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Light microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM), that uses popLight microscopy is a powerful single-cell technique that allows for quantitative spatial information at subcellular resolution. However, unlike flow cytometry and single-cell sequencing techniques, microscopy has issues achieving high-quality population-wide sample characterization while maintaining high resolution. Here, we present a general framework, data-driven microscopy (DDM), that uses pop

Brain activation mapping using fMRI data has been mostly focused on finding detections in gray matter. Activations in white matter are harder to detect due to anatomical differences between both tissue types, which are rarely acknowledged in experimental design. However, recent publications have started to show evidence for the possibility of detecting meaningful activations in white matter. The s

A design study, named $${\text {ESS}}\nu {\text {SB}}$$for European Spallation Source neutrino Super Beam, has been carried out during the years 2018–2022 of how the 5 MW proton linear accelerator of the European Spallation Source under construction in Lund, Sweden, can be used to produce the world’s most intense long-baseline neutrino beam. The high beam intensity will allow for measuring the neuA design study, named ESSνSB for European Spallation Source neutrino Super Beam, has been carried out during the years 2018–2022 of how the 5 MW proton linear accelerator of the European Spallation Source under construction in Lund, Sweden, can be used to produce the world’s most intense long-baseline neutrino beam. The high beam intensity will allow for measuring the neutrino oscillations n

Ett första steg i arbetet att uppskatta kolflödet mellan landbaserade ekosystem och atmosfären är att så precist som möjligt analysera kvantiteten ovanjordisk biomassa. LiDAR teknologi har i detta avseende visat sig vara ett värdefullt verktyg. Det överlöpande målet med denna studie är att utveckla en förenklad metod, baserad på fjärranalys, för att uppskatta ovanjordisk biomassa för individuella As a first step in the assessment of carbon flux between the terrestrial environment and the atmosphere it is important to accurately quantify the carbon stock of forest ecosystems. LiDAR technology, in this respect, has proved to be a valuable tool, able to provide accurate estimates of aboveground biomass (AGB). The overall goal of this study was to develop a simplified method for assessing AGB

Objective: The aims of this study are to validate infant vaccination data in the Swedish Vaccination Register (SVR) to the Swedish administrative coverage reports, and to assess differences in register-based vaccination coverage estimates between providers using different data reporting methods. Methods: The study population included all infants born in Sweden with a Swedish Personal Identity Numb

The resonance lines of helium in the Sun are several times stronger than expected, relative to lines of other ions. To explore the origins of this "helium enhancement,'' we have studied data from the SUMER, CDS, MDI, and EIT instruments on the Solar and Heliospheric Observatory (SOHO). Time series data obtained in a quiet region and a coronal hole indicate that the spatio-temporal properties of th

Mood is a pervasive affective state that influences cognition, including language processing, where it functions as part of the pragmatic context. When linguistic expressions carry emotional valence, mood may amplify or attenuate their perceived emotional nature. Motivated by inconsistent findings on the timing and nature of mood and valence interactions, we conducted an EEG study to investigate w

We evaluate the efficiency of the maximum likelihood estimator introduced by Aki (1965), using synthetic datasets exhibiting diverse but well defined properties. The deviation of the b-value estimation from its real value is quantified by Monte Carlo simulations as a function of catalogue features and data properties such as the sample size, the magnitude uncertainties distribution, the round-off

Drought is one of the most costly disasters worldwide. The complexity of nonlinear relationships between drought variables and drought severity during drought events poses significant challenges for accurate drought monitoring and prediction. Drought research in Morocco has been dominated by linear and conventional statistical methods, such as the pearson correlation. Although some earlier researc

Within the start-up ecosystem, young and small firms often face a significant challenge by acquiring external capital, the so-called funding gap. A trend throughout the recent years was, (and still is), the geographical clustering of financial institutions and investors in financial concentrated areas, which are referred to as financial centers. Those areas create huge disparities between differen

This paper describes a structural reliability analysis utilizing monitoring data in the ultimate limit state with consideration of the uncertainties of the monitoring procedure. For this purpose the uncertainties of the monitoring data are modeled utilizing a new framework for the determination of measurement uncertainties. The approach is based on a process equation and statistical models of obse

The microarray technique requires the organization and analysis of vast amounts of data. These data include information about the samples hybridized, the hybridization images and their extracted data matrices, and information about the physical array, the features and reporter molecules. We present a web-based customizable bioinformatics solution called BioArray Software Environment (BASE) for the

Astronomical spectroscopy has recently expanded into the near-infrared (nIR) wavelength region, raising the demands on atomic transition data. The interpretation of the observed spectra largely relies on theoretical results, and progress towards the production of accurate theoretical data must continuously be made. Spectrum calculations that target multiple atomic states at the same time are by no

Dynamic susceptibility-contrast (DSC) MRI requires deconvolution to retrieve the tissue residue function R(t) and the cerebral blood flow (CBF). In this study, deconvolution of time-series data was performed by wavelet-transform-based denoising combined with the Fourier transform (FT). Traditional FT-based deconvolution of noisy data requires frequency-domain filtering, often leading to excessive

This paper examines the sufficiency of a trading method based on singular value decomposition (SVD) of past stock prices. The SVD method is frequently used as a tool to reduce data noise, compress big-data, and analyse data components. Hence, the method is well suited to form a ground for a predictive tool of price developments. From the predicted pattern, a strategy was formed by construction of

Electroencephalography (EEG) is a medical technique for measuring brain activity through several channels connected to the scalp. Interpreting EEG data is a difficult problem because of the large amount of noise contained in the data. Using spectral methods on EEG data can improve the ability to interpret the data, especially using a time-frequency method called the scaled reassigned spectrogram w