Linear Model Approach Research Articles

Impact of sleep–wake patterns and daily rhythms including training on midsleep time in adolescent basketball players during the COVID-19 pandemic

ObjectivesThe coronavirus disease (COVID-19) had a major impact on sleep and training behavior in adolescent athletes. A crucial sleep parameter is midsleep time, which illustrates the midpoint between sleep onset and offset. The aim of this investigation was to examine the impact of chronotype, age, sex, pandemic phase, weekend, and training habits on midsleep time. The sample consisted of German elite adolescent basketball athletes (N = 91, 15.75 ± 1.15 years, female = 39.46%).MethodData were collected through a 10-day subjective monitoring program during three different pandemic phases, with more severe restrictions in phase 1. In total, 1146 measurement points were analyzed. A linear mixed model approach was used for the evaluation.ResultsA negative linear association between chronotype and midsleep time was revealed. A negative effect was identified for phases 2 and 3. The weekend parameter showed a positive effect, which may illustrate the occurrence of social jetlag. The main finding of this investigation is that a morning chronotype distribution was associated with earlier midsleep timing throughout the entire survey period.ConclusionThe results acknowledge that chronotype is one of the main influencing parameters for midsleep time. The study represents a useful contribution to the research of chronotype and sleep behavior in athletes, with a focus on the major impact of the COVID-19 pandemic.

Genome-wide association study identifies genetic variants underlying footrot in Portuguese Merino sheep

BackgroundOvine footrot caused by Dichelobacter nodosus (D. nodosus) is a contagious disease with serious economic and welfare impacts in sheep production systems worldwide. A better understanding of the host genetic architecture regarding footrot resistance/susceptibility is crucial to develop disease control strategies that efficiently reduce infection and its severity. A genome-wide association study was performed using a customized SNP array (47,779 SNPs in total) to identify genetic variants associated to footrot resistance/susceptibility in two Portuguese native breeds, i.e. Merino Branco and Merino Preto, and a population of crossbred animals. A cohort of 1375 sheep sampled across 17 flocks, located in the Alentejo region (southern Portugal), was included in the analyses.ResultsPhenotypes were scored from 0 (healthy) to 5 (severe footrot) based on visual inspection of feet lesions, following the Modified Egerton System. Using a linear mixed model approach, three SNPs located on chromosome 24 reached genome-wide significance after a Bonferroni correction (p < 0.05). Additionally, six genome-wide suggestive SNPs were identified each on chromosomes 2, 4, 7, 8, 9 and 15. The annotation and KEGG pathway analyses showed that these SNPs are located within regions of candidate genes such as the nonsense mediated mRNA decay associated PI3K related kinase (SMG1) (chromosome 24) and the RALY RNA binding protein like (RALYL) (chromosome 9), both involved in immunity, and the heparan sulfate proteoglycan 2 (HSPG2) (chromosome 2) and the Thrombospodin 1 (THBS1) (chromosome 7) implicated in tissue repair and wound healing processes.ConclusionThis is the first attempt to identify molecular markers associated with footrot in Portuguese Merino sheep. These findings provide relevant information on a likely genetic association underlying footrot resistance/susceptibility and the potential candidate genes affecting this trait. Genetic selection strategies assisted on the information obtained from this study could enhance Merino sheep-breeding programs, in combination with farm management strategies, for a more effective and sustainable long-term solution for footrot control.

Predictive multi-omics analysis of BPM31510 in combination with gemcitabine in a phase 2 study of advanced pancreatic ductal adenocarcinoma (PDAC).

696 Background: Advanced PDAC has limited therapeutic options. BPM31510-IV, a novel nano formulation of Ubidecarenone was evaluated in Phase 2 in patients with advanced PDAC. In addition to response and safety, the study included FDG-PET and omics as exploratory analyses. Multi-omics analyses from longitudinally collected patient samples including a subset of Adequately Treated Analysis Set (ATAS) (n=19) were performed to determine potential biomarkers associated with clinical outcomes. Methods: Metastatic PDAC patients (N=45) who had previously received between one and three lines of prior therapy were enrolled in this study. Omics analyses including proteomic, structural lipidomic, signaling lipidomic, and metabolomic measurements were performed on patient buffy coat and plasma samples. Biomolecules with significant association to patient outcome (PFS, OS &amp; TTP) were identified by linear regression analysis. Additional analysis with mixed effect linear modeling approach was used to relate omic values during ten weeks of treatment and patient ATAS status and time on study. Previous analysis of proteomics from patients in a phase 1 solid tumor study was mapped onto a similar omics output for phase 2 to reveal predictive biomarkers for disease status. Results: Proteomics analysis on patient buffy coats revealed enrichment of dicarboxylic and tricarboxylic metabolic processes, mitochondrial membrane space proteins, pyruvate metabolism, and malate/aspartate shuttle pathway with a positive correlation with TTP in the model, demonstrating an integrated connectivity to Ubidecarenone’s functional effects on metabolism. Medium chain acyl carnitines and c-reactive protein (CRP) were identified as having the most significant differences in trajectories between patient ATC status by mixed effect linear modeling. Medium chain acyl carnitines differences could likely be the result of an integrated adaptation of liver homeostasis and physiological activity impacted by treatment. Analysis of RECIST1.1 evaluated in Phase 1 clinical trial and subsequently validated in Phase 2 trial identified 4 proteins with predictive utility at the start of treatment (t=0) (SERPINA5, Vitronectin, Lumican, Proteosome 20S subunit alpha 4), 5 lipid molecular species, and 1 metabolite (2-keto-isovalerate). These markers exhibited differential expression in both clinical trials related to clinical outcome, thus demonstrating a biological relevance to BPM31510 treatment. Conclusions: Our results point to biomarkers associated with (a) length on trial, (b) outcome and (c) involvement of mitochondrial biology in the mechanism of BPM31510-IV in the adequately treated PDAC population. The data herein supports further study of these biomarkers in late-stage trials to confirm the biological mechanism of BPM31510 in PDAC patients. Clinical trial information: NCT02650804 .

System identification and closed-loop control of laser hot-wire directed energy deposition using the parameter-signature-quality modeling scheme

Hot-wire directed energy deposition using a laser beam (DED-LB/w) is a method of metal additive manufacturing (AM) that has benefits of high material utilization and deposition rate, but parts manufactured by DED-LB/w suffer from a substantial heat input and undesired surface finish. Hence, regulating the process parameters and monitoring the process signatures to control the final quality during the deposition is crucial to ensure the quality of the final part. This paper explores the dynamic modeling of the DED-LB/w process and introduces a parameter-signature-quality modeling and control approach to enhance the quality of modeling and control of part qualities that cannot be measured in situ. The study investigates different process parameters that influence the melt pool width (signature) and bead width (quality) in single and multi-layer beads. The proposed modeling approach utilizes a parameter-signature model as F1 and a signature-quality model as F2. Linear and nonlinear modeling approaches are compared to describe a dynamic relationship between process parameters and a process signature, the melt pool width (F1). A fully connected artificial neural network is employed to model and predict the final part quality, i.e., bead width, based on melt pool signatures (F2). Finally, the effectiveness and usefulness of the proposed parameter-signature-quality modeling is tested and verified by integrating the parameter-signature (F1) and signature-quality (F2) models in the closed-loop control of the width of the part. Compared with the control loop with only F1, the proposed method shows clear advantages and bears potential to be applied to control other part qualities that cannot be directly measured or monitored in situ.

The Effects of Neuromonitoring and Cerebrolysin Administration on Outcomes in Patients with Traumatic Brain Injury-An Interventional Pilot Study.

Traumatic brain injury (TBI) is one of the most common causes of death and an important burden to the worldwide healthcare system and society. There is a lack of guidelines for types of monitoring or neuroprotective therapy. The aim of this pilot study was to assess its feasibility and, furthermore, to evaluate the impact of Cerebrolysin on the following clinical outcomes: length of stay, Glasgow Outcome Scale (GOS) and mortality. A cohort of 56 patients was included in this non-randomised, real-time, pre-post-interventional study. The patients were assessed with the Glasgow Coma Scale (GCS) and divided into two groups: severe (GCS < 8) and non-severe (GCS > 8). After the radiological examination (CT scan), the patients were qualified for an immediate neurosurgical procedure if needed. The patients were admitted to the intensive care unit, where a standardised protocol for TBI treatment was implemented. Additional neuromonitoring was applied. There were 56 patients (19 females; 33.9%), of which 41 were considered severe cases; the patients were allocated to the Cerebrolysin (n = 25) or control groups (n = 31). In a generalised linear model (GLM) approach, the use of Cerebrolysin was associated with a decrease in the probability of death in non-severe patients (by 0.333 (standard error (SE) = 0.157, p = 0.034)) but not in severe patients (estimate (Est.) = -0.115, SE = 0.127, p = 0.364). Patients who received Cerebrolysin and who were neuromonitored had favourable outcomes and better survival rates. A multimodal treatment approach with monitoring and Cerebrolysin may have a beneficial effect on patients with less severe TBIs; however, the present study has multiple limitations, and further research is needed.

Experimental assessment and prediction of design parameter influences on a specific vacuum-based granular gripper

Innovative soft robotic grippers, such as granular grippers, enable the automated handling of a wide spectrum of different geometries, increasing the flexibility and robustness of industrial production systems. Granular grippers vary in their design as well as in their configuration, which affects the specific characteristics and capabilities regarding grippable objects. Relevant aspects are the selection of granulates and membranes, as they affect the deformability. This influences the achievable gripping forces, which vary with the gripped objects geometry. On the basis of experimental studies, the modeling of interpolations as well as through experimental validations, the present research investigates the influences of different configurations on the achievable gripping forces for a specific concept of an innovative vacuum-based granular gripper. Specifically, the focus lies on design as well as configuration parameters, which could influence the achievable gripping force. Influencing parameters are determined based on a literature review of similar gripping concepts. Various adjustment possibilities are identified, such as materials of granulates or membranes. The possible configuration options are experimentally analyzed with a one-factor-at-a-time approach. The possibility of modelling the effects of their interrelations on the achievable gripping force is examined with approaches for linear models and compared to interpolations based on Machine Learning. Especially the granulate filling level and the membrane configuration exhibit the largest influences, which were best predicted with the approach based on artificial neural networks. A selection of an optimized gripper configuration for a specified object set as well as possible further developments such as a continuous expandability of the approaches and integrations with simulations are discussed. As a result of these analyses, this research provides methodologies for an optimized selection of a gripper configuration for an improved object-specific achievable gripping force and allows for more efficient handling processes with the examined type of vacuum-based granular gripper.

Association between cigar use, with and without cigarettes, and incident diagnosed COPD: a longitudinal cohort study

BackgroundWhile regular cigar smoking is believed to carry similar health risks as regular cigarette smoking, the impact of cigar use, alone or in combination with cigarettes, on obstructive pulmonary disease (COPD) has not been well characterized. The purpose of this study was to examine the prospective association between exclusive and dual cigar and cigarette use and incident self-reported diagnosed COPD.MethodsThis study used data from Waves 1–5 (2013–2019) of the Population Assessment of Tobacco and Health (PATH) Study, a nationally representative survey of U.S. adults. Longitudinal data from adults aged 40 to 79 at Wave 1, without a pre-existing COPD diagnosis who participated at follow-up interview were analyzed. A time-varying current tobacco exposure, lagged by one wave and categorized as: (a) never/non-current use; (b) exclusive cigar use; (c) exclusive cigarette use; and (d) dual cigar/cigarette use. Multivariable models adjusted for demographics (age, sex, race or ethnicity, education), clinical risk factors (asthma, obesity), and smoking-related confounders (second-hand smoke exposure, other combustible tobacco product use, e-cigarette use, time since quitting, cigarette pack-years). The incidence of self-reported diagnosed COPD was estimated using discrete-time survival models, using a general linear modeling (GLM) approach with a binomial distribution and a complementary log-log link function.ResultsThe analytic sample consisted of 9,556 adults with a mean (SD) age of 56 (10.4), who were predominately female (52.8%) and Non-Hispanic White (70.8%). A total of 906 respondents reported a diagnosis of COPD at follow-up. In the fully adjusted model, exclusive cigar use (adjusted hazard ratio (aHR) = 1.57, 95% CI: 0.77, 3.21) was not associated with increased COPD risk compared to non-use, while exclusive cigarette use (aHR = 1.48, 95% CI: 1.13, 1.93) and dual cigar/cigarette use (aHR = 1.88, 95% CI: 1.24, 2.85) were.ConclusionsExclusive cigarette use and dual cigar/cigarette use were associated with diagnosed incident COPD. These results suggest that cigars, when used in combination with cigarettes, may be associated with poorer COPD health outcomes. Dual use may promote a higher likelihood of inhaling cigar smoke, and future research would benefit from examining whether inhalation of cigar smoke increases COPD risk.

A Linear Modeling Approach on LTGP Signals and Seismic Activity

A Linear Modeling Approach on LTGP Signals and Seismic Activity

Design and optimization of a pharmaceutical supply chain network under COVID-19 pandemic disruption

Efficient management of pharmaceutical supply chains during the COVID-19 pandemic is critical to mitigate material and human losses. This paper addresses the design of a pharmaceutical supply chain network under pandemic disruption, introducing a novel bi-objective mathematical model. Traditional supply chain management strategies often fall short in the face of sudden disruptions, necessitating the development of resilient systems. Our model seeks to minimise economic costs and shortages as primary objectives, addressing the specific challenge of sudden surges in demand for pharmaceuticals. To enhance resilience, we propose solutions including the establishment of temporary distribution points and the creation of backup inventory. We employ a scenario-based, discrete, and linear modelling approach, solving the model using goal-planning methods and validating its efficacy through numerical examples. Additionally, we conduct a case study in the metropolitan area of Mashhad, further demonstrating the applicability and effectiveness of our approach. This research contributes to the advancement of resilient supply chain design in the pharmaceutical sector, offering insights that can inform improved management practices and bolster resilience in pharmaceutical supply chains.

Forecasting Steel Demand: Comparative Analysis of Predictability across diverse Countries and Regions

Steel demand forecasting is crucial for steel industries, enabling effective production planning, inventory management, resource optimization, and strategic decision-making. This also holds true for steel plant manufacturers, who require insights into future demand trends to assess market potential, evaluate project feasibility, and identify growth regions. This paper addresses the predictability of steel demand across countries and regions worldwide, employing a comparative analysis of linear and non-linear modeling approaches to forecast steel demand per capita. A global model is trained in order to integrate information from diverse countries at varying stages of development, allowing the model to learn from comparable conditions and historical patterns. For each modeling approach hyperparameter tuning and selection of lagged input variables was performed. The performance of different model configurations was evaluated and compared for individual countries. This evaluation reveals that the predictability varies across countries and regions and shows for which countries a simple regression model based on past data is sufficient and for which countries more sophisticated models are needed. The developed models are useful in providing insights into consumption patterns, comparative risk assessment, and development of steel consumption.

A new estimator of between study variance of standardized mean difference in meta-analysis.

Meta-analysis is a statistical technique that combines the results of different environmental experiments regarding the populations, location, time, and so on. These results will differ more than the within-study variance, and the true effects being evaluated differ between studies. Thus, heterogeneity is present and should be measured. There are different estimators that were introduced to estimate between-study variance, which has received a lot of criticism from previous researchers. All of the estimators encountered the same problem, which was the correlation. To minimize the potential biases caused by interventions between the three estimators (i.e., overall effect size, within-study variance, and between-study variance), we proposed a new measure of heterogeneity known as the Environmental Effect Ratio (EER), the treatment-by-lab variability relative to the experimental error, under individual participant data (IPD) using the linear mixed model approach. We assume different between-study variances instead of constant between-study variances. The simulation of this study focuses on the performance of meta-analyses with small sample sizes. We compared our proposed estimator under two different expressions ([Formula: see text], and [Formula: see text]) with the best estimator nominated from previous studies to determine which one is the best performance. Based on the findings, our estimator ([Formula: see text]) was better for estimating between-study variance.

Exploring organizational challenges and psychological wellbeing among remote working employees during the COVID-19 pandemic: A general linear model approach

Exploring organizational challenges and psychological wellbeing among remote working employees during the COVID-19 pandemic: A general linear model approach

Assessing Phytoplankton Primary Productivity Variability in the Changjiang Estuary, East China Sea From Coupled Fast Repetition Rate (FRR) Fluorometry and Chlorophyll‐a Measurements

AbstractPhytoplankton primary productivity (PP) varies significantly over environmental gradients, particularly in physically‐dynamic systems such as estuaries and coastal seas. During summer, runoff peaks in the Changjiang River driving large environmental gradients in both the Changjiang estuary and adjacent East China Sea (ECS), likely driving significant variability in PP. As satellite models of PP often underperform in coastal waters, we aimed to develop a novel approach for assessing net PP variability in such a dynamic environment. Parallel in situ measurements of Fast Repetition Rate (FRR) fluorometry and carbon (C) uptake rates were conducted for the first time in this region during two summer cruises in 2019 and 2021. A series of 13C‐incubations (n = 31) were performed, with measured PP ranging from ∼6 to 1,700 mgC m−3 d−1. Net PP values were significantly correlated with salinity (r = 0.45), phytoplankton chlorophyll a (Chl‐a, r = 0.88), Photosystem II (PSII) functional absorption cross‐section (σPSII, r = −0.76) and maximum PSII quantum yield (Fv/Fm, r = 0.59). Stepwise regression analysis showed that Chl‐a and σPSII were the strongest predictors of net PP. A generalized additive model (GAM) was also used to estimate net PP considering nonlinear effects of Chl‐a and σPSII. We demonstrate that GAM outperforms linear modeling approaches in estimating net PP in this study, as evidenced by a lower root mean square error (∼140 vs. 250 mgC m−3 d−1). Our novel approach provides a valuable tool to examine carbon cycling dynamics in this important region.

Cerebrovascular reactivity increases across development in multiple networks as revealed by a breath-holding task: A longitudinal fMRI study.

Functional magnetic resonance imaging (fMRI) has been widely used to understand the neurodevelopmental changes that occur in cognition and behavior across childhood. The blood-oxygen-level-dependent (BOLD) signal obtained from fMRI is understood to be comprised of both neuronal and vascular information. However, it is unclear whether the vascular response is altered across age in studies investigating development in children. Since the breath-hold (BH) task is commonly used to understand cerebrovascular reactivity (CVR) in fMRI studies, it can be used to account for developmental differences in vascular response. This study examines how the cerebrovascular response changes over age in a longitudinal children's BH data set from the Nathan Kline Institute (NKI) Rockland Sample (aged 6-18 years old at enrollment). A general linear model approach was applied to derive CVR from BH data. To model both the longitudinal and cross-sectional effects of age on BH response, we used mixed-effects modeling with the following terms: linear, quadratic, logarithmic, and quadratic-logarithmic, to find the best-fitting model. We observed increased BH BOLD signals in multiple networks across age, in which linear and logarithmic mixed-effects models provided the best fit with the lowest Akaike information criterion scores. This shows that the cerebrovascular response increases across development in a brain network-specific manner. Therefore, fMRI studies investigating the developmental period should account for cerebrovascular changes that occur with age.

Productivity and Costs of Mechanized Skidding operations at Sao Hill Forest Plantation, Tanzania

Due to global advancement of technology in forest operations, utilization of advanced machineries such as grapple skidder (GS) in timber harvesting has been increasing in the last decades. However, in order to understand their contribution in sustainable harvesting operations, it is important to understand their performance under different operating environment. Therefore, this study aimed to quantify productivity and cost of mechanised skidding operations at Sao Hill Forest plantation (SHFP). Six variables; diameter at a breast height (dbh), tree height, skidding distance, slope, costs, and cycle time (determined using detailed continuous time study) were collected in 120 GS observations.GS productivity and costs were estimated using productive machine hour (PMH) and delays inclusion approach. Regression models were developed using a generalized linear model (GLM) approach. GS productivity under PMH was 2.6% higher than the one including delay time, while skidding costs was 2.1% higher in the approach including delays. This study revealed significant variations (p-value <0.05) in productivity and cost on various terrain classes. At 0 m – 50 m distance, an average delays free GS productivity was 85.5 m3/h, with costs amounting to 1.7 USD/m3. On the distance exceeding 150 m, productivity dropped to 20.1 m3/h, and costs increased to 12.7 USD/m3. Likewise, in 0.0% - 10.0% slope range, average delays free GS productivity and costs was 100 m3/h and 1.5 USD/m3 respectively, while at 20.1% - 30.0% slope range, productivity dropped to 32.6 m3/h and costs raised to 3.9 USD/m3. Skidding distance, slope, and volume per trip were robust predictors of the GS productivity and costs, yielding pseudo-R2 values of 58.1% and 64.3%, respectively. Therefore, this study developed statistical models useful for predicting GS productivity and costs, however, their applications are recommended to be within the ranges of the variables used to develop the models.

DIFFERENCES IN WHITE MATTER MICROSTRUCTURE IN MIDDLE-TO-OLDER AGE ADULTS WITH CHRONIC KNEE PAIN

Abstract Abundant research suggests aberrant white matter microstructure in patients with chronic pain as well as in older adults independently, where pain can significantly impact daily function. However, most studies fail to consider pain and aging interactions, particularly in middle and older-age. We aimed to explore differences in white matter microstructure (i.e.,Fractional Anisotropy (FA)) by pain’s impact on daily function (i.e., high (n=60) versus low pain impact (n=111) in middle-(45-59 years old, n=129) versus older adults (60-XX years old, n=77) using diffusion tensor imaging. Participants (n=171) underwent laboratory visits to collect multi-modal pain and neuroimaging measures. We employed a linear mixed model approach to test main effects of pain impact and age groups as well as their interaction with Bonferroni-corrected multiple comparisons. Findings demonstrated a significant pain impact and age interaction on FA (p’s≤0.05) in the left caudal anterior cingulate, left caudal middle frontal, left and right rostral middle frontal and left and right insular regions. Specifically, middle-aged adults with high impact pain had significantly worse brain microstructure across these regions compared to the older adults reporting high impact pain (p’s≤0.05). These findings highlight the need to study pain across the lifespan. Given the higher prevalence of chronic pain in middle-age compared to older age, future studies are urgently needed to test whether interventions in middle-age could halt pain’s impact on brain and function.

PHARMACIST INTERVENTION IS ASSOCIATED WITH MEDICATION ADHERENCE IN OLDER ADULTS WITH CHRONIC CONDITIONS

Abstract Hypertension, hyperlipidemia, and type 2 diabetes (T2D) are prevalent chronic diseases among older Americans, but related medication adherence rates are less than optimal. Consequences of poor adherence include increased mortality, healthcare utilization, and costs. Compounding evidence supports the integration of pharmacists in patients’ care across the care continuum. This study’s purpose was to examine the impact of a large national pharmacy chain’s pharmacist-led interventions to improve medication adherence among older adults with hypertension, hyperlipidemia, or T2D. Participants were Medicare D patients who had ≥2 prescription fills in ≥1 of the three therapeutic classes. The primary outcome, optimal adherence (OA), was defined as proportion of days covered (PDC) ≥80%. A difference-in-differences (DID) design with a generalized linear model approach was applied to examine differences between participants exposed to the intervention and controls across the study period (2020-2022). At baseline, intervention participants (n=317,613, age 70.1, female 57.0%) had lower OA for diabetes (76.9% vs. 79.8%), hypertension (79.0% vs. 83.0%), and cholesterol (78.6% vs. 82.1%) compared to controls (n=943.389, age 73.3, female 56.1%). The DID results showed that between 2020 - 2022, OA had significant absolute increases for intervention participants (diabetes: +4.0%, hypertension: +6.3%, cholesterol: +6.1%) versus controls (diabetes: -1.6%, hypertension: -0.4%, cholesterol: -1.4%). All DID models were significant at p&amp;lt; 0.0001. The pharmacist-led intervention was significantly associated with increased OA over 2 years. These findings reinforce the potential for pharmacists as care providers among older adults with chronic conditions. Future research should examine the expansion of pharmacist-provided care.

Genome-wide association study of leaf photosynthesis using a high-throughput gas exchange system in rice.

Enhancing leaf photosynthetic capacity is essential for improving the yield of rice (Oryza sativa L.). Although the exploitation of natural genetic resources is considered a promising approach to enhance photosynthetic capacity, genomic factors related to the genetic diversity of leaf photosynthetic capacity have yet to be fully elucidated due to the limitation of measurement efficiency. In this study, we aimed to identify novel genomic regions for the net CO2 assimilation rate (A) by combining genome-wide association study (GWAS) and the newly developed rapid closed gas exchange system MIC-100. Using three MIC-100 systems in the field at the vegetative stage, we measured A of 168 temperate japonica rice varieties with six replicates for three years. We found that the modern varieties exhibited higher A than the landraces, while there was no significant relationship between the release year and A among the modern varieties. Our GWAS scan revealed two major peaks located on chromosomes 4 and 8, which were repeatedly detected in the different experiments and in the generalized linear modelling approach. We suggest that high-throughput gas exchange measurements combined with GWAS is a reliable approach for understanding the genetic mechanisms underlying photosynthetic diversities in crop species.

Potential of Geospatial technologies in Mechanized Timber harvesting planning

Mechanized timber harvesting involves various activities including; road planning and selection of harvesting systems and machineries. The emergence of geospatial technology (GSPT) i.e., geographical information system (GIS) and remote sensing in the recent decades, has been considered as the best tools to facilitate timber harvesting planning in plantation forests. GSPT provide accurate stand information enabling better decision-making and optimizing forest operations. This study was conducted at Sao hill Forest Plantation (SHFP) in Tanzania, with the objective of determining relative efficiency (RE) between geospatial approach (GSPA) and conventional approach (CA) on planning mechanized timber harvesting. 120 grapple skidder (GS) time study observations in 30 sample plots covering different elevation terrain ranges were studied in both approaches. Productivity and costs under the two approaches were estimated and modelled using generalized linear model (GLM) approach. To obtain large scale estimates of productivity and costs, Inverse Distance Weighted (IDW) interpolation approach was used. The results showed that, GSPA demonstrated higher productivity and lower unit skidding costs (i.e., 71.1m3/hr and 2.121USD/m3) compared to CA (i.e., 67.5m3/hr and 2.914USD/m3) respectively. Skidding distance and slope (p-value &lt; 0.05) were significant predictors of the GS performance in both approaches. The pseudo R2 ranging from 58.1% to 64.3% under CA, and from 62.9% to 60.8% under GSPA. Likewise, relative root mean square error (RMSEr) for the models under CA ranged from 49.3% to 50.4% and 33.4% to 35.2% under GSPA. Generally, the results showed that, models under GSPA have better fits and accuracy, compared to CA. Furthermore, the GSPA provided a raster representation of productivity and costs over the entire study area. Moreover, computed RE values (i.e., 1.18 and 6.17) indicated that parameter estimates for the GS productivity and costs were more precise in geospatial models (GSPM) compared to conventional models (CM). These findings highlight the potential of GSPT for an efficient large scale timber harvesting planning, by considering terrain constraints.

Feasibility of using fNIRS to explore motor-related regional haemodynamic signal changes in patients with sensorimotor impairment and healthy controls: A pilot study.

While functional near-infrared spectroscopy (fNIRS) can provide insight into cortical brain activity during motor tasks in healthy and diseased populations, the feasibility of using fNIRS to assess haemoglobin-evoked responses to reanimated upper limb motor function in patients with tetraplegia remains unknown. The primary objective of this pilot study is to determine the feasibility of using fNIRS to assess cortical signal intensity changes during upper limb motor tasks in individuals with surgically restored grip functions. The secondary objectives are: 1) to collect pilot data on individuals with tetraplegia to determine any trends in the cortical signal intensity changes as measured by fNIRS and 2) to compare cortical signal intensity changes in affected individuals versus age-appropriate healthy volunteers. Specifically, patients presented with tetraplegia, a type of paralysis resulting from a cervical spinal cord injury causing loss of movement and sensation in both lower and upper limbs. All patients have their grip functions restored by surgical tendon transfer, a procedure which constitutes a unique, focused stimulus for brain plasticity. fNIRS is used to assess changes in cortical signal intensity during the performance of two motor tasks (isometric elbow and thumb flexion). Six individuals with tetraplegia and six healthy controls participate in the study. A block paradigm is utilized to assess contralateral and ipsilateral haemodynamic responses in the premotor cortex (PMC) and primary motor cortex (M1). We assess the amplitude of the optical signal and spatial features during the paradigms. The accuracy of channel locations is maximized through 3D digitizations of channel locations and co-registering these locations to template atlas brains. A general linear model approach, with short-separation regression, is used to extract haemodynamic response functions at the individual and group levels. Peak oxyhaemoglobin (oxy-Hb) changes in PMC appear to be particularly bilateral in nature in the tetraplegia group during both pinch and elbow trials whereas for controls, a bilateral PMC response is not especially evident. In M1 / primary sensory cortex (S1), the oxy-Hb responses to the pinch task are mainly contralateral in both groups, while for the elbow flexion task, lateralization is not particularly clear. This pilot study shows that the experimental setup is feasible for assessing brain activation using fNIRS during volitional upper limb motor tasks in individuals with surgically restored grip functions. Cortical signal changes in brain regions associated with upper extremity sensorimotor processing appear to be larger and more bilateral in nature in the tetraplegia group than in the control group. The bilateral hemispheric response in the tetraplegia group may reflect a signature of adaptive brain plasticity mechanisms. Larger studies than this one are needed to confirm these findings and draw reliable conclusions.