Social Events in a Time-Varying Mobile Phone Graph

The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Social Events in a Time-Varying Mobile Phone Graph

The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.Sociedad Argentina de Informática e Investigación Operativa (SADIO

Struma Ovarii associated with Pseudo-Meigs Syndrome and elevated serum Ca 125: Case report and literature review

Struma Ovarii is a highly specialized monodermal teratoma in which the major component is thyroid tissue. Its relationship with Pseudo Meigs syndrome, hyperthyroidism and elevation of Ca 125 is a rare condition; this could mimic malignancy. Ultrasound and axial tomography may be useful in diagnosis; but histopathological criteria play a very important role in the definitive diagnosis. Our objective is to present a case report of Struma ovarii, ascites, pleural effusion (pseudo meigs syndrome), elevation of Ca 125, hyperthyroidism, and review the published literature in relation to epidemiology and diagnostic characteristics

Social Events in a Time-Varying Mobile Phone Graph

The large-scale study of human mobility has been significantly enhanced over the last decade by the massive use of mobile phones in urban populations. Studying the activity of mobile phones allows us, not only to infer social networks between individuals, but also to observe the movements of these individuals in space and time. In this work, we investigate how these two related sources of information can be integrated within the context of detecting and analyzing large social events. We show that large social events can be characterized not only by an anomalous increase in activity of the antennas in the neighborhood of the event, but also by an increase in social relationships of the attendants present in the event. Moreover, having detected a large social event via increased antenna activity, we can use the network connections to infer whether an unobserved user was present at the event. More precisely, we address the following three challenges: (i) automatically detecting large social events via increased antenna activity; (ii) characterizing the social cohesion of the detected event; and (iii) analyzing the feasibility of inferring whether unobserved users were in the event.Sociedad Argentina de Informática e Investigación Operativa (SADIO

LipidFinder on LIPID MAPS: peak filtering, MS searching and statistical analysis for lipidomics

Summary We present LipidFinder online, hosted on the LIPID MAPS website, as a liquid chromatography/mass spectrometry (LC/MS) workflow comprising peak filtering, MS searching and statistical analysis components, highly customized for interrogating lipidomic data. The online interface of LipidFinder includes several innovations such as comprehensive parameter tuning, a MS search engine employing in-house customized, curated and computationally generated databases and multiple reporting/display options. A set of integrated statistical analysis tools which enable users to identify those features which are significantly-altered under the selected experimental conditions, thereby greatly reducing the complexity of the peaklist prior to MS searching is included. LipidFinder is presented as a highly flexible, extensible user-friendly online workflow which leverages the lipidomics knowledge base and resources of the LIPID MAPS website, long recognized as a leading global lipidomics portal

cDC2 plasticity and acquisition of a DC3-like phenotype mediated by IL-6 and PGE2 in a patient-derived colorectal cancer organoids model

Metastatic colorectal cancer (CRC) is highly resistant to therapy and prone to recur. The tumor-induced local and systemic immunosuppression allows cancer cells to evade immunosurveillance, facilitating their proliferation and dissemination. Dendritic cells (DCs) are required for the detection, processing, and presentation of tumor antigens, and subsequently for the activation of antigen-specific T cells to orchestrate an effective antitumor response. Notably, successful tumors have evolved mechanisms to disrupt and impair DC functions, underlining the key role of tumor-induced DC dysfunction in promoting tumor growth, metastasis initiation, and treatment resistance. Conventional DC type 2 (cDC2) are highly prevalent in tumors and have been shown to present high phenotypic and functional plasticity in response to tumor-released environmental cues. This plasticity reverberates on both the development of antitumor responses and on the efficacy of immunotherapies in cancer patients. Uncovering the processes, mechanisms, and mediators by which CRC shapes and disrupts cDC2 functions is crucial to restoring their full antitumor potential. In this study, we use our recently developed 3D DC-tumor co-culture system to investigate how patient-derived primary and metastatic CRC organoids modulate cDC2 phenotype and function. We first demonstrate that our collagen-based system displays extensive interaction between cDC2 and tumor organoids. Interestingly, we show that tumor-corrupted cDC2 shift toward a CD14+ population with defective expression of maturation markers, an intermediate phenotype positioned between cDC2 and monocytes, and impaired T-cell activating abilities. This phenotype aligns with the newly defined DC3 (CD14(+) CD1c(+) CD163(+)) subset. Remarkably, a comparable population was found to be present in tumor lesions and enriched in the peripheral blood of metastatic CRC patients. Moreover, using EP2 and EP4 receptor antagonists and an anti-IL-6 neutralizing antibody, we determined that the observed phenotype shift is partially mediated by PGE2 and IL-6. Importantly, our system holds promise as a platform for testing therapies aimed at preventing or mitigating tumor-induced DC dysfunction. Overall, our study offers novel and relevant insights into cDC2 (dys)function in CRC that hold relevance for the design of therapeutic approaches

Predictive modeling of above-ground biomass in Brachiaria pastures from satellite and UAV Imagery using machine learning approaches

Grassland pastures are crucial for the global food supply through their milk and meat production; hence, forage species monitoring is essential for cattle feed. Therefore, knowledge of pasture above-ground canopy features help understand the crop status. This paper finds how to construct machine learning models to predict above-ground canopy features in Brachiaria pasture from ground truth data (GTD) and remote sensing at larger (satellite data on the cloud) and smaller (unmanned aerial vehicles (UAV)) scales. First, we used above-ground biomass (AGB) data obtained from Brachiaria to evaluate the relationship between vegetation indices (VIs) with the dry matter (DM). Next, the performance of machine learning algorithms was used for predicting AGB based on VIs obtained from ground truth and satellite and UAV imagery. When comparing more than twenty-five machine learning models using an Auto Machine Learning Python API, the results show that the best algorithms were the Huber with R² = 0.60, Linear with R² = 0.54, and Extra Trees with R² = 0.45 to large scales using satellite. On the other hand, short-scale best regressions are K Neighbors with an R2 of 0.76, Extra Trees with an R² of 0.75, and Bayesian Ridge with an R² of 0.70, demonstrating a high potential to predict AGB and DM. This study is the first prediction model approach that assesses the rotational grazing system and pasture above-ground canopy features to predict the quality and quantity of cattle feed to support pasture management in Colombia

Exploring the vomeronasal organ transcriptome in Rasa aragonesa rams with different sexual behaviour

Raza Rasa aragonesaPublishe

Differential Release and Phagocytosis of Tegument Glycoconjugates in Neurocysticercosis: Implications for Immune Evasion Strategies

Neurocysticercosis (NCC) is an infection of the central nervous system (CNS) by the metacestode of the helminth Taenia solium. The severity of the symptoms is associated with the intensity of the immune response. First, there is a long asymptomatic period where host immunity seems incapable of resolving the infection, followed by a chronic hypersensitivity reaction. Since little is known about the initial response to this infection, a murine model using the cestode Mesocestoides corti (syn. Mesocestoides vogae) was employed to analyze morphological changes in the parasite early in the infection. It was found that M. corti material is released from the tegument making close contact with the nervous tissue. These results were confirmed by infecting murine CNS with ex vivo–labeled parasites. Because more than 95% of NCC patients exhibit humoral responses against carbohydrate-based antigens, and the tegument is known to be rich in glycoconjugates (GCs), the expression of these types of molecules was analyzed in human, porcine, and murine NCC specimens. To determine the GCs present in the tegument, fluorochrome-labeled hydrazides as well as fluorochrome-labeled lectins with specificity to different carbohydrates were used. All the lectins utilized labeled the tegument. GCs bound by isolectinB4 were shed in the first days of infection and not resynthesized by the parasite, whereas GCs bound by wheat germ agglutinin and concavalinA were continuously released throughout the infectious process. GCs bound by these three lectins were taken up by host cells. Peanut lectin-binding GCs, in contrast, remained on the parasite and were not detected in host cells. The parasitic origin of the lectin-binding GCs found in host cells was confirmed using antibodies against T. solium and M. corti. We propose that both the rapid and persistent release of tegumental GCs plays a key role in the well-known immunomodulatory effects of helminths, including immune evasion and life-long inflammatory sequelae seen in many NCC patients