Using Covariates to Improve the Efficacy of Univariate Bubble Detection Methods

We explore how information additional to a specific price series can be used to improve the power of popular univariate autoregressive-based methods for detecting and dating speculative price bubble episodes. Following Phillips, Wu and Yu (2011) and Phillips, Shi and Yu (2015) we base our approach on sequences of sub-sample regression-based augmented Dickey-Fuller [ADF] statistics. Our point of departure from these extant procedures is to allow for additional information in the testing and dating procedures. To do so we follow the approach of Hansen (1995) and augment the sub-sample ADF regressions with covariate regressors. The limiting null distributions of the resulting statistics depend on the long-run squared correlation between the covariates and the regression error. We show that this dependence can be accounted for by using a residual bootstrap re-sampling method. Simulation evidence shows that including relevant covariates can significantly improve the efficacy of both the resulting bubble detection tests and the associated date-stamping procedure, relative to using standard sub-sample ADF statistics. An empirical application of the proposed methodology to monthly S&P 500 data is considered, using a variety of candidate covariates. Using these covariates, the onset of the dotcom bubble and the bubble associated with Black Monday are both identified significantly earlier than when using standard methods

The acute effects of motor imagery and cervical transcutaneous electrical stimulation on manual dexterity and neural excitability

Transcutaneous electrical stimulation (TCES) of the spinal cord induces changes in spinal excitability. Motor imagery (MI) elicits plasticity in the motor cortex. It has been suggested that plasticity occurring in both cortical and spinal circuits might underlie the improvements in performance observed when training is combined with stimulation. We investigated the acute effects of cervical TCES and MI delivered in isolation or combined on corticospinal excitability, spinal excitability and manual performance. Participants (N = 17) completed three sessions during which they engaged in 20 min of: 1) MI, listening to an audio recording instructing to complete the purdue pegboard test (PPT) of manual performance; 2) TCES at the spinal level of C5–C6; 3) MI + TCES, listening to the MI script while receiving TCES. Before and after each condition, we measured corticospinal excitability via transcranial magnetic stimulation (TMS) at 100% and 120% motor threshold (MT), spinal excitability via single-pulse TCES and manual performance with the PPT. Manual performance was not improved by MI, TCES or MI + TCES. Corticospinal excitability assessed at 100% MT intensity increased in hand and forearm muscles after MI and MI + TCES, but not after just TCES. Conversely, corticospinal excitability assessed at 120% MT intensity was not affected by any of the conditions. The effects on spinal excitability depended on the recorded muscle: it increased after all conditions in biceps brachii (BB) and flexor carpi radialis (FCR); did not change after any conditions in the abductor pollicis brevis (APB); increased after TCES and MI + TCES, but not after just MI in the extensor carpi radialis (ECR). These findings suggest that MI and TCES increase the excitability of the central nervous system through different but complementary mechanisms, inducing changes in the excitability of spinal and cortical circuits. MI and TCES can be used in combination to modulate spinal/cortical excitability, an approach particularly relevant for people with limited residual dexterity who cannot engage in motor practice

Acute Effects of Strength and Skill Training on the Cortical and Spinal Circuits of Contralateral Limb

Unilateral strength and skill training increase strength and performance in the contralateral untrained limb, a phenomenon known as cross-education. Recent evidence suggests that similar neural mechanisms might be responsible for the increase in strength and skill observed in the untrained hand after unimanual training. The aims of this study were to: investigate whether a single session of unimanual strength and skill (force-tracking) training increased strength and skill in the opposite hand; measure ipsilateral (untrained) brain (via transcranial magnetic stimulation, TMS) and spinal (via the monosynaptic reflex) changes in excitability occurring after training; measure ipsilateral (untrained) pathway-specific changes in neural excitability (via TMS-conditioning of the monosynaptic reflex) occurring after training. Participants (N = 13) completed a session of unimanual strength (ballistic isometric wrist flexions) and skill (force-tracking wrist flexions) training on two separate days. Strength increased after training in the untrained hand (p = 0.025) but not in the trained hand (p = 0.611). Force-tracking performance increased in both the trained (p = 0.007) and untrained (p = 0.010) hand. Corticospinal excitability increased after force-tracking and strength training (p = 0.027), while spinal excitability was not affected (p = 0.214). TMS-conditioned monosynaptic reflex increased after force-tracking (p = 0.001) but not strength training (p = 0.689), suggesting a possible role of polysynaptic pathways in the increase of cortical excitability observed after training. The results suggest that cross-education of strength and skill at the acute stage is supported by increased excitability of the untrained motor cortex. New & Noteworthy: A single session of isometric wrist flexion strength and skill straining increased strength and skill in the untrained limb. The excitability of the untrained motor cortex increased after strength and skill training. TMS-conditioned H-reflexes increased after skill but not strength training in the untrained hand, indicating that polysynaptic pathways in the increase of cortical excitability observed after skill training

Robust Tests for Deterministic Seasonality and Seasonal Mean Shifts

We develop tests for the presence of deterministic seasonal behaviour and seasonal mean shifts in a seasonally observed univariate time series. These tests are designed to be asymptotically robust to the order of integration of the series at both the zero and seasonal frequencies. Motivated by the approach of Hylleberg, Engle, Granger and Yoo [1990, Journal of Econometrics vol. 44, pp. 215-238], we base our approach on linear filters of the data which remove any potential unit roots at the frequencies not associated with the deterministic component(s) under test. Test statistics are constructed using the filtered data such that they have well defined limiting null distributions regardless of whether the data are either integrated or stationary at the frequency associated with the deterministic component(s) under test. In the same manner as Vogelsang [1998, Econometrica vol. 66, pp. 123-148], Bunzel and Vogelsang [2005, Journal of Business and Economic Statistics vol. 23, pp. 381-394] and Sayginsoy and Vogelsang [2011, Econometric Theory vol. 27, pp. 992-1025], we scale these statistics by a function of an auxiliary seasonal unit root statistic. This allows us to construct tests which are asymptotically robust to the order of integration of the data at both the zero and seasonal frequencies. Monte Carlo evidence suggests that our proposed tests have good finite sample size and power properties. An empirical application to U.K. GDP indicates the presence of seasonal mean shifts in the data

Accuracy of identification of tissue types in endoscopic esophageal mucosal biopsies used for molecular biology studies

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Consolidation/reconsolidation therapies for the prevention and treatment of PTSD and re-experiencing: a systematic review and meta-analysis

Translational research highlights the potential of novel 'memory consolidation/reconsolidation therapies' to treat re-experiencing symptoms and post-traumatic stress disorder (PTSD). This systematic review and meta-analysis assessed the efficacy of so-called memory consolidation/reconsolidation therapies in randomised controlled trials (RCTs) for prevention and treatment of PTSD and symptoms of re-experiencing in children and adults (PROSPERO: CRD42020171167). RCTs were identified and rated for risk of bias. Available data was pooled to calculate risk ratios (RR) for PTSD prevalence and standardised mean differences (SMD) for PTSD/re-experiencing severity. Twenty-five RCTs met inclusion criteria (16 prevention and nine treatment trials). The methodology of most studies had a significant risk of bias. We found a large effect of reconsolidation interventions in the treatment of PTSD (11 studies, n = 372, SMD: −1.42 (−2.25 to −0.58), and a smaller positive effect of consolidation interventions in the prevention of PTSD (12 studies, n = 2821, RR: 0.67 (0.50 to 0.90). Only three protocols (hydrocortisone for PTSD prevention, Reconsolidation of Traumatic Memories (RTM) for treatment of PTSD symptoms and cognitive task memory interference procedure with memory reactivation (MR) for intrusive memories) were superior to control. There is some emerging evidence of consolidation and reconsolidation therapies in the prevention and treatment of PTSD and intrusive memories specifically. Translational research should strictly adhere to protocols/procedures describing precise reconsolidation conditions (e.g. MR) to both increase the likelihood of positive findings and more confidently interpret negative findings of putative reconsolidation agents

Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus

Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched

Corrigendum: Detecting and Predicting Emerging Disease in Poultry With the Implementation of New Technologies and Big Data: A Focus on Avian Influenza Virus

Future demands for food will place agricultural systems under pressure to increase production. Poultry is accepted as a good source of protein and the poultry industry will be forced to intensify production in many countries, leading to greater numbers of farms that house birds at elevated densities. Increasing farmed poultry can facilitate enhanced transmission of infectious pathogens among birds, such as avian influenza virus among others, which have the potential to induce widespread mortality in poultry and cause considerable economic losses. Additionally, the capability of some emerging poultry pathogens to cause zoonotic human infection will be increased as greater numbers of poultry operations could increase human contact with poultry pathogens. In order to combat the increased risk of spread of infectious disease in poultry due to intensified systems of production, rapid detection and diagnosis is paramount. In this review, multiple technologies that can facilitate accurate and rapid detection and diagnosis of poultry diseases are highlighted from the literature, with a focus on technologies developed specifically for avian influenza virus diagnosis. Rapid detection and diagnostic technologies allow for responses to be made sooner when disease is detected, decreasing further bird transmission and associated costs. Additionally, systems of rapid disease detection produce data that can be utilized in decision support systems that can predict when and where disease is likely to emerge in poultry. Other sources of data can be included in predictive models, and in this review two highly relevant sources, internet based-data and environmental data, are discussed. Additionally, big data and big data analytics, which will be required in order to integrate voluminous and variable data into predictive models that function in near real-time are also highlighted. Implementing new technologies in the commercial setting will be faced with many challenges, as will designing and operating predictive models for poultry disease emergence. The associated challenges are summarized in this review. Intensified systems of poultry production will require new technologies for detection and diagnosis of infectious disease. This review sets out to summarize them, while providing advantages and limitations of different types of technologies being researched