Error estimates for density-functional theory predictions of surface energy and work function

Density-functional theory (DFT) predictions of materials properties are becoming ever more widespread. With increased use comes the demand for estimates of the accuracy of DFT results. In view of the importance of reliable surface properties, this work calculates surface energies and work functions for a large and diverse test set of crystalline solids. They are compared to experimental values by performing a linear regression, which results in a measure of the predictable and material-specific error of the theoretical result. Two of the most prevalent functionals, the local density approximation (LDA) and the Perdew-Burke-Ernzerhof parametrization of the generalized gradient approximation (PBE-GGA), are evaluated and compared. Both LDA and GGA-PBE are found to yield accurate work functions with error bars below 0.3 eV, rivaling the experimental precision. LDA also provides satisfactory estimates for the surface energy with error bars smaller than 10%, but GGA-PBE significantly underestimates the surface energy for materials with a large correlation energy

Passive case detection of malaria in Ratanakiri Province (Cambodia) to detect villages at higher risk for malaria

Additional file 9. Spatial clusters of villages with significantly higher risk of falciparum malaria cases from 2010 to 2014 in Ratanakiri Province. Only significant clusters are showed. RR: Relative risk. LLR: Log likelihood ratio

Electrical vestibular stimulation in humans. A narrative review

Background: In patients with bilateral vestibulopathy, the regular treatment options, such as medication, surgery, and/ or vestibular rehabilitation, do not always suffice. Therefore, the focus in this field of vestibular research shifted to electri- cal vestibular stimulation (EVS) and the development of a system capable of artificially restoring the vestibular func- tion. Key Message: Currently, three approaches are being investigated: vestibular co-stimulation with a cochlear im- plant (CI), EVS with a vestibular implant (VI), and galvanic vestibular stimulation (GVS). All three applications show promising results but due to conceptual differences and the experimental state, a consensus on which application is the most ideal for which type of patient is still missing. Summa- ry: Vestibular co-stimulation with a CI is based on “spread of excitation,” which is a phenomenon that occurs when the currents from the CI spread to the surrounding structures and stimulate them. It has been shown that CI activation can indeed result in stimulation of the vestibular structures. Therefore, the question was raised whether vestibular co- stimulation can be functionally used in patients with bilat- eral vestibulopathy. A more direct vestibular stimulation method can be accomplished by implantation and activa- tion of a VI. The concept of the VI is based on the technology and principles of the CI. Different VI prototypes are currently being evaluated regarding feasibility and functionality. So far, all of them were capable of activating different types of vestibular reflexes. A third stimulation method is GVS, which requires the use of surface electrodes instead of an implant- ed electrode array. However, as the currents are sent through the skull from one mastoid to the other, GVS is rather unspe- cific. It should be mentioned though, that the reported spread of excitation in both CI and VI use also seems to in- duce a more unspecific stimulation. Although all three ap- plications of EVS were shown to be effective, it has yet to be defined which option is more desirable based on applicabil- ity and efficiency. It is possible and even likely that there is a place for all three approaches, given the diversity of the pa- tient population who serves to gain from such technologies

Rodent abundance, stone bund density and its effects on crop damage in the Tigray highlands, Ethiopia

In areas of subsistence agriculture, a variety of soil conservation methods have been implemented in the last few decades to improve crop yields, however these can have unintended consequences such as providing habitat for rodent pests. We studied rodent population dynamics and estimated crop damage in high and low stone bund density fields for four cropping seasons in Tigray highlands, northern Ethiopia. Stone bunds are physical structures for soil and water conservation, and potentially habitat for rodents. We used a general model to relate the proportion of crop damage to rodent abundance, stone bund density and crop stages. Generally, rodent abundance remained relatively low during the study period, except during the fourth quarter of the 2010 cropping season. We found a positive correlation between rodent abundance and crop damage, and significant variation in rodent abundance and crop damage between high and low stone bund density fields. Furthermore, crop damage also varied significantly between crop stages. We concluded that Mastomys awashensis (Lavrenchenko, Likhnova and Baskevich 1998) and Arvicanthis dembeensis (Ruppel 1842) were the two most important crop pests in Tigray highlands causing significant damage. Fields with high stone bund density (~10m average distance apart) harbor more rodents and endure a significantly higher proportion of crop damage compared to fields with lower stone bund density (~15m average distance 4 apart). The fact that rodent abundances peaked during the reproductive stage of the crop and around harvest implies the need for management intervention before these crop stages are attained

Automated PGP9.5 immunofluorescence staining: a valuable tool in the assessment of small fiber neuropathy?

BACKGROUND: In this study we explored the possibility of automating the PGP9.5 immunofluorescence staining assay for the diagnosis of small fiber neuropathy using skin punch biopsies. The laboratory developed test (LDT) was subjected to a validation strategy as required by good laboratory practice guidelines and compared to the well-established gold standard method approved by the European Federation of Neurological Societies (EFNS). To facilitate automation, the use of thinner sections. (16 µm) was evaluated. Biopsies from previously published studies were used. The aim was to evaluate the diagnostic performance of the LDT compared to the gold standard. We focused on technical aspects to reach high-quality standardization of the PGP9.5 assay and finally evaluate its potential for use in large scale batch testing. RESULTS: We first studied linear nerve fiber densities in skin of healthy volunteers to establish reference ranges, and compared our LDT using the modifications to the EFNS counting rule to the gold standard in visualizing and quantifying the epidermal nerve fiber network. As the LDT requires the use of 16 µm tissue sections, a higher incidence of intra-epidermal nerve fiber fragments and a lower incidence of secondary branches were detected. Nevertheless, the LDT showed excellent concordance with the gold standard method. Next, the diagnostic performance and yield of the LDT were explored and challenged to the gold standard using skin punch biopsies of capsaicin treated subjects, and patients with diabetic polyneuropathy. The LDT reached good agreement with the gold standard in identifying small fiber neuropathy. The reduction of section thickness from 50 to 16 µm resulted in a significantly lower visualization of the three-dimensional epidermal nerve fiber network, as expected. However, the diagnostic performance of the LDT was adequate as characterized by a sensitivity and specificity of 80 and 64 %, respectively. CONCLUSIONS: This study, designed as a proof of principle, indicated that the LDT is an accurate, robust and automated assay, which adequately and reliably identifies patients presenting with small fiber neuropathy, and therefore has potential for use in large scale clinical studies

Compact representations of microstructure images using triplet networks

The microstructure of a material, typically characterized through a set of microscopy images of two-dimensional cross-sections, is a valuable source of information about the material and its properties. Every pixel of the image is a degree of freedom causing the dimensionality of the information space to be extremely high. This makes it difficult to recognize and extract all relevant information from the images. Human experts circumvent this by manually creating a lower-dimensional representation of the microstructure. However, the question of how a microstructure image can be best represented remains open. From the field of deep learning, we present triplet networks as a method to build highly compact representations of the microstructure, condensing the relevant information into a much smaller number of dimensions. We demonstrate that these representations can be created even with a limited amount of example images, and that they are able to distinguish between visually very similar microstructures. We discuss the interpretability and generalization of the representations. Having compact microstructure representations, it becomes easier to establish processing-structure-property links that are key to rational materials design