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Gravitational waves from coalescing neutron stars encode information about nuclear matter at extreme densities, inaccessible by laboratory experiments. The late inspiral is influenced by the presence of tides, which depend on the neutron star equation of state. Neutron star mergers are expected to often produce rapidly rotating remnant neutron stars that emit gravitational waves. These will provide clues to the extremely hot post-merger environment. This signature of nuclear matter in gravitational waves contains most information in the 2–4 kHz frequency band, which is outside of the most sensitive band of current detectors. We present the design concept and science case for a Neutron Star Extreme Matter Observatory (NEMO): a gravitational-wave interferometer optimised to study nuclear physics with merging neutron stars. The concept uses high-circulating laser power, quantum squeezing, and a detector topology specifically designed to achieve the high-frequency sensitivity necessary to probe nuclear matter using gravitational waves. Above 1 kHz, the proposed strain sensitivity is comparable to full third-generation detectors at a fraction of the cost. Such sensitivity changes expected event rates for detection of post-merger remnants from approximately one per few decades with two A+ detectors to a few per year and potentially allow for the first gravitational-wave observations of supernovae, isolated neutron stars, and other exotica.
Pro-vitamin A carotenoids namely α-, β-carotene and β-cryptoxanthin have potential roles in neurocognitive development, but current literature on these carotenoids mainly focused on preventing cognitive decline in the elderly. This study examined the associations of maternal plasma pro-vitamin A carotenoids concentrations with offspring cognitive development up to 54 months in the GUSTO mother-offspring cohort study.
Materials and Methods
Maternal plasma pro-vitamin A carotenoids concentrations at delivery were determined by ultra-performance liquid chromatography. At age 24 months, the Bayley Scales of Infant and Toddler Development (BSID-III) was used to assess children's development for the following domains: cognitive, receptive and expressive language, and fine and gross motor. At age 54 months, the Kaufman Brief Intelligence Test (KBIT-2) was used to assess children's verbal and non-verbal intelligence. Associations of maternal pro-vitamin A carotenoids with offspring cognitive development at each time point were examined in 419 mother-offspring pairs using linear regressions adjusted for confounders (e.g. maternal demographics, antenatal mental health and breastfeeding duration).
Median (IQR) maternal plasma concentrations (mg/L) were: α-carotene 0.052 (0.032–0.081), β-carotene 0.189 (0.134–0.286), and β-cryptoxanthin 0.199 (0.123–0.304). In 24 months old infants, higher maternal β-cryptoxanthin (per SD increment) were associated with higher scores in most of BSID-III domains: cognitive [β 0.18, (0.08, 0.28) SD], receptive language [β 0.17 (0.07, 0.27) SD], fine motor [β 0.16 (0.06, 0.27) SD], and gross motor [β 0.16 (0.06, 0.27) SD]. Additionally, a 1-SD increment in maternal β-carotene concentrations were associated with 0.16 SD higher scores in BSID-III cognitive domain (95%: 0.04, 0.28), which was attenuated after adjusting for breastfeeding duration. No significant associations were observed between maternal α-carotene concentrations and BSID-III in children at 24 months of age, or between maternal pro-vitamin A carotenoids and KBIT-2 in children at 54 months of age.
Our study provides novel data suggesting a role of maternal pro-vitamin A carotenoids, especially β-cryptoxanthin, in offspring early cognitive development. This adds support to the importance of consuming sufficient amounts of red- and orange-coloured fruit and vegetables (rich sources of β-cryptoxanthin and β-carotene) during pregnancy. Further studies are required in other mother-offspring cohort with larger sample sizes, and intervention trials to confirm an effect of pro-vitamin A carotenoids on neurocognitive development.
Evidence on long-term influences of maternal vitamin B12 deficiency or concentrations on infant cognition is limited. We examined associations between maternal plasma vitamin B12 and cognitive development in 24-month-old infants. Maternal plasma vitamin B12 concentrations were measured at 26–28 weeks’ gestation; infant cognitive development was assessed with the Bayley Scales of Infant and Toddler Development-III at 24 months, for 443 mother–infant pairs from the Growing Up in Singapore Towards Healthy Outcomes cohort. Linear regressions adjusted for key confounders examined associations of maternal vitamin B12 with cognitive, receptive and expressive language, fine and gross motor subscales. Co-occurrence of maternal vitamin B12 with folate or vitamin B6 insufficiencies on child’s cognition was explored. Average maternal plasma vitamin B12 concentrations was 220·5 ± 80·5 pmol/l; 15 % and 41 % of mothers were vitamin B12 deficient (<148 pmol/l) and insufficient (148–220·9 pmol/l), respectively. Infants of mothers with vitamin B12 deficiency had 0·42 (95 % CI −0·70, −0·14) sd lower cognitive scores, compared with infants of mothers with sufficient vitamin B12. Co-occurrence of maternal vitamins B12 and B6 insufficiencies was associated with 0·37 (95 % CI −0·69, −0·06) sd lower cognitive scores in infants compared with infants of mothers sufficient in both vitamins. No significant associations were observed with other subscales. Study findings suggest the possible need to ensure adequate vitamin B12 during pregnancy. The impact of co-occurrence of maternal B-vitamins insufficiencies on early cognitive development warrants further investigation.
Malformed data-structures can lead to runtime errors such as arbitrary memory access or corruption. Despite this, reasoning over data-structure properties for low-level heap manipulating programs remains challenging. In this paper we present a constraint-based program analysis that checks data-structure integrity, w.r.t. given target data-structure properties, as the heap is manipulated by the program. Our approach is to automatically generate a solver for properties using the type definitions from the target program. The generated solver is implemented using a Constraint Handling Rules (CHR) extension of built-in heap, integer and equality solvers. A key property of our program analysis is that the target data-structure properties are shape neutral, i.e., the analysis does not check for properties relating to a given data-structure graph shape, such as doubly-linked-lists versus trees. Nevertheless, the analysis can detect errors in a wide range of data-structure manipulating programs, including those that use lists, trees, DAGs, graphs, etc. We present an implementation that uses the Satisfiability Modulo Constraint Handling Rules (SMCHR) system. Experimental results show that our approach works well for real-world C programs.
The quality of prenatal maternal mental health, from psychological stress and depressive symptoms to anxiety and other nonpsychotic mental disorders, profoundly affects fetal neurodevelopment. Despite the evidence for the influence of positive mental well-being on health, there is, to our knowledge, no research examining the possible effects of positive antenatal mental health on the development of the offspring. Using exploratory bifactor analysis, this prospective study (n = 1,066) demonstrated the feasibility of using common psychiatric screening tools to examine the effect of positive maternal mental health. Antenatal mental health was assessed during 26th week of pregnancy. The effects on offspring were assessed when the child was 12, 18, and 24 months old. Results showed that positive antenatal mental health was uniquely associated with the offspring's cognitive, language and parentally rated competences. This study shows that the effects of positive maternal mental health are likely to be specific and distinct from the sheer absence of symptoms of depression or anxiety.
Faster eating rates are associated with increased energy intake, but little is known about the relationship between children’s eating rate, food intake and adiposity. We examined whether children who eat faster consume more energy and whether this is associated with higher weight status and adiposity. We hypothesised that eating rate mediates the relationship between child weight and ad libitum energy intake. Children (n 386) from the Growing Up in Singapore Towards Healthy Outcomes cohort participated in a video-recorded ad libitum lunch at 4·5 years to measure acute energy intake. Videos were coded for three eating-behaviours (bites, chews and swallows) to derive a measure of eating rate (g/min). BMI and anthropometric indices of adiposity were measured. A subset of children underwent MRI scanning (n 153) to measure abdominal subcutaneous and visceral adiposity. Children above/below the median eating rate were categorised as slower and faster eaters, and compared across body composition measures. There was a strong positive relationship between eating rate and energy intake (r 0·61, P<0·001) and a positive linear relationship between eating rate and children’s BMI status. Faster eaters consumed 75 % more energy content than slower eating children (Δ548 kJ (Δ131 kcal); 95 % CI 107·6, 154·4, P<0·001), and had higher whole-body (P<0·05) and subcutaneous abdominal adiposity (Δ118·3 cc; 95 % CI 24·0, 212·7, P=0·014). Mediation analysis showed that eating rate mediates the link between child weight and energy intake during a meal (b 13·59; 95 % CI 7·48, 21·83). Children who ate faster had higher energy intake, and this was associated with increased BMI z-score and adiposity.
Optimal control problems of stochastic switching type appear frequently when making decisions under uncertainty and are notoriously challenging from a computational viewpoint. Although numerous approaches have been suggested in the literature to tackle them, typical real-world applications are inherently high dimensional and usually drive common algorithms to their computational limits. Furthermore, even when numerical approximations of the optimal strategy are obtained, practitioners must apply time-consuming and unreliable Monte Carlo simulations to assess their quality. In this paper, we show how one can overcome both difficulties for a specific class of discrete-time stochastic control problems. A simple and efficient algorithm which yields approximate numerical solutions is presented and methods to perform diagnostics are provided.
Vitamin D plays an important role in skeletal health throughout life. Some studies have hypothesised that vitamin D may reduce the risk of other diseases. Our study aimed to estimate age-specific and sex-specific serum 25-hydroxyvitamin D (25(OH)D) status and to identify the determinants of serum 25(OH)D status in Hong Kong, a subtropical city in southern China. In 2009–2010, households in Hong Kong were followed up to identify acute respiratory illnesses, and sera from 2694 subjects were collected in three to four different study phases to permit measurement of 25(OH)D levels at different times of the year. A questionnaire survey on diet and lifestyle was conducted among children, with simultaneous serum collection in April and May 2010. The mean of serum 25(OH)D levels in age groups ranged from 39 to 63 nmol/l throughout the year with the mean values in all age groups in spring below 50 nmol/l. Children aged 6–17 years, and girls and women had significantly lower serum 25(OH)D levels than adults, and boys and men, respectively (all P< 0·001). We estimated that serum 25(OH)D levels in Hong Kong followed a lagged pattern relative to climatic season by 5 weeks with lowest observed levels in early spring (March). For children aged 6–17 years, reporting a suntan, having at least 1 servings of fish/week and having at least 1 serving of eggs/week were independently associated with higher serum 25(OH)D levels. Adequate sunlight exposure and increased intake of dietary vitamin D could improve vitamin D status, especially for children and females in the winter and spring.
Early life environments interact with genotype to determine stable phenotypic outcomes. Here we examined the influence of a variant in the brain-derived neurotropic factor (BDNF) gene (Val66Met), which underlies synaptic plasticity throughout the central nervous system, on the degree to which antenatal maternal anxiety associated with neonatal DNA methylation. We also examined the association between neonatal DNA methylation and brain substructure volume, as a function of BDNF genotype. Infant, but not maternal, BDNF genotype dramatically influences the association of antenatal anxiety on the epigenome at birth as well as that between the epigenome and neonatal brain structure. There was a greater impact of antenatal maternal anxiety on the DNA methylation of infants with the methionine (Met)/Met compared to both Met/valine (Val) and Val/Val genotypes. There were significantly more cytosine–phosphate–guanine sites where methylation levels covaried with right amygdala volume among Met/Met compared with both Met/Val and Val/Val carriers. In contrast, more cytosine–phosphate–guanine sites covaried with left hippocampus volume in Val/Val infants compared with infants of the Met/Val or Met/Met genotype. Thus, antenatal Maternal Anxiety × BDNF Val66Met Polymorphism interactions at the level of the epigenome are reflected differently in the structure of the amygdala and the hippocampus. These findings suggest that BDNF genotype regulates the sensitivity of the methylome to early environment and that differential susceptibility to specific environmental conditions may be both tissue and function specific.
Selective single-walled carbon nanotube (SWNT) growth is a challenging problem, limiting their use in a wide variety of applications. Significant degrees of freedom in these experiments may lead to synthesis of multi-walled carbon nanotubes (MWNTs), which are less preferred. Thus, a method for constraining the synthesis results to only SWNTs is desired. A machine learning based approach for selectively growing SWNTs using a laser-induced chemical vapor deposition growth system is introduced. This approach models the complex relationships between the associated synthesis parameters to predict SWNT growth. The parameters under consideration include argon, ethylene, hydrogen and carbon dioxide partial pressures, growth temperature, and water vapor concentration. The catalyst consists of 10 nm of alumina and 1 nm of nickel deposited onto 10 µm diameter silicon pillars with a height of 10 µm. Determination of SWNT growth is performed through in-situ Raman spectroscopy using a 532 nm excitation laser. A total of 121 experiments are used to train a SWNT vs. MWNT classifier with a resulting model accuracy of 94.21%. The classifier model is applied to a range of simulated inputs, and the subset of these inputs that meet a >90% probability of SWNT growth are investigated further. The simulated inputs consist of 531,201,645 unique growth parameter combinations spanning the entire parameter space. A reduced dataset of 449,117 growth parameter combinations define 90% probability of SWNT growth according to the model. Randomly selected input parameters from this reduced dataset were tested experimentally, resulting in SWNT growth for all performed experiments validating the classifier model. This approach maps input growth conditions to SWNT growth selectivity using a limited set of experimental data and allows for further investigation into SWNT growth rates and chiral dependencies.
An inherent prerequisite to mental health first-aid (MHFA) is the ability to identify that there is a mental health problem, but little is known about the association between psychiatric labelling and MHFA. This study examined this association using data from two national surveys of Australian young people.
This study involved a national telephonic survey of 3746 Australian youth aged 12–25 years in 2006, and a similar survey in 2011 with 3021 youth aged 15–25 years. In both surveys, respondents were presented with a vignette portraying depression, psychosis or social phobia in a young person. The 2011 survey also included depression with suicidal thoughts and post-traumatic stress disorder. Respondents were asked what they thought was wrong with the person, and reported on their first-aid intentions and beliefs, which were scored for quality of the responses.
Accurate labelling of the mental disorder was associated with more helpful first-aid intentions and beliefs across vignettes, except for the intention to listen non-judgementally in the psychosis vignette.
Findings suggest that community education programmes that improve accurate psychiatric label use may have the potential to improve the first-aid responses young people provide to their peers, although caution is required in the case of psychosis.
Emerging evidence suggests that psychiatric labels may facilitate help seeking in young people. This study examined whether young people's use of accurate labels for five disorders would predict their help-seeking preferences.
Young people's help-seeking intentions were assessed by a national telephone survey of 3021 Australian youths aged 15–25. Respondents were presented with a vignette of a young person portraying depression, depression with suicidal thoughts, psychosis, social phobia or post-traumatic stress disorder (PTSD). They were then asked what they thought was wrong with the person, and where they would go for help if they had a similar problem.
Accurate psychiatric label use was associated with a preference to seek help from a general practitioner or mental health specialist. Accurately labelling the psychosis vignette was also associated with a preference to not seek help from family or friends.
Findings add to the emerging evidence that accurate psychiatric labelling may facilitate help seeking for various mental disorders in young people, and support the promise of community awareness campaigns designed to improve young people's ability to accurately identify mental disorders.
The problem of wide treatment gaps in mental disorders is endemic world wide. The study aims to establish the treatment gap of common mental disorders in Singapore.
A national sample of 6616 persons aged 18 years and above was surveyed with the World Mental Health Composite International Diagnostic Interview in which for each diagnostic module, respondents were asked a series of questions regarding treatment contact.
Treatment gap varied considerably between disorders; alcohol abuse had the largest treatment gap (96.2%), followed by obsessive compulsive disorder (89.8%) and alcohol dependence (88.3%). The disorder for which people were most likely to seek help was major depressive disorder. Women with dysthmia were more likely than men to seek help but this help seeking behavior was reversed among those with alcohol abuse and dependence. Age of onset was significantly associated with treatment contact with those who had an earlier age of onset less likely to have treatment contact than those with late age of onset for all disorders except obsessive compulsive disorder.
Our findings suggest that treatment gaps are wide even in an economically developed country like Singapore and other than sociodemographic factors, cultural influences might play an important role in help seeking behavior.
We studied the self-assembly mechanisms of Graphene Nanoribbon (GNR) with unsaturated edges and demonstrated the ability of GNR to self-assemble into novel stable structures. We proposed three mechanisms which dictate the self-assembly evolution of GNR with unsaturated edges. Using the Adaptive Intermolecular Reactive Empirical Bond-Order (AIREBO) potential, we performed molecular dynamics simulations on initially-planar GNRs with unsaturated edges. The simulation results showed that the self-assembly mechanisms and final conformations of the GNRs correlate well with the proposed GNR self-assembly mechanisms. Furthermore, the simulations also showed the ability of a narrow GNR to self-assemble into various nanostructures, such as tapered graphene nano-rings and graphene nanoscrolls with an embedded nanotube.
In this paper, we discuss the low-frequency range of the Raman spectrum of individual suspended index-identified single-walled (SWCNTs) and double-walled carbon nanotubes (DWCNTs). In SWCNTs, the role of environment on the radial breathing mode (RBM) frequency is discussed. We show that the interaction between the surrounding air and the nanotube does not induce a RBM upshift. In several DWCNTs, we evidence that the low-frequency modes cannot be connected to the RBM of each related layer. We discuss this result in terms of mechanical coupling between the layers which results in collective radial breathing-like modes. The mechanical coupling qualitatively explains the observation of Raman lines of radial breathing-like modes, whenever only one of the layers is in resonance with the incident laser energy.
The metal-catalyst-free growth of carbon nanotubes (CNTs) using chemical vapor deposition and the application in field-effect transistors (FETs) is presented. The CNT growth process used a 3-nm-thick Ge layer on SiO2 that was subsequently annealed to produce Ge nanoparticles. Raman measurements show the presence of radial breathing mode (RBM) peaks and the absence of the disorder induced D-band, indicating single walled CNTs (SWNTs) with a low defect density. The synthesized CNTs are used to fabricate CNTFETs and the best device has a state-of-the-art on/off current ratio of 3×108 and a steep sub-threshold slope of 110 mV/decade.
Although mental health information on the internet is often of poor quality, relatively little is known about the quality of websites, such as Wikipedia, that involve participatory information sharing. The aim of this paper was to explore the quality of user-contributed mental health-related information on Wikipedia and compare this with centrally controlled information sources.
Content on 10 mental health-related topics was extracted from 14 frequently accessed websites (including Wikipedia) providing information about depression and schizophrenia, Encyclopaedia Britannica, and a psychiatry textbook. The content was rated by experts according to the following criteria: accuracy, up-to-dateness, breadth of coverage, referencing and readability.
Ratings varied significantly between resources according to topic. Across all topics, Wikipedia was the most highly rated in all domains except readability.
The quality of information on depression and schizophrenia on Wikipedia is generally as good as, or better than, that provided by centrally controlled websites, Encyclopaedia Britannica and a psychiatry textbook.