@article{17868,
abstract = {Reversed conductance decay describes increasing conductance of a molecular chain series with increasing chain length. Realizing reversed conductance decay is an important step toward making long and highly conducting molecular wires. Recent work has shown that one-dimensional topological insulators (1D TIs) can exhibit reversed conductance decay due to their nontrivial edge states. The Su–Schrieffer–Heeger (SSH) model for 1D TIs relates to the electronic structure of these isolated molecules but not their electron transport properties as single-molecule junctions. Herein, we use a tight-binding approach to demonstrate that polyacetylene and other diradicaloid 1D TIs show a reversed conductance decay at the short chain limit. We explain these conductance trends by analyzing the impact of the edge states in these 1D systems on the single-molecule junction transmission. Additionally, we discuss how the self-energy from the electrode-molecule coupling and the on-site energy of the edge sites can be tuned to create longer wires with reversed conductance decays.},
author = {Li, Liang and Gunasekaran, Suman and Wei, Yujing and Nuckolls, Colin and Venkataraman, Latha},
issn = {1948-7185},
journal = {The Journal of Physical Chemistry Letters},
number = {41},
pages = {9703--9710},
publisher = {American Chemical Society},
title = {{Reversed conductance decay of 1D topological insulators by tight-binding analysis}},
doi = {10.1021/acs.jpclett.2c02812},
volume = {13},
year = {2022},
}
@article{17869,
abstract = {The formation of carbon–carbon bonds with transition metal reagents serves as a cornerstone of organic synthesis. Here, we show that the reactivity of an otherwise kinetically inert transition metal complex can be induced by an external electric field to affect a coupling reaction. These results highlight the importance of electric field effects in reaction chemistry and offers a new strategy to modulate organometallic reactivity.},
author = {Orchanian, Nicholas M. and Guizzo, Sophia and Steigerwald, Michael L. and Nuckolls, Colin and Venkataraman, Latha},
issn = {1364-548X},
journal = {Chemical Communications},
number = {90},
pages = {12556--12559},
publisher = {Royal Society of Chemistry},
title = {{Electric-field-induced coupling of aryl iodides with a nickel(0) complex}},
doi = {10.1039/d2cc03671a},
volume = {58},
year = {2022},
}
@article{17870,
abstract = {The electric fields created at solid–liquid interfaces are important in heterogeneous catalysis. Here we describe the Ullmann coupling of aryl iodides on rough gold surfaces, which we monitor in situ using the scanning tunneling microscope-based break junction (STM-BJ) and ex situ using mass spectrometry and fluorescence spectroscopy. We find that this Ullmann coupling reaction occurs only on rough gold surfaces in polar solvents, the latter of which implicates interfacial electric fields. These experimental observations are supported by density functional theory calculations that elucidate the roles of surface roughness and local electric fields on the reaction. More broadly, this touchstone study offers a facile method to access and probe in real time an increasingly prominent yet incompletely understood mode of catalysis.},
author = {Stone, Ilana B. and Starr, Rachel L. and Hoffmann, Norah and Wang, Xiao and Evans, Austin M. and Nuckolls, Colin and Lambert, Tristan H. and Steigerwald, Michael L. and Berkelbach, Timothy C. and Roy, Xavier and Venkataraman, Latha},
issn = {2041-6539},
journal = {Chemical Science},
number = {36},
pages = {10798--10805},
publisher = {Royal Society of Chemistry},
title = {{Interfacial electric fields catalyze Ullmann coupling reactions on gold surfaces}},
doi = {10.1039/d2sc03780g},
volume = {13},
year = {2022},
}
@article{17873,
abstract = {AbstractA critical overview of the theory of the chirality‐induced spin selectivity (CISS) effect, that is, phenomena in which the chirality of molecular species imparts significant spin selectivity to various electron processes, is provided. Based on discussions in a recently held workshop, and further work published since, the status of CISS effects—in electron transmission, electron transport, and chemical reactions—is reviewed. For each, a detailed discussion of the state‐of‐the‐art in theoretical understanding is provided and remaining challenges and research opportunities are identified.},
author = {Evers, Ferdinand and Aharony, Amnon and Bar‐Gill, Nir and Entin‐Wohlman, Ora and Hedegård, Per and Hod, Oded and Jelinek, Pavel and Kamieniarz, Grzegorz and Lemeshko, Mikhail and Michaeli, Karen and Mujica, Vladimiro and Naaman, Ron and Paltiel, Yossi and Refaely‐Abramson, Sivan and Tal, Oren and Thijssen, Jos and Thoss, Michael and van Ruitenbeek, Jan M. and Venkataraman, Latha and Waldeck, David H. and Yan, Binghai and Kronik, Leeor},
issn = {0935-9648},
journal = {Advanced Materials},
number = {13},
publisher = {Wiley},
title = {{Theory of chirality induced spin selectivity: Progress and challenges}},
doi = {10.1002/adma.202106629},
volume = {34},
year = {2022},
}
@article{17874,
abstract = {Redox-active two-dimensional polymers (RA-2DPs) are promising lithium battery organic cathode materials due to their regular porosities and high chemical stabilities. However, weak electrical conductivities inherent to the non-conjugated molecular motifs used thus far limit device performance and the practical relevance of these materials. We herein address this problem by developing a modular approach to construct π-conjugated RA-2DPs with a new polycyclic aromatic redox-active building block PDI-DA. Efficient imine-condensation between PDI-DA and two polyfunctional amine nodes followed by quantitative alkyl chain removal produced RA-2DPs TAPPy-PDI and TAPB-PDI as conjugated, porous, polycrystalline networks. In-plane conjugation and permanent porosity endow these materials with high electrical conductivity and high ion diffusion rates. As such, both RA-2DPs function as organic cathode materials with good rate performance and excellent cycling stability. Importantly, the improved design enables higher areal mass-loadings than were previously available, which drives a practical demonstration of TAPPy-PDI as the power source for a series of LED lights. Collectively, this investigation discloses viable synthetic methodologies and design principles for the realization of high-performance organic cathode materials.},
author = {Jin, Zexin and Cheng, Qian and Evans, Austin M. and Gray, Jesse and Zhang, Ruiwen and Bao, Si Tong and Wei, Fengkai and Venkataraman, Latha and Yang, Yuan and Nuckolls, Colin},
issn = {2041-6539},
journal = {Chemical Science},
number = {12},
pages = {3533--3538},
publisher = {Royal Society of Chemistry},
title = {{π-Conjugated redox-active two-dimensional polymers as organic cathode materials}},
doi = {10.1039/d1sc07157b},
volume = {13},
year = {2022},
}
@article{18191,
abstract = {Large-scale quantum devices provide insights beyond the reach of classical simulations. However, for a reliable and verifiable quantum simulation, the building blocks of the quantum device require exquisite benchmarking. This benchmarking of large-scale dynamical quantum systems represents a major challenge due to lack of efficient tools for their simulation. Here, we present a scalable algorithm based on neural networks for Hamiltonian tomography in out-of-equilibrium quantum systems. We illustrate our approach using a model for a forefront quantum simulation platform: ultracold atoms in optical lattices. Specifically, we show that our algorithm is able to reconstruct the Hamiltonian of an arbitrary sized bosonic ladder system using an accessible amount of experimental measurements. We are able to significantly increase the previously known parameter precision.},
author = {Valenti, Agnes and Jin, Guliuxin and Leonard, Julian and Huber, Sebastian D. and Greplova, Eliska},
issn = {2469-9934},
journal = {Physical Review A},
number = {2},
publisher = {American Physical Society},
title = {{Scalable Hamiltonian learning for large-scale out-of-equilibrium quantum dynamics}},
doi = {10.1103/physreva.105.023302},
volume = {105},
year = {2022},
}
@article{18220,
abstract = {Synonymous codons translate into the same amino acid. Although the identity of synonymous codons is often considered inconsequential to the final protein structure, there is mounting evidence for an association between the two. Our study examined this association using regression and classification models, finding that codon sequences predict protein backbone dihedral angles with a lower error than amino acid sequences, and that models trained with true dihedral angles have better classification of synonymous codons given structural information than models trained with random dihedral angles. Using this classification approach, we investigated local codon–codon dependencies and tested whether synonymous codon identity can be predicted more accurately from codon context than amino acid context alone, and most specifically which codon context position carries the most predictive power.},
author = {Ackerman-Schraier, Linor and Rosenberg, Aviv A. and Marx, Ailie and Bronstein, Alexander},
issn = {2045-2322},
journal = {Scientific Reports},
publisher = {Springer Nature},
title = {{Machine learning approaches demonstrate that protein structures carry information about their genetic coding}},
doi = {10.1038/s41598-022-25874-z},
volume = {12},
year = {2022},
}
@article{18221,
abstract = {Synonymous codons translate into chemically identical amino acids. Once considered inconsequential to the formation of the protein product, there is evidence to suggest that codon usage affects co-translational protein folding and the final structure of the expressed protein. Here we develop a method for computing and comparing codon-specific Ramachandran plots and demonstrate that the backbone dihedral angle distributions of some synonymous codons are distinguishable with statistical significance for some secondary structures. This shows that there exists a dependence between codon identity and backbone torsion of the translated amino acid. Although these findings cannot pinpoint the causal direction of this dependence, we discuss the vast biological implications should coding be shown to directly shape protein conformation and demonstrate the usefulness of this method as a tool for probing associations between codon usage and protein structure. Finally, we urge for the inclusion of exact genetic information into structural databases.},
author = {Rosenberg, Aviv A. and Marx, Ailie and Bronstein, Alexander},
issn = {2041-1723},
journal = {Nature Communications},
publisher = {Springer Nature},
title = {{Codon-specific Ramachandran plots show amino acid backbone conformation depends on identity of the translated codon}},
doi = {10.1038/s41467-022-30390-9},
volume = {13},
year = {2022},
}
@article{18222,
abstract = {STUDY QUESTION: What is the accuracy and agreement of embryologists when assessing the implantation probability of blastocysts using time-lapse imaging (TLI), and can it be improved with a data-driven algorithm?
SUMMARY ANSWER: The overall interobserver agreement of a large panel of embryologists was moderate and prediction accuracy was modest, while the purpose-built artificial intelligence model generally resulted in higher performance metrics.
WHAT IS KNOWN ALREADY: Previous studies have demonstrated significant interobserver variability amongst embryologists when assessing embryo quality. However, data concerning embryologists’ ability to predict implantation probability using TLI is still lacking. Emerging technologies based on data-driven tools have shown great promise for improving embryo selection and predicting clinical outcomes.
STUDY DESIGN, SIZE, DURATION: TLI video files of 136 embryos with known implantation data were retrospectively collected from two clinical sites between 2018 and 2019 for the performance assessment of 36 embryologists and comparison with a deep neural network (DNN).
PARTICIPANTS/MATERIALS, SETTING, METHODS: We recruited 39 embryologists from 13 different countries. All participants were blinded to clinical outcomes. A total of 136 TLI videos of embryos that reached the blastocyst stage were used for this experiment. Each embryo’s likelihood of successfully implanting was assessed by 36 embryologists, providing implantation probability grades (IPGs) from 1 to 5, where 1 indicates a very low likelihood of implantation and 5 indicates a very high likelihood. Subsequently, three embryologists with over 5 years of experience provided Gardner scores. All 136 blastocysts were categorized into three quality groups based on their Gardner scores. Embryologist predictions were then converted into predictions of implantation (IPG ≥ 3) and no implantation (IPG ≤ 2). Embryologists’ performance and agreement were assessed using Fleiss kappa coefficient. A 10-fold cross-validation DNN was developed to provide IPGs for TLI video files. The model’s performance was compared to that of the embryologists.
MAIN RESULTS AND THE ROLE OF CHANCE: Logistic regression was employed for the following confounding variables: country of residence, academic level, embryo scoring system, log years of experience and experience using TLI. None were found to have a statistically significant impact on embryologist performance at α = 0.05. The average implantation prediction accuracy for the embryologists was 51.9% for all embryos (N = 136). The average accuracy of the embryologists when assessing top quality and poor quality embryos (according to the Gardner score categorizations) was 57.5% and 57.4%, respectively, and 44.6% for fair quality embryos. Overall interobserver agreement was moderate (κ = 0.56, N = 136). The best agreement was achieved in the poor + top quality group (κ = 0.65, N = 77), while the agreement in the fair quality group was lower (κ = 0.25, N = 59). The DNN showed an overall accuracy rate of 62.5%, with accuracies of 62.2%, 61% and 65.6% for the poor, fair and top quality groups, respectively. The AUC for the DNN was higher than that of the embryologists overall (0.70 DNN vs 0.61 embryologists) as well as in all of the Gardner groups (DNN vs embryologists—Poor: 0.69 vs 0.62; Fair: 0.67 vs 0.53; Top: 0.77 vs 0.54).
LIMITATIONS, REASONS FOR CAUTION: Blastocyst assessment was performed using video files acquired from time-lapse incubators, where each video contained data from a single focal plane. Clinical data regarding the underlying cause of infertility and endometrial thickness before the transfer was not available, yet may explain implantation failure and lower accuracy of IPGs. Implantation was defined as the presence of a gestational sac, whereas the detection of fetal heartbeat is a more robust marker of embryo viability. The raw data were anonymized to the extent that it was not possible to quantify the number of unique patients and cycles included in the study, potentially masking the effect of bias from a limited patient pool. Furthermore, the lack of demographic data makes it difficult to draw conclusions on how representative the dataset was of the wider population. Finally, embryologists were required to assess the implantation potential, not embryo quality. Although this is not the traditional approach to embryo evaluation, morphology/morphokinetics as a means of assessing embryo quality is believed to be strongly correlated with viability and, for some methods, implantation potential.
WIDER IMPLICATIONS OF THE FINDINGS: Embryo selection is a key element in IVF success and continues to be a challenge. Improving the predictive ability could assist in optimizing implantation success rates and other clinical outcomes and could minimize the financial and emotional burden on the patient. This study demonstrates moderate agreement rates between embryologists, likely due to the subjective nature of embryo assessment. In particular, we found that average embryologist accuracy and agreement were significantly lower for fair quality embryos when compared with that for top and poor quality embryos. Using data-driven algorithms as an assistive tool may help IVF professionals increase success rates and promote much needed standardization in the IVF clinic. Our results indicate a need for further research regarding technological advancement in this field.},
author = {Fordham, Daniel E and Rosentraub, Dror and Polsky, Avital L and Aviram, Talia and Wolf, Yotam and Perl, Oriel and Devir, Asnat and Rosentraub, Shahar and Silver, David H and Gold Zamir, Yael and Bronstein, Alexander and Lara Lara, Miguel and Ben Nagi, Jara and Alvarez, Adrian and Munné, Santiago},
issn = {1460-2350},
journal = {Human Reproduction},
number = {10},
pages = {2275--2290},
publisher = {Oxford University Press},
title = {{Embryologist agreement when assessing blastocyst implantation probability: Is data-driven prediction the solution to embryo assessment subjectivity?}},
doi = {10.1093/humrep/deac171},
volume = {37},
year = {2022},
}
@article{18224,
abstract = {Learning from one or few visual examples is one of the key capabilities of humans since early infancy, but is still a significant challenge for modern AI systems. While considerable progress has been achieved in few-shot learning from a few image examples, much less attention has been given to the verbal descriptions that are usually provided to infants when they are presented with a new object. In this paper, we focus on the role of additional semantics that can significantly facilitate few-shot visual learning. Building upon recent advances in few-shot learning with additional semantic information, we demonstrate that further improvements are possible by combining multiple and richer semantics (category labels, attributes, and natural language descriptions). Using these ideas, we offer the community new results on the popular miniImageNet and CUB few-shot benchmarks, comparing favorably to the previous state-of-the-art results for both visual only and visual plus semantics-based approaches. We also performed an ablation study investigating the components and design choices of our approach. Code available on github.com/EliSchwartz/mutiple-semantics.},
author = {Schwartz, Eli and Karlinsky, Leonid and Feris, Rogerio and Giryes, Raja and Bronstein, Alexander},
issn = {0167-8655},
journal = {Pattern Recognition Letters},
pages = {142--147},
publisher = {Elsevier},
title = {{Baby steps towards few-shot learning with multiple semantics}},
doi = {10.1016/j.patrec.2022.06.012},
volume = {160},
year = {2022},
}
@article{18225,
abstract = {Isometric feature mapping is an established time-honored algorithm in manifold learning and non-linear dimensionality reduction. Its prominence can be attributed to the output of a coherent global low-dimensional representation of data by preserving intrinsic distances. In order to enable an efficient and more applicable isometric feature mapping, a diverse set of sophisticated advancements have been proposed to the original algorithm to incorporate important factors like sparsity of computation, conformality, topological constraints and spectral geometry. However, a significant shortcoming of most approaches is the dependence on large-scale dense-spectral decompositions and the inability to generalize to points far away from the sampling of the manifold.
In this paper, we explore an unsupervised deep learning approach for computing distance-preserving maps for non-linear dimensionality reduction. We demonstrate that our framework is general enough to incorporate all previous advancements and show a significantly improved local and non-local generalization of the isometric mapping. Our approach involves training with only a few landmark points and avoids the need for population of dense matrices as well as computing their spectral decomposition.},
author = {Pai, Gautam and Bronstein, Alexander and Talmon, Ronen and Kimmel, Ron},
issn = {0262-8856},
journal = {Image and Vision Computing},
publisher = {Elsevier},
title = {{Deep isometric maps}},
doi = {10.1016/j.imavis.2022.104461},
volume = {123},
year = {2022},
}
@article{18226,
abstract = {Spontaneous parametric downconversion (SPDC) in quantum optics is an invaluable resource for the realization of high-dimensional qudits with spatial modes of light. One of the main open challenges is how to directly generate a desirable qudit state in the SPDC process. This problem can be addressed through advanced computational learning methods; however, due to difficulties in modeling the SPDC process by a fully differentiable algorithm, progress has been limited. Here, we overcome these limitations and introduce a physically constrained and differentiable model, validated against experimental results for shaped pump beams and structured crystals, capable of learning the relevant interaction parameters in the process. We avoid any restrictions induced by the stochastic nature of our physical model and integrate the dynamic equations governing the evolution under the SPDC Hamiltonian. We solve the inverse problem of designing a nonlinear quantum optical system that achieves the desired quantum state of downconverted photon pairs. The desired states are defined using either the second-order correlations between different spatial modes or by specifying the required density matrix. By learning nonlinear photonic crystal structures as well as different pump shapes, we successfully show how to generate maximally entangled states. Furthermore, we simulate all-optical coherent control over the generated quantum state by actively changing the profile of the pump beam. Our work can be useful for applications such as novel designs of high-dimensional quantum key distribution and quantum information processing protocols. In addition, our method can be readily applied for controlling other degrees of freedom of light in the SPDC process, such as spectral and temporal properties, and may even be used in condensed-matter systems having a similar interaction Hamiltonian.},
author = {Rozenberg, Eyal and Karnieli, Aviv and Yesharim, Ofir and Foley-Comer, Joshua and Trajtenberg-Mills, Sivan and Freedman, Daniel and Bronstein, Alexander and Arie, Ady},
issn = {2334-2536},
journal = {Optica},
number = {6},
pages = {602--615},
publisher = {Optica Publishing Group},
title = {{Inverse design of spontaneous parametric downconversion for generation of high-dimensional qudits}},
doi = {10.1364/optica.451115},
volume = {9},
year = {2022},
}
@inproceedings{18229,
abstract = {We present Self-Classifier – a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for same-class prediction of two augmented views of the same sample. To guarantee non-degenerate solutions (i.e., solutions where all labels are assigned to the same class) we propose a mathematically motivated variant of the cross-entropy loss that has a uniform prior asserted on the predicted labels. In our theoretical analysis, we prove that degenerate solutions are not in the set of optimal solutions of our approach. Self-Classifier is simple to implement and scalable. Unlike other popular unsupervised classification and contrastive representation learning approaches, it does not require any form of pre-training, expectation-maximization, pseudo-labeling, external clustering, a second network, stop-gradient operation, or negative pairs. Despite its simplicity, our approach sets a new state of the art for unsupervised classification of ImageNet; and even achieves comparable to state-of-the-art results for unsupervised representation learning. Code is available at https://github.com/elad-amrani/self-classifier.},
author = {Amrani, Elad and Karlinsky, Leonid and Bronstein, Alexander},
booktitle = {17th European Conference on Computer Vision},
isbn = {9783031198205},
issn = {1611-3349},
location = {Tel Aviv, Israel},
pages = {116--132},
publisher = {Springer Nature},
title = {{Self-supervised classification network}},
doi = {10.1007/978-3-031-19821-2_7},
volume = {13691},
year = {2022},
}
@misc{18291,
author = {Katsaros, Georgios and Jirovec, Daniel},
publisher = {Institute of Science and Technology Austria},
title = {{Dynamics of Hole Singlet-Triplet Qubits with Large 𝑔-Factor Differences}},
doi = {10.15479/AT:ISTA:18291},
year = {2022},
}
@article{18606,
abstract = {Shear thickening is an intriguing rheological behaviour which consists in a brutal increase in the viscosity above a critical shear rate. It is famously encountered in suspensions of corn starch in water. Despite having been discovered in the early 1930's, its underlying mechanisms remained a mystery for a long time. In 2013–14, numerical and theoretical works [[1], [2], [3]] put forward a frictional transition scenario to explain this phenomenon.
In this talk, I will present experimental work investigating this frictional transition scenario. In order to test the ideas of this model, one has to go further than standard rheological techniques, since they do not provide access to the frictional state of the measured suspension. I will thus focus on the techniques that we developed in order to evidence the frictional transition and link it to the presence of a shear-thickening behaviour.},
author = {Clavaud, Cécile},
issn = {2772-5693},
journal = {Science Talks},
publisher = {Elsevier},
title = {{Shear thickening in dense suspensions: an experimental study}},
doi = {10.1016/j.sctalk.2022.100038},
volume = {3},
year = {2022},
}
@article{12261,
abstract = {Dose–response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose–response curves. The shape of the dose–response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose–response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose–response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose–response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.},
author = {Angermayr, Andreas and Pang, Tin Yau and Chevereau, Guillaume and Mitosch, Karin and Lercher, Martin J and Bollenbach, Mark Tobias},
issn = {1744-4292},
journal = {Molecular Systems Biology},
keywords = {Applied Mathematics, Computational Theory and Mathematics, General Agricultural and Biological Sciences, General Immunology and Microbiology, General Biochemistry, Genetics and Molecular Biology, Information Systems},
number = {9},
publisher = {Embo Press},
title = {{Growth‐mediated negative feedback shapes quantitative antibiotic response}},
doi = {10.15252/msb.202110490},
volume = {18},
year = {2022},
}
@article{12262,
abstract = {The AAA-ATPase Drg1 is a key factor in eukaryotic ribosome biogenesis that initiates cytoplasmic maturation of the large ribosomal subunit. Drg1 releases the shuttling maturation factor Rlp24 from pre-60S particles shortly after nuclear export, a strict requirement for downstream maturation. The molecular mechanism of release remained elusive. Here, we report a series of cryo-EM structures that captured the extraction of Rlp24 from pre-60S particles by Saccharomyces cerevisiae Drg1. These structures reveal that Arx1 and the eukaryote-specific rRNA expansion segment ES27 form a joint docking platform that positions Drg1 for efficient extraction of Rlp24 from the pre-ribosome. The tips of the Drg1 N domains thereby guide the Rlp24 C terminus into the central pore of the Drg1 hexamer, enabling extraction by a hand-over-hand translocation mechanism. Our results uncover substrate recognition and processing by Drg1 step by step and provide a comprehensive mechanistic picture of the conserved modus operandi of AAA-ATPases.},
author = {Prattes, Michael and Grishkovskaya, Irina and Hodirnau, Victor-Valentin and Hetzmannseder, Christina and Zisser, Gertrude and Sailer, Carolin and Kargas, Vasileios and Loibl, Mathias and Gerhalter, Magdalena and Kofler, Lisa and Warren, Alan J. and Stengel, Florian and Haselbach, David and Bergler, Helmut},
issn = {1545-9985},
journal = {Nature Structural & Molecular Biology},
keywords = {Molecular Biology, Structural Biology},
number = {9},
pages = {942--953},
publisher = {Springer Nature},
title = {{Visualizing maturation factor extraction from the nascent ribosome by the AAA-ATPase Drg1}},
doi = {10.1038/s41594-022-00832-5},
volume = {29},
year = {2022},
}
@article{12264,
abstract = {Reproductive isolation (RI) is a core concept in evolutionary biology. It has been the central focus of speciation research since the modern synthesis and is the basis by which biological species are defined. Despite this, the term is used in seemingly different ways, and attempts to quantify RI have used very different approaches. After showing that the field lacks a clear definition of the term, we attempt to clarify key issues, including what RI is, how it can be quantified in principle, and how it can be measured in practice. Following other definitions with a genetic focus, we propose that RI is a quantitative measure of the effect that genetic differences between populations have on gene flow. Specifically, RI compares the flow of neutral alleles in the presence of these genetic differences to the flow without any such differences. RI is thus greater than zero when genetic differences between populations reduce the flow of neutral alleles between populations. We show how RI can be quantified in a range of scenarios. A key conclusion is that RI depends strongly on circumstances—including the spatial, temporal and genomic context—making it difficult to compare across systems. After reviewing methods for estimating RI from data, we conclude that it is difficult to measure in practice. We discuss our findings in light of the goals of speciation research and encourage the use of methods for estimating RI that integrate organismal and genetic approaches.},
author = {Westram, Anja M and Stankowski, Sean and Surendranadh, Parvathy and Barton, Nicholas H},
issn = {1420-9101},
journal = {Journal of Evolutionary Biology},
keywords = {Ecology, Evolution, Behavior and Systematics},
number = {9},
pages = {1143--1164},
publisher = {Wiley},
title = {{What is reproductive isolation?}},
doi = {10.1111/jeb.14005},
volume = {35},
year = {2022},
}
@article{12265,
author = {Westram, Anja M and Stankowski, Sean and Surendranadh, Parvathy and Barton, Nicholas H},
issn = {1420-9101},
journal = {Journal of Evolutionary Biology},
keywords = {Ecology, Evolution, Behavior and Systematics},
number = {9},
pages = {1200--1205},
publisher = {Wiley},
title = {{Reproductive isolation, speciation, and the value of disagreement: A reply to the commentaries on ‘What is reproductive isolation?’}},
doi = {10.1111/jeb.14082},
volume = {35},
year = {2022},
}
@article{12268,
abstract = {The complexity of the microenvironment effects on cell response, show accumulating evidence that glioblastoma (GBM) migration and invasiveness are influenced by the mechanical rigidity of their surroundings. The epithelial–mesenchymal transition (EMT) is a well-recognized driving force of the invasive behavior of cancer. However, the primary mechanisms of EMT initiation and progression remain unclear. We have previously showed that certain substrate stiffness can selectively stimulate human GBM U251-MG and GL15 glioblastoma cell lines motility. The present study unifies several known EMT mediators to uncover the reason of the regulation and response to these stiffnesses. Our results revealed that changing the rigidity of the mechanical environment tuned the response of both cell lines through change in morphological features, epithelial-mesenchymal markers (E-, N-Cadherin), EGFR and ROS expressions in an interrelated manner. Specifically, a stiffer microenvironment induced a mesenchymal cell shape, a more fragmented morphology, higher intracellular cytosolic ROS expression and lower mitochondrial ROS. Finally, we observed that cells more motile showed a more depolarized mitochondrial membrane potential. Unravelling the process that regulates GBM cells’ infiltrative behavior could provide new opportunities for identification of new targets and less invasive approaches for treatment.},
author = {Basilico, Bernadette and Palamà, Ilaria Elena and D’Amone, Stefania and Lauro, Clotilde and Rosito, Maria and Grieco, Maddalena and Ratano, Patrizia and Cordella, Federica and Sanchini, Caterina and Di Angelantonio, Silvia and Ragozzino, Davide and Cascione, Mariafrancesca and Gigli, Giuseppe and Cortese, Barbara},
issn = {2234-943X},
journal = {Frontiers in Oncology},
keywords = {Cancer Research, Oncology},
publisher = {Frontiers Media},
title = {{Substrate stiffness effect on molecular crosstalk of epithelial-mesenchymal transition mediators of human glioblastoma cells}},
doi = {10.3389/fonc.2022.983507},
volume = {12},
year = {2022},
}