66 research outputs found
The Relationship Between Teacher Efficacy and Use of Data to Inform Instruction in Two Turnaround Middle Schools
Schools across our county must ensure that an increasing percentage of students meet state-specified proficiency standards for the schools to be rated as making Adequate Yearly Progress (AYP). The longer a school fails to make AYP, the more severe are the corrective actions that must be undertaken.
This study looks at two turnaround middle schools in the western United States, which were determined to be among the lowest-performing five percent in their state. The turnaround model adopted by this school district is the transformational model of school turnaround. This model requires replacing at least 50% of the staff and principal, adopting new governance, and implementing a new or revised instructional model.
This study looks at teacher efficacy and teacher use of data to inform instruction. Teacher efficacy is the teacher\u27s self-assessment of his or her ability to support student learning. Teachers with high teacher efficacy believe they can positively impact student achievement despite challenges, while teachers with low efficacy believe they have a limited ability influence student learning and achievement (Ashton & Webb, 1986; Bandura, 1993; 1994; Bruce et al, 2010; Gibson & Dembow, 1984; Hoy & Woolfolk, 1993). Teacher use of data to inform instruction is critical in school turnaround conditions. It is essential that teachers are provided a means to quickly assess student learning in order to differentiate instruction, provide extended services or reteach so that student achievement can improve.
The findings may be used to inform successful transformation in other persistently low performing schools. Such information is critical given the large numbers of struggling learners, the high number of dropouts, and the tremendous investment in resources to turnaround chronically low-performing schools
Hyperactivity, impulsivity, and inattention in boys with cleft lip and palate: relationship to ventromedial prefrontal cortex morphology
The purpose of this study is to evaluate quantitative structural measures of the ventromedial prefrontal cortex (vmPFC) in boys with isolated clefts of the lip and/or palate (ICLP) relative to a comparison group and to associate measures of brain structure with quantitative measures of hyperactivity, impulsivity, and inattentiveness. A total of 50 boys with ICLP were compared to 60 healthy boys without clefts. Magnetic resonance imaging brain scans were used to evaluate vmPFC structure. Parents and teachers provided quantitative measures of hyperactivity, impulsivity, and inattentiveness using the Pediatric Behavior Scale. Boys with ICLP had significantly higher ratings of hyperactivity/impulsivity/inattention (HII) and significantly increased volume of the right vmPFC relative to the comparison group. There was a direct relationship between HII score and vmPFC volume in both the ICLP group and control group, but the relationship was in the opposite direction: in ICLP, the higher the vmPFC volume, the higher the HII score; for the comparison group, the lower the vmPFC volume, the greater the HII score. The vmPFC is a region of the brain that governs behaviors of hyperactivity, impulsivity and inattention (HII). In boys with ICLP, there are higher levels of HII compared to the controls and this is directly related to a significantly enlarged volume of the right vmPFC. Enlargement of this region of the brain is therefore considered to be pathological in the ICLP group and supports the notion that abnormal brain structure (from abnormal brain development) is the underlying etiology for the abnormal behaviors seen in this population
Advancing Crop Transformation in the Era of Genome Editing
Plant transformation has enabled fundamental insights into plant biology and revolutionized commercial agriculture. Unfortunately, for most crops, transformation and regeneration remain arduous even after more than 30 years of technological advances. Genome editing provides novel opportunities to enhance crop productivity but relies on genetic transformation and plant regeneration, which are bottlenecks in the process. Here, we review the state of plant transformation and point to innovations needed to enable genome editing in crops. Plant tissue culture methods need optimization and simplification for efficiency and minimization of time in culture. Currently, specialized facilities exist for crop transformation. Single-cell and robotic techniques should be developed for high-throughput genomic screens. Plant genes involved in developmental reprogramming, wound response, and/or homologous recombination should be used to boost the recovery of transformed plants. Engineering universal Agrobacterium tumefaciens strains and recruiting other microbes, such as Ensifer or Rhizobium, could facilitate delivery of DNA and proteins into plant cells. Synthetic biology should be employed for de novo design of transformation systems. Genome editing is a potential game-changer in crop genetics when plant transformation systems are optimized
Lower Cancer Incidence in Amsterdam-I Criteria Families Without Mismatch Repair Deficiency: Familial Colorectal Cancer Type X
Approximately 60% of families that meet the Amsterdam-I criteria (AC-I) for hereditary nonpolyposis colorectal cancer (HNPCC) have a hereditary abnormality in a DNA mismatch repair (MMR) gene. Cancer incidence in AC-I families with MMR gene mutations is reported to be very high, but cancer incidence for individuals in AC-I families with no evidence of an MMR defect is unknown
Differences in neuropsychological and behavioral parameters and brain structure in patients with familial adenomatous polyposis: a sibling-paired study
An original phylogenetic approach identified mitochondrial haplogroup T1a1 as inversely associated with breast cancer risk in BRCA2 mutation carriers
Introduction: Individuals carrying pathogenic mutations in the BRCA1 and BRCA2 genes have a high lifetime risk of breast cancer. BRCA1 and BRCA2 are involved in DNA double-strand break repair, DNA alterations that can be caused by exposure to reactive oxygen species, a main source of which are mitochondria. Mitochondrial genome variations affect electron transport chain efficiency and reactive oxygen species production. Individuals with different mitochondrial haplogroups differ in their metabolism and sensitivity to oxidative stress. Variability in mitochondrial genetic background can alter reactive oxygen species production, leading to cancer risk. In the present study, we tested the hypothesis that mitochondrial haplogroups modify breast cancer risk in BRCA1/2 mutation carriers. Methods: We genotyped 22,214 (11,421 affected, 10,793 unaffected) mutation carriers belonging to the Consortium of Investigators of Modifiers of BRCA1/2 for 129 mitochondrial polymorphisms using the iCOGS array. Haplogroup inference and association detection were performed using a phylogenetic approach. ALTree was applied to explore the reference mitochondrial evolutionary tree and detect subclades enriched in affected or unaffected individuals. Results: We discovered that subclade T1a1 was depleted in affected BRCA2 mutation carriers compared with the rest of clade T (hazard ratio (HR) = 0.55; 95% confidence interval (CI), 0.34 to 0.88; P = 0.01). Compared with the most frequent haplogroup in the general population (that is, H and T clades), the T1a1 haplogroup has a HR of 0.62 (95% CI, 0.40 to 0.95; P = 0.03). We also identified three potential susceptibility loci, including G13708A/rs28359178, which has demonstrated an inverse association with familial breast cancer risk. Conclusions: This study illustrates how original approaches such as the phylogeny-based method we used can empower classical molecular epidemiological studies aimed at identifying association or risk modification effects.Peer reviewe
The genetic architecture of the human cerebral cortex
The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder
Management of chronic pain with chronic opioid therapy in patients with substance use disorders
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Brain multiplexes reveal morphological connectional biomarkers fingerprinting late brain dementia states
Accurate diagnosis of mild cognitive impairment (MCI) before conversion to Alzheimer’s disease (AD) is invaluable for patient treatment. Many works showed that MCI and AD affect functional and structural connections between brain regions as well as the shape of cortical regions. However, ‘shape connections’ between brain regions are rarely investigated -e.g., how morphological attributes such as cortical thickness and sulcal depth of a specific brain region change in relation to morphological attributes in other regions. To fill this gap, we unprecedentedly design morphological brain multiplexes for late MCI/AD classification. Specifically, we use structural T1-w MRI to define morphological brain networks, each quantifying similarity in morphology between different cortical regions for a specific cortical attribute. Then, we define a brain multiplex where each intra-layer represents the morphological connectivity network of a specific cortical attribute, and each inter-layer encodes the similarity between two consecutive intra-layers. A significant performance gain is achieved when using the multiplex architecture in comparison to other conventional network analysis architectures. We also leverage this architecture to discover morphological connectional biomarkers fingerprinting the difference between late MCI and AD stages, which included the right entorhinal cortex and right caudal middle frontal gyrus
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Multimodal and Multiscale Deep Neural Networks for the Early Diagnosis of Alzheimer’s Disease using structural MR and FDG-PET images
Alzheimer’s Disease (AD) is a progressive neurodegenerative disease where biomarkers for disease based on pathophysiology may be able to provide objective measures for disease diagnosis and staging. Neuroimaging scans acquired from MRI and metabolism images obtained by FDG-PET provide in-vivo measurements of structure and function (glucose metabolism) in a living brain. It is hypothesized that combining multiple different image modalities providing complementary information could help improve early diagnosis of AD. In this paper, we propose a novel deep-learning-based framework to discriminate individuals with AD utilizing a multimodal and multiscale deep neural network. Our method delivers 82.4% accuracy in identifying the individuals with mild cognitive impairment (MCI) who will convert to AD at 3 years prior to conversion (86.4% combined accuracy for conversion within 1–3 years), a 94.23% sensitivity in classifying individuals with clinical diagnosis of probable AD, and a 86.3% specificity in classifying non-demented controls improving upon results in published literature
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