Leslie Population Estimate for a Large Lake

The accuracy of a population estimate made by the Leslie removal method was evaluated for adult northern pike (Esox lucius) in a 288-hectare lake. The Leslie estimate of 6,290 fish was within 1% of that obtained from direct enumeration

High School Breeze, The, Vol. 6, No. 1, Feb. 1906

https://digitalmaine.com/milo_yearbooks/1006/thumbnail.jp

UWB-based early breast cancer existence prediction using artificial intelligence for large data set

Breast cancer is the most often identified cancer among women and the main reason for cancer-related deaths worldwide. The most effective methods for controlling and treating this disease through breast screening and emerging detection techniques. This paper proposes an intelligent classifier for the early detection of breast cancer using a larger dataset since there is limited researcher focus on that for better analytic models. To ensure that the issue is tackled, this project proposes an intelligent classifier using the Probabilistic Neural Network (PNN) with a statistical feature model that uses a more significant size of data set to analyze the prediction of the presence of breast cancer using Ultra Wideband (UWB). The proposed method is able to detect breast cancer existence with an average accuracy of 98.67%. The proposed module might become a potential user-friendly technology for early breast cancer detection in domestic use

Existing and emerging breast cancer detection technologies and its challenges: a review

Breast cancer is the most leading cancer occurring in women and is a significant factor in female mortality. Early diagnosis of breast cancer with Artificial Intelligent (AI) developments for breast cancer detection can lead to a proper treatment to affected patients as early as possible that eventually help reduce the women mortality rate. Reliability issues limit the current clinical detection techniques, such as Ultra-Sound, Mammography, and Magnetic Resonance Imaging (MRI) from screening images for precise elucidation. The capability to detect a tumor in early diagnosis, expensive, relatively long waiting time due to pandemic and painful procedure for a patient to perform. This article aims to review breast cancer screening methods and recent technological advancements systematically. In addition, this paper intends to explore the progression and challenges of AI in breast cancer detection. The next state of the art between image and signal processing will be presented, and their performance is compared. This review will facilitate the researcher to insight the view of breast cancer detection technologies advancement and its challenges

Optimized intelligent classifier for early breast cancer detection using ultra-wide band transceiver

Breast cancer is the most common cancer diagnosed in women and the leading cause of cancer-related deaths among women worldwide. The death rate is high because of the lack of early signs. Due to the absence of a cure, immediate treatment is necessary to remove the cancerous cells and prolong life. For early breast cancer detection, it is crucial to propose a robust intelligent classifier with statistical feature analysis that considers parameter existence, size, and location. This paper proposes a novel Multi-Stage Feature Selection with Binary Particle Swarm Optimization (MSFS–BPSO) using Ultra-Wideband (UWB). A collection of 39,000 data samples from non-tumor and with tumor sizes ranging from 2 to 7 mm was created using realistic tissue-like dielectric materials. Subsequently, the tumor models were inserted into the heterogeneous breast phantom. The breast phantom with tumors was imaged and represented in both time and frequency domains using the UWB signal. Consequently, the dataset was fed into the MSFS–BPSO framework and started with feature normalization before it was reduced using feature dimension reduction. Then, the feature selection (based on time/frequency domain) using seven different classifiers selected the frequency domain compared to the time domain and continued to perform feature extraction. Feature selection using Analysis of Variance (ANOVA) is able to distinguish between class-correlated data. Finally, the optimum feature subset was selected using a Probabilistic Neural Network (PNN) classifier with the Binary Particle Swarm Optimization (BPSO) method. The research findings found that the MSFS–BPSO method has increased classification accuracy up to 96.3% and given good dependability even when employing an enormous data sample

Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes.

Mutations in whole organisms are powerful ways of interrogating gene function in a realistic context. We describe a program, the Sanger Institute Mouse Genetics Project, that provides a step toward the aim of knocking out all genes and screening each line for a broad range of traits. We found that hitherto unpublished genes were as likely to reveal phenotypes as known genes, suggesting that novel genes represent a rich resource for investigating the molecular basis of disease. We found many unexpected phenotypes detected only because we screened for them, emphasizing the value of screening all mutants for a wide range of traits. Haploinsufficiency and pleiotropy were both surprisingly common. Forty-two percent of genes were essential for viability, and these were less likely to have a paralog and more likely to contribute to a protein complex than other genes. Phenotypic data and more than 900 mutants are openly available for further analysis. PAPERCLIP

The International Consortium Investigating Neurocognition in Bipolar Disorder (ICONICâ BD)

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/148257/1/bdi12748.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/148257/2/bdi12748_am.pd

Mouse large-scale phenotyping initiatives: overview of the European Mouse Disease Clinic (EUMODIC) and of the Wellcome Trust Sanger Institute Mouse Genetics Project.

Two large-scale phenotyping efforts, the European Mouse Disease Clinic (EUMODIC) and the Wellcome Trust Sanger Institute Mouse Genetics Project (SANGER-MGP), started during the late 2000s with the aim to deliver a comprehensive assessment of phenotypes or to screen for robust indicators of diseases in mouse mutants. They both took advantage of available mouse mutant lines but predominantly of the embryonic stem (ES) cells resources derived from the European Conditional Mouse Mutagenesis programme (EUCOMM) and the Knockout Mouse Project (KOMP) to produce and study 799 mouse models that were systematically analysed with a comprehensive set of physiological and behavioural paradigms. They captured more than 400 variables and an additional panel of metadata describing the conditions of the tests. All the data are now available through EuroPhenome database (www.europhenome.org) and the WTSI mouse portal (http://www.sanger.ac.uk/mouseportal/), and the corresponding mouse lines are available through the European Mouse Mutant Archive (EMMA), the International Knockout Mouse Consortium (IKMC), or the Knockout Mouse Project (KOMP) Repository. Overall conclusions from both studies converged, with at least one phenotype scored in at least 80% of the mutant lines. In addition, 57% of the lines were viable, 13% subviable, 30% embryonic lethal, and 7% displayed fertility impairments. These efforts provide an important underpinning for a future global programme that will undertake the complete functional annotation of the mammalian genome in the mouse model

GDF15 mediates the effects of metformin on body weight and energy balance.

Metformin, the world's most prescribed anti-diabetic drug, is also effective in preventing type 2 diabetes in people at high risk1,2. More than 60% of this effect is attributable to the ability of metformin to lower body weight in a sustained manner3. The molecular mechanisms by which metformin lowers body weight are unknown. Here we show-in two independent randomized controlled clinical trials-that metformin increases circulating levels of the peptide hormone growth/differentiation factor 15 (GDF15), which has been shown to reduce food intake and lower body weight through a brain-stem-restricted receptor. In wild-type mice, oral metformin increased circulating GDF15, with GDF15 expression increasing predominantly in the distal intestine and the kidney. Metformin prevented weight gain in response to a high-fat diet in wild-type mice but not in mice lacking GDF15 or its receptor GDNF family receptor α-like (GFRAL). In obese mice on a high-fat diet, the effects of metformin to reduce body weight were reversed by a GFRAL-antagonist antibody. Metformin had effects on both energy intake and energy expenditure that were dependent on GDF15, but retained its ability to lower circulating glucose levels in the absence of GDF15 activity. In summary, metformin elevates circulating levels of GDF15, which is necessary to obtain its beneficial effects on energy balance and body weight, major contributors to its action as a chemopreventive agent