Static Aggregation Research Articles

Profiling biomanufactured extracellular vesicles of human forebrain spheroids in a Vertical-Wheel Bioreactor.

Extracellular vesicles (EVs) secreted by human brain cells have great potential as cell-free therapies in various diseases, including stroke. However, because of the significant amount of EVs needed in preclinical and clinical trials, EV applicationis still challenging. Vertical-Wheel Bioreactors (VWBRs) have designed features that allow for scaling up the generation of human forebrain spheroid EVs under low shear stress. In this study, EV secretion by human forebrain spheroids derived from induced pluripotent stem cells as 3D aggregates and on Synthemax II microcarriers in VWBRs were investigated with static aggregate culture as a control. The spheroids were characterized by metabolite and transcriptome analysis. The isolated EVs were characterized by nanoparticle tracking analysis, electron microscopy, and Western blot. The EV cargo was analyzed using proteomics and miRNA sequencing. The in vitro functional assays of an oxygen and glucose-deprived stroke model were conducted. Proof of concept in vivo study was performed, too. Human forebrain spheroid differentiated on microcarriers showed a higher growth rate than 3D aggregates. Microcarrier culture had lower glucose consumption per million cells and lower glycolysis gene expression but higher EV biogenesis genes. EVs from the three culture conditions showed no differences in size, but the yields from high to low were microcarrier cultures, dynamic aggregates, and static aggregates. The cargo is enriched with proteins (proteomics) and miRNAs (miRNA-seq), promoting axon guidance, reducing apoptosis, scavenging reactive oxygen species, and regulating immune responses. Human forebrain spheroid EVs demonstrated the ability to improve recovery in an in vitro stroke model and in vivo. Human forebrain spheroid differentiation in VWBR significantly increased the EV yields (up to 240-750 fold) and EV biogenesis compared to static differentiation due to the dynamic microenvironment and metabolism change. The biomanufactured EVs from VWBRs have exosomal characteristics and more therapeutic cargo and are functional in in vitro assays, which paves the way for future in vivo stroke studies.

Toxicity of the model protein 3×GFP arises from degradation overload, not from aggregate formation.

Although protein aggregation can cause cytotoxicity, such aggregates can also form to mitigate cytotoxicity from misfolded proteins, although the nature of these contrasting aggregates remains unclear. We previously found that overproduction (op) of a three green fluorescent protein-linked protein (3×GFP) induces giant aggregates and is detrimental to growth. Here, we investigated the mechanism of growth inhibition by 3×GFP-op using non-aggregative 3×MOX-op as a control in Saccharomyces cerevisiae. The 3×GFP aggregates were induced by misfolding, and 3×GFP-op had higher cytotoxicity than 3×MOX-op because it perturbed the ubiquitin-proteasome system. Static aggregates formed by 3×GFP-op dynamically trapped Hsp70 family proteins (Ssa1 and Ssa2 in yeast), causing the heat-shock response. Systematic analysis of mutants deficient in the protein quality control suggested that 3×GFP-op did not cause a critical Hsp70 depletion and aggregation functioned in the direction of mitigating toxicity. Artificial trapping of essential cell cycle regulators into 3×GFP aggregates caused abnormalities in the cell cycle. In conclusion, the formation of the giant 3×GFP aggregates itself is not cytotoxic, as it does not entrap and deplete essential proteins. Rather, it is productive, inducing the heat-shock response while preventing an overload to the degradation system.

Sequential ensemble learning for next item recommendation

Predicting the next item that users may engage in is a key task of recommender systems, and many methods have been proposed to deal with this task from different aspects. Theoretically, the proper ensemble of multiple different methods (a.k.a. base models) can make more accurate and stable recommendations. However, most of the existing ensemble methods rely on static aggregation strategies, which fail to capture base models’ dynamic predictive abilities for each user over time. In addition, most of the existing diversity measures used in regression or classification ensemble methods rely on a distance metric of base models’ outputs, which makes it intractably apply for next-item recommendation whose base models output sequential ranking lists.To solve the above problems, we propose a Sequential Ensemble Method, named SEM, to aggregate different base models for next-item recommendation. We assume users’ concentration and base models’ expertise can be inferred from users’ sequential behaviors and base models’ prediction results. Therefore, we propose to explicitly model base models’ dynamic predictive abilities on different users over time based on users’ concentration and base models’ expertise. In addition, we propose a new diversity measure for sequential ranking ensemble, which can perform diversity-based learning over time for better performance of next-item recommendation. Extensive experiments on six real-world data sets show that our method consistently outperforms state-of-the-art methods.

Optically tunable engineered macromolecular fluorescent material analyzed using optical parameters

AbstractThere are extensive reports on bathochromic emissive material associated with non‐covalent interaction but the reports on emissive material with covalent crosslinked approach is limited. Therefore, we highlight unique method for synthesis of crosslinked polymeric materials formed via thermal Click reaction where the static aggregate of naphthalene (Np) in Polym‐Np or pyrene (Py) in Polym‐Py showed energy tunable emission. Initially, the crosslinked polymeric materials were well‐characterized using spectroscopic and analytical methods like NMR, ATR‐FTIR, rheometer, TGA and optical analysis. The energy tunability to fluorescence emission with polynuclear aromatic hydrocarbons (PAHs) ‐ naphthalene and pyrene was observed. The quantum efficiency for the respective crosslinked polymers—Polym‐Np and Polym‐Py was found to be 2.54% and 8.72%. Moreover, the Full polarization fluorescence Mueller matrix (FLMM) spectroscopy was invoked to extract and quantify the molecular orientation of the ground and excited state of the crosslinked polymers—Polym‐Np and Polym‐Py. The polarization parameters (Fluorescence Diattenuation and Polarizance) based on fluorescence polarization showed that the PAHs within the crosslinked polymers found to be well organized in the excited state with respect to the ground state. Such materials have potential to application such as photocatalyst, chemosensor, etc.

Engineering Human Mesenchymal Bodies in a Novel 3D-Printed Microchannel Bioreactor for Extracellular Vesicle Biogenesis.

Human Mesenchymal Stem Cells (hMSCs) and their derived products hold potential in tissue engineering and as therapeutics in a wide range of diseases. hMSCs possess the ability to aggregate into "spheroids", which has been used as a preconditioning technique to enhance their therapeutic potential by upregulating stemness, immunomodulatory capacity, and anti-inflammatory and pro-angiogenic secretome. Few studies have investigated the impact on hMSC aggregate properties stemming from dynamic and static aggregation techniques. hMSCs' main mechanistic mode of action occur through their secretome, including extracellular vesicles (EVs)/exosomes, which contain therapeutically relevant proteins and nucleic acids. In this study, a 3D printed microchannel bioreactor was developed to dynamically form hMSC spheroids and promote hMSC condensation. In particular, the manner in which dynamic microenvironment conditions alter hMSC properties and EV biogenesis in relation to static cultures was assessed. Dynamic aggregation was found to promote autophagy activity, alter metabolism toward glycolysis, and promote exosome/EV production. This study advances our knowledge on a commonly used preconditioning technique that could be beneficial in wound healing, tissue regeneration, and autoimmune disorders.

Molecular Uniting Set Identified Characteristic (MUSIC) of Organic Optoelectronic Material

Comprehensive SummaryResearchers investigated the organic optoelectronic materials and facilitated their development in organic light‐emitting diodes (OLEDs), chemo‐ and biosensors, organic solar cells, data storage, and anticounterfeiting devices. Atoms make up molecules through chemical bonds, and molecular aggregates are formed through weak intermolecular interactions. The opto‐electronic performance of these materials depends on not only the properties of the well‐designed molecules with specific function groups, but also their aggregate states. The molecular aggregates in the form of nanoparticles can be applied in biological imaging, and films can be applied to photovoltaic and photodeformable devices, in which, the alignment of optoelectronic molecules can be either an ordered crystalline or an amorphous state. Generally, the crystalline materials could be deeply investigated by single crystal/powder X‐ray diffraction analysis, which could provide the accurate information about molecular conformations, interactions and packing characteristics. It afforded a convenient way to investigate the possible relationship between molecular aggregates and opto‐electronic properties. Among various opto‐electronic materials, organic room temperature phosphorescence (RTP) materials exhibit the extremely sensitive luminescence property to molecular aggregates, even the dynamic properties can be detected by the tiny change of molecular aggregates. Thus, we selected the organic RTP emission as the output information of molecular aggregates, and afforded typical examples to find the possible relation between RTP effect and molecular packing. Accordingly, molecular packing can be adjusted by the external force as light, mechanical force, temperature, electric field, and so on, as well as the molecular structures as the building blocks, and the systematic investigation in the dynamic and static aggregation structures is of great value to the design of various optoelectronic materials. This review discusses the relationship among molecular structures, aggregation behaviors and corresponding optoelectronic properties by a comprehensive summary of recent research in our group, and the concept of molecular uniting set identified characteristic (MUSIC) is afforded. What is the most favorite and original chemistry developed in your research group?The concept of “Suitable Isolation Group” for molecular design of organic second‐order nonlinear optical (NLO) materials. Molecular packing is highlighted as the key point to opto‐electronic materials, which are partially summarized in this mini review to present “MUSIC” in the molecular world.How do you supervise your students?Discussions. We do discussions for science, interests and other topics related to research together, through which to solve the encountered problems.What is the most important personality for scientific research?Curiosity, desire to advance, persistence, sense of urgency, and teamspirit.What are your hobbies? What's your favorite book(s)??Listening to music and reading books, and my favorite book is “Tao Te Ching (道德经)”.Could you please give us some advices on improving Chinese Journal of Chemistry?There are many different approaches to improve CJC, perhaps, to attract and publish good papers is the key, especially those from Chinese authors since the chemistry in China develops rapidly.If you have anything else to tell our readers, please feel free to do so?While pursuing new published exciting papers, it is a very good habit to read related old excellent literatures and the classics.

A natural product targets BRD4 to inhibit phase separation and gene transcription

SummaryThe BET-bromodomain protein BRD4 uses two bromodomains to target acetyl-histones and other domains to recruit P-TEFb and other transcription factors to stimulate transcription of proto-oncogenes and key cell identity genes. Recent studies show that its ability to form phase-separated condensates that cluster preferentially at the super-enhancer regions of target genes is key for BRD4 to exert its functions. Here, we describe the identification of a natural product called PCG from polygonum cuspidatum Sieb.et Zucc., a traditional Chinese medicinal herb, that directly binds to BRD4. This binding inhibits BRD4 phase separation, turns dynamic BRD4 nuclear condensates into static aggregates, and effectively shuts down transcription of BRD4-dependent genes. Thus, through PCG we have discovered a BET inhibitor that not only selectively targets BRD4 but also works by suppressing phase separation, a mechanism of action that is different from those of the other known BET inhibitors.

Dynamic Optimization of Government Data Transmission Based on Blockchain Technology

With the development of terminal technology and the expansion of application fields, the Internet of Things’ application value and service requirements continue to increase. Efficient data transmission is a reliable guarantee for the development and application of the Internet of Things. Blockchain technology provides a solution for storing and delivering distributed data. On this basis, taking the Industrial Internet of Things as the research object, a blockchain-based data transmission optimization method was established. First, an undirected complete graph model is used to describe the network scene. A matrix grid model is used to replace the randomly distributed set of data nodes. Then, a double optimization method is proposed. We designed the mathematical description and modeling method of the lattice matching decision problem and designed the artificial neural network to find the optimal solution to the problem. Finally, an example is used to verify the government data transmission method’s technical performance and packet loss rate. It has achieved at least 20% and 30% improvements in optimizing the network life of static aggregation nodes and data transmission, respectively. While improving the robustness of the network, it also shows a stable advantage in terms of network energy efficiency indicators.

Glucose Availability Affects Extracellular Matrix Synthesis During Chondrogenesis In Vitro.

Understanding in vitro chondrogenesis of human mesenchymal stem cells (hMSCs) is important as it holds great promise for cartilage tissue engineering and other applications. The current technology produces the end tissue quality that is highly variable and dependent on culture conditions. We investigated the effect of nutrient availability on hMSC chondrogenesis in a static aggregate culture system by varying the medium-change frequency together with starting glucose levels. Glucose uptake and lactate secretion profiles were obtained to monitor the metabolism change during hMSC chondrogenesis with different culture conditions. Higher medium-change frequency led to increases in cumulative glucose uptake for all starting glucose levels. Furthermore, increase in glucose uptake by aggregates led to increased end tissue glycosaminoglycan (GAG) and hydroxyproline (HYP) content. The results suggest that increased glucose availability either through increased medium-change frequency or higher initial glucose levels lead to improved chondrogenesis. Also, cumulative glucose uptake and lactate secretion were found to correlate well with GAG and HYP content, indicating both molecules are promising biomarkers for noninvasive assessment of hMSC chondrogenesis. Collectively, our results can be used to design optimal culture conditions and develop dynamic assessment strategies for cartilage tissue engineering applications. Impact statement In this study, we investigated how culture conditions, medium-change frequency and glucose levels, affect chondrogenesis of human mesenchymal stem cells in an aggregate culture model. Doubling the medium-change frequency significantly increased the biochemical quality of the resultant tissue aggregates, as measured by their glycosaminoglycan and hydroxyproline content. We attribute this to increased glucose uptake through the glycolysis pathway, as secretion of lactate, a key endpoint product of the glycolysis pathway, increased concurrently. These findings can be used to design optimal culture conditions for tissue engineering and regenerative medicine applications.

Statically Aggregate Verifiable Random Functions and Application to E-Lottery

Cohen, Goldwasser, and Vaikuntanathan (TCC’15) introduced the concept of aggregate pseudo-random functions (PRFs), which allow efficiently computing the aggregate of PRF values over exponential-sized sets. In this paper, we explore the aggregation augmentation on verifiable random function (VRFs), introduced by Micali, Rabin and Vadhan (FOCS’99), as well as its application to e-lottery schemes. We introduce the notion of static aggregate verifiable random functions (Agg-VRFs), which perform aggregation for VRFs in a static setting. Our contributions can be summarized as follows: (1) we define static aggregate VRFs, which allow the efficient aggregation of VRF values and the corresponding proofs over super-polynomially large sets; (2) we present a static Agg-VRF construction over bit-fixing sets with respect to product aggregation based on the q-decisional Diffie–Hellman exponent assumption; (3) we test the performance of our static Agg-VRFs instantiation in comparison to a standard (non-aggregate) VRF in terms of costing time for the aggregation and verification processes, which shows that Agg-VRFs lower considerably the timing of verification of big sets; and (4) by employing Agg-VRFs, we propose an improved e-lottery scheme based on the framework of Chow et al.’s VRF-based e-lottery proposal (ICCSA’05). We evaluate the performance of Chow et al.’s e-lottery scheme and our improved scheme, and the latter shows a significant improvement in the efficiency of generating the winning number and the player verification.

Microstructures and dynamic processes within the five-phase system: regarding COVID-19 as a complex system

The research uses the development of COVID-19 in the human body as an example to explore the microstructures and dynamic processes of a concise complex system from the lens of the five-phase system. Based on the structural balance theory and system dynamics, the research finds that transitive triads and cyclic triads in the five-phase system are both imbalanced. The integration of these differentiated triads comprises of a balanced intermediate form in the shape of quadrangular cycles. These cycles serve as microstructures of the five-phase system, due to the inherent balancing feedback mechanism, and support the generation of resultants. The alternation of quadrangular cycles drives the spiraling development of the whole system. By orderly and regular interweaving of signed directed links, the research provides a holistic, process-oriented demonstration for the development processes of COVID-19. It clarifies that the essence of the five-phase system is phase-transition processes with the quadrangular cycle as carrier and supporter, rather than the static aggregation of five elements. The research deepens the understanding of system nonlinearity by visualizing the circular causality and promotes the academic dialogue between the Western process theory and the Chinese inherited notion of the five-phase system.

Dynamic Layer Aggregation for Neural Machine Translation with Routing-by-Agreement

With the promising progress of deep neural networks, layer aggregation has been used to fuse information across layers in various fields, such as computer vision and machine translation. However, most of the previous methods combine layers in a static fashion in that their aggregation strategy is independent of specific hidden states. Inspired by recent progress on capsule networks, in this paper we propose to use routing-by-agreement strategies to aggregate layers dynamically. Specifically, the algorithm learns the probability of a part (individual layer representations) assigned to a whole (aggregated representations) in an iterative way and combines parts accordingly. We implement our algorithm on top of the state-of-the-art neural machine translation model TRANSFORMER and conduct experiments on the widely-used WMT14 sh⇒German and WMT17 Chinese⇒English translation datasets. Experimental results across language pairs show that the proposed approach consistently outperforms the strong baseline model and a representative static aggregation model.

New distributed routing algorithm in wireless network models

The wireless sensor network present all sensor hubs to produce an equal proportion of information packages in a WSN. The centers around a sink need to exchange a more noteworthy number of groups and will all in all fail miserably sooner than various focus focuses in light of the way that in vitality usage of sensor focus focuses in system lifetime can be drawn out by altering the correspondence stack around a sink. This paper introduces another way to deal with tackle this issue by thinking about the psychological capacity of hubs. Most importantly, we misuse the directional gathering receiving wires to propose a directional gathering two-advance recreation routing plan to set up multicast tree for remote multi-jump networks. we think about that terminals make utilization of cutting edge energy collection transmission/gathering methods, for example, maximal proportion consolidating gathering of redundancy codes, or on the other hand information accumulation with rate less codes. Aggregate methods increment communication dependability; diminish energy utilization, and abatement inactivity. Our first calculation is incorporated, expecting that routing calculations should be possible at a focal processor with full access to channel state information for the whole framework. We likewise plan two disseminated routing calculations that require just nearby channel state information. essentially, given us to characterize two recursive calculations of the way a chance to weight utilizing the moderate way weights to each transfer, either from the source (forward) or from the Destination (in reverse). In particular, in the task exhibits a Weight Based Synchronization (WBS) conventional that utilizes the extent of synchronized hub groups as an impetus for synchronization. We outline these outcomes by concentrate the base energy routing issue in static aggregate multi-bounce networks for various sending techniques at transfers. Proposed framework empowers noteworthy performance through the most limited way routing.

Impact of temporal network structures on the speed of consensus formation in opinion dynamics

Opinion dynamics on networks has wide applications to empirical and engineered systems and profound prospects in the general study of complex systems. Many efforts have been devoted to understanding how opinion dynamics is affected by network topology. However, social interactions are best characterized as temporal networks in which ordering of interactions cannot be ignored. Temporal activity patterns including heterogeneous contact strength and interevent times, turnover edge/node dynamics, and daily patterns could have significant effects that would not be captured by static aggregate network representations. In this paper, we study the effects of such temporal patterns on the speed of consensus formation in various models of continuous opinion dynamics using four empirical data sets, including three human face-to-face networks from different real-world settings and a social insect interaction network. We find that static, aggregated networks consistently overestimate the speed of simulated consensus formation while weight heterogeneity associated with frequency of interactions has an inhibitory effect on consensus formation relative to the behavior on unweighted networks. Moreover, the speed of consensus formation is found to be highly sensitive to nodal lifetimes, suggesting that randomization protocols that dramatically alter the distribution of lifetimes cannot be relied upon as reference models. On the other hand, temporal patterns including burstiness of interevent times and the lifetime of edges are found to have insignificant effects on consensus formation. The sensitivity to nodal lifetimes is further demonstrated via synthetic networks generated by an activity-driven model with tunable nodal lifetimes.

Novel sulfonamidospirobifluorenes as fluorescent sensors for mercury(ii) ion and glutathione.

Novel spirobifluorene derivatives containing two and four sulfonamide groups are successfully synthesized from the commercially available bromo-9,9′-spirobifluorene by Sonogashira couplings. These compounds exhibit an excellent selective fluorescence quenching by Hg(ii) in DMSO/HEPES buffer mixture with three-times-noise detection limits of 10.4 to 103.8 nM. A static aggregation induced quenching mechanism is proposed based on the data from 1H-NMR and UV-Vis spectroscopy, as well as the observation of the Tyndall effect. Quantifications of Hg(ii) using these sensors are in good agreement with those obtained from ICP-OES. The reversibility of these sensors is demonstrated by a complete fluorescence restoration upon addition of EDTA or l-glutathione. The application as a turn-on sensor for l-glutathione is demonstrated in a quantitative analysis of three samples of l-glutathione supplement drinks.

Identity policies of education: struggles for inclusion and exclusion in Peru and Colombia

ABSTRACTPolicy initiatives that seek to account for ethno-cultural differences in education and schooling have become increasingly popular over the past few decades. These include affirmative action measures and bilingual education models. The rationale for the implementation of these policies focuses on their potential to rectify historical discrimination by both levelling horizontal inequalities and granting equal value to different cultures and languages in the schooling process. In this framework, however, ethnic communities are often treated as discrete and static social aggregates, and social heterogeneity and spillover effects between groups are disregarded. This paper draws on empirical case studies from Colombia and Peru to show how identity policies of education can increase inter-ethnic competition, leading to protracted social conflicts. These outcomes, beyond negatively impacting local communities, raise important dilemmas surrounding the theoretical and operational foundations of these popular policy measures.

Hypothetical Outcome Plots Help Untrained Observers Judge Trends in Ambiguous Data.

Animated representations of outcomes drawn from distributions (hypothetical outcome plots, or HOPs) are used in the media and other public venues to communicate uncertainty. HOPs greatly improve multivariate probability estimation over conventional static uncertainty visualizations and leverage the ability of the visual system to quickly, accurately, and automatically process the summary statistical properties of ensembles. However, it is unclear how well HOPs support applied tasks resembling real world judgments posed in uncertainty communication. We identify and motivate an appropriate task to investigate realistic judgments of uncertainty in the public domain through a qualitative analysis of uncertainty visualizations in the news. We contribute two crowdsourced experiments comparing the effectiveness of HOPs, error bars, and line ensembles for supporting perceptual decision-making from visualized uncertainty. Participants infer which of two possible underlying trends is more likely to have produced a sample of time series data by referencing uncertainty visualizations which depict the two trends with variability due to sampling error. By modeling each participant's accuracy as a function of the level of evidence presented over many repeated judgments, we find that observers are able to correctly infer the underlying trend in samples conveying a lower level of evidence when using HOPs rather than static aggregate uncertainty visualizations as a decision aid. Modeling approaches like ours contribute theoretically grounded and richly descriptive accounts of user perceptions to visualization evaluation.

Big Data Based Dynamic Flow Aggregation over 5G Network Slicing

Today, smart grids, smart homes, smart water networks, and intelligent transportation, are infrastructure systems that connect our world more than we ever thought possible and are associated with a single concept, the Internet of Things (IoT). The number of devices connected to the IoT and hence the number of traffic flow increases continuously, as well as the emergence of new applications. Although cutting-edge hardware technology can be employed to achieve a fast implementation to handle this huge data streams, there will always be a limit on size of traffic supported by a given architecture. However, recent cloud-based big data technologies fortunately offer an ideal environment to handle this issue. Moreover, the ever-increasing high volume of traffic created on demand presents great challenges for flow management. As a solution, flow aggregation decreases the number of flows needed to be processed by the network. The previous works in the literature prove that most of aggregation strategies designed for smart grids aim at optimizing system operation performance. They consider a common identifier to aggregate traffic on each device, having its independent static aggregation policy. In this paper, we propose a dynamic approach to aggregate flows based on traffic characteristics and device preferences. Our algorithm runs on a big data platform to provide an end-to-end network visibility of flows, which performs high-speed and high-volume computations to identify the clusters of similar flows and aggregate massive number of mice flows into a few meta-flows. Compared with existing solutions, our approach dynamically aggregates large number of such small flows into fewer flows, based on traffic characteristics and access node preferences. Using this approach, we alleviate the problem of processing a large amount of micro flows, and also significantly improve the accuracy of meeting the access node QoS demands. We conducted experiments, using a dataset of up to 100,000 flows, and studied the performance of our algorithm analytically. The experimental results are presented to show the promising effectiveness and scalability of our proposed approach.

Visualizing Structural Details of Disordered Domain Phase Separation Associated with ALS and Cancers

Phase separation of RNA-binding proteins via multivalent interactions between aromatic/polar-rich disordered domains contributes to the formation of functional cytoplasmic granules and nuclear puncta. These domains have also been identified as the nucleators of neuronal inclusions in amyotrophic lateral sclerosis (ALS) and frontotemporal dementia. We use atomic resolution nuclear magnetic resonance spectroscopy approaches for visualizing low complexity domain structure and interactions along the pathway from monomer, to liquid-liquid phase separated state, to static aggregates and hydrogels. We show that the low complexity domain of RNA-binding protein Fused in Sarcoma (FUS LC) remains disordered even within liquid phase separated states and recruits unphosphorylated RNA-polymerase II C-terminal domain into the liquid phase separated state, adding a potential explanation for FUS LC transcriptional activation in cancer. Importantly, phase separation is reversible and is modulated by ionic strength and interaction with RNA, distinguishing these assemblies from static inclusions. In contrast, we show that liquid-liquid phase separation of TDP-43 is mediated in part by α-helical assembly and extension. Some ALS-associated mutations disrupt helix-helix interaction inhibiting liquid-liquid phase separation while leading to enhanced aggregation.

Temporal correlation coefficient for directed networks.

Previous studies dealing with network theory focused mainly on the static aggregation of edges over specific time window lengths. Thus, most of the dynamic information gets lost. To assess the quality of such a static aggregation the temporal correlation coefficient can be calculated. It measures the overall possibility for an edge to persist between two consecutive snapshots. Up to now, this measure is only defined for undirected networks. Therefore, we introduce the adaption of the temporal correlation coefficient to directed networks. This new methodology enables the distinction between ingoing and outgoing edges. Besides a small example network presenting the single calculation steps, we also calculated the proposed measurements for a real pig trade network to emphasize the importance of considering the edge direction. The farm types at the beginning of the pork supply chain showed clearly higher values for the outgoing temporal correlation coefficient compared to the farm types at the end of the pork supply chain. These farm types showed higher values for the ingoing temporal correlation coefficient. The temporal correlation coefficient is a valuable tool to understand the structural dynamics of these systems, as it assesses the consistency of the edge configuration. The adaption of this measure for directed networks may help to preserve meaningful additional information about the investigated network that might get lost if the edge directions are ignored.Electronic supplementary materialThe online version of this article (doi:10.1186/s40064-016-2875-0) contains supplementary material, which is available to authorized users.