Foundling and abortions?5 answersThe contexts provided shed light on two distinct yet interconnected topics: foundlings and abortions. Foundlings refer to children abandoned by their mothers, as seen in the Foundling Museum's history of caring for deserted children. On the other hand, abortions are explored in studies focusing on women's emotional experiences and socio-demographic factors related to induced abortion decisions. The Foundling Hospital in London cared for abandoned children, highlighting the societal challenges faced by unwed mothers unable to support their offspring. In contrast, abortion studies in Sweden reveal the complex interplay of socio-demographic factors, emotional distress post-abortion, and the importance of accessible and compassionate reproductive healthcare services. Both topics reflect historical and contemporary societal attitudes towards unwanted pregnancies and the care of vulnerable children.
How does recurrent selection as a breeding method improve population genetics?5 answersRecurrent selection is a breeding method that improves population genetics by gradually increasing the frequency of favorable alleles in a population. It is a successful approach for improving breeding populations and achieving prolonged response to selection. Recurrent selection can be combined with marker-aided selection to increase breeding efficiency by selecting for favorable alleles of quantitative trait loci (QTL). Marker-assisted recurrent selection (MARS) is a novel iteration of recurrent selection that incorporates data from molecular markers, allowing for the selection of desirable QTL alleles. Recurrent selection can also be optimized through the use of genomic prediction, which allows for early evaluation and selection of candidates based on their genomic estimated breeding values. By incorporating genomic information, recurrent selection can lead to high rates of genetic gain and shorten the breeding cycle length. Overall, recurrent selection is a valuable breeding method for improving population genetics and increasing the rate of genetic gain.
Difference between neural net and recurrent neural net?4 answersNeural networks are a type of artificial intelligence model that can be used for system identification. They require fewer modeling assumptions and can be applied to both linear and nonlinear systems. However, they may need larger amounts of data to learn and generalize. On the other hand, recurrent neural networks (RNNs) are a specific type of neural network that employ recurrence, using information from a previous forward pass over the network. RNNs have internal loops that introduce delayed activation dependencies across the processing elements in the network. This recursive dynamics enable RNNs to perform tasks such as large-scale visual recognition on natural images more effectively than feedforward neural networks. RNNs also allow for flexible trading of speed for accuracy and can emulate the behavior of feedforward models while outperforming them in terms of accuracy and computational cost.
What is recurrent neural network?5 answersRecurrent neural network (RNN) is an important model in the field of deep learning. It is a type of artificial neural network that establishes connections between different nodes to form a directed or undirected graph for temporal dynamical analysis. RNNs use a similar network structure to recursively form a more complex deep network with a relatively simple structure. By adding extra weights to the network to create cycles in the network graph and using long-distance dependence information, RNNs can achieve high prediction accuracy with sufficient data. However, RNNs have limitations such as the inability to improve training speed and the issue of the gradient gradually disappearing. To address these limitations, the Long Short Term Memory (LSTM) model was introduced, which improves the hidden layer nodes of RNN into special cell structures and performs better in longer sequences.
What is breast cancer recurrence?5 answersBreast cancer recurrence refers to the reappearance of cancerous tumors in the breast after initial treatment. It can occur locally, in the same area as the original cancer, or in distant parts of the body such as the bone, breast, liver, and lungs. Recurrence can happen within 5 years or even up to 10-20 years after surgery. Risk factors for recurrence include factors such as age, type of surgery performed, tumor size, breast subtype, estrogen receptor, progesterone receptor, undergoing chemotherapy or not, and lymph node involvement. Late recurrence is a characteristic of breast cancer, and long-term hormone therapy for 10 years is necessary to prevent it. The acceptance of breast cancer recurrence is a process that involves emotional reactions, psychological preparedness, mobilizing support, and returning to the path of treatment. Nurses play a crucial role in supporting patients through the acceptance process by providing effective education, strengthening communication, and mobilizing support from family and relatives.
What is Recurrent Neural Networks (RNNs)?3 answersRecurrent Neural Networks (RNNs) are a type of neural network that can handle and process sequential data, making them suitable for analyzing temporal data such as time series. RNNs have an internal state that allows them to build a hierarchical representation of the current and past sequence steps. They have been used in various domains and have advantages over simpler feedforward models in terms of their ability to handle sequences of varying length and process data in a hierarchical manner. Some well-known variants of RNNs include Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). RNNs have been used to model circuits in the brain, solve computational problems requiring memory and error-correction, and perform autofocusing and phase recovery in holographic imaging. They can also be adapted to deal with irregularly sampled data by resampling the RNN in time to match the time of the data.