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Nuclear phase retrieval spectroscopy using resonant x-ray scattering
Abstract Light-matter interaction is exploited in spectroscopic techniques to access information about molecular, atomic or nuclear constituents of a sample. While scattered light carries both amplitude and phase information of the electromagnetic field, the latter is lost in intensity measurements. However, often the phase information is paramount to reconstruct the desired information of the target, as it is well known from coherent x-ray imaging. Here we introduce a phase retrieval method which allows us to reconstruct the field phase information from two-dimensional time- and energy-resolved spectra. We apply this method to the case of x-ray scattering off Mössbauer nuclei at a synchrotron radiation source. Knowledge of the phase allows also for the reconstruction of energy spectra from two-dimensional experimental data sets with excellent precision, without theoretical modelling of the sample. Our approach provides an efficient and accurate data analysis tool which will benefit x-ray quantum optics and Mössbauer spectroscopy with synchrotron radiation alike.
Senescence-associated lineage-aberrant plasticity evokes T-cell-mediated tumor control
Abstract Cellular senescence is a stress-inducible state switch relevant in aging, tumorigenesis and cancer therapy. Beyond a lasting arrest, senescent cells are characterized by profound chromatin remodeling and transcriptional reprogramming. We show here myeloid-skewed aberrant lineage plasticity and its immunological ramifications in therapy-induced senescence (TIS) of primary human and murine B-cell lymphoma. We find myeloid transcription factor (TF) networks, specifically AP-1-, C/EBPβ- and PU.1-governed transcriptional programs, enriched in TIS but not in equally chemotherapy-exposed senescence-incapable cancer cells. Dependent on these master TF, TIS lymphoma cells adopt a lineage-promiscuous state with properties of monocytic-dendritic cell (DC) differentiation. TIS lymphoma cells are preferentially lysed by T-cells in vitro, and mice harboring DC-skewed Eμ-myc lymphoma experience significantly longer tumor-free survival. Consistently, superior long-term outcome is also achieved in diffuse large B-cell lymphoma patients with high expression of a TIS-related DC signature. In essence, these data demonstrate a therapeutically exploitable, prognostically favorable immunogenic role of senescence-dependent aberrant myeloid plasticity in B-cell lymphoma.
Treatment, toxicity, and mortality after subsequent breast cancer in female survivors of childhood cancer
Abstract Childhood cancer survivors, particularly those who received chest radiotherapy, are at high risk for developing subsequent breast cancer. Minimizing long-term toxicity risks associated with additional radiotherapy and chemotherapy is a priority, but therapeutic tradeoffs have not been comprehensively characterized and their impact on survival is unknown. In this study, 431 female childhood cancer survivors with subsequent breast cancer from a multicenter retrospective cohort study were evaluated. Compared with one-to-one matched females with first primary breast cancer, survivors are as likely to be prescribed guideline-concordant treatment (N = 344 pairs; survivors: 94%, controls: 93%), but more frequently undergo mastectomy (survivors: 81%, controls: 60%) and are less likely to be treated with anthracyclines (survivors: 47%, controls: 66%) or radiotherapy (survivors: 18%, controls: 61%). Despite this, survivors have nearly 3.5-fold (95% CI = 2.17-5.57) greater mortality risk. Here, we show survivors with subsequent breast cancer face excess mortality despite therapeutic tradeoffs and require specialized treatment guidelines.
Selective phase separation of transcription factors is driven by orthogonal molecular grammar
Tuning ultrafast demagnetization with ultrashort spin polarized currents in multi-sublattice ferrimagnets
Unlocking property constraints through a multi-level ordered structure strategy
Urban sensing using existing fiber-optic networks
On-chip real-time detection of optical frequency variations with ultrahigh resolution using the sine-cosine encoder approach
Optogenetic activation of mechanical nociceptions to enhance implant osseointegration
Anomalous thickness dependence of the vortex pearl length in few-layer NbSe2
Abstract The coexistence of multiple types of orders is a common thread in condensed matter physics and unconventional superconductors. The nature of superconducting orders may be unveiled by analyzing local perturbations such as vortices. For thin films, the vortex magnetic profile is characterized by the Pearl-length $$\Lambda$$ Λ , which is inversely proportional to the 2D superfluid density; hence, normally, also inversely proportional to the film thickness, $$d$$ d . Here we employ the scanning SQUID-on-tip microscopy to measure $$\Lambda$$ Λ in NbSe2 flakes with thicknesses ranging from $$N=3$$ N = 3 to $$53$$ 53 layers. For $$N\, > \,10$$ N > 10 , we find the expected dependence $$\Lambda \propto 1/d$$ Λ ∝ 1 / d . However, six-layer films show a sharp increase of $$\Lambda$$ Λ deviating by a factor of three from the expected value. This value remains fixed for $$N=3$$ N = 3 to $$6$$ 6 . This unexpected behavior suggests the competition between two orders; one residing only on the first and last layers of the film while the other prevails in all layers.
Study on air-pipe cooling effect and cooling strategy of mass reinforced concrete wall
Coherence analysis of local randomness and nonlocal correlation through polarization-basis projections of entangled photon pairs
New insights into the cultivability of human milk bacteria from ingestion to digestion and implications for their Immunomodulatory properties
Abstract Human milk (HM) microbiota is increasingly studied for its potential health benefits. However, the physiological state of HM bacteria and consequently their effects on gut homeostasis remain a question. This study investigated the physiological state of the HM microbiota by characterizing its cultivable fraction as it might be at the point of ingestion and assessing the effects of digestion, in the specific context of the immature infant digestive tract, on the cultivability and immunomodulatory properties of six HM strains representative of prevalent genera in HM. Twenty-eight HM samples were analysed by 16 S metabarcoding either directly on raw milk (raw milk microbiota, RM) or on the complete cultivable fraction obtained from seven non-selective media (cultivable milk microbiota, CM). This approach enabled a more in-depth investigation of CM than conventional methods based on the individual sequencing of a subset of isolates and resulted in a moderate gain in diversity within each HM sample. It confirmed that diversity was lower in CM than in RM, with ~ 7 versus 69 genera per sample in CM and RM respectively, and an under-representation of strictly anaerobic genera in CM. In vitro infant gastrointestinal digestion resulted in overall good survival of the 6 HM strains but partial or complete loss of their immunomodulatory properties on the monocyte THP1 cell line, except for a Staphylococcus epidermidis strain that gained immunomodulatory potential. These results highlight the potential of HM bacteria to survive during the infant gastrointestinal digestion and interact with the intestinal epithelium and immune system, as well as the importance of considering the digestion process when evaluating host-bacteria interactions.
How scale affects N2O emissions in heterogeneous fields of a diversified agricultural landscape
Abstract Nitrous oxide (N2O) emissions from agricultural soils vary due to factors such as soil organic matter, soil moisture, and crop type, leading to short-term variations and concentrated zones of high emissions, known as “hot moments” and “hotspots.” These peaks, occurring at various scales, contribute significantly to total N2O emissions. This is particularly relevant for sandy soils, where high porosity and low water-holding capacity promote gas diffusion and create moisture variability, leading to highly heterogeneous N2O emissions. We investigated N2O fluxes along a transect in six agriculturally used patches (0.52 ha) with varying texture, yield potential and crop rotation. We measured N2O fluxes bi-weekly over 2 years, using a non-flow-through non-steady-state (NFT-NSS) manual closed chamber system, covering different crops and weather conditions. Hot moments accounted for 6–71% of total crop N2O emissions and were mostly driven by soil physical properties. On a small scale, soil texture and environment determined spatial heterogeneity of N2O emissions being more pronounced for sandier soils. On patch level, N2O emissions differed more strongly than on microplot level and were mainly driven by crop-type and management. Our findings highlight the importance of accounting for intrinsic variability in soil texture, topography, and microclimate within patches. Additionally, broader differences across management-influenced patches must be considered to better understand the drivers of N2O emissions. This dual-scale approach emphasizes the need for high-resolution soil monitoring for mitigation strategies and to refine models. At the same time, it guides farmers toward soil-specific fertilization to reduce emissions and maintain yields in diverse agricultural landscapes.
Monocular depth estimation via a detail semantic collaborative network for indoor scenes
Association between subretinal fluid duration in central serous chorioretinopathy and chorioretinal structure in unaffected fellow eyes
Computational discovery of natural inhibitors targeting enterovirus D68 3C protease using molecular docking pharmacokinetics and dynamics simulations
Abstract Enterovirus D68 (EV-D68) is a significant global health threat, responsible for severe respiratory and neurological complications, with no FDA-approved antiviral treatments currently available. The 3C protease of EV-D68, an essential enzyme involved in viral replication, represents a key target for therapeutic development. In this study, we employed a comprehensive computational approach, including molecular docking, pharmacokinetic predictions, and molecular dynamics simulations, to identify potential natural inhibitors of the EV-D68 3C protease. Screening a library of natural compounds, Withaferin-A (CID: 265,237) and Baicalin (CID: 64,982) emerged as top candidates due to their favorable pharmacokinetic profiles, high binding affinities (-10.7 kcal/mol for Withaferin-A and -9.5 kcal/mol for Baicalin), and interactions with key residues in the protease’s active site. The molecular dynamics simulations demonstrated the stability of the protein–ligand complexes, with low root mean square deviation (RMSD) and fluctuation (RMSF) values over a 100-ns trajectory. Free energy calculations further supported the superior binding efficiency of Withaferin-A. These findings suggest that Withaferin-A and Baicalin have significant potential as natural inhibitors of EV-D68 3C protease, offering a promising foundation for future experimental validation and the development of targeted antiviral therapies against EV-D68.
Carboxylic acids dendrimer functionalized magnetic nanoparticles for carbohydrates conversion to 5-hydroxymethylfurfural
Computational network biology analysis revealed COVID-19 severity markers: Molecular interplay between HLA-II with CIITA
COVID-19, severe acute respiratory syndrome coronavirus 2, rapidly spread worldwide. Severe and critical patients are expected to rapidly deteriorate. Although several studies have attempted to uncover the mechanisms underlying COVID-19 severity, most have focused on the perturbations of single genes. However, the complex mechanism of COVID-19 involves numerous perturbed genes in a molecular network rather than a single abnormal gene. Thus, we aimed to identify COVID-19 severity-specific markers in the Japanese population using gene network analysis. In order to reveal the severity-specific molecular interplays, we developed a novel computational network biology strategy that measures dissimilarity between networks based on the comprehensive information of gene network (i.e., expression levels of genes and network structure) by using Kullback–Leibler divergence. Monte Carlo simulations demonstrated the effectiveness of our strategy for differential gene network analysis. We applied this method to publicly available whole blood RNA-seq data from the Japan coronavirus disease 2019 Task Force and identified differentially regulated molecular interplays between 368 severe and 105 non-severe samples. Our analysis suggests the gene network between HLA class II, CIITA, and CD74 as a COVID-19 severity specific molecular marker. Although the association between HLA class II and COVID-19 has been demonstrated, our data analysis revealed that the molecular interplay of HLA class II with its target and/or regulator is a crucial marker for COVID-19 severity. Our findings from computational network biology analysis suggest that suppression and activation of the molecular interplay between HLA class II, CIITA, and CD74 provide crucial clues to uncover the mechanisms of COVID-19 severity.