Semaphorin-plexin signaling: From axonal guidance to a new X-linked intellectual disability syndrome

BACKGROUND: Semaphorins and plexins are ligands and cell surface receptors that regulate multiple neurodevelopmental processes such as axonal growth and guidance. PLXNA3 is a plexin gene located on the X chromosome that encodes the most widely expressed plexin receptor in fetal brain, plexin-A3. Plexin-A3 knockout mice demonstrate its role in semaphorin signaling in vivo. The clinical manifestations of semaphorin/plexin neurodevelopmental disorders have been less widely explored. This study describes the neurological and neurodevelopmental phenotypes of boys with maternally inherited hemizygous PLXNA3 variants. METHODS: Data-sharing through GeneDx and GeneMatcher allowed identification of individuals with autism or intellectual disabilities (autism/ID) and hemizygous PLXNA3 variants in collaboration with their physicians and genetic counselors, who completed questionnaires about their patients. In silico analyses predicted pathogenicity for each PLXNA3 variant. RESULTS: We assessed 14 boys (mean age, 10.7 [range 2 to 25] years) with maternally inherited hemizygous PLXNA3 variants and autism/ID ranging from mild to severe. Other findings included fine motor dyspraxia (92%), attention-deficit/hyperactivity traits, and aggressive behaviors (63%). Six patients (43%) had seizures. Thirteen boys (93%) with PLXNA3 variants showed novel or very low allele frequencies and probable damaging/disease-causing pathogenicity in one or more predictors. We found a genotype-phenotype correlation between PLXNA3 cytoplasmic domain variants (exons 22 to 32) and more severe neurodevelopmental disorder phenotypes (P \u3c 0.05). CONCLUSIONS: We report 14 boys with maternally inherited, hemizygous PLXNA3 variants and a range of neurodevelopmental disorders suggesting a novel X-linked intellectual disability syndrome. Greater understanding of PLXNA3 variant pathogenicity in humans will require additional clinical, computational, and experimental validation

Pyroclastic density currents (PDC) of the 16-17 August 2006 eruptions of Tungurahua volcano, Ecuador: Geophysical registry and characteristics

Tungurahua, located in the Eastern Cordillera of the Ecuadorian Andes, is a 5023 m-high active volcano, notable for its extreme relief (3200 m), steep sides, and frequent eruptive cycles. From 1999 until 2006 Tungurahua experienced short periods of low to moderate strombolian activity, characterized by fire fountaining, explosions, frequent ash falls and debris flows, and no PDC events. Without warning, Tungurahua initiated PDC activity on 15–16 July 2006, which became more intense on the night of 16–17 August 2006, which is the focus of this study. Continuous monitoring of Tungurahua has employed seismic (both short period and broadband (BB) instruments), SO2 gas emission (COSPEC and DOAS), and geodetic methods (EDM, tilt meters, and GPS), in addition to thermal imagery (airborne and ground-based). Acoustic flow monitors (AFM) installed to monitor lahar activity were important for detecting PDC events. Acoustic signals were monitored at Riobamba, 40 km to the SW, as well as by infrasound sensors at Tungurahua's BB seismic stations. Based on geophysical parameters, visual observations, and PDC deposit characteristics, four phases of distinct eruptive activity are recognized during the 16–17 August episode. Phase I (08H37 to 21H13 of 16 Aug.) (local time) experienced low to moderate strombolian activity with occasional high energy impulsive bursts and small PDC. Phase II (21H13-16 Aug. to 00H12-17 Aug.) was characterized by a number of discrete events with high amplitude seismo-acoustic signals, followed by the generation of larger PDC that overran monitoring stations and had velocities of 30–33 m/s. After midnight, Phase III (00H12 to 01H14) saw an intense period of unrelenting eruptive activity corresponding to the episode's greatest energy release. It was characterized by subplinian activity accompanied by a series of high energy outbursts and constant low frequency jetting that together formed a continuous plume. It was during this phase that the largest PDC were produced, reaching the surrounding river valleys. Phase IV (after 01H14) followed the cessation of the paroxysmal eruption, but witnessed many granular PDC generated by degassed lava spill outs from the crater that developed lobe and channel morphology on the cone's lower flanks. Hours later a blocky lava flow issued from the crater. During these episodes, more than 30 PDC events were detected, the majority being small flows that remained high on the cone. The two largest PDC occurred after midnight, probably generated by fountain collapse. Their descent down the cone's upper steep flanks (~ 28°) and 2.4 km in length favored air entrainment, resulting in PDC with greater fluidity. These flows had volumes of 9 to 17 × 106 m3 and produced widespread, but relatively thin (1–2 m thick) normally-graded deposits at their distal ends. The character and evolution of the PDC activity apparently reflect decreasing volatile contents of the magma and a diminishing magma supply

A psychometric evaluation of the Female Sexual Function Index in women treated for breast cancer.

BACKGROUND: We aimed to determine the psychometric properties and factor structure of the 19-item Female Sexual Function Index (FSFI) in 132 sexually active women previously treated for breast cancer. METHODS: Confirmatory factor analysis explored three models: (a) second-order six-factor, (b) six-factor, and (c) five-factor models combining the desire and arousal subscales. RESULTS: Results revealed excellent reliability for the total score (Cronbach's α = 0.94), and domain scores (all Cronbach's αs > 0.90), and good convergent and discriminant validity. The six-factor model provided the best fit of the models assessed, but a marginal overall fit (Tucker-Lewis index = 0.91, comparative fit index = 0.93, root mean square error of approximation = 0.09). Exploratory factor analyses (EFA) supported a four-factor structure, revealing an arousal/orgasm factor alongside the original pain, lubrication, and satisfaction domains. CONCLUSION: The arousal/orgasm factor suggests a "sexual response" construct, potentially arising from an underlying latent factor involving physical and mental stimulation in conceptualizations of arousal and orgasm in women treated for breast cancer. Finally, the EFA failed to capture an underlying desire factor, potentially due to measurement error associated with the small number of items (two) in this domain. Despite evidence that the FSFI has sound psychometric properties, our results suggest that the current conceptualizations of the FSFI might not accurately represent sexual functioning in women previously treated for breast cancer. Further research is required to elucidate the factors that influence desire, arousal, and orgasm in sexually active women in this population, and the reasons underlying sexual inactivity. Practical and theoretical implications for FSFI use in this population are discussed

The Classification of Periodic Light Curves from non-survey optimized observational data through Automated Extraction of Phase-based Visual Features

We implement two hidden-layer feedforward networks to classify 3011 variable star light curves. These light curves are generated from a reduction of non-survey optimized observational images gathered by wide-field cameras mounted on the Liverpool Telescope. We extract 16 features found to be highly informative in previous studies but achieve only 19.82% accuracy on a 30% test set, 5.56% above a random model. Noise and sampling defects present in these light curves poison these features primarily by reducing our Periodogram period match rate to fewer than 5%. We propose using an automated visual feature extraction technique by transforming the phase-folded light curves into image based representations. This eliminates much of the noise and the missing phase data, due to sampling defects, should have a less destructive effect on these shape features as they still remain at least partially present. We produced a set of scaled images with pixels turned either on or off based on a threshold of data points in each pixel defined as at minimum one fifth of those of the most populated pixel for each light curve. Training on the same feedforward network, we achieve 29.13% accuracy, a 13.16% improvement over a random model and we also show this technique scales with an improvement to 33.51% accuracy by increasing the number of hidden layer neurons. We concede that this improvement is not yet sufficient to allow these light curves to be used for automated classification and in conclusion we discuss a new pipeline currently being developed that simultaneously incorporates period estimation and classification. This method is inspired by approximating the manual methods employed by astronomers

GRAPE: Genetic Routine for Astronomical Period Estimation

Period estimation is an important task in the classification of many variable astrophysical objects. Here we present GRAPE: A Genetic Routine for Astronomical Period Estimation, a genetic algorithm optimised for the processing of survey data with spurious and aliased artefacts. It uses a Bayesian Generalised Lomb-Scargle (BGLS) fitness function designed for use with the Skycam survey conducted at the Liverpool Telescope. We construct a set of simulated light curves using both regular survey cadence and the unique Skycam variable cadence with four types of signal: sinusoidal, sawtooth, symmetric eclipsing binary and eccentric eclipsing binary. We apply GRAPE and a frequency spectrum BGLS periodogram to the light curves and show that the performance of GRAPE is superior to the frequency spectrum for any signal well modelled by the fitness function. This is due to treating the parameter space as a continuous variable.We also show that the Skycam sampling is sufficient to correctly estimate the period of over 90% of the sinusoidal shape light curves relative to the more standard regular cadence.We note that GRAPE has a computational overhead which makes it slower on light curves with low numbers of observations and faster with higher numbers of observations and discuss the potential optimisations used to speedup the runtime. Finally, we analyse the period dependence and baseline importance of the performance of both methods and propose improvements which will extend this method to the detection of quasi-periodic signals

Classifying Periodic Astrophysical Phenomena from non-survey optimized variable-cadence observational data

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. Therefore, the production of methods and systems for the automated classification of time-domain astronomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure, the Small Telescopes Installed at the Liverpool Telescope. These instruments have been in operation since March 2009 gathering data of large areas of sky around the current field of view of the main telescope generating a large dataset containing millions of light sources. The instruments are inexpensive to run as they do not require a separate telescope to operate but this style of surveying the sky introduces structured artifacts into our data due to the variable cadence at which sky fields are resampled. These artifacts can make light sources appear variable and must be addressed in any processing method. The data from large sky surveys can lead to the discovery of interesting new variable objects. Efficient software and analysis tools are required to rapidly determine which potentially variable objects are worthy of further telescope time. Machine learning offers a solution to the quick detection of variability by characterising the detected signals relative to previously seen exemplars. In this paper, we introduce a processing system designed for use with the Liverpool Telescope identifying potentially interesting objects through the application of a novel representation learning approach to data collected automatically from the wide-field instruments. Our method automatically produces a set of classification features by applying Principal Component Analysis on set of variable light curves using a piecewise polynomial fitted via a genetic algorithm applied to the epoch-folded data. The epoch-folding requires the selection of a candidate period for variable light curves identified using a genetic algorithm period estimation method specifically developed for this dataset. A Random Forest classifier is then used to classify the learned features to determine if a light curve is generated by an object of interest. This system allows for the telescope to automatically identify new targets through passive observations which do not affect day-to-day operations as the unique artifacts resulting from such a survey method are incorporated into the methods. We demonstrate the power of this feature extraction method compared to feature engineering performed by previous studies by training classification models on 859 light curves of 12 known variable star classes from our dataset. We show that our new features produce a model with a superior mean cross-validation F1 score of 0.4729 with a standard deviation of 0.0931 compared with the engineered features at 0.3902 with a standard deviation of 0.0619. We show that the features extracted from the representation learning are given relatively high importance in the final classification model. Additionally, we compare engineered features computed on the interpolated polynomial fits and show that they produce more reliable distributions than those fit to the raw light curve when the period estimation is correct

A Dynamic, Modular Intelligent-Agent framework for Astronomical Light Curve Analysis and Classification

Modern time-domain astronomy is capable of collecting a staggeringly large amount of data on millions of objects in real time. This makes it almost impossible for objects to be identified manually. Therefore the production of methods and systems for the automated classification of time-domain astro-nomical objects is of great importance. The Liverpool Telescope has a number of wide-field image gathering instruments mounted upon its structure. These in-struments have been in operation since March 2009 gathering data of multi-degree sized areas of sky around the current field of view of the main telescope. Utilizing a Structured Query Language database established by a pre-processing operation upon the resultant images, which has identified millions of candidate variable stars with multiple time-varying magnitude observations, we applied a method designed to extract time-translation invariant features from the time-series light curves of each object for future input into a classification system. These efforts were met with limited success due to noise and uneven sampling within the time-series data. Additionally, finely surveying these light curves is a processing intensive task. Fortunately, these algorithms are capable of multi-threaded implementations based on available resources. Therefore we propose a new system designed to utilize multiple intelligent agents that distribute the data analysis across multiple machines whilst simultaneously a powerful intelligence service operates to constrain the light curves and eliminate false signals due to noise and local alias periods. This system will be highly scalable, capable of operating on a wide range of hardware whilst maintaining the production of ac-curate features based on the fitting of harmonic models to the light curves within the initial Structural Query Language database

Physical activity and menopausal symptoms in women who have received menopause-inducing cancer treatments: results from the Women's Wellness After Cancer Program.

ObjectiveThis randomized controlled trial tested a digitally-delivered whole-of-lifestyle program for women previously treated for cancer. We investigated (1) associations between self-reported physical activity (PA) and menopausal symptoms and (2) if the intervention was associated with beneficial changes in PA and menopausal symptoms.MethodsWomen were randomized to intervention (n = 142) or control (n = 138). The intervention targeted lifestyle behavior including PA. Self-reported PA (International Physical Activity Questionnaire - Short Form) and menopausal symptom (Green Climacteric Scale, GCS) data were collected at baseline, with measures repeated at 12 weeks (end of intervention) and 24 weeks (to assess sustainability). Generalized estimating equation models explored associations between PA and GCS scores. Mixed-effects generalized equation models analyzed changes within and between groups in PA and GCS scores.ResultsTotal GCS scores were 1.83 (95% CI: 0.11-3.55) and 2.72 (95% CI: 1.12-4.33) points lower in women with medium and high levels of PA, respectively, than in women with low levels of PA. Total average GCS scores were 1.02 (0.21-2.26) and 1.61 (0.34-2.87) points lower in those undertaking moderate or vigorous intensity PA, respectively. Time spent walking, and performing moderate and vigorous PA were not different between intervention and control. The average GCS decrease of 0.66 points (95% CI: 0.03-1.29; p time = 0.03) over 24 weeks was not different between groups.ConclusionThis exploratory study established a stepwise association between moderate and vigorous PA and a lower total menopausal symptom score. The intervention did not appear to increase self-reported PA in women treated for early stage breast, reproductive, and blood cancers

Improving health-related quality of life in women with breast, blood, and gynaecological Cancer with an eHealth-enabled 12-week lifestyle intervention: the women's wellness after Cancer program randomised controlled trial.

BACKGROUND: The residual effects of cancer and its treatment can profoundly affect women's quality of life. This paper presents results from a multisite randomized controlled trial that evaluated the clinical benefits of an e-health enabled health promotion intervention (the Women's Wellness after Cancer Program or WWACP) on the health-related quality of life of women recovering from cancer treatment. METHODS: Overall, 351 women previously treated for breast, blood or gynaecological cancers were randomly allocated to the intervention (WWACP) or usual care arms. The WWACP comprised a structured 12-week program that included online coaching and an interactive iBook that targeted physical activity, healthy diet, stress and menopause management, sexual wellbeing, smoking cessation, alcohol intake and sleep hygiene. Data were collected via a self-completed electronic survey at baseline (t0), 12 weeks (post-intervention, t1) and 24 weeks (to assess sustained behaviour change, t2). The primary outcome, health-related quality of life (HRQoL), was measured using the Short Form Health Survey (SF-36). RESULTS: Following the 12-week lifestyle program, intervention group participants reported statistically significant improvements in general health, bodily pain, vitality, and global physical and mental health scores. Improvements were also noted in the control group across several HRQoL domains, though the magnitude of change was less. CONCLUSIONS: The WWACP was associated with improved HRQoL in women previously treated for blood, breast, and gynaecological cancers. Given how the synergy of different lifestyle factors influence health behaviour, interventions accounting for the reciprocity of multiple health behaviours like the WWACP, have real potential for immediate and sustainable change. TRIAL REGISTRATION: The protocol for this randomised controlled trial was submitted to the Australian and New Zealand Clinical Trials Registry on 15/07/2014 and approved on 28/07/2014 ( ACTRN12614000800628 )