Discerning Aggregation in Homogeneous Ensembles: A General Description of Photon Counting Spectroscopy in Diffusing Systems

In order to discern aggregation in solutions, we present a quantum mechanical analog of the photon statistics from fluorescent molecules diffusing through a focused beam. A generating functional is developed to fully describe the experimental physical system as well as the statistics. Histograms of the measured time delay between photon counts are fit by an analytical solution describing the static as well as diffusing regimes. To determine empirical fitting parameters, fluorescence correlation spectroscopy is used in parallel to the photon counting. For expedient analysis, we find that the distribution's deviation from a single Poisson shows a difference between two single fluor moments or a double fluor aggregate of the same total intensities. Initial studies were performed on fixed-state aggregates limited to dimerization. However preliminary results on reactive species suggest that the method can be used to characterize any aggregating system.Comment: 30 pages, 5 figure

Analysis of ``Gauge Modes'' in Linearized Relativity

By writing the complete set of $3 + 1$ (ADM) equations for linearized waves, we are able to demonstrate the properties of the initial data and of the evolution of a wave problem set by Alcubierre and Schutz. We show that the gauge modes and constraint error modes arise in a straightforward way in the analysis, and are of a form which will be controlled in any well specified convergent computational discretization of the differential equations.Comment: 11pages LaTe

Assessment of Early Postpartum Reproductive Performance in Two High Producing Estonian Dairy Herds

Early postpartum (6 weeks) ovarian activity, hormonal profiles, uterine involution, uterine infections, serum electrolytes, glucose, milk acetoacetate and blood urea nitrogen (BUN) levels were studied in 2 Estonian high producing dairy herd with annual milk production of 7688 (Farm A) and 9425 (Farm B). From each farm 10 cows, with normal calving performance were used. Blood samples for the hormonal (PGF(2α)-metabolite, progesterone) analyses were withdrawn. On day 25 PP blood serum samples were taken for the evaluation of metabolic/electrolyte status. On the same day estimation of milk acetoacetate values was done. The ultrasound (US) was started on day 7 PP and was performed every 3(rd )day until the end of experiment. Uterine content, follicular activity and sizes of the largest follicle and corpus luteum were monitored and measured. Vaginal discharge and uterine tone were recorded during the rectal palpation. Each animal in the study was sampled for bacteriological examination using endometrial biopsies once a week. Two types of PGF(2α)-metabolite patterns were detected: elevated levels during 14 days PP, then decline to the basal level and then a second small elevation at the time of final elimination of the bacteria from the uterus; or elevated levels during first 7 days PP, then decline to the basal level and a second small elevation before the final elimination of bacteria. Endometritis was diagnosed in 5 cows in farm A and in 3 cows in farm B respectively. In farm A, 5 cows out of 10 ovulated during experimental period and in 1 cow cystic ovaries were found. In farm B, 3 cows out of 10 ovulated. In 3 cows cystic ovaries were found. Altogether 40% of cows had their first ovulation during the experimental period. Three cows in farm A and 5 cows in farm B were totally bacteria negative during the experimental period. The most frequent bacteria found were A. pyogenes, Streptococcus spp., E. coli., F. necrophorum and Bacteroides spp. The highest incidence of bacteriological species was found during the first 3 weeks in both farms. All animals were free from bacteria after 5(th )week PP in farm A and after 4(th )week in farm B respectively. Serum electrolytes and glucose levels were found to be within the reference limits for the cows in both farms. No significant difference was found between farms (p > 0.05). Low phosphorus levels were found in both farms. Significant difference (p < 0.05) was found in BUN levels between farms. In both farms milk acetoacetate values were staying within the reference range given for the used test (<100 μmol/l). The uterine involution and bacterial elimination in the investigated cows could consider as normal but more profound metabolic studies could be needed to find reasons for later resumption of ovarian activity. Some recommendations to changing feeding regimes and strategies should also be given

Explore the concept of “light” and its interaction with matter: an inquiry-based science education project in primary school

The exploration process leading to the understanding of physical phenomena, such as light and its interaction with matter, raises great interest and curiosity in children. However, in most primary schools, children rarely have the opportunity to conduct science activities in which they can engage in an enquiry process even if by the action of the teacher. In this context, we have organised several in-service teacher training courses and carried out several pedagogic interventions in Portuguese primary schools, with the aim of promoting inquirybased science education. This article describes one of those projects, developed with a class of the third grade, which explored the curricular topic “Light Experiments”. Various activities were planned and implemented, during a total of ten hours spread over five lessons. The specific objectives of this paper are: to illustrate and analyse the teaching and learning process promoted in the classroom during the exploration of one of these lessons, and to assess children’s learning three weeks after the lessons. The results suggest that children made significant learning which persisted. We conclude discussing some processes that stimulated children’ learning, including the importance of teacher questioning in scaffolding children's learning and some didactic implications for teacher training.CIEC – Research Centre on Child Studies, IE, UMinho (FCT R&D unit 317), Portuga

Fatty oil accumulation in vegetable soybean seeds and its thin-layer chromatography

Received: February 23rd, 2021 ; Accepted: May 5th, 2021 ; Published: May 20th, 2021 ; Correspondence: [email protected] paper studies the accumulation of crude oil (triacylglycerides, monoacylglycerides, diacylglycerides, free fatty acids, phospholipids, tocopherols, pigments, sterols, waxes) in soybean vegetable samples. Samples were taken from two groups: grown in an experimental field and in protected ground of the Federal Scientific Center for Vegetable Growing in the Moscow Region. Both groups were observed in the phase of technical ripeness and in the phase of complete biological ripeness (finally ripe seeds). Soxhlet method as arbitration in analysis was used as suitable for the extraction of lipophilic substances. It was determined that the fat content in the technical ripeness phase in most soybean samples averaged 10.5%. In the phase of biological ripeness, the highest accumulation of fatty oil was observed in Hidaka and Nordic (17.6%). The oil content in vegetable forms of soybeans was consistently lower than that of grain varieties: in the phases of technical and biological ripeness by 55.6% and 22.0% (in relative values) respectively. Thus, he accumulation of oil in seeds is determined mainly genetically. The refractive index of vegetable and oil soybean was established equal on average 1.4755. According to this finding the soybean oil can be classified as semi-drying. Thin layer chromatography (TLC) was used to study the lipophilic components of soybean fatty oil. It was found experimentally that the best separation of the components is achieved using an eluent system: carbon tetrachloride: chloroform in a 2: 3 ratio. It was found that the main fatsoluble compounds are the following (in order of increasing Rf in the chromatogram): phospholipids, monoacylglycerides, triacylglycerides, tocopherols, fatty acid esters. As a finding of the research vegetable soybean cultivated at 55 °N in both technical and biological ripeness phases significantly accumulate crude oil in the seeds. This crude oil contained ω-6, ω-3, phospholipids, and vitamin E

Improving the reach of vaccines to low-resource regions, with a needle-free vaccine delivery device and long-term thermostabilization

Dry-coated microprojections can deliver vaccine to abundant antigen-presenting cells in the skin and induce efficient immune responses and the dry-coated vaccines are expected to be thermostable at elevated temperatures. In this paper, we show that we have dramatically improved our previously reported gas-jet drying coating method and greatly increased the delivery efficiency of coating from patch to skin to from 6.5% to 32.5%, by both varying the coating parameters and removing the patch edge. Combined with our previous dose sparing report of influenza vaccine delivery in a mouse model, the results show that we now achieve equivalent protective immune responses as intramuscular injection (with the needle and syringe), but with only 1/30th of the actual dose. We also show that influenza vaccine coated microprojection patches are stable for at least 6 months at 23 degrees C. inducing comparable immunogenicity with freshly coated patches. The dry-coated microprojection patches thus have key and unique attributes in ultimately meeting the medical need in certain low-resource regions with low vaccine affordability and difficulty in maintaining "cold-chain" for vaccine storage and transport. (C) 2011 Elsevier B.V. All rights reserved

AI-algorithm training and validation for identification of endometrial CD138+ cells in infertility-associated conditions; polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF)

Abstract Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138− cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138− and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36–0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p < 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (p = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples. Conclusion: Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial CD138+ plasma cells, offering distinct advantages over manual inspection, such as rapid analysis of whole slide images, reduction of intra- and interobserver variations, sparing the valuable time of trained specialists, and consistent productivity. This supports the application of AI technology to help clinical decision-making, for example, in understanding endometrial cycle phase-related dynamics, as well as different reproductive disorders.Abstract Background: Endometrial CD138+ plasma cells serve as a diagnostic biomarker for endometrial inflammation, and their elevated occurrence correlates positively with adverse pregnancy outcomes. Infertility-related conditions like polycystic ovary syndrome (PCOS) and recurrent implantation failure (RIF) are closely associated with systemic and local chronic inflammatory status, wherein endometrial CD138+ plasma cell accumulation could also contribute to endometrial pathology. Current methods for quantifying CD138+ cells typically involve laborious and time-consuming microscopic assessments of only a few random areas from a slide. These methods have limitations in accurately representing the entire slide and are susceptible to significant biases arising from intra- and interobserver variations. Implementing artificial intelligence (AI) for CD138+ cell identification could enhance the accuracy, reproducibility, and reliability of analysis. Methods: Here, an AI algorithm was developed to identify CD138+ plasma cells within endometrial tissue. The AI model comprised two layers of convolutional neural networks (CNNs). CNN1 was trained to segment epithelium and stroma across 28,363 mm2 (2.56 mm2 of epithelium and 24.87 mm2 of stroma), while CNN2 was trained to distinguish stromal cells based on CD138 staining, encompassing 7345 cells in the object layers (6942 CD138− cells and 403 CD138+ cells). The training and performance of the AI model were validated by three experienced pathologists. We collected 193 endometrial tissues from healthy controls (n = 73), women with PCOS (n = 91), and RIF patients (n = 29) and compared the CD138+ cell percentages based on cycle phases, ovulation status, and endometrial receptivity utilizing the AI model. Results: The AI algorithm consistently and reliably distinguished CD138− and CD138+ cells, with total error rates of 6.32% and 3.23%, respectively. During the training validation, there was a complete agreement between the decisions made by the pathologists and the AI algorithm, while the performance validation demonstrated excellent accuracy between the AI and human evaluation methods (intraclass correlation; 0.76, 95% confidence intervals; 0.36–0.93, p = 0.002) and a positive correlation (Spearman's rank correlation coefficient: 0.79, p < 0.01). In the AI analysis, the AI model revealed higher CD138+ cell percentages in the proliferative phase (PE) endometrium compared to the secretory phase or anovulatory PCOS endometrium, irrespective of PCOS diagnosis. Interestingly, CD138+ percentages differed according to PCOS phenotype in the PE (p = 0.03). On the other hand, the receptivity status had no impact on the cell percentages in RIF samples. Conclusion: Our findings emphasize the potential and accuracy of the AI algorithm in detecting endometrial CD138+ plasma cells, offering distinct advantages over manual inspection, such as rapid analysis of whole slide images, reduction of intra- and interobserver variations, sparing the valuable time of trained specialists, and consistent productivity. This supports the application of AI technology to help clinical decision-making, for example, in understanding endometrial cycle phase-related dynamics, as well as different reproductive disorders

Variants Near MC4R Are Associated With Obesity and Influence Obesity-Related Quantitative Traits in a Population of Middle-Aged People: Studies of 14,940 Danes

OBJECTIVE— Variants downstream of the melanocortin-4 receptor gene (MC4R) have been reported to associate with obesity. We examined rs17782313, rs17700633, rs12970134, rs477181, rs502933, and rs4450508 near MC4R for association with obesity-related quantitative traits, obesity, and type 2 diabetes in Danish individuals