Search

Cancers are characterized by chromosome instability (CIN), which drives tumor formation, evolution, and drug resistance. In this study, we investigated an intriguing observation that deletion of the evolutionarily conserved helicase Rrm3 leads to a wide range of chromosome instability suppression in budding yeast. CIN suppression appears to occur through increased potential for kinetochore–micro­tubule error correction. These results provide a novel link between the Rrm3 helicase localization of error-correcting enzymes and implicate the homologous human helicase, Pif1, as a potential therapeutic target for CIN suppression.

Mobility is an essential factor in the 5G core network (CN). If the network control plane can predict mobility, the operation will be more efficient and agile with intelligent and proactive decisions. So far, mobility patterns and their prediction models have been extensively studied in the research community with cellular network datasets. Recently, 3GPP initiated the specification of a CN architecture for data analysis and machine learning. In this article, in accordance with this trend, we provide a taxonomy of mobility prediction frameworks in 5G CNs ranging from data collection to model serving, with consideration of the 3GPP architecture and interfaces; and we introduce two key use cases in 5G CNs, where the gains from mobility predictions are evaluated on datasets from live networks. In particular, one of the proposed methods, machine-learning-assisted adaptive paging, reduces signaling overhead by up to 75 percent.

Information from microblogs is gaining increasing attention from researchers interested in analyzing fluctuations in stock markets. Behavioral financial theory draws on social psychology to explain...

Hedonic feeding is driven by the “pleasure” derived from consuming palatable food and occurs in the absence of metabolic need. It plays a critical role in the excessive feeding that underlies obesity. Compared to other pathological motivated behaviors, little is known about the neural circuit mechanisms mediating excessive hedonic feeding. Here, we show that modulation of prefrontal cortex (PFC) and anterior paraventricular thalamus (aPVT) excitatory inputs to the nucleus accumbens (NAc), a key node of reward circuitry, has opposing effects on high fat intake in mice. Prolonged high fat intake leads to input- and cell type-specific changes in synaptic strength. Modifying synaptic strength via plasticity protocols, either in an input-specific optogenetic or non-specific electrical manner, causes sustained changes in high fat intake. These results demonstrate that input-specific NAc circuit adaptations occur with repeated exposure to a potent natural reward and suggest that neuromodulatory interventions may be therapeutically useful for individuals with pathologic hedonic feeding., Hedonic feeding occurs in the absence of metabolic need and plays a critical role in the excessive feeding that underlies obesity. The authors show that optogenetic manipulation of NAc inputs from the prefrontal cortex versus inputs from the anterior paraventricular nucleus of the thalamus has opposite effects on high fat intake.

Formation of solid-phase protein aggregates in mitochondria is associated with mitochondrial lipid metabolism., Proteostasis declines with age, characterized by the accumulation of unfolded or damaged proteins. Recent studies suggest that proteins constituting pathological inclusions in neurodegenerative diseases also enter and accumulate in mitochondria. How unfolded proteins are managed within mitochondria remains unclear. Here, we found that excessive unfolded proteins in the mitochondrial matrix of yeast cells are consolidated into solid-phase inclusions, which we term deposits of unfolded mitochondrial proteins (DUMP). Formation of DUMP occurs in mitochondria near endoplasmic reticulum–mitochondria contact sites and is regulated by mitochondrial proteins controlling the production of cytidine 5′-diphosphate–diacylglycerol. DUMP formation is age dependent but accelerated by exogenous unfolded proteins. Many enzymes of the tricarboxylic acid cycle were enriched in DUMP. During yeast cell division, DUMP formation is necessary for asymmetric inheritance of damaged mitochondrial proteins between mother and daughter cells. We provide evidence that DUMP-like structures may be induced by excessive unfolded proteins in human cells.

With the information overload of user-generated content in microblogging, users find it extremely challenging to browse and find valuable information in their first attempt. In this paper we propose a microblogging recommendation algorithm, TSI-MR Topic-Level Social Influence-based Microblogging Recommendation, which can significantly improve users' microblogging experiences. The main innovation of this proposed algorithm is that we consider social influences and their indirect structural relationships, which are largely based on social status theory, from the topic level. The primary advantage of this approach is that it can build an accurate description of latent relationships between two users with weak connections, which can improve the performance of the model; furthermore, it can solve sparsity problems of training data to a certain extent. The realization of the model is mainly based on Factor Graph. We also applied a distributed strategy to further improve the efficiency of the model. Finally, we use data from Tencent Weibo, one of the most popular microblogging services in China, to evaluate our methods. The results show that incorporating social influence can improve microblogging performance considerably, and outperform the baseline methods.

Planetary exploration requires a balance between preemptive planning and financial feasibility. The risk of mid-mission equipment failure, power shortages, or supply depletion incentivizes precautionary measures, but the financial strain of sending unnecessary mass into space limits this practice.To balance the two, our team explored the advantages of biological solutions, namely the self-sustaining abilities of low-mass organisms, to make planetary exploration more self-sufficient and economical. Prioritizing repair over replacement, we are developing self-healing materials embedded with Bacillus subtilis. For longer-lasting energy, we are designing a “biobactery” using linearly oriented Escherichia coli to generate power. For renewable materials, we are engineering bacteria to synthesize and degrade rubber. Individually, these projects offer sustainable alternatives for repair, power, and materials. But when combined, these consolidated insights can provide us with the power to get to Mars and resources to sustain us while we’re there.

As is known to us all, media is an instrument on communication, such as newspaper, radio or video. Thus social media would be a social instrument on communication. Kaplan and Haenlein (2010) define social media as ‘‘a group of Internet-based applications that build on the ideological and technological foundations of Web 2.0, and that allow the creation and exchange of user-generated content.’’ The key difference between social and conventional media is the user relationships and activities. This clearly poses new challenges to traditional information retrieval technologies including

A thorough QT/QTc study is generally conducted at the early stage of a new drug development program. To demonstrate the assay sensitivity in the study, an active control is often included in the tr...

SummaryThe serial ordering of individual movements into sequential patterns is thought to require synaptic plasticity within corticostriatal circuits that route information through the basal ganglia. We used genetically and anatomically targeted manipulations of specific circuit elements in mice to isolate the source and target of a corticostriatal synapse that regulates the performance of a serial order task. This excitatory synapse originates in secondary motor cortex, terminates on direct pathway medium spiny neurons in the dorsolateral striatum, and is strengthened by serial order learning. This experience-dependent and synapse-specific form of plasticity may sculpt the balance of activity in basal ganglia circuits during sequential movements, driving a disparity in striatal output that favors the direct pathway. This disparity is necessary for execution of responses in serial order, even though both direct and indirect pathways are active during movement initiation, suggesting dynamic modulation of corticostriatal circuitry contributes to the choreography of behavioral routines.

Objective To evaluate the safety of anakinra (a recombinant human interleukin-1 receptor antagonist) in a large population of patients with rheumatoid arthritis (RA), typical of those seen in clinical practice. Methods A total of 1,414 patients were randomly assigned to treatment with 100 mg of anakinra or placebo, administered daily by subcutaneous injection. Background medications included disease-modifying antirheumatic drugs, corticosteroids, and nonsteroidal antiinflammatory drugs, alone or in combination. The primary end point was safety, which was evaluated by adverse events (including infections), discontinuation from study due to adverse events, and death. Results Safety was evaluated in 1,399 patients (1,116 in the anakinra group and 283 in the placebo group; 15 patients were randomized but did not receive any study drug) during the initial 6-month, double-blind, placebo-controlled phase of this long-term safety study. Baseline demographics, disease characteristics, and concomitant medications were similar between the 2 groups. The study group included patients with numerous comorbid conditions and a wide range of RA disease activity. Serious adverse events occurred at a similar rate in the anakinra group and the placebo group (7.7% and 7.8%, respectively). Serious infectious episodes were observed more frequently in the anakinra group (2.1% versus 0.4% in the placebo group). The rate of withdrawal due to adverse events was 13.4% in the anakinra group and 9.2% in the placebo group. Conclusion Results from this large, placebo-controlled safety study demonstrate that anakinra is safe and well tolerated in a diverse population of patients with RA, including those with comorbid conditions and those using multiple combinations of concomitant therapies. Although the frequency of serious infection was slightly higher in the anakinra group, no infection was attributed to opportunistic microorganisms or resulted in death.

Improving coordination of care and containing healthcare costs are prominent goals of healthcare reform. Specialist involvement in healthcare delivery transformation efforts like Accountable Care Organizations (ACOs) is necessary to achieve these goals. However, patients’ self-referrals to specialists may undermine care coordination and incur unnecessary costs if patients frequently receive care from specialists not engaged in such healthcare delivery transformation efforts. Additionally, frequent self-referrals may also diminish the incentive for specialist participation in reform endeavors like ACOs to get access to a referral base.To examine recent national trends in self-referred new visits to specialists.A descriptive cross-sectional study of new ambulatory visits to specialists from 2000 to 2009 using data from the National Ambulatory Medical Care Survey.We calculated nationally representative estimates of the proportion of new specialist visits through self-referrals among Medicare and private insurance beneficiaries. We also estimated the nationally representative absolute number of self-referred new specialist visits among both groups of beneficiaries.Among Medicare and private insurance beneficiaries, self-referred visits declined from 32.2% (95% confidence interval [CI], 24.0%-40.4%) to 19.6% (95% CI, 13.9%-23.3%) and from 32.4% (95% CI, 27.9%-36.8%) to 24.1% (95% CI,18.8%-29.4%), respectively. Hence, at least 1 in 5 and 1 in 4 new visits to specialists among Medicare and private insurance beneficiaries, respectively, are self-referred.The current considerable rate of self-referred new specialist visits among both Medicare and private insurance beneficiaries may have adverse implications for organizations attempting to transform healthcare delivery with improved care coordination.

The ICH E14 guidance recommends the use of a time-matched baseline, while others recommend alternative baseline definitions including a day-averaged baseline. In this article we consider six models adjusting for baselines. We derive the explicit covariances and compare their power under various conditions. Simulation results are provided. We conclude that type I error rates are controlled. However, one model outperforms the others on statistical power under certain conditions. In general, the analysis of covariance (ANCOVA) model using a day-averaged baseline is preferred. If the time-matched baseline has to be used as per requests from regulatory agencies, the analysis by time point using ANCOVA model should be recommended.

Using historical studies, we compared the impact of using the average baseline or time-matched baseline on diurnal effect correction, treatment effect estimation, and analysis of variance/covariance (ANOVA/ANCOVA) efficiency in a parallel thorough QT/QTc (TQT) study. Under a multivariate normal distribution assumption, we derived conditions for achieving unbiasness and better efficiency when using the average baseline, and confirmed these conditions using historical TQT studies. Furthermore, simulations were conducted under the randomized trial with and without observed imbalanced baseline settings. We conclude that the analyses using average baseline yield better efficiency and unbiased or less biased results under our TQT study conditions.

Both the quality and quantity of training data have significant impact on the performance of ranking functions in the context of learning to rank for web search. Due to resource constraints, training data for smaller search engine markets are scarce and we need to leverage existing training data from large markets to enhance the learning of ranking function for smaller markets. In this paper, we present a boosting framework for learning to rank in the multi-task learning context for this purpose. In particular, we propose to learn non-parametric common structures adaptively from multiple tasks in a stage-wise way. An algorithm is developed to iteratively discover super-features that are effective for all the tasks. The estimation of the functions for each task is then learned as a linear combination of those super-features. We evaluate the performance of this multi-task learning method for web search ranking using data from a search engine. Our results demonstrate that multi-task learning methods bring significant relevance improvements over existing baseline methods.

Machine Learned Ranking approaches have shown successes in web search engines. With the increasing demands on developing effective ranking functions for different search domains, we have seen a big bottleneck, i.e., the problem of insufficient training data, which has significantly limited the fast development and deployment of machine learned ranking functions for different web search domains. In this paper, we propose a new approach called tree based ranking function adaptation ("tree adaptation") to address this problem. Tree adaptation assumes that ranking functions are trained with regression-tree based modeling methods, such as Gradient Boosting Trees. It takes such a ranking function from one domain and tunes its tree-based structure with a small amount of training data from the target domain. The unique features include (1) it can automatically identify the part of model that needs adjustment for the new domain, (2) it can appropriately weight training examples considering both local and global distributions. Experiments are performed to show that tree adaptation can provide better-quality ranking functions for a new domain, compared to other modeling methods.

Ranking functions determine the relevance of search results of search engines, and learning ranking functions has become an active research area at the interface between Web search, information retrieval and machine learning. Generally, the training data for learning to rank come in two different forms: (1) absolute relevance judgments assessing the degree of relevance of a document with respect to a query. This type of judgments is also called labeled data and are usually obtained through human editorial efforts; and (2) relative relevance judgments indicating that a document is more relevant than another with respect to a query. This type of judgments is also called preference data and can usually be extracted from the abundantly available user click-through data recording users' interactions with the search results. Most existing learning to rank methods ignore the query boundaries, treating the labeled data or preference data equally across queries. In this paper, we propose a minimum effort optimization method that takes into account the entire training data within a query at each iteration. We tackle this optimization problem using functional iterative methods where the update at each iteration is computed by solving an isotonic regression problem. This more global approach results in faster convergency and signficantly improved performance of the learned ranking functions over existing state-of-the-art methods. We demonstrate the effectiveness of the proposed method using data sets obtained from a commercial search engine as well as publicly available data.

In Web search ranking, the expected results for some queries could vary greatly depending upon location of the user. We name such queries regional sensitive queries. Identifying regional sensitivity of queries is important to meet users' needs. The objective of this work is to identify whether a user expects only regional results for a query. We present three novel features generated from search logs and build a meta query classifier to identify regional sensitive query. Experimental results show that the proposed method achieves high accuracy in identifying regional sensitive queries.

Proximity of query terms in a document is an important criterion in IR. However, no investigation has been made to determine the most useful term sequences for which proximity should be considered. In this study, we test the effectiveness of using proximity of partial term sequences (n-grams) for Web search. We observe that the proximity of sequences of 3 to 5 terms is most effective for long queries, while shorter or longer sequences appear less useful. This suggests that combinations of 3 to 5 terms can best capture the intention in user queries. In addition, we also experiment with weighing the importance of query sub-sequences using query log frequencies. Our preliminary tests show promising empirical results.

Effective ranking functions are an essential part of commercial search engines. We focus on developing a regression framework for learning ranking functions for improving relevance of search engines serving diverse streams of user queries. We explore supervised learning methodology from machine learning, and we distinguish two types of relevance judgments used as the training data: 1) absolute relevance judgments arising from explicit labeling of search results; and 2) relative relevance judgments extracted from user click throughs of search results or converted from the absolute relevance judgments. We propose a novel optimization framework emphasizing the use of relative relevance judgments. The main contribution is the development of an algorithm based on regression that can be applied to objective functions involving preference data, i.e., data indicating that a document is more relevant than another with respect to a query. Experimental results are carried out using data sets obtained from a commercial search engine. Our results show significant improvements of our proposed methods over some existing methods.

We discuss information retrieval methods that aim at serving a diverse stream of user queries. We propose methods that emphasize the importance of taking into consideration of query difference in learning effective retrieval functions. We formulate the problem as a multi-task learning problem using a risk minimization framework. In particular, we show how to calibrate the empirical risk to incorporate query difference in terms of introducing nuisance parameters in the statistical models, and we also propose an alternating optimization method to simultaneously learn the retrieval function and the nuisance parameters. We illustrate the effectiveness of the proposed methods using modeling data extracted from a commercial search engine.

We discuss information retrieval methods that aim at serving a diverse stream of user queries such as those submitted to commercial search engines. We propose methods that emphasize the importance of taking into consideration of query difference in learning effective retrieval functions. We formulate the problem as a multi-task learning problem using a risk minimization framework. In particular, we show how to calibrate the empirical risk to incorporate query difference in terms of introducing nuisance parameters in the statistical models, and we also propose an alternating optimization method to simultaneously learn the retrieval function and the nuisance parameters. We work out the details for both L1 and L2 regularization cases, and provide convergence analysis for the alternating optimization method for the special case when the retrieval functions belong to a reproducing kernel Hilbert space. We illustrate the effectiveness of the proposed methods using modeling data extracted from a commercial search engine. We also point out how the current framework can be extended in future research.

SummaryNeurexins are considered central organizers of synapse architecture that are implicated in neuropsychiatric disorders. Expression of neurexins in hundreds of alternatively spliced isoforms suggested that individual neurons might exhibit a cell-type-specific neurexin expression pattern (a neurexin code). To test this hypothesis, we quantified the single-cell levels of neurexin isoforms and other trans-synaptic cell-adhesion molecules by microfluidics-based RT-PCR. We show that the neurexin repertoire displays pronounced cell-type specificity that is remarkably consistent within each type of neuron. Furthermore, we uncovered region-specific regulation of neurexin transcription and splice-site usage. Finally, we demonstrate that the transcriptional profiles of neurexins can be altered in an experience-dependent fashion by exposure to a drug of abuse. Our data provide evidence of cell-type-specific expression patterns of multiple neurexins at the single-cell level and suggest that expression of synaptic cell-adhesion molecules overlaps with other key features of cellular identity and diversity.