Early Tithonian Saturnalidae (Radiolaria) from the Solnhofen area (Southern Franconian Alb, Southern Germany)

In order to complete the study of the very rich early Tithonian (Hybonoticeras hybonotum Zone) radiolarian fauna from the Muhlheim Member of the Mornsheim Formation outcropping in the Solnhofen area, the taxa of the family Saturnalidae are described. Although rather rare, the Saturnalidae of this member contain 14 species, ten of which are new. These species belong to four genera, one of which is new (Moebicircus n. gen.), and two subfamilies (Hexasaturnalinae and Saturnalinae). The taxonomy at generic level of these late Jurassic radiolarians is founded on the basis of the position of the blades along the ring and number and morphology of the spines. Type of spines (simple or forked) has either species level value or none, depending on species. Special attention was given to anomalies, which sometimes are rather frequent, since they can give information of paleobiological and paleoecological orders. Among them frequent cases of open ring and additional spines with Dicerosaturnalis and Siamese twins skeletons with Spongosaturninus and Dicerosaturnalis are to be noted. The authors hope that this new taxonomy will give a better image of the evolution and radiation of the Saturnalidae during the Tithonian.

Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Acute painful diabetic neuropathy: an uncommon, remittent type of acute distal small fibre neuropathy.

INTRODUCTION: Acute painful diabetic neuropathy (APDN) is a distinctive diabetic polyneuropathy and consists of two subtypes: treatment-induced neuropathy (TIN) and diabetic neuropathic cachexia (DNC). The characteristics of APDN are (1.) the small-fibre involvement, (2.) occurrence paradoxically after short-term achievement of good glycaemia control, (3.) intense pain sensation and (4.) eventual recovery. In the face of current recommendations to achieve quickly glycaemic targets, it appears necessary to recognise and understand this neuropathy. METHODS AND RESULTS: Over 2009 to 2012, we reported four cases of APDN. Four patients (three males and one female) were identified and had a mean age at onset of TIN of 47.7 years (±6.99 years). Mean baseline HbA1c was 14.2% (±1.42) and 7.0% (±3.60) after treatment. Mean estimated time to correct HbA1c was 4.5 months (±3.82 months). Three patients presented with a mean time to symptom resolution of 12.7 months (±1.15 months). One patient had an initial normal electroneuromyogram (ENMG) despite the presence of neuropathic symptoms, and a second abnormal ENMG showing axonal and myelin neuropathy. One patient had a peroneal nerve biopsy showing loss of large myelinated fibres as well as unmyelinated fibres, and signs of microangiopathy. CONCLUSIONS: According to the current recommendations of promptly achieving glycaemic targets, it appears necessary to recognise and understand this neuropathy. Based on our observations and data from the literature we propose an algorithmic approach for differential diagnosis and therapeutic management of APDN patients.