Leaf-associated macroinvertebrate assemblage and leaf litter breakdown in headwater streams depend on local riparian vegetation

Headwater streams harbor diverse macroinvertebrate communities and are hotspots for leaf litter breakdown. The process of leaf litter breakdown mediated by macroinvertebrates forms an important link between terrestrial and aquatic ecosystems. Yet, how the vegetation type in the local riparian zone influences leaf-associated macroinvertebrate assemblages and leaf litter breakdown rates is still not resolved. We investigated how leaf-associated macroinvertebrate assemblages and leaf litter fragmentation rates differ between forested and non-forested sites using experimental leaf litter bags in sixteen sites paired across eight headwater streams in Switzerland. Our results show that sensitive taxa of the invertebrate orders Ephemeroptera, Plecoptera and Trichoptera (EPT) and the functional group of shredders were strongly associated with forested sites with overall higher values of abundance, diversity, and biomass of EPTs in forested compared to non-forested sites. However, the importance of riparian vegetation differed between study regions, especially for shredders. Fragmentation rates, which are primarily the result of macroinvertebrate shredding, were on average three times higher in forested compared to non-forested sites. Our results demonstrate that not only the composition of the aquatic fauna but also the functioning of an essential ecosystem process depend on the vegetation type in the local riparian zone

Assessing logistic regression applied to respondent-driven sampling studies : a simulation study with an application to empirical data

The aim of this study is to investigate the impact of different logistic regression estimators applied to RDS studies via simulation and the analysis of empirical data. Four simulated populations were created with different connectivity characteristics. Each simulated individual received two attributes, one of them associated to the infection process. RDS samples with different sizes were obtained. The observed coverage of three logistic regression estimators were applied to assess the association between the attributes and the infection status. In simulated datasets, unweighted logistic regression estimators emerged as the best option, although all estimators showed a fairly good performance. In the empirical dataset, the performance of weighted estimators presented an unexpected behavior, making them a risky option. The unweighted logistic regression estimator is a reliable option to be applied to RDS samples, with a performance roughly similar to random samples and, therefore, should be the preferred option

Characterization of the mechanisms underlying the crosstalk between galectins and notch in gastric cancer

Gastric cancer is the fourth most common cancer and the second leading cause of cancer-related deaths worldwide. Galectins form a family of β-galactosides binding proteins that recognize a variety of glycan-containing proteins at the cell surface and are overexpressed in various tumors, including gastric cancer. Galectins overexpression as well as changes in their subcellular distribution has been associated with gastric cancer progression and poor prognosis. It is not well understood, however, how the interaction between galectins and glycosylated receptors modulates tumor development and growth. Since Notch receptors and ligands contain glycan structures known to bind galectins, we aim to demonstrate that galectins expression in the tumor microenvironment may interfere with Notch signaling activation during tumor development and progression.\ud \ud Materials and methods\ud Immunoprecipitation procedures with gastric cancer cell line AGS (ATCC CRL-1739) and MKN45 (ACC 409) were used to test for association between galectin-1/-3 and Notch-1 receptor. Furthermore, we transfected AGS cell line with siRNA against galectin-1/-3 or scramble using standard protocols (IDT DNA technologies), stimulate them with immobilized human recombinant delta-4 or Jagged-1 and assessed Notch-1 receptor activation.\ud \ud Results\ud Galectin-1 and -3 interact with Notch-1 receptor and differentially modulate Notch signaling pathway upon activation by Delta/Jagged ligands. Galectin-1 knockdown alters Notch-1 activation induced by Delta-4 whereas galectin-3 knockdown alters jagged-1-mediated Notch-1 activation. Furthermore, we found that exogenously added galectin-3 can enhance Notch-1 activation by Jagged-1.\ud \ud Conclusion\ud Our results suggest that galectin-1 and -3 interact with Notch-1 receptor and differentially modulate Notch signaling activation induced by Jagged-1 and Delta-4

EEG-Based Epileptic Seizure Prediction Using Temporal Multi-Channel Transformers

Epilepsy is one of the most common neurological diseases, characterized by transient and unprovoked events called epileptic seizures. Electroencephalogram (EEG) is an auxiliary method used to perform both the diagnosis and the monitoring of epilepsy. Given the unexpected nature of an epileptic seizure, its prediction would improve patient care, optimizing the quality of life and the treatment of epilepsy. Predicting an epileptic seizure implies the identification of two distinct states of EEG in a patient with epilepsy: the preictal and the interictal. In this paper, we developed two deep learning models called Temporal Multi-Channel Transformer (TMC-T) and Vision Transformer (TMC-ViT), adaptations of Transformer-based architectures for multi-channel temporal signals. Moreover, we accessed the impact of choosing different preictal duration, since its length is not a consensus among experts, and also evaluated how the sample size benefits each model. Our models are compared with fully connected, convolutional, and recurrent networks. The algorithms were patient-specific trained and evaluated on raw EEG signals from the CHB-MIT database. Experimental results and statistical validation demonstrated that our TMC-ViT model surpassed the CNN architecture, state-of-the-art in seizure prediction.Comment: 15 pages, 10 figure