Static Variables Research Articles

Effects of Myofascial Induction Therapy on Ankle Range of Motion and Pressure Pain Threshold in Trigger Points of the Gastrocnemius-A Clinical Trial.

The myofascial induction technique (MIT) has been shown to increase shoulder range of motion (ROM) in breast cancer survivors and decrease pain pressure threshold over the radial nerve in patients with epicondylalgia. To the authors' best knowledge, no study on trigger points and MIT has been published to date. The effect on ROM of latent trigger points is also unknown. A total of 20 twins with one latent trigger point of the gastrocnemius muscle were evaluated pre- and post-MIT in the calf. We measured static footprint variables in a pre-post study. We found differences in PPT (p = 0.001) and no differences in ROM with knee flexed (p = 0.420) or stretched (p = 0.069). After Calf MIT, latent myofascial trigger points improve PPT but no change in ankle dorsiflexion with knee bent or knee flexed were found in non-restriction healthy subjects.

Pulse Pressure Variance (PPV)-Guided Fluid Management in Adult Patients Undergoing Supratentorial Tumor Surgeries: A Randomized Controlled Trial.

Objective Appropriate fluid management in neurosurgery is critical due to the risk of secondary brain injury. Determination of volume status is challenging with static variables being unreliable. Goal-directed fluid therapy with dynamic variables allows reliable determination of fluid responsiveness and promises better outcomes. We aimed to compare the intraoperative fluid requirement between conventional central venous pressure (CVP)-guided and pulse pressure variance (PPV)-guided fluid management in supratentorial tumor surgeries. Materials and Methods This prospective, randomized, double-blind, single-center trial was conducted with 72 adults undergoing supratentorial tumor surgery in a supine position. Patients were divided into two groups of 36 patients each receiving CVP- and PPV-guided fluid therapy. The CVP-guided group received boluses to target CVP greater than 8 mm Hg along with hourly replacement of intraoperative losses and maintenance fluids. The PPV-guided group received boluses to target PPV less than 13% in addition to maintenance fluids. Total intraoperative fluids administered and the incidence of hypotension was recorded along with the brain relaxation score. Postoperatively, serum lactate levels, periorbital and conjunctival edema, as well as postoperative nausea and vomiting were assessed. Statistical Analyses All statistical analyses were performed with Statistical Package for Social Sciences, version-20 (SPSS-20, IBM, Chicago, Illinois, United States). To compare the means between the two groups (CVP vs. PPV), independent samples t -test was used for normal distribution data and Mann-Whitney U test for nonnormal distribution data. The chi-square test or Fischer's exact test was used for categorical variables. Results The CVP group received significantly more intraoperative fluids than the PPV group (4,340 ± 1,010 vs. 3,540 ± 740 mL, p < 0.01). Incidence of hypotension was lower in the PPV group (4 [11.1%] vs. 0 [0%], p = 0.04). Brain relaxation scores, serum lactate levels, periorbital and conjunctival edema, and incidence of postoperative nausea and vomiting were comparable between the groups. Conclusion The requirement for intraoperative fluids was less in PPV-guided fluid management with better hemodynamic stability, adequate brain conditions, and no compromise of perfusion.

Mining the contribution of intensive care clinical course to outcome after traumatic brain injury

Existing methods to characterise the evolving condition of traumatic brain injury (TBI) patients in the intensive care unit (ICU) do not capture the context necessary for individualising treatment. Here, we integrate all heterogenous data stored in medical records (1166 pre-ICU and ICU variables) to model the individualised contribution of clinical course to 6-month functional outcome on the Glasgow Outcome Scale -Extended (GOSE). On a prospective cohort (n = 1550, 65 centres) of TBI patients, we train recurrent neural network models to map a token-embedded time series representation of all variables (including missing values) to an ordinal GOSE prognosis every 2 h. The full range of variables explains up to 52% (95% CI: 50–54%) of the ordinal variance in functional outcome. Up to 91% (95% CI: 90–91%) of this explanation is derived from pre-ICU and admission information (i.e., static variables). Information collected in the ICU (i.e., dynamic variables) increases explanation (by up to 5% [95% CI: 4–6%]), though not enough to counter poorer overall performance in longer-stay (>5.75 days) patients. Highest-contributing variables include physician-based prognoses, CT features, and markers of neurological function. Whilst static information currently accounts for the majority of functional outcome explanation after TBI, data-driven analysis highlights investigative avenues to improve the dynamic characterisation of longer-stay patients. Moreover, our modelling strategy proves useful for converting large patient records into interpretable time series with missing data integration and minimal processing.

Attention-based LSTM Block Model Framework based on static and dynamic variables for modeling fuel ethanol fermentation process

In the industrial fuel ethanol process, the initial feed conditions, which are static variables, and each control operation, which is dynamic variable and is changing during the producing process, have an impact on the concentration of ethanol out of the tank. Developing the accurate concentration model of ethanol out of the tank with the static variables and as early as possible dynamic variables is a challenging work and is useful in analyzing and optimizing the production process. Given that the ethanol fermentation process is multiphase and dynamic, a block concentration model of ethanol out of the tank based on multilayer perceptron (MLP) and long short-term memory (LSTM) is proposed to deal with the coexistence state of the static variables and the dynamic variables. A Channel-Squeeze-and-Excitation (CSE) module is constructed to solve the problem of high-dimensional input and low-dimensional output caused by the block model. The proposed model is applied for the industrial ethanol fermentation process. The results show that CSE-MLSTM is significantly different from other prediction models and improves the prediction accuracy. Temperature control experiments and ablation experiments effectively verify the reliability and effectiveness of CSE-MLSTM.

Becalm: Intelligent Monitoring of Respiratory Patients.

The Becalm project is an open and low-cost solution for the remote monitoring of respiratory support therapies like the ones used in COVID-19 patients. Becalm combines a decision-making system based on Case-Based Reasoning with a low-cost, non-invasive mask that enables the remote monitoring, detection, and explanation of risk situations for respiratory patients. This paper first describes the mask and the sensors that allow remote monitoring. Then, it describes the intelligent decision-making system that detects anomalies and raises early warnings. This detection is based on the comparison of cases that represent patients using a set of static variables plus the dynamic vector of the patient time series from sensors. Finally, personalized visual reports are created to explain the causes of the warning, data patterns, and patient context to the healthcare professional. To evaluate the case-based early-warning system, we use a synthetic data generator that simulates patients' clinical evolution from the physiological features and factors described in healthcare literature. This generation process has been verified with a real dataset and allows the validation of the reasoning system with noisy and incomplete data, threshold values, and life/death situations. The evaluation demonstrates promising results and good accuracy (0.91) for the proposed low-cost solution to monitor respiratory patients.

Wildlife habitat association over a twelve-year period (2008–2020) in the Greater Mahale Ecosystem, western Tanzania

The Greater Mahale Ecosystem (GME) in western Tanzania hosts high faunal biodiversity, including charismatic species such as elephants and chimpanzees. Across the GME, habitat occurs in differently administered, unprotected and protected areas, including village land, forest reserves, and national parks. Areas of lower protective status are prone to deforestation and land cover changes. These land cover changes influence suitable habitat itself as well as the connectivity between habitat patches, but the impact on different mammal species in this region is unknown. In this study, we investigated changes in habitat availability by creating species distribution models for 11 medium-large mammal species based on species occurrence data collected from 2008 to 2020. We tested habitat associations for each species and evaluated the importance of the static predictor variables elevation, slope, aspect and terrain-ruggedness and the dynamic predictor variables distance to deforestation, percent forest, vegetation intensity, and vegetation variability, retrieved through remotely sensed data analysis. Our models suggest habitat declined for most, but not all species. Whilst elephants and buffalo lost more than 50 % of available habitat during this period, primates exhibited habitat stability. Habitat losses were pronounced in the southeast of the GME. Slope, elevation and median tasseled-cap wetness index (vegetation intensity) and distance to deforestation were the most important predictor variables. Our study presents a spatially and temporally explicit estimation of habitat changes in a critically important, biodiverse region undergoing drastic land cover changes. We call for consideration in land-use planning to foster connectivity and landscape integrity to protect and conserve wildlife across the ecosystem.

Assessing spatial and temporal variation in obligate resprouting, obligate seeding, and facultative seeding shrub species in California’s Mediterranean-type climate region

Mediterranean-climate region (MCR) shrublands have evolved a set of regeneration strategies in response to periodic, high intensity wildfires: obligate seeding (OS), obligate resprouting (OR), and facultative seeding (FS) species. In the North American MCR, data on their spatial and temporal variability is currently lacking, which is a significant information gap for resource managers. We developed a multinomial model using dynamic and static variables to predict the distribution of the three shrub post-fire regeneration strategies, plus trees and herbs, in southern California. Cross-validation showed 50% of the predicted values for each of the five plant groups were within 8–24 percent of the actual value. We assessed variation in shrub regeneration strategies in three ways. First, we found the three major shrub community types (mixed chaparral, chamise-redshank chaparral, and coastal sage scrub) had proportionally greater biomass of FS (37–43% of total biomass) than OR or OS. Second, we assessed the spatial variability using (a) moisture availability (climatic water deficit and solar radiation) and found FS accounted for an increasingly greater proportion of total aboveground live biomass as water became limited, while OR biomass decreased; and (b) ecosystem productivity (NDVI) which showed the proportion of OS, OR, and tree biomass increased with productivity (with a corresponding dramatic decrease in herb biomass). We also assessed temporal variability using time since fire and found OS represented an initially small proportion of total biomass immediately post-fire (8–10%) which increased with time since fire (13–17%). Third, spatial outputs from the multinomial model indicated FS dominated pixels were most widely distributed across the study area (~3.4 million ha, 71%), compared to OR and OS covering 5% each. FS also occurred more frequently on warmer, south facing slopes and summits whereas OR preferred cooler, north facing slopes and valleys (p &amp;lt; 0.0001). Better understanding the distribution of OS, OR, and FS shrubs is important for resource management, including quantifying shrubland carbon storage and identifying areas for post-fire restoration, as described in a case study of the Bobcat Fire on the Angeles National Forest.

Evaluating the Potential of Crowdsourced Data to Estimate Network-Wide Bicycle Volumes

This research integrated and evaluated emerging user data sources (Strava Metro, StreetLight, and hybrid docked/dockless bike share) of bicycle activity data with conventional “static” demand determinants (land use, built environment, sociodemographics) and measures (permanent and short-duration counts) to estimate annual average daily bicycle traffic (AADBT). We selected six locations (Boulder, Charlotte, Dallas, Portland, Bend, and Eugene) covering varied urban and suburban contexts and specified three sets of Poisson regression models: city-specific models, an Oregon pooled model, and all cities pooled. Static variables, Strava, and StreetLight complemented one another, with each additional data source tending to improve the model performance. Sites with lower volumes were more difficult to predict, with considerable error in even the best-performing models. City-specific models in general exhibited improved fit and prediction performance. Expected prediction error increased by a factor of about 1.4 when using Strava or StreetLight alone, but without static adjustment variables, to predict AADBT. Combining Strava plus StreetLight, but without static variables, increased error slightly less; by 1.3 times. We also found that transferring the model specifications from one year to the next without re-estimating the model parameters resulted in a 10% to 50% increase in error rates across models, so such transfer is not recommended. The findings from this study indicate that rather than replacing conventional bike data sources and count programs, old “small” data sources will likely be very important for big data sources like Strava and StreetLight to achieve their potential for predicting AADBT.

A DEM Investigation on the Influence of Cyclic and Static Stress Ratios and State Variables on the Pore Water Pressure Generation in Granular Materials

A granular soil may undergo liquefaction failure due to excess pore water pressure (Δu) generation when subjected to cyclic loading such as an earthquake. Several past studies indicated Δu generation may be independent of cyclic stress ratio (CSR), whereas others contradicted this observation. A soil element in its natural state often remains in K0 consolidation state, i.e., subjected to nonzero static shear stress ratio (SSR). Using the discrete element method (DEM), this study performs a large number of cyclic triaxial tests and evaluates various factors influencing the magnitude and rate of Δu generation under symmetric and nonsymmetric cyclic loading conditions. The study also evaluates the capabilities of several existing Δu generation models to predict the development of Δu under different failure scenarios. It was observed that the generation of Δu can be influenced by CSR for specimens subjected to symmetrical cyclic loading and by both CSR and SSR for nonsymmetric cyclic loading conditions. A new prediction model for Δu generation was developed along with an objective framework to estimate its input parameters. The model showed good agreement with the DEM simulation results.

The impact of bilateral anodal transcranial direct current stimulation of the premotor and cerebellar cortices on physiological and performance parameters of gymnastic athletes: a randomized, cross-over, sham-controlled study

Professional sports performance relies critically on the interaction between the brain and muscles during movement. Transcranial direct current stimulation (tDCS) is a noninvasive brain stimulation technique which modulates cortical excitability and can be used to improve motor performance in athletes. The present study aimed to investigate the effect of bilateral anodal tDCS (2 mA, 20 min) over the premotor cortex or cerebellum on motor and physiological functions and peak performance of professional gymnastics athletes. Seventeen professional gymnastics athletes participated in a randomized, sham-controlled, crossover study. In this study, we assessed the efficacy of two anodal tDCS protocols (2 mA, 20 min) with stimulation over the bilateral premotor cortex or cerebellum with the return electrodes placed over the opposite supraorbital areas. Power speed, strength coordination, endurance, static and dynamic strength, static and dynamic flexibility, and rating of perceived exertion were measured before and immediately after tDCS interventions (bilateral anodal tDCS over premotor cortices, anodal tDCS over the cerebellum, and sham tDCS). Additionally, physiological muscle performance parameters, including maximum voluntary isometric contraction (MVIC) of upper body muscles, were assessed during tDCS. Bilateral anodal tDCS over the premotor cortex, compared to anodal tDCS over the cerebellum and sham tDCS conditions, significantly improved power speed, strength coordination, and static and dynamic strength variables of professional gymnastics athletes. Furthermore, bilateral anodal tDCS over the cerebellum, compared to sham tDCS, significantly improved strength coordination. Moreover, bilateral premotor anodal tDCS significantly increased MVIC of all upper body muscles during stimulation, while anodal tDCS over the cerebellum increased MVIC in only some muscles. Bilateral anodal tDCS over the premotor cortex, and to a minor degree over the cerebellum, might be suited to improve some aspects of motor and physiological functions and peak performance levels of professional gymnastics athletes.Clinical Trial Registration ID: IRCT20180724040579N2.

Quantitative Analysis of Postural Balance in Faller and Nonfaller Patients with Parkinson's Disease.

Postural instability has been identified as a fall risk factor with a significant impact on the quality of life of patients with Parkinson's disease (PD). The aim of this study was to compare the center of pressure (COP) between faller and nonfaller patients with PD during static standing. Thirty-two faller patients and 32 nonfaller patients with PD participated in this study. All patients performed the static balance test on a force plate. COP data were recorded during quiet standing. Mean distance, sway area, mean velocity, mean frequency, and peak power were derived from the COP data. Statistical analysis was performed using independent t-tests to compare faller and nonfaller patients. Fallers presented a greater average distance, wider sway area, faster average speed, and greater peak power than nonfallers (p < 0.05). In contrast, no significant group differences were observed in peak frequency and mean frequency (p > 0.05). Although falls occur during dynamic activities, our study demonstrated that even a safe and simple static postural balance test could significantly differentiate between faller and nonfaller patients. Thus, these results suggest that quantitatively assessed static postural sway variables would be useful for distinguishing prospective fallers among PD patients.

Spatial distribution patterns of striped venus clam (Chamelea gallina, L. 1758) natural beds in the Gulf of Cádiz (SW Spain): Influence of environmental variables and management considerations

Spatial distribution of characteristic population parameters (density, biomass and mean size) of Chamelea gallina were analyzed from systematic survey data applying a geostatistical approach in the Gulf of Cádiz (SW Spain). The influence of static (depth and distance to inland outflows), dynamic (sediment and water column features) environmental variables on density, biomass and mean size was also investigated. The global estimations of kriged density mean was 8.30 ind m−2, biomass mean was 23.78 g m−2, and mean size was 18.71 mm, with the species heterogeneously distributed along the whole sandy subtidal strip of the coast of Huelva. The resulting kriged map shows the location and extension of the patches (5 NM range) in the study area; where the highest density and biomass hot-spots were observed near the river mouths, except for the Guadiana River. This species was present from 3 to 16 m, but preferred the depth window between 6-8 m, having a density and biomass peak at approximately 7 m depth. A negative and significant correlation was found between density/biomass and the coefficient of variation of mean size suggesting a density-dependent process. The most influential factors on density and biomass were depth, sediment characteristics (grain size and percentage of organic content), distance from river mouths and waste water treatment plants, and sea bottom temperature. This study, in addition to reporting on the current status of these natural beds and the influence of dynamic and static environmental variables on their distribution, proposes an additional approach to estimating several biological parameters in C. gallina that could be valuable for the spatial management of this commercial resource and highlight the role of marine protected areas.

Long-term ecological vulnerability assessment of Indian Sundarban region under present and future climatic conditions under CMIP6 model

Vulnerability assessment of ecosystem bestows an idea about the ecosystem health and its ability to resist environmental stress. Indian Sundarban region situated at the southwest part of Ganga-Brahmaputra-Meghna delta has been under constant threat of frequent climate hazards and long-term climate change. Various attempts have been made for vulnerability assessment of this mangrove ecosystem focusing only on static non-temporal variables. The present work hypothesises that mangrove ecosystems are highly adaptive and respond to the changing environment by various natural resilience strategies at the ecosystem level. So, a better understanding of the dynamics of mangrove ecosystem will provide an idea about the state of vulnerability for this ecosystem. The present study uses 16 parameters to create exposure, sensitivity and adaptive capacity risk indices and constructs the vulnerability status for the Indian Sundarban. The results showed that the sea-level rise will happen between 0.7 m to 0.9 m under baseline climate conditions. The CMIP6 multi-model ensemble showed that the future minimum temperature for the region will go up to 29.48 °C, thereby reducing the max and min temperature difference for the region. The fuzzy AHP-based vulnerability assessment revealed that the western Sundarban is more prone to climate vulnerability. The island-like Surendranagar, Lothian, and West-Ajmalmari are extremely vulnerable. The proximity to human habitat will play an important role in climate change sensitivity to the Sundarban region. The time series analysis of mangrove forests showed the Mann-Kendall ‘τ’ value varies between 0.82 to −0.83. The central Sundarban forests area shows a varying degree of forest health deterioration. The assessments from the present study and the maps will help the environmental and risk-managers to identify the regions needing more climate change adaptation strategy.

Potential impacts of future climate on the spatio-temporal variability of landslide susceptibility in Iran using machine learning algorithms and CMIP6 climate-change scenarios

The objective of this research is to examine the possible impacts of climate change on landslide susceptibility in Iran. To accomplish this, 15 independent variables including 11 static variables, and 4 climatic dynamic variables that can affect landslide susceptibility were applied to predict landslide susceptibility from 3903 landslide susceptibility locations. These data points were separated into two phases for training and testing. Phase 6 of the Coupled Model Intercomparison Project (CMIP6) downscaled the data and enabled the combination of nine global climate models (GCMs) under shared socio-economic pathways (SSPs) to examine the impacts of climate change. The combination of demographic (SSP) and climatic (representative concentration pathways) scenarios were used (e.g., SSP1-2.6 and SSP5-8.5 were used to predict precipitation impacts resulting from climate change at four future dates – 2030, 2050, 2070, and 2090. The machine learning algorithms extreme gradient boosting (XGBoost) and random forest (RF) were employed to model and map landslide susceptibility. The area under curve (AUC) results generated with the testing (validation) phase demonstrates that the predictive power is suitable. The RF model produced the best results (AUC = 0.95). The XGBoost model was not as robust (AUC = 0.93). The investigation of how climate change effects on landslide susceptibility in Iran revealed that climate change should cause shifts in the ranges of susceptibility at different times. The RF model using CMIP SSP1-2.6 predicted the proportion of sites with very-high susceptibility in 2030, 2050, 2070, and 2090 would be 11.45, 11.42, 11.55, and 11.38%. The proportion of sites with high susceptibility would be 15.93, 16.03, 15.79, and 15.95%. And sites with moderate susceptibility will comprise 21.54, 20.92, 21.41, and 20.92%. With the CMIP SSP5-8.5 model, the percentages of locations with very-high susceptibility would be 11.59, 11.70, 11.88, and 12.17%.

Artificial Intelligent Power Forecasting for Wind Farm Based on Multi-Source Data Fusion

Wind power forecasting is a typical high-dimensional and multi-step time series prediction problem. Data-driven prediction methods using machine learning show advantages over traditional physical or statistical methods, especially for wind farms with complex meteorological conditions. Thus, effective use of different data sources and data types will help improve power forecasting accuracy. In this paper, a multi-source data fusion method is proposed, which integrates the static information of the wind turbine with observational and forecasting meteorological information together to further improve the power forecasting accuracy. Firstly, the characteristics of each time step are re-characterized by using the self-attention mechanism to integrate the global information of multi-source data, and the Res-CNN network is used to fuse multi-source data to improve the prediction ability of input variables. Secondly, static variable encoding and feature selection are carried out, and the time-varying variables are combined with static variables for collaborative feature selection, so as to effectively eliminate redundant information. A forecasting model based on the Encoder–Decoder framework is constructed with LSTM as the basic unit, and the Add&amp;Norm mechanism is introduced to further enhance the input variable information. In addition, the self-attention mechanism is used to integrate the global time information of the decoded results, and the Time Distributed mechanism is used to carry out multi-step prediction. Our training and testing data are obtained from an operating wind farm in northwestern China. Results show that the proposed method outperforms a classic AI forecasting method such as that using the Seq2Seq+attention model in terms of prediction accuracy, thus providing an effective solution for multi-step forecasting of wind power in wind farms.

A Novel Similarity Measure of Spatiotemporal Event Setting Sequences: Method Development and Case Study

Examining the similarity of event environments or surroundings—more precisely, settings—provides additional insight in analyzing event sequences, as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences, we consider spatial and temporal scales to define the bounds of the setting and incorporate dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index, we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, the developed similarity measures have wide application beyond the case study to other disciplinary contexts and geographical settings. They offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences.

Estimating treatment effects for time-to-treatment antibiotic stewardship in sepsis.

Sepsis is a life-threatening condition with a high in-hospital mortality rate. The timing of antibiotic administration poses a critical problem for sepsis management. Existing work studying antibiotic timing either ignores the temporality of the observational data or the heterogeneity of the treatment effects. Here we propose a novel method (called T4) to estimate treatment effects for time-to-treatment antibiotic stewardship in sepsis. T4 estimates individual treatment effects by recurrently encoding temporal and static variables as potential confounders, and then decoding the outcomes under different treatment sequences. We propose mini-batch balancing matching that mimics the randomized controlled trial process to adjust the confounding. The model achieves interpretability through a global-level attention mechanism and a variable-level importance examination. Meanwhile, we equip T4 with an uncertainty quantification to help prevent overconfident recommendations. We demonstrate that T4 can identify effective treatment timing with estimated individual treatment effects for antibiotic stewardship on two real-world datasets. Moreover, comprehensive experiments on a synthetic dataset exhibit the outstanding performance of T4 compared with the state-of-the-art models on estimation of individual treatment effect.

Muscle strength, balance and upper extremity function are not predictors of cervical proprioception in healthy young subjects

Purpose/Aim: The aim of this study is to examine the relationship between cervical proprioception sense and balance, hand grip strength, cervical region muscle strength and upper extremity functionality in healthy young subjects. Methods: A total of 200 people with a mean age of 20.8 ± 1.8 participated in the study. Cervical proprioception sense of the participants was evaluated with Cervical Joint Position Error Test (CJPET), balance with Biodex Stability System, hand grip strength with hand dynamometer, and upper extremity functionality with Perdue Pegboard test. The relationship of variables with cervical proprioception was evaluated with Pearson Correlation analysis. Results: According to this study results, there was no significant relationship between CJPET (extension, left rotation, right rotation) and sub-parameters of dynamic balance (anteroposterior, mediolateral, overall), cervical muscle strength and hand grip strength (p > 0.05). There was a significant correlation between CJPET flexion and static balance variables (p < 0.05). Conclusion: According to this study, there is no relationship between cervical proprioception and balance, hand grip muscle strength, cervical region muscle strength and upper extremity functionality in healthy young subjects.

Contrasting ecological information content in whaling archives with modern cetacean surveys for conservation planning and identification of historical distribution changes.

Many species are restricted to a marginal or suboptimal fraction of their historical range due to anthropogenic impacts, making it hard to interpret their ecological preferences from modern-day data alone. However, inferring past ecological states is limited by the availability of robust data and biases in historical archives, posing a challenge for policy makers . To highlight how historical records can be used to understand the ecological requirements of threatened species and inform conservation, we investigated sperm whale (Physeter macrocephalus) distribution in the Western Indian Ocean. We assessed differences in information content and habitat suitability predictions based on whale occurrence data from Yankee whaling logs (1792-1912) and from modern cetacean surveys (1995-2020). We built maximum entropy habitat suitability models containing static (bathymetry-derived) variables to compare models comprising historical-only and modern-only data. Using both historical and modern habitat suitability predictions we assessed marine protected area (MPA) placement by contrasting suitability in- and outside MPAs. The historical model predicted high habitat suitability in shelf and coastal regions near continents and islands, whereas the modern model predicted a less coastal distribution with high habitat suitability more restricted to areas of steep topography. The proportion of high habitat suitability inside versus outside MPAs was higher when applying the historical predictions than the modern predictions, suggesting that different marine spatial planning optimums can be reached from either data sources. Moreover, differences in relative habitat suitability predictions between eras were consistent with the historical depletion of sperm whales from coastal regions, which were easily accessed and targeted by whalers, resulting in a modern distribution limited more to steep continental margins and remote oceanic ridges. The use of historical data can provide important new insights and, through cautious interpretation, inform conservation planning and policy, for example, by identifying refugee species and regions of anticipated population recovery.

Controls on Flood Trends Across the United States

AbstractTrends in flood magnitudes vary across the conterminous USA (CONUS). There have been attempts to identify what controls these regionally varying trends, but these attempts were limited to certain—for example, climatic—variables or to smaller regions, using different methods and datasets each time. Here we attribute the trends in annual maximum streamflow for 4,390 gauging stations across the CONUS in the period 1960–2010, while using a novel combination of methods and an unprecedented variety of potential controlling variables to allow large‐scale comparisons and minimize biases. Using process‐based flood classification and complex networks, we find 10 distinct clusters of catchments with similar flood behavior. We compile a set of 31 hydro‐climatological and land use variables as predictors for 10 separate Random Forest models, allowing us to find the main controls the flood magnitude trends for each cluster. By using Accumulated Local Effect plots, we can understand how these controls influence the trends in the flood magnitude. We show that hydro‐climatologic changes and land use are of similar importance for flood magnitude trends across the CONUS. Static land use variables are more important than their trends, suggesting that land use is able to attenuate (forested areas) or amplify (urbanized areas) the effects of climatic changes on flood magnitudes. For some variables, we find opposing effects in different regions, showing that flood trend controls are highly dependent on regional characteristics and that our novel approach is necessary to attribute flood magnitude trends reliably at the continental scale while maintaining sensitivity to regional controls.