Working memory (WM) is critically important in cognitive tasks. The functional connectivity has been a powerful tool for understanding the mechanism underlying the information processing during WM tasks. The aim of this study is to investigate how to effectively characterize the dynamic variations of the functional connectivity in low dimensional space among the principal components (PCs) which were extracted from the instantaneous firing rate series. Spikes were obtained from medial prefrontal cortex (mPFC) of rats with implanted microelectrode array and then transformed into continuous series via instantaneous firing rate method. Granger causality method is proposed to study the functional connectivity. Then three scalar metrics were applied to identify the changes of the reduced dimensionality functional network during working memory tasks: functional connectivity (GC), global efficiency (E) and casual density (CD). As a comparison, GC, E and CD were also calculated to describe the functional connectivity in the original space. The results showed that these network characteristics dynamically changed during the correct WM tasks. The measure values increased to maximum, and then decreased both in the original and in the reduced dimensionality. Besides, the feature values of the reduced dimensionality were significantly higher during the WM tasks than they were in the original space. These findings suggested that functional connectivity among the spikes varied dynamically during the WM tasks and could be described effectively in the low dimensional space.
Expression of the SodA superoxide dismutase (MnSOD) in Escherichia coli is regulated by superoxide concentration through the SoxRS system and also by Fur (Ferric uptake regulator) through a mixed incoherent feed forward loop (FFL) containing the RyhB small regulatory RNA. In this work I theoretically analyze the function of this feed forward loop as part of the network controlling expression of the two cytoplasmic superoxide dismutases, SodA and SodB. I find that feed forward regulation allows faster response to superoxide stress at low intracellular iron levels compared to iron rich conditions. That is, it can conditionally modulate the response time of a superimposed transcriptional control mechanism.
Bacterial persistence, where a fraction of a population presents a transient resistance to bactericidal substances, has great medical importance due to its relation with the appearance of antibiotic resistances and untreatable bacterial chronic infections. The mechanisms behind this phenomenon remain largely unknown in spite of recent advances, in great part because of the difficulty in isolating the very small fraction of the population that is in this state at any given time. Current protocols for persister isolation have resulted in possible biases because of the induction of this state by the protocol itself. Here we present a novel protocol that allows rapid isolation of persister cells both from exponential and stationary phase. Moreover, it is capable of differentiating between type I and type II persister cells, which should allow the field to move beyond its current state of studying only one type. While this protocol prompts a revision of many of the current results, it should greatly facilitate further advances in the field.
In this study, we demonstrate closed-loop motion control of self-propelled microjets under the influence of external magnetic fields. We control the orientation of the microjets using external magnetic torque, whereas the linear motion towards a reference position is accomplished by the thrust and pulling magnetic forces generated by the ejecting oxygen bubbles and field gradients, respectively. The magnetic dipole moment of the microjets is characterized using the U-turn technique, and its average is calculated to be 1.310−10 A.m2 at magnetic field and linear velocity of 2 mT and 100 µm/s, respectively. The characterized magnetic dipole moment is used in the realization of the magnetic force-current map of the microjets. This map in turn is used for the design of a closed-loop control system that does not depend on the exact dynamical model of the microjets and the accurate knowledge of the parameters of the magnetic system. The motion control characteristics in the transient- and steady-states depend on the concentration of the surrounding fluid (hydrogen peroxide solution) and the strength of the applied magnetic field. Our control system allows us to position microjets at an average velocity of 115 m/s, and within an average region-of-convergence of 365 m.
We used psychometric functions to estimate the joint entropy for space discrimination and spatial frequency discrimination. Space discrimination was taken as discrimination of spatial extent. Seven subjects were tested. Gábor functions comprising unidimensionalsinusoidal gratings (0.4, 2, and 10 cpd) and bidimensionalGaussian envelopes (1°) were used as reference stimuli. The experiment comprised the comparison between reference and test stimulithat differed in grating's spatial frequency or envelope's standard deviation. We tested 21 different envelope's standard deviations around the reference standard deviation to study spatial extent discrimination and 19 different grating's spatial frequencies around the reference spatial frequency to study spatial frequency discrimination. Two series of psychometric functions were obtained for 2%, 5%, 10%, and 100% stimulus contrast. The psychometric function data points for spatial extent discrimination or spatial frequency discrimination were fitted with Gaussian functions using the least square method, and the spatial extent and spatial frequency entropies were estimated from the standard deviation of these Gaussian functions. Then, joint entropy was obtained by multiplying the square root of space extent entropy times the spatial frequency entropy. We compared our results to the theoretical minimum for unidimensional Gábor functions, 1/4π or 0.0796. At low and intermediate spatial frequencies and high contrasts, joint entropy reached levels below the theoretical minimum, suggesting non-linear interactions between two or more visual mechanisms. We concluded that non-linear interactions of visual pathways, such as the M and P pathways, could explain joint entropy values below the theoretical minimum at low and intermediate spatial frequencies and high contrasts. These non-linear interactions might be at work at intermediate and high contrasts at all spatial frequencies once there was a substantial decrease in joint entropy for these stimulus conditions when contrast was raised.
We consider the design and evaluation of short barcodes, with a length between six and eight nucleotides, used for parallel sequencing on platforms where substitution errors dominate. Such codes should have not only good error correction properties but also the code words should fulfil certain biological constraints (experimental parameters). We compare published barcodes with codes obtained by two new constructions methods, one based on the currently best known linear codes and a simple randomized construction method. The evaluation done is with respect to the error correction capabilities, barcode size and their experimental parameters and fundamental bounds on the code size and their distance properties. We provide a list of codes for lengths between six and eight nucleotides, where for length eight, two substitution errors can be corrected. In fact, no code with larger minimum distance can exist.
Medical educators and patients are turning to YouTube to teach and learn about medical conditions. These videos are from authors whose credibility cannot be verified & are not peer reviewed. As a result, studies that have analyzed the educational content of YouTube have reported dismal results. These studies have been unable to exclude videos created by questionable sources and for non-educational purposes. We hypothesize that medical education YouTube videos, authored by credible sources, are of high educational value and appropriately suited to educate the public. Credible videos about cardiovascular diseases were identified using the Mayo Clinic's Center for Social Media Health network. Content in each video was assessed by the presence/absence of 7 factors. Each video was also evaluated for understandability using the Suitability Assessment of Materials (SAM). User engagement measurements were obtained for each video. A total of 607 videos (35 hours) were analyzed. Half of all videos contained 3 educational factors: treatment, screening, or prevention. There was no difference between the number of educational factors present & any user engagement measurement (p NS). SAM scores were higher in videos whose content discussed more educational factors (p<0.0001). However, none of the user engagement measurements correlated with higher SAM scores. Videos with greater educational content are more suitable for patient education but unable to engage users more than lower quality videos. It is unclear if the notion “content is king” applies to medical videos authored by credible organizations for the purposes of patient education on YouTube.
Synaptic clustering on dendritic branches enhances plasticity, input integration and neuronal firing. However, the mechanisms guiding axons to cluster synapses at appropriate sites along dendritic branches are poorly understood. We searched for such a mechanism by investigating the structural overlap between dendritic branches and axons in a simplified model of neuronal networks - the hippocampal cell culture. Using newly developed software, we converted images of meshes of overlapping axonal and dendrites into topological maps of intersections, enabling quantitative study of overlapping neuritic geometry at the resolution of single dendritic branch-to-branch and axon-to-branch crossings. Among dendro-dendritic crossing configurations, it was revealed that the orientations through which dendritic branches cross is a regulated attribute. While crossing angle distribution among branches thinner than 1 µm appeared to be random, dendritic branches 1 µm or wider showed a preference for crossing each other at angle ranges of either 50°–70° or 80°–90°. It was then found that the dendro-dendritic crossings themselves, as well as their selective angles, both affected the path of axonal growth. Axons displayed 4 fold stronger tendency to traverse within 2 µm of dendro-dendritic intersections than at farther distances, probably to minimize wiring length. Moreover, almost 70% of the 50°–70° dendro-denritic crossings were traversed by axons from the obtuse angle’s zone, whereas only 15% traversed through the acute angle’s zone. By contrast, axons showed no orientation restriction when traversing 80°–90° crossings. When such traverse behavior was repeated by many axons, they converged in the vicinity of dendro-dendritic intersections, thereby clustering their synaptic connections. Thus, the vicinity of dendritic branch-to-branch crossings appears to be a regulated structure used by axons as a target for efficient wiring and as a preferred site for synaptic clustering. This synaptic clustering mechanism may enhance synaptic co-activity and plasticity.
Much effort has been devoted to the study of swarming and collective navigation of micro-organisms, insects, fish, birds and other organisms, as well as multi-agent simulations and to the study of real robots. It is well known that insect swarms can carry cargo. The studies here are motivated by a less well-known phenomenon: cargo transport by bacteria swarms. We begin with a concise review of how bacteria swarms carry natural, micrometre-scale objects larger than the bacteria (e.g. fungal spores) as well as man-made beads and capsules (for drug delivery). A comparison of the trajectories of virtual beads in simulations (using different putative coupling between the virtual beads and the bacteria) with the observed trajectories of transported fungal spores implies the existence of adaptable coupling. Motivated by these observations, we devised new, multi-agent-based studies of cargo transport by agent swarms. As a first step, we extended previous modelling of collective navigation of simple bacteria-inspired agents in complex terrain, using three putative models of agent–cargo coupling. We found that cargo-carrying swarms can navigate efficiently in a complex landscape. We further investigated how the stability, elasticity and other features of agent–cargo bonds influence the collective motion and the transport of the cargo, and found sharp phase shifts and dual successful strategies for cargo delivery. Further understanding of such mechanisms may provide valuable clues to understand cargo-transport by smart swarms of other organisms as well as by man-made swarming robots.
collective behaviour; swarming intelligence; bacteria swarming; agent-based modelling; social behaviour of bacteria; bacteria cargo transport
Bacterial swarming is a type of motility characterized by a rapid and collective migration of bacteria on surfaces. Most swarming species form densely packed dynamic clusters in the form of whirls and jets, in which hundreds of rod-shaped rigid cells move in circular and straight patterns, respectively. Recent studies have suggested that short-range steric interactions may dominate hydrodynamic interactions and that geometrical factors, such as a cell's aspect ratio, play an important role in bacterial swarming. Typically, the aspect ratio for most swarming species is only up to 5, and a detailed understanding of the role of much larger aspect ratios remains an open challenge. Here we study the dynamics of Paenibacillus dendritiformis C morphotype, a very long, hyperflagellated, straight (rigid), rod-shaped bacterium with an aspect ratio of ∼20. We find that instead of swarming in whirls and jets as observed in most species, including the shorter T morphotype of P. dendritiformis, the C morphotype moves in densely packed straight but thin long lines. Within these lines, all bacteria show periodic reversals, with a typical reversal time of 20 s, which is independent of their neighbors, the initial nutrient level, agar rigidity, surfactant addition, humidity level, temperature, nutrient chemotaxis, oxygen level, illumination intensity or gradient, and cell length. The evolutionary advantage of this unique back-and-forth surface translocation remains unclear.
Chitin is the second most produced biopolymer on Earth after cellulose. Chitin degrading enzymes are promising but untapped sources for developing novel industrial biocatalysts. Hidden amongst uncultivated micro-organisms, new bacterial enzymes can be discovered and exploited by metagenomic approaches through extensive cloning and screening. Enrichment is also a well-known strategy, as it allows selection of organisms adapted to feed on a specific compound. In this study, we investigated how the soil bacterial community responded to chitin enrichment in a microcosm experiment. An integrative metagenomic approach coupling phylochips and high throughput shotgun pyrosequencing was established in order to assess the taxonomical and functional changes in the soil bacterial community. Results indicate that chitin enrichment leads to an increase of Actinobacteria, γ-proteobacteria and β-proteobacteria suggesting specific selection of chitin degrading bacteria belonging to these classes. Part of enriched bacterial genera were not yet reported to be involved in chitin degradation, like the members from the Micrococcineae sub-order (Actinobacteria). An increase of the observed bacterial diversity was noticed, with detection of specific genera only in chitin treated conditions. The relative proportion of metagenomic sequences related to chitin degradation was significantly increased, even if it represents only a tiny fraction of the sequence diversity found in a soil metagenome.
For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results.
To study the microscopic origination of the return-volatility correlation in financial systems, we take into account the individual and collective behaviors of investors in real markets, and construct an agent-based model. The agents are linked with each other and trade in groups, and particularly, two novel microscopic mechanisms, i.e., investors’ asymmetric trading and herding in bull and bear markets, are introduced. Further, we propose effective methods to determine the key parameters in our model from historical market data.
With the model parameters determined for six representative stock-market indices in the world, respectively, we obtain the corresponding leverage or anti-leverage effect from the simulation, and the effect is in agreement with the empirical one on amplitude and duration. At the same time, our model produces other features of the real markets, such as the fat-tail distribution of returns and the long-term correlation of volatilities.
We reveal that for the leverage and anti-leverage effects, both the investors’ asymmetric trading and herding are essential generation mechanisms. Among the six markets, however, the investors’ trading is approximately symmetric for the five markets which exhibit the leverage effect, thus contributing very little. These two microscopic mechanisms and the methods for the determination of the key parameters can be applied to other complex systems with similar asymmetries.
Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods – which utilize a knowledge network derived from biological knowledge – have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes.
We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone.
The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization.
Traumatic brain injury (TBI) is the leading cause of death and disability in the US. Approximately 70-90% of the TBI cases are classified as mild, and up to 25% of them will not recover and suffer chronic neurocognitive impairments. The main pathology in these cases involves diffuse brain injuries, which are hard to detect by anatomical imaging yet noticeable in metabolic imaging. The current study tested the effectiveness of Hyperbaric Oxygen Therapy (HBOT) in improving brain function and quality of life in mTBI patients suffering chronic neurocognitive impairments.
Methods and Findings
The trial population included 56 mTBI patients 1–5 years after injury with prolonged post-concussion syndrome (PCS). The HBOT effect was evaluated by means of prospective, randomized, crossover controlled trial: the patients were randomly assigned to treated or crossover groups. Patients in the treated group were evaluated at baseline and following 40 HBOT sessions; patients in the crossover group were evaluated three times: at baseline, following a 2-month control period of no treatment, and following subsequent 2-months of 40 HBOT sessions. The HBOT protocol included 40 treatment sessions (5 days/week), 60 minutes each, with 100% oxygen at 1.5 ATA. “Mindstreams” was used for cognitive evaluations, quality of life (QOL) was evaluated by the EQ-5D, and changes in brain activity were assessed by SPECT imaging. Significant improvements were demonstrated in cognitive function and QOL in both groups following HBOT but no significant improvement was observed following the control period. SPECT imaging revealed elevated brain activity in good agreement with the cognitive improvements.
HBOT can induce neuroplasticity leading to repair of chronically impaired brain functions and improved quality of life in mTBI patients with prolonged PCS at late chronic stage.
Multi-cellular segmentation of bright field microscopy images is an essential computational step when quantifying collective migration of cells in vitro. Despite the availability of various tools and algorithms, no publicly available benchmark has been proposed for evaluation and comparison between the different alternatives.
A uniform framework is presented to benchmark algorithms for multi-cellular segmentation in bright field microscopy images. A freely available set of 171 manually segmented images from diverse origins was partitioned into 8 datasets and evaluated on three leading designated tools.
The presented benchmark resource for evaluating segmentation algorithms of bright field images is the first public annotated dataset for this purpose. This annotated dataset of diverse examples allows fair evaluations and comparisons of future segmentation methods. Scientists are encouraged to assess new algorithms on this benchmark, and to contribute additional annotated datasets.
Collective cell migration; Wound healing assay; Segmentation; Benchmarking
Biofilms are microbial communities that adhere to biotic or abiotic surfaces and are enclosed in a protective matrix of extracellular compounds. An important advantage of the biofilm lifestyle for soil bacteria (rhizobacteria) is protection against water deprivation (desiccation or osmotic effect). The rhizosphere is a crucial microhabitat for ecological, interactive, and agricultural production processes. The composition and functions of bacterial biofilms in soil microniches are poorly understood. We studied multibacterial communities established as biofilm-like structures in the rhizosphere of Medicago sativa (alfalfa) exposed to 3 experimental conditions of water limitation. The whole biofilm-forming ability (WBFA) for rhizospheric communities exposed to desiccation was higher than that of communities exposed to saline or nonstressful conditions. A culture-dependent ribotyping analysis indicated that communities exposed to desiccation or saline conditions were more diverse than those under the nonstressful condition. 16S rRNA gene sequencing of selected strains showed that the rhizospheric communities consisted primarily of members of the Actinobacteria and α- and γ-Proteobacteria, regardless of the water-limiting condition. Our findings contribute to improved understanding of the effects of environmental stress factors on plant-bacteria interaction processes and have potential application to agricultural management practices.
Methylocystis sp. strain SC2 can adapt to a wide range of methane concentrations. This is due to the presence of two isozymes of particulate methane monooxygenase exhibiting different methane oxidation kinetics. To gain insight into the underlying genetic information, its genome was sequenced and found to comprise a 3.77 Mb chromosome and two large plasmids.
We report important features of the strain SC2 genome. Its sequence is compared with those of seven other methanotroph genomes, comprising members of the Alphaproteobacteria, Gammaproteobacteria, and Verrucomicrobia. While the pan-genome of all eight methanotroph genomes totals 19,358 CDS, only 154 CDS are shared. The number of core genes increased with phylogenetic relatedness: 328 CDS for proteobacterial methanotrophs and 1,853 CDS for the three alphaproteobacterial Methylocystaceae members, Methylocystis sp. strain SC2 and strain Rockwell, and Methylosinus trichosporium OB3b. The comparative study was coupled with physiological experiments to verify that strain SC2 has diverse nitrogen metabolism capabilities. In correspondence to a full complement of 34 genes involved in N2 fixation, strain SC2 was found to grow with atmospheric N2 as the sole nitrogen source, preferably at low oxygen concentrations. Denitrification-mediated accumulation of 0.7 nmol 30N2/hr/mg dry weight of cells under anoxic conditions was detected by tracer analysis. N2 production is related to the activities of plasmid-borne nitric oxide and nitrous oxide reductases.
Presence of a complete denitrification pathway in strain SC2, including the plasmid-encoded nosRZDFYX operon, is unique among known methanotrophs. However, the exact ecophysiological role of this pathway still needs to be elucidated. Detoxification of toxic nitrogen compounds and energy conservation under oxygen-limiting conditions are among the possible roles. Relevant features that may stimulate further research are, for example, absence of CRISPR/Cas systems in strain SC2, high number of iron acquisition systems in strain OB3b, and large number of transposases in strain Rockwell.
The objective of the present study is to evaluate the cost and effectiveness of decontamination strategies in the special decontamination areas in Fukushima in regard to external radiation dose. A geographical information system (GIS) was used to relate the predicted external dose in the affected areas to the number of potential inhabitants and the land use in the areas. A comprehensive review of the costs of various decontamination methods was conducted as part of the analysis. The results indicate that aerial decontamination in the special decontamination areas in Fukushima would be effective for reducing the air dose rate to the target level in a short period of time in some but not all of the areas. In a standard scenario, analysis of cost and effectiveness suggests that decontamination costs for agricultural areas account for approximately 80% of the total decontamination cost, of which approximately 60% is associated with storage. In addition, the costs of decontamination per person per unit area are estimated to vary greatly. Appropriate selection of decontamination methods may significantly decrease decontamination costs, allowing more meaningful decontamination in terms of the limited budget. Our analysis can help in examining the prioritization of decontamination areas from the viewpoints of cost and effectiveness in reducing the external dose. Decontamination strategies should be determined according to air dose rates and future land-use plans.
Purpose: Cochlear implants (CIs) enable children with severe and profound hearing impairments to perceive the sensation of sound sufficiently to permit oral language acquisition. So far, studies have focused mainly on technological improvements and general outcomes of implantation for speech perception and spoken language development. This study quantitatively explored the organization of the semantic networks of children with CIs in comparison to those of age-matched normal hearing (NH) peers.
Method: Twenty seven children with CIs and twenty seven age- and IQ-matched NH children ages 7–10 were tested on a timed animal verbal fluency task (Name as many animals as you can). The responses were analyzed using correlation and network methodologies. The structure of the animal category semantic network for both groups were extracted and compared.
Results: Children with CIs appeared to have a less-developed semantic network structure compared to age-matched NH peers. The average shortest path length (ASPL) and the network diameter measures were larger for the NH group compared to the CIs group. This difference was consistent for the analysis of networks derived from animal names generated by each group [sample-matched correlation networks (SMCN)] and for the networks derived from the common animal names generated by both groups [word-matched correlation networks (WMCN)].
Conclusions: The main difference between the semantic networks of children with CIs and NH lies in the network structure. The semantic network of children with CIs is under-developed compared to the semantic network of the age-matched NH children. We discuss the practical and clinical implications of our findings.
cochlear implants; semantic networks; verbal fluency; network science; spreading activation; mental lexicon
Changes in electroencephalography (EEG) amplitude modulations have recently been linked with early-stage Alzheimer’s disease (AD). Existing tools available to perform such analysis (e.g., detrended fluctuation analysis), however, provide limited gains in discriminability power over traditional spectral based EEG analysis. In this paper, we explore the use of an innovative EEG amplitude modulation analysis technique based on spectro-temporal signal processing. More specifically, full-band EEG signals are first decomposed into the five well-known frequency bands and the envelopes are then extracted via a Hilbert transform. Each of the five envelopes are further decomposed into four so-called modulation bands, which were chosen to coincide with the delta, theta, alpha and beta frequency bands. Experiments on a resting-awake EEG dataset collected from 76 participants (27 healthy controls, 27 diagnosed with mild-AD, and 22 with moderate-AD) showed significant differences in amplitude modulations between the three groups. Most notably, i) delta modulation of the beta frequency band disappeared with an increase in disease severity (from mild to moderate AD), ii) delta modulation of the theta band appeared with an increase in severity, and iii) delta modulation of the beta frequency band showed to be a reliable discriminant feature between healthy controls and mild-AD patients. Taken together, it is hoped that the developed tool can be used to assist clinicians not only with early detection of Alzheimer’s disease, but also to monitor its progression.
Complex networks have been extensively used in the last decade to characterize and analyze complex systems, and they have been recently proposed as a novel instrument for the analysis of spectra extracted from biological samples. Yet, the high number of measurements composing spectra, and the consequent high computational cost, make a direct network analysis unfeasible. We here present a comparative analysis of three customary feature selection algorithms, including the binning of spectral data and the use of information theory metrics. Such algorithms are compared by assessing the score obtained in a classification task, where healthy subjects and people suffering from different types of cancers should be discriminated. Results indicate that a feature selection strategy based on Mutual Information outperforms the more classical data binning, while allowing a reduction of the dimensionality of the data set in two orders of magnitude.
For biopharmaceutical companies, investments in research and development are risky, and the results from clinical trials are key inflection points in the process. Few studies have explored how and to what extent the public equity market values clinical trial results.
Our study dataset matched announcements of clinical trial results for investigational compounds from January 2011 to May 2013 with daily stock market returns of large United States-listed pharmaceutical and biotechnology companies. Event study methodology was used to examine the relationship between clinical research events and changes in stock returns.
We identified public announcements for clinical trials of 24 investigational compounds, including 16 (67%) positive and 8 (33%) negative events. The majority of announcements were for Phase 3 clinical trials (N = 13, 54%), and for oncologic (N = 7, 29%) and neurologic (N = 6, 24%) indications. The median cumulative abnormal returns on the day of the announcement were 0.8% (95% confidence interval [CI]: –2.3, 13.4%; P = 0.02) for positive events and –2.0% (95% CI: –9.1, 0.7%; P = 0.04) for negative events, with statistically significant differences from zero. In the day immediately following the announcement, firms with positive events were associated with stock price corrections, with median cumulative abnormal returns falling to 0.4% (95% CI: –3.8, 12.3%; P = 0.33). For firms with negative announcements, the median cumulative abnormal returns were –1.7% (95% CI: –9.5, 1.0%; P = 0.03), and remained significantly negative over the two day event window. The magnitude of abnormal returns did not differ statistically by indication, by trial phase, or between biotechnology and pharmaceutical firms.
The release of clinical trial results is an economically significant event and has meaningful effects on market value for large biopharmaceutical companies. Stock return underperformance due to negative events is greater in magnitude and persists longer than abnormal returns due to positive events, suggesting asymmetric market reactions.
The relationship between a market index and its constituent stocks is complicated. While an index is a weighted average of its constituent stocks, when the investigated time scale is one day or longer the index has been found to have a stronger effect on the stocks than vice versa. We explore how this interaction changes in short time scales using high frequency data. Using a correlation-based analysis approach, we find that in short time scales stocks have a stronger influence on the index. These findings have implications for high frequency trading and suggest that the price of an index should be published on shorter time scales, as close as possible to those of the actual transaction time scale.
Many biological networks are signed molecular networks which consist of positive and negative links. To reveal the distinct features between links with different signs, we proposed signed link-clustering coefficients that assess the similarity of inter-action profiles between linked molecules. We found that positive links tended to cluster together, while negative links usually behaved like bridges between positive clusters. Positive links with higher adhesiveness tended to share protein domains, be associated with protein-protein interactions and make intra-connections within protein complexes. Negative links that were more bridge-like tended to make interconnections between protein complexes. Utilizing the proposed measures to group positive links, we observed hierarchical modules that could be well characterized by functional annotations or known protein complexes. Our results imply that the proposed sign-specific measures can help reveal the network structural characteristics and the embedded biological contexts of signed links, as well as the functional organization of signed molecular networks.