bluuebunny/crossref_metadata_2025_split huggingface dataset:
Can the Schrödinger dynamics explain measurement?
Authors: Kryukov Alexey A 
Published: June 2023

Abstract
The motion of a ball through an appropriate lattice of round obstacles models the behavior of a Brownian particle and can be used to describe measurement on a macro system. On another hand, such motion is chaotic and a known conjecture asserts that the Hamiltonian of the corresponding quantum system must follow the random matrix statistics of an appropriate ensemble. We use the Hamiltonian represented by a random matrix in the Gaussian unitary ensemble to study the Schrödinger evolution of non-stationary states. For Gaussian states representing a classical system, the Brownian motion that describes the behavior of the system under measurement is obtained. For general quantum states, the Born rule for the probability of transition between states is derived. It is then shown that the Schrödinger evolution with such a Hamiltonian models measurement on macroscopic and microscopic systems, provides an explanation for the classical behavior of macroscopic bodies and for irreversibility of a measurement, and identifies the boundary between micro and macro worlds.
What quantum matter tells about quantisation of gravity in a statistical mechanics context
Authors: Mandrin Pierre A 
Published: June 2023

Abstract
We propose a “guide” towards quantisation of gravity based on quantum matter in a statistical mechanics context. On one hand, a statistical mechanics model naturally arises from the thermodynamic interpretation of horizons in Rindler space. On the other hand, the path integral formulation of quantum field theory can be interpreted from the point of view of statistical mechanics. From these perspectives, gravity and matter are related to each other in the same way as a gas and its chemical potential are. This statistical mechanics interpretation of gravity and matter suggests that gravity should be quantised in a precise way which is determined by the quantisation of matter. Although, in a first step, quantisation of gravity applies for small perturbations of the metric with respect to the vacuum, the most central and general features of quantisation (quantisation prescription, quantum space dimensions) are supported from statistical mechanics and remain valid non-perturbatively.
Dynamical atom-wall Casimir-Polder effect after a sudden change of the atomic position
Authors: Noto Antonio , Passante Roberto , Rizzuto Lucia , Spagnolo Salvatore 
Published: June 2023

Abstract
We investigate the dynamical Casimir-Polder force between an atom and a conducting wall during the time evolution of the system from a partially dressed state. This state is obtained by a sudden change of the atomic position with respect to the plate. To evaluate the time-dependent atom-plate Casimir-Polder force we solve the Heisenberg equations for the field and atomic operators by an iterative technique. We find that the dynamical atom-plate Casimir-Polder interaction exhibits oscillation in time, and can be attractive or repulsive depending on time and the atom-wall distance. We also investigate the time dependence of global observables, such as the field and atomic Hamiltonians, and discuss some interesting features of the dynamical process bringing the interaction energy to the equilibrium configuration.
Field observables near a fluctuating boundary
Authors: Armata Federico , Butera Salvatore , Montalbano Federico , Passante Roberto , Rizzuto Lucia 
Published: June 2023

Abstract
We review several aspects related to the confinement of a massless scalar field in a cavity with a movable conducting wall of finite mass, free to move around its equilibrium position to which it is bound by a harmonic potential, and whose mechanical degrees of freedom are described quantum mechanically. This system, for small displacements of the movable wall from its equilibrium position, can be described by an effective interaction Hamiltonian between the field and the mirror, quadratic in the field operators and linear in the mirror operators. In the interacting, i.e. dressed, ground state, we first consider local field observables such as the field energy density: we evaluate changes of the field energy density in the cavity with the movable wall with respect to the case of a fixed wall, and corrections to the usual Casimir forces between the two walls. We then investigate the case of two one-dimensional cavities separated by a movable wall of finite mass, with two massless scalar fields defined in the two cavities. We show that in this case correlations between the squared fields in the two cavities exist, mediated by the movable wall, at variance with the fixed-wall case.
A QFT based on Einstein-Hilbert action and hidden dimensions as consistent theory of measurement, dark energy, dark matter and McGaugh’s universal law for rotating galaxies
Authors: Doyen G , Drakova D 
Published: June 2023

Abstract
We present a quantum field theory of gravity in 11 dimensional spacetime, based on Einstein-Hilbert action and expanded up to 4th order in the metric. In the theory the deviation of the local metric from Minkowski metric is a quantum field, called gravonon field. It is responsible for the localization of a matter particle on a definite position in four dimensional spacetime (warp resonance, beable). This is the solution of the measurament problem of Quantum Mechanics coming out as result of the deterministic Schrödinger equation. The quantum gravonon field is also decisive for calculating the expansion of the universe. In lowest order of the metric the interaction of the matter field with the gravonon field is the cause for the expansion of the universe and is the quantum analogue of the often mentioned dark energy. In higher orders of the metric the interaction of ordinary matter and gravonons becomes attractive. The gravonon field then assumes the role of dark matter. McGaugh’s universal radial acceleration relation in rotationally supported galaxies is obtained using this quantum gravitational appoach. The concept of dark matter emerges as do the results of Milgrom’s MOND analysis.
Fermion mixing in curved spacetime
Authors: Capolupo Antonio , Lambiase Gaetano , Quaranta Aniello 
Published: June 2023

Abstract
We develop the quantum field theory of fermion mixing in curved spacetime and discuss the role of unitarily inequivalent representations in the particle interpretation of the theory. We derive general oscillation formulae and apply them to specific spcetimes of interest, such as spatially flat FRW metrics and the Schwarzschild spacetime. We exhibit the main deviations from the usual quantum mechanical approach.
Development of a low-cost monitoring system for the measurement of DC and AC electrical parameters in grid-connected photovoltaic systems based on IEC standards
Authors: Zamudio M A , Carhuavilca A M , Conde L A , García M A , Berastain A E , Montes-Romero J , De la Casa J , Töfflinger J A , Cataño M A 
Published: June 2023

Abstract
The rapid development of renewable energy sources, such as photovoltaic solar energy, makes it possible to sustainably cover part of the energy demand in different parts of the world. Thus, higher efficiency of photovoltaic systems is sought to provide higher yields, security, and stability in their operation and energy production. Therefore, it is crucial to monitor the variables of interest and verify the correct operation of photovoltaic systems. This article presents the development of a monitoring system that allows the acquisition and recording of the main electrical parameters, both in direct and alternating current, enabling the evaluation of the array and final yield of grid-connected photovoltaic systems. The developed monitoring system follows the concept of the Internet of Things for remote data analysis. In addition, it allows local data logging in a computer for data acquisition over a long period. The measurements of the developed monitoring system comply with the International Electrotechnical Commission standards specified in IEC 61724-1. This validation is performed after contrasting the measurements obtained by the developed system with those obtained by the high-precision WT5000 power analyzer, where the relative differences of each electrical parameter analysed are less than 2%. Finally, the system developed is characterised by its low cost, which is less than US$ 140.
A Bootstrap Approach for Evaluating Uncertainty in the Number of Groups Identified by Latent Class Growth Models
Authors: Mésidor Miceline , Sirois Caroline , Simard Marc , Talbot Denis 
Published: June 2023

Abstract
The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last few decades in the medical literature. However, these methods have been criticized, especially because of the data-driven modeling process, which involves statistical decision-making. In this paper, we propose an approach that uses the bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigation of the statistical validity and uncertainty of the groups identified in the original data by checking to see whether the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replicationwise variability. We evaluated the ability of 3 commonly used adequacy criteria (average posterior probability, odds of correct classification, and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrate the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes.
Elucidation of the mechanism by which the internal structure of food controls the quality
Authors: Ogawa Takenobu 
Published: June 2023

ABSTRACT
Several of the existing food manufacturing processes are based on empirical knowledge, and not many are rationally designed and operated based on a sufficient understanding of the underlying phenomena. Drying and rehydration processes are one such example of this, and a new method for measuring the moisture distribution was developed, focusing on the fact that the brightness of food varies depending on its moisture content. Using this method, new mechanisms of water transfer inside food were proposed based on the rehydration process of noodles. In addition, as a new analysis method for understanding of extremely complex phenomena, we suggest the “artificial intelligence comprehensive and reverse analysis methods”. As a future prospect, we discussed the possibility that this method could contribute to elucidating various unknown complex phenomena.
RNAMotifComp: a comprehensive method to analyze and identify structurally similar RNA motif families
Authors: Rahaman Md Mahfuzur , Khan Nabila Shahnaz , Zhang Shaojie 
Published: June 2023

Abstract

Motivation
The 3D structures of RNA play a critical role in understanding their functionalities. There exist several computational methods to study RNA 3D structures by identifying structural motifs and categorizing them into several motif families based on their structures. Although the number of such motif families is not limited, a few of them are well-studied. Out of these structural motif families, there exist several families that are visually similar or very close in structure, even with different base interactions. Alternatively, some motif families share a set of base interactions but maintain variation in their 3D formations. These similarities among different motif families, if known, can provide a better insight into the RNA 3D structural motifs as well as their characteristic functions in cell biology.

Results
In this work, we proposed a method, RNAMotifComp, that analyzes the instances of well-known structural motif families and establishes a relational graph among them. We also have designed a method to visualize the relational graph where the families are shown as nodes and their similarity information is represented as edges. We validated our discovered correlations of the motif families using RNAMotifContrast. Additionally, we used a basic Naïve Bayes classifier to show the importance of RNAMotifComp. The relational analysis explains the functional analogies of divergent motif families and illustrates the situations where the motifs of disparate families are predicted to be of the same family.

Availability and implementation
Source code publicly available at https://github.com/ucfcbb/RNAMotifFamilySimilarity.
TSignal: a transformer model for signal peptide prediction
Authors: Dumitrescu Alexandru , Jokinen Emmi , Paatero Anja , Kellosalo Juho , Paavilainen Ville O , Lähdesmäki Harri 
Published: June 2023

Abstract

Motivation
Signal peptides (SPs) are short amino acid segments present at the N-terminus of newly synthesized proteins that facilitate protein translocation into the lumen of the endoplasmic reticulum, after which they are cleaved off. Specific regions of SPs influence the efficiency of protein translocation, and small changes in their primary structure can abolish protein secretion altogether. The lack of conserved motifs across SPs, sensitivity to mutations, and variability in the length of the peptides make SP prediction a challenging task that has been extensively pursued over the years.

Results
We introduce TSignal, a deep transformer-based neural network architecture that utilizes BERT language models and dot-product attention techniques. TSignal predicts the presence of SPs and the cleavage site between the SP and the translocated mature protein. We use common benchmark datasets and show competitive accuracy in terms of SP presence prediction and state-of-the-art accuracy in terms of cleavage site prediction for most of the SP types and organism groups. We further illustrate that our fully data-driven trained model identifies useful biological information on heterogeneous test sequences.

Availability and implementation
TSignal is available at: https://github.com/Dumitrescu-Alexandru/TSignal.
Transfer learning for drug–target interaction prediction
Authors: Dalkıran Alperen , Atakan Ahmet , Rifaioğlu Ahmet S , Martin Maria J , Atalay Rengül Çetin , Acar Aybar C , Doğan Tunca , Atalay Volkan 
Published: June 2023

Abstract

Motivation
Utilizing AI-driven approaches for drug–target interaction (DTI) prediction require large volumes of training data which are not available for the majority of target proteins. In this study, we investigate the use of deep transfer learning for the prediction of interactions between drug candidate compounds and understudied target proteins with scarce training data. The idea here is to first train a deep neural network classifier with a generalized source training dataset of large size and then to reuse this pre-trained neural network as an initial configuration for re-training/fine-tuning purposes with a small-sized specialized target training dataset. To explore this idea, we selected six protein families that have critical importance in biomedicine: kinases, G-protein-coupled receptors (GPCRs), ion channels, nuclear receptors, proteases, and transporters. In two independent experiments, the protein families of transporters and nuclear receptors were individually set as the target datasets, while the remaining five families were used as the source datasets. Several size-based target family training datasets were formed in a controlled manner to assess the benefit provided by the transfer learning approach.

Results
Here, we present a systematic evaluation of our approach by pre-training a feed-forward neural network with source training datasets and applying different modes of transfer learning from the pre-trained source network to a target dataset. The performance of deep transfer learning is evaluated and compared with that of training the same deep neural network from scratch. We found that when the training dataset contains fewer than 100 compounds, transfer learning outperforms the conventional strategy of training the system from scratch, suggesting that transfer learning is advantageous for predicting binders to under-studied targets.

Availability and implementation
The source code and datasets are available at https://github.com/cansyl/TransferLearning4DTI. Our web-based service containing the ready-to-use pre-trained models is accessible at https://tl4dti.kansil.org.
Deriving spatial features from in situ proteomics imaging to enhance cancer survival analysis
Authors: Dayao Monica T , Trevino Alexandro , Kim Honesty , Ruffalo Matthew , D’Angio H Blaize , Preska Ryan , Duvvuri Umamaheswar , Mayer Aaron T , Bar-Joseph Ziv 
Published: June 2023

Abstract

Motivation
Spatial proteomics data have been used to map cell states and improve our understanding of tissue organization. More recently, these methods have been extended to study the impact of such organization on disease progression and patient survival. However, to date, the majority of supervised learning methods utilizing these data types did not take full advantage of the spatial information, impacting their performance and utilization.

Results
Taking inspiration from ecology and epidemiology, we developed novel spatial feature extraction methods for use with spatial proteomics data. We used these features to learn prediction models for cancer patient survival. As we show, using the spatial features led to consistent improvement over prior methods that used the spatial proteomics data for the same task. In addition, feature importance analysis revealed new insights about the cell interactions that contribute to patient survival.

Availability and implementation
The code for this work can be found at gitlab.com/enable-medicine-public/spatsurv.
Getting ‘ϕψχal’ with proteins: minimum message length inference of joint distributions of backbone and sidechain dihedral angles
Authors: Amarasinghe Piyumi R , Allison Lloyd , Stuckey Peter J , Garcia de la Banda Maria , Lesk Arthur M , Konagurthu Arun S 
Published: June 2023

Abstract The tendency of an amino acid to adopt certain configurations in folded proteins is treated here as a statistical estimation problem. We model the joint distribution of the observed mainchain and sidechain dihedral angles (〈ϕ,ψ,χ1,χ2,…〉) of any amino acid by a mixture of a product of von Mises probability distributions. This mixture model maps any vector of dihedral angles to a point on a multi-dimensional torus. The continuous space it uses to specify the dihedral angles provides an alternative to the commonly used rotamer libraries. These rotamer libraries discretize the space of dihedral angles into coarse angular bins, and cluster combinations of sidechain dihedral angles (〈χ1,χ2,…〉) as a function of backbone 〈ϕ,ψ〉 conformations. A ‘good’ model is one that is both concise and explains (compresses) observed data. Competing models can be compared directly and in particular our model is shown to outperform the Dunbrack rotamer library in terms of model complexity (by three orders of magnitude) and its fidelity (on average 20% more compression) when losslessly explaining the observed dihedral angle data across experimental resolutions of structures. Our method is unsupervised (with parameters estimated automatically) and uses information theory to determine the optimal complexity of the statistical model, thus avoiding under/over-fitting, a common pitfall in model selection problems. Our models are computationally inexpensive to sample from and are geared to support a number of downstream studies, ranging from experimental structure refinement, de novo protein design, and protein structure prediction. We call our collection of mixture models as PhiSiCal (ϕψχal).Availability and implementationPhiSiCal mixture models and programs to sample from them are available for download at http://lcb.infotech.monash.edu.au/phisical.
Foreign RNA spike-ins enable accurate allele-specific expression analysis at scale
Authors: Mendelevich Asia , Gupta Saumya , Pakharev Aleksei , Teodosiadis Athanasios , Mironov Andrey A , Gimelbrant Alexander A 
Published: June 2023

Abstract

Motivation
Analysis of allele-specific expression is strongly affected by the technical noise present in RNA-seq experiments. Previously, we showed that technical replicates can be used for precise estimates of this noise, and we provided a tool for correction of technical noise in allele-specific expression analysis. This approach is very accurate but costly due to the need for two or more replicates of each library. Here, we develop a spike-in approach which is highly accurate at only a small fraction of the cost.

Results
We show that a distinct RNA added as a spike-in before library preparation reflects technical noise of the whole library and can be used in large batches of samples. We experimentally demonstrate the effectiveness of this approach using combinations of RNA from species distinguishable by alignment, namely, mouse, human, and Caenorhabditis elegans. Our new approach, controlFreq, enables highly accurate and computationally efficient analysis of allele-specific expression in (and between) arbitrarily large studies at an overall cost increase of ∼5%.

Availability and implementation
Analysis pipeline for this approach is available at GitHub as R package controlFreq (github.com/gimelbrantlab/controlFreq).
RawHash: enabling fast and accurate real-time analysis of raw nanopore signals for large genomes
Authors: Firtina Can , Mansouri Ghiasi Nika , Lindegger Joel , Singh Gagandeep , Cavlak Meryem Banu , Mao Haiyu , Mutlu Onur 
Published: June 2023

Abstract
Summary: Nanopore sequencers generate electrical raw signals in real-time while sequencing long genomic strands. These raw signals can be analyzed as they are generated, providing an opportunity for real-time genome analysis. An important feature of nanopore sequencing, Read Until, can eject strands from sequencers without fully sequencing them, which provides opportunities to computationally reduce the sequencing time and cost. However, existing works utilizing Read Until either (i) require powerful computational resources that may not be available for portable sequencers or (ii) lack scalability for large genomes, rendering them inaccurate or ineffective. We propose RawHash, the first mechanism that can accurately and efficiently perform real-time analysis of nanopore raw signals for large genomes using a hash-based similarity search. To enable this, RawHash ensures the signals corresponding to the same DNA content lead to the same hash value, regardless of the slight variations in these signals. RawHash achieves an accurate hash-based similarity search via an effective quantization of the raw signals such that signals corresponding to the same DNA content have the same quantized value and, subsequently, the same hash value. We evaluate RawHash on three applications: (i) read mapping, (ii) relative abundance estimation, and (iii) contamination analysis. Our evaluations show that RawHash is the only tool that can provide high accuracy and high throughput for analyzing large genomes in real-time. When compared to the state-of-the-art techniques, UNCALLED and Sigmap, RawHash provides (i) 25.8× and 3.4× better average throughput and (ii) significantly better accuracy for large genomes, respectively. Source code is available at https://github.com/CMU-SAFARI/RawHash.