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Exome analysis links kidney malformations to developmental disorders and reveals causal genes
The dissemination potential of Microsporidia MB in Anopheles arabiensis mosquitoes is modulated by temperature
Abstract Microsporidia MB , a vertically transmitted endosymbiont of Anopheles mosquitoes, shows strong potential as a malaria control agent due to its ability to inhibit Plasmodium development within the mosquito host. To support its deployment in malaria transmission reduction strategies, it is critical to understand how environmental factors, particularly temperature, influence its infection dynamics. In this study, we investigated the impact of four temperature regimes (22 °C, 27 °C, 32 °C, and 37 °C) on Microsporidia MB prevalence and infection intensity by rearing mosquito larvae under controlled laboratory conditions. Our results demonstrate that elevated temperatures, especially 32 °C, significantly enhance both larval growth and Microsporidia MB infection rates. Population growth modeling further indicates that at 32 °C, an infected mosquito population can reach 1000 offspring within 15–35 days, representing a 4.7-, 1.3-, and 1.7-fold increase in dissemination potential compared to 22 °C, 27 °C, and 37 °C, respectively. Although mortality at 32 °C was approximately 20% higher than at 27 °C, this temperature emerged as the most favorable for mass-rearing Microsporidia MB -infected larvae. These findings provide the first insights into temperature-mediated dynamics of Microsporidia MB and support its potential for scalable implementation in malaria-endemic regions.
On the influence of bending energy on the assembly of spherical viral capsids
The protective shell, or capsid, of many spherical viruses is formed via a self-assembly process whose underlying physical principles have not yet been fully elucidated. In this article, we analyze the role of elastic bending energy in the in vitro self-assembly of a spherical capsid in the limit where such energetic contribution dominates over compression stress. The model predicts that the capsid closes prematurely, and its final size is completely determined by a dimensionless constant fr, which is the ratio of the bending modulus to the line tension of the edge. In addition, we compute the critical size, the nucleation barrier, and the assembly rate of capsids and compare our results with those previously obtained by the original classical nucleation theory of viral capsids, where the elastic energy was neglected. Our model suggests that the competition between line tension and bending energy accelerates the rate of capsid nuclei production and causes capsids to close at suboptimal sizes, suggesting that capsomers have optimal bending angles that differ from the values measured in native viruses.
Joint, multifaceted genomic analysis enables diagnosis of diverse, ultra-rare monogenic presentations
Abstract Genomics for rare disease diagnosis has advanced at a rapid pace due to our ability to perform in-depth analyses on individual patients with ultra-rare diseases. The increasing sizes of ultra-rare disease cohorts internationally newly enables cohort-wide analyses for new discoveries, but well-calibrated statistical genetics approaches for jointly analyzing these patients are still under development. The Undiagnosed Diseases Network (UDN) brings multiple clinical, research and experimental centers under the same umbrella across the United States to facilitate and scale case-based diagnostic analyses. Here, we present the first joint analysis of whole genome sequencing data of UDN patients across the network. We introduce new, well-calibrated statistical methods for prioritizing disease genes with de novo recurrence and compound heterozygosity. We also detect pathways enriched with candidate and known diagnostic genes. Our computational analysis, coupled with a systematic clinical review, recapitulated known diagnoses and revealed new disease associations. We further release a software package, RaMeDiES, enabling automated cross-analysis of deidentified sequenced cohorts for new diagnostic and research discoveries. Gene-level findings and variant-level information across the cohort are available in a public-facing browser ( https://dbmi-bgm.github.io/udn-browser/ ). These results show that case-level diagnostic efforts should be supplemented by a joint genomic analysis across cohorts.
Exploring the clinical value of concept-based AI explanations in gastrointestinal disease detection
Abstract Complex artificial intelligence models, like deep neural networks, have shown exceptional capabilities to detect early-stage polyps and tumors in the gastrointestinal tract. These technologies are already beginning to assist gastroenterologists in the endoscopy suite. To understand how these complex models work and their limitations, model explanations can be useful. Moreover, medical doctors specialized in gastroenterology can provide valuable feedback on the model explanations. This study explores three different explainable artificial intelligence methods for explaining a deep neural network detecting gastrointestinal abnormalities. The model explanations are presented to gastroenterologists. Furthermore, the clinical applicability of the explanation methods from the healthcare personnel’s perspective is discussed. Our findings indicate that the explanation methods are not meeting the requirements for clinical use, but that they can provide valuable information to researchers and model developers. Higher quality datasets and careful considerations regarding how the explanations are presented might lead to solutions that are more welcome in the clinic.
Non-equilibrium origin of cavity-induced resonant modifications of chemical reactivities
In this work, we investigate the influence of light–matter coupling on reaction dynamics and equilibrium properties of a single molecule inside an optical cavity. The reactive molecule is modeled using a triple-well potential, allowing two competing reaction pathways that yield distinct products. Dynamical and equilibrium simulations are performed using the numerically exact hierarchical equations of motion approach in real- and imaginary-time formulations, respectively, both implemented with tree tensor network decomposition schemes. We consider two illustrative cases: one dominated by slow kinetics and another by ultrafast processes. Our results demonstrate that the rates of ground-state reaction pathways can be selectively enhanced when the cavity frequency is tuned into resonance with a vibrational transition directly leading to the formation of the corresponding product, even when that transition is spectroscopically dark. However, tuning cavity frequency to match an absorption-dominant transition shared across both reaction pathways does not necessarily result in pronounced rate enhancements and selectivity. Together with an additional analysis using an asymmetric double-well model, we highlight the greater complexity of underlying factors governing chemical reactivity, which extend beyond considerations of transition dipole strengths and thermal population distributions that shape linear spectroscopy. Furthermore, we found that in all scenarios, the equilibrium populations remain unchanged when the molecule is moved into the cavity, regardless of the cavity frequency. Thus, our proof-of-concept study confirms at a fully quantum-mechanical level that cavity-induced modifications of chemical reactivities in resonant conditions arise from dynamical and non-equilibrium interactions between the cavity mode and molecular vibrations, rather than from the significant changes in equilibrium properties.
Crosstalk between inovirus core gene and accessory toxin-antitoxin system mediates polylysogeny
Advanced skin cancer prediction with medical image data using MobileNetV2 deep learning and optimized techniques
Vibronic spectrum of pyrazine: New insights from multi-state-multi-mode simulations parameterized with equation-of-motion coupled-cluster methods
This study reports simulations of the lowest band in the electronic absorption spectrum of pyrazine carried out using a multi-state-multimode vibronic Hamiltonian parameterized using equation-of-motion coupled-cluster methods. The simulations explain the main spectral features and show how peaks of vibronic nature appear. The most complete vibronic model includes four electronic states and six vibrational modes. The simulations reveal that non-adiabatic coupling with bright states located as high as 3 eV above the studied state can lead to discernible features in the absorption spectrum. This study demonstrates the power of fully ab initio treatments of electronic and vibrational structure and their utility in understanding the mechanisms leading to complex molecular spectra.
PABPC1 SUMOylation enhances cell survival by promoting mitophagy through stabilizing U-rich mRNAs within stress granules
Explainable illicit drug abuse prediction using hematological differences
Mitigating error cancellation in density functional approximations via machine learning correction
The integration of machine learning (ML) with density functional theory has emerged as a promising strategy to enhance the accuracy of density functional methods. While practical implementations of density functional approximations (DFAs) often exploit error cancellation between chemical species to achieve high accuracy in thermochemical and kinetic energy predictions, this approach is inherently system-dependent, which severely limits the transferability of DFAs. To address this challenge, we developed a novel ML-based correction to the widely used B3LYP functional, directly targeting its deviations from the exact exchange-correlation functional. By utilizing highly accurate absolute energies as exclusive reference data, our approach eliminates the reliance on error cancellation. To optimize the ML model, we attribute errors to real-space pointwise contributions and design a double-cycle protocol that incorporates self-consistent field calculations into the training workflow. Numerical tests demonstrate that the ML model, trained solely on absolute energies, improves the accuracy of calculated relative energies, demonstrating that robust DFAs can be constructed without resorting to error cancellation. Comprehensive benchmarks further show that our ML-corrected B3LYP functional significantly outperforms the original B3LYP across diverse thermochemical and kinetic energy calculations, offering a versatile and superior alternative for practical applications.
Author Correction: An extensive disulfide bond network prevents tail contraction in Agrobacterium tumefaciens phage Milano
Association between gastroesophageal reflux disease and DMFT index in the PERSIAN Guilan Cohort Study
The mutagenic forces shaping the genomes of lung cancer in never smokers
Quantitation of the piezoelectric coefficients of room temperature ionic liquids
We report the development of a means to quantitatively evaluate the piezoelectric coefficient, d33, of room temperature ionic liquids (RTILs) and apply it to investigate how d33 varies with RTIL cation structure. The d33 quantitation method we developed also enables evaluation of the average size of pressure-induced, piezoelectrically active RTIL crystals. We evaluated d33 for RTILs composed of seven different cations and the common anion bis(trifluoromethylsulfonyl)imide (TFSI−) and found that its magnitude varies predictably with the extent of conjugation and the length of the cation’s aliphatic chain. These findings offer insights into how the constituent ion structures of RTILs can be rationally optimized to enhance piezoelectric activity.
RNAi epimutations conferring antifungal drug resistance are inheritable
Abstract Epimutations modify gene expression and lead to phenotypic variation while the encoding DNA sequence remains unchanged. Epimutations mediated by RNA interference (RNAi) and/or chromatin modifications can confer antifungal drug resistance and may impact virulence traits in fungi. However, whether these epigenetic modifications can be transmitted across generations following sexual reproduction was unclear. This study demonstrates that RNAi epimutations conferring antifungal drug resistance are transgenerationally inherited in the human fungal pathogen Mucor circinelloides . Our research reveals that RNAi-based antifungal resistance follows a DNA sequence-independent, non-Mendelian inheritance pattern. Small RNAs (sRNAs) are the exclusive determinants of inheritance, transmitting drug resistance independently of other known repressive epigenetic modifications. Unique sRNA signature patterns can be traced through inheritance from parent to progeny, further supporting RNA as an alternative molecule for transmitting information across generations. Understanding how epimutations occur, propagate, and confer resistance may enable their detection in other eukaryotic pathogens, provide solutions for challenges posed by rising antimicrobial drug resistance, and advance research on phenotypic adaptability and its evolutionary implications.
Analysis of data on biosimilar prescription rates in rheumatology practice from a reference center in Turkey
Vibrational energy flow in adenosine triphosphate
Intermolecular vibrational energy transfer from H2O to adenosine triphosphate (ATP) molecules and intramolecular energy redistribution in ATP have been studied using the semiclassical Wentzel–Kramers–Brillouin procedure and quasiclassical trajectory calculations. The hydrogen bond interaction between the excited vibrational stretches of H2O (symmetric stretching mode in v = 1) and OH vibration of the γ-phosphate of the ground state ATP leads to efficient intermolecular energy flow, which is followed by intramolecular energy distribution in ATP. The phosphorus–oxygen chain functions as an efficient pathway for energy distribution to the ribose moiety and then ultimately to the terminal stretches of the adenine moiety, distributing most of the available energy to high-frequency OH, CH, and NH bonds on a sub-picosecond scale, while the hydrogen bond maintains its lifetime of ∼2 ps.