bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluation criteria versus firm characteristics as determinants of public R&D funding
Authors: Thomas Falk Martin , Svensson Roger 
Published: May 2020

Abstract
This study provides new empirical evidence regarding the relevance of evaluation criteria and firm characteristics for public R&D funding decisions. The database used contains both accepted and rejected R&D project proposals, project evaluation scores, and several firm characteristics. The probit estimations show that proposals with high scores on innovative content, spillover, and knowledge gain are significantly more likely to be approved and that most firm-level characteristics are not significant, except for firm size. For example, good or very good assessments of innovative content raise the acceptance probability by between 18 and 37 percentage points, respectively. Small firms are more likely to receive a grant.
0001 Activation of Nociceptin/Orphanin-FQ Peptide (NOP) Receptors Produces an Increase in Non-REM Sleep in Rats and Constitutes a Novel and Attractive Target for the Treatment of Insomnia
Authors: Whiteside G T , Hummel M , Knappenberger T , Hiroyama S , Itoh T , Takai N , Kyle D J 
Published: May 2020

Abstract

Introduction
Treatments for insomnia have targeted GABA, histamine, serotonin, melatonin and orexin receptors. The nociceptin/orphanin-FQ peptide (NOP) receptor is widely expressed in the nervous system. High doses of NOP agonists administered systemically or locally into the CNS can result in sedation, however, the utility of targeting this receptor to treat insomnia has not been fully described.

Methods
V117957 is a recently described investigational oral, potent and selective NOP receptor partial agonist. We determined the brain Kp in whole brain and multiple sub-regions (50mg/kg) and receptor occupancy in the hypothalamus (30, 300mg/kg) via in vivo displacement using [3H]-NOP-1A. EEG/EMG were determined in rats chronically implanted with electrodes (cortex and dorsal neck muscle) and recorded via telemetry following dosing (3, 30, 300mg/kg); sleep stage was determined from visual analysis of EEG level. Sleep parameters were also assessed in NOP receptor knock-out rats (300mg/kg). The side-effect profile for V117957 was determined by functional observation battery, whole-body plethysmography, Morris water maze (MWM) (up to 600mg/kg) and conditioned place preference (CPP) assay (up to 300mg/kg).

Results
V117957 displayed limited distribution into the CNS but achieved a high level of receptor occupancy (75% at 30mg/kg). Administration of V117957 produced dose-dependent and statistically significant increases in non-REM sleep with a minimally efficacious dose of 30mg/kg; a coincident dose-dependent and statistically significant decrease in wakefulness and a non-dose-dependent effect on REM sleep occurred. These changes were not seen in knock-out animals demonstrating effects are via NOP receptors. At doses higher than those that increased non-REM sleep, V117957 had no effects in a functional observational battery, did not affect escape latency in MWM or produce CPP; additionally, V117957 did not affect respiratory parameters.

Conclusion
We conclude that activation of NOP receptors decreases wakefulness and increases non-REM sleep in rats with an improved preclinical profile compared to historical profiles of current treatments and, therefore, may represent a novel and attractive target for the treatment of insomnia.

Support
Funded by Shionogi and Imbrium Therapeutics, a subsidiary of Purdue Pharma L.P.
0009 Anti-Streptococcal Antibodies in Chinese Patients with Type -1 Narcolepsy
Authors: Ding Q , Li J , Xiao F , Zhang C , Dong X , Han F 
Published: May 2020

Abstract

Introduction
Narcolepsy type 1 (NT1) is considered to be an autoimmune disease, and streptococcal infection may be an environmental trigger. However, previous studies from Asian narcolepsy patients did not reveal elevated anti-streptolysin O [ASO]. The aim is to investigate whether large sample Chinese patients with NT1 have an increase in antistreptococcal antibody titers.

Methods
A total of 214 narcolepsy patients and 360 healthy controls were recruited. All patients were DQB1*0602 positive with clear-cut cataplexy or had low CSF hypocretin-1. Participants were tested for ASO and anti DNAse B [ADB]. These patients were divided into five groups according to disease duration, including 29 patients less than 3 months; 25 from 3 months to 1 year; 40 from 1 to 3 years; 61 from 3 to 10 years and 59 patients over 10 years. Comparison was also made between children and adults with age matched controls, respectively.

Results
There were no significant differences between patients and healthy controls in regard to both ASO ≥ 200 IU (19.2% vs. 16.9%, p = 0.50) and ADB≥480IU (9.8% vs. 10.3%, p = 0.86). For children narcolepsy patients, ASO positive rates(19.8% vs. 18%, p = 0.68) and ADB positive rates(10.4% vs. 12%, p = 0.72) had no differences compared to age matched controls. And no difference was observed in adult narcolepsy patients either, with ASO positive rates (18.5% vs. 13.8%, p = 0.39) and ADB positive rates (9.3% vs. 5.3%, p = 0.42) compared to age matched controls, respectively. ASO (ADB) positive rates had no significant differences among different disease duration groups(p= 0.55, 0.9).

Conclusion
It is indicated that positive rates of ASO and ADB were not significantly different between Chinese patients with NT1 and healthy controls, including recent onset cases and children.

Support
The study was supported by the National Natural Science Foundation of China (No. 81420108002 and NO. 81570083)
0013 Mobile Device Use in Bed and Relationships to Work Productivity: Impact of Anxiety
Authors: Gozar A , Seixas A , Hale L , Branas C , Barrett M , Killgore W D , Wills C C , Grandner M A 
Published: May 2020

Abstract

Introduction
Mobile phone use at night is associated with worse sleep quality. It may also be associated with daytime productivity, possibly via anxiety.

Methods
Data were obtained from the Sleep and Healthy Activity, Diet, Environment, and Socialization (SHADES) study, including N=1007 adults age 22–60. Mobile device use in bed was assessed as the frequency that participants reported: a device in the bedroom, use of the device in bed, texting, emails, internet browsing, calls, and/or social networking in bed, being woken up by the device in a planned (alarm) or unplanned (alert/call/message) way, and checking the phone at night. Each of these were coded as “never,” “rarely,” or “often.” Work productivity was assessed with the Well-Being Assessment of Productivity (WBA-P; scores 0–22 measure productivity loss). Regressions with WBA-P score as outcome and mobile phone variables as predictors were adjusted for age, sex, race/ethnicity, education, and income level. Post-hoc analyses included GAD7 score to examine the mediating role of anxiety.

Results
The presence of a device was not associated with productivity loss, but frequent use (“often”) was (B=1.26,p=0.01). Increased productivity loss was also seen in those who frequently (“often”) sent texts (B=1.20,p=0.008), browsed internet (B=1.14,p=0.01), emailed (B=2.09,p&lt;0.0005), called (B=1.42,p=0.004), and used social media (B=1.26,p=0.004). Productivity loss was associated with being woken by a call/alert “rarely” (B=1.20,p=0.001) or “often” (B=1.72,p=0.005), but not by alarm. Checking the phone at night “rarely” (B=0.89,p=0.01) and “often” (B=1.73,p&lt;0.0005) were also associated with productivity loss. When anxiety was entered into the model, all relationships except those with frequent emails and calls in bed became nonsignificant.

Conclusion
Anxiety may be the underlying cause for both increased mobile phone usage and reduced productivity. Reducing anxiety levels may indirectly aid in decreasing nighttime mobile phone use and increasing daytime productivity.

Support
The SHADES study was funded by R21ES022931
Dr. Grandner is supported by R01MD011600
0015 Manipulating Body Temperature: Effects on Sleep in Postmenopausal Women
Authors: Reid K J , Kräuchi K , Grimaldi D , Sbarboro J , Attarian H , Zee P C 
Published: May 2020

Abstract

Introduction
A decline in sleep quality and reduction in slow wave sleep (SWS) and slow wave activity (SWA) are common in older adults. Prior studies have shown that manipulating body temperature during sleep can increase SWS/SWA. The aim of this study was to determine the effects of manipulation of body temperatures during sleep, using a high heat capacity mattress, on SWS/SWA and heart rate variability in post-menopausal women.

Methods
Twenty-four healthy postmenopausal women between 40–75 years of age (mean age 62.4 ± 8.2 years, mean BMI 25.4 ± 3.5 kg/m2) were randomized in a single-blind, counterbalanced, cross-over manner to sleep on either a high heat capacity mattress (HHCM) or a low heat capacity mattress(LHCM) a week apart. Sleep was recorded using polysomnography during an 8-hour sleep opportunity. Core and peripheral temperatures were recorded using Equivital and ibutton respectively.

Results
In comparison to the LHCM, sleep on HHCM exhibited a selective increase in SWS (average increase in Stage N3 of 9.6 minutes (2.1%), p = 0.04) and in slow oscillatory activity (0.5-1Hz) in the first NREM/REM cycle (p=0.04). In addition, the HHCM induced a greater reduction in core body temperature (p=0.002), and delayed the increase in mattress surface temperature (maximal difference LHCM-HHCM: 4.66±0.17°C). Average heart rate was 2.7 beats/minute lower across the night on the HHCM compared to the LHCM (p=0.001).

Conclusion
The results of this study indicate that manipulation of body temperature during sleep may be a useful approach to enhance SWS sleep and cardiovascular function in postmenopausal women.

Support
Technogel
0036 Inconsistent Bedtimes Associated with Unhealthier Body Size in Older Adults
Authors: Zendels P , Gaultney J 
Published: May 2020

Abstract

Introduction
A variety of sleep related variables have been shown to impact measures of health, including duration of sleep, consistency of sleep, quality of sleep, and sleep disorders. These can impact respiratory health, metabolic health, immune function, and more. Older adults have been shown to have different sleep schedules, with mostly consistent weeknight and weekend bedtimes and more opportunities to nap. The study investigated which aspects of sleep best predicted body size (averaged standardized body mass index and percent body fat indices).

Methods
A sample of 304 older adults (55+) participated in surveys and health measures in an urban area in the southeastern United States. Survey data collected were reports of sleep during the last month, including duration at night, nap duration, measures of quality, typical weeknight and weekend bedtime, and reports of possible sleep disorders. Physiological measures, including height, weight, BMI, body fat percentage, blood sugars, blood fats, and fitness tests were conducted. Sleep data were weighted ((5*weeknight+2*weekend)/7) across the week. A hierarchical multiple regression model was run with a standardized average of BMI and body fat percentage with multiple sleep variables as a predictor, controlling for age and socioeconomic status.

Results
After controlling for age and socioeconomic status (SES), symptoms of obstructive sleep apnea, night sleep duration, nap duration, difficulty initiating and maintaining sleep, sleep midpoint, duration inconsistencies and midpoint inconsistences were added to the regression. SES and duration inconsistencies were significant predictors, explaining 16% of the variance in body size.

Conclusion
Inconsistent bedtimes may be associated with larger body size. Encouraging older adults to have consistent sleep schedules could help preserve health as they age. This may also reduce rates of disorders like obstructive sleep apnea, which are associated with higher BMI, and help promote better overall sleep quality and health. However, these older adults reported fairly consistent sleep midpoint, limiting interpretation of this variable.

Support
Psychological Sciences department funding
0045 Biobehavioral Markers for Sleep/Wake Disturbance and Fatigue in Young Childhood Brain Tumor Survivors
Authors: Johnson A H , Bashore L , Hines A , Aufricht J , Smith A M , Pearson H 
Published: May 2020

Abstract

Introduction
Survivors of childhood and adolescent brain tumors and subsequent treatment may experience many neurological processes involving the forebrain, brainstem, and hypothalamus as well as the symptom cluster of stress, sleep, and fatigue. As a result, the impact of brain tumor treatment (chemotherapy/biotherapy, radiotherapy, and surgery) may have lasting biobehavioral effects. Description of symptoms during early survivorship is not always evident in the literature.

Methods
Convenience sampling and the following inclusion criteria were utilized: brain tumor survivors ages 8–17 years; ≥6 months, &lt;6 years from completion of treatment; disease free or stable disease. Participants completed polysomnography (PSG) followed by a multiple sleep latency test (MSLT), and subjective measures of sleep, fatigue, stress, and pubertal status. Collection of salivary biomarkers for stress (cortisol) and sleep (melatonin) was completed the evening of and morning after the PSG.

Results
Analysis of the first 12 participants (5 males; 3 Hispanic/Latino; average age 14 years; 9–72 months post treatment) revealed mean (minutes) total sleep time (TST) 442, sleep latency (SL) 42 and waking (WASO) 88; sleep efficiency (SE) mean 83%, There were large magnitude correlations between several variables of interest, notably PM Cortisol with fatigue, TST (r= .472; -.453); AM Cortisol with SL (r=.479); AM Melatonin with SE, SL, WASO (r= -.459; .692; .458). Average AM melatonin level (26.6 pg/dl) was higher than PM (6.66 pg/dl). Seven participants were diagnosed with clinical sleep disorders, including one with narcolepsy and two with hypersomnia.

Conclusion
During early survivorship after pediatric brain tumor treatment, survivors may be at high risk for sleep/wake disturbance (SWD). Morning melatonin and biomarker correlations with sleep and fatigue in this sample warrant further exploration and may be related to first night effect versus circadian rhythm differences or clinical sleep disorder. Recommendations for future practice include developmentally matched protocols and routine screening of biobehavioral markers to assess risk for stress, SWD, and fatigue.

Support
1. Center for Oncology Education and Research Harris College of Nursing & Health Sciences Texas Christian University 2. Neuro-Oncology Program Hematology/Oncology Center Cook Children’s Health Care System 3. Nursing Research and Evidence-Based Practice James A. “Buddy” Davidson Endowed Fund
0046 Diffusion Tensor Imaging Evidence of Hypothalamic Injury in Traumatic Brain Injury Warfighters with Sleep Dysfunction
Authors: Werner K , Gerstenslager B , Yeh P , Srikanchana R , Kenney K , Ollinger J 
Published: May 2020

Abstract

Introduction
While sleep disorders occur in 40–70% of chronic traumatic brain injury (TBI) patients, the pathophysiology remains unknown. Increasingly, DTI has been used to evaluate gray matter structures, but no prior studies have evaluated hypothalamic regions in TBI. We hypothesized that TBI patients with poor sleep quality by questionnaire and/or polysomnography (PSG) may have structural injury to hypothalamic sleep circuitry and that this may be detectable by diffusion magnetic resonance imaging (dMRI). We examined diffusion tensor parameters in warfighters using dMRI within the hypothalamus of poor sleepers and compared them to good sleepers.

Methods
A retrospective review of 92 warfighters with blast TBI and loss of consciousness included demographics, structural MRI, dMRI, PSG and Pittsburgh Sleep Quality Index (PSQI) questionnaire. Acquisition of diffusion-weighted and structural data was performed with three Tesla MRI. Using the California Institute of Technology probabilistic high-resolution in vivo atlas as a prior, the hypothalamic nuclei were segmented by applying diffeomorphic registration of T1- and T2-weighted structural images and mapped to dMRI space.

Results
TBI patients within the lowest quartile of hypothalamic fractional anisotropy (FA) measures demonstrated decreased total sleep time (320 +/- 52 minutes vs. 382 +/- 19, p=0.006) on PSG and had more sleep complaints on PSQI (p=0.029) compared to those with the highest quartile of FA measures. There was no difference in BMI, age or AHI among the quartiles. Radial, mean and axial diffusivity quartiles did not carry significant differences in TST or PSQI. Linear models did not show significant correlation between any imaging parameter and sleep quality measures.

Conclusion
Our results reveal microstructural differences in the hypothalami of military TBI patients that may be related to clinical sleep dysfunction. Biomarkers of sleep circuitry damage may further our understanding of sleep dysfunction after TBI. Lack of correlations in linear models may be a reflection of the small sample size or a complex interaction, and removal of outliers did not change our results. Larger longitudinal studies may help clarify the association between hypothalamic and brainstem circuitry structure after TBI and sleep dysfunction.

Support
This work was supported by a grant 130132 from USAMRMC.
0051 Respiratory Cycle-Related EEG Changes (RCREC) Predict Incidence and Recurrence of Cardiovascular Disease
Authors: Tsimpanouli M , Chervin R D , Gliske S V 
Published: May 2020

Abstract

Introduction
Sleep-disordered breathing (SDB) is a common sleep disturbance and is associated with both incident and recurrent cardiovascular disease (CVD). Respiratory Cycle-Related EEG Changes (RCREC), an SDB biomarker, are thought to reflect inspiratory microarousals and are based on quantitative analysis of sleep EEG and breathing. The objective of this study was to assess whether RCREC may predict CVD incidence or recurrence in a large cohort of adults. The RCREC for several frequency bands have been previously shown to predict either higher or lower all-cause mortality in the same cohort.

Methods
Data were obtained from the Sleep Heart Health Study (SHHS), a multicenter longitudinal study that included polysomnograms in middle-aged to older adults. Information about CVD events was collected at baseline and for up to 16 years later. The RCREC values at baseline were computed in the delta, theta, alpha, sigma, beta, and gamma frequency bands during scored epochs of sleep. Cox Proportional Hazard models, were used to assess the relation of each RCREC frequency band and incidence or recurrence of CVD. These models were stratified by sex and adjusted for body-mass index, age, race, smoking status, diabetic status, hypertensive status, HDL cholesterol, LDL cholesterol, and the apnea-hypopnea index (AHI).

Results
There were 3,032 adults with sufficient data quality (mean age at baseline 62±11(SD) years, 58% female). Among 2,500 adults with no reported prior CVD history at baseline, the adjusted odds ratios (95% CI) for delta RCREC 0.948(0.920–0.977), theta RCREC 0.938(0.895–0.984), and alpha RCREC 0.946(0.902–0.993) separately suggested associations with lower CVD incidence, whereas gamma RCREC 1.017(1.001–1.032) predicted a marginal increase. Among 532 adults having prior CVD history at baseline, delta RCREC 0.958(0.927–0.989) and sigma RCREC 0.931(0.895–0.969) separately predicted decreased CVD recurrence. The apnea-hypopnea index (AHI) was not similarly predictive in any model.

Conclusion
The RCREC for several frequency bands, in contrast to AHI, may predict CVD incidence and recurrence. The directionality of the association was surprising and merits further exploration.

Support
NIH:NCATS-TL1-TR-002242, BD2K-K01-ES-026839, HL105999
0056 Identification of a Stable Human Metabolomics-Based Biomarker of Insufficient Sleep and its Association with Cognitive Performance
Authors: Depner C M , Reisdorph N , Wright K 
Published: May 2020

Abstract

Introduction
There are numerous potential benefits of developing objective biomarkers of insufficient sleep including providing screening and diagnostic tools, increasing our understanding of poor sleep health, and supporting sleep-based countermeasures. We analyzed plasma metabolites in humans during two separate occasions of experimental sleep restriction to identify stable biomarkers of insufficient sleep, and assessed the association of these biomarkers with cognitive performance.

Methods
12 healthy adults (6M/6F), aged 24±5y (mean±SD), completed two 18-day protocols separated by 10 days. For each 18-day protocol, participants maintained habitual 9h sleep schedules for two weeks at home, and then completed 4-day laboratory visits with sleep opportunities of: 9h on night 1, 5h on nights 2 and 3, and recovery sleep on night 4. Energy balanced diets were provided 2 days before and throughout laboratory visits. Blood was collected every 2h during scheduled wakefulness on days 1–2 (baseline) and days 3–4 (insufficient sleep), and was analyzed by untargeted liquid chromatography/mass-spectrometry. Sustained attention was assessed every 2 h during scheduled wakefulness with the Psychomotor Vigilance Test (PVT).

Results
After filtering, we detected 6,822 metabolites. Elastic-net regression identified 21 metabolites consistently altered by insufficient sleep in both 18-day protocols. We developed a logistic regression-based biomarker fingerprint of insufficient sleep using 11 of these 21 metabolites with consistent responses to insufficient sleep. This biomarker fingerprint has 74.4% accuracy and 0.822 (0.782–0.858; 95%CI) area under the receiver operator curve. Median reaction time was slower (P&lt;0.05) during insufficient sleep versus baseline. Mediation analyses show our biomarker fingerprint accounts for ~6% of the slower reaction time.

Conclusion
We identified a plasma metabolomics-based biomarker of insufficient sleep with “good” performance. However, this biomarker only accounts for ~6% of reduced PVT performance during insufficient sleep. Our findings suggest that specific biomarkers for insufficient sleep versus cognitive deficits associated with insufficient sleep may be required.

Support
This work was supported by NIH R01HL132150, NIH K01HL145099, NIH F32DK111161, NIH/NCATS Colorado CTSA Grant UL1TR002535, Sleep Research Society Foundation 011-JP-16, and the University of Colorado Boulder Undergraduate Research Opportunities Grant.