Loss-of-function mutations in Lysyl-tRNA synthetase cause various leukoencephalopathy phenotypes

Objective: To expand the clinical spectrum of lysyl-tRNA synthetase (KARS) gene–related diseases, which so far includes Charcot-Marie-Tooth disease, congenital visual impairment and microcephaly, and nonsyndromic hearing impairment. Methods: Whole-exome sequencing was performed on index patients from 4 unrelated families with leukoencephalopathy. Candidate pathogenic variants and their cosegregation were confirmed by Sanger sequencing. Effects of mutations on KARS protein function were examined by aminoacylation assays and yeast complementation assays. Results: Common clinical features of the patients in this study included impaired cognitive ability, seizure, hypotonia, ataxia, and abnormal brain imaging, suggesting that the CNS involvement is the main clinical presentation. Six previously unreported and 1 known KARS mutations were identified and cosegregated in these families. Two patients are compound heterozygous for missense mutations, 1 patient is homozygous for a missense mutation, and 1 patient harbored an insertion mutation and a missense mutation. Functional and structural analyses revealed that these mutations impair aminoacylation activity of lysyl-tRNA synthetase, indicating that de- fective KARS function is responsible for the phenotypes in these individuals. Conclusions: Our results demonstrate that patients with loss-of-function KARS mutations can manifest CNS disorders, thus broadening the phenotypic spectrum associated with KARS-related disease

The application of remote sensing to resource management and environmental quality programs in Kansas

The activities of the Kansas Applied Remote Sensing (KARS) Program during the period April 1, 1982 through Marsh 31, 1983 are described. The most important work revolved around the Kansas Interagency Task Force on Applied Remote Sensing and its efforts to establish an operational service oriented remote sensing program in Kansas state government. Concomitant with this work was the upgrading of KARS capabilities to process data for state agencies through the vehicle of a low cost digital data processing system. The KARS Program continued to take an active role in irrigation mapping. KARS is now integrating data acquired through analysis of LANDSAT into geographic information systems designed for evaluating groundwater resources. KARS also continues to work at the national level on the national inventory of state natural resources information systems

Distinct subunits in heteromeric kainate receptors mediate ionotropic and metabotropic function at hippocampal mossy fiber synapses

Heteromeric kainate receptors (KARs) containing both glutamate receptor 6 (GluR6) and KA2 subunits are involved in KAR-mediated EPSCs at mossy fiber synapses in CA3 pyramidal cells. We report that endogenous glutamate, by activating KARs, reversibly inhibits the slow Ca2+-activated K+ current I(sAHP) and increases neuronal excitability through a G-protein-coupled mechanism. Using KAR knockout mice, we show that KA2 is essential for the inhibition of I(sAHP) in CA3 pyramidal cells by low nanomolar concentrations of kainate, in addition to GluR6. In GluR6(-/-) mice, both ionotropic synaptic transmission and inhibition of I(sAHP) by endogenous glutamate released from mossy fibers was lost. In contrast, inhibition of I(sAHP) was absent in KA2(-/-) mice despite the preservation of KAR-mediated EPSCs. These data indicate that the metabotropic action of KARs did not rely on the activation of a KAR-mediated inward current. Biochemical analysis of knock-out mice revealed that KA2 was required for the interaction of KARs with Galpha(q/11)-proteins known to be involved in I(sAHP) modulation. Finally, the ionotropic and metabotropic actions of KARs at mossy fiber synapses were differentially sensitive to the competitive glutamate receptor ligands kainate (5 nM) and kynurenate (1 mM). We propose a model in which KARs could operate in two modes at mossy fiber synapses: through a direct ionotropic action of GluR6, and through an indirect G-protein-coupled mechanism requiring the binding of glutamate to KA2

The application of remote sensing to resource management and environmental quality programs in Kansas

There are no author-identified significant results in this report

The application of remote sensing to resource management and environmental quality programs in Kansas

Activities of the Kansas Applied Remote Sensing Program (KARS) designed to establish interactions on cooperative projects with decision makers in Kansas agencies in the development and application of remote sensing procedures are reported. Cooperative demonstration projects undertaken with several different agencies involved three principal areas of effort: Wildlife Habitat and Environmental Analysis; Urban and Regional Analysis; Agricultural and Rural Analysis. These projects were designed to concentrate remote sensing concepts and methodologies on existing agency problems to insure the continued relevancy of the program and maximize the possibility for immediate operational use. Completed projects are briefly discussed

Deriving item features relevance from collaborative domain knowledge

An Item based recommender system works by computing a similarity between items, which can exploit past user interactions (collaborative filtering) or item features (content based filtering). Collaborative algorithms have been proven to achieve better recommendation quality then content based algorithms in a variety of scenarios, being more effective in modeling user behaviour. However, they can not be applied when items have no interactions at all, i.e. cold start items. Content based algorithms, which are applicable to cold start items, often require a lot of feature engineering in order to generate useful recommendations. This issue is specifically relevant as the content descriptors become large and heterogeneous. The focus of this paper is on how to use a collaborative models domain-specific knowledge to build a wrapper feature weighting method which embeds collaborative knowledge in a content based algorithm. We present a comparative study for different state of the art algorithms and present a more general model. This machine learning approach to feature weighting shows promising results and high flexibility