Hasil untuk "q-bio.BM"

L. Summers

J. Lukierski, H. Ruegg, A. Nowicki et al.

M. Ablikim, M. N. Achasov, P. Adlarson et al.

with small, protruding marginal tubercles. Abdominal dorsum with large marginal sclerites on tergites II-IV and small postsiphuncular ones in addition to the sclerites developed in apterae. The scleroites on tergites I-III and VII usually into small plates and sometimes transversal bars. Abdominal tergite VIII usually with 4, rarely 5 hairs. Antennae 0.93-0.99 of body length. Processus terminalis 4.2-5.1 times as long as base of segment VI. Secondary rhinaria 46-70 on the whole length of segment III and sometimes 3-5 on basal half of segment IV. Ultimate rostral segment with 5 or 6 subsidiary hairs. Cauda with 16-17 hairs only. Other characters as in apterous viviparous female.

Ming-Jia Fu

The biochronometers used to keep time in eukaryotes include short-period biochronometer (SPB) and long-period biochronometer (LPB). Because the circadian clock reflects the biological time rhythm of a day, it is considered as SPB. Telomere shortening, which reflects the decreasing of telomere DNA length of chromosomes with the increase of cell division times, can be used to time the lifespan of organisms, so it is regarded as LPB. It is confirmed that SPB and LPB exist in most eukaryotes, and it is speculated that SPB and LPB are closely related. In this paper, based on existing studies, it is speculated that SPB and LPB of most eukaryotes can be co-attenuated with cell division in the process of aging. Due to the attenuated phenomenon of key components in the biochronometers during the growth and development of organisms, the biochronometers attenuate with the aging. Based on existing research results, it is preliminarily determined that the biochronometers can be rebuilt in the co-attenuated process. When the key components of biochronometers are reversed and increased in the organism, it can lead to the reversal of biochronometers, which further leads to the phenomenon of biological rejuvenation and makes the organism younger. In addition, the rebuilding of biochronometers can also lead to the acceleration of biochronometers and the shortening of the original timing time of biochronometers, thus shortening the life span of organisms. The rebuilding of biochronometers includes the reversal of biochronometers, the truncation of biochronometers timing and Uncoordinated co-attenuation of biochronometer and so on. The reversal of the biochronometers, which leads to rejuvenation, can give us a whole new understanding of life expectancy to be different from anti-aging.

Zhiwei Wang, Yongkang Wang, Wen Zhang

Accurately predicting antibody-antigen binding residues, i.e., paratopes and epitopes, is crucial in antibody design. However, existing methods solely focus on uni-modal data (either sequence or structure), disregarding the complementary information present in multi-modal data, and most methods predict paratopes and epitopes separately, overlooking their specific spatial interactions. In this paper, we propose a novel Multi-modal contrastive learning and Interaction informativeness estimation-based method for Paratope and Epitope prediction, named MIPE, by using both sequence and structure data of antibodies and antigens. MIPE implements a multi-modal contrastive learning strategy, which maximizes representations of binding and non-binding residues within each modality and meanwhile aligns uni-modal representations towards effective modal representations. To exploit the spatial interaction information, MIPE also incorporates an interaction informativeness estimation that computes the estimated interaction matrices between antibodies and antigens, thereby approximating them to the actual ones. Extensive experiments demonstrate the superiority of our method compared to baselines. Additionally, the ablation studies and visualizations demonstrate the superiority of MIPE owing to the better representations acquired through multi-modal contrastive learning and the interaction patterns comprehended by the interaction informativeness estimation.

Jiaxing Yang

Structural prediction has long been considered critical in RNA research, especially following the success of AlphaFold2 in protein studies, which has drawn significant attention to the field. While recent advances in machine learning and data accumulation have effectively addressed many biological tasks, particularly in protein related research. RNA structure prediction remains a significant challenge due to data limitations. Obtaining RNA structural data is difficult because traditional methods such as nuclear magnetic resonance spectroscopy, Xray crystallography, and electron microscopy are expensive and time consuming. Although several RNA 3D structure prediction methods have been proposed, their accuracy is still limited. Predicting RNA structural information at another level, such as distance maps, remains highly valuable. Distance maps provide a simplified representation of spatial constraints between nucleotides, capturing essential relationships without requiring a full 3D model. This intermediate level of structural information can guide more accurate 3D modeling and is computationally less intensive, making it a useful tool for improving structural predictions. In this work, we demonstrate that using only primary sequence information, we can accurately infer the distances between RNA bases by utilizing a large pretrained RNA language model coupled with a well trained downstream transformer.

Kairi Furui, Masahito Ohue

Accurate prediction and optimization of protein-protein binding affinity is crucial for therapeutic antibody development. Although machine learning-based prediction methods $ΔΔG$ are suitable for large-scale mutant screening, they struggle to predict the effects of multiple mutations for targets without existing binders. Energy function-based methods, though more accurate, are time consuming and not ideal for large-scale screening. To address this, we propose an active learning workflow that efficiently trains a deep learning model to learn energy functions for specific targets, combining the advantages of both approaches. Our method integrates the RDE-Network deep learning model with Rosetta's energy function-based Flex ddG to efficiently explore mutants. In a case study targeting HER2-binding Trastuzumab mutants, our approach significantly improved the screening performance over random selection and demonstrated the ability to identify mutants with better binding properties without experimental $ΔΔG$ data. This workflow advances computational antibody design by combining machine learning, physics-based computations, and active learning to achieve more efficient antibody development.

D. Popa, Z. Sun, T. Hasan et al.

We demonstrate a wideband-tunable Q-switched fiber laser exploiting a graphene saturable absorber. We get ∼2 μs pulses, tunable between 1522 and 1555 nm with up to ∼40 nJ energy. This is a simple and low-cost light source for metrology, environmental sensing, and biomedical diagnostics.

W. Twitchell, Sean P. Brown, K. Mackie

T. Okubo, S. Chung, H. Kawamura

Ordering of the frustrated classical Heisenberg model on the triangular lattice with an incommensurate spiral structure is studied under magnetic fields by means of a mean-field analysis and a Monte Carlo simulation. Several types of multiple-q states including the Skyrmion-lattice state is observed in addition to the standard single-q state. In contrast to the Dzyaloshinskii-Moriya interaction driven system, the present model allows both Skyrmions and anti-Skyrmions, together with a new thermodynamic phase where Skyrmion and anti-Skyrmion lattices form a domain state.

Mohammed Mouhcine, Youness Kadil1, Imane Rahmoune et al.

Colorectal cancer is a public health problem.Approximately 30 to 50 \% of colorectal tumors are caused by mutations in the KRAS gene.These mutations induce uncontrolled proliferation.To date,There is no approved effective treatment for the mutated KRAS oncogene.Farnesyltransferase (FTI) inhibitors are considered a therapeutic target against the mutated KRAS oncogene.Tipifarnib is a farnesyltransferase inhibitor that was analyzed in a Phase II trial.In the present study, the three-dimensional structure of farnesyltransferase complexed with tipifarnib [1SA4] was used as a basis to exploit the characteristics of tipifarnib.A pharmacophore model was generated based on the structure using the Asinex (Gold and Platinum Collections) database.A total of 299 molecules were obtained after screening.The 299 molecules were anchored to the tipifarnib binding site in the farnesyltransferase crystal structure for docking analysis.During the molecular docking process, the pharmacophore that was modeled, and was used as a constraint to eliminate the molecules that do not satisfy the pharmacophore.Finally, four Hits identified as farnesyltransferase inhibitors for biological tests. Keywords: colorectal cancer, structure-based pharmacophore, molecular docking, KRAS, farnesyltransferase inhibitors, Virtual Screening.

Jeff Guo, Philippe Schwaller

Sample efficiency is a fundamental challenge in de novo molecular design. Ideally, molecular generative models should learn to satisfy a desired objective under minimal oracle evaluations (computational prediction or wet-lab experiment). This problem becomes more apparent when using oracles that can provide increased predictive accuracy but impose a significant cost. Consequently, these oracles cannot be directly optimized under a practical budget. Molecular generative models have shown remarkable sample efficiency when coupled with reinforcement learning, as demonstrated in the Practical Molecular Optimization (PMO) benchmark. Here, we propose a novel algorithm called Augmented Memory that combines data augmentation with experience replay. We show that scores obtained from oracle calls can be reused to update the model multiple times. We compare Augmented Memory to previously proposed algorithms and show significantly enhanced sample efficiency in an exploitation task and a drug discovery case study requiring both exploration and exploitation. Our method achieves a new state-of-the-art in the PMO benchmark which enforces a computational budget, outperforming the previous best performing method on 19/23 tasks.

B. Min, E. Ostby, V. Sorger et al.

V. Bazhanov, S. Lukyanov, A. Zamolodchikov

Abstract:This paper is a direct continuation of [1] where we began the study of the integrable structures in Conformal Field Theory. We show here how to construct the operators ${\bf Q}_{\pm}(\lambda)$ which act in the highest weight Virasoro module and commute for different values of the parameter λ. These operators appear to be the CFT analogs of the Q - matrix of Baxter [2], in particular they satisfy Baxter's famous T- Q equation. We also show that under natural assumptions about analytic properties of the operators as the functions of λ the Baxter's relation allows one to derive the nonlinear integral equations of Destri-de Vega (DDV) [3] for the eigenvalues of the Q-operators. We then use the DDV equation to obtain the asymptotic expansions of the Q - operators at large λ; it is remarkable that unlike the expansions of the T operators of [1], the asymptotic series for Q(λ) contains the “dual” nonlocal Integrals of Motion along with the local ones. We also discuss an intriguing relation between the vacuum eigenvalues of the Q - operators and the stationary transport properties in the boundary sine-Gordon model. On this basis we propose a number of new exact results about finite voltage charge transport through the point contact in the quantum Hall system.

F. Harper, D. Raban, S. Rafaeli et al.

Jarek Duda, Sabina Podlewska

While there is a general focus on predictions of values, mathematically more appropriate is prediction of probability distributions: with additional possibilities like prediction of uncertainty, higher moments and quantiles. For the purpose of the computer-aided drug design field, this article applies Hierarchical Correlation Reconstruction approach, previously applied in the analysis of demographic, financial and astronomical data. Instead of a single linear regression to predict values, it uses multiple linear regressions to independently predict multiple moments, finally combining them into predicted probability distribution, here of several ADMET properties based on substructural fingerprint developed by Klekota\&Roth. Discussed application example is inexpensive selection of a percentage of molecules with properties nearly certain to be in a predicted or chosen range during virtual screening. Such an approach can facilitate the interpretation of the results as the predictions characterized by high rate of uncertainty are automatically detected. In addition, for each of the investigated predictive problems, we detected crucial structural features, which should be carefully considered when optimizing compounds towards particular property. The whole methodology developed in the study constitutes therefore a great support for medicinal chemists, as it enable fast rejection of compounds with the lowest potential of desired physicochemical/ADMET characteristic and guides the compound optimization process.

Nicola Dietler, Umberto Lupo, Anne-Florence Bitbol

Local and global inference methods have been developed to infer structural contacts from multiple sequence alignments of homologous proteins. They rely on correlations in amino-acid usage at contacting sites. Because homologous proteins share a common ancestry, their sequences also feature phylogenetic correlations, which can impair contact inference. We investigate this effect by generating controlled synthetic data from a minimal model where the importance of contacts and of phylogeny can be tuned. We demonstrate that global inference methods, specifically Potts models, are more resilient to phylogenetic correlations than local methods, based on covariance or mutual information. This holds whether or not phylogenetic corrections are used, and may explain the success of global methods. We analyse the roles of selection strength and of phylogenetic relatedness. We show that sites that mutate early in the phylogeny yield false positive contacts. We consider natural data and realistic synthetic data, and our findings generalise to these cases. Our results highlight the impact of phylogeny on contact prediction from protein sequences and illustrate the interplay between the rich structure of biological data and inference.

J. Bai, Y. Ban, J. Bian et al.

S. Jowett, N. Ntoumanis

H. Roest, J. Tilburg, W. Hoek et al.

SUMMARY The 2007–2009 human Q fever epidemic in The Netherlands attracted attention due to its magnitude and duration. The current epidemic and the historical background of Q fever in The Netherlands are reviewed according to national and international publications. Seroprevalence studies suggest that Q fever was endemic in The Netherlands several decades before the disease was diagnosed in dairy goats and dairy sheep. This was in 2005 and the increase in humans started in 2007. Q fever abortions were registered on 30 dairy goat and dairy sheep farms between 2005 and 2009. A total of 3523 human cases were notified between 2007 and 2009. Proximity to aborting small ruminants and high numbers of susceptible humans are probably the main causes of the human Q fever outbreak in The Netherlands. In general good monitoring and surveillance systems are necessary to assess the real magnitude of Q fever.