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The rise in computational power in recent years has enabled research and national weather services to conduct high-resolution simulations down to the kilometric (Δx=𝒪(1 km)) and even to hectometric (Δx=𝒪(100 m)) scales for both weather and climate applications. We investigate with the state-of-the-art numerical weather prediction model ICON how mid-level clouds are represented on a mesh size of 1 km and 65 m, respectively, and for two bulk microphysics schemes, namely one-moment and two-moment cloud microphysics. For this analysis, we leverage the abundant observational data from two independent field campaigns in Switzerland (CLOUDLAB; hilly terrain) and Austria (CROSSINN; complex terrain). With four case studies, we show that while the temperature fields around the campaign sites are well represented in both mesh sizes, the 65 m resolution simulates a more realistic vertical velocity structure that is beneficial for cloud formation. Therefore, the largest differences for the representation of clouds lie in the two mesh sizes. The 1 km simulation in hilly terrain does not capture the observed clouds in both cloud microphysics schemes. Here, the higher resolution of the vertical velocities in the 65 m proves to be crucial for representing the investigated cloud types, and the two-moment microphysics scheme in general performs better with respect to the cloud characteristics because it considers variations in cloud droplet and ice crystal number concentrations. In complex terrain, the differences between the mesh sizes and the cloud microphysics schemes are surprisingly small, but the 65 m simulations with two-moment cloud microphysics show the most realistic cloud representation.

The ice phase in clouds is essential for precipitation formation over continents. The underlying processes for ice growth are still poorly understood, leading to large uncertainties in precipitation forecasts and climate simulations. One crucial aspect is the Wegener–Bergeron–Findeisen (WBF) process, which describes the growth of ice crystals at the expense of cloud droplets, leading to a partial or full glaciation of the cloud. In the CLOUDLAB project, we employ glaciogenic cloud seeding to initiate the ice phase in supercooled low-level clouds in Switzerland using uncrewed aerial vehicles with the goal of investigating the WBF process. An extensive setup of ground-based remote-sensing and balloon-borne in situ instrumentation allows us to observe the formation and subsequent growth of ice crystals in great detail. In this study, we compare the seeding signals observed in the field to those simulated using a numerical weather model in large-eddy mode (ICON-LEM). We first demonstrate the capability of the model to accurately simulate and reproduce the seeding experiments across different environmental conditions. Second, we investigate the WBF process in the model by comparing the simulated cloud droplet and ice crystal number concentration changes to in situ measurements. In the field experiments, simultaneous reductions in cloud droplet number concentrations with increased ice crystal number concentrations were observed, with periods showing a full depletion of cloud droplets. The model can reproduce the observed ice crystal number concentrations most of the time; however, it cannot reproduce the observed fast reductions in cloud droplet number concentrations. Our detailed analysis shows that the WBF process appears to be less efficient in the model than in the field. In the model, exaggerated ice crystal number concentrations are required to produce comparable changes in cloud droplet number concentrations, highlighting the inefficiency of the WBF process in the numerical weather model ICON.

Uncrewed aerial vehicles (UAVs) have become widely used in a range of atmospheric science research applications. Because of their small size, flexible range of motion, adaptability, and low cost, multirotor UAVs are especially well-suited for probing the lower atmosphere. However, their use so far has been limited to conditions outside of clouds, first because of the difficulty of flying beyond visual line of sight and second because of the challenge of flying in icing conditions in supercooled clouds. Here, we present two UAVs for cloud microphysical research: one UAV (the measurement UAV) equipped with a Portable Optical Particle Spectrometer (POPS) and meteorological sensors to probe the aerosol and meteorological properties in the boundary layer and one UAV (the seeding UAV) equipped with seeding flares to produce a plume of particles that can nucleate ice in supercooled clouds. A propeller heating mechanism on both UAVs allows for operating in supercooled clouds with icing conditions. These UAVs are an integral part of the CLOUDLAB project in which glaciogenic cloud seeding of supercooled low stratus clouds is utilized for studying aerosol–cloud interactions and ice crystal formation and growth. In this paper, we first show validations of the POPS on board the measurement UAV, demonstrating that the rotor turbulence has a small effect on measured particle number concentrations. We then exemplify the applicability for profiling the planetary boundary layer, as well as for sampling and characterizing aerosol plumes, in this case, the seeding plume. We also present a new method for filtering out high-concentration data to ensure good data quality of POPS. We explain the different flight patterns that are possible for both UAVs, namely horizontal or vertical leg patterns or hovering, with an extensive and flexible parameter space for designing the flight patterns according to our scientific goals. Finally, we show two examples of seeding experiments: first characterizing an out-of-cloud seeding plume with the measurement UAV flying horizontal transects through the plume and, second, characterizing an in-cloud seeding plume with downstream measurements from a POPS and a holographic imager mounted on a tethered balloon. Particle number concentrations and particle number size distributions of the seeding plume from the experiments reveal that we can successfully produce and measure the seeding plume, both in-cloud (with accompanying elevated ice crystal number concentrations) and out-of-cloud. The methods presented here will be useful for probing the lower atmosphere, for characterizing aerosol plumes, and for deepening our cloud microphysical understanding through cloud seeding experiments, all of which have the potential to benefit the atmospheric science community.

Cirrus cloud thinning (CCT) is a relatively new radiation management proposal to counteract anthropogenic climate warming by targeting Earth's terrestrial radiation balance. The efficacy of this method was presented in several general circulation model (GCM) studies that showed widely varied radiative responses, originating in part from the differences in the representation of cirrus ice microphysics between the different GCMs. The recent implementation of a new, more physically based ice microphysics scheme (Predicted Particle Properties, P3) that abandons ice hydrometeor size class separation into the ECHAM-HAM GCM, coupled to a new approach for calculating cloud fractions that increases the relative humidity (RH) thresholds for cirrus cloud formation, motivated a reassessment of CCT efficacy. In this study, we first compared CCT sensitivity between the new cloud fraction approach and the original ECHAM-HAM cloud fraction approach. Consistent with previous approaches using ECHAM-HAM, with the P3 scheme and the higher RH thresholds for cirrus cloud formation, we do not find a significant cooling response in any of our simulations. The most notable response from our extreme case is the reduction in the maximum global-mean net top-of-atmosphere (TOA) radiative anomalies from overseeding by about 50 %, from 9.9 W m−2 with the original cloud fraction approach down to 4.9 W m−2 using the new cloud fraction RH thresholds that allow partial grid-box coverage of cirrus clouds above ice saturation, unlike the original approach. Even with this reduction with the updated cloud fraction approach, the TOA anomalies from overseeding far exceed those reported in previous studies. We attribute the large positive TOA anomalies to seeding particles overtaking both homogeneous nucleation and heterogeneous nucleation on mineral dust particles within cirrus clouds to produce more numerous and smaller ice crystals. This effect is amplified by longer ice residence times in clouds due to the slower removal of ice via sedimentation in the P3 scheme. In an effort to avoid this overtaking effect of seeding particles, we increased the default critical ice saturation ratio (Si,seed) for ice nucleation on seeding particles from the default value of 1.05 to 1.35 in a second sensitivity test. With the higher Si,seed we drastically reduce overseeding, which suggests that Si,seed is a key factor to consider for future CCT studies. However, the global-mean TOA anomalies contain high uncertainty. In response, we examined the TOA anomalies regionally and found that specific regions only show a small potential for targeted CCT, which is partially enhanced by using the larger Si,seed. Finally, in a seasonal analysis of TOA responses to CCT, we find that our results do not confirm the previous finding that high-latitude wintertime seeding is a feasible strategy to enhance CCT efficacy, as seeding in our model enhances the already positive cirrus longwave cloud radiative effect for most of our simulations. Our results also show feedbacks on lower-lying mixed-phase and liquid clouds through the reduction in ice crystal sedimentation that reduces cloud droplet depletion and results in stronger cloud albedo effects. However, this is outweighed by stronger longwave trapping from cirrus clouds with more numerous and smaller ice crystals. Therefore, we conclude that CCT is unlikely to act as a feasible climate intervention strategy on a global scale.

Abstract In scientific and economic experience, the effect of zeolite-chlorella top dressing (CCP) from 28-29% zeolite, 1.5-2% Chlorella vulgaris powder and 75-76% cake on the productive effect of feed and digestion metabolism of dairy cows has been studied. Tetra-edric frame-hollow zeolite crystals have selectively adsorbing and ion-filtering properties, and chlorella enriches with amino acids and vitamins. The introduction of CCP into the diet of cows of the experimental group increased the mineral-vitamin balance and positively affected the metabolism of cicatricial digestion. The pH shift from 6.14 to 6.17 activated the ecosystem of the rumen microflora, increased the synthesis of LVH 0.79 mmol/100ml, due to an increase in the volume of acetates from 54.1±3.0 to 57.2±2.2 mmol/100ml, increased the number of infusoria by 41.1 thousand/ml more than the control group. An increase in the amylolytic activity of the rumen chyme by 2.8 mg/starch, and cellulolytic activity by 2.8% increased scar digestion. This increased the consumption of the dry matter of the diet by cows of the experimental group by 0.48 ± 0.06 kg/head. /day. and daily milk yields by 1.06 ± 0.03 kg against the control. An increase in protein and fat in milk with a decrease in somatic cells, and in the blood of erythrocytes and hemoglobin increased biosynthesis, which increased the conversion rate of feed from 0.68 to 0.72, and protein from 17.0% to 18.9%.

Objective: Determine the frequency of migraine, its triggering factors and assessment of the impact on daily life among medical students ofa private medical college in Lahore, Pakistan. Settings: First to final year MBBS students of Fatima Memorial College of Medicine, Lahore Period: February to April, 2019. Material & Methods: In a total of 708 students, 118 students were identified, as having experienced headache for the past one year. They were categorized into those having migraine, tension and cluster headache according to operational definitions. Migraine was identified in 56 participants. Further data was collected from the 56 migraineurs using a structured questionnaire, migraine disability assessment tool and visual analogue scale for pain intensity. Data was analyzed using SPSS version 22. Results: Migraineurs were 56, mean age 21.16 + 1.714, predominant in females 46 (82.1%). Most common triggering factors, sleep deprivation 53 (94.6%), mental stress 51 (91.1%), missing meals 41 (73.2%), bright light 37 (66.1%), physical activity 44 (78.6%), photophobia 44 (78.6%) and phonophobia 36 (64.3%). Most students 20 (35.7%) suffered from moderate to severe disability 19 (33.9%) (MIDAS grade III and IV). Visual Analogue Scale showed 32 (57.14%) had severe pain (VAS scale 7-10). Association of lack of sleep (p=0.017), duration of headache (p = 0.014) with higher VAS score and duration of headache (p=0.046) with moderate to severe MIDAS grade was depicted. Conclusion: Migraine can be considered to be the commonest type of headache among medical students resulting in major disability and lost days of productivity

Introduction. The burden of urinary tract infection (UTI) in pregnancy has been understudied in Cameroon.Aim: to determine the prevalence and risk factors of UTI in pregnancy in a tertiary hospital in Cameroon.Materials and methods. A hospital-based matched case-control study of pregnant women with evidence of UTI and those without who underwent antenatal care and gave birth at the Douala General Hospital from January 2014 to December 2018. Demographic, reproductive health/clinical data were collected using a pre-tested questionnaire and analyzed with SPSS.Results. The prevalence of UTI in pregnancy was 4.41 %. Poor antenatal care uptake (AOR = 5.64; 95 % CI = 2.21–14.33), multiple weekly sexual intercourses (AOR = 4.64; 95 % CI = 0.22–96.94), a history of UTI (AOR = 3.01; 95 % CI = 1.12–8.04) and drying the genitals from back to front (AOR = 5.50; 95 % CI = 1.95–15.44) were associated factors.Conclusion. The prevalence of UTI in pregnancy is low amid multiple associated factors. Screening for UTI in pregnancy should be made the standard of obstetric care in Cameroon.

Purpose: This study aimed to explore the early predictors of intensive care unit (ICU) admission and in-hospital mortality among patients diagnosed with Coronavirus disease (COVID-19). Methods & Materials: This was a case-control study of adult patients with confirmed COVID-19. Cases were defined as patients admitted to ICU during the period February 29 - May 29, 2020. For each case enrolled, one control was matched by age and gender. Univariate and multivariate logistic regression models were used to identify the predictors for ICU admission and in-hospital mortality among the COVID‐19 patients. Results: A total of 1560 patients with confirmed COVID-19 were included. Each group included 780 patients with a predominant male gender (89.7%) and a median age of 49 years (interquartile range, IQR=18). Predictors independently associated with ICU admission included having cardiovascular disease (CVD) (adjusted odds ratio (aOR)=1.64, 95% confidence interval (CI): 1.16 - 2.32, p= 0.005), diabetes (aOR=1.52, 95% CI: 1.08 - 2.13, p= 0.016), body mass index ≥30 kg/m2 (aOR=1.46, 95% CI: 1.03-2.08, p= 0.034), lymphocytes ≤0.8 × 103/μL (aOR=2.69, 95% CI: 1.80-4.02, p120 U/L (aOR= 2.59, 95% CI: 1.53-4.36, p600 μg/L (aOR=1.96, 95% CI: 1.40-2.74, p100 mg/L (aOR=4.09, 95% CI: 2.81-5.96, p120 U/L (aOR= 1.89, 95% CI: 1.04-3.43, p=0.036). Conclusion: Having CVD, diabetes, lymphopenia, and increased AST were independent predictors for both ICU admission and in-hospital mortality in patients with COVID-19. In addition, obesity, high ferritin, and CRP levels were also associated with increased risk of ICU admission, while cancer was strongly associated with in-hospital mortality. Early identification and monitoring of patients at risk is essential in planning the level of care needed to prevent delay in medical intervention.

A scientific approach for biology students in the Vertebrate course is needed in order to improve the skills and enrich the experience for new spirit of learning. The research was a quantitative research aiming to explain the influence of the encyclopedia as learning materials used in the scientific approach to improving the students’ science process skills. Meanwhile, to reveal the influence of each indicator in each predictor, a qualitative descriptive analysis was employed. The encyclopedia was found to be able to provide visualization to represent an explanation. The respondents in this study were biology students who attended the Vertebrate course. Thirty respondents were selected through a cluster random sampling technique. A test of science process skills was the instrument of this research. Furthermore, the ANOVA was utilized in testing the hypothesis. The analysis results showed that there was a different effect of the learning approach on the basic science process skills. Moreover, there was also a significant influence of the scientific approach to basic science processing skills at a significance level of 0.001 (on corrected model) with F value equal to 7.411. Meanwhile, the significance level for basic science process skills was 0.024 with the p-value

The NOAA National Status and Trends (NS&T) Program has been monitoring chemical contamination in fish livers, and surface sediments since 1984 and in molluscan tissue and sediments since 1986. Data from fine-grained sediment at 175 sites are used to describe the spatial distribution of contamination throughout the coastal and estuarine United States. Highest levels are generally found in, and considered representative of, urban areas. It should be noted, however, that these levels are not as high as have been found near discharge pipes or in isolated industrial areas through other monitoring efforts. Dramatic biological responses, such as liver tumors in fish or apparently toxic contaminant levels in sediment, are found infrequently. Subtle biological changes, especially those that affect reproductive ability, are being sought. Data from three annual collections of mollusks have been used to identify early signals of temporal trends in contamination at NS&T sites.