Solomonoff Induction Violates Nicod's Criterion

Nicod's criterion states that observing a black raven is evidence for the hypothesis H that all ravens are black. We show that Solomonoff induction does not satisfy Nicod's criterion: there are time steps in which observing black ravens decreases the belief in H. Moreover, while observing any computable infinite string compatible with H, the belief in H decreases infinitely often when using the unnormalized Solomonoff prior, but only finitely often when using the normalized Solomonoff prior. We argue that the fault is not with Solomonoff induction; instead we should reject Nicod's criterion.Comment: ALT 201

Improved social functioning following social recovery therapy in first episode psychosis: Do social cognition and neurocognition change following therapy, and do they predict treatment response?

There is a need to develop and refine psychosocial interventions to improve functioning in First Episode Psychosis (FEP). Social cognition and neurocognition are closely linked to functioning in psychosis; examinations of cognition pre- and post- psychosocial intervention may provide insights into the mechanisms of these interventions, and identify which individuals are most likely to benefit. Method: Cognition was assessed within a multi-site trial of Social Recovery Therapy (SRT) for individuals with FEP experiencing poor functioning (<30 h weekly structured activity). Fifty-nine participants were randomly allocated to the therapy group (SRT + Early intervention), and 64 were allocated to treatment as usual group (TAU - early intervention care). Social cognition and neurocognition were assessed at baseline and 9 months; assessors were blind to group allocation. It was hypothesized that social cognition would improve following therapy, and those with better social cognition prior to therapy would benefit the most from SRT. Results: There was no significant impact of SRT on individual neurocognitive or social cognitive variables, however, joint models addressing patterns of missingness demonstrate improvement across a number of cognitive outcomes following SRT. Further, regression analyses showed those who had better social cognition at baseline were most likely to benefit from the therapy (ß = 0.350; 95% CI = 0.830 to 8.891; p = .019). Conclusion: It is not clear if SRT impacts on social cognitive or neurocognitive function, however, SRT may be beneficial in those with better social cognition at baseline

A review of camera trapping for conservation behaviour research

An understanding of animal behaviour is important if conservation initiatives are to be effective. However, quantifying the behaviour of wild animals presents significant challenges. Remote-sensing camera traps are becoming increasingly popular survey instruments that have been used to non-invasively study a variety of animal behaviours, yielding key insights into behavioural repertoires. They are well suited to ethological studies and provide considerable opportunities for generating conservation-relevant behavioural data if novel and robust methodological and analytical solutions can be developed. This paper reviews the current state of camera-trap-based ethological studies, describes new and emerging directions in camera-based conservation behaviour, and highlights a number of limitations and considerations of particular relevance for camera-based studies. Three promising areas of study are discussed: (1) documenting anthropogenic impacts on behaviour; (2) incorporating behavioural responses into management planning and (3) using behavioural indicators such as giving up densities and daily activity patterns. We emphasize the importance of reporting methodological details, utilizing emerging camera trap metadata standards and central data repositories for facilitating reproducibility, comparison and synthesis across studies. Behavioural studies using camera traps are in their infancy; the full potential of the technology is as yet unrealized. Researchers are encouraged to embrace conservation-driven hypotheses in order to meet future challenges and improve the efficacy of conservation and management processes

Developmental hypoxia has negligible effects on long-term hypoxia tolerance and aerobic metabolism of Atlantic salmon (Salmo salar)

Exposure to developmental hypoxia can have long-term impacts on the physiological performance of fish because of irreversible plasticity. Wild and captive-reared Atlantic salmon (Salmo salar) can be exposed to hypoxic conditions during development and continue to experience fluctuating oxygen levels as juveniles and adults.Here,we examine whether developmental hypoxia impacts subsequent hypoxia tolerance and aerobic performance of Atlantic salmon. Individuals at 8°C were exposed to 50% (hypoxia) or 100% (normoxia) dissolved oxygen (DO) saturation (as percent of air saturation) from fertilization for ∼100 d (800 degree days) and then raised in normoxic conditions for a further 15mo. At 18mo after fertilization, aerobic scope was calculated in normoxia (100% DO) and acute (18 h) hypoxia (50% DO) from the difference between the minimum and maximum oxygen consumption rates (ṀO2 min and ṀO2 max, respectively) at 107°C. Hypoxia tolerance was determined as the DO at which loss of equilibrium (LOE) occurred in a constantly decreasing DO environment. There was no difference in ṀO2 min, ṀO2 max, or aerobic scope between fish raised in hypoxia or normoxia. There was some evidence that hypoxia tolerance was lower (higher DO at LOE) in hypoxiaraised fish compared with those raised in normoxia, but the magnitude of the effect was small (12.52% DO vs. 11.73% DO at LOE). Acute hypoxia significantly reduced aerobic scope by reducing ṀO2 max, while ṀO2 min remained unchanged. Interestingly, acute hypoxia uncovered individual-level relationships between DO at LOE and ṀO2 min, ṀO2 max, and aerobic scope. We discuss our findings in the context of developmental trajectories and the role of aerobic performance in hypoxia tolerance

Systemic exosomal siRNA delivery reduced alpha-synuclein aggregates in brains of transgenic mice.

Alpha-synuclein (α-Syn) aggregates are the main component of Lewy bodies, which are the characteristic pathological feature in Parkinson's disease (PD) brain. Evidence that α-Syn aggregation can be propagated between neurones has led to the suggestion that this mechanism is responsible for the stepwise progression of PD pathology. Decreasing α-Syn expression is predicted to attenuate this process and is thus an attractive approach to delay or halt PD progression. We have used α-Syn small interfering RNA (siRNA) to reduce total and aggregated α-Syn levels in mouse brains. To achieve widespread delivery of siRNAs to the brain we have peripherally injected modified exosomes expressing Ravies virus glycoprotein loaded with siRNA. Normal mice were analyzed 3 or 7 days after injection. To evaluate whether this approach can decrease α-Syn aggregates, we repeated the treatment using transgenic mice expressing the human phosphorylation-mimic S129D α-Syn, which exhibits aggregation. In normal mice we detected significantly reduced α-Syn messenger RNA (mRNA) and protein levels throughout the brain 3 and 7 days after treatment with RVG-exosomes loaded with siRNA to α-Syn. In S129D α-Syn transgenic mice we found a decreased α-Syn mRNA and protein levels throughout the brain 7 days after injection. This resulted in significant reductions in intraneuronal protein aggregates, including in dopaminergic neurones of the substantia nigra. This study highlights the therapeutic potential of RVG-exosome delivery of siRNA to delay and reverse brain α-Syn pathological conditions

Gene mobility promotes the spread of resistance in bacterial populations

Theory predicts that horizontal gene transfer (HGT) expands the selective conditions under which genes spread in bacterial populations. Whereas vertically inherited genes can only spread by positively selected clonal expansion, mobile genetic elements can drive fixation of genes by infectious HGT. We tested this using populations of Pseudomonas fluorescens and the conjugative mercury resistance (Hg R) plasmid pQBR57. HGT expanded the selective conditions allowing the spread of Hg R: Chromosomal Hg R only increased in frequency under positive selection, whereas plasmid-encoded Hg R reached fixation with or without positive selection. Tracking plasmid dynamics over time revealed that the mode of Hg R inheritance varied across mercury environments. Under mercury selection, the spread of Hg R was driven primarily by clonal expansion while in the absence of mercury Hg R dynamics were dominated by infectious transfer. Thus, HGT is most likely to drive the spread of resistance genes in environments where resistance is useless

Beyond Volume: The Impact of Complex Healthcare Data on the Machine Learning Pipeline

From medical charts to national census, healthcare has traditionally operated under a paper-based paradigm. However, the past decade has marked a long and arduous transformation bringing healthcare into the digital age. Ranging from electronic health records, to digitized imaging and laboratory reports, to public health datasets, today, healthcare now generates an incredible amount of digital information. Such a wealth of data presents an exciting opportunity for integrated machine learning solutions to address problems across multiple facets of healthcare practice and administration. Unfortunately, the ability to derive accurate and informative insights requires more than the ability to execute machine learning models. Rather, a deeper understanding of the data on which the models are run is imperative for their success. While a significant effort has been undertaken to develop models able to process the volume of data obtained during the analysis of millions of digitalized patient records, it is important to remember that volume represents only one aspect of the data. In fact, drawing on data from an increasingly diverse set of sources, healthcare data presents an incredibly complex set of attributes that must be accounted for throughout the machine learning pipeline. This chapter focuses on highlighting such challenges, and is broken down into three distinct components, each representing a phase of the pipeline. We begin with attributes of the data accounted for during preprocessing, then move to considerations during model building, and end with challenges to the interpretation of model output. For each component, we present a discussion around data as it relates to the healthcare domain and offer insight into the challenges each may impose on the efficiency of machine learning techniques.Comment: Healthcare Informatics, Machine Learning, Knowledge Discovery: 20 Pages, 1 Figur

Near-field examination of perovskite-based superlenses and superlens-enhanced probe-object coupling

A planar slab of negative index material works as a superlens with sub-diffraction-limited imaging resolution, since propagating waves are focused and, moreover, evanescent waves are reconstructed in the image plane. Here, we demonstrate a superlens for electric evanescent fields with low losses using perovskites in the mid-infrared regime. The combination of near-field microscopy with a tunable free-electron laser allows us to address precisely the polariton modes, which are critical for super-resolution imaging. We spectrally study the lateral and vertical distributions of evanescent waves around the image plane of such a lens, and achieve imaging resolution of wavelength/14 at the superlensing wavelength. Interestingly, at certain distances between the probe and sample surface, we observe a maximum of these evanescent fields. Comparisons with numerical simulations indicate that this maximum originates from an enhanced coupling between probe and object, which might be applicable for multifunctional circuits, infrared spectroscopy, and thermal sensors.Comment: 20 pages, 6 figures, published as open access article in Nature Communications (see http://www.nature.com/ncomms/