Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference

Many functions and signals of interest are formed by the addition of multiple underlying components, often nonlinearly transformed and modified by noise. Examples may be found in the literature on Generalized Additive Models [1] and Underdetermined Source Separation [2] or other mode decomposition techniques. Recovery of the underlying component processes often depends on finding and exploiting statistical regularities within them. Gaussian Processes (GPs) [3] have become the dominant way to model statistical expectations over functions. Recent advances make inference of the GP posterior efficient for large scale datasets and arbitrary likelihoods [4,5]. Here we extend these methods to the additive GP case [6, 7], thus achieving scalable marginal posterior inference over each latent function in settings such as those above

Residual dynamics resolves recurrent contributions to neural computation

Relating neural activity to behavior requires an understanding of how neural computations arise from the coordinated dynamics of distributed, recurrently connected neural populations. However, inferring the nature of recurrent dynamics from partial recordings of a neural circuit presents considerable challenges. Here we show that some of these challenges can be overcome by a fine-grained analysis of the dynamics of neural residuals—that is, trial-by-trial variability around the mean neural population trajectory for a given task condition. Residual dynamics in macaque prefrontal cortex (PFC) in a saccade-based perceptual decision-making task reveals recurrent dynamics that is time dependent, but consistently stable, and suggests that pronounced rotational structure in PFC trajectories during saccades is driven by inputs from upstream areas. The properties of residual dynamics restrict the possible contributions of PFC to decision-making and saccade generation and suggest a path toward fully characterizing distributed neural computations with large-scale neural recordings and targeted causal perturbations

Non-reversible Gaussian processes for identifying latent dynamical structure in neural data

A common goal in the analysis of neural data is to compress large population recordings into sets of interpretable, low-dimensional latent trajectories. This problem can be approached using Gaussian process (GP)-based methods which provide uncertainty quantification and principled model selection. However, standard GP priors do not distinguish between underlying dynamical processes and other forms of temporal autocorrelation. Here, we propose a new family of “dynamical” priors over trajectories, in the form of GP covariance functions that express a property shared by most dynamical systems: temporal non-reversibility. Non-reversibility is a universal signature of autonomous dynamical systems whose state trajectories follow consistent flow fields, such that any observed trajectory could not occur in reverse. Our new multi-output GP kernels can be used as drop-in replacements for standard kernels in multivariate regression, but also in latent variable models such as Gaussian process factor analysis (GPFA). We therefore introduce GPFADS (Gaussian Process Factor Analysis with Dynamical Structure), which models single-trial neural population activity using low-dimensional, non-reversible latent processes. Unlike previously proposed non-reversible multi-output kernels, ours admits a Kronecker factorization enabling fast and memory-efficient learning and inference. We apply GPFADS to synthetic data and show that it correctly recovers ground truth phase portraits. GPFADS also provides a probabilistic generalization of jPCA, a method originally developed for identifying latent rotational dynamics in neural data. When applied to monkey M1 neural recordings, GPFADS discovers latent trajectories with strong dynamical structure in the form of rotations

Study of Radiophotoluminescence of Eu Doped CaSO4 Phosphor for Gamma Dosimetric Applications

CaSO4:Eu phosphor is synthesised by acid distillation method with varying synthesis parameters for studying its Radiophotoluminescence (RPL) properties for gamma absorbed dose measurements. Five phosphor samples are prepared by varying quantity of solvent, distillation temperature and reaction time. XRD, SEM, particle size analysis and photoluminescence studies are carried out. The characterisation study shows polycrystalline luminescent particles of average size varying from 35 μm to 55 μm. Characteristic emission of Eu3+ is observed around 590, 615 and 620 nm at 242 nm excitation. Gamma dose response of maximum Eu3+ PL intensity sample is studied in the range 10 cGy to 1000 cGy using Co-60 source. Gamma radiation exposure induces conversion of Eu3+ to Eu2+ giving luminescence at 385 nm with 320 nm excitation. Repetitive measurements of gamma exposed samples are carried out and no significant fading is observed within one week of post-irradiation. The phosphor has the potential to be used for gamma dosimetry

Prior context in audition informs binding and shapes simple features

A perceptual phenomenon is reported, whereby prior acoustic context has a large, rapid and long-lasting effect on a basic auditory judgement. Pairs of tones were devised to include ambiguous transitions between frequency components, such that listeners were equally likely to report an upward or downward ‘pitch’ shift between tones. We show that presenting context tones before the ambiguous pair almost fully determines the perceived direction of shift. The context effect generalizes to a wide range of temporal and spectral scales, encompassing the characteristics of most realistic auditory scenes. Magnetoencephalographic recordings show that a relative reduction in neural responsivity is correlated to the behavioural effect. Finally, a computational model reproduces behavioural results, by implementing a simple constraint of continuity for binding successive sounds in a probabilistic manner. Contextual processing, mediated by ubiquitous neural mechanisms such as adaptation, may be crucial to track complex sound sources over time

Autoimmune Pancreatitis: Disease Evolution, Staging, Response Assessment, and CT Features That Predict Response to Corticosteroid Therapy

Purpose:To evaluate the evolution of morphologic features of autoimmune pancreatitis (AIP) at computed tomography (CT) and to identify imaging features that can predict AIP response to corticosteroid therapy (CST). Materials and Methods: This HIPAA-compliant retrospective study had institutional review board approval. From among a cohort of 63 Patients with AIP, 15 Patients (12 men, three women, mean age, 64.7 years, age range, 30-84 years) who underwent sequential CT examinations before treatment were included to assess the evolution of disease by reviewing pancreatic, peripancreatic, and ductal changes. Of these Patients, 13 received CST and underwent posttreatment CT, these CT studies were evaluated to determine if there were imaging features that could predict response to CST. Results: The disease evolved from changes of diffuse (14 of 15 Patients) or focal (one of 15 Patients) parenchymal swelling, peripancreatic stranding (10 of 15 Patients), halo (nine of 15 Patients), pancreatic duct changes (15 of 15 Patients), and distal common bile duct narrowing (12 of 15 Patients) to either resolution or development of ductal strictures and/or focal masslike swelling. In 13 Patients treated with CST, favorable response to treatment was seen in those with diffuse pancreatic and peripancreatic changes. Suboptimal response was seen in Patients with ductal stricture formation (two of 13 Patients) and in those in whom focal masslike swellings persisted after resolution of diffuse changes (seven of 13 Patients). Conclusion: CT features like diffuse swelling and halo respond favorably to CST and likely reflect an early inflammatory phase, whereas features like ductal strictures and focal masslike swelling are predictive of a suboptimal response and symbolize a late stage with predominance of fibrosis

Intraventricular cystic meningioma

We report a case of a 45-year-old male patient with intraventricular cystic meningioma located in the left lateral ventricle. He presented with complaints of global headache, progressively increasing loss of memory, and frequent episodes of abnormal behavior, of 1 month duration. At the time of hospital admission, his general and neurological examination was normal. Neuroimaging studies showed a left lateral ventricular enhancing mass, composed of mixed solid and cystic areas. The tumor was completely excised via the anterior transcallosal approach. A histological examination revealed a meningothelial meningioma without any atypia. The aim of this report is to present the occurrence of an intraventricular cystic meningioma

F(T) Models within Bianchi Type I Universe

In this paper, we consider spatially homogenous and anisotropic Bianchi type I universe in the context of F(T) gravity. We construct some corresponding models using conservation equation and equation of state parameter representing different phases of the universe. In particular, we take matter dominated era, radiation dominated era, present dark energy phase and their combinations. It is found that one of the models has a constant solution which may correspond to the cosmological constant. We also derive equation of state parameter by using two well-known F(T) models and discuss cosmic acceleration.Comment: 19 pages, accepted for publication in Mod. Phys. Lett.

Magnetic resonance imaging biomarkers in hepatocellular carcinoma: association with response and circulating biomarkers after sunitinib therapy

Background: To investigate the hypothesis that MRI derived diffusion-weighted imaging (DWI) and perfusion (MRP) parameters are sensitive image biomarkers for monitoring early antiangiogenic effects and predicting progression free survival (PFS) in advanced hepatocellular carcinoma (HCC). Methods: In this phase II clinical trial, 23 of 34 patients were included in the imaging and circulating biomarker study. DWI and MRP were performed at the baseline and at 2-weeks after initiation of sunitinib. The imaging protocol included an axial DWI sequence using b values of 50, 400 and 800 sec/mm2, and MRP using a series of coronal 3D-VIBE following 20 ml of Gd-DTPA at 2 ml/sec. These parameters were compared with clinical outcome and PFS at 6-months. Correlation between changes in MRI parameters and plasma biomarkers was also evaluated. Results: After 2-week of sunitinib, substantial Ktrans changes in HCC were observed from median baseline value 2.15 min−1 to 0.94 min−1 (P = 0.0001) with increases in median apparent diffusion coefficient (ADC) from 0.88 × 10-3 mm2/s to 0.98 × 10-3 mm2/s (P = 0.0001). Tumor size remained unchanged by RECIST and mRECIST (both P > 0.05). Patients who showed larger drop in Ktrans and Kep at 2 weeks correlated with favorable clinical outcome, and higher baseline Ktrans and larger drop in EVF correlated with longer PFS (all P < 0.05). There was a significant association between a decrease in sVEGFR2 and the drop in Ktrans and Kep (P = 0.044, P = 0.030), and a significant and borderline association between decrease in TNF-α and the drop in Ktrans and Kep, respectively (P = 0.051, P = 0.035). Conclusion: In HCC, MRP may be a more sensitive biomarker in predicting early response and PFS following sunitinib than RECIST and mRECIST. Trial registration ClinicalTrials.gov: NCT0036130

Using empirical science education in schools to improve climate change literacy

Providing children with a clear understanding of climate change drivers and their mitigation is crucial for their roles as future earth stewards. To achieve this, it will be necessary to reverse the declining interest in STEM (Science, Technology, Engineering and Mathematics) education in schools in the UK and other countries, as STEM skills will be critical when designing effective mitigation solutions for climate change. The ‘Heat-Cool Initiative’ was co-designed and successfully implemented in five primary/secondary UK schools, as a playful learning tool to unleash student interest in STEM subjects. 103 students from two cohorts (years 5–6 and 7–9) participated in five Heat-Cool activity sessions where they used infrared cameras to explore the issue of urban heat. Their learning was evaluated using a multi-functional quantitative assessment, including pre- and postsession quizzes. Climate change literacy increased by 9.4% in primary school children and by 4.5% in secondary school children. Analyses of >2000 infrared images taken by students, categorised into 13 common themes, revealed age-related differences in children’s cognitive development. At primary school age, images of the ‘self’ dominated; secondary school children engaged more with their physical environment. This novel approach demonstrated the importance of developing tailored technology-enhanced STEM education programmes for different age cohorts, leading to a high capacity for improving learning outcomes regarding climate change. Such programmes, embedded in school curricula nationally and internationally, could become a much-needed positive contribution to reaching the United Nation’s Sustainable Development Goals, especially Goals 4 (Quality Education) and 13 (Climate Action)