9,677 research outputs found

    HistoPerm: A Permutation-Based View Generation Approach for Improving Histopathologic Feature Representation Learning

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    Deep learning has been effective for histology image analysis in digital pathology. However, many current deep learning approaches require large, strongly- or weakly-labeled images and regions of interest, which can be time-consuming and resource-intensive to obtain. To address this challenge, we present HistoPerm, a view generation method for representation learning using joint embedding architectures that enhances representation learning for histology images. HistoPerm permutes augmented views of patches extracted from whole-slide histology images to improve classification performance. We evaluated the effectiveness of HistoPerm on two histology image datasets for Celiac disease and Renal Cell Carcinoma, using three widely used joint embedding architecture-based representation learning methods: BYOL, SimCLR, and VICReg. Our results show that HistoPerm consistently improves patch- and slide-level classification performance in terms of accuracy, F1-score, and AUC. Specifically, for patch-level classification accuracy on the Celiac disease dataset, HistoPerm boosts BYOL and VICReg by 8% and SimCLR by 3%. On the Renal Cell Carcinoma dataset, patch-level classification accuracy is increased by 2% for BYOL and VICReg, and by 1% for SimCLR. In addition, on the Celiac disease dataset, models with HistoPerm outperform the fully-supervised baseline model by 6%, 5%, and 2% for BYOL, SimCLR, and VICReg, respectively. For the Renal Cell Carcinoma dataset, HistoPerm lowers the classification accuracy gap for the models up to 10% relative to the fully-supervised baseline. These findings suggest that HistoPerm can be a valuable tool for improving representation learning of histopathology features when access to labeled data is limited and can lead to whole-slide classification results that are comparable to or superior to fully-supervised methods

    Urinary biomarkers of biofortified beef in healthy women explored by untargeted metabolomics

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    Background: The prevalence of overweight and non-communicable chronic diseases is rising all over the globe. The high consumption of energy dense foods on behalf of high nutrient-dense food leads to lower intake of essential vitamins and minerals, such as vitamins D, E, K, and selenium. These micronutrients are related with numerous human vital functions and their deficiency is positively associated with higher risk of chronic diseases and mortality. Bovine meat is an important source of several micronutrients, with higher bioavailability compared to other plant-based foods. Meat consumption is expected to increase worldwide, therefore the biofortification of bull’s feeds can be an innovative strategy to increase population’s exposure to nutrients. Metabolomics techniques are capable to explore if the supplementation will ultimately lead to a higher micronutrient’s uptake in the body. Objective: The aim of the present study was to explore the differences on urinary metabolic fingerprint of women ingesting 300g of beef a day from bulls fed concentrate supplemented with extra vitamin D, E, K, and selenium compared to the regular composite feed. Methodology: A 32 days double-blind randomized cross-over human intervention study with two intervention periods, each for 6 days, was conducted in 35 healthy women. The participants were instructed to eat 300g of grinded beef meat as raw weight per day, either from bulls fed with regular control feed or meat supplemented with vitamin D, E, K and selenium, combined with their habitual diet. Fasting urine samples were collected in the morning before and after each intervention period and were analyzed by LC-MS untargeted metabolomics. Multivariate and univariate analysis were applied do identify discriminative features between the two interventions. Results: A total of 7 and 6 metabolites for positive and negative mode, respectively, were selected as discriminative of the two interventions. Among these, markers of overall meat intake, as well as markers of animal feed, markers related with the participants diet and inflammation-related markers were identified as upregulated or downregulated for the supplemented intervention. No markers specifically related to the biofortification were observed. Conclusions: Based on our methodology, the ingestion of biofortified beef did not results in a higher level of related metabolites when comparing the two interventions. Minor changes indicate that consequences of biofortification were very small. Further research is needed to understand if a higher increase of vitamin D, E, K, and selenium on animal´s feed composite can lead to different outcomes

    Acoustic activity of bats at power lines correlates with relative humidity: a potential role for corona discharges

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    With the ever-increasing dependency on electric power, electrical grid networks are expanding worldwide. Bats exhibit a wide diversity of foraging and flight behaviours, and their sensitivity to anthropogenic stressors suggests this group is very likely to be affected by power lines in a myriad of ways. Yet the effects of power lines on bats remains unknown. Here we assessed the responses of insectivorous bats to very high voltage power lines (VHVPL; greater than 220 kV). We implemented a paired sampling design and monitored bats acoustically at 25 pairs, one pair consisting of one forest edge near to VHVPL matched with one control forest edge. Relative humidity mediates the effects of power lines on bats: we detected bat attraction to VHVPL at high relative humidity levels and avoidance of VHVPL by bats at low relative humidity levels. We argue that the former could be explained by insect attraction to the light emitted by VHVPL owing to corona discharges while the latter may be owing to the physical presence of pylons/cables at foraging height and/or because of electromagnetic fields. Our work highlights the response of bats to power lines at foraging habitats, providing new insight into the interactions between power lines and biodiversity

    Pollution-induced community tolerance in freshwater biofilms – from molecular mechanisms to loss of community functions

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    Exposure to herbicides poses a threat to aquatic biofilms by affecting their community structure, physiology and function. These changes render biofilms to become more tolerant, but on the downside community tolerance has ecologic costs. A concept that addresses induced community tolerance to a pollutant (PICT) was introduced by Blanck and Wängberg (1988). The basic principle of the concept is that microbial communities undergo pollution-induced succession when exposed to a pollutant over a long period of time, which changes communities structurally and functionally and enhancing tolerance to the pollutant exposure. However, the mechanisms of tolerance and the ecologic consequences were hardly studied up to date. This thesis addresses the structural and functional changes in biofilm communities and applies modern molecular methods to unravel molecular tolerance mechanisms. Two different freshwater biofilm communities were cultivated for a period of five weeks, with one of the communities being contaminated with 4 μg L-1 diuron. Subsequently, the communities were characterized for structural and functional differences, especially focusing on their crucial role of photosynthesis. The community structure of the autotrophs was assessed using HPLC-based pigment analysis and their functional alterations were investigated using Imaging-PAM fluorometry to study photosynthesis and community oxygen profiling to determine net primary production. Then, the molecular fingerprints of the communities were measured with meta-transcriptomics (RNA-Seq) and GC-based community metabolomics approaches and analyzed with respect to changes in their molecular functions. The communities were acute exposed to diuron for one hour in a dose-response design, to reveal a potential PICT and uncover related adaptation to diuron exposure. The combination of apical and molecular methods in a dose-response design enabled the linkage of functional effects of diuron exposure and underlying molecular mechanisms based on a sensitivity analysis. Chronic exposure to diuron impaired freshwater biofilms in their biomass accrual. The contaminated communities particularly lost autotrophic biomass, reflected by the decrease in specific chlorophyll a content. This loss was associated with a change in the molecular fingerprint of the communities, which substantiates structural and physiological changes. The decline in autotrophic biomass could be due to a primary loss of sensitive autotrophic organisms caused by the selection of better adapted species in the course of chronic exposure. Related to this hypothesis, an increase in diuron tolerance has been detected in the contaminated communities and molecular mechanisms facilitating tolerance have been found. It was shown that genes of the photosystem, reductive-pentose phosphate cycle and arginine metabolism were differentially expressed among the communities and that an increased amount of potential antioxidant degradation products was found in the contaminated communities. This led to the hypothesis that contaminated communities may have adapted to oxidative stress, making them less sensitive to diuron exposure. Moreover, the photosynthetic light harvesting complex was altered and the photoprotective xanthophyll cycle was increased in the contaminated communities. Despite these adaptation strategies, the loss of autotrophic biomass has been shown to impair primary production. This impairment persisted even under repeated short-term exposure, so that the tolerance mechanisms cannot safeguard primary production as a key function in aquatic systems.:1. The effect of chemicals on organisms and their functions .............................. 1 1.1 Welcome to the anthropocene .......................................................................... 1 1.2 From cellular stress responses to ecosystem resilience ................................... 3 1.2.1 The individual pursuit for homeostasis ....................................................... 3 1.2.2 Stability from diversity ................................................................................. 5 1.3 Community ecotoxicology - a step forward in monitoring the effects of chemical pollution? ................................................................................................................. 6 1.4 Functional ecotoxicological assessment of microbial communities ................... 9 1.5 Molecular tools – the key to a mechanistic understanding of stressor effects from a functional perspective in microbial communities? ...................................... 12 2. Aims and Hypothesis ......................................................................................... 14 2.1 Research question .......................................................................................... 14 2.2 Hypothesis and outline .................................................................................... 15 2.3 Experimental approach & concept .................................................................. 16 2.3.1 Aquatic freshwater biofilms as model community ..................................... 16 2.3.2 Diuron as model herbicide ........................................................................ 17 2.3.3 Experimental design ................................................................................. 18 3. Structural and physiological changes in microbial communities after chronic exposure - PICT and altered functional capacity ................................................. 21 3.1 Introduction ..................................................................................................... 21 3.2 Methods .......................................................................................................... 23 3.2.1 Biofilm cultivation ...................................................................................... 23 3.2.2 Dry weight and autotrophic index ............................................................. 23 3.2.4 Pigment analysis of periphyton ................................................................. 23 3.2.4.1 In-vivo pigment analysis for community characterization ....................... 24 3.2.4.2 In-vivo pigment analysis based on Imaging-PAM fluorometry ............... 24 3.2.4.3 In-vivo pigment fluorescence for tolerance detection ............................. 26 3.2.4.4 Ex-vivo pigment analysis by high-pressure liquid-chromatography ....... 27 3.2.5 Community oxygen metabolism measurements ....................................... 28 3.3 Results and discussion ................................................................................... 29 3.3.1 Comparison of the structural community parameters ............................... 29 3.3.2 Photosynthetic activity and primary production of the communities after selection phase ................................................................................................. 33 3.3.3 Acquisition of photosynthetic tolerance .................................................... 34 3.3.4 Primary production at exposure conditions ............................................... 36 3.3.5 Tolerance detection in primary production ................................................ 37 3.4 Summary and Conclusion ........................................................................... 40 4. Community gene expression analysis by meta-transcriptomics ................... 41 4.1 Introduction to meta-transcriptomics ............................................................... 41 4.2. Methods ......................................................................................................... 43 4.2.1 Sampling and RNA extraction................................................................... 43 4.2.2 RNA sequencing analysis ......................................................................... 44 4.2.3 Data assembly and processing................................................................. 45 4.2.4 Prioritization of contigs and annotation ..................................................... 47 4.2.5 Sensitivity analysis of biological processes .............................................. 48 4.3 Results and discussion ................................................................................... 48 4.3.1 Characterization of the meta-transcriptomic fingerprints .......................... 49 4.3.2 Insights into community stress response mechanisms using trend analysis (DRomic’s) ......................................................................................................... 51 4.3.3 Response pattern in the isoform PS genes .............................................. 63 4.5 Summary and conclusion ................................................................................ 65 5. Community metabolome analysis ..................................................................... 66 5.1 Introduction to community metabolomics ........................................................ 66 5.2 Methods .......................................................................................................... 68 5.2.1 Sampling, metabolite extraction and derivatisation................................... 68 5.2.2 GC-TOF-MS analysis ............................................................................... 69 5.2.3 Data processing and statistical analysis ................................................... 69 5.3 Results and discussion ................................................................................... 70 5.3.1 Characterization of the metabolic fingerprints .......................................... 70 5.3.2 Difference in the metabolic fingerprints .................................................... 71 5.3.3 Differential metabolic responses of the communities to short-term exposure of diuron ............................................................................................................ 73 5.4 Summary and conclusion ................................................................................ 78 6. Synthesis ............................................................................................................. 79 6.1 Approaches and challenges for linking molecular data to functional measurements ...................................................................................................... 79 6.2 Methods .......................................................................................................... 83 6.2.1 Summary on the data ............................................................................... 83 6.2.2 Aggregation of molecular data to index values (TELI and MELI) .............. 83 6.2.3 Functional annotation of contigs and metabolites using KEGG ................ 83 6.3 Results and discussion ................................................................................... 85 6.3.1 Results of aggregation techniques ........................................................... 85 6.3.2 Sensitivity analysis of the different molecular approaches and endpoints 86 6.3.3 Mechanistic view of the molecular stress responses based on KEGG functions ............................................................................................................ 89 6.4 Consolidation of the results – holistic interpretation and discussion ............... 93 6.4.1 Adaptation to chronic diuron exposure - from molecular changes to community effects.............................................................................................. 93 6.4.2 Assessment of the ecological costs of Pollution-induced community tolerance based on primary production ............................................................. 94 6.5 Outlook ............................................................................................................ 9

    Shuffled ATG8 interacting motifs form an ancestral bridge between UFMylation and autophagy

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    UFMylation involves the covalent modification of substrate proteins with UFM1 (Ubiquitin‐fold modifier 1) and is important for maintaining ER homeostasis. Stalled translation triggers the UFMylation of ER‐bound ribosomes and activates C53‐mediated autophagy to clear toxic polypeptides. C53 contains noncanonical shuffled ATG8‐interacting motifs (sAIMs) that are essential for ATG8 interaction and autophagy initiation. However, the mechanistic basis of sAIM‐mediated ATG8 interaction remains unknown. Here, we show that C53 and sAIMs are conserved across eukaryotes but secondarily lost in fungi and various algal lineages. Biochemical assays showed that the unicellular alga Chlamydomonas reinhardtii has a functional UFMylation pathway, refuting the assumption that UFMylation is linked to multicellularity. Comparative structural analyses revealed that both UFM1 and ATG8 bind sAIMs in C53, but in a distinct way. Conversion of sAIMs into canonical AIMs impaired binding of C53 to UFM1, while strengthening ATG8 binding. Increased ATG8 binding led to the autoactivation of the C53 pathway and sensitization of Arabidopsis thaliana to ER stress. Altogether, our findings reveal an ancestral role of sAIMs in UFMylation‐dependent fine‐tuning of C53‐mediated autophagy activation

    Nonparametric Two-Sample Test for Networks Using Joint Graphon Estimation

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    This paper focuses on the comparison of networks on the basis of statistical inference. For that purpose, we rely on smooth graphon models as a nonparametric modeling strategy that is able to capture complex structural patterns. The graphon itself can be viewed more broadly as density or intensity function on networks, making the model a natural choice for comparison purposes. Extending graphon estimation towards modeling multiple networks simultaneously consequently provides substantial information about the (dis-)similarity between networks. Fitting such a joint model - which can be accomplished by applying an EM-type algorithm - provides a joint graphon estimate plus a corresponding prediction of the node positions for each network. In particular, it entails a generalized network alignment, where nearby nodes play similar structural roles in their respective domains. Given that, we construct a chi-squared test on equivalence of network structures. Simulation studies and real-world examples support the applicability of our network comparison strategy.Comment: 25 pages, 6 figure

    Decoding spatial location of attended audio-visual stimulus with EEG and fNIRS

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    When analyzing complex scenes, humans often focus their attention on an object at a particular spatial location in the presence of background noises and irrelevant visual objects. The ability to decode the attended spatial location would facilitate brain computer interfaces (BCI) for complex scene analysis. Here, we tested two different neuroimaging technologies and investigated their capability to decode audio-visual spatial attention in the presence of competing stimuli from multiple locations. For functional near-infrared spectroscopy (fNIRS), we targeted dorsal frontoparietal network including frontal eye field (FEF) and intra-parietal sulcus (IPS) as well as superior temporal gyrus/planum temporal (STG/PT). They all were shown in previous functional magnetic resonance imaging (fMRI) studies to be activated by auditory, visual, or audio-visual spatial tasks. We found that fNIRS provides robust decoding of attended spatial locations for most participants and correlates with behavioral performance. Moreover, we found that FEF makes a large contribution to decoding performance. Surprisingly, the performance was significantly above chance level 1s after cue onset, which is well before the peak of the fNIRS response. For electroencephalography (EEG), while there are several successful EEG-based algorithms, to date, all of them focused exclusively on auditory modality where eye-related artifacts are minimized or controlled. Successful integration into a more ecological typical usage requires careful consideration for eye-related artifacts which are inevitable. We showed that fast and reliable decoding can be done with or without ocular-removal algorithm. Our results show that EEG and fNIRS are promising platforms for compact, wearable technologies that could be applied to decode attended spatial location and reveal contributions of specific brain regions during complex scene analysis

    EVALUACIÓN ANALGÉSICA PERIOPERATORIA DEL ACETAMINOFÉN EN PERRAS SOMETIDAS A OVARIOHISTERECTOMÍA ELECTIVA

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    Tesis de doctorado que evalúa el efecto analgésico del acetaminofén en perras ovarihisterectomizadas.La administración de analgésicos antiinflamatorios no esteroidales (AINES) para el control del dolor post-quirúrgico en perros es una práctica común, debido a sus efectos analgésicos, antiinflamatorios y antipiréticos. En el presente trabajo se realizaron dos estudios. En el experimento 1, el objetivo fue evaluar la analgesia post-operatoria del acetaminofén (paracetamol) a través de la utilización de las escalas de reconocimiento clínico del dolor DIVAS (Escala Dinámica e Interactiva Analógica Visual) y UMPS (Escala de la Universidad de Melbourne), en perras sometidas a ovariohisterectomía electiva. Además de valorar la seguridad y eficacia clínica del uso del acetaminofén en perros mediante pruebas de funcionamiento hepático y renal en el post-operatorio inmediato. Para ello, se utilizaron 30 perras de diferentes razas que fueron asignadas aleatoriamente a uno de los tres grupos de tratamiento: acetaminofén [GACET; n=10, 15 mg kg-1 intravenoso (IV)], carprofeno (GCARP; n=10, 4 mg kg-1 IV) y meloxicam (GMELOX; n=10, 0.2 mg kg-1 IV). Todos los tratamientos se administraron 30 minutos antes de la cirugía y posterior a esta durante 48 horas. En este período el acetaminofén se administró por vía oral cada 8 horas (15 mg kg-1); el carprofeno (4 mg kg-1) y el meloxicam (0.1 mg kg-1) se administraron por vía IV cada 24 horas. Durante el postoperatorio, los sistemas de puntuación del dolor DIVAS y UMPS fueron medidos a las 1, 2, 4, 6, 8, 12, 16, 20, 24, 36 y 48 horas post-cirugía. Para evaluar la seguridad clínica de los tratamientos, se recolectaron muestras de sangre de la vena yugular para realizar la medición de enzimas ALT, AST, ALP, y los metabolitos bilirrubina directa, bilirrubina indirecta, bilirrubina total, creatinina, urea, albúmina y glucosa. Esto fue realizado en T0 (pre-anestesia; TBASAL), 48 y 96 horas después de la cirugía (T48, T96). Los resultados indican que en la evaluación clínica del dolor de todos los grupos de estudio, hubo una reducción gradual en la percepción del mismo durante el postoperatorio en ambos sistemas de puntuación; no obstante, también fue observado que ninguna escala difirió significativamente entre los tres grupos de tratamiento (P>0.05) en cada momento de evaluación durante las 48 horas post-cirugía. En cuanto a los parámetros bioquímico séricos, sólo la ALT aumentó significativamente en T96 en el GACET y GCARP con respecto a los valores basales (P<0.01). El resto de los analitos séricos evaluados se mantuvo en rangos normales. En el experimento 2 bajo el mismo diseño experimental de tratamientos administrados, el objetivo fue evaluar el efecto analgésico perioperatorio del acetaminofén 2 administrado pre y post-quirúrgicamente en perras sometidas a ovariohisterectomía electiva a través de la medición del índice de la actividad del tono parasimpático (PTA). Este parámetro hemodinámico fue medido 60 minutos antes de la cirugía (TBASAL) y durante el transquirúrgico en la aplicación de estímulos nociceptivos: colocación de las pinzas de campo backhouse (TPINZ), incisión de piel y abordaje quirúrgico primario (TINC), ligadura y extracción de pedículo ovárico izquierdo (TOVI) y derecho (TOVD), ligadura y transfixión del cuello uterino (TLIGUT), sección quirúrgica del cuello uterino (TCUT), reconstrucción de peritoneo y planos anatómicos musculares (TMUSC) y sutura de piel (TSUT). Durante el postoperatorio, el índice PTA fue valorado a las 1, 2, 4, 6, 8, 12, 16, 20, 24, 36 y 48 horas, en los mismos tiempos en que fueron evaluadas las escalas de reconocimiento de dolor DIVAS y UMPS. Los resultados obtenidos en la medición del índice PTA basal para GACET fue 65 ± 8, para GCARP 65 ± 7 y para GMELOX 62 ± 5. Durante los diferentes tiempos transquirúrgicos, los valores promedio de índice PTA indican que GACET (76 ± 14) y GMELOX (72 ± 18) muestran tendencia a manifestar mayores niveles en comparación con GCARP (62 ± 13) desde el inicio del procedimiento quirúrgico sin que esto pudiera comprobarse estadísticamente, ya que no hubo diferencias significativas entre grupos de tratamiento ni entre los tiempos quirúrgicos evaluados (P>0.05). En el postoperatorio, el índice PTA fue de 65 ± 9 en el GACET, 63 ± 8 en el GCARP y 65 ± 8 en el GMELOX. Los resultados tampoco mostraron diferencias estadísticamente significativas con los valores basales o entre los tratamientos (P>0.05). El índice PTA postoperatorio mostró una sensibilidad del 40%, especificidad del 98.46% y valor predictivo negativo del 99.07% con respecto a la escala validada de UMPS. En conclusión, el acetaminofén puede considerarse una herramienta para el tratamiento efectivo del dolor perioperatorio agudo en perros, ya que mostró la misma eficacia clínica que el meloxicam y el carprofeno para la analgesia postquirúrgica en perras sometidas a ovariohisterectomía electiva. Además, la evidencia del uso de este medicamento no condujo a reacciones adversas o cambios en los parámetros evaluados, lo que indica su seguridad clínica. Finalmente, destacar que el índice PTA representa una medición objetiva del comfort y analgesia postoperatoria, por lo que es una herramienta que podría ayudar a predecir las respuestas hemodinámicas asociadas con el dolor
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