An efficient Parkinson's disease detection framework: Leveraging time-frequency representation and AlexNet convolutional neural network.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting the quality of life of over 10 million individuals worldwide. Early diagnosis is crucial for timely intervention and better patient outcomes. Electroencephalogram (EEG) signals are commonly used for early PD diagnosis due to their potential in monitoring disease progression. But traditional EEG-based methods lack exploration of brain regions that provide essential information about PD, and their performance falls short for real-time applications. To address these limitations, this study proposes a novel approach using a Time-Frequency Representation (TFR) based AlexNet Convolutional Neural Network (CNN) model to explore EEG channel-based analysis and identify critical brain regions efficiently diagnosing PD from EEG data. The Wavelet Scattering Transform (WST) is employed to capture distinct temporal and spectral characteristics, while AlexNet CNN is utilized to detect complex spatial patterns at different scales, accurately identifying intricate EEG patterns associated with PD. The experiment results on two real-time EEG PD datasets: San Diego dataset and the Iowa dataset demonstrate that frontal and central brain regions, including AF4 and AFz electrodes, contribute significantly to providing more representative features compared to other regions for PD detection. The proposed architecture achieves an impressive accuracy of 99.84% for the San Diego dataset and 95.79% for the Iowa dataset, outperforming existing EEG-based PD detection methods. The findings of this research will assist to create an essential technology for efficient PD diagnosis, enhancing patient care and quality of life.

HEIDI: an experiment-management platform enabling high-throughput fragment and compound screening.

The Swiss Light Source facilitates fragment-based drug-discovery campaigns for academic and industrial users through the Fast Fragment and Compound Screening (FFCS) software suite. This framework is further enriched by the option to utilize the Smart Digital User (SDU) software for automated data collection across the PXI, PXII and PXIII beamlines. In this work, the newly developed HEIDI webpage (https://heidi.psi.ch) is introduced: a platform crafted using state-of-the-art software architecture and web technologies for sample management of rotational data experiments. The HEIDI webpage features a data-review tab for enhanced result visualization and provides programmatic access through a representational state transfer application programming interface (REST API). The migration of the local FFCS MongoDB instance to the cloud is highlighted and detailed. This transition ensures secure, encrypted and consistently accessible data through a robust and reliable REST API tailored for the FFCS software suite. Collectively, these advancements not only significantly elevate the user experience, but also pave the way for future expansions and improvements in the capabilities of the system.

Measurement of the

The cross section of the ^{13}C(α,n)^{16}O reaction is needed for nuclear astrophysics and applications to a precision of 10% or better, yet inconsistencies among 50 years of experimental studies currently lead to an uncertainty of ≈15%. Using a state-of-the-art neutron detection array, we have performed a high resolution differential cross section study covering a broad energy range. These measurements result in a dramatic improvement in the extrapolation of the cross section to stellar energies potentially reducing the uncertainty to ≈5% and resolving long standing discrepancies in higher energy data.

Exploring mental health approaches and curriculum in physiotherapy: an Australasian perspective.

BACKGROUND: Exposure to mental health problems are becoming increasingly more frequent within physiotherapy. Physiotherapists recognize the need for a biopsychosocial approach, however their knowledge, assessment, and treatment skills specific to mental health problems requires further research. PURPOSE: To profile the level of education and perception of education that physiotherapists have acquired specific to mental health problems; and to profile an understanding of the current practice of physiotherapists specific to mental health problems. METHODS: An online survey addressed the aims of the study and collected data from physiotherapists in Australia and New Zealand (open between 20th of October 2022 to the 20th of March 2023). RESULTS: 139 respondents were included in the analysis. Physiotherapists had a perception that a significantly greater amount of coursework related to mental health needed to be included within their initial degree compared to what they received (mean difference of 20.0%, 95% CI: 17.5 to 22.5). This trend was evident irrespective of the degree level or the year of graduation. Higher perceived knowledge of anxiety (MD of 11.4, p = 0.001, ES: 0.5) and depression (MD of 11.8, p = 0.001, ES: 0.5) was evident in outpatient care physiotherapists. Lack of perceived knowledge is a reason for whether an assessment or treatment strategy is used with patients experiencing a mental health problem. Motivational interviewing and mindfulness were the most frequently used psychologically based techniques. CONCLUSION: This study reveals the need to increase the amount of mental health and psychologically based techniques within Physiotherapy curriculum.

Perinatal sleep disruption and postpartum psychosis in bipolar disorder: Findings from the UK BDRN Pregnancy Study.

BACKGROUND: Women with bipolar disorder (BD) are at high risk of postpartum psychosis (PP). The factors that increase risk of PP among women with BD are not fully understood. Here, we examine whether sleep disruption in the perinatal period (poor sleep quality in late pregnancy and sleep deprivation related to childbirth) is associated with PP in a longitudinal study of pregnant women with BD. METHODS: Participants were 76 pregnant women with lifetime DSM-5 bipolar I disorder or schizoaffective-BD, followed from week 12 of pregnancy to 12 weeks postpartum. Demographics and lifetime psychopathology were assessed at baseline via semi-structured interview (Schedules for Clinical Assessment in Neuropsychiatry). Psychopathology and sleep disruption within the current perinatal period were assessed in the third trimester and at 12 weeks postpartum. Data were supplemented by clinician questionnaires and case-note review. RESULTS: After controlling for prophylactic use of mood stabilising medication, the loss of at least one complete night of sleep across labour/delivery was associated with five times the odds of experiencing PP compared to no or less than one night of sleep loss across labour/delivery (OR 5.19, 95 % CI 1.45-18.54; p = 0.011). Sleep quality in late pregnancy was not associated with PP, and perinatal sleep disruption was not associated with postpartum depression. LIMITATIONS: Lack of objective measures of sleep factors. CONCLUSIONS: In the context of other aetiological factors, severe sleep loss associated with childbirth/the immediate postpartum may act as a final trigger of PP. These findings could have important clinical implications for risk prediction and prevention of PP.

Diet quality is associated with nutrition knowledge and physical activity in the US military veterans enrolled in university programmes.

INTRODUCTION: Military veterans are at greater risk for chronic medical conditions, many of which are associated with greater body mass index (BMI). Detrimental changes to diet and physical activity (PA) levels after separation from military service contribute to this disparity which may be mitigated by nutrition education (NE) during service. METHODS: We conducted a survey in student veterans attending two southeastern US universities to determine current nutrition knowledge and hypothesised that NE received during time of service would be associated with better current diet quality (DQ), PA and BMI. Food group knowledge (FGK), and nutrient knowledge, DQ measured by Healthy Eating Index (HEI), and PA reported in metabolic equivalent minutes per week (MET-min/week) were assessed using previously validated questionnaires. Height and weight were also self-reported to calculate BMI. Differences in these variables between NE groups were assessed using Mann-Whitney U tests. Change in DQ, PA and BMI were assessed with Wilcoxon signed rank tests. Stepwise backward regression analysis was used to identify significant predictors related to HEI and BMI status. RESULTS: Sixty-three out of 83 total responses were valid. Respondents were 60% male, 81% white, 43% and 27% served in the Army and Navy, respectively and 30% reported receiving NE while in the military. Veterans who received NE while serving did not have higher FGK, nutrition knowledge, current DQ, MET-min/week or BMI than their counterparts. Overall, respondents reported decreased MET-min/week (p<0.001), increased BMI (p=0.01) and no change in DQ. PA (p=0.014) and FGK (p<0.001) were significant predictors of current DQ, while no variables significantly predicted current BMI. CONCLUSIONS: Inverse relationships between BMI and PA were observed after separation from duty. These results warrant the development and implementation of effective lifestyle interventions in veterans to prevent chronic disease and improve quality of life.

Developmental activities of elite junior hockey players: an analysis of early sport specialization.

Early sport specialization is a popular and contentious topic in the scientific literature and popular media. The lure of extrinsic rewards has led to increasing rates of specialization among young athletes, while expert recommendations promote multisport participation. The purpose of this study was to describe and analyze developmental activities of a group of elite junior hockey players in Canada. Within this context, elements of specialization were investigated in accordance with existing theoretical frameworks and long-term athlete development models to enhance the literature. Fifteen participants from the Ontario Hockey League completed quantitative retrospective interviews, detailing past sport and recreational activities. Thirty-one developmental milestones were assessed. Accumulated hours of activity were categorized in accordance with Côté's (1999) Developmental Model of Sports Participation, along with the number and types of sports in which they participated during childhood. Jayanthi et al.'s (2015) continuum was utilized to determine the age at which the athletes became moderately and highly specialized. Accrued hours of deliberate practice reported by participants increased from ages 6 to 16 years, as did competition in organized hockey games. Reported hours of deliberate play peaked at 9 years of age and decreased thereafter. Participants played a combined 16 sports other than hockey, ranging from an average of 2.0 at age 6, to a maximum average of 5.6 at 12 years old, and decreasing each year to 2.3 by age 15. The greatest number of hours in other sports was accumulated at 12 years of age. Using a three-point scale, participants considered themselves "highly specialized" at 14 years old; however, other quantitative indicators suggested this may have occurred at 12 years of age. Relative to previous research on early sport specialization, participants in this study spent more time practicing hockey, while ceasing hockey-specific play and other sports at younger ages. Despite a diverse sport history, hockey competition was initiated earlier than recommended, showing high levels of sport commitment as young as 9 years old. The early specialization path remains a popular trajectory among coaches, parents, and athletes in Canadian ice hockey.

Pilot feasibility and acceptability trial of BE REAL's BodyKind: A universal school-based body image intervention for adolescents.

Body dissatisfaction is prevalent among adolescents and a primary risk factor for eating disorders, yet there are few body image interventions for older adolescents that support development of positive body image. Therefore, we assessed the feasibility, acceptability and preliminary effectiveness of BodyKind, a four-lesson, mixed gender, teacher-led, school-based curriculum for older adolescents, that combines principles of self-compassion, compassion for others, cognitive dissonance, and social activism to address contemporary adolescent body image concerns (i.e., appearance bias, comparisons on social media) and strengthen positive body image development. The sample contained 147 adolescents, predominantly racial/ethnic minorities (>95%), 54.8% male, 41.5% female and 4.1% gender-minority students aged 15-18 years (M=16.24, SD=.96) from a low-income, inner-city high school in the Midwestern US. Two teachers received training and delivered the curriculum to students. This single arm, mixed methods trial assessed student and teacher acceptability, teacher fidelity and student intervention outcomes. Despite reasonable teacher fidelity, recruitment/attendance rates, post-intervention data loss (35% attrition) limited evaluations of program effectiveness and study feasibility. Important learnings regarding study feasibility will inform optimisation for future school-based trials. Findings demonstrate high acceptability of BodyKind among teachers and adolescents in a lower socioeconomic school setting, and further randomized controlled effectiveness trials are required.

Protein dose requirements to maximize skeletal muscle protein synthesis after repeated bouts of resistance exercise in young trained women.

Studies examining the effect of protein (PRO) feeding on post resistance exercise (RE) muscle protein synthesis (MPS) have primarily been performed in men, and little evidence is available regarding the quantity of PRO required to maximally stimulate MPS in trained women following repeated bouts of RE. We therefore quantified acute (4 h and 8 h) and extended (24 h) effects of two bouts of resistance exercise, alongside protein-feeding, in women, and the PRO requirement to maximize MPS. Twenty-four RE trained women (26.6 ± 0.7 years, mean ± SEM) performed two bouts of whole-body RE (3 × 8 repetitions/maneuver at 75% 1-repetition maximum) 4 h apart, with post-exercise ingestion of 15 g, 30 g, or 60 g whey PRO (n = 8/group). Saliva, venous blood, and a vastus lateralis muscle biopsy were taken at 0 h, 4 h, 8 h, and 24 h post-exercise. Plasma leucine and branched chain amino acids were quantified using gas chromatography mass spectrometry (GC-MS) after ingestion of D2 O. Fifteen grams PRO did not alter plasma leucine concentration or myofibrillar synthetic rate (MyoFSR). Thirty and sixty grams PRO increased plasma leucine concentration above baseline (105.5 ± 5.3 µM; 120.2 ± 7.4 µM, respectively) at 4 h (151.5 ± 8.2 µM, p < 0.01; 224.8 ± 16.0 µM, p < 0.001, respectively) and 8 h (176.0 ± 7.3 µM, p < 0.001; 281.7 ± 21.6 µM, p < 0.001, respectively). Ingestion of 30 g PRO increased MyoFSR above baseline (0.068 ± 0.005%/h) from 0 to 4 h (0.140 ± 0.021%/h, p < 0.05), 0 to 8 h (0.121 ± 0.012%/h, p < 0.001), and 0 to 24 h (0.099 ± 0.011%/h, p < 0.01). Ingestion of 60 g PRO increased MyoFSR above baseline (0.063 ± 0.003%/h) from 0 to 4 h (0.109 ± 0.011%/h, p < 0.01), 0 to 8 h (0.093 ± 0.008%/h, p < 0.01), and 0 to 24 h (0.086 ± 0.006%/h, p < 0.01). Post-exercise ingestion of 30 g or 60 g PRO, but not 15 g, acutely increased MyoFSR following two consecutive bouts of RE and extended the anabolic window over 24 h. There was no difference between the 30 g and 60 g responses.

Virtual reality as a countermeasure for astronaut motion sickness during simulated post-flight water landings.

Entry motion sickness (EMS) affects crewmembers upon return to Earth following extended adaptation to microgravity. Anticholinergic pharmaceuticals (e.g., Meclizine) are often taken prior to landing; however, they have operationally adverse side effects (e.g., drowsiness). There is a need to develop non-pharmaceutical countermeasures to EMS. We assessed the efficacy of a technological countermeasure providing external visual cues following splashdown, where otherwise only nauseogenic internal cabin visual references are available. Our countermeasure provided motion-congruent visual cues of an Earth-fixed scene in virtual reality, which was compared to a control condition with a head-fixed fixation point in virtual reality in a between-subject design with 15 subjects in each group. We tested the countermeasure's effectiveness at mitigating motion sickness symptoms at the end of a ground-based reentry analog: approximately 1 h of 2Gx centrifugation followed by up to 1 h of wave-like motion. Secondarily, we explored differences in vestibular-mediated balance performance between the two conditions. While Motion Sickness Questionnaire outcomes did not differ detectably between groups, we found significantly better survival rates (with dropout dictated by reporting moderate nausea consecutively over 2 min) in the visual countermeasure group than the control group (79% survival vs. 33%, t(14) = 2.50, p = 0.027). Following the reentry analogs, subjects demonstrated significantly higher sway prior to recovery (p = 0.0004), which did not differ between control and countermeasure groups. These results imply that providing motion-congruent visual cues may be an effective mean for curbing the development of moderate nausea and increasing comfort following future space missions.

Ascending thoracic aortic aneurysm size at presentation and growth by diameter.

AIM: To investigate the hypothesis that lung cancer screening allows for earlier identification of ascending thoracic aortic aneurysms (aTAAs) and that growth rates for aTAAs are greatest at larger sizes. MATERIALS AND METHODS: This single referral centre retrospective study manually gathered computed tomography (CT) data from 732 patients presenting from July 2002 to August 2022. Five hundred and seventeen patients with aTAA >39 mm were identified to compare presenting diameter by year of presentation. Four hundred and thirty-two patients had CT examinations >3 months apart, allowing for growth analysis. Patients were separated by initial examination date (before or after 12/31/2013) for presenting size comparison. Patients were then divided into five groups based on aTAA diameter for growth rate analysis. RESULTS: At identification, patients had a median aTAA diameter of 44 mm (IQR 41-47 mm). Patients with aTAAs identified prior to December 2013 (n=129) had an average aTAA diameter 1.7 mm larger than those identified later (n=388; p=0.003). The growth analysis showed an average growth rate of 0.1 mm/year (p<0.001) across the entire cohort. Patients with an aTAA diameter of ≥55 mm (n=12) grew the fastest at 1.9 mm/year (p<0.001). In the <40 mm group (n=43), the aTAAs expanded at 0.2 mm/year, faster than the 0.1 mm/year of the slowest expanding 45-49 mm group (n=130; p=0.04). CONCLUSION: aTAA size at discovery was larger before lung cancer screening guidelines took effect in December 2013. The largest aTAAs expanded fastest, but growth rates were slowest in the medium-sized 45-49 mm diameter group.

Ensemble Wavelet Decomposition-Based Detection of Mental States Using Electroencephalography Signals.

Technological advancements in healthcare, production, automobile, and aviation industries have shifted working styles from manual to automatic. This automation requires smart, intellectual, and safe machinery to develop an accurate and efficient brain-computer interface (BCI) system. However, developing such BCI systems requires effective processing and analysis of human physiology. Electroencephalography (EEG) is one such technique that provides a low-cost, portable, non-invasive, and safe solution for BCI systems. However, the non-stationary and nonlinear nature of EEG signals makes it difficult for experts to perform accurate subjective analyses. Hence, there is an urgent need for the development of automatic mental state detection. This paper presents the classification of three mental states using an ensemble of the tunable Q wavelet transform, the multilevel discrete wavelet transform, and the flexible analytic wavelet transform. Various features are extracted from the subbands of EEG signals during focused, unfocused, and drowsy states. Separate and fused features from ensemble decomposition are classified using an optimized ensemble classifier. Our analysis shows that the fusion of features results in a dimensionality reduction. The proposed model obtained the highest accuracies of 92.45% and 97.8% with ten-fold cross-validation and the iterative majority voting technique. The proposed method is suitable for real-time mental state detection to improve BCI systems.

Examining the long-term effects of traumatic brain injury on fear extinction in male rats.

There is a strong association between traumatic brain injuries (TBIs) and the development of psychiatric disorders, including post-traumatic stress disorder (PTSD). Exposure-based therapy is a first-line intervention for individuals who suffer from PTSD and other anxiety-related disorders; however, up to 50% of individuals with PTSD do not respond well to this approach. Fear extinction, a core mechanism underlying exposure-based therapy, is a procedure in which a repeated presentation of a conditioned stimulus in the absence of an unconditioned stimulus leads to a decrease in fear expression, and is a useful tool to better understand exposure-based therapy. Identifying predictors of extinction would be useful in developing alternative treatments for the non-responders. We recently found that CO2 reactivity predicts extinction phenotypes in rats, likely through the activation of orexin receptors in the lateral hypothalamus. While studies have reported mixed results in extinction of fear after TBI, none have examined the long-term durability of this phenotype in the more chronically injured brain. Here we tested the hypothesis that TBI results in a long-term deficit in fear extinction, and that CO2 reactivity would be predictive of this extinction phenotype. Isoflurane-anesthetized adult male rats received TBI (n = 59) (produced by a controlled cortical impactor) or sham surgery (n = 29). One month post-injury or sham surgery, rats underwent a CO2 or air challenge, followed by fear conditioning, extinction, and fear expression testing. TBI rats exposed to CO2 (TBI-CO2) showed no difference during extinction or fear expression relative to shams exposed to CO2 (sham-CO2). However, TBI-CO2 rats, showed significantly better fear expression than TBI rats exposed to air (TBI-air). In contrast to previous findings, we observed no relationship between CO2 reactivity and post-extinction fear expression in either the sham or TBI rats. However, compared to the previously observed naïve sample, we observed more variability in post-extinction fear expression but a very similar distribution of CO2 reactivity in the current sample. Isoflurane anesthesia may lead to interoceptive threat habituation, possibly via action on orexin receptors in the lateral hypothalamus, and may interact with CO2 exposure, resulting in enhanced extinction. Future work will directly test this possibility.

First Direct Measurement Constraining the

The rate of the final step in the astrophysical αp process, the ^{34}Ar(α,p)^{37}K reaction, suffers from large uncertainties due to a lack of experimental data, despite having a considerable impact on the observable light curves of x-ray bursts and the composition of the ashes of hydrogen and helium burning on accreting neutron stars. We present the first direct measurement constraining the ^{34}Ar(α,p)^{37}K reaction cross section, using the Jet Experiments in Nuclear Structure and Astrophysics gas jet target. The combined cross section for the ^{34}Ar,Cl(α,p)^{37}K,Ar reaction is found to agree well with Hauser-Feshbach predictions. The ^{34}Ar(α,2p)^{36}Ar cross section, which can be exclusively attributed to the ^{34}Ar beam component, also agrees to within the typical uncertainties quoted for statistical models. This indicates the applicability of the statistical model for predicting astrophysical (α,p) reaction rates in this part of the αp process, in contrast to earlier findings from indirect reaction studies indicating orders-of-magnitude discrepancies. This removes a significant uncertainty in models of hydrogen and helium burning on accreting neutron stars.

Use of milk proteins as biomarkers of changes in the rumen metaproteome of Holstein cows fed low-fiber, high-starch diets.

Dietary levels of undegraded neutral detergent fiber (uNDF240) and rumen-fermentable starch (RFS) can affect the rumen microbiome and milk composition. The objective of the study is to investigate the use of milk proteins as biomarkers of rumen microbial activity through a comparative evaluation of the rumen microbial and milk protein profiles produced by Holstein cows fed diets with varying contents of physically effective uNDF240 (peuNDF240) and RFS. Eight ruminally cannulated lactating Holstein cows were included in a larger study as part of a 4 × 4 Latin square design with 4 28-d periods to assess 4 diets varying in peuNDF240 and RFS content. For this experiment, cows received one of 2 dietary treatments: (1) low-peuNDF240, high-RFS (LNHR) diet or (2) high-peuNDF240, low-RFS (HNLR) diet. Within each period, rumen fluid samples were collected from each cow on d 26 (1400 h) and d 27 (0600 h and 1000 h), and milk samples were collected from each cow on d 25 (2030 h), d 26 (0430 h, 1230 h, and 2030 h), and d 27 (0430 h and 1230 h). Microbial proteins were isolated from each rumen fluid sample. For milk samples, milk proteins were fractionated, and the whey fraction was subsequently isolated. Isolated proteins within each rumen fluid or milk sample were isobarically labeled and analyzed by liquid chromatography-tandem mass spectrometry. Product ion spectra acquired from rumen fluid samples were searched using SEQUEST against 71 composite databases. In contrast, product ion spectra acquired from milk samples were searched against the Bos taurus database. Data were analyzed using the PROC MIXED procedure in SAS 9.4 to assess the effect of diet and time of sampling. To increase stringency, the false discovery rate-adjusted P-value (PFDR) was also calculated to account for multiple comparisons. Using the mixed procedure, a total of 129 rumen microbial proteins were quantified across 24 searched microbial species. Of these, the abundance of 14 proteins across 9 microbial species was affected due to diet and diet × time interaction, including 7 proteins associated with energetics pathways. Among the 159 quantified milk proteins, the abundance of 21 proteins was affected due to the diet and diet × time interaction. The abundance of 19 of these milk proteins was affected due to diet × time interactions. Of these, 16 proteins had the disparity across diets at the 0430 h sampling time, including proteins involved in host defense, nutrient synthesis, and transportation, suggesting that biological shifts resulting from diet-induced rumen changes are not diurnally uniform across milkings. The concentration of lipoprotein lipase (LPL) was statistically higher in the milk from the cows fed with the LNHR diet, which was numerically confirmed with an ELISA. Further, as determined by ELISA, the LPL concentration was significantly higher in the milk from the cows fed with the LNHR diet at 0430 h sampling point, suggesting that LPL concentration may indicate dietary carbohydrate-induced ruminal changes. The results of this study suggest that diet-induced rumen changes can be reflected in milk in a diurnal pattern, further highlighting the need to consider sampling time points for using milk proteins as a representative biomarker of rumen microbial activity.

Developing an integrated genomic selection approach beyond biomass for varietal protection and nutritive traits in perennial ryegrass (Lolium perenne L.).

KEY MESSAGE: Breeding target traits can be broadened to include nutritive value and plant breeder's rights traits in perennial ryegrass by using in-field regression-based spectroscopy phenotyping and genomic selection. Perennial ryegrass breeding has focused on biomass yield, but expansion into a broader set of traits is needed to benefit livestock industries whilst also providing support for intellectual property protection of cultivars. Numerous breeding objectives can be targeted simultaneously with the development of sensor-based phenomics and genomic selection (GS). Of particular interest are nutritive value (NV), which has been difficult and expensive to measure using traditional phenotyping methods, resulting in limited genetic improvement to date, and traits required to obtain varietal protection, known as plant breeder's rights (PBR) traits. In order to assess phenotyping requirements for NV improvement and potential for genetic improvement, in-field reflectance-based spectroscopy was assessed and GS evaluated in a single population for three key NV traits, captured across four timepoints. Using three prediction approaches, the possibility of targeting PBR traits using GS was evaluated for five traits recorded across three years of a breeding program. Prediction accuracy was generally low to moderate for NV traits and moderate to high for PBR traits, with heritability highly correlated with GS accuracy. NV did not show significant or consistent correlation between timepoints highlighting the need to incorporate seasonal NV into selection indexes and the value of being able to regularly monitor NV across seasons. This study has demonstrated the ability to implement GS for both NV and PBR traits in perennial ryegrass, facilitating the expansion of ryegrass breeding targets to agronomically relevant traits while ensuring necessary varietal protection is achieved.

SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals.

Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.

An explainable and interpretable model for attention deficit hyperactivity disorder in children using EEG signals.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder that affects a person's sleep, mood, anxiety, and learning. Early diagnosis and timely medication can help individuals with ADHD perform daily tasks without difficulty. Electroencephalogram (EEG) signals can help neurologists to detect ADHD by examining the changes occurring in it. The EEG signals are complex, non-linear, and non-stationary. It is difficult to find the subtle differences between ADHD and healthy control EEG signals visually. Also, making decisions from existing machine learning (ML) models do not guarantee similar performance (unreliable). METHOD: The paper explores a combination of variational mode decomposition (VMD), and Hilbert transform (HT) called VMD-HT to extract hidden information from EEG signals. Forty-one statistical parameters extracted from the absolute value of analytical mode functions (AMF) have been classified using the explainable boosted machine (EBM) model. The interpretability of the model is tested using statistical analysis and performance measurement. The importance of the features, channels and brain regions has been identified using the glass-box and black-box approach. The model's local and global explainability has been visualized using Local Interpretable Model-agnostic Explanations (LIME), SHapley Additive exPlanations (SHAP), Partial Dependence Plot (PDP), and Morris sensitivity. To the best of our knowledge, this is the first work that explores the explainability of the model prediction in ADHD detection, particularly for children. RESULTS: Our results show that the explainable model has provided an accuracy of 99.81%, a sensitivity of 99.78%, 99.84% specificity, an F-1 measure of 99.83%, the precision of 99.87%, a false detection rate of 0.13%, and Mathew's correlation coefficient, negative predicted value, and critical success index of 99.61%, 99.73%, and 99.66%, respectively in detecting the ADHD automatically with ten-fold cross-validation. The model has provided an area under the curve of 100% while the detection rate of 99.87% and 99.73% has been obtained for ADHD and HC, respectively. CONCLUSIONS: The model show that the interpretability and explainability of frontal region is highest compared to pre-frontal, central, parietal, occipital, and temporal regions. Our findings has provided important insight into the developed model which is highly reliable, robust, interpretable, and explainable for the clinicians to detect ADHD in children. Early and rapid ADHD diagnosis using robust explainable technologies may reduce the cost of treatment and lessen the number of patients undergoing lengthy diagnosis procedures.

Ostreopsis Schmidt and Coolia Meunier (Dinophyceae, Gonyaulacales) from Cook Islands and Niue (South Pacific Ocean), including description of Ostreopsis tairoto sp. nov.

It is important to decipher the diversity and distribution of benthic dinoflagellates, as there are many morphologically indistinct taxa that differ from one another in production of potent toxins. To date, the genus Ostreopsis comprises twelve described species, of which seven are potentially toxic and produce compounds presenting a threat to human and environmental health. In this study, isolates previously identified as "Ostreopsis sp. 3" were sampled from the area where it was first reported, Rarotonga, Cook Islands, and have been taxonomically and phylogenetically characterised as Ostreopsis tairoto sp. nov. Phylogenetically, the species is closely related to "Ostreopsis sp. 8", O. mascarenensis, "O. sp. 4", O. fattorussoi, O. rhodesiae and O. cf. siamensis. Previously, it was considered a part of the O. cf. ovata complex but can be distinguished from O. cf. ovata based on the small pores identified on this study, and from O. fattorussoi and O. rhodesiae based on relative lengths of the 2' plates. No known palytoxin -like compounds were detected in strains investigated in this study. Strains of O. lenticularis, Coolia malayensis and C. tropicalis were also identified and described. This study advances our knowledge of biogeography, distribution, and toxins of Ostreopsis and Coolia species.