Short and Long Term Investor Synchronization Caused by Decoupling

The dynamics of collective decision making is not yet well understood. Its practical relevance however can be of utmost importance, as experienced by people who lost their fortunes in turbulent moments of financial markets. In this paper we show how spontaneous collective “moods” or “biases” emerge dynamically among human participants playing a trading game in a simple model of the stock market. Applying theory and computer simulations to the experimental data generated by humans, we are able to predict the onset of such moments before they actually happen.     

PSYCHIATRY AND THE LAW

In Rex vs. Arnold (1724) it was held that to avail himself of the defense of insanity “a man must be totally deprived of his understanding and memory, so as not to know what he is doing, no more than an infant, a brute, or a wild beast.” Although there has been some modification of this formula in most jurisdictions, the courts still operate under the McNaghten Rule (1843) which is no more logical and actually is more difficult to apply. That such a situation exists in 1956 is a reflection on the indifference of society—and particularly the courts which it elects—as well as on the failure of modern psychiatry to communicate its viewpoint to society. If we are to correct the sad formulae of the “right and wrong” and “policeman at the elbow” tests, we must have more study and better methods of communication in this area.A similar state of confusion exists in the methods of commitment of mentally ill people to psychiatric hospitals. The methods prescribed by law are archaic and cruel—and again reflect the failure of modern psychiatry to communicate its understanding to the legislatures and courts.There are many other areas of conflict between law (which looks to the past for its insights) and psychiatry (which seeks for its concepts in the current scientific advances).     

Effects of insularity on genetic diversity within and among natural populations

We conducted a quantitative literature review of genetic diversity (GD) within and among populations in relation to categorical population size and isolation (together referred to as “insularity”). Using populations from within the same studies, we were able to control for between‐study variation in methodology, as well as demographic and life histories of focal species. Contrary to typical expectations, insularity had relatively minor effects on GD within and among populations, which points to the more important role of other factors in shaping evolutionary processes. Such effects of insularity were sometimes seen—particularly in study systems where GD was already high overall. That is, insularity influenced GD in a study system when GD was high even in non‐insular populations of the same study system—suggesting an important role for the “scope” of influences on GD. These conclusions were more robust for within population GD versus among population GD, although several biases might underlie this difference. Overall, our findings indicate that population‐level genetic assumptions need to be tested rather than assumed in nature, particularly for topics underlying current conservation management practices.     

Advantages and Limitations of Anticipating Laboratory Test Results from Regression- and Tree-Based Rules Derived from Electronic Health-Record Data

Laboratory testing is the single highest-volume medical activity, making it useful to ask how well one can anticipate whether a given test result will be high, low, or within the reference interval (“normal”). We analyzed 10 years of electronic health records—a total of 69.4 million blood tests—to see how well standard rule-mining techniques can anticipate test results based on patient age and gender, recent diagnoses, and recent laboratory test results. We evaluated rules according to their positive and negative predictive value (PPV and NPV) and area under the receiver-operator characteristic curve (ROC AUCs). Using a stringent cutoff of PPV and/or NPV≥0.95, standard techniques yield few rules for sendout tests but several for in-house tests, mostly for repeat laboratory tests that are part of the complete blood count and basic metabolic panel. Most rules were clinically and pathophysiologically plausible, and several seemed clinically useful for informing pre-test probability of a given result. But overall, rules were unlikely to be able to function as a general substitute for actually ordering a test. Improving laboratory utilization will likely require different input data and/or alternative methods.     

High Quality Topic Extraction from Business News Explains Abnormal Financial Market Volatility

Understanding the mutual relationships between information flows and social activity in society today is one of the cornerstones of the social sciences. In financial economics, the key issue in this regard is understanding and quantifying how news of all possible types (geopolitical, environmental, social, financial, economic, etc.) affects trading and the pricing of firms in organized stock markets. In this article, we seek to address this issue by performing an analysis of more than 24 million news records provided by Thompson Reuters and of their relationship with trading activity for 206 major stocks in the S&P US stock index. We show that the whole landscape of news that affects stock price movements can be automatically summarized via simple regularized regressions between trading activity and news information pieces decomposed, with the help of simple topic modeling techniques, into their “thematic” features. Using these methods, we are able to estimate and quantify the impacts of news on trading. We introduce network-based visualization techniques to represent the whole landscape of news information associated with a basket of stocks. The examination of the words that are representative of the topic distributions confirms that our method is able to extract the significant pieces of information influencing the stock market. Our results show that one of the most puzzling stylized facts in financial economies, namely that at certain times trading volumes appear to be “abnormally large,” can be partially explained by the flow of news. In this sense, our results prove that there is no “excess trading,” when restricting to times when news is genuinely novel and provides relevant financial information.     

CANCER DETECTION CENTERS—The Experience to Date in California

Cancer “detection centers” (that is, centers for the examination of presumably well or asymptomatic persons) have been tried out in four different California communities during the last three years. In all instances—as in most other such centers throughout the United States—they have not been successful in restricting examination to well persons.The detection centers in California may therefore be described more accurately as “cancer examination and detection clinics.”Three of the four centers have been closed owing to the small yield of cancer cases discovered, plus the fact that the cost of operation exceeded the total available funds of the local branch of the Cancer Society. In addition, it was extremely difficult to obtain and maintain competence on the part of the professional staff in such centers.A more practical approach to the problem of earlier tumor detection would appear to be emphasis on making “every physician''s office a detection center,” and stressing the annual examination of persons over 40 years of age for tumors in the five common accessible sites. These are the tumors most readily curable today.     

A dynamic formin-dependent deep F-actin network in axons

Although actin at neuronal growth cones is well-studied, much less is known about actin organization and dynamics along axon shafts and presynaptic boutons. Using probes that selectively label filamentous-actin (F-actin), we found focal “actin hotspots” along axons—spaced ∼3–4 µm apart—where actin undergoes continuous assembly/disassembly. These foci are a nidus for vigorous actin polymerization, generating long filaments spurting bidirectionally along axons—a phenomenon we call “actin trails.” Super-resolution microscopy reveals intra-axonal deep actin filaments in addition to the subplasmalemmal “actin rings” described recently. F-actin hotspots colocalize with stationary axonal endosomes, and blocking vesicle transport diminishes the actin trails, suggesting mechanistic links between vesicles and F-actin kinetics. Actin trails are formin—but not Arp2/3—dependent and help enrich actin at presynaptic boutons. Finally, formin inhibition dramatically disrupts synaptic recycling. Collectively, available data suggest a two-tier F-actin organization in axons, with stable “actin rings” providing mechanical support to the plasma membrane and dynamic "actin trails" generating a flexible cytoskeletal network with putative physiological roles.     

Use of artificial genomes in assessing methods for atypical gene detection

Parametric methods for identifying laterally transferred genes exploit the directional mutational biases unique to each genome. Yet the development of new, more robust methods—as well as the evaluation and proper implementation of existing methods—relies on an arbitrary assessment of performance using real genomes, where the evolutionary histories of genes are not known. We have used the framework of a generalized hidden Markov model to create artificial genomes modeled after genuine genomes. To model a genome, “core” genes—those displaying patterns of mutational biases shared among large numbers of genes—are identified by a novel gene clustering approach based on the Akaike information criterion. Gene models derived from multiple “core” gene clusters are used to generate an artificial genome that models the properties of a genuine genome. Chimeric artificial genomes—representing those having experienced lateral gene transfer—were created by combining genes from multiple artificial genomes, and the performance of the parametric methods for identifying “atypical” genes was assessed directly. We found that a hidden Markov model that included multiple gene models, each trained on sets of genes representing the range of genotypic variability within a genome, could produce artificial genomes that mimicked the properties of genuine genomes. Moreover, different methods for detecting foreign genes performed differently—i.e., they had different sets of strengths and weaknesses—when identifying atypical genes within chimeric artificial genomes.     

What are lightness illusions and why do we see them?

Lightness illusions are fundamental to human perception, and yet why we see them is still the focus of much research. Here we address the question by modelling not human physiology or perception directly as is typically the case but our natural visual world and the need for robust behaviour. Artificial neural networks were trained to predict the reflectance of surfaces in a synthetic ecology consisting of 3-D “dead-leaves” scenes under non-uniform illumination. The networks learned to solve this task accurately and robustly given only ambiguous sense data. In addition—and as a direct consequence of their experience—the networks also made systematic “errors” in their behaviour commensurate with human illusions, which includes brightness contrast and assimilation—although assimilation (specifically White's illusion) only emerged when the virtual ecology included 3-D, as opposed to 2-D scenes. Subtle variations in these illusions, also found in human perception, were observed, such as the asymmetry of brightness contrast. These data suggest that “illusions” arise in humans because (i) natural stimuli are ambiguous, and (ii) this ambiguity is resolved empirically by encoding the statistical relationship between images and scenes in past visual experience. Since resolving stimulus ambiguity is a challenge faced by all visual systems, a corollary of these findings is that human illusions must be experienced by all visual animals regardless of their particular neural machinery. The data also provide a more formal definition of illusion: the condition in which the true source of a stimulus differs from what is its most likely (and thus perceived) source. As such, illusions are not fundamentally different from non-illusory percepts, all being direct manifestations of the statistical relationship between images and scenes.     

Inferring Social Status and Rich Club Effects in Enterprise Communication Networks

Social status, defined as the relative rank or position that an individual holds in a social hierarchy, is known to be among the most important motivating forces in social behaviors. In this paper, we consider the notion of status from the perspective of a position or title held by a person in an enterprise. We study the intersection of social status and social networks in an enterprise. We study whether enterprise communication logs can help reveal how social interactions and individual status manifest themselves in social networks. To that end, we use two enterprise datasets with three communication channels — voice call, short message, and email — to demonstrate the social-behavioral differences among individuals with different status. We have several interesting findings and based on these findings we also develop a model to predict social status. On the individual level, high-status individuals are more likely to be spanned as structural holes by linking to people in parts of the enterprise networks that are otherwise not well connected to one another. On the community level, the principle of homophily, social balance and clique theory generally indicate a “rich club” maintained by high-status individuals, in the sense that this community is much more connected, balanced and dense. Our model can predict social status of individuals with 93% accuracy.     

Variations in Kinematics during Clinical Gait Analysis in Stroke Patients

In addition to changes in spatio-temporal and kinematic parameters, patients with stroke exhibit fear of falling as well as fatigability during gait. These changes could compromise interpretation of data from gait analysis. The aim of this study was to determine if the gait of hemiplegic patients changes significantly over successive gait trials. Forty two stroke patients and twenty healthy subjects performed 9 gait trials during a gait analysis session. The mean and variability of spatio-temporal and kinematic joint parameters were analyzed during 3 groups of consecutive gait trials (1–3, 4–6 and 7–9). Principal component analysis was used to reduce the number of variables from the joint kinematic waveforms and to identify the parts of the gait cycle which changed during the gait analysis session. The results showed that i) spontaneous gait velocity and the other spatio-temporal parameters significantly increased, and ii) gait variability decreased, over the last 6 gait trials compared to the first 3, for hemiplegic patients but not healthy subjects. Principal component analysis revealed changes in the sagittal waveforms of the hip, knee and ankle for hemiplegic patients after the first 3 gait trials. These results suggest that at the beginning of the gait analysis session, stroke patients exhibited phase of adaptation,characterized by a “cautious gait” but no fatigue was observed.     

Why is Real-World Visual Object Recognition Hard?

Progress in understanding the brain mechanisms underlying vision requires the construction of computational models that not only emulate the brain's anatomy and physiology, but ultimately match its performance on visual tasks. In recent years, “natural” images have become popular in the study of vision and have been used to show apparently impressive progress in building such models. Here, we challenge the use of uncontrolled “natural” images in guiding that progress. In particular, we show that a simple V1-like model—a neuroscientist's “null” model, which should perform poorly at real-world visual object recognition tasks—outperforms state-of-the-art object recognition systems (biologically inspired and otherwise) on a standard, ostensibly natural image recognition test. As a counterpoint, we designed a “simpler” recognition test to better span the real-world variation in object pose, position, and scale, and we show that this test correctly exposes the inadequacy of the V1-like model. Taken together, these results demonstrate that tests based on uncontrolled natural images can be seriously misleading, potentially guiding progress in the wrong direction. Instead, we reexamine what it means for images to be natural and argue for a renewed focus on the core problem of object recognition—real-world image variation.     

Individual risk perception and empirical social structures shape the dynamics of infectious disease outbreaks

The dynamics of a spreading disease and individual behavioral changes are entangled processes that have to be addressed together in order to effectively manage an outbreak. Here, we relate individual risk perception to the adoption of a specific set of control measures, as obtained from an extensive large-scale survey performed via Facebook—involving more than 500,000 respondents from 64 countries—showing that there is a “one-to-one” relationship between perceived epidemic risk and compliance with a set of mitigation rules. We then develop a mathematical model for the spreading of a disease—sharing epidemiological features with COVID-19—that explicitly takes into account non-compliant individual behaviors and evaluates the impact of a population fraction of infectious risk-deniers on the epidemic dynamics. Our modeling study grounds on a wide set of structures, including both synthetic and more than 180 real-world contact patterns, to evaluate, in realistic scenarios, how network features typical of human interaction patterns impact the spread of a disease. In both synthetic and real contact patterns we find that epidemic spreading is hindered for decreasing population fractions of risk-denier individuals. From empirical contact patterns we demonstrate that connectivity heterogeneity and group structure significantly affect the peak of hospitalized population: higher modularity and heterogeneity of social contacts are linked to lower peaks at a fixed fraction of risk-denier individuals while, at the same time, such features increase the relative impact on hospitalizations with respect to the case where everyone correctly perceive the risks.     

Phenotype Bias Determines How Natural RNA Structures Occupy the Morphospace of All Possible Shapes

Morphospaces—representations of phenotypic characteristics—are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavoring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that noncoding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon a random sampling of sequences. We show that: 1) only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored; 2) remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far; and 3) perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon a uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather a strong phenotype bias in the RNA genotype–phenotype map, a type of developmental bias or “findability constraint,” which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to “find.”     

What Is Stochastic Resonance? Definitions, Misconceptions, Debates, and Its Relevance to Biology

Stochastic resonance is said to be observed when increases in levels of unpredictable fluctuations—e.g., random noise—cause an increase in a metric of the quality of signal transmission or detection performance, rather than a decrease. This counterintuitive effect relies on system nonlinearities and on some parameter ranges being “suboptimal”. Stochastic resonance has been observed, quantified, and described in a plethora of physical and biological systems, including neurons. Being a topic of widespread multidisciplinary interest, the definition of stochastic resonance has evolved significantly over the last decade or so, leading to a number of debates, misunderstandings, and controversies. Perhaps the most important debate is whether the brain has evolved to utilize random noise in vivo, as part of the “neural code”. Surprisingly, this debate has been for the most part ignored by neuroscientists, despite much indirect evidence of a positive role for noise in the brain. We explore some of the reasons for this and argue why it would be more surprising if the brain did not exploit randomness provided by noise—via stochastic resonance or otherwise—than if it did. We also challenge neuroscientists and biologists, both computational and experimental, to embrace a very broad definition of stochastic resonance in terms of signal-processing “noise benefits”, and to devise experiments aimed at verifying that random variability can play a functional role in the brain, nervous system, or other areas of biology.     

THE THORNY CHILD

Too many physicians—and parents—hide behind the overworked excuse that “Johnny is just going through a stage.” If the remark is inaccurate a great disservice can be done to both mother and child, and ultimately to society. The well oriented physician would no more permit a young mother to unwittingly feel “guilty” because her two-year-old “little stinker” behaves like a two-year-old little stinker than he would casually reassure when a ten-year-old behaves as though he were two.Actually much of the unpleasant behavior of children is quite normal. If physicians would help all young mothers to recognize this without dismissing abnormal behavior, it would do much to avert the overwhelming sense of inadequacy that so many modern young mothers feel—especially with their first baby. If they can be made comfortable with their first the others usually come easily. Many physicians who care for children are not trained in the rudiments of developmental behavior. By means of a simple outline and drawing of “the thorny child” even the least of the experts can better understand some of the chronologic variations in developmental behavior.     

Efficient Synthesis and Anti-Tubercular Activity of a Series of Spirocycles: An Exercise in Open Science

Tuberculosis afflicts an estimated 2 billion people worldwide and causes 1.3 million deaths annually. Chemotherapeutic solutions rely on drugs developed many years ago, with only one new therapeutic having been approved in the last 40 years. Given the rise of drug-resistant strains, there is an urgent need for the development of a more robust drug development pipeline. GlaxoSmithKline recently placed the structures and activities of 177 novel anti-tubercular leads in the public domain, as well as the results of ongoing optimisation of some of the series. Since many of the compounds arose from screening campaigns, their provenance was unclear and synthetic routes were in many cases not reported. Here we present the efficient synthesis of several novel analogues of one family of the GSK compounds—termed “Spiros”—using an oxa-Pictet–Spengler reaction. The new compounds are attractive from a medicinal chemistry standpoint and some were potent against the virulent strain, suggesting this class is worthy of further study. The research was carried out using open source methodology, providing the community with full access to all raw experimental data in real time.     

Cell Lineages and the Logic of Proliferative Control

It is widely accepted that the growth and regeneration of tissues and organs is tightly controlled. Although experimental studies are beginning to reveal molecular mechanisms underlying such control, there is still very little known about the control strategies themselves. Here, we consider how secreted negative feedback factors (“chalones”) may be used to control the output of multistage cell lineages, as exemplified by the actions of GDF11 and activin in a self-renewing neural tissue, the mammalian olfactory epithelium (OE). We begin by specifying performance objectives—what, precisely, is being controlled, and to what degree—and go on to calculate how well different types of feedback configurations, feedback sensitivities, and tissue architectures achieve control. Ultimately, we show that many features of the OE—the number of feedback loops, the cellular processes targeted by feedback, even the location of progenitor cells within the tissue—fit with expectations for the best possible control. In so doing, we also show that certain distinctions that are commonly drawn among cells and molecules—such as whether a cell is a stem cell or transit-amplifying cell, or whether a molecule is a growth inhibitor or stimulator—may be the consequences of control, and not a reflection of intrinsic differences in cellular or molecular character.