Hot streaks in artistic, cultural, and scientific careers.

The hot streak-loosely defined as 'winning begets more winnings'-highlights a specific period during which an individual's performance is substantially better than his or her typical performance. Although hot streaks have been widely debated in sports1,2, gambling3-5 and financial markets6,7 over the past several decades, little is known about whether they apply to individual careers. Here, building on rich literature on the lifecycle of creativity8-22, we collected large-scale career histories of individual artists, film directors and scientists, tracing the artworks, films and scientific publications they produced. We find that, across all three domains, hit works within a career show a high degree of temporal regularity, with each career being characterized by bursts of high-impact works occurring in sequence. We demonstrate that these observations can be explained by a simple hot-streak model, allowing us to probe quantitatively the hot streak phenomenon governing individual careers. We find this phenomemon to be remarkably universal across diverse domains: hot streaks are ubiquitous yet usually unique across different careers. The hot streak emerges randomly within an individual's sequence of works, is temporally localized, and is not associated with any detectable change in productivity. We show that, because works produced during hot streaks garner substantially more impact, the uncovered hot streaks fundamentally drive the collective impact of an individual, and ignoring this leads us to systematically overestimate or underestimate the future impact of a career. These results not only deepen our quantitative understanding of patterns that govern individual ingenuity and success, but also may have implications for identifying and nurturing individuals whose work will have lasting impact.

Why Do Immigrant and Swedish Adolescents Engage in Ethnic Victimization? Common and Distinct Underlying Factors.

Youth of immigrant background are at risk of experiencing victimization due to their ethnic or cultural background. However, limited knowledge is available regarding why youth victimize their immigrant peers, and whether the factors associated with engagement in ethnic victimization vary across adolescents of different background. To address this gap in knowledge, the present study aimed to elucidate the common or differential factors associated with engagement in ethnic victimization among immigrant and native youth. The analytical sample included seventh grade students residing in Sweden from 55 classrooms (N = 963, Mage = 13.11, SD = 0.41; 46% girls; 38% youth of immigrant background). The results showed that being morally disengaged and engaging in general victimization are the common denominators of engagement in ethnic victimization for immigrant and Swedish youth. Low levels of positive attitudes toward immigrants provide a foundation for ethnic victimization among Swedish youth, but not youth of immigrant background. Classroom ethnic composition was not significantly related to engagement in ethnic victimization in either group. Predictors of engagement in ethnic victimization seem to have similarities and differences among immigrant and Swedish youth. The factors involved require further attention in developing strategies to combat bias-based hostile behaviors in diverse school settings.

An Entropy Metric for Regular Grammar Classification and Learning with Recurrent Neural Networks.

Recently, there has been a resurgence of formal language theory in deep learning research. However, most research focused on the more practical problems of attempting to represent symbolic knowledge by machine learning. In contrast, there has been limited research on exploring the fundamental connection between them. To obtain a better understanding of the internal structures of regular grammars and their corresponding complexity, we focus on categorizing regular grammars by using both theoretical analysis and empirical evidence. Specifically, motivated by the concentric ring representation, we relaxed the original order information and introduced an entropy metric for describing the complexity of different regular grammars. Based on the entropy metric, we categorized regular grammars into three disjoint subclasses: the polynomial, exponential and proportional classes. In addition, several classification theorems are provided for different representations of regular grammars. Our analysis was validated by examining the process of learning grammars with multiple recurrent neural networks. Our results show that as expected more complex grammars are generally more difficult to learn.

Shapley Homology: Topological Analysis of Sample Influence for Neural Networks.

Data samples collected for training machine learning models are typically assumed to be independent and identically distributed (i.i.d.). Recent research has demonstrated that this assumption can be problematic as it simplifies the manifold of structured data. This has motivated different research areas such as data poisoning, model improvement, and explanation of machine learning models. In this work, we study the influence of a sample on determining the intrinsic topological features of its underlying manifold. We propose the Shapley homology framework, which provides a quantitative metric for the influence of a sample of the homology of a simplicial complex. Our proposed framework consists of two main parts: homology analysis, where we compute the Betti number of the target topological space, and Shapley value calculation, where we decompose the topological features of a complex built from data points to individual points. By interpreting the influence as a probability measure, we further define an entropy that reflects the complexity of the data manifold. Furthermore, we provide a preliminary discussion of the connection of the Shapley homology to the Vapnik-Chervonenkis dimension. Empirical studies show that when the zero-dimensional Shapley homology is used on neighboring graphs, samples with higher influence scores have a greater impact on the accuracy of neural networks that determine graph connectivity and on several regular grammars whose higher entropy values imply greater difficulty in being learned.

An Empirical Evaluation of Rule Extraction from Recurrent Neural Networks.

Rule extraction from black box models is critical in domains that require model validation before implementation, as can be the case in credit scoring and medical diagnosis. Though already a challenging problem in statistical learning in general, the difficulty is even greater when highly nonlinear, recursive models, such as recurrent neural networks (RNNs), are fit to data. Here, we study the extraction of rules from second-order RNNs trained to recognize the Tomita grammars. We show that production rules can be stably extracted from trained RNNs and that in certain cases, the rules outperform the trained RNNs.

Antihypertensive agents do not prevent blood-brain barrier dysfunction and cognitive deficits in dietary-induced obese mice.

BACKGROUND: While vascular risk factors including Western-styled diet and obesity are reported to induce cognitive decline and increase dementia risk, recent reports consistently suggest that compromised integrity of cerebrovascular blood-brain barrier (BBB) may have an important role in neurodegeneration and cognitive deficits. A number of studies report that elevated blood pressure increases the permeability of BBB. METHODS: In this study, we investigated the effects of antihypertensive agents, candesartan or ursodeoxycholic acid (UDCA), on BBB dysfunction and cognitive decline in wild-type mice maintained on high fat and fructose (HFF) diet for 24 weeks. RESULTS: In HFF-fed mice, significantly increased body weight with elevated blood pressure, plasma insulin and glucose compared with mice fed with low-fat control chow was observed. Concomitantly, significant disruption of BBB and cognitive decline were evident in the HFF-fed obese mice. Hypertension was completely prevented by the coprovision of candesartan or UDCA in mice maintained on HFF diet, while only candesartan significantly reduced the body weight compared with HFF-fed mice. Nevertheless, BBB dysfunction and cognitive decline remained unaffected by candesartan or UDCA. CONCLUSIONS: These data conclusively indicate that modulation of blood pressure and/or body weight may not be directly associated with BBB dysfunction and cognitive deficits in Western diet-induced obese mice, and hence antihypertensive agents may not be effective in preventing BBB disruption and cognitive decline. The findings may provide important mechanistical insights to obesity-associated cognitive decline and its therapy.

Unifying Adversarial Training Algorithms with Data Gradient Regularization.

Many previous proposals for adversarial training of deep neural nets have included directly modifying the gradient, training on a mix of original and adversarial examples, using contractive penalties, and approximately optimizing constrained adversarial objective functions. In this article, we show that these proposals are actually all instances of optimizing a general, regularized objective we call DataGrad. Our proposed DataGrad framework, which can be viewed as a deep extension of the layerwise contractive autoencoder penalty, cleanly simplifies prior work and easily allows extensions such as adversarial training with multitask cues. In our experiments, we find that the deep gradient regularization of DataGrad (which also has L1 and L2 flavors of regularization) outperforms alternative forms of regularization, including classical L1, L2, and multitask, on both the original data set and adversarial sets. Furthermore, we find that combining multitask optimization with DataGrad adversarial training results in the most robust performance.

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

It is believed that anomalous mental states such as stress and anxiety not only cause suffering for the individuals, but also lead to tragedies in some extreme cases. The ability to predict the mental state of an individual at both current and future time periods could prove critical to healthcare practitioners. Currently, the practical way to predict an individual's mental state is through mental examinations that involve psychological experts performing the evaluations. However, such methods can be time and resource consuming, mitigating their broad applicability to a wide population. Furthermore, some individuals may also be unaware of their mental states or may feel uncomfortable to express themselves during the evaluations. Hence, their anomalous mental states could remain undetected for a prolonged period of time. The objective of this work is to demonstrate the ability of using advanced machine learning based approaches to generate mathematical models that predict current and future mental states of an individual. The problem of mental state prediction is transformed into the time series forecasting problem, where an individual is represented as a multivariate time series stream of monitored physical and behavioral attributes. A personalized mathematical model is then automatically generated to capture the dependencies among these attributes, which is used for prediction of mental states for each individual. In particular, we first illustrate the drawbacks of traditional multivariate time series forecasting methodologies such as vector autoregression. Then, we show that such issues could be mitigated by using machine learning regression techniques which are modified for capturing temporal dependencies in time series data. A case study using the data from 150 human participants illustrates that the proposed machine learning based forecasting methods are more suitable for high-dimensional psychological data than the traditional vector autoregressive model in terms of both magnitude of error and directional accuracy. These results not only present a successful usage of machine learning techniques in psychological studies, but also serve as a building block for multiple medical applications that could rely on an automated system to gauge individuals' mental states.

State-level estimates of childhood obesity prevalence in the United States corrected for report bias.

BACKGROUND/OBJECTIVES: State-specific obesity prevalence data are critical to public health efforts to address the childhood obesity epidemic. However, few states administer objectively measured body mass index (BMI) surveillance programs. This study reports state-specific childhood obesity prevalence by age and sex correcting for parent-reported child height and weight bias. SUBJECTS/METHODS: As part of the Childhood Obesity Intervention Cost Effectiveness Study (CHOICES), we developed childhood obesity prevalence estimates for states for the period 2005-2010 using data from the 2010 US Census and American Community Survey (ACS), 2003-2004 and 2007-2008 National Survey of Children's Health (NSCH) (n=133 213), and 2005-2010 National Health and Nutrition Examination Surveys (NHANES) (n=9377; ages 2-17). Measured height and weight data from NHANES were used to correct parent-report bias in NSCH using a non-parametric statistical matching algorithm. Model estimates were validated against surveillance data from five states (AR, FL, MA, PA and TN) that conduct censuses of children across a range of grades. RESULTS: Parent-reported height and weight resulted in the largest overestimation of childhood obesity in males ages 2-5 years (NSCH: 42.36% vs NHANES: 11.44%). The CHOICES model estimates for this group (12.81%) and for all age and sex categories were not statistically different from NHANES. Our modeled obesity prevalence aligned closely with measured data from five validation states, with a 0.64 percentage point mean difference (range: 0.23-1.39) and a high correlation coefficient (r=0.96, P=0.009). Estimated state-specific childhood obesity prevalence ranged from 11.0 to 20.4%. CONCLUSION: Uncorrected estimates of childhood obesity prevalence from NSCH vary widely from measured national data, from a 278% overestimate among males aged 2-5 years to a 44% underestimate among females aged 14-17 years. This study demonstrates the validity of the CHOICES matching methods to correct the bias of parent-reported BMI data and highlights the need for public release of more recent data from the 2011 to 2012 NSCH.

Plant-Microbiota Interactions as a Driver of the Mineral Turnover in the Rhizosphere.

A major challenge facing agriculture in the 21st century is the need to increase the productivity of cultivated land while reducing the environmentally harmful consequences of mineral fertilization. The microorganisms thriving in association and interacting with plant roots, the plant microbiota, represent a potential resource of plant probiotic function, capable of conjugating crop productivity with sustainable management in agroecosystems. However, a limited knowledge of the organismal interactions occurring at the root-soil interface is currently hampering the development and use of beneficial plant-microbiota interactions in agriculture. Therefore, a comprehensive understanding of the recruitment cues of the plant microbiota and the molecular basis of nutrient turnover in the rhizosphere will be required to move toward efficient and sustainable crop nutrition. In this chapter, we will discuss recent insights into plant-microbiota interactions at the root-soil interface, illustrate the processes driving mineral dynamics in soil, and propose experimental avenues to further integrate the metabolic potential of the plant microbiota into crop management and breeding strategies for sustainable agricultural production.

Biostatistical analysis of quantitative immunofluorescence microscopy images.

Semiquantitative immunofluorescence microscopy has become a key methodology in biomedical research. Typical statistical workflows are considered in the context of avoiding pseudo-replication and marginalising experimental error. However, immunofluorescence microscopy naturally generates hierarchically structured data that can be leveraged to improve statistical power and enrich biological interpretation. Herein, we describe a robust distribution fitting procedure and compare several statistical tests, outlining their potential advantages/disadvantages in the context of biological interpretation. Further, we describe tractable procedures for power analysis that incorporates the underlying distribution, sample size and number of images captured per sample. The procedures outlined have significant potential for increasing understanding of biological processes and decreasing both ethical and financial burden through experimental optimization.

Aging-related changes in blood-brain barrier integrity and the effect of dietary fat.

BACKGROUND: Disturbances in blood-brain barrier (BBB) integrity contribute to the onset and progression of neurodegenerative diseases including Alzheimer's disease (AD) and vascular dementia (VaD). Aging is positively associated with AD and VaD risk, but this may reflect comorbidities or the effects of other chronic modulators of vascular function such as diet. OBJECTIVE: To explore putative synergistic effects of aging with diet, in this study genetically unmanipulated mice were maintained on diets enriched in saturated fatty acids (SFA) or cholesterol and compared to mice provided with low-fat (LF) feed formula. METHODS: The functional integrity of the BBB was assessed following 3, 6 and 12 months of dietary intervention commenced at 6 weeks of age, by determining the brain parenchymal extravasation of immunoglobulin G (IgG). RESULTS: Mice maintained on the SFA- or cholesterol-enriched diet showed significant parenchymal IgG abundance following 3 months of feeding, concomitant with diminished expression of the tight junction protein occludin. LF control mice had essentially no evidence of BBB disturbances. Six months of SFA feeding exacerbated the difference in IgG abundance compared to the LF mice. At 12 months of feeding, the control LF mice also had significant parenchymal IgG that was comparable to mice fed the SFA- or cholesterol-enriched diet for 3 months. However, there may have been an adaptation to the fat-enriched diets because SFA and cholesterol did not exacerbate IgG parenchymal accumulation beyond 6 months of feeding. CONCLUSION: Collectively, the study suggests that diets enriched in SFA or cholesterol accelerate the onset of BBB dysfunction that otherwise occurs with aging.

Automating document classification with distant supervision to increase the efficiency of systematic reviews: A case study on identifying studies with HIV impacts on female sex workers.

There remains a limited understanding of the HIV prevention and treatment needs among female sex workers in many parts of the world. Systematic reviews of existing literature can help fill this gap; however, well-done systematic reviews are time-demanding and labor-intensive. Here, we propose an automatic document classification approach to a systematic review to significantly reduce the effort in reviewing documents and optimizing empiric decision making. We first describe a manual document classification procedure that is used to curate a pertinent training dataset and then propose three classifiers: a keyword-guided method, a cluster analysis-based method, and a random forest approach that utilizes a large set of feature tokens. This approach is used to identify documents studying female sex workers that contain content relevant to either HIV or experienced violence. We compare the performance of the three classifiers by cross-validation in terms of area under the curve of the receiver operating characteristic and precision and recall plot, and found random forest approach reduces the amount of manual reading for our example by 80%; in sensitivity analysis, we found that even trained with only 10% of data, the classifier can still avoid reading 75% of future documents (68% of total) while retaining 80% of relevant documents. In sum, the automated procedure of document classification presented here could improve both the precision and efficiency of systematic reviews and facilitate live reviews, where reviews are updated regularly. We expect to obtain a reasonable classifier by taking 20% of retrieved documents as training samples. The proposed classifier could also be used for more meaningfully assembling literature in other research areas and for rapid documents screening with a tight schedule, such as COVID-related work during the crisis.

A gene signature in histologically normal surgical margins is predictive of oral carcinoma recurrence.

BACKGROUND: Oral Squamous Cell Carcinoma (OSCC) is a major cause of cancer death worldwide, which is mainly due to recurrence leading to treatment failure and patient death. Histological status of surgical margins is a currently available assessment for recurrence risk in OSCC; however histological status does not predict recurrence, even in patients with histologically negative margins. Therefore, molecular analysis of histologically normal resection margins and the corresponding OSCC may aid in identifying a gene signature predictive of recurrence. METHODS: We used a meta-analysis of 199 samples (OSCCs and normal oral tissues) from five public microarray datasets, in addition to our microarray analysis of 96 OSCCs and histologically normal margins from 24 patients, to train a gene signature for recurrence. Validation was performed by quantitative real-time PCR using 136 samples from an independent cohort of 30 patients. RESULTS: We identified 138 significantly over-expressed genes (> 2-fold, false discovery rate of 0.01) in OSCC. By penalized likelihood Cox regression, we identified a 4-gene signature with prognostic value for recurrence in our training set. This signature comprised the invasion-related genes MMP1, COL4A1, P4HA2, and THBS2. Over-expression of this 4-gene signature in histologically normal margins was associated with recurrence in our training cohort (p = 0.0003, logrank test) and in our independent validation cohort (p = 0.04, HR = 6.8, logrank test). CONCLUSION: Gene expression alterations occur in histologically normal margins in OSCC. Over-expression of the 4-gene signature in histologically normal surgical margins was validated and highly predictive of recurrence in an independent patient cohort. Our findings may be applied to develop a molecular test, which would be clinically useful to help predict which patients are at a higher risk of local recurrence.

Poster presentations at medical conferences: an effective way of disseminating research?

This study aimed to ascertain the value of posters at medical meetings to presenters and delegates. The usefulness of posters to presenters at national and international meetings was evaluated by assessing the numbers of delegates visiting them and the reasons why they visited. Memorability of selected posters was assessed and factors influencing their appeal to expert delegates identified. At both the national and international meetings, very few delegates (< 5%) visited posters. Only a minority read them and fewer asked useful questions. Recall of content was so poor that it prevented identification of factors improving their memorability. Factors increasing posters' visual appeal included their scientific content, pictures/graphs and limited use of words. Few delegates visit posters and those doing so recall little of their content. To engage their audience, researchers should design visually appealing posters by presenting high quality data in pictures or graphs without an excess of words.

Understanding the onset of hot streaks across artistic, cultural, and scientific careers.

Across a range of creative domains, individual careers are characterized by hot streaks, which are bursts of high-impact works clustered together in close succession. Yet it remains unclear if there are any regularities underlying the beginning of hot streaks. Here, we analyze career histories of artists, film directors, and scientists, and develop deep learning and network science methods to build high-dimensional representations of their creative outputs. We find that across all three domains, individuals tend to explore diverse styles or topics before their hot streak, but become notably more focused after the hot streak begins. Crucially, hot streaks appear to be associated with neither exploration nor exploitation behavior in isolation, but a particular sequence of exploration followed by exploitation, where the transition from exploration to exploitation closely traces the onset of a hot streak. Overall, these results may have implications for identifying and nurturing talents across a wide range of creative domains.

Screenomics: A Framework to Capture and Analyze Personal Life Experiences and the Ways that Technology Shapes Them.

Digital experiences capture an increasingly large part of life, making them a preferred, if not required, method to describe and theorize about human behavior. Digital media also shape behavior by enabling people to switch between different content easily, and create unique threads of experiences that pass quickly through numerous information categories. Current methods of recording digital experiences provide only partial reconstructions of digital lives that weave - often within seconds - among multiple applications, locations, functions and media. We describe an end-to-end system for capturing and analyzing the "screenome" of life in media, i.e., the record of individual experiences represented as a sequence of screens that people view and interact with over time. The system includes software that collects screenshots, extracts text and images, and allows searching of a screenshot database. We discuss how the system can be used to elaborate current theories about psychological processing of technology, and suggest new theoretical questions that are enabled by multiple time scale analyses. Capabilities of the system are highlighted with eight research examples that analyze screens from adults who have generated data within the system. We end with a discussion of future uses, limitations, theory and privacy.

Null alleles of human complement C4. Evidence for pseudogenes at the C4A locus and for gene conversion at the C4B locus.

The two genes for the C4A and C4B isotypes of the fourth component of human complement are located in the MHC class III region. Previous studies have demonstrated the unusual expression of C4 genes in the form of aberrant or duplicated haplotypes. Null alleles of C4A or C4B (AQ0 or BQ0) have been defined by the absence of gene products and occur at frequencies of 0.1-0.3. However, only some C4 null alleles are due to gene deletions, the remainder were thought to be nonexpressed genes. We have analyzed the C4 gene structure of 26 individuals lacking either C4A or C4B protein. The DNA of individuals with apparently nonexpressed C4 genes was tested for the presence of C4A- and C4B-specific sequences using restriction fragment analysis and isotype-specific oligonucleotide hybridization of DNA amplified by polymerase chain reaction. All nondeleted AQ0 allels had C4A-specific sequences and may thus be described as pseudogenes, whereas the nondeleted BQ0 alleles had C4A-instead of C4B-specific sequences. Gene conversion is the probable mechanism by which a C4A gene is found at the second C4 locus normally occupied by C4B genes.

Soil biota and exotic plant invasion.

Invasive plants are an economic problem and a threat to the conservation of natural systems. Escape from natural enemies might contribute to successful invasion, with most work emphasizing the role of insect herbivores; however, microbial pathogens are attracting increased attention. Soil biota in some invaded ecosystems may promote 'exotic' invasion, and plant-soil feedback processes are also important. Thus, relatively rare species native to North America consistently demonstrate negative feedbacks with soil microbes that promote biological diversity, whereas abundant exotic and native species demonstrate positive feedbacks that reduce biological diversity. Here we report that soil microbes from the home range of the invasive exotic plant Centaurea maculosa L. have stronger inhibitory effects on its growth than soil microbes from where the weed has invaded in North America. Centaurea and soil microbes participate in different plant-soil feedback processes at home compared with outside Centaurea's home range. In native European soils, Centaurea cultivates soil biota with increasingly negative effects on the weed's growth, possibly leading to its control. But in soils from North America, Centaurea cultivates soil biota with increasingly positive effects on itself, which may contribute to the success of this exotic species in North America.

Continual Learning of Recurrent Neural Networks by Locally Aligning Distributed Representations.

Temporal models based on recurrent neural networks have proven to be quite powerful in a wide variety of applications, including language modeling and speech processing. However, training these models often relies on backpropagation through time (BPTT), which entails unfolding the network over many time steps, making the process of conducting credit assignment considerably more challenging. Furthermore, the nature of backpropagation itself does not permit the use of nondifferentiable activation functions and is inherently sequential, making parallelization of the underlying training process difficult. Here, we propose the parallel temporal neural coding network (P-TNCN), a biologically inspired model trained by the learning algorithm we call local representation alignment. It aims to resolve the difficulties and problems that plague recurrent networks trained by BPTT. The architecture requires neither unrolling in time nor the derivatives of its internal activation functions. We compare our model and learning procedure with other BPTT alternatives (which also tend to be computationally expensive), including real-time recurrent learning, echo state networks, and unbiased online recurrent optimization. We show that it outperforms these on-sequence modeling benchmarks such as Bouncing MNIST, a new benchmark we denote as Bouncing NotMNIST, and Penn Treebank. Notably, our approach can, in some instances, outperform full BPTT as well as variants such as sparse attentive backtracking. Significantly, the hidden unit correction phase of P-TNCN allows it to adapt to new data sets even if its synaptic weights are held fixed (zero-shot adaptation) and facilitates retention of prior generative knowledge when faced with a task sequence. We present results that show the P-TNCN's ability to conduct zero-shot adaptation and online continual sequence modeling.