Defects in mitochondrial fusion or fission are associated with many pathologies, raising the hope that pharmacological manipulation of mitochondrial dynamics may have therapeutic benefit. This approach assumes that organ physiology can be restored by rebalancing mitochondrial dynamics, but this concept remains to be validated. We addressed this issue by analyzing mice deficient in Mff, a protein important for mitochondrial fission. Mff mutant mice die at 13 wk as a result of severe dilated cardiomyopathy leading to heart failure. Mutant tissue showed reduced mitochondrial density and respiratory chain activity along with increased mitophagy. Remarkably, concomitant deletion of the mitochondrial fusion gene Mfn1 completely rescued heart dysfunction, life span, and respiratory chain function. Our results show for the first time that retuning the balance of mitochondrial fusion and fission can restore tissue integrity and mitochondrial physiology at the whole-organ level. Examination of liver, testis, and cerebellum suggest, however, that the precise balance point of fusion and fission is cell type specific.

Research in developmental psychology consistently shows that children explore the world thoroughly and efficiently andthat this exploration allows them to learn. While much work has gone into developing methods for exploration in machinelearning, artificial agents have not yet reached the standard set by their human counterparts. In this work we propose usingDeepMind Lab as a platform to directly compare child and agent behaviors and to develop new exploration techniques.We tested 60 children aged 4-6 examining two conditions that emulate how current reinforcement learning algorithmslearn using dense and sparse rewards and the children are then asked to find a goal in various mazes. These tasks providedata that can easily be compared to algorithms and we evaluate turn-by-turn moves the children do to what the Intrinsic-Curiosity-Module and Depth-First-Search algorithm do in the same exact maze. We show specifically where and whenchildren differ from the algorithms.

As noted by Warburg, many cancer cells depend on the consumption of glucose. We performed a genetic screen to identify factors responsible for glucose addiction and recovered the two subunits of the xCT antiporter (system x_c^-), which plays an antioxidant role by exporting glutamate for cystine. Disruption of the xCT antiporter greatly improves cell viability after glucose withdrawal, because conservation of glutamate enables cells to maintain mitochondrial respiration. In some breast cancer cells, xCT antiporter expression is upregulated through the antioxidant transcription factor Nrf2 and contributes to their requirement for glucose as a carbon source. In cells carrying patient-derived mitochondrial DNA mutations, the xCT antiporter is upregulated and its inhibition improves mitochondrial function and cell viability. Therefore, although upregulation of the xCT antiporter promotes antioxidant defence, it antagonizes glutamine metabolism and restricts nutrient flexibility. In cells with mitochondrial dysfunction, the potential utility of xCT antiporter inhibition should be further tested.

Introduction: Digital technologies, including text messaging and mobile phone apps, can be leveraged to increase people's physical activity and manage health. Chatbots, powered by artificial intelligence, can automatically interact with individuals through natural conversation. They may be more engaging than one-way messaging interventions. To our knowledge, physical activity chatbots have not been developed with low-income participants, nor in Spanish-the second most dominant language in the U.S. We recommend best practices for physical activity chatbots in English and Spanish for low-income women. Methods: We designed a prototype physical activity text-message based conversational agent based on various psychotherapeutic techniques. We recruited participants through SNAP-Ed (Supplemental Nutrition Assistance Program Education) in California (Alameda County) and Tennessee (Shelby County). We conducted qualitative interviews with participants during testing of our prototype chatbot, held a Wizard of Oz study, and facilitated a co-design workshop in Spanish with a subset of our participants. Results: We included 10 Spanish- and 8 English-speaking women between 27 and 41 years old. The majority was Hispanic/Latina (n = 14), 2 were White and 2 were Black/African American. More than half were monolingual Spanish speakers, and the majority was born outside the US (>50% in Mexico). Most participants were unfamiliar with chatbots and were initially skeptical. After testing our prototype, most users felt positively about health chatbots. They desired a personalized chatbot that addresses their concerns about privacy, and stressed the need for a comprehensive system to also aid with nutrition, health information, stress, and involve family members. Differences between English and monolingual Spanish speakers were found mostly in exercise app use, digital literacy, and the wish for family inclusion. Conclusion: Low-income Spanish- and English-speaking women are interested in using chatbots to improve their physical activity and other health related aspects. Researchers developing health chatbots for this population should focus on issues of digital literacy, app familiarity, linguistic and cultural issues, privacy concerns, and personalization. Designing and testing this intervention for and with this group using co-creation techniques and involving community partners will increase the probability that it will ultimately be effective.

Human children are proficient explorers, using causal information to great benefit. In contrast, typical AI agents do not consider underlying causal structures during exploration. To improve our understanding of the differences between children and agents—and ultimately to improve AI agents’ performance—we designed a virtual Blicket experiment to test childrens’ ability to leverage causal information while exploring a novel environment. This experiment doubles as an RL environment with a controllable causal structure, allowing us to evaluate exploration strategies used by both agents and children. Our results demonstrate that there are significant differences between information-gain optimal RL exploration and the exploration of children: in particular, children appear to consider a wide range of creative overhypotheses, including stochasticity, total weight, object ordering, and more. We leverage this new insight to lay the groundwork for future research into efficient exploration and disambiguation of causal structures for RL algorithms.

Purpose

Adding antiangiogenic therapy to standard chemotherapy has improved response rates and progression-free survival in metastatic breast cancer (BC) patients. This phase II study evaluated cardiac safety of bevacizumab with/without trastuzumab with two docetaxel-based regimens in early BC.

Methods

127 women with non-metastatic node-positive or high-risk node-negative BC were enrolled. Women with human epidermal growth factor receptor 2 (HER2)-negative BC (n = 93) received docetaxel/doxorubicin/cyclophosphamide (TAC) + bevacizumab, while women with HER2-positive disease (n = 34) received docetaxel/carboplatin/trastuzumab (TCH) + bevacizumab, every 3 weeks for six cycles. Maintenance therapy with bevacizumab alone or bevacizumab plus trastuzumab, respectively, was given every 3 weeks for 52 weeks. The primary objective was to evaluate cardiac safety, as measured by the incidence of ≥ grade 3 clinical congestive heart failure (CHF); the secondary objective was assessment of safety and toxicity.

Results

At least one cardiac adverse event (AE; CHF, cardiomyopathy, or left ventricular dysfunction) was reported in 26.1% of TAC (n = 92) and 17.6% of TCH subjects (n = 34); there were no cardiac deaths. ≥ Grade 3 clinical CHF was observed in 4.3% in the TAC plus bevacizumab stratum and 0% in the TCH plus bevacizumab stratum. A ≥ grade 3 treatment-emergent AE (any kind) related to study treatment was observed in 59.8% in the TAC with bevacizumab and 52.9% in the TCH plus bevacizumab stratum.

Conclusion

Adding bevacizumab to a docetaxel-based regimen with trastuzumab did not appear to increase cardiotoxicity.

Trial registration

ClinicalTrials.gov Identifier: NCT00446030, registered March 8, 2007.

Stem cell differentiation depends on transcriptional activation driven by lineage-specific regulators as well as changes in chromatin organization. However, the coordination of these events is poorly understood. Here, we show that T-box proteins team up with chromatin modifying enzymes to drive the expression of the key lineage regulator, Eomes during endodermal differentiation of embryonic stem (ES) cells. The Eomes locus is maintained in a transcriptionally poised configuration in ES cells. During early differentiation steps, the ES cell factor Tbx3 associates with the histone demethylase Jmjd3 at the enhancer element of the Eomes locus to allow enhancer-promoter interactions. This spatial reorganization of the chromatin primes the cells to respond to Activin signalling, which promotes the binding of Jmjd3 and Eomes to its own bivalent promoter region to further stimulate Eomes expression in a positive feedback loop. In addition, Eomes activates a transcriptional network of core regulators of endodermal differentiation. Our results demonstrate that Jmjd3 sequentially associates with two T-box factors, Tbx3 and Eomes to drive stem cell differentiation towards the definitive endoderm lineage.

Recent work in machine learning and cognitive science has suggested that understanding causal information is essential to the development of intelligence. One of the key challenges for current machine learning algorithms is modeling and understanding causal overhypotheses: transferable abstract hypotheses about sets of causal relationships. In contrast, even young children spontaneously learn causal overhypotheses, and use these to guide their exploration or to generalize to new situations. This has been demonstrated in a variety of cognitive science experiments using the “blicket detector” environment. We present a causal learning benchmark adapting the “blicket" environment for machine learning agents and evaluate a range of state-of-the-art methods in this environment. We find that although most agents have no problem learning causal structures seen during training, they are unable to learn causal overhypotheses from these experiences, and thus cannot generalize to new settings.