(单词翻译:单击)
听力文本
Hi, I'm Scientific American podcast editor Steve Mirsky. And here's a short piece from the April issue of the magazine, in the section we call Advances: Dispatches from the Frontiers of Science, Technology and Medicine:
#Flu by Rachel Berkowitz
Forecasting influenza outbreaks before they strike could help officials take early action to reduce related deaths, which total 290,000 to 650,000 worldwide every year. In a recent study, researchers say they have accurately predicted outbreaks up to two weeks in advance—using only the content of social media conversations. The findings could theoretically be used to direct resources to areas that will need them most.
A team at the Pacific Northwest National Laboratory in Washington State gathered linguistic cues from Twitter conversations about seemingly non-flu-related topics such as the weather or coffee. Based on this information, the researchers nailed down when and where the next flu outbreaks were likely to occur.
The investigators used a "deep learning" computer model that mimics the layers of neurons and memory capabilities of the human brain. Their algorithm analyzed how Twitter language style, opinions and communication behaviors changed in a given period and how such changes related to later reports of flu outbreaks.
The study was published in the journal PLOS ONE.
Computer scientist Svitlana Volkova, who led the study said "the beauty of the deep-learning model we use is that it considers emotions and linguistic clues over time to predict the future." Previous efforts to forecast flu outbreaks via the Internet—including studies that used Twitter and Wikipedia records and a project called Google Flu Trends—have scanned specifically for flu-related words. In contrast, Volkova's work examined 171 million general tweets and outperformed other models that were based exclusively on word searches or clinical data suggesting an imminent outbreak.
Epidemiologist Matthew Biggerstaff of the U.S. Centers for Disease Control and Prevention cautions that we are still in "early days" when it comes to flu forecasting. But researchers are increasingly looking to the Internet to supplement official data, which are limited to a small proportion of actual cases because many infected individuals do not seek medical care. Furthermore, such a tool might one day help identify flu trends in regions where public health data are not available at all.
That was #Flu by Rachel Berkowitz.
参考译文
大家好，我是《科学美国人》播客编辑史蒂夫·米尔斯基。下面是收录在本杂志四月刊“进展：科学、技术和医学前沿快报”版块的一篇短文。
《#流感》——雷切尔·伯克威茨
在流感来袭前预测流感爆发情况，可帮助官员尽早采取行动减少相关死亡人数，流感每年会造成全世界29万至65万人死亡。在最近的一项研究中，研究人员表示他们只用社交媒体的交流内容，就提前两周准确地预测到了流感的爆发。从理论上来说，这项研究可以用于将资源分配到最需要的地区。
华盛顿州美国太平洋西北国家实验室的一个小组收集了推特交谈中的语言线索，这些交谈内容涉及“天气”或“咖啡”等看似与流感无关的话题。但是基于这些信息，研究人员确定了下一场流感爆发可能发生的时间和地点。
研究人员使用了“深度学习”模型，这是一种可以模拟人类大脑神经元层和记忆能力的计算机模型。“深度学习”算法分析了特定时间内的推特语言风格、观点和交流行为所发生的变化，以及这种变化与之后的流感爆发报告的关联情况。
这项研究发表在《公共科学图书馆·综合》期刊上。
计算机科学家兼该研究领导者斯维特拉娜·沃尔科娃表示，“我们所使用的‘深度学习'模型的优点在于，它考虑到了随时间变化的情感和语言线索，以此来预测未来。”以前，人们通过互联网来预测流感爆发，包括用推特、维基百科记录以及“谷歌流感趋势”项目进行的研究，这些预测往往专门扫描与流感有关的词语。与此相反，沃尔科娃的模型检查了1.71亿条普通推文，与其他仅基于词语搜索或表示流感即将爆发的临床数据模型相比，这种模型的表现更好。
美国疾病预防和控制中心的流行病学家马修·毕格士塔夫提醒说，就流感预测来说，目前我们仍处于“早期阶段”。但现在研究人员越来越依赖互联网来补充官方数据，官方数据被限制在实际感染案例中的一小部分，因为许多被感染个体不寻求医疗护理。另外，这种工具也许有一天能帮助无法提供公共卫生数据的地区确定流感趋势。
以上是雷切尔·伯克威茨发表的文章《#流感》。
译文为可可英语翻译，未经授权请勿转载！
重点讲解
重点讲解：
1. in advance 提前；事先；
Because of the popularity of the region, it is advisable to book hotels or camp sites in advance.
鉴于该地区很受人们青睐，最好提前预订旅馆或宿营地。
2. nail down 弄清；确定；
It would be useful if you could nail down the source of this tension.
如果你能弄清这种紧张的根源，将会大有益处。
3. in contrast 相反；
In contrast, the lives of girls in well-to-do families were often very sheltered.
相反，生活在富裕家庭的女孩子通常都备受呵护。
4. when it comes to 谈到；涉及；
It's easy to think that individuals can't make a difference when it comes to the issue of global warming.
当说到全球变暖的问题时，人们似乎都觉得个人的力量微不足道。
