Diffusion Search Research Articles

Exploiting cryo-EM structures of actomyosin-5a to reveal the physical properties of its lever.

Myosin 5a (Myo5a) is a dimeric processive motor protein that transports cellular cargos along filamentous actin (F-actin). Its long lever is responsible for its large power-stroke, step size, and load-bearing ability. Little is known about the levers' structure and physical properties, and how they contribute to walking mechanics. Using cryoelectron microscopy (cryo-EM) and molecular dynamics (MD) simulations, we resolved the structure of monomeric Myo5a, comprising the motor domain and full-length lever, bound to F-actin. The range of its lever conformations revealed its physical properties, how stiffness varies along its length and predicts a large, 35nm, working stroke. Thus, the newly released trail head in a dimeric Myo5a would only need to perform a small diffusive search for its new binding site on F-actin, and stress would only be generated across the dimer once phosphate is released from the lead head, revealing new insight into the walking behavior of Myo5a.

Search of extended emission from HESS J1702-420 with eROSITA

is a peculiar TeV complex with a morphology changing from a diffuse source ( at $ 2$ TeV to a point-like one ( at $ 10$ TeV energies. The morphology and spectral properties of could be understood in terms of (diffusive) hadronic or leptonic models in which the observed TeV emission arises correpondingly from proton-proton or IC-radiation of relativistic particles present in the region. In this work, we perform searches of the X-ray counterpart of the source originating from the synchrotron emission of the primary or secondary relativistic electrons produced within leptonic or hadronic models. Such an emission can be extended and remain beyond the detection capabilities of narrow field-of-view instruments such as We utilised the publicly available first-6-months eROSITA dataset (DR1) fully covering the selected analysis region of $>5^ around We discuss biases connected to variable plasma temperature or neutral hydrogen column density in the region and present results based on the background modelling approach. The performed analysis does not allow us to detect the extended X-ray counterpart of of $0.07^ sizes. The derived upper limits are significantly higher than the expected hadronic model flux of the X-ray counterpart. For the leptonic model, the derived limits indicate the magnetic field in the region $B We argue that further advances in the diffuse X-ray counterpart searches could be achieved with either next-generation missions or Msec-long observational campaigns using currently operating instruments.

"Modeling Diffusive Search by Non-Adaptive Sperm: Empirical and Computational Insights".

During fertilization, mammalian sperm undergo a winnowing selection process that reduces the candidate pool of potential fertilizers from ~106-1011 cells to 101-102 cells (depending on the species). Classical sperm competition theory addresses the positive or 'stabilizing' selection that acts on sperm phenotypes within populations of organisms but does not strictly address the developmental consequences of sperm traits among individual organisms that are under purifying selection during fertilization. It is the latter that is of utmost concern for improving assisted reproductive technologies (ART) because 'low fitness' sperm may be inadvertently used for fertilization during interventions that rely heavily on artificial sperm selection, such as intracytoplasmic sperm injection (ICSI). Importantly, some form of sperm selection is used in nearly all forms of ART (e.g., differential centrifugation, swim-up, or hyaluronan binding assays, etc.). To date, there is no unifying quantitative framework (i.e., theory of sperm selection) that synthesizes causal mechanisms of selection with observed natural variation in individual sperm traits. In this report, we reframe the physiological function of sperm as a collective diffusive search process and develop multi-scale computational models to explore the causal dynamics that constrain sperm 'fitness' during fertilization. Several experimentally useful concepts are developed, including a probabilistic measure of sperm 'fitness' as well as an information theoretic measure of the magnitude of sperm selection, each of which are assessed under systematic increases in microenvironmental selective pressure acting on sperm motility patterns.

Pulse shape discrimination technique for diffuse supernova neutrino background search with JUNO

Pulse shape discrimination (PSD) is widely used in particle and nuclear physics. Specifically in liquid scintillator detectors, PSD facilitates the classification of different particle types based on their energy deposition patterns. This technique is particularly valuable for studies of the diffuse supernova neutrino background (DSNB), nucleon decay, and dark matter searches. This paper presents a detailed investigation of the PSD technique, applied in the DSNB search performed with the Jiangmen Underground Neutrino Observatory (JUNO). Instead of using conventional cut-and-count methods, we employ methods based on boosted decision trees and neural networks and compare their capability to distinguish the DSNB signals from the atmospheric neutrino neutral-current background events. The two methods demonstrate comparable performance, resulting in a 50–80% improvement in signal efficiency compared to a previous study performed for JUNO (An et al. [JUNO] in J Phys G 43(3):030401, 2016). Moreover, we study the dependence of the PSD performance on the visible energy and final state composition of the events and find a significant dependence on the presence/absence of 11\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{11}$$\\end{document}C. Finally, we evaluate the impact of the detector effects (photon propagation, PMT dark noise, and waveform reconstruction) on the PSD performance.

Disordered C-terminal domain drives spatiotemporal confinement of RNAPII to enhance search for chromatin targets.

Efficient gene expression requires RNA polymerase II (RNAPII) to find chromatin targets precisely in space and time. How RNAPII manages this complex diffusive search in three-dimensional nuclear space remains largely unknown. The disordered carboxy-terminal domain (CTD) of RNAPII, which is essential for recruiting transcription-associated proteins, forms phase-separated droplets in vitro, hinting at a potential role in modulating RNAPII dynamics. In the present study, we use single-molecule tracking and spatiotemporal mapping in living yeast to show that the CTD is required for confining RNAPII diffusion within a subnuclear region enriched for active genes, but without apparent phase separation into condensates. Both Mediator and global chromatin organization are required for sustaining RNAPII confinement. Remarkably, truncating the CTD disrupts RNAPII spatial confinement, prolongs target search, diminishes chromatin binding, impairs pre-initiation complex formation and reduces transcription bursting. The present study illuminates the pivotal role of the CTD in driving spatiotemporal confinement of RNAPII for efficient gene expression.

Transition path theory for diffusive search with stochastic resetting

Many chemical reactions can be formulated in terms of particle diffusion in a complex energy landscape. Transition path theory (TPT) is a theoretical framework for describing the direct (reaction) pathways from reactant to product states within this energy landscape, and calculating the effective reaction rate. It is now the standard method for analyzing rare events between long lived states. In this paper, we consider a completely different application of TPT, namely, a dual-aspect diffusive search process in which a particle alternates between collecting cargo from a source domain A and then delivering it to a target domain B. The rate of resource accumulation at the target, k AB , is determined by the statistics of direct (reactive or transport) paths from A to B. Rather than considering diffusion in a complex energy landscape, we focus on pure diffusion with stochastic resetting. Resetting introduces two non-trivial problems in the application of TPT. First, the process is not time-reversal invariant, which is reflected by the fact that there exists a unique non-equilibrium stationary state (NESS). Second, calculating k AB involves determining the total probability flux of direct transport paths across a dividing surface S between A and B. This requires taking into account discontinuous jumps across S due to resetting. We derive a general expression for k AB and show that it is independent of the choice of dividing surface. Finally, using the example of diffusion in a finite interval, we show that there exists an optimal resetting rate at which k AB is maximized. We explore how this feature depends on model parameters.

Truncated stochastically switching processes.

There are a large variety of hybrid stochastic systems that couple a continuous process with some form of stochastic switching mechanism. In many cases the system switches between different discrete internal states according to a finite-state Markov chain, and the continuous dynamics depends on the current internal state. The resulting hybrid stochastic differential equation(hSDE) could describe the evolution of a neuron's membrane potential, the concentration of proteins synthesized by a gene network, or the position of an active particle. Another major class of switching system is a search process with stochastic resetting, where the position of a diffusing or active particle is reset to a fixed position at a random sequence of times. In this case the system switches between a search phase and a reset phase, where the latter may be instantaneous. In this paper, we investigate how the behavior of a stochastically switching system is modified when the maximum number of switching (or reset) events in a given time interval is fixed. This is motivated by the idea that each time the system switches there is an additive energy cost. We first show that in the case of an hSDE, restricting the number of switching events is equivalent to truncating a Volterra series expansion of the particle propagator. Such a truncation significantly modifies the moments of the resulting renormalized propagator. We then investigate how restricting the number of reset events affects the diffusive search for an absorbing target. In particular, truncating a Volterra series expansion of the survival probability, we calculate the splitting probabilities and conditional MFPTs for the particle to be absorbed by the target or exceed a given number of resets, respectively.

Software Testing Using Cuckoo Search Algorithm with Machine Learning Techniques

Software testing are any errors, flaws, bugs, mistakes, failures in a piece of software that might cause the programme to produce incorrect or unexpected results. Testing in software almost always increase both the time and money needed to finish a project. And finding bugs and fixing them is a laborious and expensive software process in and of itself. While it's unrealistic to expect to completely eradicate all testing from a project, their severity may be mitigated. It is possible to predict where bugs may appear in software using a method known as software defect prediction (SDP). The goal of each software development project should be to provide a bug-free product. Predicting where bugs may appear in code, often known as software defect prediction (SDP), is an important part of fixing software. Software of a high calibre should have few bugs. A software metric is a quantitative or qualitative evaluation of some aspect of the programme or its requirements. One of the more recent population-based algorithms, Cuckoo Search (CS) was inspired by the flight patterns of some cuckoo species as well as the Lévy flying patterns of other birds and fruit flies. The needs for international convergence are met by CS. KNN is a significant non-parameter supervised learning technique. This paper presents an overview of Stochastic Diffusion Search (SDS) in the form of a social metaphor to illustrate the processes by which SDS allots resources. The best-fit pattern identification and matching difficulties were addressed by SDS using a novel probabilistic method. As a multiagent population-based global search and optimization method, SDS is a distributed model of computing that makes use of interaction amongst basic agents. The behaviour of SDS is described by studying its resource allocation, convergence to global optimum, resilience, minimum convergence criterion, and linear time complexity within a rigorous mathematical framework, setting it apart from many nature-inspired search algorithms. This paper proposes a hybrid optimization strategy based on CS-SDS techniques. By using the global search strategy solution of the SDS algorithm, this hybridization idea aims to enhance the cuckoo bird's search strategy for the optimum host nest. To that end, the SDS method would be used to place the cuckoo egg in the most advantageous location. When compared to other classifiers, PC2's improved performance may be attributed to its higher recall values. When compared to the Naive Bayes and Radial Bias Neural Network classifiers, the KNN performs 7.64% and 2.20% better, respectively.

First-passage times under frequent stochastic resetting.

We determine the full distribution and moments of the first passage time for a wide class of stochastic search processes in the limit of frequent stochastic resetting. Our results apply to any system whose short-time behavior of the search process without resetting can be estimated. In addition to the typical case of exponentially distributed resetting times, we prove our results for a variety of resetting time distributions. We illustrate our results in several examples and show that the errors of our approximations vanish exponentially fast in typical scenarios of diffusive search.

Critical number of walkers for diffusive search processes with resetting.

We consider N Brownian motions diffusing independently on a line, starting at x_{0}>0, in the presence of an absorbing target at the origin. The walkers undergo stochastic resetting under two protocols: (A) each walker resets independently to x_{0} with rate r and (B) all walkers reset simultaneously to x_{0} with rate r. We derive an explicit analytical expression for the mean first-passage time to the origin in terms of an integral which is evaluated numerically using Mathematica. We show that, as a function of r and for fixed x_{0}, it has a minimum at an optimal value r^{*}>0 as long as N<N_{c}. Thus resetting is beneficial for the search for N<N_{c}. When N>N_{c}, the optimal value occurs at r^{*}=0 indicating that resetting hinders search processes. We obtain different values of N_{c} for protocols A and B; indeed, for N≤7 resetting is beneficial in protocol A, while for N≤6 resetting is beneficial for protocol B. Our theoretical predictions are verified in numerical Langevin simulations.

Diffusion with stochastic resetting screened by a semipermeable interface

In this paper we consider the diffusive search for a bounded target with its boundary totally absorbing. We assume that the target is surrounded by a semipermeable interface given by the closed surface with . That is, the interface totally surrounds the target and thus partially screens the diffusive search process. We also assume that the position of the diffusing particle (searcher) randomly resets to its initial position x 0 according to a Poisson process with a resetting rate r. The location x 0 is taken to be outside the interface, , which means that resetting does not occur when the particle is within the interior of . (Otherwise, the particle would have to cross the interface in order to reset to x 0.) Hence, the semipermeable interface also screens out the effects of resetting. We illustrate the theory by explicitly solving the boundary value problem for a three-dimensional (3D) spherically symmetric interface and a concentric spherical target. We calculate the mean first passage time (MFPT) to find (be absorbed by) the target and explore its behavior as a function of the permeability κ 0 of the interface and the radius of the interface R. In particular, we find that increasing R for a fixed target size reduces the MFPT and increases the optimal resetting rate at which the MFPT is minimized. We also find that the sensitivity of the MFPT to changes in κ 0 is a decreasing function of R. Finally, we introduce a stochastic single-particle realization of the search process based on a generalization of so-called snapping out Brownian motion (BM). The latter sews together successive rounds of reflecting BM on either side of the interface. The main challenge is establishing that the probability density generated by the snapping out BM satisfies the permeable boundary conditions at the interface. We show how this can be achieved using renewal theory.

Large time probability of failure in diffusive search with resetting for a random target in ℝ^{𝕕}–A functional analytic approach

We consider a stochastic search model with resetting for an unknown stationary target a ∈ R d , d ≥ 1 a\in \mathbb {R}^d,\ d\ge 1 , with known distribution μ \mu . The searcher begins at the origin and performs Brownian motion with diffusion coefficient D D . The searcher is also equipped with an exponential clock with rate r &gt; 0 r&gt;0 , so that if it has failed to locate the target by the time the clock rings, then its position is reset to the origin and it continues its search anew from there. In dimension one, the target is considered located when the process hits the point a a , while in dimensions two and higher, one chooses an ϵ 0 &gt; 0 \epsilon _0&gt;0 and the target is considered located when the process hits the ϵ 0 \epsilon _0 -ball centered at a a . Denote the position of the searcher at time t t by X ( t ) X(t) , let τ a \tau _a denote the time that a target at a a is located, and let P 0 d ; ( r , 0 ) P^{d;(r,0)}_0 denote probabilities for the process starting from 0. Taking a functional analytic point of view, and using the generator of the Markovian search process and its adjoint, we obtain precise estimates, with control on the dependence on a a , for the asymptotic behavior of P 0 d ; ( r , 0 ) ( τ a &gt; t ) P^{d;(r,0)}_0(\tau _a&gt;t) for large time, and then use this to obtain large time estimates on ∫ R d P 0 d ; ( r , 0 ) ( τ a &gt; t ) d μ ( a ) \int _{\mathbb {R}^d}P^{d;(r,0)}_0(\tau _a&gt;t)d\mu (a) , the probability that the searcher has failed up to time t t to locate the random target, for a variety of families of target distributions μ \mu . Specifically, for B , l &gt; 0 B,l&gt;0 and d ∈ N d\in \mathbb {N} , let μ B , l ( d ) ∈ P ( R d ) \mu ^{(d)}_{B,l}\in \mathcal {P}(\mathbb {R}^d) denote any target distribution with density μ B , l ( d ) ( a ) \mu _{B,l}^{(d)}(a) that satisfies lim | a | → ∞ log ⁡ μ B , l ( d ) ( a ) | a | l = − B . \begin{equation*} \lim _{|a|\to \infty }\frac {\log \mu _{B,l}^{(d)}(a)}{|a|^l}=-B. \end{equation*} Then we prove that lim t → ∞ 1 ( log ⁡ t ) l log ⁡ ∫ R d P 0 d ; ( r , 0 ) ( τ a &gt; t ) μ B , l ( d ) ( d a ) = − B ( D 2 r ) l 2 . \begin{equation*} \lim _{t\to \infty }\frac 1{(\log t)^l}\log \int _{\mathbb {R}^d} P_0^{d;(r,0)}(\tau _a&gt;t)\mu _{B,l}^{(d)}(da)=-B(\frac D{2r})^\frac l2. \end{equation*} The result is independent of the dimension. In particular, for example, if the target distribution is a centered Gaussian of any dimension with variance σ 2 \sigma ^2 , then for any δ &gt; 0 \delta &gt;0 , the probability of not locating the target by time t t falls in the interval ( e − ( 1 + δ ) D 4 r σ 2 ( log ⁡ t ) 2 , e − ( 1 − δ ) D 4 r σ 2 ( log ⁡ t ) 2 ) \big (e^{-(1+\delta )\frac {D}{4r\sigma ^2}(\log t)^2}, e^{-(1-\delta )\frac {D}{4r\sigma ^2}(\log t)^2}\big ) , for sufficiently large t t .

Translation kinetics and diffusive timescales regulate mitochondrial localization of mRNAs in yeast and mammalian cells.

Eukaryotes employ various regulatory mechanisms to maintain subcellular organization in dynamic environments. Localization of nuclear-encoded mitochondrial mRNAs attunes the nuclear genome to mitochondrial protein levels and metabolic needs. Targeting nuclear-encoded mitochondrial mRNAs to the mitochondrial surface is crucial for cotranslational import and proper folding of mitochondrial membrane proteins. Hundreds of mitochondrial mRNAs contain a Mitochondria Targeting Sequence (“MTS”) and preferentially localize to mitochondria using nascent peptide-mediated interactions with mitochondrial import machinery. Recent studies in brewer's yeast have illuminated MTS-mediated localization as a post-transcriptional mechanism of protein production, identifying two broad classes of mRNA localization patterns--constitutively localized or condition dependent. Our stochastic simulation incorporates gene-specific translation kinetics, MTS exposure and maturation, and diffusive search for mitochondria to explore and predict the localization patterns of mRNAs in brewer's yeast without the need for mRNA-specific binding partners. Our results point to the interplay of global and gene-specific mechanisms for tuning mRNA localization in response to changing metabolic conditions. Interestingly, conditional and constitutive localization patterns are largely conserved for homologous mRNAs from brewer's yeast to mammalian cells despite differences in cell size and gene-specific translation parameters. Notably, the conditional class is enriched for crucial TCA cycle genes, making these metabolic mRNAs sensitive to metabolic conditions in yeast and mammalian cells. Mammalian cell types also exhibit similar patterns in gene-specific kinetics for conditional and constitutive mRNAs. Both classes have similar ribosome densities whereas conditional mRNAs have significantly faster initiation and elongation rates. Given that patterns in mRNA localization and translation kinetics are conserved from brewer's yeast to mammals, we posit that the physical mechanism of mRNA localization is conserved as well.

A penalty function-based greedy diffusion search algorithm for the optimization of constrained nonlinear dynamical processes with discrete-valued input

This paper considers an optimization problem of nonlinear dynamical processes with discrete-valued input and continuous-time inequality constraints. This problem requires to determine an order and some instants. Since the order is a discrete variable and the constraints are complex, classical optimal control approaches cannot be used directly to solve this problem. To tackle this issue, a penalty function-based greedy diffusion search algorithm is proposed based on a novel transformation for each discrete value, a novel second-order smoothing technique, a $ l_1 $ penalty function, and a novel greedy rule. In the novel greedy rule, there exist only two parameters, which can be set in advance. Thus, unlike existing hybrid approaches, no parameters is required adjusted in the penalty function-based greedy diffusion search algorithm. Following that, the global convergence results of the proposed algorithm are established. Finally, an energy management problem in an electrical vehicle is used to illustrate the effectiveness of the proposed algorithm. The numerical simulation results show that the algorithm proposed by this paper can obtain a better solution with less time-consuming, fewer iterations, and fewer errors compared with existing methods.

A Hybridized Artificial Neural Network for Automated Software Test Oracle

Software testing is the methodology of analyzing the nature of software to test if it works as anticipated so as to boost its reliability and quality. These two characteristics are very critical in the software applications of present times. When testers want to perform scenario evaluations, test oracles are generally employed in the third phase. Upon test case execution and test outcome generation, it is essential to validate the results so as to establish the software behavior’s correctness. By choosing a feasible technique for the test case optimization and prioritization as along with an appropriate assessment of the application, leads to a reduction in the fault detection work with minimal loss of information and would also greatly reduce the cost for clearing up. A hybrid Particle Swarm Optimization (PSO) with Stochastic Diffusion Search (PSO-SDS) based Neural Network, and a hybrid Harmony Search with Stochastic Diffusion Search (HS-SDS) based Neural Network has been proposed in this work. Further to evaluate the performance, it is compared with PSO- SDS based artificial Neural Network (PSO-SDS ANN) and Artificial Neural Network (ANN). The Misclassification of correction output (MCO) of HS-SDS Neural Network is 6.37 for 5 iterations and is well suited for automated testing.

Design of High-Performance Intelligent WSN based-IoT using Time Synchronized Channel Hopping and Spatial Correlation Model

Wireless Sensor Network (WSN) is one of the most significant contributors to the Internet of Things (IoT), and it plays a significant role in the lives of individuals. There are three main problems in the design of traditional WSN based-IoT. First problem about data; the WSN transmits a huge volume of data to the IoT for processing. The second problem is the energy; since sensor nodes rely on their limited battery, conserving energy is crucial, and the third problem about efficiency of transmission. This paper presents new WSN based IoT framework that integrate important techniques to solve these problems; To increase the effectiveness of data processing and storing, the intelligent Adaptive Boosting stochastic algorithm is applied. IEEE 802.15.4e time slotted channel hopping (TSCH) protocol is used because it has the benefits such as collision-free transmission and multi-hop transmission. Data reduction at the Gateway (GW) level of the network is achieved through spatial correlation between sensors with the goal of conserving energy. Principle idea of this new framework is to identify the advantages of integrating the important techniques; intelligent Adaptive Boosting Stochastic diffusion search algorithm, TSCH, and Special correlation model. As a result, the proposed framework can thereby satisfy the need for a long battery life of low-rate applications and at the same time, the need for high throughput for high-rate uses also for testing it in achieved efficient classification of data, the important performance measures are used.

Optimizing minimum spanning tree using stochastic–Variable neighborhood search for efficient clustering of cancer gene data

SummaryWith the rapid advancement of DNA microarray technologies, researchers in biology can now monitor several gene expression levels in one single experiment that is very useful in detecting or classifying specific types of tumors. The microarray data and its high‐dimensional nature, along with a small set of samples, have presented a new but well‐known phenomenon called the curse of dimensionality. The process of organizing all objects into various groups based on their traits that are either similar or dissimilar is known as clustering. The framework has the foundation of a minimum spanning tree (MST), which is the representation of one set of gene expression data that is multidimensional. The main property of such representations is that every cluster of this expression data will correspond to a subtree in the MST that can convert the problem of multidimensional Clustering to the problem of tree partitioning. A stochastic diffusion search (SDS) has been deployed for clustering. The algorithm was used to be a multi‐agent technique of search and optimization. A variable neighborhood search (VNS) algorithm has been proposed to be a metaheuristic that solves problems of global and combinatorial optimization. The goal of this method was proceeded to get a systematic change to the neighborhood in a local search algorithm. The algorithm will continue to remain the same as an optimal solution that explores the far neighborhoods until better solutions are identified.

Development of ultra-pure gadolinium sulfate for the Super-Kamiokande gadolinium project

Abstract This paper reports the development and detailed properties of about 13 metric tons of gadolinium sulfate octahydrate, $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$, which has been dissolved into Super-Kamiokande (SK) in the summer of 2020. We evaluate the impact of radioactive impurities in $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$ on diffuse supernova neutrino background searches and solar neutrino observation and confirm the need to reduce radioactive and fluorescent impurities by about three orders of magnitude from commercially available high-purity $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$. In order to produce ultra-high-purity $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$, we have developed a method to remove impurities from gadolinium oxide, Gd2O3, consisting of acid dissolution, solvent extraction, and pH control processes, followed by a high-purity sulfation process. All of the produced ultra-high-purity $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$ is assayed by inductively coupled plasma mass spectrometry and high-purity germanium detectors to evaluate its quality. Because of the long measurement time of high-purity germanium detectors, we have employed several underground laboratories for making parallel measurements including the Laboratorio Subterráneo de Canfranc in Spain, Boulby in the UK, and Kamioka in Japan. In the first half of production, the measured batch purities were found to be consistent with the specifications. However, in the latter half, the $\rm Gd_2(\rm SO_4)_3\cdot \rm 8H_2O$ contained one order of magnitude more 228Ra than the budgeted mean contamination. This was correlated with the corresponding characteristics of the raw material Gd2O3, in which an intrinsically large contamination was present. Based on their modest impact on SK physics, they were nevertheless introduced into the detector. To reduce 228Ra for the next stage of gadolinium loading to SK, a new process has been successfully established.

Spatiotemporal kinetics of the SRP pathway in live E. coli cells

Mechanistic details of the signal recognition particle (SRP)-mediated insertion of membrane proteins have been described from decades of invitro biochemical studies. However, the dynamics of the pathway inside the living cell remain obscure. By combining invivo single-molecule tracking with numerical modeling and simulated microscopy, we have constructed a quantitative reaction-diffusion model of the SRP cycle. Our results suggest that the SRP-ribosome complex finds its target, the membrane-bound translocon, through a combination of three-dimensional (3D) and 2D diffusional search, together taking on average 750 ms. During this time, the nascent peptide is expected to be elongated only 12 or 13 amino acids, which explains why, in Escherichia coli, no translation arrest is needed to prevent incorrect folding of the polypeptide in the cytosol. We also found that a remarkably high proportion (75%) of SRP bindings to ribosomes occur in the cytosol, suggesting that the majority of target ribosomes bind SRP before reaching the membrane. In combination with the average SRP cycling time, 2.2 s, this result further shows that the SRP pathway is capable of targeting all substrate ribosomes to translocons.

Correction to: An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation

Correction to: An improvement of spectral clustering algorithm based on fast diffusion search for natural neighbor and affinity propagation