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Erosion occurs when the flow of velocity in a channel exceeds the threshold velocity that causes the bed material to move. This phenomenon is observed in the Harapan River, resulting in significant impacts, such as riverbed lowering and narrowing due to the presence of water structures (bridge abutments) spanning across the river. The objective of this study is to investigate the sediment motion stability around bridge abutments before and after the installation of cribs in order to prevent structural failures. Sediment sampling was conducted at six points around the bridge abutments, using the Pebble Count method along zigzag paths. The grain size of the bed material was classified according to the Sturgess classification standard, and the material dispersion process was analysed using the Hjustorm curve. Design rainfall was transformed into design flood discharge, which was then simulated using the Two-Dimensional Hydrodynamic, comparing the river conditions before and after crib installation. In abutments without cribs, erosion occurred in direct contact with the abutment on the right side for each sediment grain diameter. However, in abutments with cribs, erosion still occurred, but it did not directly contact either abutment despite the presence of a crib. The crib prevented and protected the abutments from being eroded by the riverbed.

BackgroundAntimicrobial resistance (AMR) which has been ascribed to be due to community carriage of antibiotic-resistant bacteria is highly prevalent in the WHO South-East Asia region. One of the major reasons for this is the misuse of antibiotics in animal farming practices and at the community level, which threatens both human and animal health. However, this problem of antibiotic misuse in poultry farms and in respective farmers is not well studied in countries like Pakistan.MethodsWe conducted a cross-sectional study in rural Punjab to explore the current practices of antibiotic use in poultry and poultry farmers, associated factors, their healthcare-seeking behavior and biosecurity practices.ResultsIn the context of antibiotic use for poultry, 60% comprised of Colistin sulfate and Amoxicillin trihydrate whereas Colistin is considered as the last resort antibiotic. In addition, the significant consumption of antibiotics in poultry farms (60%) and poultry farmers (50%) was without prescription by either human health physicians or veterinarians. Most of the farms (85%) had no wastewater drainage system, which resulted in the direct shedding of poultry waste and antibiotic residue into the surrounding environment. The lack of farmers’ education, professional farm training and farming experience were the most significant factors associated with antibiotic use and knowledge of AMR.ConclusionOur study findings show that it is necessary for an integrated AMR policy with the inclusion of all poultry farmers to be educated, a mass awareness program to be undertaken and that strict antibiotic usage guidelines be available to them. Such initiatives are also important to ensure food safety and farm biosecurity practices.

Resumo Objetivo Analisar a tendência da mortalidade por suicídio no estado do Espírito Santo e em sua Região Serrana no período de 1996 a 2020. Métodos Estudo descritivo e analítico de série temporal com dados consolidados dos óbitos por suicídio na população acima de 15 anos de idade obtidos no Sistema de Informações sobre Mortalidade. As unidades de análise foram o estado do Espírito Santo e sua Região Serrana. A análise da tendência foi verificada pela regressão de Prais-Winsten e pelo cálculo da variação percentual anual. Resultados Foram notificados 3.922 óbitos por suicídio no Espírito Santo e, destes, 525 na Região Serrana. A tendência da mortalidade por suicídio no estado foi crescente na população geral acima de 15 anos (APC= 5,1; IC95%= 0,28-9,92; p=0,039). A tendência da mortalidade se manteve crescente entre a população feminina, tanto em relação ao estado (APC= 6;5; IC95%= 1,55-14,46; p=0,012) como na região serrana (APC= 15,2; IC95%= 1,21-29,20; p=0,035). A mortalidade foi crescente na faixa etária de 30 a 59 anos no estado (APC= 3,4; IC95%= 0,62-6,10; p=0,018) e na Região Serrana (APC= 2,9; IC95%= 2,02-3,86; p

This study discusses the history of the pagoda formation and its distribution along with the presence of Chinese existence in Manado. This study also discusses the factors and the impact of the pagoda in Manado. The first pagoda in Manado was beginning to exist in 1819, and in a fairly long period until 2018, the development and distribution were very slow despite the presence of Chinese in this area since the 17th century. This study was carried out using historical methods and analysis. The data obtained were processed using a qualitative descriptive approach. The results of the study show that the presence of the pagoda together with the initial settlement of Chinese people brought by the Dutch VOC was aimed to build the fort of Fort Amsterdam. Although it impressed by the slow erection and distribution of pagodas in Manado, the causal factors and the impact are interesting in relation to interfaith, interethnic relations, urban expansion, religious space contestation, opening wider economic access and become tourism destinations.

Identification of small objects in fluorescence microscopy is a non-trivial task burdened by parameter-sensitive algorithms, for which there is a clear need for an approach that adapts dynamically to changing imaging conditions. Here, we introduce an adaptive object detection method that, given a microscopy image and an image level label, uses kurtosis-based matching of the distribution of the image differential to express operator intent in terms of recall or precision. We show how a theoretical upper bound of the statistical distance in feature space enables application of belief theory to obtain statistical support for each detected object, capturing those aspects of the image that support the label, and to what extent. We validate our method on 2 datasets: distinguishing sub-diffraction limit caveolae and scaffold by stimulated emission depletion (STED) super-resolution microscopy; and detecting amyloid-β deposits in confocal microscopy retinal cross-sections of neuropathologically confirmed Alzheimer's disease donor tissue. Our results are consistent with biological ground truth and with previous subcellular object classification results, and add insight into more nuanced class transition dynamics. We illustrate the novel application of belief theory to object detection in heterogeneous microscopy datasets and the quantification of conflict of evidence in a joint belief function. By applying our method successfully to diffraction-limited confocal imaging of tissue sections and super-resolution microscopy of subcellular structures, we demonstrate multi-scale applicability.

We recently demonstrated that Cav1 (caveolin-1) is a negative regulator of Stat3 (signal transducer and activator of transcription-3) activity in mouse fibroblasts and human lung carcinoma SHP77 cells. We now examined whether the cellular context may affect their levels as well as the relationship between them, by assessing Cav1 and Stat3-ptyr705 amounts in different cell lines. In MDA-MB-231, A549, and HaCat cells, Cav1 levels were high and Stat3-ptyr705 levels were low, consistent with the notion of a negative effect of endogenous Cav1 on Stat3-ptyr705 levels in these lines. In addition, manipulation of Cav1 levels revealed a negative effect in MCF7 and mouse fibroblast cells, while Cav1 upregulation induced apoptosis in MCF7 cells. In contrast, however, line MRC9 had high Cav1 and high Stat3-ptyr705 levels, indicating that high Cav1 is insufficient to reduce Stat3-ptyr705 levels in this line. MCF7 and LuCi6 cells had very low Cav1 and Stat3-ptyr705 levels, indicating that the low Stat3-ptyr705 can be independent from Cav1 levels altogether. Our results reveal a further level of complexity in the relationship between Cav1 and Stat3-ptyr705 than previously thought. In addition, we demonstrate that in a feedback loop, Stat3 inhibition upregulates Cav1 in HeLa cells but not in other lines tested. Key words: Stat3, caveolin-1, cancer, cell lines. Les auteurs ont récemment démontré que Cav1 (cavéoline-1) est un régulateur négatif de l'activité de Stat3 dans les fibroblastes de souris et les cellules de la lignée de carcinome pulmonaire SHP77. Ils examinent maintenant si le contexte cellulaire peut affecter leurs niveaux ainsi que leurs liens, en évaluant les quantités de Cav1 et de Stat3-ptyr705 dans différentes lignées. Dans les cellules MDA-MB-231, A549 et HaCat, les niveaux de Cav1 étaient élevés et ceux de Stat3-ptyr705 étaient faibles, ce qui concorde avec la notion d'effet négatif de Cav1 endogène sur les niveaux de Stat3-ptyr705 dans ces lignées. De plus, la manipulation des niveaux de Cav1 a révélé un effet négatif dans les cellules MCF7 et les fibroblastes de souris, alors que la régulation à la hausse provoquait l'apoptose des cellules MCF7. Par contre, la lignée MRC9 montrait des niveaux élevés de Cav1 et de Stat3-ptyr705, indiquant que les niveaux élevés de Cav1 ne sont pas suffisants pour réduire les niveaux de Stat3-ptyr705 dans cette lignée. Les cellules MCF et LuCi6 montraient des niveaux très faibles de Cav1 et de Stat3-ptyr705, indiquant que les faibles niveaux de Stat3-pTyr705 peuvent être indépendants des niveaux de Cav1. Les résultats des auteurs révèlent que la relation entre Cav1 et Stat3-ptyr705 est plus complexe qu'initialement anticipée. De plus, ils démontrent que, dans une boucle de rétroaction, l'inhibition de Stat3 régule à la hausse Cav1 dans les cellules HeLa, mais pas dans les autres lignées testées. [Traduit par la Rédaction] Mots-clés: Stat3, cavéoline-1, cancer, lignées cellulaires., Introduction The Signal Transducer and Activator of Transcription-3 (Stat3) is activated by receptor and non-receptor tyrosine kinases. Upon activation, Stat3 is phosphorylated at a critical tyrosine residue (tyr-705). Stat3-ptyr705 subsequently [...]

Single-molecule localization microscopy (SMLM) is a relatively new imaging modality, winning the 2014 Nobel Prize in Chemistry, and considered as one of the key super-resolution techniques. SMLM resolution goes beyond the diffraction limit of light microscopy and achieves resolution on the order of 10–20 nm. SMLM thus enables imaging single molecules and study of the low-level molecular interactions at the subcellular level. In contrast to standard microscopy imaging that produces 2D pixel or 3D voxel grid data, SMLM generates big data of 2D or 3D point clouds with millions of localizations and associated uncertainties. This unprecedented breakthrough in imaging helps researchers employ SMLM in many fields within biology and medicine, such as studying cancerous cells and cell-mediated immunity and accelerating drug discovery. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. Researchers have been actively exploring new computational methods for SMLM data analysis to extract biosignatures of various biological structures and functions. In this survey, we describe the state-of-the-art clustering methods adopted to analyze and quantify SMLM data and examine the capabilities and shortcomings of the surveyed methods. We classify the methods according to (1) the biological application (i.e., the imaged molecules/structures), (2) the data acquisition (such as imaging modality, dimension, resolution, and number of localizations), and (3) the analysis details (2D versus 3D, field of view versus region of interest, use of machine-learning and multi-scale analysis, biosignature extraction, etc.). We observe that the majority of methods that are based on second-order statistics are sensitive to noise and imaging artifacts, have not been applied to 3D data, do not leverage machine-learning formulations, and are not scalable for big-data analysis. Finally, we summarize state-of-the-art methodology, discuss some key open challenges, and identify future opportunities for better modeling and design of an integrated computational pipeline to address the key challenges. The Bigger Picture: Recent developments in super-resolution SMLM imaging techniques enable researchers to study macromolecular structures at the nanometer scale. However, SMLM data quantification and interpretation methods have yet to keep pace with the rapid advancement of SMLM imaging. This article provides a balanced and comprehensive review of state-of-the-art SMLM image analysis methods and ties disparate approaches together in a cohesive manner. Researchers are actively exploring new computational methods to analyze SMLM data, including recent approaches to use data-driven and machine-learning approaches. However, the validation of the SMLM clustering methods remains an open challenge. Potential future directions using multi-modality imaging (e.g., SMLM and electron microscopy) might help validate quantitative SMLM image analysis methods.

Abstract Quantitative approaches to analyze the large data sets generated by single molecule localization super-resolution microscopy (SMLM) are limited. We developed a computational pipeline and applied it to analyzing 3D point clouds of SMLM localizations (event lists) of the caveolar coat protein, caveolin-1 (Cav1), in prostate cancer cells differentially expressing CAVIN1 (also known as PTRF), that is also required for caveolae formation. High degree (strongly-interacting) points were removed by an iterative blink merging algorithm and Cav1 network properties were compared with randomly generated networks to retain a sub-network of geometric structures (or blobs). Machine-learning based classification extracted 28 quantitative features describing the size, shape, topology and network characteristics of ∼80,000 blobs. Unsupervised clustering identified small S1A scaffolds corresponding to SDS-resistant Cav1 oligomers, as yet undescribed larger hemi-spherical S2 scaffolds and, only in CAVIN1-expressing cells, spherical, hollow caveolae. Multi-threshold modularity analysis suggests that S1A scaffolds interact to form larger scaffolds and that S1A dimers group together, in the presence of CAVIN1, to form the caveolae coat.

Plantar warts, caused by the human papilloma virus (HPV), are a commonly encountered condition presenting in clinic. In adults, an array of various therapies exists, frequently with modest results particularly with plantar lesions. Microwaves have had limited uses for medical purposes. Recently a new portable microwave device has been approved for the treatment of skin lesions. Prior research has demonstrated immuno-stimulatory effects against HPV infection. We report the application of a novel portable medical microwave unit to treat a long-standing plantar wart which had failed to respond to other treatment modalities.

Caveolae are plasma membrane invaginations whose formation requires caveolin-1 (Cav1), the adaptor protein polymerase I, and the transcript release factor (PTRF or CAVIN1). Caveolae have an important role in cell functioning, signaling, and disease. In the absence of CAVIN1/PTRF, Cav1 forms non-caveolar membrane domains called scaffolds. In this work, we train machine learning models to automatically distinguish between caveolae and scaffolds from single molecule localization microscopy (SMLM) data. We apply machine learning algorithms to discriminate biological structures from SMLM data. Our work is the first that is leveraging machine learning approaches (including deep learning models) to automatically identifying biological structures from SMLM data. In particular, we develop and compare three binary classification methods to identify whether or not a given 3D cluster of Cav1 proteins is a caveolae. The first uses a random forest classifier applied to 28 hand-crafted/designed features, the second uses a convolutional neural net (CNN) applied to a projection of the point clouds onto three planes, and the third uses a PointNet model, a recent development that can directly take point clouds as its input. We validate our methods on a dataset of super-resolution microscopy images of PC3 prostate cancer cells labeled for Cav1. Specifically, we have images from two cell populations: 10 PC3 and 10 CAVIN1/PTRF-transfected PC3 cells (PC3-PTRF cells) that form caveolae. We obtained a balanced set of 1714 different cellular structures. Our results show that both the random forest on hand-designed features and the deep learning approach achieve high accuracy in distinguishing the intrinsic features of the caveolae and non-caveolae biological structures. More specifically, both random forest and deep CNN classifiers achieve classification accuracy reaching 94% on our test set, while the PointNet model only reached 83% accuracy. We also discuss the pros and cons of the different approaches.