FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection.

False news articles pose a serious challenge in today\u27s information landscape, impacting public opinion and decision-making. Efforts to counter this issue have led to research in deep learning and machine learning methods. However, a gap exists in effectively using contextual cues and skip connections within models, limiting the development of comprehensive detection systems that harness contextual information and vital data propagation. Thus, we propose a model of deep learning, FakeStack, in order to identify bogus news accurately. The model combines the power of pre-trained Bidirectional Encoder Representation of Transformers (BERT) embeddings with a deep Convolutional Neural Network (CNN) having skip convolution block and Long Short-Term Memory (LSTM). The model has been trained and tested on English fake news dataset, and various performance metrics were employed to assess its effectiveness. The results showcase the exceptional performance of FakeStack, achieving an accuracy of 99.74%, precision of 99.67%, recall of 99.80%, and F1-score of 99.74%. Our model\u27s performance was extended to two additional datasets. For the LIAR dataset, our accuracy reached 75.58%, while the WELFake dataset showcased an impressive accuracy of 98.25%. Comparative analysis with other baseline models, including CNN, BERT-CNN, and BERT-LSTM, further highlights the superiority of FakeStack, surpassing all models evaluated. This study underscores the potential of advanced techniques in combating the spread of false news and ensuring the dissemination of reliable information

A bayesian meta-analysis of multiple treatment comparisons of systemic regimens for advanced pancreatic cancer

© 2014 Chan et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Background: For advanced pancreatic cancer, many regimens have been compared with gemcitabine (G) as the standard arm in randomized controlled trials. Few regimens have been directly compared with each other in randomized controlled trials and the relative efficacy and safety among them remains unclear

Practical considerations for measuring the effective reproductive number, Rt.

Estimation of the effective reproductive number Rt is important for detecting changes in disease transmission over time. During the Coronavirus Disease 2019 (COVID-19) pandemic, policy makers and public health officials are using Rt to assess the effectiveness of interventions and to inform policy. However, estimation of Rt from available data presents several challenges, with critical implications for the interpretation of the course of the pandemic. The purpose of this document is to summarize these challenges, illustrate them with examples from synthetic data, and, where possible, make recommendations. For near real-time estimation of Rt, we recommend the approach of Cori and colleagues, which uses data from before time t and empirical estimates of the distribution of time between infections. Methods that require data from after time t, such as Wallinga and Teunis, are conceptually and methodologically less suited for near real-time estimation, but may be appropriate for retrospective analyses of how individuals infected at different time points contributed to the spread. We advise caution when using methods derived from the approach of Bettencourt and Ribeiro, as the resulting Rt estimates may be biased if the underlying structural assumptions are not met. Two key challenges common to all approaches are accurate specification of the generation interval and reconstruction of the time series of new infections from observations occurring long after the moment of transmission. Naive approaches for dealing with observation delays, such as subtracting delays sampled from a distribution, can introduce bias. We provide suggestions for how to mitigate this and other technical challenges and highlight open problems in Rt estimation

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

FakeStack: Hierarchical Tri-BERT-CNN-LSTM stacked model for effective fake news detection.

False news articles pose a serious challenge in today's information landscape, impacting public opinion and decision-making. Efforts to counter this issue have led to research in deep learning and machine learning methods. However, a gap exists in effectively using contextual cues and skip connections within models, limiting the development of comprehensive detection systems that harness contextual information and vital data propagation. Thus, we propose a model of deep learning, FakeStack, in order to identify bogus news accurately. The model combines the power of pre-trained Bidirectional Encoder Representation of Transformers (BERT) embeddings with a deep Convolutional Neural Network (CNN) having skip convolution block and Long Short-Term Memory (LSTM). The model has been trained and tested on English fake news dataset, and various performance metrics were employed to assess its effectiveness. The results showcase the exceptional performance of FakeStack, achieving an accuracy of 99.74%, precision of 99.67%, recall of 99.80%, and F1-score of 99.74%. Our model's performance was extended to two additional datasets. For the LIAR dataset, our accuracy reached 75.58%, while the WELFake dataset showcased an impressive accuracy of 98.25%. Comparative analysis with other baseline models, including CNN, BERT-CNN, and BERT-LSTM, further highlights the superiority of FakeStack, surpassing all models evaluated. This study underscores the potential of advanced techniques in combating the spread of false news and ensuring the dissemination of reliable information