- 作者： Wu Kai
for filename in os.listdir(dirName):
subs = pysrt.open(infile,encoding='utf-8')
outfile = infile[:-4] + '.txt'
f = open(outfile, 'w',encoding='utf-8')
for i in range(len(subs)):
print('covert %d srt file to txt file' % (i+1)
covert 67 srt file to txt file
- 作者： Wu Kai
Masking out air sharing
The effectiveness of masks in preventing the transmission of severe acute respiratory syndrome coronavirus 2 has been debated since the beginning of the COVID-19 pandemic. One important question is whether masks are effective despite the forceful expulsion of respiratory matter during coughing and sneezing. Cheng et al. convincingly show that most people live in conditions in which the airborne virus load is low. The probability of infection changes nonlinearly with the amount of respiratory matter to which a person is exposed. If most people in the wider community wear even simple surgical masks, then the probability of an encounter with a virus particle is even further limited. In indoor settings, it is impossible to avoid breathing in air that someone else has exhaled, and in hospital situations where the virus concentration is the highest, even the best-performing masks used without other protective gear such as hazmat suits will not provide adequate protection.
Science, abg6296, this issue p. 1439
Airborne transmission by droplets and aerosols is important for the spread of viruses. Face masks are a well-established preventive measure, but their effectiveness for mitigating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission is still under debate. We show that variations in mask efficacy can be explained by different regimes of virus abundance and are related to population-average infection probability and reproduction number. For SARS-CoV-2, the viral load of infectious individuals can vary by orders of magnitude. We find that most environments and contacts are under conditions of low virus abundance (virus-limited), where surgical masks are effective at preventing virus spread. More-advanced masks and other protective equipment are required in potentially virus-rich indoor environments, including medical centers and hospitals. Masks are particularly effective in combination with other preventive measures like ventilation and distancing.
- 作者： Wu Kai
在2021年7月22日，DeepMind和欧洲分子生物学实验室（European Bioinformatics Institute）联合宣布，两家合作，用自己开发的人工智能分析工具AlphaFold2预测了36.5万种蛋白质的结构，然后把这些结果做成数据库，免费给全球所有科研人员使用。
Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the 3-D structure that a protein will adopt based solely on its amino acid sequence, the structure prediction component of the ‘protein folding problem’8, has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even where no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experiment in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm.
- 作者： Wu Kai
- 作者： Wu Kai
A4 paper size in different values.
A4 size in different values. The links below give you an overview. You will find the exact numbers for Micrometres (μm), Millimetres (mm), Centimetres (cm), Metres (m), Thou (th), Inches (in), Feet (ft), Yards (yd), Pixels, Pica, Point and HPGL. Click on the link to go to the appropriate size. You can calculate your A paper size in another unit with our calculator.
History ISO 216: ISO 216 the international standard.
The ISO 216 is used worldwide, except in North America and parts of Latin America. The standard includes the “A”, “B” and “C” series of paper sizes. The A4 size is the most widely used worldwide. The sizes A2, A3, B3, B4 and B5 were developed in France by the mathematician Lazare Carnot. These standards were published during the French Revolution in 1798.
At the beginning of the 20th century, Dr. Walter Porstmann changed Lichtenberg’s idea into a good system of different paper sizes. In 1922 Porstmann introduced his system. The DIN became the standard (DIN 476) in Germany. Porstmann’s standard replaced some other paper systems. The DIN system is used in Germany and Austria.
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