The Design of an Automatic Temperature Compensation System through Smart Heat Comparison/Judgment and Control for Stable Thermal Treatment in Hyperthermic Intraperitoneal Chemotherapy (HIPEC) Surgery.

After surgery for ovarian cancer or colorectal cancer, residual tumors are left around. A practical way to treat residual tumors is to destroy them with heat by injecting high-temperature drugs into the abdominal cavity. The injected medicinal substances are induced to flow out of the abdominal cavity; then, the spilled drug flows back into the abdominal cavity through feedback. During this process, the heat starts to decrease; thus, the treatment performance reduces. To overcome this problem, this study compares and assesses the temperature needed to maintain the heat for treatment and transmits a command signal to the heat exchanger through a look-up table (LUT). When the temperature decreases during the circulation of medications leaking out of the abdominal cavity, the LUT transmits a control signal (Tp) to the heat exchanger, which increases or vice versa. However, if the temperature (To) is within the treatment range, the LUT sends a Ts signal to the heat exchanger. This principle generates a pulse signal for the temperature difference (Tdif) in TC by comparing and determining the temperature (To) of the substance flowing out of the abdominal cavity with the reference temperature (Tref) through the temperature comparator (TC). At this time, if the signal is 41 °C or less, the LUT generates (heats) a Tp signal so that the temperature of the heat exchanger can be maintained in the range of 41 °C to 43 °C. If the Tdif is 44 °C or higher, the LUT generates (cools) the Ta signal and maintains the temperature of the heat exchanger at 41-43 °C. If the Tdif is maintained at 41-43 °C, the LUT generates a Tx signal to stop the system performance. At this time, the TC operation performance and Tdif generation process for comparing and determining the signal of To and Tref for drugs leaking out of the abdominal cavity is very important. It was observed that the faster the response signal, the lower the comparison and judgment error was; therefore, the response signal was confirmed to be 0.209 µs. The proposed method can guarantee rapid/accurate/safe treatment and automatically induce temperature adjustment; thus, it could be applied to the field of surgery.

Effects of temperature adjustment with a heating device in weak honey bee colonies in cold seasons.

Temperature adjustment was used to buffer against changes in external environmental temperature by a heating device. Two groups of honey bee colonies Apis mellifera anatoliaca were used: one as experimental (20) and the other control (10), which were equalized in terms of the number of brood frames, frames with bees, food, hive materials, and health treatments. The experimental colonies were heated during the cold season, and the temperature and humidity inside the hive were recorded. The number of frames of brood, frames of bees, and Varroa destructor mite numbers were also determined for all hives. Results suggest that the number of brood frames and frames with bees increased significantly in the hives with the heating treatment. However, varroa mite numbers also increased significantly in the heated colonies in comparison to the control colonies. Colony losses were significantly less (15%) in heated colonies compared to a 40% loss in control colonies. This is the first account demonstrating that using simple heating devices can support weak colonies during the cold season in general, and colony losses can be reduced significantly from this heating treatment in cold seasons.

Associations between short-term exposure to PM2.5, NO2 and O3 pollution and kidney-related conditions and the role of temperature-adjustment specification: A case-crossover study in New York state.

Epidemiologic evidence on the relationship between air pollution and kidney disease remains inconclusive. We evaluated associations between short-term exposure to PM2.5, NO2 and O3 and unplanned hospital visits for seven kidney-related conditions (acute kidney failure [AKF], urolithiasis, glomerular diseases [GD], renal tubulo-interstitial diseases, chronic kidney disease, dysnatremia, and volume depletion; n = 1,209,934) in New York State (2007-2016). We applied a case-crossover design with conditional logistic regression, controlling for temperature, dew point temperature, wind speed, and solar radiation. We used a three-pollutant model at lag 0-5 days of exposure as our main model. We also assessed the influence of model adjustment using different specifications of temperature by comparing seven temperature metrics (e.g., dry-bulb temperature, heat index) and five intraday temperature measures (e.g., daily mean, daily minimum, nighttime mean), according to model performance and association magnitudes between air pollutants and kidney-related conditions. In our main models, we adjusted for daytime mean outdoor wet-bulb globe temperature, which showed good model performance across all kidney-related conditions. We observed the odds ratios (ORs) for 5 µg/m3 increase in daily mean PM2.5 to be 1.013 (95% confidence interval [CI]: 1.001, 1.025) for AKF, 1.107 (95% CI: 1.018, 1.203) for GD, and 1.027 (95% CI: 1.015, 1.038) for volume depletion; and the OR for 5 ppb increase in daily 1-hour maximum NO2 to be 1.014 (95% CI; 1.008, 1.021) for AKF. We observed no associations with daily 8-hour maximum O3 exposure. Association estimates varied by adjustment for different intraday temperature measures: estimates adjusted for measures with poorer model performance resulted in the greatest deviation from estimates adjusted for daytime mean, especially for AKF and volume depletion. Our findings indicate that short-term exposure to PM2.5 and NO2 is a risk factor for specific kidney-related conditions and underscore the need for careful adjustment of temperature in air pollution epidemiologic studies.

Needle age and season influence photosynthetic temperature response and total annual carbon uptake in mature Picea mariana trees.

BACKGROUND AND AIMS: The carbon (C) balance of boreal terrestrial ecosystems is sensitive to increasing temperature, but the direction and thresholds of responses are uncertain. Annual C uptake in Picea and other evergreen boreal conifers is dependent on seasonal- and cohort-specific photosynthetic and respiratory temperature response functions, so this study examined the physiological significance of maintaining multiple foliar cohorts for Picea mariana trees within an ombrotrophic bog ecosystem in Minnesota, USA. METHODS: Measurements were taken on multiple cohorts of needles for photosynthetic capacity, foliar respiration (Rd) and leaf biochemistry and morphology of mature trees from April to October over 4 years. The results were applied to a simple model of canopy photosynthesis in order to simulate annual C uptake by cohort age under ambient and elevated temperature scenarios. KEY RESULTS: Temperature responses of key photosynthetic parameters [i.e. light-saturated rate of CO2 assimilation (Asat), rate of Rubisco carboxylation (Vcmax) and electron transport rate (Jmax)] were dependent on season and generally less responsive in the developing current-year (Y0) needles compared with 1-year-old (Y1) or 2-year-old (Y2) foliage. Temperature optimums ranged from 18·7 to 23·7, 31·3 to 38·3 and 28·7 to 36·7 °C for Asat, Vcmax and Jmax, respectively. Foliar cohorts differed in their morphology and photosynthetic capacity, which resulted in 64 % of modelled annual stand C uptake from Y1&2 cohorts (LAI 0·67 m(2 )m(-2)) and just 36 % from Y0 cohorts (LAI 0·52 m(2 )m(-2)). Under warmer climate change scenarios, the contribution of Y0 cohorts was even less; e.g. 31 % of annual C uptake for a modelled 9 °C rise in mean summer temperatures. Results suggest that net annual C uptake by P. mariana could increase under elevated temperature, and become more dependent on older foliar cohorts. CONCLUSIONS: Collectively, this study illustrates the physiological and ecological significance of different foliar cohorts, and indicates the need for seasonal- and cohort-specific model parameterization when estimating C uptake capacity of boreal forest ecosystems under ambient or future temperature scenarios.