Curriculum planning in community colleges and the emerging technological economy.

This study examined curriculum planning strategies at four Massachusetts community colleges: Massachusetts Bay Community College, Bunker Hill Community College, Roxbury Community College and Middlesex Community College. This examination was done through an interview process conducted throughout 1990. The interviewees were three knowledgeable officials at each of the four community colleges. The interviews were conducted in person and lasted approximately two hours each. It was the goal of the interview process to have interviewers respond to a number of questions related to the planning strategies used currently in the context of curriculum development, labor force preparation, and the emerging technological economy and questions related to planning strategies being contemplated for the future in the context of curriculum development, labor force preparation and the emerging technological economy. The data from these qualitative interviews were summarized, analyzed, and compared. Additionally, this study gathered data from five-year plans of each community college. Points stressed by those interviewed were compiled in the section on Findings . From the Findings, conclusions were drawn relative to the subject of this dissertation. It is interesting to note that while many might not fully have appreciated the reality, community colleges are now and will likely continue to be significantly involved in remediation as a component of their respective missions

PMP22 exon 4 deletion causes ER retention of PMP22 and a gain-of-function allele in CMT1E

OBJECTIVE: To determine whether predicted fork stalling and template switching (FoSTeS) during mitosis deletes exon 4 in peripheral myelin protein 22 KD (PMP22) and causes gain‐of‐function mutation associated with peripheral neuropathy in a family with Charcot–Marie–Tooth disease type 1E. METHODS: Two siblings previously reported to have genomic rearrangements predicted to involve exon 4 of PMP22 were evaluated clinically and by electrophysiology. Skin biopsies from the proband were studied by RT‐PCR to determine the effects of the exon 4 rearrangements on exon 4 mRNA expression in myelinating Schwann cells. Transient transfection studies with wild‐type and mutant PMP22 were performed in Cos7 and RT4 cells to determine the fate of the resultant mutant protein. RESULTS: Both affected siblings had a sensorimotor dysmyelinating neuropathy with severely slow nerve conduction velocities (<10 m/sec). RT‐PCR studies of Schwann cell RNA from one of the siblings demonstrated a complete in‐frame deletion of PMP22 exon 4 (PMP22Δ4). Transfection studies demonstrated that PMP22Δ4 protein is retained within the endoplasmic reticulum and not transported to the plasma membrane. CONCLUSIONS: Our results confirm that that FoSTeS‐mediated genomic rearrangement produced a deletion of exon 4 of PMP22, resulting in expression of both PMP22 mRNA and protein lacking this sequence. In addition, we provide experimental evidence for endoplasmic reticulum retention of the mutant protein suggesting a gain‐of‐function mutational mechanism consistent with the observed CMT1E in this family. PMP22Δ4 is another example of a mutated myelin protein that is misfolded and contributes to the pathogenesis of the neuropathy

Taxis Toward Hydrogen Gas by Methanococcus Maripaludis

Knowledge of taxis (directed swimming) in the Archaea is currently expanding through identification of novel receptors, effectors, and proteins involved in signal transduction to the flagellar motor. Although the ability for biological cells to sense and swim toward hydrogen gas has been hypothesized for many years, this capacity has yet to be observed and demonstrated. Here we show that the average swimming velocity increases in the direction of a source of hydrogen gas for the methanogen, Methanococcus maripaludis using a capillary assay with anoxic gas-phase control and time-lapse microscopy. The results indicate that a methanogen couples motility to hydrogen concentration sensing and is the first direct observation of hydrogenotaxis in any domain of life. Hydrogenotaxis represents a strategy that would impart a competitive advantage to motile microorganisms that compete for hydrogen gas and would impact the C, S and N cycles

Neural Responses to Naturalistic Clips of Behaving Animals Under Two Different Task Contexts

The human brain rapidly deploys semantic information during perception to facilitate our interaction with the world. These semantic representations are encoded in the activity of distributed populations of neurons (Haxby et al., 2001; McClelland and Rogers, 2003; Kriegeskorte et al., 2008b) and command widespread cortical real estate (Binder et al., 2009; Huth et al., 2012). The neural representation of a stimulus can be described as a location (i.e., response vector) in a high-dimensional neural representational space (Kriegeskorte and Kievit, 2013; Haxby et al., 2014). This resonates with behavioral and theoretical work describing mental representations of objects and actions as being organized in a multidimensional psychological space (Attneave, 1950; Shepard, 1958, 1987; Edelman, 1998; Gärdenfors and Warglien, 2012). Current applications of this framework to neural representation (e.g., Kriegeskorte et al., 2008b) often implicitly assume that these neural representational spaces are relatively fixed and context-invariant. In contrast, earlier work emphasized the importance of attention and task demands in actively reshaping representational space (Shepard, 1964; Tversky, 1977; Nosofsky, 1986; Kruschke, 1992). A growing body of work in both electrophysiology (e.g., Sigala and Logothetis, 2002; Sigala, 2004; Cohen and Maunsell, 2009; Reynolds and Heeger, 2009) and human neuroimaging (e.g., Hon et al., 2009; Jehee et al., 2011; Brouwer and Heeger, 2013; Çukur et al., 2013; Sprague and Serences, 2013; Harel et al., 2014; Erez and Duncan, 2015; Nastase et al., 2017) has suggested mechanisms by which behavioral goals dynamically alter neural representation

Multiple Subject Barycentric Discriminant Analysis (MUSUBADA): How to Assign Scans to Categories without Using Spatial Normalization

We present a new discriminant analysis (DA) method called Multiple Subject Barycentric Discriminant Analysis (MUSUBADA) suited for analyzing fMRI data because it handles datasets with multiple participants that each provides different number of variables (i.e., voxels) that are themselves grouped into regions of interest (ROIs). Like DA, MUSUBADA (1) assigns observations to predefined categories, (2) gives factorial maps displaying observations and categories, and (3) optimally assigns observations to categories. MUSUBADA handles cases with more variables than observations and can project portions of the data table (e.g., subtables, which can represent participants or ROIs) on the factorial maps. Therefore MUSUBADA can analyze datasets with different voxel numbers per participant and, so does not require spatial normalization. MUSUBADA statistical inferences are implemented with cross-validation techniques (e.g., jackknife and bootstrap), its performance is evaluated with confusion matrices (for fixed and random models) and represented with prediction, tolerance, and confidence intervals. We present an example where we predict the image categories (houses, shoes, chairs, and human, monkey, dog, faces,) of images watched by participants whose brains were scanned. This example corresponds to a DA question in which the data table is made of subtables (one per subject) and with more variables than observations

Open access publishing, article downloads, and citations: randomised controlled trial

Objective To measure the effect of free access to the scientific literature on article downloads and citations

Towards 50% Efficiency in Solar Cells

International audienc

Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models

Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models