Inhibition of ovine in vitro fertilization by anti-Prt antibody: hypothetical model for Prt/ZP interaction

BACKGROUND: The impact of prion proteins in the rules that dictate biological reproduction is still poorly understood. Likewise, the role of prnt gene, encoding the prion-like protein testis specific (Prt), in ram reproductive physiology remains largely unknown. In this study, we assessed the effect of Prt in ovine fertilization by using an anti-Prt antibody (APPA) in fertilization medium incubated with spermatozoa and oocytes. Moreover, a computational model was constructed to infer how the results obtained could be related to a hypothetical role for Prt in sperm-zona pellucida (ZP) binding. METHODS: Mature ovine oocytes were transferred to fertilization medium alone (control) or supplemented with APPA, or pre-immune serum (CSerum). Oocytes were inseminated with ovine spermatozoa and after 18 h, presumptive zygotes (n = 142) were fixed to evaluate fertilization rates or transferred (n = 374) for embryo culture until D6-7. Predicted ovine Prt tertiary structure was compared with data obtained by circular dichroism spectroscopy (CD) and a protein-protein computational docking model was estimated for a hypothetical Prt/ZP interaction. RESULTS: The fertilizing rate was lower (P = 0.006) in APPA group (46.0+/−6.79%) when compared to control (78.5+/−7.47%) and CSerum (64.5+/−6.65%) groups. In addition, the cleavage rate was higher (P < 0.0001) in control (44.1+/−4.15%) than in APPA group (19.7+/−4.22%). Prt CD spectroscopy showed a 22% alpha-helical structure in 30% (m/v) aqueous trifluoroethanol (TFE) and 17% alpha in 0.6% (m/v) TFE. The predominant alpha-helical secondary structure detected correlates with the predicted three dimensional structure for ovine Prt, which was subsequently used to test Prt/ZP docking. Computational analyses predicted a favorable Prt-binding activity towards ZP domains. CONCLUSIONS: Our data indicates that the presence of APPA reduces the number of fertilized oocytes and of cleaved embryos. Moreover, the CD analysis data reinforces the predicted ovine Prt trend towards an alpha-helical structure. Predicted protein-protein docking suggests a possible interaction between Prt and ZP, thus supporting an important role for Prt in ovine fertilization

Identificação de Problemas e Propostas para Melhoria

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MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

Intelligent medical image analysis: a Deep Learning approach to breast cancer diagnosis

Dissertação de mestrado integrado em Engenharia InformáticaOnce medical images were scanned and uploaded to a computer, researchers began to create automated medical imaging systems. From the 1970’s to the 1990’s, medical imaging was performed with sequential application of low-level pixel processing and mathematical modeling to solve specific tasks such as organ segmentation. At the end of the 1990’s, supervised techniques began to appear, where data extracted from the images were used to train models and classification systems. One example is the use of automated classifiers to build support systems for cancer detection and diagnosis. This pattern recognition and / or Machine Learning approach is still very popular and represented a shift from systems that were completely human-engineered to computer-trained systems with the use of specific (manually drawn) features and automatically extracted from the training data (example). The following step would be enabling the algorithms to directly learn characteristics of the pixels of the images. This is the basic concept of Deep Learning algorithms: multi-layered models that transform input data (images) into outputs (e.g. the presence or absence of pathological lesions or cancer). This study intends to present ways of using Deep Learning algorithms in the analysis of medical images, like the particular case of pathological lesions representative of breast cancer phenotypes.Assim que foi possível digitalizar e carregar imagens médicas num computador, os investigadores começaram a criar sistemas automatizados para análise de imagens médicas. No intervalo dos anos 70 até aos anos 90, a análise de imagens médicas foi feita com a aplicação sequencial de processamento de pixeis de baixo nível e modelação matemática para resolver tarefas específicas como, por exemplo, a segmentação de órgãos. No final dos anos 90, começam a aparecer as técnicas supervisionadas, onde os dados extraídos das imagens são usados para treinar modelos e sistemas de classificação. Um exemplo é o uso de classificadores automáticos para construir sistemas de apoio à deteção e diagnóstico do cancro. Esta abordagem de reconhecimento de padrões e/ou Machine Learning ainda é muito popular e representou uma mudança nos sistemas que eram completamente projetados por seres humanos para sistemas treinados por computadores com recurso ao uso de características específicas (manualmente desenhadas) e extraídas automaticamente dos dados de treino (exemplo). O passo seguinte a alcançar é que os algoritmos aprendam directamente as características dos pixeis das imagens. É este o conceito base dos algoritmos de Deep Learning: modelos (redes) compostos por muitas camadas que transformam dados de entrada (imagens) em saídas (por exemplo, a presença ou ausência de lesões patológicas ou cancro). Pretende-se com este estudo apresentar formas de usar algoritmos de Deep Learning na análise de imagens médicas, em particular para a classificação de lesões patológicas representativas de fenotipos de cancro da mama

An Innovative Faster R-CNN-Based Framework for Breast Cancer Detection in MRI

Replacing lung cancer as the most commonly diagnosed cancer globally, breast cancer (BC) today accounts for 1 in 8 cancer diagnoses and a total of 2.3 million new cases in both sexes combined. An estimated 685,000 women died from BC in 2020, corresponding to 16% or 1 in every 6 cancer deaths in women. BC represents a quarter of a total of cancer cases in females and by far the most commonly diagnosed cancer in women in 2020. However, when detected in the early stages of the disease, treatment methods have proven to be very effective in increasing life expectancy and, in many cases, patients fully recover. Several medical imaging modalities, such as X-rays Mammography (MG), Ultrasound (US), Computer Tomography (CT), Magnetic Resonance Imaging (MRI), and Digital Tomosynthesis (DT) have been explored to support radiologists/physicians in clinical decision-making workflows for the detection and diagnosis of BC. In this work, we propose a novel Faster R-CNN-based framework to automate the detection of BC pathological Lesions in MRI. As a main contribution, we have developed and experimentally (statistically) validated an innovative method improving the “breast MRI preprocessing phase” to select the patient’s slices (images) and associated bounding boxes representing pathological lesions. In this way, it is possible to create a more robust training (benchmarking) dataset to feed Deep Learning (DL) models, reducing the computation time and the dimension of the dataset, and more importantly, to identify with high accuracy the specific regions (bounding boxes) for each of the patient’s images, in which a possible pathological lesion (tumor) has been identified. As a result, in an experimental setting using a fully annotated dataset (released to the public domain) comprising a total of 922 MRI-based BC patient cases, we have achieved, as the most accurate trained model, an accuracy rate of 97.83%, and subsequently, applying a ten-fold cross-validation method, a mean accuracy on the trained models of 94.46% and an associated standard deviation of 2.43%

Experimental data: Starvation combined with continuous anode potential triggers intracellular electron storage in Geobacter dominated electro-active biofilms

The data provided in these files have been generated/collected during the study on the effect of anode potential regime and feeding mode on electron storage in electro-active biofilms (EABfs). These data contain measured current density, acetate concentrations, and biofilm development over time

Experimental data: Real-time monitoring of biofilm thickness allows for determination of acetate limitations in bio-anodes

The data provided in these files have been generated/collected during the study of the development of electro-active biofilms (EABfs) in real-time. These data contain measured current density, acetate concentrations, and biofilm development over tim