A closer look at ARSA activity in a patient with metachromatic leukodystrophy.

Metachromatic leukodystrophy (MLD) is an autosomal recessive lysosomal storage disease mainly caused by a deficiency of arylsulfatase A activity. The typical clinical course of patients with the late infantile form includes a regression in motor skills with progression to dysphagia, seizures, hypotonia and death. We present a case of a 4-year-old female with rapidly progressive developmental regression with loss of motor milestones, spasticity and dysphagia. MRI showed volume loss and markedly abnormal deep white matter. Enzymatic testing in one laboratory showed arylsulfatase A activity in their normal range. However, extraction of urine showed a large increase in sulfatide excretion in a second laboratory. Measurement of arylsulfatase A in that laboratory showed a partial decrease in arylsulfatase A activity measured under typical conditions (about 37% of the normal mean). When the concentration of substrate in the assay was lowered to one quarter of that normally used, this individual had activity \u3c10% of controls. The patient was found to be homozygous for an unusual missense mutation in the arylsulfatase A gene confirming the diagnosis of MLD. This case illustrates the importance of careful biochemical and molecular testing for MLD if there is suspicion of this diagnosis

Portable X‐ray fluorescence spectroscopy as a tool for cyclostratigraphy

Cyclostratigraphic studies are used to create relative and high‐resolution timescales for sedimentary successions based on identification of regular cycles in climate proxy data. This method typically requires the construction of long high‐resolution datasets. In this study, we have demonstrated the efficacy of portable X‐ray fluorescence spectroscopy (pXRF) as a non‐destructive method of generating compositional data for cyclostratigraphy. The rapidity (100 samples per day) and low cost of pXRF measurements provide advantages over relatively time‐consuming and costly elemental and stable isotopic measurements that are commonly used for cyclostratigraphy. The non‐destructive nature of pXRF also allows other geochemical analyses on the same samples. We present an optimized protocol for pXRF elemental concentration measurement in powdered rocks. The efficacy of this protocol for cyclostratigraphy is demonstrated through analysis of 360 Toarcian mudrock samples from North Yorkshire, UK, that were previously shown to exhibit astronomical forcing of [CaCO3], [S] and δ13Corg. Our study is the first to statistically compare the cyclostratigraphic results of pXRF analysis with more established combustion analysis. There are strong linear correlations of pXRF [Ca] with dry combustion elemental analyzer [CaCO3] (r2=0.7616), and pXRF [S] and [Fe] with dry combustion elemental analyzer [S] (r2=0.9632 and r2=0.9274 respectively). Spectral and cross‐spectral analysis demonstrates that cyclicity previously recognized in [S], significant above the 99.99% confidence level, is present above the 99.92% and 99.99% confidence levels in pXRF [S] and [Fe] data respectively. Cyclicity present in [CaCO3] data above the 99.96% confidence level is also present in pXRF [Ca] above the 98.12% confidence level

IMAGE ACQUISTION AND PROCESSING FOR ENVIRONMENTAL SITE ASSESSMENTS

Described are systems, methods, computer programs, and user interfaces for image location, acquisition, analysis, and data correlation. Results obtained via image analysis are correlated to non-spatial information. For example, images of regions of interest of the Earth are used for ESAs. Imagery and derived information delivered via the systems and methods described herein have the potential to provide the timely, accurate, and objective information about distributed global locations a user needs to identify current or potential risk and hazards associated with interaction between commercial activities and the environment. Although ESA information is formed from the integration of a broad variety of data sources, many of which involve ground-based scientific measurements, the systems and methods of the present disclosure can add value by contributing objective, quantitative information about physically observable materials from a remote perspective. Keywords associated with the present disclosure include: image acquisition, satellite imagery drone imagery, environmental site assessments, environmental contamination, vegetative land cover

IMAGE ACQUISTION AND PROCESSING FOR FINANCIAL DUE DILIGENCE

Described are systems, methods, computer programs, and user interfaces for image location, acquisition, analysis, and data correlation. Results obtained via image analysis are correlated to non-spatial information. For example, images of regions of interest of the Earth are used for financial due diligence. Due diligence (DD) generally refers to the investigative process by which an organization mitigates risk prior to engaging in a business or contractual transaction. Specifically for those in the financial world, DD entails the process by which an investor thoroughly evaluates a target company and its assets prior to investment or acquisition. Although many aspects of the due diligence process relate to the collection of financial and legal information, the systems and methods described herein can provide additional critical insights an investor or research analyst needs to qualify the physical assets and macroeconomic dynamics associated with a targeted deal. Keywords associated with the present disclosure include: image acquisition, satellite imagery drone imagery, financial due diligence, due diligence

IMAGE ACQUISTION AND PROCESSING FOR COMPETITIVE INTELLIGENCE

Described are systems, methods, computer programs, and user interfaces for image location, acquisition, analysis, and data correlation. Results obtained via image analysis are correlated to non-spatial information. For example, images of regions of interest of the Earth are used for competitive intelligence. Competitive Intelligence (CI) entails the collection and interpretation of information about products, customers, and competitors to gain perspective on changing market conditions and inform the corporate strategic decision making process. The sources of relevant information that support competitive intelligence objectives are typically difficult and time consuming to obtain and produce, particularly outside the developed world. Imagery and derived information delivered as described herein has the potential to address existing data access and timeliness challenges, and provide the information about global locations an enterprise needs to understand the competitive marketplace and develop business strategies accordingly. Keywords associated with the present disclosure include: image acquisition, satellite imagery drone imagery, competitive intelligence

System using leo satellites for centimeter-level navigation

Disclosed herein is a system for rapidly resolving position with centimeter-level accuracy for a mobile or stationary receiver [4]. This is achieved by estimating a set of parameters that are related to the integer cycle ambiguities which arise in tracking the carrier phase of satellite downlinks [5,6]. In the preferred embodiment, the technique involves a navigation receiver [4] simultaneously tracking transmissions [6] from Low Earth Orbit Satellites (LEOS) [2] together with transmissions [5] from GPS navigation satellites [1]. The rapid change in the line-of-sight vectors from the receiver [4] to the LEO signal sources [2], due to the orbital motion of the LEOS, enables the resolution with integrity of the integer cycle ambiguities of the GPS signals [5] as well as parameters related to the integer cycle ambiguity on the LEOS signals [6]. These parameters, once identified, enable real-time centimeter-level positioning of the receiver [4]. In order to achieve high-precision position estimates without the use of specialized electronics such as atomic clocks, the technique accounts for instabilities in the crystal oscillators driving the satellite transmitters, as well as those in the reference [3] and user [4] receivers. In addition, the algorithm accommodates as well as to LEOS that receive signals from ground-based transmitters, then re-transmit frequency-converted signals to the ground

Prediction and Realisation of Conversational Characteristics by Utilising Spontaneous Speech for Unit Selection

Unit selection speech synthesis has reached high levels of naturalness and intelligibility for neutral read aloud speech. However, synthetic speech generated using neutral read aloud data lacks all the attitude, intention and spontaneity associated with everyday conversations. Unit selection is heavily data dependent and thus in order to simulate human conversational speech, or create synthetic voices for believable virtual characters, we need to utilise speech data with examples of how people talk rather than how people read. In this paper we included carefully selected utterances from spontaneous conversational speech in a unit selection voice. Using this voice and by automatically predicting type and placement of lexical fillers and filled pauses we can synthesise utterances with conversational characteristics. A perceptual listening test showed that it is possible to make synthetic speech sound more conversational without degrading naturalness

GPCRTree: online hierarchical classification of GPCR function

Background: G protein-coupled receptors (GPCRs) play important physiological roles transducing extracellular signals into intracellular responses. Approximately 50% of all marketed drugs target a GPCR. There remains considerable interest in effectively predicting the function of a GPCR from its primary sequence. Findings: Using techniques drawn from data mining and proteochemometrics, an alignment-free approach to GPCR classification has been devised. It uses a simple representation of a protein's physical properties. GPCRTree, a publicly-available internet server, implements an algorithm that classifies GPCRs at the class, sub-family and sub-subfamily level. Conclusion: A selective top-down classifier was developed which assigns sequences within a GPCR hierarchy. Compared to other publicly available GPCR prediction servers, GPCRTree is considerably more accurate at every level of classification. The server has been available online since March 2008 at URL: http://igrid-ext.cryst.bbk.ac.uk/gpcrtree