Hybrid cluster-expansion and density-functional-theory approach for optical absorption in TiO2

A combined approach of first-principles density-functional calculations and the systematic cluster-expansion scheme is presented. The dipole, quadrupole, and Coulomb matrix elements obtained from ab initio calculations are used as an input to the microscopic many-body theory of the excitonic optical response. To demonstrate the hybrid approach for a nontrivial semiconductor system, the near-bandgap excitonic optical absorption of rutile TiO2 is computed. Comparison with experiments yields strong evidence that the observed near-bandgap features are due to a dipole-forbidden but quadrupole-allowed 1s-exciton state.Comment: 14 pages, 4 figure

Localization of a 64-kDa phosphoprotein in the lumen between the outer and inner envelopes of pea chloroplasts

The identification and localization of a marker protein for the intermembrane space between the outer and inner chloroplast envelopes is described. This 64-kDa protein is very rapidly labeled by [γ-32P]ATP at very low (30 nM) ATP concentrations and the phosphoryl group exhibits a high turnover rate. It was possible to establish the presence of the 64-kDa protein in this plastid compartment by using different chloroplast envelope separation and isolation techniques. In addition comparison of labeling kinetics by intact and hypotonically lysed pea chloroplasts support the localization of the 64-kDa protein in the intermembrane space. The 64-kDa protein was present and could be labeled in mixed envelope membranes isolated from hypotonically lysed plastids. Mixed envelope membranes incorporated high amounts of 32P from [γ-32P]ATP into the 64-kDa protein, whereas separated outer and inner envelope membranes did not show significant phosphorylation of this protein. Water/Triton X-114 phase partitioning demonstrated that the 64-kDa protein is a hydrophilic polypeptide. These findings suggest that the 64-kDa protein is a soluble protein trapped in the space between the inner and outer envelope membranes. After sonication of mixed envelope membranes, the 64-kDa protein was no longer present in the membrane fraction, but could be found in the supernatant after a 110000 × g centrifugation

The medical student

The Medical Student was published from 1888-1921 by the students of Boston University School of Medicine

Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques

To reduce damage caused by insect pests, farmers use insecticides to protect produce from crop pests. This practice leads to high synthetic chemical usage because a large portion of the applied insecticide does not reach its intended target; instead, it may affect non-target organisms and pollute the environment. One approach to mitigating this is through the selective application of insecticides to only those crop plants (or patches of plants) where the insect pests are located, avoiding non-targets and beneficials. The first step to achieve this is the identification of insects on plants and discrimination between pests and beneficial non-targets. However, detecting small-sized individual insect pests is challenging using image-based machine-learning techniques, especially in natural field settings. This paper proposes a method based on explainable artificial intelligence feature selection and machine learning to detect pests and beneficial insects in field crops. An insect-plant dataset reflecting real field conditions was created. It comprises two pest insects—the Colorado potato beetle (CPB, Leptinotarsa decemlineata) and green peach aphid (Myzus persicae)—and the beneficial seven-spot ladybird (Coccinella septempunctata). The specialist herbivore CPB was imaged only on potato plants (Solanum tuberosum) while green peach aphids and seven-spot ladybirds were imaged on three crops: potato, faba bean (Vicia faba), and sugar beet (Beta vulgaris subsp. vulgaris). This increased dataset diversity, broadening the potential application of the developed method for discriminating between pests and beneficial insects in several crops. The insects were imaged in both laboratory and outdoor settings. Using the GrabCut algorithm, regions of interest in the image were identified before shape, texture, and colour features were extracted from the segmented regions. The concept of explainable artificial intelligence was adopted by incorporating permutation feature importance ranking and Shapley Additive explanations values to identify the feature set that optimised a model's performance while reducing computational complexity. The proposed explainable artificial intelligence feature selection method was compared to conventional feature selection techniques, including mutual information, chi-square coefficient, maximal information coefficient, Fisher separation criterion and variance thresholding. Results showed improved accuracy (92.62 % Random forest, 90.16 % Support vector machine, 83.61 % K-nearest neighbours, and 81.97 % Naïve Bayes) and a reduction in the number of model parameters and memory usage (7.22 × 107 Random forest, 6.23 × 103 Support vector machine, 3.64 × 104 K-nearest neighbours and 1.88 × 102 Naïve Bayes) compared to using all features. Prediction and training times were also reduced by approximately half compared to conventional feature selection techniques. This demonstrates a simple machine learning algorithm combined with an ideal feature selection methodology can achieve robust performance comparable to other methods. With feature selection, model performance can be maximised and hardware requirements reduced, which are essential for real-world applications with resource constraints. This research offers a reliable approach towards automatic detection and discrimination of pest and beneficial insects which will facilitate the development of alternative pest control approaches and other targeted pest removal methods that are less harmful to the environment than the broad-scale application of synthetic insecticides

Economic-demographic interactions in long-run growth

Cliometrics confirms that Malthus’ model of the pre-industrial economy, in which increases in productivity raise population but higher population drives down wages, is a good description for much of demographic/economic history. A contributor to the Malthusian equilibrium was the Western European Marriage Pattern, the late age of female first marriage, which promised to retard the fall of living standards by restricting fertility. The demographic transition and the transition from Malthusian economies to modern economic growth attracted many Cliometric models surveyed here. A popular model component is that lower levels of mortality over many centuries increased the returns to, or preference for, human capital investment so that technical progress eventually accelerated. This initially boosted birth rates and population growth accelerated. Fertility decline was earliest and most striking in late eighteenth century France. By the 1830s the fall in French marital fertility is consistent with a response to the rising opportunity cost of children. The rest of Europe did not begin to follow until end of the nineteenth century. Interactions between the economy and migration have been modelled with Cliometric structures closely related to those of natural increase and the economy. Wages were driven up by emigration from Europe and reduced in the economies receiving immigrants

An AARS variant as the likely cause of Swedish type hereditary diffuse leukoencephalopathy with spheroids

Swedish type Hereditary Diffuse Leukoencephalopathy with Spheroids (HDLS-S) is a severe adult-onset leukoencephalopathy with the histopathological hallmark of neuraxonal degeneration with spheroids, described in a large family with a dominant inheritance pattern. The initial stage of the disease is dominated by frontal lobe symptoms that develop into a rapidly advancing encephalopathy with pyramidal, deep sensory, extrapyramidal and optic tract symptoms. Median survival is less than 10 years. Recently, pathogenic mutations in CSF1R were reported in a clinically and histologically similar leukoencephalopathy segregating in several families. Still, the cause of HDLS-S remained elusive since its initial description in 1984, with no CSF1R mutations identified in the family. Here we update the original findings associated with HDLS-S after a systematic and recent assessment of several family members. We also report the results from exome sequencing analyses indicating the p.Cys152Phe variant in the alanyl tRNA synthetase (AARS) gene as the probable cause of this disease. The variant affects an amino acid located in the aminoacylation domain of the protein and does not cause differences in splicing or expression in the brain. Brain pathology in one case after 10 years of disease duration showed the end stage of the disease to be characterized by widespread liquefaction of the white matter leaving only some macrophages and glial cells behind the centrifugally progressing front. These results point to AARS as a candidate gene for rapidly progressing adult-onset CSF1R-negative leukoencephalopathies

Molecular Logic for Cellular Specializations That Initiate the Auditory Parallel Processing Pathways

The cochlear nuclear complex (CN), the starting point for all central auditory processing, encompasses a suite of neuronal cell types highly specialized for neural coding of acoustic signals. However, the molecular logic governing these specializations remains unknown. By combining single-nucleus RNA sequencing and Patch-seq analysis, we reveal a set of transcriptionally distinct cell populations encompassing all previously observed types and discover multiple hitherto unknown subtypes with anatomical and physiological identity. The resulting comprehensive cell-type taxonomy reconciles anatomical position, morphological, physiological, and molecular criteria, enabling the determination of the molecular basis of the specialized cellular phenotypes in the CN. In particular, CN cell-type identity is encoded in a transcriptional architecture that orchestrates functionally congruent expression across a small set of gene families to customize projection patterns, input-output synaptic communication, and biophysical features required for encoding distinct aspects of acoustic signals. This high-resolution account of cellular heterogeneity from the molecular to the circuit level reveals the molecular logic driving cellular specializations, thus enabling the genetic dissection of auditory processing and hearing disorders with a high specificity

Genome-wide association study identifies multiple risk loci for renal cell carcinoma

Previous genome-wide association studies (GWAS) have identified six risk loci for renal cell carcinoma (RCC). We conducted a meta-analysis of two new scans of 5,198 cases and 7,331 controls together with four existing scans, totalling 10,784 cases and 20,406 controls of European ancestry. Twenty-four loci were tested in an additional 3,182 cases and 6,301 controls. We confirm the six known RCC risk loci and identify seven new loci at 1p32.3 (rs4381241, P=3.1 × 10−10), 3p22.1 (rs67311347, P=2.5 × 10−8), 3q26.2 (rs10936602, P=8.8 × 10−9), 8p21.3 (rs2241261, P=5.8 × 10−9), 10q24.33-q25.1 (rs11813268, P=3.9 × 10−8), 11q22.3 (rs74911261, P=2.1 × 10−10) and 14q24.2 (rs4903064, P=2.2 × 10−24). Expression quantitative trait analyses suggest plausible candidate genes at these regions that may contribute to RCC susceptibility

Monitoring of Collaborative Assembly Operations: An OEE Based Approach

International audienceIn this paper we present requirements and concept generation principles for performance monitoring of a collaborative assembly task. Life cycle aspects are considered and an Overall Equipment Efficiency (OEE) based monitoring scenario for a developed passive collaborative robot (COBOT) test system is presented. In this case main benefits of applying COBOT are expected to be: improved productivity, improved quality, reduced production cost and improved ergonomics. Since human and COBOT are working co-operatively human actions have also affects on process performance, i.e. OEE. However a human's and machines or a COBOT's efficiency are undistinguishable directly from OEE factors. It is possible to infer cause of lower efficiency from the variables from which OEE factors are calculated. One such variable is cycle time, which is used to define performance efficiency