Modelling the gait of healthy and post-stroke individuals

Gait is a complex mechanism which involves the action of the musculoskeletal system, controlled by spinal and supraspinal mechanisms [1]. After Cerebrovascular Accident (CVA), or stroke, the damage of structures involved in motor control from one side of the brain, can cause activation deficits in the muscles from the contralesional (CONTRA) side of the body – hemiparesis, leading to asymmetry in the gait pattern [2]. In gait analysis, a set of experimental methods have ben used to study the gait of one subject, including based on: visual analysis, collection of kinematic parameters acquired using cameras and reflective body markers, kinetic analysis using force platforms to determine the ground reaction force (GRF) and electromyography. Due to the recent technological advances, it became possible to create and implement complex dynamical multi-segmented models of the human body with several degrees-of-freedom to perform simulations from experimental data [3]. OpenSim is an open-source software developed in Stanford University that allows the creation of models of the musculoskeletal system and the development of subject-specific simulations of several tasks [4]. The present work describes a procedure used to perform a simulation in OpenSim of a healthy and a post-stroke individual, using real experimental data. The kinematic parameters were determined as well as the activation of two calf muscles: soleus (SOL) and medial gastrocnemius (MEDGAS).info:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Latitudinal disparity in the reproductive cycle of sharpnose shark, Rhizoprionodon lalandii (Elasmobranchii: Carcharhinidae), in Atlantic waters off South America

Geographical variation in biophysical conditions may strongly influence the life history characteristics of widely distributed species, such as the Brazilian sharpnose shark, Rhizoprionodon lalandii (Müller & Henle, 1839). Here, we use original and secondary data of reproductive traits of R. lalandii to identify population differences among northern/northeastern and southern Atlantic waters of South America. In the southeast region, birth occurs between December and March, and the young become frequent along the coast between April and September. Mating occurs mainly between March and June, when females with bite marks are common. Females in early pregnancy occur between March and September. The reproductive cycle of R. lalandii in the northern/northeastern region was approximately six months ahead of the cycle described for the southeastern region. These results support the hypothesis that environmental conditions in the North-Northeast and Southeast generate differences in life history traits, resulting in at least two distinct populations along the Brazilian coast