A Finding of No Significant Impact was signed on August 28, 2009, by Michael D. Snyder, Regional Director for the Intermountain Region of the National Park Service, for Train Operations at Grand Canyon National Park https://www.nps.gov/grca/learn/news/news_2009-08-31_trainea.htm

In spite of the rapid response time, park emergency responders were not able to revive 72-year old John Debelack of Saddlebrooke, Arizona. https://www.nps.gov/grca/learn/news/2011-06-29_death.htm

Recent heavy monsoonal rainfall has caused damage to portions of the South Kaibab Trail approximately one-half mile below Cedar Ridge and to the North Kaibab Trail below Supai Tunnel. https://www.nps.gov/grca/learn/news/heavy-monsoonal-rain-causes-trail-damage-to-grand-canyon-trails.htm

Recent heavy monsoonal rainfall caused damage to portions of the South Kaibab Trail approximately one-half mile below Cedar Ridge closing the trail to livestock use. Trail crews have completed work to remove debris from the trail. The trail is now open to livestock as well as foot traffic. https://www.nps.gov/grca/learn/news/update-crews-repair-south-kaibab-trail-after-monsoonal-rain-damage.htm

Horace P. Albright Training Center was officially listed to the National Register of Historic Places on September 30, 2013. The only National Park Service (NPS) training center in the West and located within the boundaries of Grand Canyon National Park, the Center is a state significant Historic District exemplifying simple, practical, modernist design of the Mission 66 Era. https://www.nps.gov/grca/learn/news/albright-training-center.htm

The Galahad Fire has been contained to the point where no direct threats remain to park resources. The fire will continue to burn across the southern portion of the Walla Valley Peninsula west of the W-4 Road until wetting rains arrive, typically in mid-July. Currently it is estimated to be 4,363 acres and 50% contained. The increase in containment reflects ongoing holding actions by firefighters along the W-4 Road. https://www.nps.gov/grca/learn/news/galahad-fire-likely-to-burn-until-wetting-rains-arrive.htm

The trail between the El Tovar Hotel and the train depot will close for repairs July 1. https://www.nps.gov/grca/learn/news/el-tovar-walkway-detour.htm

The Ikes Fire has seen minimal growth over the past 2 days due to multiple rain showers over the fire area. Due to moisture, fire activity has been limited, however, the fire continues to burn within the duff layer of needles, leaf litter, and downed logs along the forest floor. https://www.nps.gov/grca/learn/news/ikes-fire-remains-active-despite-rain-20190810.htm

Not only did the patients experience high rates of remission, their cognitive function also showed marked improvement as well.

The post Magnetic Brain Treatment Found to Relieve Depression in 90% of Participants With No Negative Side Effects appeared first on Good News Network.

Wildfires consume existing forest fuels but also leave behind dead shrubs and trees that become fuel to future wildfires. Harvesting firekilled trees is sometimes proposed as an economical approach for reducing future fuels and wildfire severity. Postfire logging, however, is controversial. Some question its fuel reduction benefits and its ecological impacts.

Climate change is altering growing conditions in the temperate rain forest region that extends from northern California to the Gulf of Alaska. Longer, warmer growing seasons are generally increasing the overall potential for forest growth in the region. However, species differ in their ability to adapt to changing conditions. For example, researchers with Pacific Northwest Research Station examined forest trends for southeastern and southcentral Alaska and found that, in 13 years, western redcedar showed a 4.2-percent increase in live-tree biomass, while shore pine showed a 4.6-percent decrease. In general, the researchers found that the amount of live-tree biomass in extensive areas of unmanaged, higher elevation forest in southern Alaska increased by as much as 8 percent over the 13-year period, contributing to significant carbon storage. Hemlock dwarf mistletoe is another species expected to fare well under warmer conditions in Alaska. Model projections indicate that habitat for this parasitic species could increase 374 to 757 percent over the next 100 years. This could temper the prospects for western hemlock—a tree species otherwise expected to do well under future climate conditions projected for southern Alaska. In coastal forests of Washington and Oregon, water availability may be a limiting factor in future productivity, with gains at higher elevations but declines at lower elevations.

Sitka, Alaska, has substantial hydroelectric resources, limited driving distances, and a conservation-minded community, all suggesting strong opportunities for achieving a low community carbon footprint.

Thankfully the little lamb was unharmed by the ordeal and was reunited with his mum

Newcastle United goalkeeper Rob Elliot has opened up about team-mate Isaac Hayden and what makes him such an important player

Chance of "localised torrential downpours"

Statement Highlights: While significant improvements have been made in resuscitation and post cardiac arrest resuscitation care, mortality remains high and is mainly attributed to widespread brain injury.Better science is needed to support the ...

DALLAS, March 24, 2020 — As part of its global response to the growing COVID-19 pandemic, the American Heart Association, the world’s leading voluntary organization focused on heart and brain health and research, is committing  $2.5 million to research...

DALLAS, April 2, 2020 – The American Heart Association — the world’s leading voluntary organization dedicated to a world of longer, healthier lives — announced today more than $14 million in scientific research grants are being awarded to four...

Research Highlights: A robot was used to treat brain aneurysms for the first time. The robotic system could eventually allow remote surgery, enabling surgeons to treat strokes from afar. Embargoed until 11:15 a.m. Pacific Time / 2:15 p.m. Eastern ...

Research Highlights: People with recent heart attacks have significantly higher activity in a brain area (the amygdala) involved in stress perception and emotional response. They also have more inflammation in key arteries and increased bone marrow ...

DALLAS, April 3, 2020 — With the COVID-19 pandemic, more patients are having difficulty breathing and requiring ventilators to help them breathe. As hospital and intensive care unit (ICU) volumes increase with COVID-19 patients, health care ...

Traumatic brain injury (TBI) can have lifelong and dynamic effects on health and wellbeing. Research on the longterm consequences emphasises that, for many patients, TBI should be conceptualised as a chronic health condition. Evidence suggests that functional outcomes after TBI can show improvement or deterioration up to two decades after injury, and rates of all-cause mortality remain elevated for many years. Furthermore, TBI represents a risk factor for a variety of neurological illnesses, including epilepsy, stroke, and neurodegenerative disease. With respect to neurodegeneration after TBI, post-mortem studies on the long-term neuropathology after injury have identified complex persisting and evolving abnormalities best described as polypathology, which includes chronic traumatic encephalopathy. Despite growing awareness of the lifelong consequences of TBI, substantial gaps in research exist. Improvements are therefore needed in understanding chronic pathologies and their implications for survivors of TBI, which could inform long-term health management in this sizeable patient population.

Traumatic brain injury (TBI) is a leading cause of death and disability. Older adults are more likely than younger individuals to sustain TBIs and less likely to survive them. TBI has been called the “silent epidemic,” and older adults are the “silent population” within this epidemic. This study evaluates whether indicators of preinjury health and functioning are associated with risk of incident traumatic brain injury (TBI) with loss of consciousness (LOC) and to evaluate health‐related factors associated with mortality in individuals with incident TBI.

The association of dispositional optimism with health-related factors has been well established in several clinical populations, but little is known about the role of optimism in recovery after traumatic brain injury (TBI). Given the high prevalence of cognitive complaints after TBI, the present study examined the association between optimism and cognitive functioning after TBI.

Homelessness is a global public health concern, and traumatic brain injury (TBI) could represent an underappreciated factor in the health trajectories of homeless and marginally housed individuals. We aimed to evaluate the lifetime prevalence of TBI in this population, and to summarise findings on TBI incidence and the association between TBI and health-related or functioning-related outcomes.

The national age-adjusted rate of fall-related TBI deaths increased by 17% from 2008 to 2017; rates increased significantly in 29 states and among nearly all groups, most notably persons living in noncore nonmetropolitan counties and those aged ≥75 years.

A look at how to describe clinical recovery time and factors that might impact recovery after a sports-related mild traumatic brain injury (SR-mTBI; concussion).

By turning 'Stella' into a punchline, laughter became my medicine and sharing my story became my therapy

When Olympic gold medallist Shona McCallin was hit on the side of her head by a seemingly innocuous shoulder challenge, she suffered what was originally thought to be a concussion.

"This study challenges current perceptions that most people with a sports-related mTBI recover within 10 to 14 days," said lead author Dr. Stephen Kara, from Axis Sports Medicine in Auckland, New Zealand.

The congressional Brain Injury Task Force, co-chaired by Reps. Bill Pascrell Jr. (D-N.J.) and Don Bacon (R-Neb.), spoke to hundreds of people gathered at the Rayburn House Office Building. One area of focus was the development of a national traumatic brain injury registry, a vital step for getting a handle on how best to manage this difficult-to-treat condition.

Ken Rekowski, Shawn Hill and Jodi Graham are dealing with COVID-19 in different ways

Language sets humans apart in the animal world. Language allows us to communicate complex ideas and emotions.  But too often after brain injury be it stroke or trauma, language is lost. 

Language sets humans apart in the animal world. Language allows us to communicate complex ideas and emotions.  But too often after brain injury be it stroke or trauma, language is lost. 

This week, we had a very special guest, our Livecastard of the Month, Eric, who actually signed up for our...

The post METAL INJECTION LIVECAST #544 - 33% Drained appeared first on Metal Injection.

Managing money is complicated, especially for people with a brain injury who may have trouble remembering what they spent or creating a budget. Adam shares some tips from online banking to keeping a spending journal.

If extra time on a test or memory aids can make life easier during college, why not use them? Adam talks about moving past the "stigma" of using disability services and getting the help you need to succeed in college.

Adam thanks you — his blog viewers and supporters — and encourages you to continue the discussion and awareness raising about TBI and PTSD; the battle does not stop here.

Language sets humans apart in the animal world. Language allows us to communicate complex ideas and emotions.  But too often after brain injury be it stroke or trauma, language is lost. 

Recent studies of optimization methods and GNC of spacecraft near small bodies focusing on descent, landing, rendezvous, etc., with key safety constraints such as line-of-sight conic zones and soft landings have shown promising results; this paper considers descent missions to an asteroid surface with a constraint that consists of an onboard camera and asteroid surface markers while using a stochastic convex MPC law. An undermodeled asteroid gravity and spacecraft technology inspired measurement model is established to develop the constraint. Then a computationally light stochastic Linear Quadratic MPC strategy is presented to keep the spacecraft in satisfactory field of view of the surface markers while trajectory tracking, employing chance based constraints and up-to-date estimation uncertainty from navigation. The estimation uncertainty giving rise to the tightened constraints is particularly addressed. Results suggest robust tracking performance across a variety of trajectories.

In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. It is motivated by the fact that many small and medium-size datasets have been released by the community over the years whereas there is no large scale dataset similar to ImageNet in the domain. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images. Then, we propose a simple architecture and training scheme for creating a transferable model and a robust evaluation and selection protocol in order to evaluate our method. Depending on the target task, we show that our models used as feature extractors either improve significantly over ImageNet pre-trained models or provide comparable performance. Fine-tuning improves performance over feature extraction and is able to recover the lack of specificity of ImageNet features, as both pre-training sources yield comparable performance.

Many efforts of research are devoted to semantic role labeling (SRL) which is crucial for natural language understanding. Supervised approaches have achieved impressing performances when large-scale corpora are available for resource-rich languages such as English. While for the low-resource languages with no annotated SRL dataset, it is still challenging to obtain competitive performances. Cross-lingual SRL is one promising way to address the problem, which has achieved great advances with the help of model transferring and annotation projection. In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations. Experimental results on Universal Proposition Bank show that the translation-based method is highly effective, and the automatic pseudo datasets can improve the target-language SRL performances significantly.

A brain-computer interface (BCI) enables a user to communicate with a computer directly using brain signals. Electroencephalograms (EEGs) used in BCIs are weak, easily contaminated by interference and noise, non-stationary for the same subject, and varying across different subjects and sessions. Therefore, it is difficult to build a generic pattern recognition model in an EEG-based BCI system that is optimal for different subjects, during different sessions, for different devices and tasks. Usually, a calibration session is needed to collect some training data for a new subject, which is time consuming and user unfriendly. Transfer learning (TL), which utilizes data or knowledge from similar or relevant subjects/sessions/devices/tasks to facilitate learning for a new subject/session/device/task, is frequently used to reduce the amount of calibration effort. This paper reviews journal publications on TL approaches in EEG-based BCIs in the last few years, i.e., since 2016. Six paradigms and applications -- motor imagery, event-related potentials, steady-state visual evoked potentials, affective BCIs, regression problems, and adversarial attacks -- are considered. For each paradigm/application, we group the TL approaches into cross-subject/session, cross-device, and cross-task settings and review them separately. Observations and conclusions are made at the end of the paper, which may point to future research directions.

We propose a new method for realistic human motion transfer using a generative adversarial network (GAN), which generates a motion video of a target character imitating actions of a source character, while maintaining high authenticity of the generated results. We tackle the problem by decoupling and recombining the posture information and appearance information of both the source and target characters. The innovation of our approach lies in the use of the projection of a reconstructed 3D human model as the condition of GAN to better maintain the structural integrity of transfer results in different poses. We further introduce a detail enhancement net to enhance the details of transfer results by exploiting the details in real source frames. Extensive experiments show that our approach yields better results both qualitatively and quantitatively than the state-of-the-art methods.

A graph $G=(V,E)$ is $delta$-hyperbolic if for any four vertices $u,v,w,x$, the two larger of the three distance sums $d(u,v)+d(w,x)$, $d(u,w)+d(v,x)$, and $d(u,x)+d(v,w)$ differ by at most $2delta geq 0$. Recent work shows that many real-world graphs have small hyperbolicity $delta$. This paper describes the eccentricity terrain of a $delta$-hyperbolic graph. The eccentricity function $e_G(v)=max{d(v,u) : u in V}$ partitions the vertex set of $G$ into eccentricity layers $C_{k}(G) = {v in V : e(v)=rad(G)+k}$, $k in mathbb{N}$, where $rad(G)=min{e_G(v): vin V}$ is the radius of $G$. The paper studies the eccentricity layers of vertices along shortest paths, identifying such terrain features as hills, plains, valleys, terraces, and plateaus. It introduces the notion of $eta$-pseudoconvexity, which implies Gromov's $epsilon$-quasiconvexity, and illustrates the abundance of pseudoconvex sets in $delta$-hyperbolic graphs. In particular, it shows that all sets $C_{leq k}(G)={vin V : e_G(v) leq rad(G) + k}$, $kin mathbb{N}$, are $(2delta-1)$-pseudoconvex. Additionally, several bounds on the eccentricity of a vertex are obtained which yield a few approaches to efficiently approximating all eccentricities. An $O(delta |E|)$ time eccentricity approximation $hat{e}(v)$, for all $vin V$, is presented that uses distances to two mutually distant vertices and satisfies $e_G(v)-2delta leq hat{e}(v) leq {e_G}(v)$. It also shows existence of two eccentricity approximating spanning trees $T$, one constructible in $O(delta |E|)$ time and the other in $O(|E|)$ time, which satisfy ${e}_G(v) leq e_T(v) leq {e}_G(v)+4delta+1$ and ${e}_G(v) leq e_T(v) leq {e}_G(v)+6delta$, respectively. Thus, the eccentricity terrain of a tree gives a good approximation (up-to an additive error $O(delta))$ of the eccentricity terrain of a $delta$-hyperbolic graph.

Uncertain dynamic obstacles, such as pedestrians or vehicles, pose a major challenge for optimal robot navigation with safety guarantees. Previous work on motion planning has followed two main strategies to provide a safe bound on an obstacle's space: a polyhedron, such as a cuboid, or a nonlinear differentiable surface, such as an ellipsoid. The former approach relies on disjunctive programming, which has a relatively high computational cost that grows exponentially with the number of obstacles. The latter approach needs to be linearized locally to find a tractable evaluation of the chance constraints, which dramatically reduces the remaining free space and leads to over-conservative trajectories or even unfeasibility. In this work, we present a hybrid approach that eludes the pitfalls of both strategies while maintaining the original safety guarantees. The key idea consists in obtaining a safe differentiable approximation for the disjunctive chance constraints bounding the obstacles. The resulting nonlinear optimization problem is free of chance constraint linearization and disjunctive programming, and therefore, it can be efficiently solved to meet fast real-time requirements with multiple obstacles. We validate our approach through mathematical proof, simulation and real experiments with an aerial robot using nonlinear model predictive control to avoid pedestrians.

Restless multi-armed bandits are a class of discrete-time stochastic control problems which involve sequential decision making with a finite set of actions (set of arms). This paper studies a class of constrained restless multi-armed bandits (CRMAB). The constraints are in the form of time varying set of actions (set of available arms). This variation can be either stochastic or semi-deterministic. Given a set of arms, a fixed number of them can be chosen to be played in each decision interval. The play of each arm yields a state dependent reward. The current states of arms are partially observable through binary feedback signals from arms that are played. The current availability of arms is fully observable. The objective is to maximize long term cumulative reward. The uncertainty about future availability of arms along with partial state information makes this objective challenging. Applications for CRMAB abound in the domain of cyber-physical systems. This optimization problem is analyzed using Whittle's index policy. To this end, a constrained restless single-armed bandit is studied. It is shown to admit a threshold-type optimal policy, and is also indexable. An algorithm to compute Whittle's index is presented. Further, upper bounds on the value function are derived in order to estimate the degree of sub-optimality of various solutions. The simulation study compares the performance of Whittle's index, modified Whittle's index and myopic policies.