Response of growth, yield and quality of small onion (Allium cepa L. var. aggregatum don.) to Tamil Nadu Agricultural University-Water Soluble Fertilizers (TNAU-WSF)

Enhancing the food production for the growing world population has needed application of highly sustainable and efficient inputs to produce more food per unit of land. Hence, Tamil Nadu Agricultural University (TNAU), Coimbatore, Tamil Nadu has produced Water soluble fertilizers (WSF) in its maiden attempt and it is necessary to optimize on different crops. Small onion is one of the most important vegetables in the Indian diet and it has high demand but low productivity. To enhance crop productivity and quality of small onions, the application of TNAU-WSF was taken up. A field experiment was laid out in a Randomized block design (RBD) incorporating 8 treatments comprising of application of RDF at100% NPK as TNAU WSF, soil test based application of 75%, 100%, 125% NPK ha-1 as TNAU-WSF with soil application of sulphur (S) and foliar spray of TNAU Liquid multi micronutrient (LMM) and without S and TNAU LMM and absolute control. Each treatment was replicated thrice with onion (CO 4). Soil test based application of 125% NPK ha-1 as TNAU-WSF with sulphur (S) and TNAU LMM recorded significantly higher in plant height (54.01 cm), the number of leaves per bulb (8.56), leaf greenness (67.5 SPAD), root length (5.42 cm), polar bulb diameter (4.38 cm), equatorial bulb diameter (2.72 cm) fresh bulb weight (74.21 g), bulb yield (1751 t ha-1) and quality attributes like total soluble solids (TSS) (14.78 °Brix), ascorbic acid content (15.34 mg 100 g-1), pyruvic acid content (2.27 µmol g-1). However, soil test based application of 100% NPK ha-1 as TNAU-WSF was found to be an ideal rate to attain the economic target yield of the onion crop

Missense mutation of Brain Derived Neurotrophic Factor (BDNF) alters neurocognitive performance in patients with mild traumatic brain injury: a longitudinal study

The predictability of neurocognitive outcomes in patients with traumatic brain injury is not straightforward. The extent and nature of recovery in patients with mild traumatic brain injury (mTBI) are usually heterogeneous and not substantially explained by the commonly known demographic and injury-related prognostic factors despite having sustained similar injuries or injury severity. Hence, this study evaluated the effects and association of the Brain Derived Neurotrophic Factor (BDNF) missense mutations in relation to neurocognitive performance among patients with mTBI. 48 patients with mTBI were prospectively recruited and MRI scans of the brain were performed within an average 10.1 (SD 4.2) hours post trauma with assessment of their neuropsychological performance post full Glasgow Coma Scale (GCS) recovery. Neurocognitive assessments were repeated again at 6 months follow-up. The paired t-test, Cohen’s d effect size and repeated measure ANOVA were performed to delineate statistically significant differences between the groups [wildtype G allele (Val homozygotes) vs. minor A allele (Met carriers)] and their neuropsychological performance across the time point (T1 = baseline/ admission vs. T2 = 6th month follow-up). Minor A allele carriers in this study generally performed more poorly on neuropsychological testing in comparison wildtype G allele group at both time points. Significant mean differences were observed among the wildtype group in the domains of memory (M = -11.44, SD = 10.0, p = .01, d = 1.22), executive function (M = -11.56, SD = 11.7, p = .02, d = 1.05) and overall performance (M = -6.89 SD = 5.3, p = .00, d = 1.39), while the minor A allele carriers showed significant mean differences in the domains of attention (M = -11.0, SD = 13.1, p = .00, d = .86) and overall cognitive performance (M = -5.25, SD = 8.1, p = .01, d = .66).The minor A allele carriers in comparison to the wildtype G allele group, showed considerably lower scores at admission and remained impaired in most domains across the timepoints, although delayed signs of recovery were noted to be significant in the domains attention and overall cognition. In conclusion, the current study has demonstrated the role of the BDNF rs6265 Val66Met polymorphism in influencing specific neurocognitive outcomes in patients with mTBI. Findings were more detrimentally profound among Met allele carriers

Assessment of heart rate circadianity alterations in patients with depression using a wearable device

The aim of this study is to assess whether the possible loss of circadian rhythm, a modulator of heart rate, is associated with the severity of depression. For that, Cosinor fitting method was applied to heart rate data of 203 patients with major depressive disorder, recorded continuously over 18 months by a wearable device. Results revealed that the amplitude derived from the Cosinor fit is significantly lower in patients with severe depression, implying a loss in circadian rhythmicity when depression is severe

Longitudinal Modeling of Depression Shifts Using Speech and Language

Speech analysis can provide a potential non-invasive and objective means of assessing and monitoring an individual's mental health. Most studies to date have focused on cross-sectional analysis and have not explored the benefits of speech analysis as a longitudinal monitoring tool that can assist in the management of chronic conditions such as major depressive disorder (MDD). Objectively monitoring for shifts in depression symptom severity levels over time presents a notable challenge, which we address through an automated approach using longitudinal English and Spanish speech samples collected from a clinical population. We employ time-frequency representations and linguistic embeddings to enhance the early recognition of alterations in depression levels in individuals with MDD. We investigate the suitability of using siamese-based training for modeling these changes, intending to enable personalized and adaptive interventions

Autonomic response to walk tests is useful for assessing outcome measures in people with multiple sclerosis

Objective: The aim of this study was to evaluate the association between changes in the autonomic control of cardiorespiratory system induced by walk tests and outcome measures in people with Multiple Sclerosis (pwMS). Methods: Electrocardiogram (ECG) recordings of 148 people with Relapsing-Remitting MS (RRMS) and 58 with Secondary Progressive MS (SPMS) were acquired using a wearable device before, during, and after walk test performance from a total of 386 periodical clinical visits. A subset of 90 participants repeated a walk test at home. Various MS-related symptoms, including fatigue, disability, and walking capacity were evaluated at each clinical visit, while heart rate variability (HRV) and ECG-derived respiration (EDR) were analyzed to assess autonomic nervous system (ANS) function. Statistical tests were conducted to assess differences in ANS control between pwMS grouped based on the phenotype or the severity of MS-related symptoms. Furthermore, correlation coefficients (r) were calculated to assess the association between the most significant ANS parameters and MS-outcome measures. Results: People with SPMS, compared to RRMS, reached higher mean heart rate (HRM) values during walk test, and larger sympathovagal balance after test performance. Furthermore, pwMS who were able to adjust their HRM and ventilatory values, such as respiratory rate and standard deviation of the ECG-derived respiration, were associated with better clinical outcomes. Correlation analyses showed weak associations between ANS parameters and clinical outcomes when the Multiple Sclerosis phenotype is not taken into account. Blunted autonomic response, in particular HRM reactivity, was related with worse walking capacity, yielding r = 0.36 r = 0.29 (RRMS) and r > 0.5 (SPMS). A positive strong correlation r > 0.7 r > 0.65 between cardiorespiratory parameters derived at hospital and at home was also found. Conclusion: Autonomic function, as measured by HRV, differs according to MS phenotype. Autonomic response to walk tests may be useful for assessing clinical outcomes, mainly in the progressive stage of MS. Participants with larger changes in HRM are able to walk longer distance, while reduced ventilatory function during and after walk test performance is associated with higher fatigue and disability severity scores. Monitoring of disorder severity could also be feasible using ECG-derived cardiac and respiratory parameters recorded with a wearable device at home

Fitbeat: COVID-19 estimation based on wristband heart rate using a contrastive convolutional auto-encoder

This study proposes a contrastive convolutional auto-encoder (contrastive CAE), a combined architecture of an auto-encoder and contrastive loss, to identify individuals with suspected COVID-19 infection using heart-rate data from participants with multiple sclerosis (MS) in the ongoing RADAR-CNS mHealth research project. Heart-rate data was remotely collected using a Fitbit wristband. COVID-19 infection was either confirmed through a positive swab test, or inferred through a self-reported set of recognised symptoms of the virus. The contrastive CAE outperforms a conventional convolutional neural network (CNN), a long short-term memory (LSTM) model, and a convolutional auto-encoder without contrastive loss (CAE). On a test set of 19 participants with MS with reported symptoms of COVID-19, each one paired with a participant with MS with no COVID-19 symptoms, the contrastive CAE achieves an unweighted average recall of 95.3 % , a sensitivity of 100 % and a specificity of 90.6 % , an area under the receiver operating characteristic curve (AUC-ROC) of 0.944, indicating a maximum successful detection of symptoms in the given heart rate measurement period, whilst at the same time keeping a low false alarm rate

Monitorización de la depresión mediante el análisis de la circadianidad del ritmo cardíaco proporcionado por un dispositivo wearable

En este estudio se ha aplicado el método de ajuste Cosinor, por mínimos cuadrados a una función senoidal, a los datos de frecuencia cardiaca (FC) de 203 pacientes con depresión, registrados de manera continua durante un transcurso de 18 meses por un dispositivo wearable, en condiciones de vida cotidiana. El objetivo es evaluar si la posible pérdida del ritmo circadiano, modulador de la frecuencia cardiaca, esta asociada a una depresión mas severa. Estos datos coexisten con resultados de pruebas médicas para la evaluación de la sintomatología de la depresión, como el Patient Health Questionnaire (PHQ-8) [1] y el Inventory of Depressive Symptomatology (IDS) [2], que permiten determinar la presencia y gravedad del trastorno. El estudio U de Mann-Whitney sobre el ajuste Cosinor de la frecuencia cardiaca, sincronizado a los registros de PHQ-8 e IDS basales de cada paciente, ha permitido encontrar diferencias significativas según la gravedad del trastorno: la amplitud derivada del ajuste Cosinor (es decir, la oscilación de la FC a lo largo del día) es significativamente menor en aquellos pacientes con depresión severa. Este resultado se cumple en todas las ventanas temporales de datos sobre las que se ha realizado el ajuste Cosinor (1 día, 1 semana y 2 semanas), así como para los ajustes sincronizados con PHQ-8 e IDS. Esto supone una pérdida en la circadianidad cuando la depresión es severa.Este trabajo ha sido parcialmente financiado por los proyectos TED2021-131106B-I00, PID2021-126734OB-C21 (Ministerio de Ciencia e Innovación y Fondo Social Europeo), España, el grupo BSICoS T39-23R (Gobierno de Aragón y Fondo Social Europeo) . El proyecto RADAR-CNS ha recibido financiación de Innovative Medicines Initiative 2 Joint Undertaking mediante el acuerdo No 115902. Los cálculos fueron realizados por el ICTS NAN-BIOSIS (HPC de la Universidad de Zaragoza)

Monitorización de la depresión mediante el análisis de la circadianidad del ritmo cardíaco proporcionado por un dispositivo wearable

En este estudio se ha aplicado el método de ajuste Cosinor, por mínimos cuadrados a una función senoidal, a los datos de frecuencia cardiaca (FC) de 203 pacientes con depresión, registrados de manera continua durante un transcurso de 18 meses por un dispositivo wearable, en condiciones de vida cotidiana. El objetivo es evaluar si la posible pérdida del ritmo circadiano, modulador de la frecuencia cardiaca, esta asociada a una depresión mas severa. Estos datos coexisten con resultados de pruebas médicas para la evaluación de la sintomatología de la depresión, como el Patient Health Questionnaire (PHQ-8) [1] y el Inventory of Depressive Symptomatology (IDS) [2], que permiten determinar la presencia y gravedad del trastorno. El estudio U de Mann-Whitney sobre el ajuste Cosinor de la frecuencia cardiaca, sincronizado a los registros de PHQ-8 e IDS basales de cada paciente, ha permitido encontrar diferencias significativas según la gravedad del trastorno: la amplitud derivada del ajuste Cosinor (es decir, la oscilación de la FC a lo largo del día) es significativamente menor en aquellos pacientes con depresión severa. Este resultado se cumple en todas las ventanas temporales de datos sobre las que se ha realizado el ajuste Cosinor (1 día, 1 semana y 2 semanas), así como para los ajustes sincronizados con PHQ-8 e IDS. Esto supone una pérdida en la circadianidad cuando la depresión es severa.Este trabajo ha sido parcialmente financiado por los proyectos TED2021-131106B-I00, PID2021-126734OB-C21 (Ministerio de Ciencia e Innovación y Fondo Social Europeo), España, el grupo BSICoS T39-23R (Gobierno de Aragón y Fondo Social Europeo) . El proyecto RADAR-CNS ha recibido financiación de Innovative Medicines Initiative 2 Joint Undertaking mediante el acuerdo No 115902. Los cálculos fueron realizados por el ICTS NAN-BIOSIS (HPC de la Universidad de Zaragoza)

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity

BACKGROUND: Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. METHODS: Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. RESULTS: Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. CONCLUSIONS: Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly

The relationship between wearable-derived sleep features and relapse in Major Depressive Disorder

Background: Changes in sleep and circadian function are leading candidate markers for the detection of relapse in Major Depressive Disorder (MDD). Consumer-grade wearable devices may enable remote and real-time examination of dynamic changes in sleep. Fitbit data from individuals with recurrent MDD were used to describe the longitudinal effects of sleep duration, quality, and regularity on subsequent depression relapse and severity. Methods: Data were collected as part of a longitudinal observational mobile Health (mHealth) cohort study in people with recurrent MDD. Participants wore a Fitbit device and completed regular outcome assessments via email for a median follow-up of 541 days. We used multivariable regression models to test the effects of sleep features on depression outcomes. We considered respondents with at least one assessment of relapse (n = 218) or at least one assessment of depression severity (n = 393). Results: Increased intra-individual variability in total sleep time, greater sleep fragmentation, lower sleep efficiency, and more variable sleep midpoints were associated with worse depression outcomes. Adjusted Population Attributable Fractions suggested that an intervention to increase sleep consistency in adults with MDD could reduce the population risk for depression relapse by up to 22 %. Limitations: Limitations include a potentially underpowered primary outcome due to the smaller number of relapses identified than expected. Conclusion: Our study demonstrates a role for consumer-grade activity trackers in estimating relapse risk and depression severity in people with recurrent MDD. Variability in sleep duration and midpoint may be useful targets for stratified interventions