Binding Mechanism of Riboswitch to Natural Ligand Elucidated by McMD-Based Dynamic Docking Simulations.

Flavin mononucleotide riboswitches are common among many pathogenic bacteria and are therefore considered to be an attractive target for antibiotics development. The riboswitch binds riboflavin (RBF, also known as vitamin B), and although an experimental structure of their complex has been solved with the ligand bound deep inside the RNA molecule in a seemingly unreachable state, the binding mechanism between these molecules is not yet known. We have therefore used our Multicanonical Molecular Dynamics (McMD)-based dynamic docking protocol to analyze their binding mechanism by simulating the binding process between the riboswitch aptamer domain and the RBF, starting from the apo state of the riboswitch.

Metabolome analysis of metabolic burden in Escherichia coli caused by overexpression of green fluorescent protein and delta-rhodopsin.

Overexpression of proteins by introducing a DNA vector is among the most important tools for the metabolic engineering of microorganisms such as Escherichia coli. Protein overexpression imposes a burden on metabolism because metabolic pathways must supply building blocks for protein and DNA synthesis. Different E.

CMIR: A Unified Cross-Modality Framework for Preoperative Accurate Prediction of Microvascular Invasion in Hepatocellular Carcinoma.

Microvascular invasion of HCC is an important factor affecting postoperative recurrence and prognosis of patients. Preoperative diagnosis of MVI is greatly significant to improve the prognosis of HCC. Currently, the diagnosis of MVI is mainly based on the histopathological examination after surgery, which is difficult to meet the requirement of preoperative diagnosis.

Sensing the Intentions to Speak in VR Group Discussions.

While virtual reality (VR) technologies enable remote communication through the use of 3D avatars, it is often difficult to foster engaging group discussions without addressing the limitations to the non-verbal communication among distributed participants. In this paper, we discuss a technique to detect the intentions to speak in group discussions by tapping into intricate sensor data streams from VR headsets and hand-controllers. To this end, we developed a prototype VR group discussion app equipped with comprehensive sensor data-logging functions and conducted an experiment of VR group discussions (N = 24).

Dynamic mode decomposition for Koopman spectral analysis of elementary cellular automata.

We apply dynamic mode decomposition (DMD) to elementary cellular automata (ECA). Three types of DMD methods are considered, and the reproducibility of the system dynamics and Koopman eigenvalues from observed time series is investigated. While standard DMD fails to reproduce the system dynamics and Koopman eigenvalues associated with a given periodic orbit in some cases, Hankel DMD with delay-embedded time series improves reproducibility.

Distinguishing examples while building concepts in hippocampal and artificial networks.

The hippocampal subfield CA3 is thought to function as an auto-associative network that stores experiences as memories. Information from these experiences arrives directly from the entorhinal cortex as well as indirectly through the dentate gyrus, which performs sparsification and decorrelation. The computational purpose for these dual input pathways has not been firmly established.

Automatic recording of rare behaviors of wild animals using video bio-loggers with on-board light-weight outlier detector.

Rare behaviors displayed by wild animals can generate new hypotheses; however, observing such behaviors may be challenging. While recent technological advancements, such as bio-loggers, may assist in documenting rare behaviors, the limited running time of battery-powered bio-loggers is insufficient to record rare behaviors when employing high-cost sensors (e.g.

Magnetic resonance imaging of blood perfusion rate based on Helmholtz decomposition of heat flux.

Thermal property (TP) maps of human tissues are useful for tumor treatment and diagnosis. In particular, the blood perfusion rate is significantly different for tumors and healthy tissues. Noninvasive techniques that reconstruct TPs from the temperature measured via magnetic resonance imaging (MRI) by solving an inverse bioheat transfer problem have been developed.

Impact of eHealth education to reduce anemia among school-going adolescent girls in rural Bangladesh: Study protocol of a randomized controlled trial.

Adolescent girls are highly vulnerable to developing anemia due to reproductive immaturity, poor personal hygiene, and lack of nutritional intake and health education in rural Bangladesh. Digital health technology is a promising tool to overcome barriers and provide appropriate health guidelines. We aim to evaluate eHealth education's impact and changes in adolescent girls' knowledge, attitude, and practice regarding anemia.

A multi-stage anticipated surprise model with dynamic expectation for economic decision-making.

There are many modeling works that aim to explain people's behaviors that violate classical economic theories. However, these models often do not take into full account the multi-stage nature of real-life problems and people's tendency in solving complicated problems sequentially. In this work, we propose a descriptive decision-making model for multi-stage problems with perceived post-decision information.

Charging Quantum Batteries via Indefinite Causal Order: Theory and Experiment.

In the standard quantum theory, the causal order of occurrence between events is prescribed, and must be definite. This has been maintained in all conventional scenarios of operation for quantum batteries. In this study we take a step further to allow the charging of quantum batteries in an indefinite causal order (ICO).

A piezoelectric micromachined ultrasound transducer combined with recording electrodes for acute brain preparations in vitro.

Background: Ultrasound stimulation is used to noninvasively stimulate the local and deep areas of the brain. However, the detailed cellular mechanisms of neural activation are still unclear because studies on micro-stimulation at the cellular level are lacking.

New Method: To modulate neural activity at the cellular level, we developed a piezoelectric micromachined ultrasound transducer (PMUT), having circular diaphragms for application on acute brain slice preparations.

Enhanced Coarse-Grained Molecular Dynamics Simulation with a Smoothed Hybrid Potential Using a Neural Network Model.

In all-atom (AA) molecular dynamics (MD) simulations, the rugged energy profile of the force field makes it challenging to reproduce spontaneous structural changes in biomolecules within a reasonable calculation time. Existing coarse-grained (CG) models, in which the energy profile is set to a global minimum around the initial structure, are unsuitable to explore the structural dynamics between metastable states far away from the initial structure without any bias. In this study, we developed a new hybrid potential composed of an artificial intelligence (AI) potential and minimal CG potential related to the statistical bond length and excluded volume interactions to accelerate the transition dynamics while maintaining the protein character.

Physical Noninvasive Attacks on Photoplethysmogram by Computer Controlled Blood Pressure Cuff.

Sensor data has been used in social security and welfare infrastructures such as insurance and medical care to provide personalized products and services; there is a risk that attackers can alter sensor data to obtain unfair benefits. We consider that one of the attack methods to modify sensor data is to attack the wearer's body to modify biometric information. In this study, we propose a noninvasive attack method to modify the sensor value of a photoplethysmogram.

Study on Improvement of Radio Propagation Characteristics of Cast Iron Boxes for Water Smart Meters.

Water utilities in Japan face a number of challenges, including declining water demand due to a shrinking population, shrinking workforce, and aging water supply facilities. Widespread use of smart water meters is crucial for solving these problems. The widespread use of smart water meters is expected to bring many benefits such as reduced labor by automating meter reading, early identification of leaks, and visualization of pipeline data to strengthen the infrastructure of water services, business continuity, and customer service, as detailed data can be obtained using wireless communication.

Biodiversity and Constrained Information Dynamics in Ecosystems: A Framework for Living Systems.

The increase in ecosystem biodiversity can be perceived as one of the universal processes converting energy into information across a wide range of living systems. This study delves into the dynamics of living systems, highlighting the distinction between ex post adaptation, typically associated with natural selection, and its proactive counterpart, ex ante adaptability. Through coalescence experiments using synthetic ecosystems, we (i) quantified ecosystem stability, (ii) identified correlations between some biodiversity indexes and the stability, (iii) proposed a mechanism for increasing biodiversity through moderate inter-ecosystem interactions, and (iv) inferred that the information carrier of ecosystems is species composition, or merged genomic information.

Population dynamics models for various forms of adaptation.

Adaptability to changing environments is one of the universal characteristics of living organisms. Because individual modes of adaptation are diverse, a unified understanding of these diverse modes is essential to comprehend adaptation. Adaptations can be categorized from at least two perspectives with respect to information.

Multicolor optogenetics for regulating flux ratio of three glycolytic pathways using EL222 and CcaSR in Escherichia coli.

Optogenetics is an attractive synthetic biology tool for controlling the metabolic flux distribution. Here, we demonstrated optogenetic flux ratio control of glycolytic pathways consisting of the Embden-Meyerhof-Parnas (EMP), pentose phosphate (PP), and Entner-Doudoroff (ED) pathways by illuminating multicolor lights using blue light-responsive EL222 and green/red light-responsive CcaSR in Escherichia coli. EL222 forms a dimer and binds to a particular DNA sequence under blue light; therefore, target gene expression can be reduced or induced by inserting a recognition sequence into its promoter regions.

Metropolis-Hastings algorithm in joint-attention naming game: experimental semiotics study.

We explore the emergence of symbols during interactions between individuals through an experimental semiotic study. Previous studies have investigated how humans organize symbol systems through communication using artificially designed subjective experiments. In this study, we focused on a joint-attention-naming game (JA-NG) in which participants independently categorized objects and assigned names while assuming their joint attention.

Hopfield-like network with complementary encodings of memories.

We present a Hopfield-like autoassociative network for memories representing examples of concepts. Each memory is encoded by two activity patterns with complementary properties. The first is dense and correlated across examples within concepts, and the second is sparse and exhibits no correlation among examples.

A cutoff-based method with charge-distribution-data driven pair potentials for efficiently estimating electrostatic interactions in molecular systems.

We introduce a simple cutoff-based method for precise electrostatic energy calculations in the molecular dynamics (MD) simulations of point-particle systems. Our method employs a theoretically derived smooth pair potential function to define electrostatic energy, offering stability and computational efficiency in MD simulations. Instead of imposing specific physical conditions, such as dielectric environments or charge neutrality, we focus on the relationship represented by a single summation formula of charge-weighted pair potentials.

Decentralized policy learning with partial observation and mechanical constraints for multiperson modeling.

Extracting the rules of real-world multi-agent behaviors is a current challenge in various scientific and engineering fields. Biological agents independently have limited observation and mechanical constraints; however, most of the conventional data-driven models ignore such assumptions, resulting in lack of biological plausibility and model interpretability for behavioral analyses. Here we propose sequential generative models with partial observation and mechanical constraints in a decentralized manner, which can model agents' cognition and body dynamics, and predict biologically plausible behaviors.

Data Processing of Product Ion Spectra: Redundancy of Product Ion Spectra of Small Molecules in Data-Dependent Acquisition Dataset.

Non-targeted metabolome analysis studies comprehensively acquire product ion spectra from the observed ions by the data-dependent acquisition (DDA) mode of tandem mass spectrometry (MS). A DDA dataset redundantly contains closely similar product ion spectra of metabolites commonly existing among the biological samples analyzed in a metabolome study. Moreover, a single DDA data file often includes two or more closely similar raw spectra obtained from an identical precursor ion.