Seasonal Dynamics of Lipid Metabolism and Energy Storage in the Brazilian Free-Tailed Bat

As small, flying, mammalian endotherms, insectivorous bats are adapted to operate at high levels of energy expenditure. In response to seasonally variable challenges, we predicted that bats should balance energy budgets by flexibly adjusting aspects of their physiology or behavior in ways that elevate metabolic capacity. We examined variation in energy storage and pathways for oxidative metabolism in Brazilian free-tailed bats (Tadarida brasiliensis) related to estimated costs associated with reproduction and migration. We collected pectoral muscle and liver from female T. brasiliensis at six time points during the summer and fall and measured changes in the activity of four enzymes involved with lipid metabolism. Body mass varied substantially with life-cycle stage, suggesting that rapid accumulation and use of fat stores occurs in response to current and anticipated energy demands. Catabolic enzyme activity (carnitine palmitoyltransferase [CPT], 3-hydroxyacyl-CoA dehydrogenase [HOAD], and citrate synthase [CS]) in the muscle was increased during lactation compared with early pregnancy but exhibited no change before fall migration. While there was no temporal change in lipid biosynthetic capacity in the liver, fatty acid synthase activity was negatively correlated with body mass. Variation in body mass and enzyme activity in T. brasiliensis during the summer suggests that stored energy is mobilized and lipid oxidative capacity is increased during periods of increased demand and that lipid biosynthetic capacity is increased with depletion of fat stores. These results suggest that bats are able to flexibly adjust metabolic capacity based on energy requirement to maintain energy balance despite high levels of expenditure

A spectrum of physics-informed Gaussian processes for regression in engineering

Despite the growing availability of sensing and data in general, we remain unable to fully characterise many in-service engineering systems and structures from a purely data-driven approach. The vast data and resources available to capture human activity are unmatched in our engineered world, and, even in cases where data could be referred to as ``big,'' they will rarely hold information across operational windows or life spans. This paper pursues the combination of machine learning technology and physics-based reasoning to enhance our ability to make predictive models with limited data. By explicitly linking the physics-based view of stochastic processes with a data-based regression approach, a spectrum of possible Gaussian process models are introduced that enable the incorporation of different levels of expert knowledge of a system. Examples illustrate how these approaches can significantly reduce reliance on data collection whilst also increasing the interpretability of the model, another important consideration in this context

Seasonal feeding on bark by gorillas: an unexpected keystone food?

First paragraph: There are a number of reports in the literature of primates feeding on the bark of trees, but bark has only occasionally been considered as a major food to be studied in its own right (e.g., Waser, 1977; Beeson, 1987; Norris, 1988). All the great apes feed on bark at certain times, and clearly have preferences as to which species they choose (e.g., Schaller, 1963; Jones & Sabater Pi. 1971; Casimir, 1975: Nishida, 1976; Goodall, 1977; Rodman, 1977; Sabater Pi, 1977, 1979). Evidence has been presented that bark feeding by chimpanzees (Pan troglodytes) and orangutans (Pongo pygmaeus) is a seasonal phenomenon related to scarcity of preferred fruits (Nishida, 1976; Rodman, 1977), and similar conclusions have been drawn from studies of blue monkeys (Cercopithecus mitis) living near plantations of exotic pines (Beeson, 1987; Maganga & Wright, 1992). Bark feeding is also well known in other mammals where, again, it often occurs seasonally (e.g., elephants, Wing & Buss, 1970; grey squirrels, Kenward & Parish, 1986)

Initial Metabolic Profiles Are Associated with 7-Day Survival among Infants Born at 22-25 Weeks of Gestation.

OBJECTIVE:To evaluate the association between early metabolic profiles combined with infant characteristics and survival past 7 days of age in infants born at 22-25 weeks of gestation. STUDY DESIGN:This nested case-control consisted of 465 singleton live births in California from 2005 to 2011 at 22-25 weeks of gestation. All infants had newborn metabolic screening data available. Data included linked birth certificate and mother and infant hospital discharge records. Mortality was derived from linked death certificates and death discharge information. Each death within 7 days was matched to 4 surviving controls by gestational age and birth weight z score category, leaving 93 cases and 372 controls. The association between explanatory variables and 7-day survival was modeled via stepwise logistic regression. Infant characteristics, 42 metabolites, and 12 metabolite ratios were considered for model inclusion. Model performance was assessed via area under the curve. RESULTS:The final model included 1 characteristic and 11 metabolites. The model demonstrated a strong association between metabolic patterns and infant survival (area under the curve [AUC] 0.885, 95% CI 0.851-0.920). Furthermore, a model with just the selected metabolites performed better (AUC 0.879, 95% CI 0.841-0.916) than a model with multiple clinical characteristics (AUC 0.685, 95% CI 0.627-0.742). CONCLUSIONS:Use of metabolomics significantly strengthens the association with 7-day survival in infants born extremely premature. Physicians may be able to use metabolic profiles at birth to refine mortality risks and inform postnatal counseling for infants born at <26 weeks of gestation

NICUs in the US: levels of acuity, number of beds, and relationships to population factors.

OBJECTIVE: To 1) define the number and characteristics of NICUs in the United States (US) and 2) identify hospital and population characteristics related to US NICUs. STUDY DESIGN: Cohort study of US NICUs. RESULTS: There were 1424 NICUs identified in the US. Higher number of NICU beds was positively associated with higher NICU level (p \u3c 0.0001). Higher acuity level and number of NICU beds related to being in a children\u27s hospital (p \u3c 0.0001;p \u3c 0.0001), part of an academic center (p = 0.006;p = 0.001), and in a state with Certificate of Need legislation (p = 0.023;p = 0.046). Higher acuity level related to higher population density (p \u3c 0.0001), and higher number of beds related to increasing proportions of minorities in the population up until 50% minorities. There was also significant variation in NICU level by region. CONCLUSIONS: This study contributes new knowledge by describing an updated registry of NICUs in the US in 2021 that can be used for comparisons and benchmarking

The Effect of Simulated Lunar Dust on the Absorptivity, Emissivity, and Operating Temperature on AZ-93 and Ag/FEP Thermal Control Surfaces

JSC-1AF lunar simulant has been applied to AZ-93 and AgFEP thermal control surfaces on aluminum or composite substrates in a simulated lunar environment. The temperature of these surfaces was monitored as they were heated with a solar simulator and cooled in a 30 K coldbox. Thermal modeling was used to determine the absorptivity ( ) and emissivity ( ) of the thermal control surfaces in both their clean and dusted states. Then, a known amount of power was applied to the samples while in the coldbox and the steady state temperatures measured. It was found that even a submonolayer of simulated lunar dust can significantly degrade the performance of both white paint and second-surface mirror type thermal control surfaces under these conditions. Contrary to earlier studies, dust was found to affect as well as . Dust lowered the emissivity by as much as 16 percent in the case of AZ-93, and raised it by as much as 11 percent in the case of AgFEP. The degradation of thermal control surface by dust as measured by / rose linearly regardless of the thermal control coating or substrate, and extrapolated to degradation by a factor 3 at full coverage by dust. Submonolayer coatings of dust were found to not significantly change the steady state temperature at which a shadowed thermal control surface will radiate

Infrared Variability of Two Dusty White Dwarfs

The most heavily polluted white dwarfs often show excess infrared radiation from circumstellar dust disks, which are modeled as a result of tidal disruption of extrasolar minor planets. Interaction of dust, gas, and disintegrating objects can all contribute to the dynamical evolution of these dust disks. Here, we report on two infrared variable dusty white dwarfs, SDSS J1228+1040 and G29-38. For SDSS J1228+1040, compared to the first measurements in 2007, the IRAC [3.6] and [4.5] fluxes decreased by 20% by 2014 to a level also seen in the recent 2018 observations. For G29-38, the infrared flux of the 10 $\mu$m silicate emission feature became 10% stronger between 2004 and 2007, We explore several scenarios that could account for these changes, including tidal disruption events, perturbation from a companion, and runaway accretion. No satisfactory causes are found for the flux drop in SDSS J1228+1040 due to the limited time coverage. Continuous tidal disruption of small planetesimals could increase the mass of small grains and concurrently change the strength of the 10 $\mu$m feature of G29-38. Dust disks around white dwarfs are actively evolving and we speculate that there could be different mechanisms responsible for the temporal changes of these disks.Comment: ApJ, in pres

Ascertaining invasive breast cancer cases; the validity of administrative and self-reported data sources in Australia

Background: Statutory State-based cancer registries are considered the ‘gold standard’ for researchers identifying cancer cases in Australia, but research using self-report or administrative health datasets (e.g. hospital records) may not have linkage to a Cancer Registry and need to identify cases. This study investigated the validity of administrative and self-reported data compared with records in a State-wide Cancer Registry in identifying invasive breast cancer cases. Methods: Cases of invasive breast cancer recorded on the New South Wales (NSW) Cancer Registry between July 2004 and December 2008 (the study period) were identified for women in the 45 and Up Study. Registry cases were separately compared with suspected cases ascertained from: i) administrative hospital separations records; ii) outpatient medical service claims; iii) prescription medicines claims; and iv) the 45 and Up Study baseline survey. Ascertainment flags included diagnosis codes, surgeries (e.g. lumpectomy), services (e.g. radiotherapy), and medicines used for breast cancer, as well as self-reported diagnosis. Positive predictive value (PPV), sensitivity and specificity were calculated for flags within individual datasets, and for combinations of flags across multiple datasets. Results: Of 143,010 women in the 45 and Up Study, 2039 (1.4%) had an invasive breast tumour recorded on the NSW Cancer Registry during the study period. All of the breast cancer flags examined had high specificity (\u3e97.5%). Of the flags from individual datasets, hospital-derived ‘lumpectomy and diagnosis of invasive breast cancer’ and ‘(lumpectomy or mastectomy) and diagnosis of invasive breast cancer’ had the greatest PPV (89% and 88%, respectively); the later having greater sensitivity (59% and 82%, respectively). The flag with the highest sensitivity and PPV ≥ 85% was \u27diagnosis of invasive breast cancer\u27 (both 86%). Self-reported breast cancer diagnosis had a PPV of 50% and sensitivity of 85%, and breast radiotherapy had a PPV of 73% and a sensitivity of 58% compared with Cancer Registry records. The combination of flags with the greatest PPV and sensitivity was ‘(lumpectomy or mastectomy) and (diagnosis of invasive breast cancer or breast radiotherapy)’ (PPV and sensitivity 83%). Conclusions: In the absence of Cancer Registry data, administrative and self-reported data can be used to accurately identify cases of invasive breast cancer for sample identification, removing cases from a sample, or risk adjustment. Invasive breast cancer can be accurately identified using hospital-derived diagnosis alone or in combination with surgeries and breast radiotherapy

Lionfish (\u3ci\u3ePterois volitans\u3c/i\u3e) as biomonitoring species for oil pollution effects in coral reef ecosystems

With oil spills, and other sources of aromatic hydrocarbons, being a continuous threat to coral reef systems, and most reef fish species being protected or difficult to collect, the use of the invasive lionfish (Pterois volitans) might be a good model species to monitor biomarkers in potentially exposed fish in the Caribbean and western Atlantic. The rapid expansion of lionfish in the Caribbean and western Atlantic, and the unregulated fishing for this species, would make the lionfish a suitable candidate as biomonitoring species for oil pollution effects. However, to date little has been published about the responses of lionfish to environmental pollutants. For this study lionfish were collected in the Florida Keys a few weeks after Hurricane Irma, which sank numerous boats resulting in leaks of oil and fuel, and during the winter and early spring after that. Several biomarkers indicative of exposure to PAHs (bile fluorescence, cytochrome P450-1A induction, glutathione S-transferase activity) were measured. To establish if these biomarkers are inducible in PAH exposed lionfish, dosing experiments with different concentrations of High Energy Water Accommodated Fraction of crude oil were performed. The results revealed no significant effects in the biomarkers in the field collected fish, while the exposure experiments demonstrated that lionfish did show strong effects in the measured biomarkers, even at the lowest concentration tested (0.3% HEWAF, or 25 μg/l ƩPAH50). Based on its widespread distribution, relative ease of collection, and significant biomarker responses in the controlled dosing experiment, it is concluded that lionfish has good potential to be used as a standardized biomonitoring species for oil pollution in its neotropical realm