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Background: Machine learning algorithms for bacterial strain identification using Raman spectroscopy have been widely used in microbiology. During the training phase, existing datasets are augmented and used to optimize model architecture and hyperparameters. After training, it is presumed that the models have reached their peak performance and are used for inference without being further enhanced. Our methodology combines Monte Carlo Dropout (MCD) with convolutional neural networks (CNNs) by utilizing dropout during the inference phase, which enables to measure the model uncertainty, a critical but often ignored aspect in deep learning models., Results: We categorize unseen input data into two subsets based on the uncertainty of their prediction by employing MCD and defining the threshold using the Gaussian Mixture Model (GMM). The final prediction is obtained on the subset of testing data that exhibits lower model uncertainty, thereby enhancing the reliability of the results. To validate our method, we applied it to two Raman spectra datasets. As a result, we have observed an increase in accuracy of 9 % for Dataset 1 (from 83.10 % to 92.10 %) and 12.82 % for Dataset 2 (from 83.86 % to 96.68 %). These improvements were observed within specific subsets of the data: 826 out of 1206 spectra in Dataset 1 and 1700 out of 3000 spectra in Dataset 2. This demonstrates the effectiveness of our approach in improving prediction accuracy by focusing on data with lower uncertainty., Significance: Different from routine prediction based on mere probabilities, we believe this uncertainty-guided prediction is more effective to ensure a high prediction rate rather than the prediction on the entire dataset. By guiding the decision-making of a model on higher-confidence subsets, our methodology can enhance the accuracy of classification in critical areas like disease diagnosis and safety monitoring. This targeted approach is to advance microbial identification and produces more trustworthy predictions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Black soldier fly larvae (BSFL, Hermetia illucens) contain high amounts of proteins and essential amino acids and are therefore an appropriate feed source. However, they lack essential fatty acids (FAs), specifically ω-3 and ω-6, making them a less desirable feed choice for aquaculture. The aim of this study was to increase the ω-3 and ω-6 FA concentrations in BSFL by manipulating the FA composition in their rearing substrate. Specifically, the potential of substrate fermentation using the ω-3 and ω-6 FA-producing fungus Mortierella alpina was assessed. Fermentation of two agricultural side streams (wheat bran (WB) and WB with distiller's dried grains with solubles (DDGS)) increased substrate total crude fat concentration by 2.1 - 4.6%, as well as the concentration of several essential FAs, including the ω-6 FAs arachidonic acid (from less than 0.2 mg/g fat to a maximum of 44.2 mg/g fat) and gamma-linolenic acid (from less than 1.2 mg/g fat to a maximum of 45.8 mg/g fat and the ω-3 FA eicosapentaenoic acid (EPA) (from less than 0.7 mg/g fat to a maximum of 49.9 mg/g fat). Rearing BSFL on feeds from such fermented substrates resulted in similar changes in larval FA composition, specifically a higher concentration of EPA (from less than 0.2 mg/g fat to a maximum of 26.6 mg/g fat in the larvae fed on fermented diets), however, larval growth was reduced. Feeds made from fermented substrates were prone to stickiness and dehydration, possibly limiting larval movement and feeding, thereby affecting larval growth. Furthermore, proximate analysis of the substrates revealed sugar depletion after fermentation, which could be detrimental for larval growth and illustrate important attention points going forward. This study shows that fermentation of agricultural side streams WB and a mixture of WB with DDGS with Mortierella alpina alters their FA profile, increasing their ω-3 and ω-6 FA concentrations and that of BSFL fed with those substrates. Therefore, these results suggest that BSFL with tailor-made FA profiles for a specific application could be successfully produced., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Introduction: Radioprotectors help to protect the body or at least minimize the negative consequences of radiation exposure. The present study aimed to assess the radioprotective potential of Helianthus tuberosus L. polysaccharide (HTLP) in vitality and micronuclei tests. To assess the cytotoxic effects of HTLP, both vitality and MTT reductase assays were conducted., Materials and Methods: RAW 264.7 cells viability was assessed 24 h after adding 200 μg/ml HTLP solution by staining cell cultures with propidium iodide and bis-benzimide to detect the nuclei of dead cells and the total number of cells in culture. To assess cell viability via cellular metabolic activity MTT test was used. In this work outbred 24-30 g 5-months old SHK mice have been used. Irradiation was provided with proton beams with an energy of 660 MeV at a dose rate of 80 Gy with doses 1.5 Gy for micronuclei test and 8.5 Gy for survival test. Whole body X-ray irradiation was conducted using the RUT-15 therapeutic X-ray unit with doses of 1.5 Gy for MN test and 6.5 Gy for survival. The HTLP sterile solution in dose 100 μg/animal was injected into the tail vein 15 min before X-ray or proton irradiation., Results and Conclusion: s: Vitality test showed no significant differences between the control group and cells treated with 200 μl of 200 μg/ml HTLP solution, though a greater variability was noted. In contrast, the MTT assay indicated enhanced cell viability in the HTLP-treated cells. HTLP does not exert any toxic effects in cell culture. Moreover, results of MTT reductase assay shows, that HTLP may enhance the cells' metabolic activity. Animals pre-treated with HTLP displayed a significant reduction in micronuclei formation, showing five times fewer micronuclei in bone marrow cells compared to the non-treated group. This comparison highlights HTLP's potential protective effect against radiation-induced chromosomal damage. HTLP treatment demonstrates a significant reduction in hazard compared to the control, indicating its protective effects against irradiation. Thus, it can be concluded that the use of HTLP increases the likelihood of animal survival under the ionizing effects of X-rays and protons. The survival analysis reveals that the HTLP-treated groups exhibit a higher survival rate compared to both the control and Cysteamine-treated groups, suggesting a significant protective effect of HTLP against irradiation, regardless of the type of irradiation (proton or X-ray) with p < 0.0001., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

LIBS-based analysis has experienced an ever-increasing interest in recent years as a well-suited technique for chemical analysis tasks relying on elemental fingerprinting. This method stands out for its ability to offer rapid, simultaneous multi-element analysis with the advantage of portability. In the context of this research, our aim is to bridge the gap between the analysis of simulated and real data to better account for variations in plasma temperature and electron density, which are typically not considered in LIBS analysis. To achieve this, we employ two distinct methodologies, PLS and CNNs, to construct predictive models for predicting the concentration of the 24 elements within each LIBS spectrum. The initial phase of our investigation concentrates on the training and testing of these models using simulated LIBS data, with results evaluated through RMSEP values. The IQR and median RMSEP values for all the elements demonstrate that CNNs consistently achieved values below 0.01, while PLS results ranged from 0.01 to 0.05, highlighting the superior stability and predictive accuracy of CNNs model. In the next phase, we applied the pre-trained models to analyze the real LIBS spectra, consistently identifying Aluminum (Al), Iron (Fe), and Silicon (Si) as having the highest predicted concentrations. The overall predicted values were approximately 0.5 for Al, 0.6 for Si, and 0.04 for Fe. In the third phase, deliberate adjustments are made to the training parameters and architecture of the proposed CNNs model to force the network to emphasize specific elements, prioritizing them over other components present in each real LIBS spectrum. The generation of the three modified versions of the initially proposed CNNs allows us to explore the impact of regularization, sample weighting, and a customized loss function on prediction outcomes. Some elements emerge during the prediction phase, with Calcium (Ca), Magnesium (Mg), Zinc (Zn), Titanium (Ti), and Gallium (Ga) exhibiting more pronounced patterns., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Therapeutic drug monitoring (TDM) is an important tool in precision medicine as it allows estimating pharmacodynamic and pharmacokinetic effects of drugs in clinical settings. An accurate, fast and real-time determination of the drug concentrations in patients ensures fast decision-making processes at the bedside to optimize the clinical treatment. Surface-enhanced Raman spectroscopy (SERS), which is based on the application of metallic nanostructured substrates to amplify the inherent weak Raman signal, is a promising technique in medical research due to its molecular specificity and trace sensitivity accompanied with short detection times. Therefore, we developed a SERS-based detection scheme using silicon nanowires decorated with silver nanoparticles, fabricated by means of top-down etching combined with chemical deposition, to detect the antibiotic ceftriaxone (CRO) in spiked fresh plasma and microdialysis samples. We successfully detected CRO in both matrices with an LOD of 94 μM in protein-depleted fresh plasma and 1.4 μM in microdialysate., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Giardia intestinalis is one of the most widespread intestinal parasites and is considered a major cause of epidemic or sporadic diarrhea worldwide. In this study, we aimed to develop a rapid aptameric diagnostic technique for G. intestinalis infection. First, the SELEX (Systematic Evolution of Ligands by Exponential Enrichment) process generated DNA aptamers specific to a recombinant protein of the parasite's trophozoite. Ten selection rounds were performed; each round, the DNA library was incubated with the target protein conjugated to Sepharose beads. Then, the unbound sequences were removed by washing and the specific sequences were eluted and amplified by Polymerase Chain Reaction (PCR). Two aptamers were selected, and the dissociation constants (K d ), were determined as 2.45 and 16.95 nM, showed their high affinity for the G. intestinalis trophozoite protein. Subsequently, the aptamer sequence T1, which exhibited better affinity, was employed to develop a label-free electrochemical biosensor. A thiolated aptamer was covalently immobilized onto a gold screen-printed electrode (SPGE), and the binding of the targeted protein was monitored using square wave voltammetry (SWV). The developed aptasensor enabled accurate detection of the G. intestinalis recombinant protein within the range of 0.1 pg/mL to 100 ng/mL, with an excellent sensitivity (LOD of 0.35 pg/mL). Moreover, selectivity studies showed a negligible cross-reactivity toward other proteins such as bovine serum albumin, globulin, and G. intestinalis cyst protein., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Genomic biomarkers of cancer are based on changes in nucleic acids, which include abnormal expression levels of some miRNAs, point mutations in DNA sequences, and altered levels of DNA methylation. The presence of tumor-related nucleic acids in body fluids (blood, saliva, or urine) makes it possible to achieve a non-invasive early-stage cancer diagnosis. Currently existing techniques for the discovery of nucleic acids require complex, time-consuming, costly assays and have limited multiplexing abilities. Surface-enhanced Raman spectroscopy (SERS) is a vibrational spectroscopy technique that is able to provide molecular specificity combined with trace sensitivity. SERS has gained research attention as a tool for the detection of nucleic acids because of its promising potential: label-free SERS can decrease the complexity of assays currently used with fluorescence-based detection due to the absence of the label, while labeled SERS may outperform the gold standard in terms of the multiplexing ability. The first papers about SERS-based methods for the measurement of genomic biomarkers were written in 2008, and since then, more than 150 papers have been published. The aim of this paper is to review and evaluate the proposed SERS-based methods in terms of their level of development and their potential for liquid biopsy application, as well as to contribute to their further evolution by attracting research attention to the field. This goal will be reached by grouping, on the basis of their experimental protocol, all the published manuscripts on the topic and evaluating each group in terms of its limit of detection and applicability to real body fluids. Thus, the methods are classified according to their working principles into five main groups, including capture-based, displacement-based, sandwich-based, enzyme-assisted, and specialized protocols., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Wastewater is a very complex and diverse medium, which despite low nutrient density still harbors bacteria. Especially the wastewater from hospitals contains a high germ load. However, wastewater is also very variable and differs not only from day to day, but also from house to house. Since wastewater is always changing and medium has an impact on Raman spectra of bacteria, it is necessary to find a surrogate material in which bacteria can be cultured to mimic a real hospital wastewater sample. In this study, we investigate two different artificial wastewaters for their abilities as a good alternative to real wastewater from the Jena University Hospital and to serve as a reference material for bacterial cultivation with subsequent Raman measurement. Each of the artificial wastewater on its own was not suitable to be used as a reference medium. Only the combination of the two simulated wastewaters achieved satisfactory results in the Raman spectroscopic identification of bacteria from real wastewater. These results could be used later in new experiments as a reference dataset to identify bacteria from real hospital wastewater samples., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Classical electromagnetic theory applied to infrared (IR) and vibrational circular dichroism (VCD) spectra of chiral compounds can provide useful insights, such as the fact that the area of all bands of wavenumber-normalized absorbance above zero must be the same as the area below zero. Additionally, dispersion analysis based on wave optics and dispersion theory, which was extended by Born and Kuhn to include chiral substances, can be used to quantitatively describe the dielectric function and the chiral admittance functions that shape IR and VCD spectra. For dispersion analysis, pairs of coupled oscillators, with five different kinds of parameters, namely oscillator strength, damping, oscillator position, vertical distance between coupled oscillators, and the coupling constant are used to model the dielectric functions and chiral admittance functions. We report the results of such an analysis for α-Pinene and Propylene oxide. For most bands, the oscillator model using two coupled oscillators is sufficient to achieve a good correspondence between experimental and modelled data., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Pyrazinoic acid (POA) is a metabolite of the anti-tuberculosis drug pyrazinamide (PZA), and its detection can be used to assess the resistance of Mycobacterium tuberculosis in cultures, as only sensitive strains of the bacteria can metabolize PZA into POA. Prussian blue is a well-known metal-organic framework compound widely used in various sensing platforms such as electrochemical, photochemical, and magnetic sensors. In this study, we present a novel sensing platform based on Prussian blue-modified gold nanoparticles (AuNPs) designed to enhance the affinity of POA towards the sensing surface and to capture POA molecules from aqueous solutions. This SERS-based method allows for the selective enrichment of POA, which can be detected in both pure aqueous solution and in the presence of its pro-drug PZA. The limit of detection (LOD) for POA was estimated to be 1.08 μM in pure aqueous solution and 0.18 mM in the presence of PZA. Furthermore, the precision of the SERS method was verified by the relative standard deviation (RSD) of 3.34-12.02% for three parallel samples using different matrices, i.e. aqueous solution, spiked river water and spiked simulated saliva. The recoveries of the samples ranged from 92.65 to 118.51%. These all demonstrate the potential application of the proposed detection scheme in medical research., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier B.V.)

Optical constants functions of analytes are indispensable for the effective design of plasmonic sensors. Such sensors are potentially able to enhance the sensitivity by several order of magnitudes which can greatly facilitate the determination of the generally weak spectral signals caused by vibrational circular dichroism. Accordingly, to demonstrate how to obtain these functions, we have determined the dielectric and chirality admittance functions of α-Pinene and Propylene oxide in the mid-infrared spectral range using attenuated total reflection and vibrational circular dichroism spectroscopy. Our iterative formalism starts with an estimation of the absorption index function, followed by the calculation of the refractive index function using the Kramers-Kronig relation and a modelled spectrum based on Fresnel's equations. By comparing the experimental and modelled spectra, we improve the absorption index function. To determine the chirality admittance function, we use the same iterative formalism, but with a modified 4x4 matrix formalism formulated by Berreman. Our results show that the experimental absorbance difference is independent of the dielectric function of the chiral substance and depends linearly on the cuvette thickness. Additionally, we provide a sum rule that can be used to assess the quality of VCD spectra and determine the position of the baseline. Our findings provide crucial insights into the optical properties of chiral substances in the mid-infrared spectral range, which have important implications for a range of applications in fields such as analytical chemistry and materials science., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Raman reference libraries can be used for identification of components in unknown samples as Raman spectroscopy offers fingerprint information of the measured samples. Since Raman libraries often contain many different and/or highly similar spectra, it is important that the spectra are a reliable fingerprint for each compound. However, Raman spectra are highly sensitive to the experimental conditions, and the Raman spectra will change in different conditions even though the same sample is measured. Raman data pre-treatment minimizes the differences between Raman spectra arising from different experimental conditions. In this study, different combinations of pre-treatment methods are used to quantify the effect of each pre-treatment step in minimizing the differences between Raman spectra of the same sample in different experimental conditions, e.g., different excitation wavelengths. These different pre-treatment processes are evaluated for six solvents. The spectra differences between spectra recorded with three excitation wavelengths (532 nm, 633 nm, and 830 nm) are evaluated by angular difference index and the influence on a classification model is tested. The angular difference index of each spectrum after every data pre-treatment step shows a decreasing behavior. It could be demonstrated that wavenumber calibration has the largest effect on the differences between the Raman spectra. However, ω 4 correction doesn't have a significate effect in this dataset. The classification results show that the prediction accuracy is improving by doing data pre-treatment. In the dataset obtained in 633 nm a lower amount of pre-treatment steps is needed but in the dataset 830 nm more pre-treatment steps are needed for a high accuracy. The result shows that the choice of an optimal pre-treatment method or combination of methods strongly influences the analysis results, but is far from straightforward, since it depends on the characteristics of the data set and the goal of data analysis., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

The human dura mater is known to impact vastly traumatic brain injury mechanopathology. In spite of this involvement, dura mater is typically neglected in computational and physical human head models. The lack of location-dependent microstructural and related mechanical data of dura mater may be considered a rationale behind this simplification. The anisotropic nature of dura mater under various loading conditions so far remains unelucidated. Furthermore, principal collagen fiber orientation is yet to be quantified for a morpho-mechanically-informed material model on the dura mater. This study aims to assess how location-dependent mechanical anisotropy is linked to principal collagen fiber orientation. Uniaxial extension tests were performed in a heated tissue bath for 60 samples from six individuals and correlated to the three-dimensional collagen structure in four individuals using second-harmonic generation (SHG) imaging. Failure stress and stretch at failure, elastic modulus, and a microstructurally motivated material model were integrated to examine local differences in dura mater morpho-mechanics. The quantitative observation of collagen fiber orientation and dispersion confirmed that collagen is highly aligned in the human dura mater and that both fiber orientation and dispersion differ depending on the location investigated. This observation provides a possible explanation for the previously observed isotropic mechanical behavior, as the main collagen fiber direction is not oriented along the anterior-posterior or medial-lateral direction at most of the mapped locations. Additionally, these site-dependent structural properties have implications for the mechanical load response and therefore potentially for the regional functions dura mater has to fulfill. The here chosen non-symmetrical fiber dispersion material model fits the data well and provides a comprehensive parameter base for further studies and future finite element models. STATEMENT OF SIGNIFICANCE: The human dura mater greatly affects traumatic brain injury mechanisms, but it is often ignored in computational and physical head models. This is because there is a lack of detailed microstructural and mechanical data specific to the dura mater. Its anisotropic nature and collagen fiber orientation have not been fully understood, hindering the development of an accurate material model. Hence, this study combines morphological data on collagen fiber orientation and dispersion at multiple locations of human cranial dura mater, and links microstructure to location-specific load-displacement behavior. It provides microstructurally informed mechanical information towards realistic head models for predicting location-dependent tissue behavior and failure for assessing brain injury and graft material development., Competing Interests: Declaration of Competing Interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023. Published by Elsevier Ltd.)

Polar regions are the most exposed to secondary particles and radiation produced by primary cosmic rays in the atmosphere, because naturally they are with marginal geomagnetic shielding. In addition, the secondary particle flux contributing to the complex radiation field is enhanced at high-mountain altitudes compared to sea level because of the reduced atmospheric attenuation. At present, there are very few systematic experimental measurements of environmental dose at high southern latitudes, specifically at high-altitude region. Here, we report a campaign of measurements with different devices, that is passive and Liulin-type dosimeters, of the radiation background at high-mountain Antarctic station Vostok (3488 m above sea level, 78° 27' S; 106° 50' E). We compare the measurements with a Monte Carlo-based model for the propagation of the cosmic rays through the atmosphere and assessment of the radiation field in the atmosphere. We employed the model to estimate the radiation dose at Vostok station during the ground-level enhancement at 28 October 2021. As in previous studies by other teams, we show that the annual dose equivalent at high-altitude Antarctic facilities can significantly exceed the limit of 1 mSv established for the general population by the ICRP., Competing Interests: Declaration of competing interest The authors declare that they have no conflicts of interest., (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Hybrid Diffusion Imaging (HYDI) was one of the first attempts to use multi-shell samplings of the q-space to infer diffusion properties beyond Diffusion Tensor Imaging (DTI) or High Angular Resolution Diffusion Imaging (HARDI). HYDI was intended as a flexible protocol embedding both DTI (for lower b-values) and HARDI (for higher b-values) processing, as well as Diffusion Spectrum Imaging (DSI) when the entire data set was exploited. In the latter case, the spherical sampling of the q-space is re-gridded by interpolation to a Cartesian lattice whose extent covers the range of acquired b-values, hence being acquisition-dependent. The Discrete Fourier Transform (DFT) is afterwards used to compute the corresponding Cartesian sampling of the Ensemble Average Propagator (EAP) in an entirely non-parametric way. From this lattice, diffusion markers such as the Return To Origin Probability (RTOP) or the Mean Squared Displacement (MSD) can be numerically estimated. We aim at re-formulating this scheme by means of a Fourier Transform encoding matrix that eliminates the need for q-space re-gridding at the same time it preserves the non-parametric nature of HYDI-DSI. The encoding matrix is adaptively designed at each voxel according to the underlying DTI approximation, so that an optimal sampling of the EAP can be pursued without being conditioned by the particular acquisition protocol. The estimation of the EAP is afterwards carried out as a regularized Quadratic Programming (QP) problem, which allows to impose positivity constraints that cannot be trivially embedded within the conventional HYDI-DSI. We demonstrate that the definition of the encoding matrix in the adaptive space allows to analytically (as opposed to numerically) compute several popular descriptors of diffusion with the unique source of error being the cropping of high frequency harmonics in the Fourier analysis of the attenuation signal. They include not only RTOP and MSD, but also Return to Axis/Plane Probabilities (RTAP/RTPP), which are defined in terms of specific spatial directions and are not available with the former HYDI-DSI. We report extensive experiments that suggest the benefits of our proposal in terms of accuracy, robustness and computational efficiency, especially when only standard, non-dedicated q-space samplings are available., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Author(s). Published by Elsevier B.V. All rights reserved.)

The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm 2 . The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

This work focuses on obtaining a magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T 1 and transverse T 2 ⋆ relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametric acquisitions, where only a few diffusion gradient directions and b-values are available for each pair of echo and inversion times. The method is based on the three-dimensional simple harmonic oscillator-based reconstruction and estimation (SHORE) representation of the diffusion signal, which enables the estimation of the orientation distribution function and the retrieval of various quantitative indices such as the generalized fractional anisotropy or the return-to-the-origin probability while simultaneously resolving for T 1 and T 2 ⋆ relaxation times. Our technique, the Relax-SHORE, has been tested on both in silico and in vivo diffusion-relaxation scattered MR data. The results show that Relax-SHORE is accurate in the context of scattered acquisitions while guaranteeing flexibility in the diffusion signal representation from multi-parametric sequences., (Copyright © 2022 Elsevier Inc. All rights reserved.)

We propose a method that can provide information about the anisotropy and orientation of diffusion in the brain from only 3 orthogonal gradient directions without imposing additional assumptions. The method is based on the Diffusion Anisotropy (DiA) that measures the distance from a diffusion signal to its isotropic equivalent. The original formulation based on a Spherical Harmonics basis allows to go down to only 3 orthogonal directions in order to estimate the measure. In addition, an alternative simplification and a color-coding representation are also proposed. Acquisitions from a publicly available database are used to test the viability of the proposal. The DiA succeeded in providing anisotropy information from the white matter using only 3 diffusion-encoding directions. The price to pay for such reduced acquisition is an increment in the variability of the data and a subestimation of the metric on those tracts not aligned with the acquired directions. Nevertheless, the calculation of anisotropy information from DMRI is feasible using fewer than 6 gradient directions by using DiA. The method is totally compatible with existing acquisition protocols, and it may provide complementary information about orientation in fast diffusion acquisitions., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

AMURA (Apparent Measures Using Reduced Acquisitions) was originally proposed as a method to infer micro-structural information from single-shell acquisitions in diffusion MRI. It reduces the number of samples needed and the computational complexity of the estimation of diffusion properties of tissues by assuming the diffusion anisotropy is roughly independent on the b-value. This simplification allows the computation of simplified expressions and makes it compatible with standard acquisition protocols commonly used even in clinical practice. The present work proposes an extension of AMURA that allows the calculation of general moments of the diffusion signals that can be applied to describe the diffusion process with higher accuracy. We provide simplified expressions to analytically compute a set of scalar indices as moments of arbitrary orders over either the whole 3-D space, particular directions, or particular planes. The existing metrics previously proposed for AMURA (RTOP, RTPP and RTAP) are now special cases of this generalization. An extensive set of experiments is performed on public data and a clinical clase acquired with a standard type acquisition. The new metrics provide additional information about the diffusion processes inside the brain., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses or interpretation of data; in the writing of the manuscript or in the decision to publish the results., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

An imaging biomarker is a biologic feature in an image that is relevant to a patient's diagnosis or prognosis. In order to qualify as a biomarker, a measure must be robust and reproducible. However, the usual scalar measures derived from diffusion tensor imaging are known to be highly dependent on the variation of the acquisition parameters, which prevents their possible use as biomarkers. In this work, we propose a new set of quantitative measures based on diffusion magnetic resonance imaging from single-shell acquisitions that are designed to be robust to the variations of several acquisition parameters (number of gradient directions, b-value and SNR) while keeping a high discrimination power on differences in the diffusion characteristics of the tissue. These new scalar measures are analytically obtained from a generic diffusion function that does not require the calculation of a diffusion tensor. This way, on one hand, we avoid the use of a specific diffusion model and, on the other hand, we make easier the statistical characterization of the measures. Accordingly, the analysis of the measures bias is carried out and it is used to minimize their dependency with respect to the acquisition noise for different SNRs. The robustness and discrimination power of the measures are tested for different number of gradients, b-values and SNRs using a realistic phantom and three real datasets: (1) 13 control subjects and different acquisition parameters; (2) a public data set from a single subject acquired using multiple shells and (3) 32 schizophrenia patients and 32 age and sex-matched healthy controls with a varying number of gradient directions. The proposed quantitative measures exhibit low variability to the changes of the acquisition parameters, while at the same time they preserve a discrimination power that is able to detect significant changes in the anisotropy of the diffusion., (Copyright © 2018 Elsevier Inc. All rights reserved.)

Speckle Tracking is one of the most prominent techniques used to estimate the regional movement of the heart based on ultrasound acquisitions. Many different approaches have been proposed, proving their suitability to obtain quantitative and qualitative information regarding myocardial deformation, motion and function assessment. New proposals to improve the basic algorithm usually focus on one of these three steps: (1) the similarity measure between images and the speckle model; (2) the transformation model, i.e. the type of motion considered between images; (3) the optimization strategies, such as the use of different optimization techniques in the transformation step or the inclusion of structural information. While many contributions have shown their good performance independently, it is not always clear how they perform when integrated in a whole pipeline. Every step will have a degree of influence over the following and hence over the final result. Thus, a Speckle Tracking pipeline must be analyzed as a whole when developing novel methods, since improvements in a particular step might be undermined by the choices taken in further steps. This work presents two main contributions: (1) We provide a complete analysis of the influence of the different steps in a Speckle Tracking pipeline over the motion and strain estimation accuracy. (2) The study proposes a methodology for the analysis of Speckle Tracking systems specifically designed to provide an easy and systematic way to include other strategies. We close the analysis with some conclusions and recommendations that can be used as an orientation of the degree of influence of the models for speckle, the transformation models, interpolation schemes and optimization strategies over the estimation of motion features. They can be further use to evaluate and design new strategy into a Speckle Tracking system., (Copyright © 2016 Elsevier B.V. All rights reserved.)

Fungi are where one finds them, and if one seeks fungal pathogens affecting flies, then a garbage dump may be an ideal place to find both persistent, abundant fly populations and their fungal pathogens. An obvious fungal epizootic affecting the oriental latrine fly, Chrysomya megacephala (Diptera: Calliphoridae), was observed over several days in mid-February 2015 at the local garbage dump adjacent to the city of Cavalcante, northern Goiás. This site harbored large populations of both C. megacephala and a Musca sp. (Diptera: Muscidae) but only the population of oriental latrine fly was affected by any fungal pathogen and presented unusually dense populations of fresh cadavers. The fungus was identifiable as Pandora bullata (Entomophthorales: Entomophthoraceae) only after a very small number of characteristically decorated resting spores were found in these flies two months later; this represents the first Brazilian (and South American) record of this species. P. bullata is known previously from a small number of North American, European and Australian collections, all of which have included relatively abundant production of resting spores. We cannot dismiss the possibility that the extremely sparse formation of resting spores at this Brazilian site may be due to abiotic factors such as latitude (13°46'40.53″S), day length, ambient temperatures, or even the precipitation patterns in this mid-tropical montaine site. Epizootic events affecting calliphorids in Brazil strengthen the interest in entomophthoran pathogens for biological control of flies., (Copyright © 2016 Elsevier B.V. All rights reserved.)

The strain and strain-rate measures are commonly used for the analysis and assessment of regional myocardial function. In echocardiography (EC), the strain analysis became possible using Tissue Doppler Imaging (TDI). Unfortunately, this modality shows an important limitation: the angle between the myocardial movement and the ultrasound beam should be small to provide reliable measures. This constraint makes it difficult to provide strain measures of the entire myocardium. Alternative non-Doppler techniques such as Speckle Tracking (ST) can provide strain measures without angle constraints. However, the spatial resolution and the noisy appearance of speckle still make the strain estimation a challenging task in EC. Several maximum likelihood approaches have been proposed to statistically characterize the behavior of speckle, which results in a better performance of speckle tracking. However, those models do not consider common transformations to achieve the final B-mode image (e.g. interpolation). This paper proposes a new maximum likelihood approach for speckle tracking which effectively characterizes speckle of the final B-mode image. Its formulation provides a diffeomorphic scheme than can be efficiently optimized with a second-order method. The novelty of the method is threefold: First, the statistical characterization of speckle generalizes conventional speckle models (Rayleigh, Nakagami and Gamma) to a more versatile model for real data. Second, the formulation includes local correlation to increase the efficiency of frame-to-frame speckle tracking. Third, a probabilistic myocardial tissue characterization is used to automatically identify more reliable myocardial motions. The accuracy and agreement assessment was evaluated on a set of 16 synthetic image sequences for three different scenarios: normal, acute ischemia and acute dyssynchrony. The proposed method was compared to six speckle tracking methods. Results revealed that the proposed method is the most accurate method to measure the motion and strain with an average median motion error of 0.42 mm and a median strain error of 2.0 ± 0.9%, 2.1 ± 1.3% and 7.1 ± 4.9% for circumferential, longitudinal and radial strain respectively. It also showed its capability to identify abnormal segments with reduced cardiac function and timing differences for the dyssynchrony cases. These results indicate that the proposed diffeomorphic speckle tracking method provides robust and accurate motion and strain estimation., (Copyright © 2015. Published by Elsevier B.V.)

The reliable estimation of noise characteristics in MRI is a task of great importance due to the influence of noise features in extensively used post-processing algorithms. Many methods have been proposed in the literature to retrieve noise features from the magnitude signal. However, most of them assume a stationary noise model, i.e., the features of noise do not vary with the position inside the image. This assumption does not hold when modern scanning techniques are considered, e.g., in the case of parallel reconstruction and intensity correction. Therefore, new noise estimators must be found to cope with non-stationary noise. Some methods have been recently proposed in the literature. However, they require multiple acquisitions or extra information which is usually not available (biophysical models, sensitivity of coils). In this work we overcome this drawback by proposing a new method that can accurately estimate the non-stationary parameters of noise from just a single magnitude image. In the derivation, we considered the noise to follow a non-stationary Rician distribution, since it is the most common model in real acquisitions (e.g., SENSE reconstruction), though it can be easily generalized to other models. The proposed approach makes use of a homomorphic separation of the spatially variant noise in two terms: a stationary noise term and one low frequency signal that correspond to the x-dependent variance of noise. The non-stationary variance of noise is then estimated by a low pass filtering with a Rician bias correction. Results in real and synthetic experiments evidence the better performance and the lowest error variance of the proposed methodology when compared to the state-of-the-art methods., (Copyright © 2014 Elsevier B.V. All rights reserved.)

This study presents new evidence for the events leading to Escherichia coli reduction in the absence of light irradiation on TiO2-polyester (from now on TiO2-PES. By transmission electron microscopy (TEM) the diffusion of TiO2 NP's aggregates with the E. coli outer lipo-polyssacharide (LPS) layer is shown to be a prerequisite for the loss of bacterial cultivability. Within 30 min in the dark the TiO2 aggregates interact with E. coli cell wall leading within 120 min to the complete loss of bacterial cultivability on a TiO2-PES 5% TiO2 sample. The bacterial reduction was observed to increase with a higher TiO2 loading on the PES up to 5%. Bacterial disinfection on TiO2-PES in the dark was slower compared to the runs under low intensity simulated sunlight light irradiation. The interaction between the TiO2 aggregates and the E. coli cell wall is discussed in terms of the competition between the TiO2 units collapsing to form TiO2-aggregates at a physiologic pH-value followed by the electrostatic interaction with the bacteria surface. TiO2-PES samples were able to carry repetitive bacterial inactivation. This presents a potential for practical applications. X-ray photoelectron spectroscopy (XPS) evidence was found for the reduction of Ti4+ to Ti3+ contributing to redox interactions between TiO2-PES and the bacterial cell wall. Insight is provided into the mechanism of interaction between the E. coli cell wall and TiO2 NP's. The properties of the TiO2-PES surface like percentage atomic concentration, TiO2-loading, optical absorption, surface charge and crystallographic phases are reported in this study., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Parallel imaging methods allow to increase the acquisition rate via subsampled acquisitions of the k-space. SENSE and GRAPPA are the most popular reconstruction methods proposed in order to suppress the artifacts created by this subsampling. The reconstruction process carried out by both methods yields to a variance of noise value which is dependent on the position within the final image. Hence, the traditional noise estimation methods - based on a single noise level for the whole image - fail. In this paper we propose a novel methodology to estimate the spatial dependent pattern of the variance of noise in SENSE and GRAPPA reconstructed images. In both cases, some additional information must be known beforehand: the sensitivity maps of each receiver coil in the SENSE case and the reconstruction coefficients for GRAPPA., (Copyright © 2014 Elsevier Inc. All rights reserved.)

Modern magnetic resonance (MR) imaging protocols based on multiple-coil acquisitions have carried on a new attention to noise and signal statistical modeling, as long as most of the existing techniques for data processing are model based. In particular, nonaccelerated multiple-coil and GeneRalized Autocalibrated Partially Parallel Acquisitions (GRAPPA) have brought noncentral-χ (nc-χ) statistics into stake as a suitable substitute for traditional Rician distributions. However, this model is only valid when the signals received by each coil are roughly uncorrelated. The recent literature on this topic suggests that this is often not the case, so nc-χ statistics are in principle not adequate. Fortunately, such model can be adapted through the definition of a set of effective parameters, namely, an effective noise power (greater than the actual power of thermal noise in the Radio Frequency receiver) and an effective number of coils (smaller than the actual number of RF receiving coils in the system). The implications of these artifacts in practical algorithms have not been discussed elsewhere. In the present paper, we aim to study their actual impact and suggest practical rules to cope with them. We define the main noise parameters in this context, introducing a new expression for the effective variance of noise which is of capital importance for the two image processing problems studied: first, we propose a new method to estimate the effective variance of noise from the composite magnitude signal of MR data when correlations are assumed. Second, we adapt several model-based image denoising techniques to the correlated case using the noise estimation techniques proposed. We show, through a number of experiments with synthetic, phantom, and in vivo data, that neglecting the correlated nature of noise in multiple-coil systems implies important errors even in the simplest cases. At the same time, the proper statistical characterization of noise through effective parameters drives to improved accuracy (both qualitatively and quantitatively) for both of the problems studied., (Copyright © 2013 Elsevier Inc. All rights reserved.)

Diffusion tensor imaging (DTI) constitutes the most used paradigm among the diffusion-weighted magnetic resonance imaging (DW-MRI) techniques due to its simplicity and application potential. Recently, real-time estimation in DW-MRI has deserved special attention, with several proposals aiming at the estimation of meaningful diffusion parameters during the repetition time of the acquisition sequence. Specifically focusing on DTI, the underlying model of the noise present in the acquired data is not taken into account, leading to a suboptimal estimation of the diffusion tensor. In this paper, we propose an optimal real-time estimation framework for DTI reconstruction in single-coil acquisitions. By including an online estimation of the time-changing noise variance associated to the acquisition process, the proposed method achieves the sequential best linear unbiased estimator. Results on both synthetic and real data show that our method outperforms those so far proposed, reaching the best performance of the existing proposals by processing a substantially lower number of diffusion images., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Bivalve larvae and hydrographic parameters were sampled over a range of spatio-temporal scales in a deep atoll lagoon. Bivalve larvae abundances were very high throughout the year: 18,550 m(-3) in average. Larvae were (i) concentrated at mid-depth with nocturnal ascent and diurnal descent, (ii) heterogeneously dispersed at the lagoon scale, (iii) subject to day-to-day variation in abundance and (iv) transferred between different parts of the lagoon providing evidence of intra-lagoonal connectivity. The primacy of physical factors was seen on large spatial scale with the diluting effect of water renewal and transfers by hydrodynamics. On smaller spatial scale, the primacy of biological processes was recognised, with larval swimming activity leading to dial vertical migration correlated with food concentration. Variations in larval abundance were driven by bivalve reproductive activity correlated with meteorological conditions (i.e. windy periods). Finally, relationship between bivalve larvae patterns and pearl oyster (Pinctada margaritifera) settlement structuring is discussed., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

Patterns of bivalve larvae dispersal in the deep Ahe atoll lagoon was studied by using a numerical 3D transport model (MARS3D) coupled with a vertical swimming sub-model, forced mainly by tide and wind-induced currents. The simulations were validated against observations of larval dispersal monitored several days throughout the lagoon. Connectivity matrices describing larval exchanges inside the lagoon were inferred. Larvae displayed a significant dispersal capacity at the lagoon scale, especially with dominant eastern winds. With southeastern winds, larvae mostly remained in their origin sector. The total export rate of the larvae, toward the ocean through the pass and shallow lagoon borders, was independent of the wind conditions, with 1% of the total concentration exported per day. However, the tide-driven currents efficiently flushed larvae in sectors close to the pass. Connectivity matrices suggest that the south and west sectors were more suitable for spat collecting and that central sectors would be efficient sanctuaries if genitors were accumulated., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

A model for the distribution of the sample local variance (SLV) of magnetic resonance data is proposed. It is based on a bimodal Gamma distribution, whose maxima are related to the signal and background areas of the image. The model is valid for single- and multiple-coil systems. The proposed distribution allows us to characterize some signal/background properties in MR data. As an example, the model is used to study the effect of the background size over noise estimation techniques and a method to test the validity of background-based noise estimators is presented., (Copyright 2010 Elsevier Inc. All rights reserved.)

Noise estimation is a challenging task in magnetic resonance imaging (MRI), with applications in quality assessment, filtering or diffusion tensor estimation. Main noise estimators based on the Rician model are revisited and classified in this article, and new useful methods are proposed. Additionally, all the surveyed estimators are extended to the noncentral chi model, which applies to multiple-coil MRI and some important parallel imaging algorithms for accelerated acquisitions. The proposed new noise estimation procedures, based on the distribution of local moments, show better performance in terms of smaller variance and unbiased estimation over a wide range of experiments, with the additional advantage of not needing to explicitly segment the background of the image.

It has been shown that the tensor calculation is very sensitive to the presence of noise in the acquired images, yielding to very low quality Diffusion Tensor Images (DTI) data. Recent investigations have shown that the noise present in the Diffusion Weighted Images (DWI) causes bias effects on the DTI data which cannot be corrected if the noise characteristic is not taken into account. One possible solution is to increase the minimum number of acquired measurements (which is 7) to several tens (or even several hundreds). This has the disadvantage of increasing the acquisition time by one (or two) orders of magnitude, making the process inconvenient for a clinical setting. We here proposed a turn-around procedure for which the number of acquisitions is maintained but, the DWI data are filtered prior to determining the DTI. We show a significant reduction on the DTI bias by means of a simple and fast procedure which is based on linear filtering; well-known drawbacks of such filters are circumvented by means of anisotropic neighborhoods and sequential application of the filter itself. Information of the first order probability density function of the raw data, namely, the Rice distribution, is also included. Results are shown both for synthetic and real datasets. Some error measurements are determined in the synthetic experiments, showing how the proposed scheme is able to reduce them. It is worth noting a 50% increase in the linear component for real DTI data, meaning that the bias in the DTI is considerably reduced. A novel fiber smoothness measure is defined to evaluate the resulting tractography for real DWI data. Our findings show that after filtering, fibers are considerably smoother on the average. Execution times are very low as compared to other reported approaches which allows for a real-time implementation.

In this paper, a novel method for the boundary detection of human kidneys from three dimensional (3D) ultrasound (US) is proposed. The inherent difficulty of interpretation of such images, even by a trained expert, makes the problem unsuitable for classical methods. The method here proposed finds the kidney contours in each slice. It is a probabilistic Bayesian method. The prior defines a Markov field of deformations and imposes the restriction of contour smoothness. The likelihood function imposes a probabilistic behavior to the data, conditioned to the contour position. This second function, which is also Markov, uses an empirical model of distribution of the echographical data and a function of the gradient of the data. The model finally includes, as a volumetric extension of the prior, a term that forces smoothness along the depth coordinate. The experiments that have been carried out on echographies from real patients validate the model here proposed. A sensitivity analysis of the model parameters has also been carried out.