite were also determined. By the end of the study, males gained more weight than females in control and Na-bentonite groups (p ≤ 0.0001); the interaction between treatment and sex was not significant (p = 0.6780), however. Some significant differences between the control group and bentonite treatments were observed in serum biochemistry and vitamin and minerals measurements; however, parameters fell within reference clinical values reported for SD rats and no evidence of dose-dependency was found. Serum Na and Na/K ratios were increased, while K levels were decreased in males and females from Na-bentonite groups. Serum Zn levels were decreased only in males from Na-bentonite treatments. Overall, results showed that inclusion of Na-bentonite at 0.25% and 2% did not cause any observable toxicity in a 3-month rodent study.

We present a comparison between the performance of a selection of source finders (SFs) using a new software tool called Hydra. The companion paper, Paper I, introduced the Hydra tool and demonstrated its performance using simulated data. Here we apply Hydra to assess the performance of different source finders by analysing real observational data taken from the Evolutionary Map of the Universe (EMU) Pilot Survey. EMU is a wide-field radio continuum survey whose primary goal is to make a deep ($20\mu$Jy/beam RMS noise), intermediate angular resolution ($15^{\prime\prime}$), 1 GHz survey of the entire sky south of $+30^{\circ}$ declination, and expecting to detect and catalogue up to 40 million sources. With the main EMU survey it is highly desirable to understand the performance of radio image SF software and to identify an approach that optimises source detection capabilities. Hydra has been developed to refine this process, as well as to deliver a range of metrics and source finding data products from multiple SFs. We present the performance of the five SFs tested here in terms of their completeness and reliability statistics, their flux density and source size measurements, and an exploration of case studies to highlight finder-specific limitations.

The latest generation of radio surveys are now producing sky survey images containing many millions of radio sources. In this context it is highly desirable to understand the performance of radio image source finder (SF) software and to identify an approach that optimises source detection capabilities. We have created Hydra to be an extensible multi-SF and cataloguing tool that can be used to compare and evaluate different SFs. Hydra, which currently includes the SFs Aegean, Caesar, ProFound, PyBDSF, and Selavy, provides for the addition of new SFs through containerisation and configuration files. The SF input RMS noise and island parameters are optimised to a 90% ‘percentage real detections’ threshold (calculated from the difference between detections in the real and inverted images), to enable comparison between SFs. Hydra provides completeness and reliability diagnostics through observed-deep ($\mathcal{D}$) and generated-shallow ($\mathcal{S}$) images, as well as other statistics. In addition, it has a visual inspection tool for comparing residual images through various selection filters, such as S/N bins in completeness or reliability. The tool allows the user to easily compare and evaluate different SFs in order to choose their desired SF, or a combination thereof. This paper is part one of a two part series. In this paper we introduce the Hydra software suite and validate its $\mathcal{D/S}$ metrics using simulated data. The companion paper demonstrates the utility of Hydra by comparing the performance of SFs using both simulated and real images.

Background: Cognitive impairment is a common manifestation of anti-LGI1 encephalitis and is typically defined as prominent memory deficits. We frequently encounter frontal cognitive-behavioural deficits when evaluating these patients, but this has yet to be well described in the literature. Methods: Patients with anti-LGI1 encephalitis were retrospectively identified from three tertiary centres in Toronto, Ontario between 2013 and 2022. Their medical records were evaluated and frontal features were categorized based on diagnostic criteria for behavioural variant frontotemporal dementia (bvFTD). Results: Nineteen patients were identified (median age 60 years [range 18–84]; 10 [52.6%] male). Eighteen (94.7%) had frontal cognitive-behavioural symptoms. Two developed these symptoms during treatment with steroids and were excluded from further analysis. The remaining 16 presented with behavioural disinhibition (n=13), apathy or inertia (n=6), perseverative, stereotyped or compulsive/ritualistic behaviours (n=6), hyperorality and dietary changes (n=4), a neuropsychological profile with predominant deficits in executive tasks (n=4), and loss of sympathy or empathy (n=4). Nine (47.3%) met diagnostic criteria for possible bvFTD. Anterograde memory impairment was common (n=14). Of the 16 patients with frontal features, 6 had faciobrachial dystonic seizures. Conclusions: Patients with anti-LGI1 encephalitis exhibit frontal cognitive-behavioural symptoms in addition to memory impairment. Clinicians should consider anti-LGI1 encephalitis in the differential diagnosis of bvFTD.

Background: Our aim was to develop a National Quality Indicators Set for the Care of Adults Hospitalized for Neurological Problems, to serve as a foundation to build regional or national quality initiatives in Canadian neurology centres. Methods: We used a national eDelphi process to develop a suite of quality indicators and a parallel process of surveys and patient focus groups to identify patient priorities. Canadian content and methodology experts were invited to participate. To be included, >70% of participants had to rate items as critical and <15% had to rate it as not important. Two rounds of surveys and consensus meetings were used identify and rank indicators, followed by national consultation with members of the Canadian Neurological Society. Results: 38 neurologists and methodologists and 56 patients/caregivers participated in this project. An initial list of 91 possible quality indicators was narrowed to 40 indicators across multiple categories of neurological conditions. 21 patient priorities were identified. Conclusions: This quality indicators suite can be used regionally or nationally to drive improvement initiatives for inpatient neurology care. In addition, we identified multiple opportunities for further research where evidence was lacking or patient and provider priorities did not align.

Background: Eye movements reveal neurodegenerative disease processes due to overlap between oculomotor circuitry and disease-affected areas. Characterizing oculomotor behaviour in context of cognitive function may enhance disease diagnosis and monitoring. We therefore aimed to quantify cognitive impairment in neurodegenerative disease using saccade behaviour and neuropsychology. Methods: The Ontario Neurodegenerative Disease Research Initiative recruited individuals with neurodegenerative disease: one of Alzheimer’s disease, mild cognitive impairment, amyotrophic lateral sclerosis, frontotemporal dementia, Parkinson’s disease, or cerebrovascular disease. Patients (n=450, age 40-87) and healthy controls (n=149, age 42-87) completed a randomly interleaved pro- and anti-saccade task (IPAST) while their eyes were tracked. We explored the relationships of saccade parameters (e.g. task errors, reaction times) to one another and to cognitive domain-specific neuropsychological test scores (e.g. executive function, memory). Results: Task performance worsened with cognitive impairment across multiple diseases. Subsets of saccade parameters were interrelated and also differentially related to neuropsychology-based cognitive domain scores (e.g. antisaccade errors and reaction time associated with executive function). Conclusions: IPAST detects global cognitive impairment across neurodegenerative diseases. Subsets of parameters associate with one another, suggesting disparate underlying circuitry, and with different cognitive domains. This may have implications for use of IPAST as a cognitive screening tool in neurodegenerative disease.

The incidence of scarlet fever has increased dramatically in recent years in Chongqing, China, but there has no effective method to forecast it. This study aimed to develop a forecasting model of the incidence of scarlet fever using a seasonal autoregressive integrated moving average (SARIMA) model. Monthly scarlet fever data between 2011 and 2019 in Chongqing, China were retrieved from the Notifiable Infectious Disease Surveillance System. From 2011 to 2019, a total of 5073 scarlet fever cases were reported in Chongqing, the male-to-female ratio was 1.44:1, children aged 3–9 years old accounted for 81.86% of the cases, while 42.70 and 42.58% of the reported cases were students and kindergarten children, respectively. The data from 2011 to 2018 were used to fit a SARIMA model and data in 2019 were used to validate the model. The normalised Bayesian information criterion (BIC), the coefficient of determination (R2) and the root mean squared error (RMSE) were used to evaluate the goodness-of-fit of the fitted model. The optimal SARIMA model was identified as (3, 1, 3) (3, 1, 0)12. The RMSE and mean absolute per cent error (MAPE) were used to assess the accuracy of the model. The RMSE and MAPE of the predicted values were 19.40 and 0.25 respectively, indicating that the predicted values matched the observed values reasonably well. Taken together, the SARIMA model could be employed to forecast scarlet fever incidence trend, providing support for scarlet fever control and prevention.