A study published in Child Development,the top－ranked educational psychology journal,provides some of the strongest evidence yet that gesturing may have a unique effect on learning.Teachers in the United States tend to use gestures less than teachers in other countries.

     "Gesturing can be a very beneficial tool that is completely free and easily employed in classrooms," said Kimberly Fenn,study co－author and assistant professor of psychology at Michigan State University."And I think it can have long－lasting effects."

    Fenn and Ryan Duffy of MSU and Susan Cook of the University of Iowa conducted an experiment with 184 second－,third－ and fourth－graders in Michigan elementary classrooms.

    Half of the students were shown videos of an instructor teaching math problems using only speech.The others were shown videos of the instructor teaching the same problems using both speech and gestures.In the speech－only videos,the instructor simply explains the problem.In the other videos,the instructor uses two hand gestures while speaking,using different hands to refer to the two sides of the equation.Students who learned from the gesture videos performed better on a test given immediately afterward than those who learned from the speech－only video.

    Another test was given 24 hours later,and the gesture students actually showed improvement in their performance while the speech－only students did not.

    While previous research has shown the benefits of gestures in a one－on－one tutoring－style environment,the new study is the first to test the role of gestures in equivalence learning in a regular classroom.

    The study also is the first to show that gestures can help students transfer learning to new contexts—such as transferring the knowledge learned in an addition－based equation to a multiplication－based equation.

    Fenn noted that U.S.students lag behind those in many other Western countries in math and have a particularly hard time mastering equivalence problems in early grades."So if we can help them grasp this foundational knowledge earlier," she said,"it will help them as they learn algebra and higher levels of mathematics."

Reader's Travel Photography Competition

    This month's images include some original and brilliant shots. The overall 2018 winner will receive a $200 holiday ticket and go on a 16-night wildlife holiday.

    Elephants are able to know the difference between a man and a woman, and can tell an adult (成年人) from a child—all from the sound of a human voice. This is according to a study in which researchers played voice recordings to wild African elephants.

    The animals showed more fear when they heard the voices of adult Masai men. Usually Masai people hunt elephants, and this suggests that animals have grown to listen for and avoid them.

    Prof. Karen McComb and Dr Graeme Shannon from the University of Sussex led the study. They explained that in former research they had used similar experiments to show that elephants could tell—from the sound of a lion—whether the animal was a female (雌性) or a more dangerous male (雄性).

    Prof  McComb wanted to find out if the animals used their very sharp sense of hearing to recognize danger from humans.

    The scientists recorded Masai men, women and children saying, in their own language, "Look, look over there, a group of elephants are coming." They also recorded Kamba men saying this phrase.

    Masai people often come across elephants, which can result in violent (暴力的) hunting. Kamba people, however, mainly feed on agriculture, which does not generally bring them into violent touch with the animals.

    When the team played recordings of these different voices through a hidden speaker, they found that elephant family groups showed more fear in response to the voice of a Masai man, than to a Kamba man's voice. And the adult male Masai voices caused far more violent response than the voices of women or boys.

    Many of us have had this experience: we lie down in a bed other than our own, perhaps at a friend's house or in a hotel room, and find it difficult or impossible to fall asleep. Is it because the bed is uncomfortable? Maybe, but perhaps there can be other reasons.

    According to a new study published in Current Biology, a significant reason is what the scientists call "first night effect". They believe that one side of the brain acts as a "night watch" to warn us about potential dangers. It forces us to stay awake on the first night in a new environments.

    For the study, 35 young volunteers were asked to sleep in a sleep lab for several days. Meanwhile, researchers watched their brain activities.

    According to the researchers, on their first night, the left brain was more active than the right brain and people had a hard time sleeping. However, left-brain activities decreased as days went by, falling even to the point of complete calm. In this process, the participants got an increasingly better sleep experience.

    The findings suggest that the different rhythms of the sides of the brain affect our sleep. When the two sides work differently, the balance between them is broken. Thus, the brain can't relax and is sensitive to anything strange in the surroundings, just as it is in the daytime.

    "At some level, the brain is continuing to analyze things, even though you are not aware of the analysis, " US professor Jerome Siegel told Smithsonian

    "If something unusual happens - if a door opens or you hear a key in a lock --you can be alert, even though the intensity of the stimulus (刺激强度) is quite low." More surprisingly, this phenomenon is similar to the way some animals sleep.

    The researchers think that it is the result of evolution (进化), and works to protect us in potentially dangerous environments.

    If you have ever had what you think is "first night effect", researchers suggest that you bring your own pillow or sleep in a room similar to your bedroom next time you sleep away from home.

    Traditional Chinese Medicine (TCM) needs to prove its worth through rigorous clinical trials, according to the head of one of China's largest herbal remedy companies, as a contentious new law to boost the $40bn sector comes into effect.

    Traditional Chinese Medicine is estimated by analysts to account for a third of sales in China's $117bn pharmaceutical (药学) market, the world's second largest. But most of those sales are of relatively cheap over-the-counter drugs, with many Chinese hospital doctors unwilling to prescribe TCM remedies because of the lack of evidence for their effectiveness.

    China's first law promising equal status for TCM and western medicine in the country's state-dominated healthcare system comes into effect this weekend. Provisions include encouragement for hospitals to set up TCM centres, and a licensing system for practitioners (从业者). "This law is very important for securing the status of TCM," said Wu Yiling, chairman of Yiling Pharmaceutical, a company that makes herbal remedies based on traditional recipes in the northern province of Hebei.

    Listed in Shenzhen, Wu Yiling has a market capitalization of RMB 20.7bn ($3bn). Mr Wu, the son of a herbal practitioner, controls a family fortune of $1.6bn, according to the Hurun Rich List.

    Mr Wu is both a supporter of the nature of Qi—the mystical energy force that provides the basis for much of TCM theory—and the clinical trials vital to western pharmaceutical companies. "TCM needs to develop using modern research methods," he said.

    For instance, researchers from Peking and Cardiff Universities tested the health benefits of Yiling's herbal medicine Yangzheng Xiaoji, publishing papers in the International Journal of Oncology that showed the drug can slow the growth of cancer tumours. "The theory and recipe is TCM, but in practice the evaluation of our medicines is carried out according to western evidence-based methods," Mr Wu said.

    Analysts say such tests can help gain support from doubtful hospital doctors and boost prescriptions. "Doctors need strong evidence that drugs definitely work," said Serena Shao, healthcare analyst at brokerage CLSA. "Some of these companies are currently doing clinical trials, and getting proof that their drugs have the same efficacy (功效) as chemical drugs. That's the way to go." she added.

    The TCM law has been greeted with doubt from China's western-schooled medical establishment, which points to a lack of rigorous training for TCM doctors, and a recent series of shocking events involving herbal injections believed to have been harmful. "Officials will try and encourage TCM, but also will be very cautious about what kinds of TCM they use in hospitals," added Ms Shao.

Tests have shown robots can diagnose heart problems in as little as four seconds, as a review of artificial intelligence (AI) finds machines are now as good at spotting illness as doctors.

Analyzing a patient's heart function on a cardiac MRI (心脏磁共振成像) scan currently takes doctors around 13 minutes. But a new trial by University College London (UCL) showed an AI program could read the scans in less time with equal accuracy. There are approximately 150,000 such scans performed in the UK each year, and researchers estimate that fully using AI to read them could save 54 clinician-days (临床天数) at each cardiac centre per year. So it can make up for the shortage of doctors.

It is hoped that AI where computer systems are able to learn from data to identify new patterns with minimal human intervention will transform medicine by helping doctors spot diseases such as heart disease and cancer faster and earlier. However, most scans are still read by specially trained doctors.

Dr Charlotte Manisty, who led the UCL research, said, "Cardiovascular MRI offers in- comparable image quality for assessing heart structure and function. However, current manual analysis remains basic and outdated. Automated machine techniques offer the potential to change this and completely improve efficiency and accuracy, and we look forward to further research that could confirm the superiority to human analysis."

She added, "Our dataset of patients with a range of heart disease who received scans enabled us to demonstrate that the greatest sources of measurement errors arise from human factors. This indicates that automated techniques are at least as good as humans, with the potential soon to be 'super-human'—transforming clinical and research measurement precision."

Professor Alastair Denniston said, "Within those handful of high-quality studies, we found that by deep learning AI could indeed detect disease ranging from cancer to eye disease as accurately as health professionals. But it's important to note that it did not absolutely exceed human professional diagnosis. "