New geometric shape in cells

As an embryo develops, tissues bend into complex three-dimensional shapes that lead to organs. Epithelial cells are the building blocks of this process forming, for example, the outer layer of skin. They also line the blood vessels and organs of all animals. These cells pack together tightly. To accommodate the curving that occurs during embryonic development, it has been assumed that epithelial cells adopt either columnar or bottle-like shapes.


Credit: Lehigh University

However, a group of scientists dug deeper into this phenomenon and discovered a new geometric shape in the process. They uncovered that, during tissue bending, epithelial cells adopt a previously undescribed shape that enables the cells to minimize energy use and maximize packing stability. The team’s results will be published in Nature Communications in a paper called “Scutoids are a geometrical solution to three-dimensional packing of epithelia.” The study is the result of a United States-European Union collaboration between the teams of Luis M. Escudero (Seville University, Spain) and that of Javier Buceta (Lehigh University, USA). Pedro Gomez-Galvez and Pablo Vicente-Munuera are the first authors of this work that also includes scientists from the Andalucian Center of Developmental Biology, and the Severo Ochoa Center of Molecular Biology, among others.

Read more (Lehigh University. “New geometric shape used by nature to pack cells efficiently.” ScienceDaily. ScienceDaily, 28 July 2018.)

Original paper: Gómez-Gálvez, P., Vicente-Munuera, P., Tagua, A., Forja, C., Castro, A.M., Letrán, M., Valencia-Expósito, A., Grima, C., Bermúdez-Gallardo, M., Serrano-Pérez-Higueras, Ó. and Cavodeassi, F., 2018. Scutoids are a geometrical solution to three-dimensional packing of epithelia. Nature communications9(1), pp.1-14.

Machine learning without negative data

A research team from the RIKEN Center for Advanced Intelligence Project (AIP) has successfully developed a new method for machine learning that allows an AI to make classifications without what is known as “negative data,” a finding which could lead to wider application to a variety of classification tasks.


Credit: Riken

According to lead author Takashi Ishida from RIKEN AIP, “Previous classification methods could not cope with the situation where negative data were not available, but we have made it possible for computers to learn with only positive data, as long as we have a confidence score for our positive data, constructed from information such as buying intention or the active rate of app users. Using our new method, we can let computers learn a classifier only from positive data equipped with confidence.”

Ishida proposed, together with researcher Niu Gang from his group and team leader Masashi Sugiyama, that they let computers learn well by adding the confidence score, which mathematically corresponds to the probability whether the data belongs to a positive class or not. They succeeded in developing a method that can let computers learn a classification boundary only from positive data and information on its confidence (positive reliability) against classification problems of machine learning that divide data positively and negatively.

To see how well the system functioned, they used it on a set of photos that contains various labels of fashion items. For example, they chose “T-shirt,” as the positive class and one other item, e.g., “sandal,” as the negative class. Then they attached a confidence score to the “T-shirt” photos. They found that without accessing the negative data (e.g., “sandal” photos), in some cases, their method was just as good as a method that involves using positive and negative data.

According to Ishida, “This discovery could expand the range of applications where classification technology can be used. Even in fields where machine learning has been actively used, our classification technology could be used in new situations where only positive data can be gathered due to data regulation or business constraints. In the near future, we hope to put our technology to use in various research fields, such as natural language processing, computer vision, robotics, and bioinformatics.”

Read more here (RIKEN. “Smarter AI: Machine learning without negative data.” ScienceDaily. ScienceDaily, 26 November 2018.)

Practical application of 3D holography

Japanese computer scientists have succeeded in developing a special purpose computer that can project high-quality three-dimensional (3D) holography as a video. The research team led by Tomoyoshi Ito, who is a professor at the Institute for Global Prominent Research, Chiba University, has been working to increase the speed of the holographic projections by developing new hardware.


Credit: Tomoyoshi Ito

Ito, who is an astronomer and a computer scientist, began working on specially designed computers for holography, called HORN, in 1992. The HORN-8, which adopts a calculation method called the “amplitude type” for adjusting the intensity of light, was recognized as the world’s fastest computer for holography in a publication in the international science journal Nature Electronics on April 17, 2018.

With the newly developed “phase type” HORN-8, the calculation method for adjusting the phase of light was implemented, and the researchers were successful at projecting holography information as a 3D video with high-quality images. This research was published in Optics Express on September 28, 2018.

In the latest phase type of HORN-8, eight chips are mounted on the FPGA (Field Programmable Gate Array) board. This enables one to avoid a bottleneck problem for the processing speed with the calculation method, by which the chips are prevented from communicating with each other. With this approach, HORN-8 increases the computing speed in proportion to the number of chips, so that it can project video holography more clearly.

Source (Chiba University. “A big step toward the practical application of 3D holography with high- performance computers.” ScienceDaily. ScienceDaily, 28 November 2018.)

Original paper: Nishitsuji, T., Yamamoto, Y., Sugie, T., Akamatsu, T., Hirayama, R., Nakayama, H., Kakue, T., Shimobaba, T. and Ito, T., 2018. Special-purpose computer HORN-8 for phase-type electro-holography. Optics express26(20), pp.26722-26733.

About dancing

ENG: A group of scientists have written a technical report on their research named “Male dance moves that catch a woman’s eyes“.


Researchers at Northumbria University and the University of Gottingen wanted to know what women look for in a dancing partner, since “dancing ability, particularly that of men, may serve as a signal of mate quality.”

So the researchers set up an experiment in which they had 30 men to dance to a core drum beat for 30 seconds. The dancers were given no specific instructions on how to dance beforehand, and their movements were recorded via a motion-capture system. An avatar was generated to animate their dance moves. Then 37 women were asked to view each of the dancing avatars and rate their performance on a seven-point scale.

They found that women rated dancers higher when they showed larger and more variable movements of the head, neck and torso. Speed of leg movements mattered too, particularly bending and twisting of the right knee. In what might be bad news for the 20% of the population who is left-footed, left knee movement didn’t seem to matter. In fact, certain left-legged movements had a small negative correlation with dancing ability, meaning that dancers who favored left leg motion were rated more poorly. While not statistically significant, these findings suggest that there might be something to that old adage about “two left feet” after all. One final surprise – arm movement didn’t correlate with perceived dancing ability in any significant way.

So boys, learn to dance and have fun.

RO: Un grup de oameni de știință au scris un raport tehnic privind cercetarea lor, intitulat “Mișcările de dans ale bărbaților care atrag privirile femeilor“.

Cercetătorii de la Universitatea Northumbria și de la Universitatea din Gottingen au vrut să afle ce caută femeile la un partener de dans, întrucât “abilitățile de dans, în special cele ale bărbaților, pot servi drept semnal al calității partenerului”.

Astfel, cercetătorii au efectuat un experiment în care au pus 30 de bărbați să danseze pe un ritm de tobă timp de 30 de secunde. Dansatorii nu au primit în prealabil instrucțiuni specifice despre cum să danseze, iar mișcările lor au fost înregistrate printr-un sistem de captare a mișcărilor. Un avatar a fost generat pentru a anima mișcările lor de dans. Apoi, 37 de femei au fost rugate să vadă fiecare dintre avatarele dansatoare și să le evalueze performanța pe o scară de șapte puncte.

Ei au descoperit că femeile au acordat o notă mai mare dansatorilor atunci când aceștia prezentau mișcări mai mari și mai variabile ale capului, gâtului și trunchiului. Viteza mișcărilor picioarelor a contat, de asemenea, în special îndoirea și răsucirea genunchiului drept. În ceea ce ar putea fi o veste proastă pentru cei 20% din populație care sunt stângaci, mișcarea genunchiului stâng nu părea să conteze. De fapt, anumite mișcări ale piciorului stâng au avut o mică corelație negativă cu capacitatea de dans, ceea ce înseamnă că dansatorii care au favorizat mișcarea piciorului stâng au fost evaluați mai slab. Deși nu sunt semnificative din punct de vedere statistic, aceste constatări sugerează că ar putea exista ceva în acel vechi adagiu despre “două picioare stângi”, până la urmă. O ultimă surpriză – mișcarea brațelor nu a fost corelată cu abilitatea de dans percepută în mod semnificativ.

Așadar, băieți, învățați să dansați și distrați-vă.

Source (The Washington Post, “Male dance moves that catch a woman’s eye”, 24.03.2014)