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The tympanoperiotic complex (TPC) bones of the fin whale skull were studied using experimental measurements and simulation modeling to provide insight into the low frequency hearing of these animals. The study focused on measuring the sounds emitted by the left and right TPC bones when the bones were tapped at designated locations. Radiated sound was recorded by eight microphones arranged around the tympanic bulla. A finite element model was also created to simulate the natural mode vibrations of the TPC and ossicular chain, using a 3D mesh generated from a CT scan. The simulations produced mode shapes and frequencies for various Young's modulus and density values. The recorded sound amplitudes were compared with the normal component of the simulated displacement and it was found that the modes identified in the experiment most closely resembled those found with Young's modulus for stiff and flexible bone set to 25 and 5 GPa, respectively. The first twelve modes of vibration of the TPC had resonance frequencies between 100Hz and 6kHz. Many vibrational modes focused energy at the sigmoidal process, and therefore the ossicular chain. The resonance frequencies of the left and right TPC were offset, suggesting a mechanism for the animals to have improved hearing at a range of frequencies as well as a mechanism for directionality in their perception of sounds.

Long-duration space exploratory missions to the Earth's moon and the planet Mars are actively being planned. Such missions will require humans to live for prolonged periods beyond low earth orbit where astronauts will be continuously exposed to high energy galactic cosmic rays (GCRs). A major unknown is the potential impact of GCRs on the risks of developing degenerative cardiovascular disease, which is a concern to NASA. A ground-based rat model has been used to provide a detailed characterization of the risk of long-term cardiovascular disease from components of GCRs at radiation doses relevant to future human missions beyond low earth orbit. Six month old male WAG/RijCmcr rats were irradiated at a ground-based charged particle accelerator facility with high energy ion beams broadly representative of GCRs: protons, silicon and iron. Irradiation was given either as a single ion beam or as a combination of three ion beams. For the doses used, the single ion beam studies did not show any significant changes in the known cardiac risk factors and no evidence of cardiovascular disease could be demonstrated. In the three ion beam study, the total cholesterol levels in the circulation increased modestly over the 270 day follow up period, and inflammatory cytokines were also increased, transiently, 30 days after irradiation. Perivascular cardiac collagen content, systolic blood pressure and the number of macrophages found in the kidney and in the heart were each increased 270 days after irradiation with 1.5 Gy of the three ion beam grouping. These findings provide evidence for a cardiac vascular pathology and indicate a possible threshold dose for perivascular cardiac fibrosis and increased systemic systolic blood pressure for complex radiation fields during the 9 month follow up period. The development of perivascular cardiac fibrosis and increased systemic systolic blood pressure occurred at a physical dose of the three ion beam grouping (1.5 Gy) that was much lower than that required to show similar outcomes in earlier studies with the same rat strain exposed to photons. Further studies with longer follow up periods may help determine whether humans exposed to lower, mission-relevant doses of GCRs will develop radiation-induced heart disease.

Human exposure to low-to-moderate dose ionizing radiation (LMD-IR) is increasing via environmental, medical, occupational sources. Acute exposure to LMD-IR can cause subclinical damage to cells, resulting in altered gene expression and cellular function within the human brain. It has been difficult to identify diagnostic and predictive biomarkers of exposure using traditional research models due to factors including lack of 3D structure in monolayer cell cultures, limited ability of animal models to accurately predict human responses, and technical limitations of studying functional human brain tissue. To address this gap, we generated brain/cerebral organoids from human induced pluripotent stem cells to study the radiosensitivity of human brain cells, including neurons, astrocytes, and oligodendrocytes. While organoids have become popular models for studying brain physiology and pathology, there is little evidence to confirm that exposing brain organoids to LMD-IR will recapitulate previous in vitro and in vivo observations. We hypothesized that exposing brain organoids to proton radiation would (1) cause a time- and dose-dependent increase in DNA damage, (2) induce cell type-specific differences in radiosensitivity, and (3) increase expression of oxidative stress and DNA damage response genes. Organoids were exposed to 0.5 or 2 Gy of 250 MeV protons and samples were collected at 30 minute, 24 hour, and 48 hour timepoints. Using immunofluorescence and RNA sequencing, we found time- and dose-dependent increases in DNA damage in irradiated organoids; no changes in cell populations for neurons, oligodendrocytes, and astrocytes by 24 hours; decreased expression of genes related to oligodendrocyte lineage, astrocyte lineage, mitochondrial function, and cell cycle progression by 48 hours; increased expression of genes related to neuron lineage, oxidative stress, and DNA damage checkpoint regulation by 48 hours. Our findings demonstrate the possibility of using organoids to characterize cell-specific radiosensitivity and early radiation-induced gene expression changes within the human brain, providing new avenues for further study of the mechanisms underlying acute neural cell responses to IR exposure at low-to-moderate doses.

Manifold visualisation techniques are commonly used to visualise high-dimensional datasets in physical sciences. In this paper, we apply a recently introduced manifold visualisation method, slisemap, on datasets from physics and chemistry. slisemap combines manifold visualisation with explainable artificial intelligence. Explainable artificial intelligence investigates the decision processes of black box machine learning models and complex simulators. With slisemap, we find an embedding such that data items with similar local explanations are grouped together. Hence, slisemap gives us an overview of the different behaviours of a black box model, where the patterns in the embedding reflect a target property. In this paper, we show how slisemap can be used and evaluated on physical data and that it is helpful in finding meaningful information on classification and regression models trained on these datasets. [ABSTRACT FROM AUTHOR]

Power laws are of interest to several scientific disciplines because they can provide important information about the underlying dynamics (e.g. scale invariance and self-similarity) of a given system. Because power laws are of increasing interest to the cardiac sciences as potential indicators of cardiac dysfunction, it is essential that rigorous, standardized analytical methods are employed in the evaluation of power laws. This study compares the methods currently used in the fields of condensed matter physics, geoscience, neuroscience, and cardiology in order to provide a robust analytical framework for evaluating power laws in stem cell-derived cardiomyocyte cultures. One potential power law-obeying phenomenon observed in these cultures is pacemaker translocations, or the spatial and temporal instability of the pacemaker region, in a 2D cell culture. Power law analysis of translocation data was performed using increasingly rigorous methods in order to illustrate how differences in analytical robustness can result in misleading power law interpretations. Non-robust methods concluded that pacemaker translocations adhere to a power law while robust methods convincingly demonstrated that they obey a doubly truncated power law. The results of this study highlight the importance of employing comprehensive methods during power law analysis of cardiomyocyte cultures.

Transcranial ultrasound stimulation (tUS) shows potential as a noninvasive brain stimulation (NIBS) technique, offering increased spatial precision compared to other NIBS techniques. However, its reported effects on primary motor cortex (M1) are limited. We aimed to better understand tUS effects in human M1 by performing tUS of the hand area of M1 (M1hand) during tonic muscle contraction of the index finger. Stimulation during muscle contraction was chosen because of the transcranial magnetic stimulation-induced phenomenon known as cortical silent period (cSP), in which transcranial magnetic stimulation (TMS) of M1hand involuntarily suppresses voluntary motor activity. Since cSP is widely considered an inhibitory phenomenon, it presents an ideal parallel for tUS, which has often been proposed to preferentially influence inhibitory interneurons. Recording electromyography (EMG) of the first dorsal interosseous (FDI) muscle, we investigated effects on muscle activity both during and after tUS. We found no change in FDI EMG activity concurrent with tUS stimulation. Using single-pulse TMS, we found no difference in M1 excitability before versus after sparsely repetitive tUS exposure. Using acoustic simulations in models made from structural MRI of the participants that matched the experimental setups, we estimated in-brain pressures and generated an estimate of cumulative tUS exposure experienced by M1hand for each subject. We were unable to find any correlation between cumulative M1hand exposure and M1 excitability change. We also present data that suggest a TMS-induced MEP always preceded a near-threshold cSP.

PurposeA previously developed ordinary differential equation (ODE) that models the dynamic interaction and distinct radiosensitivity between cancer stem cells (CSC) and differentiated cancer cells (DCC) was used to explain the definitive treatment failure in Glioblastoma Multiforme (GBM) for conventionally and hypo-fractionated treatments. In this study, optimization of temporal dose modulation based on the ODE equation is performed to explore the feasibility of improving GBM treatment outcome.MethodsA non-convex optimization problem with the objective of minimizing the total cancer cell number while maintaining the normal tissue biological effective dose (BEDnormal) at 100 Gy, equivalent to the conventional 2 Gy × 30 dosing scheme was formulated. With specified total number of dose fractions and treatment duration, the optimization was performed using a paired simulated annealing algorithm with fractional doses delivered to the CSC and DCC compartments and time intervals between fractions as variables. The recurrence time, defined as the time point at which the total tumor cell number regrows to 2.8×109 cells, was used to evaluate optimization outcome. Optimization was performed for conventional treatment time frames equivalent to currently and historically utilized fractionation schemes, in which limited improvement in recurrence time delay was observed. The efficacy of a super hyperfractionated approach with a prolonged treatment duration of one year was therefore tested, with both fixed regular and optimized variable time intervals between dose fractions corresponding to total number of fractions equivalent to weekly, bi-weekly, and monthly deliveries (n = 53, 27, 13). Optimization corresponding to BEDnormal of 150 Gy was also obtained to evaluate the possibility in further recurrence delay with dose escalation.ResultsFor the super hyperfractionated schedules with dose fraction number equivalent to weekly, bi-weekly, and monthly deliveries, the recurrence time points were found to be 430.5, 423.9, and 413.3 days, respectively, significantly delayed compared with the recurrence time of 250.3 days from conventional fractionation. Results show that optimal outcome was achieved by first delivering infrequent fractions followed by dense once per day fractions in the middle and end of the treatment course, with sparse and low dose treatments in the between. The dose to the CSC compartment was held relatively constant throughout while larger dose fractions to the DCC compartment were observed in the beginning and final fractions that preceded large time intervals. Dose escalation to BEDnormal of 150 Gy was shown capable of further delaying recurrence time to 452 days.ConclusionThe development and utilization of a temporal dose fractionation optimization framework in the context of CSC dynamics have demonstrated that substantial delay in GBM local tumor recurrence could be achieved with a super hyperfractionated treatment approach. Preclinical and clinical studies are needed to validate the efficacy of this novel treatment delivery method.

The aim of this work was to use TOPAS Monte Carlo simulations to model the effect of magnetic fields on dose distributions in brachytherapy lung treatments, under ideal and clinical conditions. Idealistic studies were modeled consisting of either a monoenergetic electron source of 432 keV, or a polyenergetic electron source using the spectrum of secondary electrons produced by 192Ir gamma-ray irradiation. The electron source was positioned in the center of a homogeneous, lung tissue phantom (ρ = 0.26 g/cm3). Conversely, the clinical study was simulated using the VariSource VS2000 192Ir source in a patient with a lung tumor. Three contoured volumes were considered: the tumor, the planning tumor volume (PTV), and the lung. In all studies, dose distributions were calculated in the presence or absence of a constant magnetic field of 3T. Also, TG-43 parameters were calculated for the VariSource and compared with published data from EGS-brachy (EGSnrc) and PENELOPE. The magnetic field affected the dose distributions in the idealistic studies. For the monoenergetic and poly-energetic studies, the radial distance of the 10% iso-dose line was reduced in the presence of the magnetic field by 64.9% and 24.6%, respectively. For the clinical study, the magnetic field caused differences of 10% on average in the patient dose distributions. Nevertheless, differences in dose-volume histograms were below 2%. Finally, for TG-43 parameters, the dose-rate constant from TOPAS differed by 0.09% ± 0.33% and 0.18% ± 0.33% with respect to EGS-brachy and PENELOPE, respectively. The geometry and anisotropy functions differed within 1.2% ± 1.1%, and within 0.0% ± 0.3%, respectively. The Lorentz forces inside a 3T magnetic resonance machine during 192Ir brachytherapy treatment of the lung are not large enough to affect the tumor dose distributions significantly, as expected. Nevertheless, large local differences were found in the lung tissue. Applications of this effect are therefore limited by the fact that meaningful differences appeared only in regions containing air, which is not abundant inside the human.

Despite the diversity in fish auditory structures, it remains elusive how otolith morphology and swim bladder-inner ear (= otophysic) connections affect otolith motion and inner ear stimulation. A recent study visualized sound-induced otolith motion; but tank acoustics revealed a complex mixture of sound pressure and particle motion. To separate sound pressure and sound-induced particle motion, we constructed a transparent standing wave tube-like tank equipped with an inertial shaker at each end while using X-ray phase contrast imaging. Driving the shakers in phase resulted in maximised sound pressure at the tank centre, whereas particle motion was maximised when shakers were driven out of phase (180°). We studied the effects of two types of otophysic connections-i.e. the Weberian apparatus (Carassius auratus) and anterior swim bladder extensions contacting the inner ears (Etroplus canarensis)-on otolith motion when fish were subjected to a 200 Hz stimulus. Saccular otolith motion was more pronounced when the swim bladder walls oscillated under the maximised sound pressure condition. The otolith motion patterns mainly matched the orientation patterns of ciliary bundles on the sensory epithelia. Our setup enabled the characterization of the interplay between the auditory structures and provided first experimental evidence of how different types of otophysic connections affect otolith motion.

The left ventricular (LV) end-systolic (ES) pressure volume relationship (ESPVR) is the cornerstone of systolic LV function analysis. We describe a 2D real-time (RT) MRI-based method (RTPVR) with separate software tools for 1) semi-automatic level set-based shape prior method (LSSPM) of the LV, 2) generation of synchronized pressure area loops and 3) calculation of the ESPVR. We used the RTPVR method to measure ventricular geometry, ES pressure area relationship (ESPAR) and ESPVR during vena cava occlusion (VCO) in normal sheep. 14 adult sheep were anesthetized and underwent measurement of LV systolic function. Ten of the 14 sheep underwent RTMRI and eight of the 14 underwent measurement with conductance catheter; 4 had both RTMRI and conductance measurements. 2D cross sectional RTMRI were performed at apex, mid-ventricle and base levels during separate VCOs. The Dice similarity coefficient was used to compare LSSPM and manual image segmentation and thus determine LSSPM accuracy. LV cross-sectional area, major and minor axis length, axis ratio, major axis orientation angle and ESPAR were measured at each LV level. ESPVR was calculated with a trapezoidal rule. The Dice similarity coefficient between LSSPM and manual segmentation by two readers was 87.31±2.51% and 88.13±3.43%. All cross sections became more elliptical during VCO. The major axis orientation shifted during VCO but remained in the septo-lateral direction. LV chamber obliteration at the apical level occurred during VCO in 7 of 10 sheep that underwent RTMRI. ESPAR was non-linear at all levels. Finally, ESPVR was non-linear because of apical collapse. ESPVR measured by conductance catheter (EES,Index = 2.23±0.66 mmHg/ml/m2) and RT (EES,Index = 2.31±0.31 mmHg/ml/m2) was not significantly different. LSSPM segmentation of 2D RT MRI images is accurate and allows calculation of LV geometry, ESPAR and ESPVR during VCO. In the future, RTPVR will facilitate determination of regional systolic material parameters underlying ESPVR.

We have previously demonstrated that the use of a commercially-available immersion-based optical clearing agent (OCA) enables, within 3-6 hours, three-dimensional visualization of subsurface exogenous fluorescent and absorbing markers of vascular architecture and neurodegenerative disease in thick (0.5-1.0mm) mouse brain sections. Nonetheless, the relative performance of immersion-based OCAs has remained unknown. Here, we show that immersion of brain sections in specific OCAs (FocusClear, RIMS, sRIMS, or ScaleSQ) affects both their transparency and volume; the optical clearing effect occurs over the entire visible spectrum and is reversible; and that ScaleSQ had the highest optical clearing potential and increase in imaging depth of the four evaluated OCAs, albeit with the largest change in sample volume and a concomitant decrease in apparent microvascular density of the sample. These results suggest a rational, quantitative framework for screening and characterization of the impact of optical clearing, to streamline experimental design and enable a cost-benefit assessment.

[This corrects the article DOI: 10.1371/journal.pone.0180374.].

In retinal raster imaging modalities, fixational eye movements manifest as image warp, where the relative positions of the beam and retina change during the acquisition of single frames. To remove warp artifacts, strip-based registration methods-in which fast-axis strips from target images are registered to a reference frame-have been applied in adaptive optics (AO) scanning light ophthalmoscopy (SLO) and optical coherence tomography (OCT). This approach has enabled object tracking and frame averaging, and methods have been described to automatically select reference frames with minimal motion. However, inconspicuous motion artifacts may persist in reference frames and propagate themselves throughout the processes of registration, tracking, and averaging. Here we test a previously proposed method for removing movement artifacts in reference frames, using biases in stripwise cross-correlation statistics. We applied the method to synthetic retinal images with simulated eye motion artifacts as well as real AO-SLO images of the cone mosaic and volumetric AO-OCT images, both affected by eye motion. In the case of synthetic images, the method was validated by direct comparison with motion-free versions of the images. In the case of real AO images, performance was validated by comparing the correlation of uncorrected images with that of corrected images, by quantifying the effect of motion artifacts on the image power spectra, and by qualitative examination of AO-OCT B-scans and en face projections. In all cases, the proposed method reduced motion artifacts and produced more faithful images of the retina.

Compositional data, which is data consisting of fractions or probabilities, is common in many fields including ecology, economics, physical science and political science. If these data would otherwise be normally distributed, their spread can be conveniently represented by a multivariate normal distribution truncated to the non-negative space under a unit simplex. Here this distribution is called the simplex-truncated multivariate normal distribution. For calculations on truncated distributions, it is often useful to obtain rapid estimates of their integral, mean and covariance; these quantities characterising the truncated distribution will generally possess different values to the corresponding non-truncated distribution. In this paper, three different approaches that can estimate the integral, mean and covariance of any simplex-truncated multivariate normal distribution are described and compared. These three approaches are (1) naive rejection sampling, (2) a method described by Gessner et al. that unifies subset simulation and the Holmes-Diaconis-Ross algorithm with an analytical version of elliptical slice sampling, and (3) a semi-analytical method that expresses the integral, mean and covariance in terms of integrals of hyperrectangularly-truncated multivariate normal distributions, the latter of which are readily computed in modern mathematical and statistical packages. Strong agreement is demonstrated between all three approaches, but the most computationally efficient approach depends strongly both on implementation details and the dimension of the simplex-truncated multivariate normal distribution. For computations in low-dimensional distributions, the semi-analytical method is fast and thus should be considered. As the dimension increases, the Gessner et al. method becomes the only practically efficient approach of the methods tested here. [ABSTRACT FROM AUTHOR]

A model-based clustering method for compositional data is explored in this article. Most methods for compositional data analysis require some kind of transformation. The proposed method builds a mixture model using Dirichlet distribution which works with the unit sum constraint. The mixture model uses a hard EM algorithm with some modification to overcome the problem of fast convergence with empty clusters. This work includes a rigorous simulation study to evaluate the performance of the proposed method over varied dimensions, number of clusters, and overlap. The performance of the model is also compared with other popular clustering algorithms often used for compositional data analysis (e.g. KMeans, Gaussian mixture model (GMM) Gaussian Mixture Model with Hard EM (Hard GMM), partition around medoids (PAM), Clustering Large Applications based on Randomized Search (CLARANS), Density-Based Spatial Clustering of Applications with Noise (DBSCAN) etc.) for simulated data as well as two real data problems coming from the business and marketing domain and physical science domain, respectively. The study has shown promising results exploiting different distributional patterns of compositional data. [ABSTRACT FROM AUTHOR]

Graphlets are small connected induced subgraphs of a larger graph G. Graphlets are now commonly used to quantify local and global topology of networks in the field. Methods exist to exhaustively enumerate all graphlets (and their orbits) in large networks as efficiently as possible using orbit counting equations. However, the number of graphlets in G is exponential in both the number of nodes and edges in G. Enumerating them all is already unacceptably expensive on existing large networks, and the problem will only get worse as networks continue to grow in size and density. Here we introduce an efficient method designed to aid statistical sampling of graphlets up to size k = 8 from a large network. We define graphettes as the generalization of graphlets allowing for disconnected graphlets. Given a particular (undirected) graphette g, we introduce the idea of the canonical graphette [Formula: see text] as a representative member of the isomorphism group Iso(g) of g. We compute the mapping [Formula: see text], in the form of a lookup table, from all 2k(k - 1)/2 undirected graphettes g of size k ≤ 8 to their canonical representatives [Formula: see text], as well as the permutation that transforms g to [Formula: see text]. We also compute all automorphism orbits for each canonical graphette. Thus, given any k ≤ 8 nodes in a graph G, we can in constant time infer which graphette it is, as well as which orbit each of the k nodes belongs to. Sampling a large number N of such k-sets of nodes provides an approximation of both the distribution of graphlets and orbits across G, and the orbit degree vector at each node.

Exposure to a small number of high-energy heavy charged particles (HZE ions), as found in the deep space environment, could significantly affect astronaut health following prolonged periods of space travel if these ions induce mutations and related cancers. In this study, we used an in vivo mutagenesis assay to define the mutagenic effects of accelerated 56Fe ions (1 GeV/amu, 151 keV/μm) in the mouse kidney epithelium exposed to doses ranging from 0.25 to 2.0 Gy. These doses represent fluences ranging from 1 to 8 particle traversals per cell nucleus. The Aprt locus, located on chromosome 8, was used to select induced and spontaneous mutants. To fully define the mutagenic effects, we used multiple endpoints including mutant frequencies, mutation spectrum for chromosome 8, translocations involving chromosome 8, and mutations affecting non-selected chromosomes. The results demonstrate mutagenic effects that often affect multiple chromosomes for all Fe ion doses tested. For comparison with the most abundant sparsely ionizing particle found in space, we also examined the mutagenic effects of high-energy protons (1 GeV, 0.24 keV/μm) at 0.5 and 1.0 Gy. Similar doses of protons were not as mutagenic as Fe ions for many assays, though genomic effects were detected in Aprt mutants at these doses. Considered as a whole, the data demonstrate that Fe ions are highly mutagenic at the low doses and fluences of relevance to human spaceflight, and that cells with considerable genomic mutations are readily induced by these exposures and persist in the kidney epithelium. The level of genomic change produced by low fluence exposure to heavy ions is reminiscent of the extensive rearrangements seen in tumor genomes suggesting a potential initiation step in radiation carcinogenesis.

Current approaches to dynamic community detection in complex networks can fail to identify multi-scale community structure, or to resolve key features of community dynamics. We propose a targeted node removal technique to improve the resolution of community detection. Using synthetic oscillator networks with well-defined "ground truth" communities, we quantify the community detection performance of a common modularity maximization algorithm. We show that the performance of the algorithm on communities of a given size deteriorates when these communities are embedded in multi-scale networks with communities of different sizes, compared to the performance in a single-scale network. We demonstrate that targeted node removal during community detection improves performance on multi-scale networks, particularly when removing the most functionally cohesive nodes. Applying this approach to network neuroscience, we compare dynamic functional brain networks derived from fMRI data taken during both repetitive single-task and varied multi-task experiments. After the removal of regions in visual cortex, the most coherent functional brain area during the tasks, community detection is better able to resolve known functional brain systems into communities. In addition, node removal enables the algorithm to distinguish clear differences in brain network dynamics between these experiments, revealing task-switching behavior that was not identified with the visual regions present in the network. These results indicate that targeted node removal can improve spatial and temporal resolution in community detection, and they demonstrate a promising approach for comparison of network dynamics between neuroscientific data sets with different resolution parameters.

ObjectiveThe brain's functional architecture of interconnected network-related oscillatory patterns in discrete cortical regions has been well established with functional magnetic resonance imaging (fMRI) studies or direct cortical electrophysiology from electrodes placed on the surface of the brain, or electrocorticography (ECoG). These resting state networks exhibit a robust functional architecture that persists through all stages of sleep and under anesthesia. While the stability of these networks provides a fundamental understanding of the organization of the brain, understanding how these regions can be perturbed is also critical in defining the brain's ability to adapt while learning and recovering from injury.MethodsPatients undergoing an awake craniotomy for resection of a tumor were studied as a unique model of an evolving injury to help define how the cortical physiology and the associated networks were altered by the presence of an invasive brain tumor.ResultsThis study demonstrates that there is a distinct pattern of alteration of cortical physiology in the setting of a malignant glioma. These changes lead to a physiologic sequestration and progressive synaptic homogeneity suggesting that a de-learning phenomenon occurs within the tumoral tissue compared to its surroundings.SignificanceThese findings provide insight into how the brain accommodates a region of "defunctionalized" cortex. Additionally, these findings may have important implications for emerging techniques in brain mapping using endogenous cortical physiology.

We describe an 8-spot confocal setup for high-throughput smFRET assays and illustrate its performance with two characteristic experiments. First, measurements on a series of freely diffusing doubly-labeled dsDNA samples allow us to demonstrate that data acquired in multiple spots in parallel can be properly corrected and result in measured sample characteristics consistent with those obtained with a standard single-spot setup. We then take advantage of the higher throughput provided by parallel acquisition to address an outstanding question about the kinetics of the initial steps of bacterial RNA transcription. Our real-time kinetic analysis of promoter escape by bacterial RNA polymerase confirms results obtained by a more indirect route, shedding additional light on the initial steps of transcription. Finally, we discuss the advantages of our multispot setup, while pointing potential limitations of the current single laser excitation design, as well as analysis challenges and their solutions.

A new methodology based on tensor algebra that uses a higher order singular value decomposition to perform three-dimensional voxel reconstruction from a series of temporal images obtained using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is proposed. Principal component analysis (PCA) is used to robustly extract the spatial and temporal image features and simultaneously de-noise the datasets. Tumour segmentation on enhanced scaled (ES) images performed using a fuzzy C-means (FCM) cluster algorithm is compared with that achieved using the proposed tensorial framework. The proposed algorithm explores the correlations between spatial and temporal features in the tumours. The multi-channel reconstruction enables improved breast tumour identification through enhanced de-noising and improved intensity consistency. The reconstructed tumours have clear and continuous boundaries; furthermore the reconstruction shows better voxel clustering in tumour regions of interest. A more homogenous intensity distribution is also observed, enabling improved image contrast between tumours and background, especially in places where fatty tissue is imaged. The fidelity of reconstruction is further evaluated on the basis of five new qualitative metrics. Results confirm the superiority of the tensorial approach. The proposed reconstruction metrics should also find future applications in the assessment of other reconstruction algorithms.

Discussions about science and engineering postdoctoral researchers focus almost exclusively on academic postdocs and their chances of eventually securing tenure-track faculty positions. Further, biological sciences dominate policy research and published advice for new PhDs regarding postdoctoral employment. Our analysis uses the Survey of Earned Doctorates and Survey of Doctorate Recipients to understand employment implications for physical sciences and engineering (PSE) and life sciences (LS) graduates who took postdoctoral positions in government, industry, and academic sectors. We examine postdoc duration, reasons for staying in a postdoc, movement between sectors, and salary implications. There is considerable movement between employment sectors within the first six years post-PhD. Additionally, postdocs in PSE are shorter, better paid, and more often in nonacademic sectors than postdocs in LS. These results can help science and engineering faculty discuss a broader range of career pathways with doctoral students and help new PhDs make better informed early career decisions. [ABSTRACT FROM AUTHOR]

DNA sequencing by imaging in an electron microscope is an approach that holds promise to deliver long reads with low error rates and without the need for amplification. Earlier work using transmission electron microscopes, which use high electron energies on the order of 100 keV, has shown that low contrast and radiation damage necessitates the use of heavy atom labeling of individual nucleotides, which increases the read error rates. Other prior work using scattering electrons with much lower energy has shown to suppress beam damage on DNA. Here we explore possibilities to increase contrast by employing two methods, X-ray photoelectron and Auger electron spectroscopy. Using bulk DNA samples with monomers of each base, both methods are shown to provide contrast mechanisms that can distinguish individual nucleotides without labels. Both spectroscopic techniques can be readily implemented in a low energy electron microscope, which may enable label-free DNA sequencing by direct imaging.

Single-molecule Förster Resonance Energy Transfer (smFRET) allows probing intermolecular interactions and conformational changes in biomacromolecules, and represents an invaluable tool for studying cellular processes at the molecular scale. smFRET experiments can detect the distance between two fluorescent labels (donor and acceptor) in the 3-10 nm range. In the commonly employed confocal geometry, molecules are free to diffuse in solution. When a molecule traverses the excitation volume, it emits a burst of photons, which can be detected by single-photon avalanche diode (SPAD) detectors. The intensities of donor and acceptor fluorescence can then be related to the distance between the two fluorophores. While recent years have seen a growing number of contributions proposing improvements or new techniques in smFRET data analysis, rarely have those publications been accompanied by software implementation. In particular, despite the widespread application of smFRET, no complete software package for smFRET burst analysis is freely available to date. In this paper, we introduce FRETBursts, an open source software for analysis of freely-diffusing smFRET data. FRETBursts allows executing all the fundamental steps of smFRET bursts analysis using state-of-the-art as well as novel techniques, while providing an open, robust and well-documented implementation. Therefore, FRETBursts represents an ideal platform for comparison and development of new methods in burst analysis. We employ modern software engineering principles in order to minimize bugs and facilitate long-term maintainability. Furthermore, we place a strong focus on reproducibility by relying on Jupyter notebooks for FRETBursts execution. Notebooks are executable documents capturing all the steps of the analysis (including data files, input parameters, and results) and can be easily shared to replicate complete smFRET analyzes. Notebooks allow beginners to execute complex workflows and advanced users to customize the analysis for their own needs. By bundling analysis description, code and results in a single document, FRETBursts allows to seamless share analysis workflows and results, encourages reproducibility and facilitates collaboration among researchers in the single-molecule community.

PurposeAnalyze inter-fraction volumetric changes of lung tumors treated with stereotactic body radiation therapy (SBRT) and determine if the volume changes during treatment can be predicted and thus considered in treatment planning.Methods and materialsKilo-voltage cone-beam CT (kV-CBCT) images obtained immediately prior to each fraction were used to monitor inter-fraction volumetric changes of 15 consecutive patients (18 lung nodules) treated with lung SBRT at our institution (45-54 Gy in 3-5 fractions) in the year of 2011-2012. Spearman's (ρ) correlation and Spearman's partial correlation analysis was performed with respect to patient/tumor and treatment characteristics. Multiple hypothesis correction was performed using False Discovery Rate (FDR) and q-values were reported.ResultsAll tumors studied experienced volume change during treatment. Tumor increased in volume by an average of 15% and regressed by an average of 11%. The overall volume increase during treatment is contained within the planning target volume (PTV) for all tumors. Larger tumors increased in volume more than smaller tumors during treatment (q = 0.0029). The volume increase on CBCT was correlated to the treatment planning gross target volume (GTV) as well as internal target volumes (ITV) (q = 0.0085 and q = 0.0039 respectively) and could be predicted for tumors with a GTV less than 22 mL. The volume increase was correlated to the integral dose (ID) in the ITV at every fraction (q = 0.0049). The peak inter-fraction volume occurred at an earlier fraction in younger patients (q = 0.0122).ConclusionsWe introduced a new analysis method to follow inter-fraction tumor volume changes and determined that the observed changes during lung SBRT treatment are correlated to the initial tumor volume, integral dose (ID), and patient age. Furthermore, the volume increase during treatment of tumors less than 22mL can be predicted during treatment planning. The volume increase remained significantly less than the overall PTV expansion, and radiation re-planning was therefore not required for the purpose of tumor control. The presence of the studied correlations suggests that the observed volumetric changes may reflect some underlying biologic process rather than random fluctuations.

Automated segmentation is a useful method for studying large brain structures such as the cerebellum and brainstem. However, automated segmentation may lead to inaccuracy and/or undesirable boundary. The goal of the present study was to investigate whether SegAdapter, a machine learning-based method, is useful for automatically correcting large segmentation errors and disagreement in anatomical definition. We further assessed the robustness of the method in handling size of training set, differences in head coil usage, and amount of brain atrophy. High resolution T1-weighted images were acquired from 30 healthy controls scanned with either an 8-channel or 32-channel head coil. Ten patients, who suffered from brain atrophy because of fragile X-associated tremor/ataxia syndrome, were scanned using the 32-channel head coil. The initial segmentations of the cerebellum and brainstem were generated automatically using Freesurfer. Subsequently, Freesurfer's segmentations were both manually corrected to serve as the gold standard and automatically corrected by SegAdapter. Using only 5 scans in the training set, spatial overlap with manual segmentation in Dice coefficient improved significantly from 0.956 (for Freesurfer segmentation) to 0.978 (for SegAdapter-corrected segmentation) for the cerebellum and from 0.821 to 0.954 for the brainstem. Reducing the training set size to 2 scans only decreased the Dice coefficient ≤0.002 for the cerebellum and ≤ 0.005 for the brainstem compared to the use of training set size of 5 scans in corrective learning. The method was also robust in handling differences between the training set and the test set in head coil usage and the amount of brain atrophy, which reduced spatial overlap only by

Correlative fluorescence light microscopy and electron microscopy allows the imaging of spatial distributions of specific biomolecules in the context of cellular ultrastructure. Recent development of super-resolution fluorescence microscopy allows the location of molecules to be determined with nanometer-scale spatial resolution. However, correlative super-resolution fluorescence microscopy and electron microscopy (EM) still remains challenging because the optimal specimen preparation and imaging conditions for super-resolution fluorescence microscopy and EM are often not compatible. Here, we have developed several experiment protocols for correlative stochastic optical reconstruction microscopy (STORM) and EM methods, both for un-embedded samples by applying EM-specific sample preparations after STORM imaging and for embedded and sectioned samples by optimizing the fluorescence under EM fixation, staining and embedding conditions. We demonstrated these methods using a variety of cellular targets.

In support of the RNA world hypothesis, previous studies identified trimetaphosphate (Tmp) as a plausible energy source for RNA world organisms. In one of these studies, catalytic RNAs (ribozymes) that catalyze the triphosphorylation of RNA 5'-hydroxyl groups using Tmp were obtained by in vitro selection. One ribozyme (TPR1) was analyzed in more detail. TPR1 catalyzes the triphosphorylation reaction to a rate of 0.013 min-1 under selection conditions (50 mM Tmp, 100 mM MgCl2, 22°C). To identify a triphosphorylation ribozyme that catalyzes faster triphosphorylation, and possibly learn about its secondary structure TPR1 was subjected to a doped selection. The resulting ribozyme, TPR1e, contains seven mutations relative to TPR1, displays a previously unidentified duplex that constrains the ribozyme's structure, and reacts at a 24-fold faster rate than the parent ribozyme. Under optimal conditions (150 mM Tmp, 650 mM MgCl2, 40°C), the triphosphorylation rate of TRP1e reaches 6.8 min-1.

Receptor coated resonant nanoparticles and quantum dots are proposed to provide a cellular-level resolution image of neural activities inside the brain. The functionalized nanoparticles and quantum dots in this approach will selectively bind to different neurotransmitters in the extra-synaptic regions of neurons. This allows us to detect neural activities in real time by monitoring the nanoparticles and quantum dots optically. Gold nanoparticles (GNPs) with two different geometries (sphere and rod) and quantum dots (QDs) with different sizes were studied along with three different neurotransmitters: dopamine, gamma-Aminobutyric acid (GABA), and glycine. The absorption/emission spectra of GNPs and QDs before and after binding of neurotransmitters and their corresponding receptors are reported. The results using QDs and nanorods with diameter 25nm and aspect rations larger than three were promising for the development of the proposed functional brain mapping approach.

Transcranial Magnetic Stimulation (TMS) is an important tool for testing causal relationships in cognitive neuroscience research. However, the efficacy of TMS can be variable across individuals and difficult to measure. This variability is especially a challenge when TMS is applied to regions without well-characterized behavioral effects, such as in studies using TMS on multi-modal areas in intrinsic networks. Here, we examined whether perfusion fMRI recordings of Cerebral Blood Flow (CBF), a quantitative measure sensitive to slow functional changes, reliably index variability in the effects of stimulation. Twenty-seven participants each completed four combined TMS-fMRI sessions during which both resting state Blood Oxygen Level Dependent (BOLD) and perfusion Arterial Spin Labeling (ASL) scans were recorded. In each session after the first baseline day, continuous theta-burst TMS (TBS) was applied to one of three locations: left dorsolateral prefrontal cortex (L dlPFC), left anterior insula/frontal operculum (L aI/fO), or left primary somatosensory cortex (L S1). The two frontal targets are components of intrinsic networks and L S1 was used as an experimental control. CBF changes were measured both before and after TMS on each day from a series of interleaved resting state and perfusion scans. Although TBS led to weak selective increases under the coil in CBF measurements across the group, individual subjects showed wide variability in their responses. TBS-induced changes in rCBF were related to TBS-induced changes in functional connectivity of the relevant intrinsic networks measured during separate resting-state BOLD scans. This relationship was selective: CBF and functional connectivity of these networks were not related before TBS or after TBS to the experimental control region (S1). Furthermore, subject groups with different directions of CBF change after TBS showed distinct modulations in the functional interactions of targeted networks. These results suggest that CBF is a marker of individual differences in the effects of TBS.

Recent research demonstrates that white matter of the brain contains not only long T2 components, but a minority of ultrashort T2* components. Adiabatic inversion recovery prepared dual echo ultrashort echo time (IR-dUTE) sequences can be used to selectively image the ultrashort T2* components in white matter of the brain using a clinical whole body scanner. The T2*s of the ultrashort T2* components can be quantified using mono-exponential decay fitting of the IR-dUTE signal at a series of different TEs. However, accurate T1 measurement of the ultrashort T2* components is technically challenging. Efficient suppression of the signal from the majority of long T2 components is essential for robust T1 measurement. In this paper we describe a novel approach to this problem based on the use of IR-dUTE data acquisitions with different TR and TI combinations to selectively detect the signal recovery of the ultrashort T2* components. Exponential recovery curve fitting provides efficient T1 estimation, with minimized contamination from the majority of long T2 components. A rubber phantom and a piece of bovine cortical bone were used for validation of this approach. Six healthy volunteers were studied. An averaged T2* of 0.32 ± 0.09 ms, and a short mean T1 of 226 ± 46 ms were demonstrated for the healthy volunteers at 3T.

Background and purposeBladder radiotherapy is a management option for carefully selected patients with muscle-invasive bladder cancer. However, the inability to visualize the tumor site during treatment and normal bladder movement limits targeting accuracy and increases collateral radiation. A means to accurately and reliably target the bladder during radiotherapy is needed.Materials and methodsEighteen consecutive patients with muscle-invasive bladder cancer (T1-T4) elected bladder-preserving treatment with maximal transurethral resection (TUR), radiation and concurrent chemotherapy. All underwent endoscopic placement of 24-K gold fiducial markers modified with micro-tines (70 [2.9×0.9 mm.]; 19 [2.1×0.7 mm.) into healthy submucosa 5-10 mm. from the resection margin, using custom-made coaxial needles. Marker migration was assessed for with intra-op bladder-filling cystogram and measurement of distance between markers. Set-up error and marker retention through completion of radiotherapy was confirmed by on-table portal imaging.ResultsBetween 1/2007 and 7/2012, a total of 89 markers (3-5 per tumor site) were placed into 18 patients of mean age 73.6 years. Two patients elected cystectomy before starting treatment; 16/18 completed chemo-radiotherapy. All (100%) markers were visible with all on-table (portal, cone-beam CT), fluoroscopy, plain-film, and CT-scan imaging. In two patients, 1 of 4 markers placed at the tumor site fell-out (voided) during the second half of radiotherapy. All other markers (80/82, 98%) were present through the end of radio-therapy. No intraoperative (e.g. uncontrolled bleeding, collateral injury) or post-operative complications (e.g. stone formation, urinary tract infection, post-TUR hematuria >48 hours) occurred. Use of micro-tined fiducial tumor-site markers afforded a 2 to 6-fold reduction in bladder-area targeted with high-dose radiation.DiscussionPlacement of the micro-tined fiducial markers into the bladder was feasible and associated with excellent retention-rate and no complications. All markers were well-visualized during radiotherapy with all imaging modalities. Bladder fiducial markers improve targeting accuracy, and may increase treatment efficacy and reduce morbidity from collateral radiation.

Four-stranded G-quadruplex DNA secondary structures have recently been visualized in the nuclei of human cultured cells. Here, we show that BG4, a G-quadruplex-specific antibody, can be used to stain DNA G-quadruplex structures in patient-derived tissues using immunohistochemistry. We observe a significantly elevated number of G-quadruplex-positive nuclei in human cancers of the liver and stomach as compared to background non-neoplastic tissue. Our results suggest that G-quadruplex formation can be detected and measured in patient-derived material and that elevated G-quadruplex formation may be a characteristic of some cancers.

A number of social-ecological systems exhibit complex behaviour associated with nonlinearities, bifurcations, and interaction with stochastic drivers. These systems are often prone to abrupt and unexpected instabilities and state shifts that emerge as a discontinuous response to gradual changes in environmental drivers. Predicting such behaviours is crucial to the prevention of or preparation for unwanted regime shifts. Recent research in ecology has investigated early warning signs that anticipate the divergence of univariate ecosystem dynamics from a stable attractor. To date, leading indicators of instability in systems with multiple interacting components have remained poorly investigated. This is a major limitation in the understanding of the dynamics of complex social-ecological networks. Here, we develop a theoretical framework to demonstrate that rising variance--measured, for example, by the maximum element of the covariance matrix of the network--is an effective leading indicator of network instability. We show that its reliability and robustness depend more on the sign of the interactions within the network than the network structure or noise intensity. Mutualistic, scale free and small world networks are less stable than their antagonistic or random counterparts but their instability is more reliably predicted by this leading indicator. These results provide new advances in multidimensional early warning analysis and offer a framework to evaluate the resilience of social-ecological networks.

Electron tomography (ET) of biological samples is used to study the organization and the structure of the whole cell and subcellular complexes in great detail. However, projections cannot be acquired over full tilt angle range with biological samples in electron microscopy. ET image reconstruction can be considered an ill-posed problem because of this missing information. This results in artifacts, seen as the loss of three-dimensional (3D) resolution in the reconstructed images. The goal of this study was to achieve isotropic resolution with a statistical reconstruction method, sequential maximum a posteriori expectation maximization (sMAP-EM), using no prior morphological knowledge about the specimen. The missing wedge effects on sMAP-EM were examined with a synthetic cell phantom to assess the effects of noise. An experimental dataset of a multivesicular body was evaluated with a number of gold particles. An ellipsoid fitting based method was developed to realize the quantitative measures elongation and contrast in an automated, objective, and reliable way. The method statistically evaluates the sub-volumes containing gold particles randomly located in various parts of the whole volume, thus giving information about the robustness of the volume reconstruction. The quantitative results were also compared with reconstructions made with widely-used weighted backprojection and simultaneous iterative reconstruction technique methods. The results showed that the proposed sMAP-EM method significantly suppresses the effects of the missing information producing isotropic resolution. Furthermore, this method improves the contrast ratio, enhancing the applicability of further automatic and semi-automatic analysis. These improvements in ET reconstruction by sMAP-EM enable analysis of subcellular structures with higher three-dimensional resolution and contrast than conventional methods.

Head movement during a dynamic brain PET/CT imaging results in mismatch between CT and dynamic PET images. It can cause artifacts in CT-based attenuation corrected PET images, thus affecting both the qualitative and quantitative aspects of the dynamic PET images and the derived parametric images. In this study, we developed an automated retrospective image-based movement correction (MC) procedure. The MC method first registered the CT image to each dynamic PET frames, then re-reconstructed the PET frames with CT-based attenuation correction, and finally re-aligned all the PET frames to the same position. We evaluated the MC method's performance on the Hoffman phantom and dynamic FDDNP and FDG PET/CT images of patients with neurodegenerative disease or with poor compliance. Dynamic FDDNP PET/CT images (65 min) were obtained from 12 patients and dynamic FDG PET/CT images (60 min) were obtained from 6 patients. Logan analysis with cerebellum as the reference region was used to generate regional distribution volume ratio (DVR) for FDDNP scan before and after MC. For FDG studies, the image derived input function was used to generate parametric image of FDG uptake constant (Ki) before and after MC. Phantom study showed high accuracy of registration between PET and CT and improved PET images after MC. In patient study, head movement was observed in all subjects, especially in late PET frames with an average displacement of 6.92 mm. The z-direction translation (average maximum = 5.32 mm) and x-axis rotation (average maximum = 5.19 degrees) occurred most frequently. Image artifacts were significantly diminished after MC. There were significant differences (P

Improved understanding of how the human brain is "wired" on a macroscale may now be possible due to the emerging field of MRI connectomics. However, mapping the rapidly developing infant brain networks poses challenges. In this study, we applied an automated template-free "baby connectome" framework using diffusion MRI to non-invasively map the structural brain networks in subjects of different ages, including premature neonates, term-born neonates, six-month-old infants, and adults. We observed increasing brain network integration and decreasing segregation with age in term-born subjects. We also explored how the equal area nodes can be grouped into modules without any prior anatomical information--an important step toward a fully network-driven registration and analysis of brain connectivity.

PurposeTo minimize feature loss in T1- and T2-weighted MRI by merging multiple MR images acquired at different TR and TE to generate an image with increased dynamic range.Materials and methodsHigh Dynamic Range (HDR) processing techniques from the field of photography were applied to a series of acquired MR images. Specifically, a method to parameterize the algorithm for MRI data was developed and tested. T1- and T2-weighted images of a number of contrast agent phantoms and a live mouse were acquired with varying TR and TE parameters. The images were computationally merged to produce HDR-MR images. All acquisitions were performed on a 7.05 T Bruker PharmaScan with a multi-echo spin echo pulse sequence.ResultsHDR-MRI delineated bright and dark features that were either saturated or indistinguishable from background in standard T1- and T2-weighted MRI. The increased dynamic range preserved intensity gradation over a larger range of T1 and T2 in phantoms and revealed more anatomical features in vivo.ConclusionsWe have developed and tested a method to apply HDR processing to MR images. The increased dynamic range of HDR-MR images as compared to standard T1- and T2-weighted images minimizes feature loss caused by magnetization recovery or low SNR.

ObjectiveTo test the hypothesis that wide area detector face transplant surgical planning CT angiograms with simulated lower radiation dose and iterative reconstruction (AIDR3D) are comparable in image quality to those with standard tube current and filtered back projection (FBP) reconstruction.Materials and methodsThe sinograms from 320-detector row CT angiography of four clinical candidates for face transplantation were processed utilizing standard FBP, FBP with simulated 75, 62, and 50% tube current, and AIDR3D with corresponding dose reduction. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) were measured at muscle, fat, artery, and vein. Image quality for each reconstruction strategy was assessed by two independent readers using a 4-point scale.ResultsCompared to FBP, the median SNR and CNR for AIDR3D images were higher at all sites for all 4 different tube currents. The AIDR3D with simulated 50% tube current achieved comparable SNR and CNR to FBP with standard dose (median muscle SNR: 5.77 vs. 6.23; fat SNR: 6.40 vs. 5.75; artery SNR: 43.8 vs. 45.0; vein SNR: 54.9 vs. 55.7; artery CNR: 38.1 vs. 38.6; vein CNR: 49.0 vs. 48.7; all p-values >0.19). The interobserver agreement in the image quality score was good (weighted κ = 0.7). The overall score and the scores for smaller arteries were significantly lower when FBP with 50% dose reduction was used. The AIDR3D reconstruction images with 4 different simulated doses achieved a mean score ranging from 3.68 to 3.82 that were comparable to the scores from images reconstructed using FBP with original dose (3.68-3.77).ConclusionsSimulated radiation dose reduction applied to clinical CT angiography for face transplant planning suggests that AIDR3D allows for a 50% reduction in radiation dose, as compared to FBP, while preserving image quality.

We present the design, synthesis and characterization of new functionalized fluorescent optical switches for rapid, all-visible light-mediated manipulation of fluorescence signals from labelled structures within living cells, and as probes for high-contrast optical lock-in detection (OLID) imaging microscopy. A triazole-substituted BIPS (TzBIPS) is identified from a rational synthetic design strategy that undergoes robust, rapid and reversible, visible light-driven transitions between a colorless spiro- (SP) and a far-red absorbing merocyanine (MC) state within living cells. The excited MC-state of TzBIPS may also decay to the MC-ground state emitting near infra-red fluorescence, which is used as a sensitive and quantitative read-out of the state of the optical switch in living cells. The SP to MC transition for a membrane-targeted TzBIPS probe (C₁₂-TzBIPS) is triggered at 405 nm at an energy level compatible with studies in living cells, while the action spectrum of the reverse transition (MC to SP) has a maximum at 650 nm. The SP to MC transition is complete within the 790 ns pixel dwell time of the confocal microscope, while a single cycle of optical switching between the SP and MC states in a region of interest is complete within 8 ms (125 Hz) within living cells, the fastest rate attained for any optical switch probe in a biological sample. This property can be exploited for real-time correction of background signals in living cells. A reactive form of TzBIPS is linked to secondary antibodies and used, in conjunction with an enhanced scope-based analysis of the modulated MC-fluorescence in immuno-stained cells, for high-contrast immunofluorescence microscopic analysis of the actin cytoskeleton.

Based on partially-coherent digital in-line holography, we report a field-portable microscope that can render lensfree colour images over a wide field-of-view of e.g., >20 mm(2). This computational holographic microscope weighs less than 145 grams with dimensions smaller than 17×6×5 cm, making it especially suitable for field settings and point-of-care use. In this lensfree imaging design, we merged a colorization algorithm with a source shifting based multi-height pixel super-resolution technique to mitigate 'rainbow' like colour artefacts that are typical in holographic imaging. This image processing scheme is based on transforming the colour components of an RGB image into YUV colour space, which separates colour information from brightness component of an image. The resolution of our super-resolution colour microscope was characterized using a USAF test chart to confirm sub-micron spatial resolution, even for reconstructions that employ multi-height phase recovery to handle dense and connected objects. To further demonstrate the performance of this colour microscope Papanicolaou (Pap) smears were also successfully imaged. This field-portable and wide-field computational colour microscope could be useful for tele-medicine applications in resource poor settings.

Plasmid pRN1 from Sulfolobus islandicus REN1H1 is believed to replicate by a rolling circle mechanism but its origin and mechanism of replication are not well understood. We sought to create minimal expression vectors based on pRN1 that would be useful for heterologous gene expression in S. acidocaldarius, and in the process improve our understanding of the mechanism of replication. We constructed and transformed shuttle vectors that harbored different contiguous stretches of DNA from pRN1 into S. acidocaldarius E4-39, a uracil auxotroph. A 232-bp region 3' of orf904 was found to be critical for pRN1 replication and is therefore proposed to be the putative origin of replication. This 232-bp region contains a 100-bp stem-loop structure believed to be the double-strand origin of replication. The loop of the 100-bp structure contains a GTG tri-nucleotide motif, a feature that was previously reported to be important for the primase activity of Orf904. This putative origin and the associated orf56 and orf904 were identified as the minimal replicon of pRN1 because transformants of plasmids lacking any of these three features were not recovered. Plasmids lacking orf904 and orf56 but harboring the putative origin were transformable when orf904 and orf56 were provided in-trans; a 75-bp region 5' of the orf904 start codon was found to be essential for this complementation. Detailed knowledge of the pRN1 origin of replication will broaden the application of the plasmid as a genetic tool for Sulfolobus species.

In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.

We report Giga-pixel lensfree holographic microscopy and tomography using color sensor-arrays such as CMOS imagers that exhibit Bayer color filter patterns. Without physically removing these color filters coated on the sensor chip, we synthesize pixel super-resolved lensfree holograms, which are then reconstructed to achieve ~350 nm lateral resolution, corresponding to a numerical aperture of ~0.8, across a field-of-view of ~20.5 mm(2). This constitutes a digital image with ~0.7 Billion effective pixels in both amplitude and phase channels (i.e., ~1.4 Giga-pixels total). Furthermore, by changing the illumination angle (e.g., ± 50°) and scanning a partially-coherent light source across two orthogonal axes, super-resolved images of the same specimen from different viewing angles are created, which are then digitally combined to synthesize tomographic images of the object. Using this dual-axis lensfree tomographic imager running on a color sensor-chip, we achieve a 3D spatial resolution of ~0.35 µm × 0.35 µm × ~2 µm, in x, y and z, respectively, creating an effective voxel size of ~0.03 µm(3) across a sample volume of ~5 mm(3), which is equivalent to >150 Billion voxels. We demonstrate the proof-of-concept of this lensfree optical tomographic microscopy platform on a color CMOS image sensor by creating tomograms of micro-particles as well as a wild-type C. elegans nematode.

BackgroundRadial intra- and interlaminar connections form a basic microcircuit in primary auditory cortex (AI) that extracts acoustic information and distributes it to cortical and subcortical networks. Though the structure of this microcircuit is known, we do not know how the functional connectivity between layers relates to laminar processing.Methodology/principal findingsWe studied the relationships between functional connectivity and receptive field properties in this columnar microcircuit by simultaneously recording from single neurons in cat AI in response to broadband dynamic moving ripple stimuli. We used spectrotemporal receptive fields (STRFs) to estimate the relationship between receptive field parameters and the functional connectivity between pairs of neurons. Interlaminar connectivity obtained through cross-covariance analysis reflected a consistent pattern of information flow from thalamic input layers to cortical output layers. Connection strength and STRF similarity were greatest for intralaminar neuron pairs and in supragranular layers and weaker for interlaminar projections. Interlaminar connection strength co-varied with several STRF parameters: feature selectivity, phase locking to the stimulus envelope, best temporal modulation frequency, and best spectral modulation frequency. Connectivity properties and receptive field relationships differed for vertical and horizontal connections.Conclusions/significanceThus, the mode of local processing in supragranular layers differs from that in infragranular layers. Therefore, specific connectivity patterns in the auditory cortex shape the flow of information and constrain how spectrotemporal processing transformations progress in the canonical columnar auditory microcircuit.

Several areas, such as physical and health sciences, require the use of matrices as fundamental tools for solving various problems. Matrices are used in real-life contexts, such as control, automation, and optimization, wherein results are expected to improve with increase of computational precision. However, special attention should be paid to ill-conditioned matrices, which can produce unstable systems; an inadequate handling of precision might worsen results since the solution found for data with errors might be too far from the one for data without errors besides increasing other costs in hardware resources and critical paths. In this paper, we make a wake-up call, using 2 × 2 matrices to show how ill-conditioning and precision can affect system design (resources, cost, etc.). We first demonstrate some examples of real-life problems where ill-conditioning is present in matrices obtained from the discretization of the operational equations (ill-posed in the sense of Hadamard) that model these problems. If these matrices are not handled appropriately (i.e., if ill-conditioning is not considered), large errors can result in the computed solutions to the systems of equations in the presence of errors. Furthermore, we illustrate the generated effect in the calculation of the inverse of an ill-conditioned matrix when its elements are approximated by truncation. We present two case studies to illustrate the effects on calculation errors caused by increasing or reducing precision to s digits. To illustrate the costs, we implemented the adjoint matrix inversion algorithm on different field-programmable gate arrays (FPGAs), namely, Spartan-7, Artix-7, Kintex-7, and Virtex-7, using the full-unrolling hardware technique. The implemented architecture is useful for analyzing trade-offs when precision is increased; this also helps analyze performance, efficiency, and energy consumption. By means of a detailed description of the trade-offs among these metrics, concerning precision and ill-conditioning, we conclude that the need for resources seems to grow not linearly when precision is increased. We also conclude that, if error is to be reduced below a certain threshold, it is necessary to determine an optimal precision point. Otherwise, the system becomes more sensitive to measurement errors and a better alternative would be to choose precision carefully, and/or to apply regularization or preconditioning methods, which would also reduce the resources required. [ABSTRACT FROM AUTHOR]

Chromosomal aberrations are relatively frequent pathologies in both humans and animals. Among them, translocations present a specific meiotic segregation pattern able to give a higher percentage of unbalanced gametes that can induce fertility problems. In this study, the meiotic segregation patterns of 1p, 1q and 18 Bubalus bubalis chromosomes were analyzed in both total sperm fraction and motile sperm fraction of a t(1p;18) carrier and a control bulls by triple-color FISH analysis with a pool of specific BAC probes. The frequencies of each total sperm fraction products in the carrier resulting from alternate, adjacent I, adjacent II and 3:1 segregation were 39%, 20%, 1% and 38%, respectively. On the other hand, the frequencies of each motile sperm fraction products in the carrier resulting from alternate, adjacent I, adjacent II and 3:1 segregation were 93%, 5%, 0% and 2%, respectively. The frequencies of normal sperms in the carrier were 27% and 69% in total sperm fraction and motile sperm fraction, respectively. The frequencies detected in motile sperm fraction were also validated by comparison with bull's progeny. To our knowledge, this is the first report on the meiotic segregation patterns in motile sperm fractions of B. bubalis bull carrying a chromosomal translocation. These data suggest that translocation has a very limited effect on aneuploidy in the gametes, and therefore, on the reproductive abilities of the bull. [ABSTRACT FROM AUTHOR]

This study examines differences across demographic subgroups in the phenomenon of recent doctoral recipients seeking work but having no job offers for employment. Gender and race/ethnicity have been identified as two characteristics with considerable issues of representation in a number of science and engineering fields, particularly at the doctoral level. Using the NSF Survey of Earned Doctorates dataset, which includes over 298,000 respondents in the biological sciences, engineering, and physical sciences since 1977, we use logistic regression modelling to examine the likelihood of doctoral recipients having no offers at the time of graduation as a function of race, gender, family and funding variables. We find that across the fields of biology, engineering, and physical sciences, women and underrepresented minorities have a higher prevalence of having no job offers, but this relationship has notable interaction effects for family variables and doctoral program funding mechanism. Importantly, marital status accounts for differences in job offers between genders that deserves further exploration. [ABSTRACT FROM AUTHOR]

What factors predict the underreporting of sexual harassment in academe? We used logistic regression and sentiment analysis to examine 2,343 reports of sexual harassment involving members of university communities. Results indicate students were 1.6 times likely to not report their experiences when compared to faculty. Respondents in the life and physical sciences were 1.7 times more likely to not report their experiences when compared to respondents in other disciplines. Men represented 90% of the reported perpetrators of sexual harassment. Analysis of respondents' written accounts show variation of overall sentiment based on discipline, student type, and the type of institution attended, particularly with regard to mental health. Our results suggest that institutional and departmental barriers driven by power asymmetries play a large role in the underreporting sexual harassment among students—especially those in STEM disciplines. [ABSTRACT FROM AUTHOR]

Protracted conflict is one of the largest human challenges that have persistently undermined economic and social progress. In recent years, there has been increased emphasis on using statistical and physical science models to better understand both the universal patterns and the underlying mechanics of conflict. Whilst macroscopic power-law fractal patterns have been shown for death-toll in wars and self-excitation models have been shown for roadside ambush attacks, very few works deal with the challenge of complex dynamics between gangs at the intra-city scale. Here, based on contributions to the historical memory of the conflict in Colombia, Medellin's gang-confrontation-network is presented. It is shown that socio-economic and violence indexes are moderate to highly correlated to the structure of the network. Specifically, the death-toll of conflict is strongly influenced by the leading eigenvalues of the gangs' conflict adjacency matrix, which serves a proxy for unstable self-excitation from revenge attacks. The distribution of links based on the geographic distance between gangs in confrontation leads to the confirmation that territorial control is a main catalyst of violence and retaliation among gangs. As a first attempt to explore the time evolution of the confrontation network, the Boltzmann-Lotka-Volterra (BLV) dynamic interaction network analysis is applied to quantify the spatial embeddedness of the dynamic relationship between conflicting gangs in Medellin. However, the non-stationary character of the violence in Medellin during the observation period restricts the application of the BLV model and results suggest that more involved and comprehensive models are needed to described the dynamics of Medellin's armed conflict. [ABSTRACT FROM AUTHOR]