Forecast of omicron wave time evolution

Adopting an early doubling time of three days for the rate of new infections with the omicron mutant the temporal evolution of the omicron wave in different countries is predicted. The predictions are based on the susceptible-infectious-recovered/removed (SIR) epidemic compartment model with a constant stationary ratio k=mu(t)/a(t) between the infection (a(t)) and recovery (mu(t)) rate. The fixed early doubling time then uniquely relates the initial infection rate a0 to the ratio k, which therefore determines the full temporal evolution of the omicron waves. For each country three scenarios (optimistic, pessimistic, intermediate) are considered and the resulting pandemic parameters are calculated. These include the total number of infected persons, the maximum rate of new infections, the peak time and the maximum 7-day incidence per 100000 persons. Among the considered European countries Denmark has the smallest omicron peak time and the recently observed saturation of the 7-day incidence value at 2478 is in excellent agreement with the prediction in the optimistic scenario. For Germany we predict peak times of the omicron wave ranging from 32 to 38 and 45 days after the start of the omicron wave in the optimistic, intermediate and pessimistic scenario, respectively, with corresponding maximum SDI values of 7090, 13263 and 28911, respectively. Adopting Jan 1st, 2022 as the starting date our predictions implies that the maximum of the omicron wave is reached between Feb 1 and Feb 15, 2022. Rather similar values are predicted for Switzerland. Due to an order of magnitude smaller omicron hospitalization rate, due to the high percentage of vaccinated and boostered population, the German health system can cope with maximum omicron SDI value of 2800 which is about a factor 2.5 smaller than the maximum omicron SDI value 7090 in the optimistic case. By either reducing the duration of intensive care during this period of maximum, and/or by making use of the nonuniform spread of the omicron wave across Germany, it seems that the German health system can barely cope with the omicron wave avoiding triage decisions. The reduced omicron hospitalization rate also causes significantly smaller mortality rates compared to the earlier mutants in Germany. In the optimistic scenario one obtains for the total number of fatalities 7445 and for the maximum death rate 418 per day which are about one order of magnitude smaller than the beta fatality rate and total number.

Effect of Endogenous Lipids and Proteins on the Antioxidant, in vitro Starch Digestibility, and Pasting Properties of Sorghum Flour

This study evaluated the effect of endogenous lipids and proteins on the antioxidants, starch digestibility, and pasting properties of sorghum (Sorghum bicolor) flour (SF). Endogenous lipids and/or proteins were removed from different portions of SF to obtain defatted (DF), deproteinized (DP), and defatted and deproteinized (DF-DP) flours. Bioactive constituents (total phenolics, tannins, flavonoids, saponins, and anthocyanins), antioxidant activities [2,2-Azinobis (3-ethyl-benzothiazoline-6-sulfonic acid) radical cation (ABTS*+) and 2, 2-Diphenyl-2-picrylhydrazyl radical (DPPH*) scavenging activities, reducing power, and Fe2+ chelating capacity], starch, amylose, starch hydrolysis index (HI), estimated glycemic index (eGI), and pasting properties of treated and control (untreated) flours were determined. The control flour (SF) had significantly higher (p < 0.05) levels of all the bioactive constituents and antioxidant activity tested than the DF, DP, and DF-DP flours, while the DF-DP flour had the least levels of bioactive constituents and antioxidant activity. In contrast, the starch, amylose, HI, and eGI were consistently in the order of DF-DP > DF > DP > control flour (p < 0.05). The control flour had the highest (p < 0.05) peak viscosity, and the least peak time and pasting temperature, while the DF flour had the highest final viscosity. Therefore, endogenous lipids and proteins contribute to the antioxidant, starch digestibility, and pasting properties of sorghum flour.

Use of Protein Content, Amylose Content, and RVA Parameters to Evaluate the Taste Quality of Rice

Taste quality of rice is the key to its value. However, it is greatly affected by rice types and the environment. It is a complex but necessary factor to accurately evaluate the taste quality of various types of rice in different environments. In this study, 7 different types of rice with different taste values were used as materials, and 12 nitrogen fertilizer treatments were applied to obtain 84 different rice taste values. We used protein content, amylose content, and RVA to evaluate changes in the taste value of rice. Rice with high taste value tended to have higher amylose content, peak viscosity, hold viscosity, final viscosity, and breakdown, as well as lower protein content, pasting temperature, and peak time. Protein and amylose contents affected the taste value of rice by affecting the RVA profiles except for setback. For high and low taste-value rice types, protein content could explain 66.8 and 42.9% of the variation in taste value, respectively. In the case of medium taste-value type, protein content was not enough to evaluate the taste quality of rice. Stickiness could explain 59.6% of the variation in taste value. When the protein content of rice was less than 6.61% or greater than 9.34%, it could be used to reflect the taste quality of rice. When the protein content was in between the two, protein content was not enough to reflect the taste quality of rice. Our results suggested that protein content could better reflect the taste quality change for rice, which provided a theoretical and technical basis for the accurate evaluation of the taste value of various types of rice.

Acquisition of Rainfall in Ungauged Basins: a Study of Rainfall Distribution Heterogeneity Based on a New Method

High-density precipitation data is always desired to capture the heterogeneity of precipitation to accurately describe the components of the hydrological cycle. However, equipping and maintaining a high-density rain gauge network involves high costs, and the existing rain gauges are often unable to meet the density requirements. The objective of this study is to provide a new method to analyze the spatiotemporal variability of the precipitation field and to solve the problem of insufficient site density. To this end, the Proper Orthogonal Decomposition (POD) method is proposed, which can analyze the spatial distribution characteristics of rainfall fields to solve data shortages. To demonstrate the feasibility and advantages of the proposed methodology, four districts and counties (Hongshan District, Jianli County, Sui County, and Xuanen County) in Hubei province in China were selected as case studies. The principal results are as follows. (1) The proposed method is effective in analyzing the spatiotemporal variability of the rainfall field to reconstruct rainfall data in ungauged basins. (2) Compared with the commonly used Thiessen Polygon method, the Inverse Distance Weighting method, and the Kriging method, POD is more accurate and convenient, and the root mean squared error is reduced from 3.22, 1.83, 2.19 to 2.09; the correlation coefficients are improved from 0.60, 0.85, 0.79 to 0.89, respectively. (3) The POD method performs particularly well in simulating the peak value and the peak time and can offer a meaningful reference for analyzing the spatial distribution of rainfall.

A Machine Learning Based Model for Energy Usage Peak Prediction in Smart Farms

Context: Energy utilization is one of the most closely related factors affecting many areas of the smart farm, plant growth, crop production, device automation, and energy supply to the same degree. Recently, 4th industrial revolution technologies such as IoT, artificial intelligence, and big data have been widely used in smart farm environments to efficiently use energy and control smart farms’ conditions. In particular, machine learning technologies with big data analysis are actively used as one of the most potent prediction methods supporting energy use in the smart farm. Purpose: This study proposes a machine learning-based prediction model for peak energy use by analyzing energy-related data collected from various environmental and growth devices in a smart paprika farm of the Jeonnam Agricultural Research and Extension Service in South Korea between 2019 and 2021. Scientific method: To find out the most optimized prediction model, comparative evaluation tests are performed using representative ML algorithms such as artificial neural network, support vector regression, random forest, K-nearest neighbors, extreme gradient boosting and gradient boosting machine, and time series algorithm ARIMA with binary classification for a different number of input features. Validate: This article can provide an effective and viable way for smart farm managers or greenhouse farmers who can better manage the problem of agricultural energy economically and environmentally. Therefore, we hope that the recommended ML method will help improve the smart farm’s energy use or their energy policies in various fields related to agricultural energy. Conclusion: The seven performance metrics including R-squared, root mean squared error, and mean absolute error, are associated with these two algorithms. It is concluded that the RF-based model is more successful than in the pre-others diction accuracy of 92%. Therefore, the proposed model may be contributed to the development of various applications for environment energy usage in a smart farm, such as a notification service for energy usage peak time or an energy usage control for each device.

Assessing the energy transition in China towards carbon neutrality with a probabilistic framework

A profound transformation of China’s energy system is required to achieve carbon neutrality. Here, we couple Monte Carlo analysis with a bottom-up energy-environment-economy model to generate 3,000 cases with different carbon peak times, technological evolution pathways and cumulative carbon budgets. The results show that if emissions peak in 2025, the carbon neutrality goal calls for a 45–62% electrification rate, 47–78% renewable energy in primary energy supply, 5.2–7.9 TW of solar and wind power, 1.5–2.7 PWh of energy storage usage and 64–1,649 MtCO2 of negative emissions, and synergistically reducing approximately 80% of local air pollutants compared to the present level in 2050. The emission peak time and cumulative carbon budget have significant impacts on the decarbonization pathways, technology choices, and transition costs. Early peaking reduces welfare losses and prevents overreliance on carbon removal technologies. Technology breakthroughs, production and consumption pattern changes, and policy enhancement are urgently required to achieve carbon neutrality.

The Impact of Heart Rate Circadian Rhythm on In-hospital Mortality in Stroke and Critically Ill Patients: Insights from the eICU Collaborative Research Database

Background: The dysregulation of the heart rate circadian rhythm has been documented to be an independent risk factor in multiple diseases. However, data showing the impact of dysregulated heart rate circadian rhythm in stroke and critically ill patients are scarce.Methods: Stroke and critically ill patients in the ICU between 2014 and 2015 from the recorded eICU Collaborative Research Database were included in the current analyses. The impact of circadian rhythm of heart rate on in-hospital mortality was analyzed. Three variables, Mesor (rhythm-adjusted mean of heart rate), Amplitude (distance from the highest point of circadian rhythm of heart rate to Mesor), and Peak time (time when the circadian rhythm of heart rate reaches the highest point) were used to evaluate the heart rate circadian rhythm. The incremental value of circadian rhythm variables in addition to Acute Physiology and Chronic Health Evaluation (APACHE) IV score to predict in-hospital mortality was also explored.Results: A total of 6,201 eligible patients were included. The in-hospital mortality was 16.2% (1,002/6,201). The circadian rhythm variables of heart rate, Mesor, Amplitude, and Peak time, were identified to be independent risk factors of in-hospital mortality. After adjustments, Mesor per 10 beats per min (bpm) increase was associated with a 1.17-fold (95%CI: 1.11, 1.24, P<0.001) and Amplitude per 5 bpm was associated with a 1.14-fold (95%CI: 1.06, 1.24, P<0.001) increase in the risk of in-hospital mortality, respectively. The risk of in-hospital mortality was lower in patients who had Peak time reached between 18:00-24:00 or 00:00-06:00; whereas the risk was highest in patients who had Peak time reached between 12:00-18:00 (OR: 1.33, 95%CI: 1.05, 1.68, P=0.017). Compared with APACHE IV score only (c-index=0.757), combining APACHE IV score and circadian rhythm variables of heart rate (c-index=0.766) was associated with increased discriminative ability (P=0.003).Conclusion: Circadian rhythm of heart rate is an independent risk factor of the in-hospital mortality in stroke and critically ill patients. Including circadian rhythm variables regarding heart rate might increase the discriminative ability of the risk score to predict the short-term prognosis of patients.

Ultrasound-Induced Release of Nimodipine from Drug-Loaded Block Copolymer Micelles: In Vivo Analysis

Nimodipine prevents cerebral vasospasm and improves functional outcome after aneurysmal subarachnoid hemorrhage (aSAH). The beneficial effect is limited by low oral bioavailability of nimodipine, which resulted in an increasing use of nanocarriers with sustained intrathecal drug release in order to overcome this limitation. However, this approach facilitates only a continuous and not an on-demand nimodipine release during the peak time of vasospasm development. In this study, we aimed to assess the concept of controlled drug release from nimodipine-loaded copolymers by ultrasound application in the chicken chorioallantoic membrane (CAM) model. Nimodipine-loaded copolymers were produced with the direct dissolution method. Vasospasm of the CAM vessels was induced by means of ultrasound (Physiomed, continuous wave, 3 MHz, 1.0 W/cm2). The ultrasound-mediated nimodipine release (Physiomed, continuous wave, 1 MHz, 1.7 W/cm2) and its effect on the CAM vessels were evaluated. Measurements of vessel diameter before and after ultrasound-induced nimodipine release were performed using ImageJ. The CAM model could be successfully carried out in all 25 eggs. After vasospasm induction and before drug release, the mean vessel diameter was at 57% (range 44–61%) compared to the baseline diameter (set at 100%). After ultrasound-induced drug release, the mean vessel diameter of spastic vessels increased again to 89% (range 83–91%) of their baseline diameter, which was significant (p = 0.0002). We were able to provide a proof of concept for in vivo vasospasm induction by ultrasound application in the CAM model and subsequent resolution by ultrasound-mediated nimodipine release from nanocarriers. This concept merits further evaluation in a rat SAH model. Graphical abstract

Percentile Curves for Multiple Physical Fitness Components Among Chinese Han Children and Adolescents Aged 7–18 Years From a National Survey Based on the Total and the Normal Weight Population

Introduction: To develop sex- and age-specific percentile curves for seven physical fitness components for Chinese Han children and adolescents aged 7–18 years based on the total and the normal weight population using a nationally representative sample.Methods: A total of 214,228 Chinese Han children and adolescents aged 7–18 years old with all nutritional status and 161,999 with normal weight were examined. Seven physical fitness components [forced vital capacity (FVC), standing long jump (SLJ), 50-m dash, sit-and-reach (SR), grip strength (GS), body muscle strength (BMS), and endurance running (ER)] were measured, and percentile curves for each physical fitness component at the 20th, 40th, 60th, and 80th percentiles were calculated using the general additive model for location, scale, and shape (GAMLSS).Results: Physical fitness presents different characteristics in each subgroup of sex, age, and nutritional status among children and adolescents. Sex- and age-specific percentiles for the seven physical fitness components among the Chinese Han children and adolescents aged 7–18 years based on the total and the normal weight population were provided as curves. Boys performed better than girls in FVC, SLJ, 50-m dash, GS, and ER but worse in SR. The performances of FVC, SLJ, 50-m dash, GS, BMS, and ER increased with age, but the estimates of SR were at the bottom among boys aged 12 years and girls aged 11 years. The annual increments of all components were larger in boys than girls at the peak time, which was earlier in girls than boys. The gap of physical fitness components between sexes increased with age, especially during puberty (since after 11 years old).Conclusion: The present study described the percentile curves of seven physical fitness components among the Chinese Han children and adolescents based on the total and the normal weight population at the national level, which could help to chart the level of physical fitness across age span and identify the extreme populations with either health concerns or potential talents.