Crowd Sensors Research Articles

Direct activation of HSF1 by macromolecular crowding and misfolded proteins.

Stress responses play a vital role in cellular survival against environmental challenges, often exploited by cancer cells to proliferate, counteract genomic instability, and resist therapeutic stress. Heat shock factor protein 1 (HSF1), a central transcription factor in stress response pathways, exhibits markedly elevated activity in cancer. Despite extensive research into the transcriptional role of HSF1, the mechanisms underlying its activation remain elusive. Upon exposure to conditions that induce protein damage, monomeric HSF1 undergoes rapid conformational changes and assembles into trimers, a key step for DNA binding and transactivation of target genes. This study investigates the role of HSF1 as a sensor of proteotoxic stress conditions. Our findings reveal that purified HSF1 maintains a stable monomeric conformation independent of molecular chaperones in vitro. Moreover, while it is known that heat stress triggers HSF1 trimerization, a notable increase in trimerization and DNA binding was observed in the presence of protein-based crowders. Conditions inducing protein misfolding and increased protein crowding in cells directly trigger HSF1 trimerization. In contrast, proteosynthesis inhibition, by reducing denatured proteins in the cell, prevents HSF1 activation. Surprisingly, HSF1 remains activated under proteotoxic stress conditions even when bound to Hsp70 and Hsp90. This finding suggests that the negative feedback regulation between HSF1 and chaperones is not directly driven by their interaction but is realized indirectly through chaperone-mediated restoration of cytoplasmic proteostasis. In summary, our study suggests that HSF1 serves as a molecular crowding sensor, trimerizing to initiate protective responses that enhance chaperone activities to restore homeostasis.

A cytoplasmic osmosensing mechanism mediated by molecular crowding-sensitive DCP5.

Plants are frequently challenged by osmotic stresses. How plant cells sense environmental osmolarity changes is not fully understood. We report that Arabidopsis Decapping 5 (DCP5) functions as a multifunctional cytoplasmic osmosensor that senses and responds to extracellular hyperosmolarity. DCP5 harbors a plant-specific intramolecular crowding sensor (ICS) that undergoes conformational change and drives phase separation in response to osmotically intensified molecular crowding. Upon hyperosmolarity exposure, DCP5 rapidly and reversibly assembles to DCP5-enriched osmotic stress granules (DOSGs), which sequestrate plenty of mRNA and regulatory proteins, and thus adaptively reprograms both the translatome and transcriptome to facilitate plant osmotic stress adaptation. Our findings uncover a cytoplasmic osmosensing mechanism mediated by DCP5 with plant-specific molecular crowding sensitivity and suggest a stress sensory function for hyperosmotically induced stress granules.

Cell stress and phase separation stabilize the monomeric state of pseudoisocyanine chloride employed as a self-assembly crowding sensor

Cellular stress and ageing involve an increase in crowding and aggregation of amylogenic proteins. We here investigate if crowding is the intrinsic cause of aggregation and utilise a previously established non-protein aggregation sensor, namely pseudoisocyanine chloride (PIC). PIC shows fibrillization in cells into a highly fluorescent J-aggregated state and is sensitive to crowding. Surprisingly, cell stress conditions stabilise the monomeric rather than the aggregated state of PIC both in the cytoplasm and in stress granules. Regarding the different physiochemical changes of the cytoplasm occurring upon cell stress, involving volume reduction, phase separation and solidification, the intrinsic crowding effect is not the key factor to drive associated self-assembly processes.

CrowdWaterSens: An uncertainty-aware crowdsensing approach to groundwater contamination estimation

Groundwater contamination poses serious threats to public health and environmental sustainability. In this paper, we explore smart groundwater contamination sensing, which aims to accurately estimate the nitrate concentration in groundwater via a crowdsensing approach. Existing solutions often require professional groundwater collection and high-quality measurement of groundwater properties, making the data collection process time-consuming and unscalable. In this work, we leverage the approximate nitrate concentration measured by crowd sensors (i.e., participants from well-dependent communities) to accurately estimate nitrate concentration in groundwater samples. Three critical challenges exist in developing the crowdsensing-based groundwater contamination estimation solution: (i) the spatial irregularity of the crowdsensing groundwater contamination data, (ii) the hidden temporal dependency of groundwater contamination in the anthropogenic context, and (iii) the uncertainty of crowdsensing nitrate measurements from crowd sensors. To address the above challenges, we develop CrowdWaterSens, an uncertainty-aware graph neural network framework that explicitly examines the uncertainty and spatial irregularity of the crowdsensing groundwater contamination data and its relevant anthropogenic context to accurately estimate groundwater nitrate concentration. We evaluate the CrowdWaterSens framework through two real-world case studies in well-dependent communities in Northern Indiana, United States. The evaluation results not only show the effectiveness of CrowdWaterSens in accurately estimating nitrate concentration, but also demonstrate the viability of crowdsensing for community-level groundwater quality monitoring.

WNK1, a molecular crowding sensor, links phase separation to cellular physiological stress.

WNK1, a molecular crowding sensor, links phase separation to cellular physiological stress.

Cell wall damage increases macromolecular crowding effects in the Escherichia coli cytoplasm

The intracellular milieu is crowded with biomacromolecules. Macromolecular crowding changes the interactions, diffusion, and conformations of biomacromolecules. Changes in intracellular crowding have been mostly ascribed to differences in biomacromolecule concentration. However, spatial organization of these molecules should play a significant role in crowding effects. Here, we find that cell wall damage causes increased crowding effects in the Escherichia coli cytoplasm. Using a genetically encoded macromolecular crowding sensor, we see that crowding effects in spheroplasts and penicillin-treated cells well surpass crowding effects obtained using hyperosmotic stress. The crowding increase is not because of osmotic pressure, cell shape, or volume changes and therefore not crowder concentration. Instead, a genetically encoded nucleic acid stain and a DNA stain show cytoplasmic mixing and nucleoid expansion, which could cause these increased crowding effects. Our data demonstrate that cell wall damage alters the biochemical organization in the cytoplasm and induces significant conformational changes in a probe protein.

Affirmed Crowd Sensor Selection based Cooperative Spectrum Sensing

The Cooperative Spectrum sensing model is gaining importance among the cognitive radio network sharing groups. While the crowd-sensing model (technically the cooperative spectrum sensing) model has positive developments, one of the critical challenges plaguing the model is the false or manipulated crowd sensor data, which results in implications for the secondary user’s network. Considering the efficacy of the spectrum sensing by crowd-sensing model, it is vital to address the issues of falsifications and manipulations, by focusing on the conditions of more accurate determination models. Concerning this, a method of avoiding falsified crowd sensors from the process of crowd sensors centric cooperative spectrum sensing has portrayed in this article. The proposal is a protocol that selects affirmed crowd sensor under diversified factors of the decision credibility about spectrum availability. An experimental study is a simulation approach that evincing the competency of the proposal compared to the other contemporary models available in recent literature.

A Blockchain-based privacy preserving mechanism for mobile crowdsensing

In blockchain-based mobile crowdsensing, reporting of real-time data is stored on a public blockchain in which the address of every user/node is public. Now, the problem lies in the fact that if their addresses get shown to adversaries, all their transactions history is also going to be revealed. Therefore, crowdsensing demands a little privacy preservation strategy in which the identity of a user is unable to be revealed to an adversary or we can say that crowd sensors while reporting the real-time data must provide some level of anonymity to crowdsensing users/nodes [1]. The current crowdsensing architecture is not secure because of its centralized nature and the reason is a single point of failure also numerous kinds of attacks are possible by adversaries such as linkage attacks, Sybil attacks, and DDOS attacks to get the identity or any other valuable information about the nodes. The location of crowd sensors is also a threat that could lead to adversarial attacks. Consequently, some blockchain-based models must be proposed to attain privacy on the blockchain ledger. The solution can either be made up crowdsensing environment on a private blockchain or smart contracts may be the answer to this problem by which we can make the users secure from several attacks conducted by adversaries on the blockchain.

An improved macromolecular crowding sensor CRONOS for detection of crowding changes in membrane-less organelles under stressed conditions

Membrane-less organelles (MLOs) formed by liquid-liquid phase separation (LLPS) play pivotal roles in biological processes. During LLPS, proteins and nucleotides are extremely condensed, resulting in changes in their conformation and biological functions. Disturbed LLPS homeostasis in MLOs is thought to associate with fatal diseases such as amyotrophic lateral sclerosis. Therefore, it is important to detect changes in the degree of crowding in MLOs. However, it has not been investigated well due to the lack of an appropriate method. To address this, we developed a genetically encoded macromolecular crowding sensor CRONOS (crowding sensor with mNeonGreen and mScarlet-I) that senses the degree of macromolecular crowding in MLOs using a fluorescence resonance energy transfer (FRET) system. CRONOS is a bright biosensor with a wide dynamic range and successfully detects changes in the macromolecular volume fraction in solution. By fusing to the scaffold protein of each MLO, we delivered CRONOS to MLO of interest and detected previously undescribed differences in the degree of crowding in each MLO. CRONOS also detected changes in the degree of macromolecular crowding in nucleolus induced by environmental stress or inhibition of transcription. These findings suggest that CRONOS can be a useful tool for the determination of molecular crowding and detection of pathological changes in MLOs in live cells.

ADS-B Crowd-Sensor Network and Two-Step Kalman Filter for GNSS and ADS-B Cyber-Attack Detection.

Automatic Dependent Surveillance-Broadcast is an Air Traffic Control system in which aircraft transmit their own information (identity, position, velocity, etc.) to ground sensors for surveillance purposes. This system has many advantages compared to the classical surveillance radars: easy and low-cost implementation, high accuracy of data, and low renewal time, but also limitations: dependency on the Global Navigation Satellite System, a simple unencrypted and unauthenticated protocol. For these reasons, the system is exposed to attacks like jamming/spoofing of the on-board GNSS receiver or false ADS-B messages’ injection. After a mathematical model derivation of different types of attacks, we propose the use of a crowd sensor network capable of estimating the Time Difference Of Arrival of the ADS-B messages together with a two-step Kalman filter to detect these attacks (on-board GNSS/ADS-B tampering, false ADS-B message injection, GNSS Spoofing/Jamming). Tests with real data and simulations showed that the algorithm can detect all these attacks with a very high probability of detection and low probability of false alarm.

Exploiting Real-time Search Engine Queries for Earthquake Detection: A Summary of Results

Online search engine has been widely regarded as the most convenient approach for information acquisition. Indeed, the intensive information-seeking behaviors of search engine users make it possible to exploit search engine queries as effective “crowd sensors” for event monitoring. While some researchers have investigated the feasibility of using search engine queries for coarse-grained event analysis, the capability of search engine queries for real-time event detection has been largely neglected. To this end, in this article, we introduce a large-scale and systematic study on exploiting real-time search engine queries for outbreak event detection, with a focus on earthquake rapid reporting. In particular, we propose a realistic system of real-time earthquake detection through monitoring millions of queries related to earthquakes from a dominant online search engine in China. Specifically, we first investigate a large set of queries for selecting the representative queries that are highly correlated with the outbreak of earthquakes. Then, based on the real-time streams of selected queries, we design a novel machine learning–enhanced two-stage burst detection approach for detecting earthquake events. Meanwhile, the location of an earthquake epicenter can be accurately estimated based on the spatial-temporal distribution of search engine queries. Finally, through the extensive comparison with earthquake catalogs from China Earthquake Networks Center, 2015, the detection precision of our system can achieve 87.9%, and the accuracy of location estimation (province level) is 95.7%. In particular, 50% of successfully detected results can be found within 62 s after earthquake, and 50% of successful locations can be found within 25.5 km of seismic epicenter. Our system also found more than 23.3% extra earthquakes that were felt by people but not publicly released, 12.1% earthquake-like special outbreaks, and meanwhile, revealed many interesting findings, such as the typical query patterns of earthquake rumor and regular memorial events. Based on these results, our system can timely feed back information to the search engine users according to various cases and accelerate the information release of felt earthquakes.

A physicochemical perspective of aging from single-cell analysis of pH, macromolecular and organellar crowding in yeast.

Cellular aging is a multifactorial process that is characterized by a decline in homeostatic capacity, best described at the molecular level. Physicochemical properties such as pH and macromolecular crowding are essential to all molecular processes in cells and require maintenance. Whether a drift in physicochemical properties contributes to the overall decline of homeostasis in aging is not known. Here, we show that the cytosol of yeast cells acidifies modestly in early aging and sharply after senescence. Using a macromolecular crowding sensor optimized for long-term FRET measurements, we show that crowding is rather stable and that the stability of crowding is a stronger predictor for lifespan than the absolute crowding levels. Additionally, in aged cells, we observe drastic changes in organellar volume, leading to crowding on the micrometer scale, which we term organellar crowding. Our measurements provide an initial framework of physicochemical parameters of replicatively aged yeast cells.

Bayesian filter based on the wisdom of crowds

Abstract Nowadays, the wisdom of crowds aids in data labeling via crowd sensors. One of the successful tools worth mentioning is Amazon Mechanical Turk. Uncertainty in crowd labels, however, deteriorates the result of the learning algorithm. In some applications, such as weather and stock forecasting and object tracking, the temporal dependency of data (dynamics) is effective in decreasing label uncertainty. In order to benefit from the existing knowledge in label dynamics, the current study first employs a traditional state-space model and it shows that these models have serious drawbacks, for instance, sensors with a low coverage rate and the existence of a random labeler are the main challenges posed in this process. Then, an appropriate dynamic model for crowd sensors is presented and the Bayesian filter is applied so that true label inference and system parameter learning are performed jointly. The present work will show that the proposed method is robust enough to meet these challenges and performs better in comparison to the existing methods. The results of experiments on synthetic and real data confirm this issue.

Game based incentive mechanism for cooperative spectrum sensing with mobile crowd sensors

Mobile crowd sensing emerges as a new paradigm where widespread personal sensors can help to collect information beyond the previously possible scale. Existing spectrum sensing papers consider only secondary users in a cognitive system as the cooperative nodes. While if there are not enough secondary users exist, the cooperative detection performance will be degraded,and which will incur to a low spectrum efficiency or an inevitable interference to primary users. Hence in this paper, we take advantage of the Mobile Crowd Sensing to study the cooperative spectrum sensing where the sensing users are not only the secondary users but also a crowd of mobile users equipped with personal spectrum sensors (such as smartphones, vehicle sensors and tablets). Since spectrum sensing will incur costs for sensing users, an incentive mechanism is necessary to motivate adequate participation. In this paper, a Stackelberg game based incentive mechanism is proposed where the fusion center is leader and mobile sensing users are followers. In Leader game, the fusion center provides an optimal reward to incentive sensing users. And in Follower game, mobile sensing users adjust their own detection probabilities for obtaining more profits when given the reward published by the fusion center. Furthermore, the strategy adjusting process for each sensing user is modeled as a Coalition game where sensing users decide whether join in a coalition based on its utility maximization. We prove the existence of unique Nash Equilibrium in both Follower game and Leader game. Extensive simulations evaluate the feasibility and practically of proposed incentive mechanism. And the results show that a better detection performance can be obtained by the fusion center in our algorithm.

Direct Imaging of Protein Stability and Folding Kinetics in Hydrogels.

We apply fast relaxation imaging (FReI) as a novel technique for investigating the folding stability and dynamics of proteins within polyacrylamide hydrogels, which have diverse and widespread uses in biotechnology. FReI detects protein unfolding in situ by imaging changes in fluorescence resonance energy transfer (FRET) after temperature jump perturbations. Unlike bulk measurements, diffraction-limited epifluorescence imaging combined with fast temperature perturbations reveals the impact of local environment effects on protein-biomaterial compatibility. Our experiments investigated a crowding sensor protein (CrH2) and phosphoglycerate kinase (PGK), which undergoes cooperative unfolding. The crowding sensor quantifies the confinement effect of the cross-linked hydrogel: the 4% polyacrylamide hydrogel is similar to aqueous solution (no confinement), while the 10% hydrogel is strongly confining. FRAP measurements and protein concentration gradients in the 4% and 10% hydrogels further support this observation. PGK reveals that noncovalent interactions of the protein with the polymer surface are more important than confinement for determining protein properties in the gel: the mere presence of hydrogel increases protein stability, speeds up folding relaxation, and promotes irreversible binding to the polymer even at the solution-gel interface, whereas the difference between the 4% and the 10% hydrogels is negligible despite their large difference in confinement. The imaging capabilities of FReI, demonstrated to be diffraction limited, further revealed spatially homogeneous protein unfolding across the hydrogels at 500 nm length scales and revealed differences in protein properties at the gel-solution boundary.

Robust Localization with Crowd Sensors: A Data Cleansing Approach

In this paper, the source localization problem with crowd of anchor nodes is investigated, under the circumstances that abnormal data could be sporadically and randomly produced for the reason of either accidental equipment failures or random malicious behaviors. To cope with the problem that abnormal data brings, we formulate a generalized modeling of abnormal data in localization problem, which involves the impacts of both unexpected equipment failures and malicious data falsifications. The corresponding Cramer-Rao lower bound (CRLB) of the specific localization problem is derived. For the localization enhancement, we propose a data cleansing-based robust localization algorithm which exploits the low occupancy of channel band by sources and the sparsity of abnormal data. The data cleansing approach achieves both the new sensing data matrix that cleansed out abnormal data component and the estimated abnormal data matrix, which are respectively used for the correct source detection process and the position estimation process of the final source localization. The root mean squared error (RMSE) is derived to assess the performance of the proposed robust localization algorithm. Computer simulations show that the proposed data cleansing-based robust localization algorithm can effectively eliminate the impairment of the abnormal data and hence improve the localization performance evidently.

Emergency Event Web Information Acquisition using Crowd Web Sensors

In the domain of emergent event analysis, it is still a difficult issue to acquire the event information from the Web efficiently. To solve the problem, this paper proposes a crowdsensing-based Web crawler for emergent event analysis. When an emergent event occurs, some web users post event information on the Web with geographical position. These web users can be regarded as crowd sensors. In the proposed method, the crawler takes advantage of the information from these crowd sensors, such as semantic information, geographical information, sentiment information, etc., to get the information of event efficiently. Experimental results show that the proposed method can improve the efficiency of crawlers when compared with universal crawlers both in the period of sparse information and in the period of eruptible information.

The use of a genetically encoded molecular crowding sensor in various biological phenomena.

We evaluated usability of a previously developed genetically encoded molecular crowding sensor in various biological phenomena. Molecular crowding refers to intracellular regions that are occupied more by proteins and nucleotides than by water molecules and is thought to have a strong effect on protein function. To evaluate intracellular molecular crowding, usually the diffusion coefficient of a probe is used because it is related to mobility of the surrounding molecular crowding agents. Recently, genetically encoded molecular crowding sensors based on Förster resonance energy transfer were reported. In the present study, to evaluate the usability of a genetically encoded molecular crowding sensor, molecular crowding was monitored during several biological events. Changes in molecular crowding during stem cell differentiation, cell division, and focal adhesion development and difference in molecular crowding in filopodia locations were examined. The results show usefulness of the genetically encoded molecular crowding sensor for understanding the biological phenomena relating to molecular crowding.

Shaping City Neighborhoods Leveraging Crowd Sensors

Location-based social networks (LBSN) are capturing large amount of data related to whereabouts of their users. This has become a social phenomenon, that is changing the normal communication means and it opens new research perspectives on how to compute descriptive models out of this collection of geo-spatial data. In this paper, we propose a methodology for clustering location-based information in order to provide first glance summaries of geographic areas. The summaries are a composition of fingerprints, each being a cluster, generated by a new subspace clustering algorithm, named GeoSubClu, that is proposed in this paper. The algorithm is parameter-less: it automatically recognizes areas with homogeneous density of similar points of interest and provides clusters with a rich characterization in terms of the representative categories. We measure the validity of the generated clusters using both a qualitative and a quantitative evaluation. In the former, we benchmark the results of our methodology over an existing gold standard, and we compare the achieved results against two baselines. We then further validate the generated clusters using a quantitative analysis, over the same gold standard and a new geographic extent, using statistical validation measures. Results of the qualitative and quantitative experiments show the robustness of our approach in creating geographic clusters which are significant both for humans (holding a F-measure of 88.98% over the gold standard) and from a statistical point of view.

In-Cell Protein Folding - PAPS Synthases

Here, we study the stabilities of different naturally fragile mutants of 3' phosphoadenosine 5' phosphosulfate (PAPS) synthases. The goal is to establish a relationship between the stability of the mutants in the cellular environment and their enzymatic activity. In vitro stability measurements have suggested that protein stability is a major factor for cellular PAPS availability [1]. We aim to specifically analyze different cellular factors for in-cell PAPSS stability. One crucial factor is the macromolecular crowding effect. The cell is filled up to a volume of 40 % with macromolecules. Often, artificial macromolecular crowding agents are used to mimic these conditions. We previously studied macromolecular crowding effects via a thermodynamic analysis of the thermal unfolding of ubiquitin [2]. We observed enthalpic stabilization and entropic destabilization forces for all tested crowders. Further, we tested how such artificial cosolutes reflect the physicochemical properties of the complex cellular environment. Therefore, we developed a FRET-based macromolecular crowding sensor to study the crowding effect in living cells [3]. We found that the in-cell crowding effect is distributed heterogeneously and can change significantly upon osmotic stress. In comparison, we now used Fast Relaxation Imaging [4] to study the stability of PAPSS within the cellular environment and compare the results to in vitro crowding studies.