Effect of natural abiotic soil vibrations, rainfall and wind on anuran calling behavior: a test with captive-bred midwife toads (Alytes obstetricans)

Anurans are known to detect vibrations, but few studies explore relationships between vibrations and resultant behaviors. We studied the reaction of calling captive-bred male midwife toads (Alytes obstetricans) to the randomized playback of a vibrational crescendo stimulus train. We considered two sources of natural abiotic vibrational stimuli: rainfall and wind. Rainfall was expected to induce calling and wind was expected to inhibit it. Playback experiments with two synthetic tones (200 Hz and 300 Hz) tested the sensitivity to pure tones and could possibly reveal a hearing sensitivity trend between these frequencies. The toads did not increase call rate in response to rainfall vibrations and only one of the five wind stimulus levels caused a significant decrease in call rate. This limited response could be explained, because the tested toads came from a captive population, where emergence may not be mediated by rainfall vibrations. We found that A. obstetricans is highly sensitive to very low frequencies, which could explain the sensitivity observed to vibrational stimuli. Playback of a random crescendo stimulus train proves to be a valid approach for addressing behavioral questions. However, the use of a captive population may have been a limitation in the clarity of the results

Testing the applicability and performance of Auto ML for potential applications in diagnostic neuroradiology.

To investigate the applicability and performance of automated machine learning (AutoML) for potential applications in diagnostic neuroradiology. In the medical sector, there is a rapidly growing demand for machine learning methods, but only a limited number of corresponding experts. The comparatively simple handling of AutoML should enable even non-experts to develop adequate machine learning models with manageable effort. We aim to investigate the feasibility as well as the advantages and disadvantages of developing AutoML models compared to developing conventional machine learning models. We discuss the results in relation to a concrete example of a medical prediction application. In this retrospective IRB-approved study, a cohort of 107 patients who underwent gross total meningioma resection and a second cohort of 31 patients who underwent subtotal resection were included. Image segmentation of the contrast enhancing parts of the tumor was performed semi-automatically using the open-source software platform 3D Slicer. A total of 107 radiomic features were extracted by hand-delineated regions of interest from the pre-treatment MRI images of each patient. Within the AutoML approach, 20 different machine learning algorithms were trained and tested simultaneously. For comparison, a neural network and different conventional machine learning algorithms were trained and tested. With respect to the exemplary medical prediction application used in this study to evaluate the performance of Auto ML, namely the pre-treatment prediction of the achievable resection status of meningioma, AutoML achieved remarkable performance nearly equivalent to that of a feed-forward neural network with a single hidden layer. However, in the clinical case study considered here, logistic regression outperformed the AutoML algorithm. Using independent test data, we observed the following classification results (AutoML/neural network/logistic regression): mean area under the curve = 0.849/0.879/0.900, mean accuracy = 0.821/0.839/0.881, mean kappa = 0.465/0.491/0.644, mean sensitivity = 0.578/0.577/0.692 and mean specificity = 0.891/0.914/0.936. The results obtained with AutoML are therefore very promising. However, the AutoML models in our study did not yet show the corresponding performance of the best models obtained with conventional machine learning methods. While AutoML may facilitate and simplify the task of training and testing machine learning algorithms as applied in the field of neuroradiology and medical imaging, a considerable amount of expert knowledge may still be needed to develop models with the highest possible discriminatory power for diagnostic neuroradiology

Sensory profile of kombucha brewed with New Zealand ingredients by focus group and word clouds

Kombucha is a yeast and bacterially fermented tea that is often described as having an acetic, fruity and sour flavour. There is a particular lack of sensory research around the use of Kombucha with additional ingredients such as those from the pepper family, or with hops. The goal of this project was to obtain a sensory profile of Kombucha beverages with a range of different ingredients, particularly of a novel Kombucha made with only Kawakawa (Piper excelsum) leaves. Other samples included hops and black pepper. Instrumental data were collected for all the Kombucha samples, and a sensory focus group of eight semi-trained panellists were set up to create a sensory profile of four products. Commercially available Kombucha, along with reference training samples were used to train the panel. Kawakawa Kombucha was found to be the sourest of the four samples and was described as having the bitterest aftertaste. The instrumental results showed that the Kawakawa Kombucha had the highest titratable acidity (1.55 vs. 1.21–1.42 mL) as well as the highest alcohol percentage (0.40 vs. 0.15–0.30%). The hops sample had the highest pH (3.72 vs. 3.49–3.54), with the lowest titratable acidity (1.21), and, from a basic poll, was the most liked of the samples. Each Kombucha had its own unique set of sensory descriptors with particular emphasis on the Kawakawa product, having unique mouthfeel descriptors as a result of some of the compounds found in Kawakawa. This research has led to a few areas that could be further studied, such as the characteristics of the Piperaceae family under fermentation and the different effects or the foaminess of the Kawakawa Kombucha, which is not fully explained

Plasma extracellular vesicle tau and TDP-43 as diagnostic biomarkers in FTD and ALS

Minimally invasive biomarkers are urgently needed to detect molecular pathology in frontotemporal dementia (FTD) and amyotrophic lateral sclerosis (ALS). Here, we show that plasma extracellular vesicles (EVs) contain quantifiable amounts of TDP-43 and full-length tau, which allow the quantification of 3-repeat (3R) and 4-repeat (4R) tau isoforms. Plasma EV TDP-43 levels and EV 3R/4R tau ratios were determined in a cohort of 704 patients, including 37 genetically and 31 neuropathologically proven cases. Diagnostic groups comprised patients with TDP-43 proteinopathy ALS, 4R tauopathy progressive supranuclear palsy, behavior variant FTD (bvFTD) as a group with either tau or TDP-43 pathology, and healthy controls. EV tau ratios were low in progressive supranuclear palsy and high in bvFTD with tau pathology. EV TDP-43 levels were high in ALS and in bvFTD with TDP-43 pathology. Both markers discriminated between the diagnostic groups with area under the curve values &gt;0.9, and between TDP-43 and tau pathology in bvFTD. Both markers strongly correlated with neurodegeneration, and clinical and neuropsychological markers of disease severity. Findings were replicated in an independent validation cohort of 292 patients including 34 genetically confirmed cases. Taken together, the combination of EV TDP-43 levels and EV 3R/4R tau ratios may aid the molecular diagnosis of FTD, FTD spectrum disorders and ALS, providing a potential biomarker to monitor disease progression and target engagement in clinical trials.</p

Optimasi Portofolio Resiko Menggunakan Model Markowitz MVO Dikaitkan dengan Keterbatasan Manusia dalam Memprediksi Masa Depan dalam Perspektif Al-Qur`an

Risk portfolio on modern finance has become increasingly technical, requiring the use of sophisticated mathematical tools in both research and practice. Since companies cannot insure themselves completely against risk, as human incompetence in predicting the future precisely that written in Al-Quran surah Luqman verse 34, they have to manage it to yield an optimal portfolio. The objective here is to minimize the variance among all portfolios, or alternatively, to maximize expected return among all portfolios that has at least a certain expected return. Furthermore, this study focuses on optimizing risk portfolio so called Markowitz MVO (Mean-Variance Optimization). Some theoretical frameworks for analysis are arithmetic mean, geometric mean, variance, covariance, linear programming, and quadratic programming. Moreover, finding a minimum variance portfolio produces a convex quadratic programming, that is minimizing the objective function ðð¥with constraintsð ð 𥠥 ðandð´ð¥ = ð. The outcome of this research is the solution of optimal risk portofolio in some investments that could be finished smoothly using MATLAB R2007b software together with its graphic analysis

Differential cross section measurements for the production of a W boson in association with jets in proton–proton collisions at √s = 7 TeV

Measurements are reported of differential cross sections for the production of a W boson, which decays into a muon and a neutrino, in association with jets, as a function of several variables, including the transverse momenta (pT) and pseudorapidities of the four leading jets, the scalar sum of jet transverse momenta (HT), and the difference in azimuthal angle between the directions of each jet and the muon. The data sample of pp collisions at a centre-of-mass energy of 7 TeV was collected with the CMS detector at the LHC and corresponds to an integrated luminosity of 5.0 fb[superscript −1]. The measured cross sections are compared to predictions from Monte Carlo generators, MadGraph + pythia and sherpa, and to next-to-leading-order calculations from BlackHat + sherpa. The differential cross sections are found to be in agreement with the predictions, apart from the pT distributions of the leading jets at high pT values, the distributions of the HT at high-HT and low jet multiplicity, and the distribution of the difference in azimuthal angle between the leading jet and the muon at low values.United States. Dept. of EnergyNational Science Foundation (U.S.)Alfred P. Sloan Foundatio