A Deep Spatial Context Guided Framework for Infant Brain Subcortical Segmentation

AbstractMost cities in the United States rely on zoning to address important planning-related issues within their jurisdictions. Planners often use GIS tools to analyze these issues in a spatial context. ESRI’s ArcGIS Urban software seeks to provide the planning profession with a GIS-based solution for various challenges, including zoning’s impacts on the built environment and housing capacity.This research explores the use of ArcGIS Urban for assessing the existing zoning and comprehensive plans in meeting the projected residential growth in the near future using the City of Arlington, Texas as a case study. The exploration provides examples and lessons for how ArcGIS Urban might be used by planners to accomplish their tasks and highlights the capabilities and limitations of ArcGIS Urban in its current stand. The paper is concluded with some suggestions for future studies.

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Evaluation of risk factors for cerebral palsy

The Egyptian Journal of Neurology Psychiatry and Neurosurgery ◽

10.1186/s41983-020-00265-1 ◽

2021 ◽

Vol 57 (1) ◽

Author(s):

Dina Salama Abd Elmagid ◽

Hend Magdy

Keyword(s):

Risk Factors ◽

Cerebral Palsy ◽

Intracranial Hemorrhage ◽

Cns Infection ◽

Motor Impairments ◽

Cerebrovascular Accidents ◽

Common Causes ◽

Children With Cerebral Palsy ◽

Infant Brain ◽

Cns Malformation

Abstract Background Cerebral palsy (CP) has been identified as one of the most important and common causes of childhood disabilities worldwide and is often accompanied by multiple comorbidities. CP is defined as a group of disorders of the development of movement and posture, causing activity limitation that are attributed to non-progressive disturbances that occurred in the developing fetal or infant brain. The objective of our study was to describe main clinical pattern and motor impairments of our patients, and to evaluate the presence of risk factors and if there is a relation to the type of cerebral palsy. Methods Children with cerebral palsy were retrospectively enrolled over 2 years from the neurology outpatient clinics. Cerebral palsy risk factors and motor impairments were determined through caregiver interviews, review of medical records, and direct physical examination. Results One thousand children with cerebral palsy were enrolled. Subjects were 64.4% male, with a median age of 2.5 years. The risk factors for cerebral palsy in our study were antenatal (21%), natal and post-natal (30.5%), post-neonatal (17.1%), and unidentified (31.4%). Antenatal as CNS malformation (26.6%), maternal DM (17.6%), prolonged rupture of membrane (11.9%), maternal hemorrhage (10.4%), and pre-eclampsia (4.7%). Natal and post-natal as hypoxic ischemic encephalopathy (28.5%), infection (16.3%), hyperbilirubinemia (12.7%), cerebrovascular accidents (8.8%), meconium aspiration (6.2%), and intracranial hemorrhage. Post-neonatal as CNS infection (34.5%), cerebrovascular accidents (28.6%), sepsis (23.9%), and intracranial hemorrhage (8.7%). Conclusions Cerebral palsy has different etiologies and risk factors. Further studies are necessary to determine optimal preventative strategies in these patients.

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Measuring spatial allocative efficiency in basketball

Journal of Quantitative Analysis in Sports ◽

10.1515/jqas-2019-0126 ◽

2020 ◽

Vol 16 (4) ◽

pp. 271-289

Author(s):

Nathan Sandholtz ◽

Jacob Mortensen ◽

Luke Bornn

Keyword(s):

Spatial Distribution ◽

Hierarchical Model ◽

Opportunity Cost ◽

Allocative Efficiency ◽

Bayesian Hierarchical Model ◽

National Basketball Association ◽

Spatial Context ◽

Bayesian Hierarchical ◽

The Impact

AbstractEvery shot in basketball has an opportunity cost; one player’s shot eliminates all potential opportunities from their teammates for that play. For this reason, player-shot efficiency should ultimately be considered relative to the lineup. This aspect of efficiency—the optimal way to allocate shots within a lineup—is the focus of our paper. Allocative efficiency should be considered in a spatial context since the distribution of shot attempts within a lineup is highly dependent on court location. We propose a new metric for spatial allocative efficiency by comparing a player’s field goal percentage (FG%) to their field goal attempt (FGA) rate in context of both their four teammates on the court and the spatial distribution of their shots. Leveraging publicly available data provided by the National Basketball Association (NBA), we estimate player FG% at every location in the offensive half court using a Bayesian hierarchical model. Then, by ordering a lineup’s estimated FG%s and pairing these rankings with the lineup’s empirical FGA rate rankings, we detect areas where the lineup exhibits inefficient shot allocation. Lastly, we analyze the impact that sub-optimal shot allocation has on a team’s overall offensive potential, demonstrating that inefficient shot allocation correlates with reduced scoring.

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