Catálogo de publicaciones - revistas

<jats:title>Credit assignment in the brain</jats:title> <jats:p>To discover relevant clues for survival, an organism must bridge the gap between the short time periods when a clue occurs and the potentially long waiting times after which feedback arrives. This so-called temporal credit-assignment problem is also a major challenge in machine learning. Gütig developed a representation of the responses of spiking neurons, whose derivative defines the direction along which a neuron's response changes most rapidly. By using a learning rule that follows this development, the temporal credit-assignment problem can be solved by training a neuron to match its number of output spikes to the number of clues. The same learning rule endows unsupervised neural networks with powerful learning capabilities.</jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" related-article-type="in-this-issue" xlink:href="10.1126/science.aab4113">10.1126/science.aab4113</jats:related-article> </jats:p>

<jats:title>Have cancer stem cells MET their match?</jats:title> <jats:p> Solid tumors have been hypothesized to contain a subset of highly aggressive cells that fuel tumor growth and metastasis. The search is on for drugs that selectively kill or diminish the malignant properties of these tumor-initiating cells (TICs; previously called “cancer stem cells”). Pattabiraman <jats:italic>et al.</jats:italic> hypothesized that compounds that induce TICs to undergo a phenotypic change called the mesenchymal-to-epithelial transition (MET) would therefore cause TICs to lose their tumor-initiating ability. Indeed, drugs activating the protein kinase A signaling pathway triggered an epigenetic reprogramming of TICs that resulted in the cells acquiring a more benign epithelial-like phenotype. </jats:p> <jats:p> <jats:italic>Science</jats:italic> , this issue p. <jats:related-article xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="doi" related-article-type="in-this-issue" xlink:href="10.1126/science.aad3680">10.1126/science.aad3680</jats:related-article> </jats:p>

<jats:p> In the midst of steady progress in policies for data sharing, a recent editorial expressed a contrarian view. <jats:xref ref-type="fn">*</jats:xref> The authors described the concern of some scientists about the rise of an underclass of “research parasites” who exploit data sets that are collected and curated by others. Even worse, these parasites might use such data to try to disprove the conclusions posited in the data's original source studies. The editorial raised the points of how anyone not involved in the original study could use the data without misrepresenting it, and the danger of perhaps arriving at erroneous conclusions. The editorial advised instead that data sharing be implemented by involving the authors of the original study as coauthors in follow-up research. The research community immediately took to Twitter under the hashtag #IAmAResearchParasite to voice opposition to the editorial. </jats:p>

<jats:p> The <jats:italic>Science</jats:italic> family of journals is committed to sharing data relevant to public health emergencies, and therefore we are signatories to, and wholeheartedly endorse, the following statement by funders and journals. <jats:xref ref-type="fn">*</jats:xref> </jats:p>

<jats:p>A roundup of weekly science policy and related news.</jats:p>

<jats:p>Conflicting results suggest the potent blasts come in two flavors.</jats:p>