FDI and Performance: An Investigation of EU Manufacturing Industries

This paper investigates the impact of foreign direct investment (FDI) on the performance of companies in low- and high-technologically intensive industries in the manufacturing sector in EU member countries, using datasets covering the period between 2010 and 2020. The industries were selected according to the EU High-tech classification of manufacturing industries based on NACE Rev.2 at 2-digit codes. The performance of companies in our study was measured based on turnover. We used a set of independent variables, such as inward and outward FDI stocks, imports and exports of goods and services, gross domestic product (GDP) at market prices, real effective exchange rate, gross domestic expenditure on research and development, and gross fixed capital formation. On applying the panel data methodology, our findings indicated that inward FDI stocks, imports of goods and services, and real effective exchange rates have a significant impact on the performance of companies in high-technologically intensive industries. For low-tech companies, exports of goods and services are important driving factors behind their performance. Keywords: high-tech industries, low-tech industries, foreign direct investment, performance, European Unio

Multivariate MR Biomarkers Better Predict Cognitive Dysfunction in Mouse Models of Alzheimers Disease

To understand multifactorial conditions such as Alzheimers disease (AD) we need brain signatures that predict the impact of multiple pathologies and their interactions. To help uncover the relationships between brain circuits and cognitive markers we have used mouse models that represent, at least in part, the complex interactions altered in AD. In particular, we aimed to understand the relationship between vulnerable brain circuits and memory deficits measured in the Morris water maze, and we tested several predictive modeling approaches. We used in vivo manganese enhanced MRI voxel based analyses to reveal regional differences in volume (morphometry), signal intensity (activity), and magnetic susceptibility (iron deposition, demyelination). These regions included the hippocampus, olfactory areas, entorhinal cortex and cerebellum. The image based properties of these regions were used to predict spatial memory. We next used eigenanatomy, which reduces dimensionality to produce sets of regions that explain the variance in the data. For each imaging marker, eigenanatomy revealed networks underpinning a range of cognitive functions including memory, motor function, and associative learning. Finally, the integration of multivariate markers in a supervised sparse canonical correlation approach outperformed single predictor models and had significant correlates to spatial memory. Among a priori selected regions, the fornix also provided good predictors, raising the possibility of investigating how disease propagation within brain networks leads to cognitive deterioration. Our results support that modeling approaches integrating multivariate imaging markers provide sensitive predictors of AD-like behaviors. Such strategies for mapping brain circuits responsible for behaviors may help in the future predict disease progression, or response to interventions.Comment: 23 pages, 3 Tables, 6 Figures; submitted for publicatio

The organization of frequency and binaural cues in the gerbil inferior colliculus: GRAÑA et al.

The inferior colliculus (IC) is the common target of separate pathways that transmit different types of auditory information. Beyond tonotopy, little is known about the organization of response properties within the 3-dimensional layout of the auditory midbrain in most species. Through study of interaural time difference (ITD) processing, the functional properties of neurons can be readily characterized and related to specific pathways. To characterize the representation of ITDs relative to the frequency and hodological organization of the IC, the properties of neurons were recorded and the sites recovered histologically. Subdivisions of the IC were identified based on cytochrome oxidase (CO) histochemistry. The results were plotted within a framework formed by an MRI atlas of the gerbil brain. The central nucleus was composed of two parts, and lateral and dorsal cortical areas were identified. The lateral part of the central nucleus had the highest CO activity in the IC and a high proportion of neurons sensitive to ITDs. The medial portion had lower CO activity and fewer ITD-sensitive neurons. A common tonotopy with a dorsolateral to ventromedial gradient of low to high frequencies spanned the two regions. The distribution of physiological responses was in close agreement with known patterns of ascending inputs. An understanding of the 3-dimensional organization of the IC is needed to specify how the single tonotopic representation in the IC central nucleus leads to the multiple tonotopic representations in core areas of the auditory cortex

Learning sources of variability from high-dimensional observational studies

Causal inference studies whether the presence of a variable influences an observed outcome. As measured by quantities such as the "average treatment effect," this paradigm is employed across numerous biological fields, from vaccine and drug development to policy interventions. Unfortunately, the majority of these methods are often limited to univariate outcomes. Our work generalizes causal estimands to outcomes with any number of dimensions or any measurable space, and formulates traditional causal estimands for nominal variables as causal discrepancy tests. We propose a simple technique for adjusting universally consistent conditional independence tests and prove that these tests are universally consistent causal discrepancy tests. Numerical experiments illustrate that our method, Causal CDcorr, leads to improvements in both finite sample validity and power when compared to existing strategies. Our methods are all open source and available at github.com/ebridge2/cdcorr

Investigating the tradeoffs between spatial resolution and diffusion sampling for brain mapping with diffusion tractography: Time well spent?: Spatial vs. Q-Space Sampling for Tractography

Interest in mapping white matter pathways in the brain has peaked with the recognition that altered brain connectivity may contribute to a variety of neurologic and psychiatric diseases. Diffusion tractography has emerged as a popular method for postmortem brain mapping initiatives, including the ex-vivo component of the human connectome project, yet it remains unclear to what extent computer-generated tracks fully reflect the actual underlying anatomy. Of particular concern is the fact that diffusion tractography results vary widely depending on the choice of acquisition protocol. The two major acquisition variables that consume scan time, spatial resolution, and diffusion sampling, can each have profound effects on the resulting tractography. In this analysis we determined the effects of the temporal tradeoff between spatial resolution and diffusion sampling on tractography in the ex-vivo rhesus macaque brain, a close primate model for the human brain. We used the wealth of autoradiography-based connectivity data available for the rhesus macaque brain to assess the anatomic accuracy of six time-matched diffusion acquisition protocols with varying balance between spatial and diffusion sampling. We show that tractography results vary greatly, even when the subject and the total acquisition time are held constant. Further, we found that focusing on either spatial resolution or diffusion sampling at the expense of the other is counterproductive. A balanced consideration of both sampling domains produces the most anatomically accurate and consistent results

A diffusion tensor MRI atlas of the postmortem rhesus macaque brain

The rhesus macaque (Macaca mulatta) is the most widely used nonhuman primate for modeling the structure and function of the brain. Brain atlases, and particularly those based on magnetic resonance imaging (MRI), have become important tools for understanding normal brain structure, and for identifying structural abnormalities resulting from disease states, exposures, and/or aging. Diffusion tensor imaging (DTI) -based MRI brain atlases are widely used in both human and macaque brain imaging studies because of the unique contrasts, quantitative diffusion metrics, and diffusion tractography that they can provide. Previous MRI and DTI atlases of the rhesus brain have been limited by low contrast and/or low spatial resolution imaging. Here we present a microscopic resolution MRI/DTI atlas of the rhesus brain based on 10 postmortem brain specimens. The atlas includes both structural MRI and DTI image data, a detailed three-dimensional segmentation of 241 anatomic structures, diffusion tractography, cortical thickness estimates, and maps of anatomic variability amongst atlas specimens. This atlas incorporates many useful features from previous work, including anatomic label nomenclature and ontology, data orientation, and stereotaxic reference frame, and further extends prior analyses with the inclusion of high-resolution multi-contrast image data