bluuebunny/crossref_metadata_2025 huggingface dataset:
Cosmic neutrino decoupling and its observable imprints: insights from entropic-dual transport
Authors: Bond J. Richard , Fuller George M. , Grohs Evan , Meyers Joel , Wilson Matthew James
Published: September 2024

Very different processes characterize the decoupling of neutrinos to form the cosmic neutrino background (CνB) and the much later decoupling of photons from thermal equilibrium to form the cosmic microwave background (CMB). The CνB emerges from the fuzzy, energy-dependent neutrinosphere and encodes the physics operating in the early universe in the temperature rangeT∼ 10 MeV toT∼ 10 keV. This is the epoch where beyond Standard Model (BSM) physics, especially in the neutrino sector, may be influential in setting the light element abundances, the necessarily distorted fossil neutrino energy spectra, and other light particle energy density contributions. Here we use techniques honed in extensive CMB studies to analyze the CνB as calculated in detailed neutrino energy transport and nuclear reaction simulations of the protracted weak decoupling and primordial nucleosynthesis epochs. Our moment method, relative entropy, and differential visibility approach can leverage future high precision CMB and light element primordial abundance measurements to provide new insights into the CνB and any BSM physics it encodes. We demonstrate that the evolution of the energy spectrum of the CνB throughout the weak decoupling epoch is accurately captured in the Standard Model by only three parameters per species, a non-trivial conclusion given the deviation from thermal equilibrium and the impact of the decrease of electron-positron pairs. Furthermore, we can interpret each of the three parameters as physical characteristics of a non-equilibrium system. Though the treatment presented here makes some simplifying assumptions including ignoring neutrino flavor oscillations, the success of our compact description within the Standard Model motivates its use also in BSM scenarios. We further demonstrate how observations of primordial light element abundances can be used to place constraints on the CνB energy spectrum, deriving response functions that can be applied for general deviations from a thermal spectrum. Combined with the description of those deviations that we develop here, our methods provide a convenient and powerful framework to constrain the impact of BSM physics on the CνB.
Searching for cosmological collider in the Planck CMB data
Authors: Sohn Wuhyun , Wang Dong-Gang , Fergusson James R. , Shellard E.P.S.
Published: September 2024

In this paper, we present the first comprehensive CMB data analysis of cosmological collider physics.
New heavy particles during inflation can leave imprints in the primordial correlators which are observable in today's cosmological surveys.
This remarkable detection channel provides an unsurpassed opportunity to probe new physics at extremely high energies.
Here we initiate the search for these relic signals in the cosmic microwave background (CMB) data from the Planck legacy release.
On the theory side, guided by recent progress from the cosmological bootstrap, we first propose a family of analytic bispectrum templates that incorporate the distinctive signatures of cosmological collider physics. Our consideration includes the oscillatory signals in the squeezed limit, the angular dependence from spinning fields, and several new shapes from nontrivial sound speed effects.
On the observational side, we apply the recently developed pipeline, CMB Bispectrum Estimator (CMB-BEST), to efficiently analyze the three-point statistics and search directly for these new templates in the Planck 2018 temperature and polarization data.
We report stringent CMB constraints on these new templates. Furthermore, we perform parameter scans to search for the best-fit values with maximum significance.
For a benchmark example of collider templates, we find f
NL = -91 ± 40 at the 68% confidence level. After
accounting for the look-elsewhere effect, the biggest adjusted significance we get is 1.8σ.
In general, we find no significant evidence of cosmological collider signals in the Planck data.

However, our innovative analysis, together with the recent work [107] using the BOSS data, sets the stage for probing cosmological collider and demonstrates the potential for discovering new heavy particles during inflation in forthcoming cosmological surveys.
Digital Beamforming Demonstration of a Microstrip Antenna Phased Array Feeds (PAF) at 1.25 GHz
Authors: Wu 仵 Peng 鹏 , Chen 陈 Mao-Zheng 卯蒸 , Li 李 Jian 健 , Wang 王 Kai 凯 , Cao 曹 Liang 亮 , Yu 于 Han 涵
Published: September 2024

We introduce the structure of a radio astronomy phased array feeds (PAF) beamforming demonstrator. In a laboratory environment, we have demonstrated beamforming on a received 1.25 GHz sinusoidal signal and used digital weighting techniques to plot the 2D pattern of the PAF. The radio frequency part of the demonstrator includes a 4 × 4 linearly polarized microstrip antenna array, all of which is connected in series with a low-noise amplifier. The signals from the central 4 × 2 array elements are injected into a radio frequency system-on-chip digital board, which can receive eight inputs with a bandwidth of 512 MHz. Combining the principle of undersampling, the beamforming is completed at a frequency of 1.25 GHz for the offline data, and a 2D image of the beam is plotted using beam scanning technology.
Heterostructure VO2@VS2 tailored by one-step hydrothermal synthesis for stable and highly efficient Zn-ion storage
Authors: Zhao Enyan , Gao Kesheng , Luo Xuan , Li Lin , Zhao Jinkui , Li Hongliang
Published: September 2024

The increasing demand for advanced energy storage solutions has driven extensive research into Zn-ion batteries due to their safety, cost-effectiveness, and environmental compatibility. This study presents a synthesis and evaluation of VO2@VS2 hollow nanospheres as a novel cathode material for Zn-ion batteries. The VO2@VS2 composite, synthesized via a one-step hydrothermal method, demonstrates a significant improvement in electrochemical performance. The material exhibits a reversible capacity of 468 mAh g−1 at 0.1 A g−1 and maintains a high capacity of 237 mAh g−1 at 1.0 A g−1 over 1000 cycles with a retention rate of 85%. Electrochemical analyses reveal enhanced charge transfer and Zn-ion storage, attributed to the synergistic effect and built-in electric field of the VO2 and VS2 heterostructure. Additionally, the composite shows superior electrochemical kinetics, facilitating rapid ion transport and charge transfer. In-situ Raman analysis confirms the reversible Zn-ion storage mechanism, further validating the composite’s structural stability during cycling. Density functional theory calculations further support these findings, indicating the composite’s potential for high-rate capability and long-term cycling stability. This research highlights the promise of VO2@VS2 hollow nanospheres in advancing the performance of aqueous Zn-ion batteries.
Teichmüller dynamics in the eyes of an algebraic geometer
Authors: Chen Dawei
Published: September 2024

This is an introduction to the algebraic aspect of Teichmüller dynamics, with a focus on its interplay with the geometry of moduli spaces of curves as well as recent advances in the field.
Cycles, derived categories, and rationality
Authors: Auel Asher , Bernardara Marcello
Published: September 2024

Our main goal is to give a sense of recent developments in the (stable) rationality problem from the point of view of unramified cohomology and

0
0

-cycles as well as derived categories and semiorthogonal decompositions, and how these perspectives intertwine and reflect each other. In particular, in the case of algebraic surfaces, we explain the relationship between Bloch’s conjecture, Chow-theoretic decompositions of the diagonal, categorical representability, and the existence of phantom subcategories of the derived category.
Scaling limits and universality: Critical percolation on weighted graphs converging to an 𝐿³ graphon
Authors: Baslingker Jnaneshwar , Bhamidi Shankar , Broutin Nicolas , Sen Sanchayan , Wang Xuan
Published: September 2024

We develop a general universality technique for establishing metric scaling limits of critical random discrete structures exhibiting mean-field behavior that requires four ingredients: (i) from the barely subcritical regime to the critical window, components merge approximately like the multiplicative coalescent, (ii) asymptotics of the susceptibility functions are the same as that of the Erdős-Rényi random graph, (iii) asymptotic negligibility of the maximal component size and the diameter in the barely subcritical regime, and (iv) macroscopic averaging of distances between vertices in the barely subcritical regime.

As an application of the general universality theorem, we establish, under some regularity conditions, the critical percolation scaling limit of graphs that converge, in a suitable topology, to an

L
3

L^3

graphon. In particular, we define a notion of the critical window in this setting. The

L
3

L^3

assumption ensures that the model is in the Erdős-Rényi universality class and that the scaling limit is Brownian. Our results do not assume any specific functional form for the graphon. As a consequence of our results on graphons, we obtain the metric scaling limit for Aldous-Pittel’s RGIV model inside the critical window (see D.J. Aldous and B. Pittel [Random Structures Algorithms 17 (2000), pp. 79–102]).

Our universality principle has applications in a number of other problems including in the study of noise sensitivity of critical random graphs (see E. Lubetzky and Y. Peled [Israel J. Math. 252 (2022), pp. 187–214]). In Bhamidi et al. [Scaling limits and universality II: geometry of maximal components in dynamic random graph models in the critical regime, In preparation], we use our universality theorem to establish the metric scaling limit of critical bounded size rules. Our method should yield the critical metric scaling limit of Ruciński and Wormald’s random graph process with degree restrictions provided an additional technical condition about the barely subcritical behavior of this model can be proved (see A. Ruciński and N. C. Wormald [Combin. Probab. Comput. 1 (1992), pp. 169–180]).
Revolutionary Emotions
Authors: Toska Silvana
Published: September 2024

Revolutionary Emotions: The Emotional Roots of Revolutionary Waves proposes a novel emotional theory of revolutionary diffusion and success and argues that emotional mobilization is at the root of both. For centuries, revolutionaries have spoken of the emotional arousal—the anger, the hope, and the jettisoning of fear—that motivated their participation in revolt. Studies of revolutions, however, rarely give these emotional narratives the power that actors themselves recount. This book argues that revolutionary waves, including the 1848 revolts and the Arab Uprisings, cannot be explained without the emotional arousal that motivated potential revolutionaries to imitate neighboring revolts. The shared identity of revolutionaries across borders leads to a shared emotional arousal and adoption of protest frames and methods. Absent this shared identity, revolutions are unlikely to cause sufficient emotional resonance to spur imitators across borders. In addition to a shared identity, previous history of protests makes diffusion more likely, as seasoned activists capitalize on this emotional jolt to mobilize against their regimes. The emotional power of the crowds is tangible—if difficult to measure—and influences the decisions of key elite players, including the military, on whether to continue supporting the regime. It is emotional arousal that both conditions the regional diffusion of revolt and determines the strength of mobilization and potential for revolutionary success. By grounding theory in the emotional narratives of revolutionaries and by breaking down the various dichotomies that plague the study of revolutions—structure versus agency, domestic versus international—Revolutionary Emotions provides the first coherent theory of revolutionary diffusion and success.
Introduction
Authors: Toska Silvana
Published: September 2024

This introduction examines the question of why some revolts diffuse across borders and why some of them succeed in becoming revolutions, a puzzle without a clear answer in the existing literature. The chapter then explores the role of emotions in revolutionary diffusion; provides an analysis of existing explanations on revolts, revolutions, emotions, rationality, social movements, military behavior in revolutionary settings, and revolutionary diffusion, and establishes the necessity for a coherent theory; it provides a brief overview of the emotional theory of the diffusion of revolt and revolutionary success; it establishes the theory’s strengths and limitations; it elaborates on the interview-based research and cross-national analysis that test this theory; and it provides an overview of the rest of the book.
Theory
Authors: Toska Silvana
Published: September 2024

This chapter builds on theories of the causes of revolution and studies of diffusion to devise an emotional theory of the spread and success of revolt. Because emotions are rarely theorized, it first defines emotions, explains which emotions are mobilizing and which ones have paralyzing tendencies, and describes how we can study them during episodes of contention. It further examines the relationship between emotions, cognitive liberation, and identity and shows how, because of its ability to engender an emotional reaction, identity determines the limits of where revolutionary movements are likely to spread. The final section of the theory outlines the domestic conditions that allow these movements to spread and succeed: namely, that preexisting opportunity structures are essential to mobilization, and that the relationship between protesters and the military and the military’s evaluation of the emotional strength of the protesters greatly affect the success of revolts.
Why Revolts Spread and Often Succeed
Authors: Toska Silvana
Published: September 2024

This chapter presents a cross-national study that first tests whether diffusion follows on from a revolution in an identity neighbor and then, for revolts between 1945 and 2013, measures the effects of revolt size and nature on the likelihood of success. It tests the relationship between neighborhood revolt and domestic opportunity structures on the probability of revolt in the home country. The chapter shows how a revolt in an identity neighbor greatly increases the likelihood that revolt will spread, especially so when the home country has preexisting opportunity structures. It also tests the relationship between the size and nature of the opposition on the probability of revolt success. This cross-national analysis shows a clear relationship between an identity neighbor revolt and spread of revolt, and the size and nature of protests on their success.
learnMSA2: deep protein multiple alignments with large language and hidden Markov models
Authors: Becker Felix , Stanke Mario
Published: September 2024

Motivation

For the alignment of large numbers of protein sequences, tools are predominant that decide to align two residues using only simple prior knowledge, e.g. amino acid substitution matrices, and using only part of the available data. The accuracy of state-of-the-art programs declines with decreasing sequence identity and when increasingly large numbers of sequences are aligned. Recently, transformer-based deep-learning models started to harness the vast amount of protein sequence data, resulting in powerful pretrained language models with the main purpose of generating high-dimensional numerical representations, embeddings, for individual sites that agglomerate evolutionary, structural, and biophysical information.

Results

We extend the traditional profile hidden Markov model so that it takes as inputs unaligned protein sequences and the corresponding embeddings. We fit the model with gradient descent using our existing differentiable hidden Markov layer. All sequences and their embeddings are jointly aligned to a model of the protein family. We report that our upgraded HMM-based aligner, learnMSA2, combined with the ProtT5-XL protein language model aligns on average almost 6% points more columns correctly than the best amino acid-based competitor and scales well with sequence number. The relative advantage of learnMSA2 over other programs tends to be greater when the sequence identity is lower and when the number of sequences is larger. Our results strengthen the evidence on the rich information contained in protein language models’ embeddings and their potential downstream impact on the field of bioinformatics.

Availability and implementation:  https://github.com/Gaius-Augustus/learnMSA, PyPI and Bioconda, evaluation: https://github.com/felbecker/snakeMSA
Predicting spatially resolved gene expression via tissue morphology using adaptive spatial GNNs
Authors: Song Tianci , Cosatto Eric , Wang Gaoyuan , Kuang Rui , Gerstein Mark , Min Martin Renqiang , Warrell Jonathan
Published: September 2024

Motivation

Spatial transcriptomics technologies, which generate a spatial map of gene activity, can deepen the understanding of tissue architecture and its molecular underpinnings in health and disease. However, the high cost makes these technologies difficult to use in practice. Histological images co-registered with targeted tissues are more affordable and routinely generated in many research and clinical studies. Hence, predicting spatial gene expression from the morphological clues embedded in tissue histological images provides a scalable alternative approach to decoding tissue complexity.

Results

Here, we present a graph neural network based framework to predict the spatial expression of highly expressed genes from tissue histological images. Extensive experiments on two separate breast cancer data cohorts demonstrate that our method improves the prediction performance compared to the state-of-the-art, and that our model can be used to better delineate spatial domains of biological interest.

Availability and implementation

https://github.com/song0309/asGNN/
CIBRA identifies genomic alterations with a system-wide impact on tumor biology
Authors: Lakbir Soufyan , Buranelli Caterina , Meijer Gerrit A , Heringa Jaap , Fijneman Remond J A , Abeln Sanne
Published: September 2024

Motivation

Genomic instability is a hallmark of cancer, leading to many somatic alterations. Identifying which alterations have a system-wide impact is a challenging task. Nevertheless, this is an essential first step for prioritizing potential biomarkers. We developed CIBRA (Computational Identification of Biologically Relevant Alterations), a method that determines the system-wide impact of genomic alterations on tumor biology by integrating two distinct omics data types: one indicating genomic alterations (e.g. genomics), and another defining a system-wide expression response (e.g. transcriptomics). CIBRA was evaluated with genome-wide screens in 33 cancer types using primary and metastatic cancer data from the Cancer Genome Atlas and Hartwig Medical Foundation.

Results

We demonstrate the capability of CIBRA by successfully confirming the impact of point mutations in experimentally validated oncogenes and tumor suppressor genes (0.79 AUC). Surprisingly, many genes affected by structural variants were identified to have a strong system-wide impact (30.3%), suggesting that their role in cancer development has thus far been largely under-reported. Additionally, CIBRA can identify impact with only 10 cases and controls, providing a novel way to prioritize genomic alterations with a prominent role in cancer biology. Our findings demonstrate that CIBRA can identify cancer drivers by combining genomics and transcriptomics data. Moreover, our work shows an unexpected substantial system-wide impact of structural variants in cancer. Hence, CIBRA has the potential to preselect and refine current definitions of genomic alterations to derive more nuanced biomarkers for diagnostics, disease progression, and treatment response.

Availability and implementation

The R package CIBRA is available at https://github.com/AIT4LIFE-UU/CIBRA.
Metagenomic functional profiling: to sketch or not to sketch?
Authors: Hera Mahmudur Rahman , Liu Shaopeng , Wei Wei , Rodriguez Judith S , Ma Chunyu , Koslicki David
Published: September 2024

Motivation

Functional profiling of metagenomic samples is essential to decipher the functional capabilities of microbial communities. Traditional and more widely used functional profilers in the context of metagenomics rely on aligning reads against a known reference database. However, aligning sequencing reads against a large and fast-growing database is computationally expensive. In general, k-mer-based sketching techniques have been successfully used in metagenomics to address this bottleneck, notably in taxonomic profiling. In this work, we describe leveraging FracMinHash (implemented in sourmash, a publicly available software), a k-mer-sketching algorithm, to obtain functional profiles of metagenome samples.

Results

We show how pieces of the sourmash software (and the resulting FracMinHash sketches) can be put together in a pipeline to functionally profile a metagenomic sample. We named our pipeline fmh-funprofiler. We report that the functional profiles obtained using this pipeline demonstrate comparable completeness and better purity compared to the profiles obtained using other alignment-based methods when applied to simulated metagenomic data. We also report that fmh-funprofiler is 39–99× faster in wall-clock time, and consumes up to 40–55× less memory. Coupled with the KEGG database, this method not only replicates fundamental biological insights but also highlights novel signals from the Human Microbiome Project datasets.

Availability and implementation

This fast and lightweight metagenomic functional profiler is freely available and can be accessed here: https://github.com/KoslickiLab/fmh-funprofiler. All scripts of the analyses we present in this manuscript can be found on GitHub.
A physics-informed neural SDE network for learning cellular dynamics from time-series scRNA-seq data
Authors: Jiang Qi , Wan Lin
Published: September 2024

Motivation: Learning cellular dynamics through reconstruction of the underlying cellular potential energy landscape (aka Waddington landscape) from time-series single-cell RNA sequencing (scRNA-seq) data is a current challenge. Prevailing data-driven computational methods can be hampered by the lack of physical principles to guide learning from complex data, resulting in reduced prediction accuracy and interpretability when applied to infer cell population dynamics.

Results: Here, we propose PI-SDE, a physics-informed neural stochastic differential equation (SDE) framework that combines the Hamilton–Jacobi (HJ) equation and neural SDE to learn cellular dynamics. Grounded in potential energy theory of biological systems, PI-SDE integrates the principle of least action by enforcing the HJ equation when reconstructing cellular potential energy function. This approach not only facilitates accurate predictions, but also improves interpretability, especially in the reconstructed potential energy landscape. Through benchmarking on two real scRNA-seq datasets, we demonstrate the importance of incorporating the HJ regularization term in dynamic inference, especially in predicting gene expression at held-out time points. Meanwhile, the learned potential energy landscape provides biologically interpretable insights into the process of cell differentiation. Our framework enhances model performance, while maintaining robustness and stability.

Availability: PI-SDE software is available at https://github.com/QiJiang-QJ/PI-SDE.
GORetriever: reranking protein-description-based GO candidates by literature-driven deep information retrieval for protein function annotation
Authors: Yan Huiying , Wang Shaojun , Liu Hancheng , Mamitsuka Hiroshi , Zhu Shanfeng
Published: September 2024

The vast majority of proteins still lack experimentally validated functional annotations, which highlights the importance of developing high-performance automated protein function prediction/annotation (AFP) methods. While existing approaches focus on protein sequences, networks, and structural data, textual information related to proteins has been overlooked. However, roughly 82% of SwissProt proteins already possess literature information that experts have annotated. To efficiently and effectively use literature information, we present GORetriever, a two-stage deep information retrieval-based method for AFP. Given a target protein, in the first stage, candidate Gene Ontology (GO) terms are retrieved by using annotated proteins with similar descriptions. In the second stage, the GO terms are reranked based on semantic matching between the GO definitions and textual information (literature and protein description) of the target protein. Extensive experiments over benchmark datasets demonstrate the remarkable effectiveness of GORetriever in enhancing the AFP performance. Note that GORetriever is the key component of GOCurator, which has achieved first place in the latest critical assessment of protein function annotation (CAFA5: over 1600 teams participated), held in 2023–2024.

Availability and implementation

GORetriever is publicly available at https://github.com/ZhuLab-Fudan/GORetriever.
A hybrid constrained continuous optimization approach for optimal causal discovery from biological data
Authors: Zhu Yuehua , Benos Panayiotis V , Chikina Maria
Published: September 2024

Motivation

Understanding causal effects is a fundamental goal of science and underpins our ability to make accurate predictions in unseen settings and conditions. While direct experimentation is the gold standard for measuring and validating causal effects, the field of causal graph theory offers a tantalizing alternative: extracting causal insights from observational data. Theoretical analysis has shown that this is indeed possible, given a large dataset and if certain conditions are met. However, biological datasets, frequently, do not meet such requirements but evaluation of causal discovery algorithms is typically performed on synthetic datasets, which they meet all requirements. Thus, real-life datasets are needed, in which the causal truth is reasonably known. In this work we first construct such a large-scale real-life dataset and then we perform on it a comprehensive benchmarking of various causal discovery methods.

Results

We find that the PC algorithm is particularly accurate at estimating causal structure, including the causal direction which is critical for biological applicability. However, PC does only produces cause-effect directionality, but not estimates of causal effects. We propose PC-NOTEARS (PCnt), a hybrid solution, which includes the PC output as an additional constraint inside the NOTEARS optimization. This approach combines PC algorithm’s strengths in graph structure prediction with the NOTEARS continuous optimization to estimate causal effects accurately. PCnt achieved best aggregate performance across all structural and effect size metrics.

Availability and implementation

https://github.com/zhu-yh1/PC-NOTEARS.
A distribution-free and analytic method for power and sample size calculation in single-cell differential expression
Authors: Hsu Chih-Yuan , Liu Qi , Shyr Yu
Published: September 2024

Motivation

Differential expression analysis in single-cell transcriptomics unveils cell type-specific responses to various treatments or biological conditions. To ensure the robustness and reliability of the analysis, it is essential to have a solid experimental design with ample statistical power and sample size. However, existing methods for power and sample size calculation often assume a specific distribution for single-cell transcriptomics data, potentially deviating from the true data distribution. Moreover, they commonly overlook cell–cell correlations within individual samples, posing challenges in accurately representing biological phenomena. Additionally, due to the complexity of deriving an analytic formula, most methods employ time-consuming simulation-based strategies.

Results

We propose an analytic-based method named scPS for calculating power and sample sizes based on generalized estimating equations. scPS stands out by making no assumptions about the data distribution and considering cell–cell correlations within individual samples. scPS is a rapid and powerful approach for designing experiments in single-cell differential expression analysis.

Availability and implementation

scPS is freely available at https://github.com/cyhsuTN/scPS and Zenodo https://zenodo.org/records/13375996.
The evolution of sensory systems after signal change in threespine stickleback
Authors: Firneno Thomas J , Welsh Gabrielle T , Gumm Jennifer M , Larson Erica L , Tinghitella Robin M
Published: September 2024

Sensory drive can lead to the evolution of signals that are optimized to the environment in which they are perceived. However, when environmental conditions change, the interactions between signal, environment, and receiver may also shift, leading to the evolution of a new signal optimum or more categorical shifts in sexual signals (gains or losses). We evaluated how visual systems have evolved following a change in environment and male signal, and whether visual system divergence contributes to reproductive isolation between ancestral and derived types in red and black morphs of Pacific Northwest freshwater threespine stickleback. We found that opsin sequence was tuned to enhance the perceived contrast of black fish on a red-shifted light background, whereas opsin expression was not. Further, we found no evidence for homotypic preferences or assortative mating between colour morphs; males of both morphs were equally successful in no-choice mating contexts, perhaps because black males are more vigorous courters. Together, our results suggest that habitat transitions in black stickleback have led to a shift in sensory-drive dynamics with some aspects of the visual system and behaviour evolving in response to other factors (foraging or predation) or lagging behind the evolution of opsin sequences in red-shifted environments.
Using mindfulness-based intervention to promote executive function in young children: a multivariable and multiscale sample entropy study
Authors: Xie Sha , Lu Shuqi , Lu Jiahao , Gong Chaohui , Chang Chunqi
Published: September 2024

Early childhood marks a pivotal period in the maturation of executive function, the cognitive ability to consciously regulate actions and thoughts. Mindfulness-based interventions have shown promise in bolstering executive function in children. This study used the functional near-infrared spectroscopy technique to explore the impact of mindfulness-based training on young children. Brain imaging data were collected from 68 children (41 boys, aged 61.8 ± 10.7 months) who were randomly assigned to either an intervention group (N = 37, aged 60.03 ± 11.14 months) or a control group (N = 31, aged 59.99 ± 10.89 months). Multivariate and multiscale sample entropy analyses were used. The results showed that: (1) brain complexity was reduced in the intervention group after receiving the mindfulness-based intervention in all three executive function tasks (ps < 0.05), indicating a more efficient neural processing mechanism after the intervention; (2) difference comparisons between the intervention and control groups showed significant differences in relevant brain regions during cognitive shifting (left dorsolateral prefrontal cortex and medial prefrontal cortex) and working memory tasks (left dorsolateral prefrontal cortex), which corroborates with improved behavioral results in the intervention group (Z = −3.674, P < 0.001 for cognitive shifting; Z = 2.594, P < 0.01 for working memory). These findings improve our understanding of early brain development in young children and highlight the neural mechanisms by which mindfulness-based interventions affect executive function. Implications for early intervention to promote young children’s brain development are also addressed.
Preliminary studies on ion-pair extractions of Zr, Hf, Nb, and Ta using extractants having tertiary N atom from H2SO4 and HF
Authors: Sasaki Yuji , Kaneko Masashi , Matsumiya Masahiko
Published: September 2024

Ion-pair extraction enables the recovery of anionic metal ions, such as pertechnetate and chlorinated rhodium ions, by protonated extractants with tertiary amino N atom in their structures. We expand this technique for other anionic species dissolved in different mineral acids. Extractions of Zr, Hf, Nb, and Ta, metal anions present in sulfuric and hydrofluoric acids (H2SO4 and HF), are examined. Zr and Hf in H2SO4, and Zr, Hf, Nb, and Ta in HF can be extracted by ion-pair extraction using NTAamide (nitrilotriacetamide), methylimino-N, N’-dioctylacetamide (MIDOA), and trioctylamine (TOA). Basic information about their extraction behavior was obtained through this study.
DEFINE: A Prospective, Randomized, Phase 4 Trial to Assess a Protease Inhibitor–Based Regimen Switch Strategy to Manage Integrase Inhibitor–Related Weight Gain
Authors: Anderson David , Ramgopal Moti , Hagins Debbie P , Lee Johnnie , Simonson Richard Bruce , Hsu Tien-Huei , Xu Ping , Ahmad Nina , Short William R
Published: September 2024

Background

Integrase strand transfer inhibitor (INSTI)–based antiretroviral (ARV) therapies have been associated with greater weight gain in people living with human immunodeficiency virus (HIV) versus those on protease inhibitor (PI)–based regimens. The DEFINE study investigated whether switching from an INSTI- to a PI-based regimen could mitigate/reverse weight gain.

Methods

DEFINE (NCT04442737) was a randomized, 48-week, open-label, prospective, phase 4 study in virologically suppressed adults with HIV-1 and ≥10% weight gain on INSTI + tenofovir alafenamide (TAF)/emtricitabine (FTC; <36 months prescreening). Participants either switched immediately to darunavir/cobicistat/FTC/TAF (D/C/F/TAF) or continued INSTI + TAF/FTC during weeks 0–24 then switched to D/C/F/TAF for weeks 24–48. The primary endpoint was least squares (LS) mean (95% confidence interval [CI]) percent weight change from baseline to week 24.

Results

Overall, 103 adults were randomized (D/C/F/TAF, n = 53; INSTI + TAF/FTC, n = 50); 30% were female, and 61% were Black/African American. No significant difference in weight change was observed at week 24 (LS mean change: D/C/F/TAF, 0.63% [95% CI, −.44% to 1.70%] vs INSTI + TAF/FTC, −0.24% [95% CI, −1.35% to .87%]; P = .24); however, a trend toward weight loss was observed with extended time post–ARV switch to D/C/F/TAF (baseline to week 48, −0.36% [95% CI, −1.77% to 1.06%]), particularly in subgroups at higher weight gain risk (eg, female and Black/African American participants). Metabolic endpoints paralleled weight change over time. D/C/F/TAF was well tolerated, with comparable virologic efficacy between arms.

Conclusions

While no significant change in body weight was observed at 24 weeks after switching from INSTI + TAF/FTC to D/C/F/TAF among adults with weight gain, a trend toward weight loss emerged with longer time post–ARV switch, supporting further investigation of ARV selection/switch for weight management.

Clinical Trials Registration. NCT04442737.
Prospective Exploratory Evaluation of Cepheid Xpert Mycobacterium tuberculosis Host Response Cartridge: A Focus on Adolescents and Young Adults
Authors: Seifert Marva , Catanzaro Donald G , Gracia Michael , Hillery Naomi , Tahseen Sabira , Masood Faisal , Hussain Alamdar , Majeed Uzma , Colman Rebecca E , Syed Rehan R , Catanzaro Antonino , Rodwell Timothy
Published: September 2024

Background

An accurate, rapid, non-sputum-based triage test for diagnosing tuberculosis (TB) is needed.

Methods

A prospective evaluation of the Cepheid GeneXpert Mycobacterium tuberculosis Host Response cartridge (Xpert-MTB-HR), a prototype blood-based host response mRNA signature assay, among individuals presenting with TB-like symptoms was performed in Pakistan and results were compared to 3 reference standards: Xpert MTB/RIF Ultra, bacteriological confirmation (Xpert MTB/RIF Ultra and/or culture positivity), and composite clinical diagnosis (clinician diagnosis, treatment initiation, Xpert MTB/RIF Ultra, and/or culture positivity). Analyses were conducted both for the entire study cohort and separately in the adolescent and young adult cohort (aged 10–24 years).

Results

A total of 497 participants, aged 6–83 years, returned valid Xpert-MTB-HR results. When a diagnostic threshold was set for a sensitivity of >90%, specificity was 32% (95% confidence interval [CI], 28%–37%) compared to Xpert MTB/RIF Ultra, 29% (95% CI, 25%–34%) compared to a bacteriological confirmation, and 22% (95% CI, 18%–26%) compared to a composite clinical diagnosis. However, when evaluating only the adolescent and young adult cohort with a diagnostic threshold set for sensitivity of >90%, specificity was 82% (95% CI, 74%–89%) compared to Xpert MTB/RIF Ultra, 84% (95% CI, 75%–90%) compared to a bacteriological confirmation, and 54% (95% CI, 44%–64%) compared to a composite clinical diagnosis.

Conclusions

While the Xpert-MTB-HR does not meet World Health Organization minimum criteria in the general population, in our study it does meet the minimum sensitivity and specificity requirements for a non-sputum-based triage test among adolescents and young adults when compared to Xpert MTB/RIF Ultra or bacteriological confirmation.
Intellectual Property and the European Courts, Conference at the Court of Justice of the European Union, Luxembourg, 5 June 2024
Authors: Khuchua Tamar , Galeotta Giorgia
Published: September 2024

On 5 June 2024, the German Association for Intellectual Property Law (GRUR) and the University of Luxembourg organised a conference on Intellectual Property and the European Courts at the Court of Justice of the European Union (CJEU) in Luxembourg. This one-day event featured fruitful discussions on the role of courts in protecting and enforcing intellectual property rights. Among the renowned speakers and moderators were judges from European courts, prominent attorneys, representatives of various organisations and associations, and leading academics from across Europe.
Statistical inference for sketching algorithms
Authors: Browne Ryan P , Andrews Jeffrey L
Published: September 2024

Sketching algorithms use random projections to generate a smaller sketched data set, often for the purposes of modelling. Complete and partial sketch regression estimates can be constructed using information from only the sketched data set or a combination of the full and sketched data sets. Previous work has obtained the distribution of these estimators under repeated sketching, along with the first two moments for both estimators. Using an alternative approach, we derive the distribution of the complete sketch estimator and additionally consider the error term under both repeated sketching and repeated sampling. Importantly, we obtain pivotal quantities which are based solely on the sketched data set—specifically not requiring information from the full data model fit. These pivotal quantities can be used for inference on the full data set regression estimates or the model parameters. For partial sketching, we derive pivotal quantities for a marginal test and an approximate distribution for the partial sketch under repeated sketching or repeated sampling—again avoiding reliance on a full data model fit. We extend these results to include the Hadamard and Clarkson–Woodruff sketches then compare them in a simulation study.
New Banach spaces-based mixed finite element methods for the coupled poroelasticity and heat equations
Authors: Careaga Julio , Gatica Gabriel N , Inzunza Cristian , Ruiz-Baier Ricardo
Published: September 2024

In this paper, we introduce and analyze a Banach spaces-based approach yielding a fully-mixed finite element method for numerically solving the coupled poroelasticity and heat equations, which describe the interaction between the fields of deformation and temperature. A nonsymmetric pseudostress tensor is utilized to redefine the constitutive equation for the total stress, which is an extension of Hooke’s law to account for thermal effects. The resulting continuous formulation, posed in suitable Banach spaces, consists of a coupled system of three saddle point-type problems, each with right-hand terms that depend on data and the unknowns of the other two. The well-posedness of it is analyzed by means of a fixed-point strategy, so that the classical Banach theorem, along with the Babuška–Brezzi theory in Banach spaces, allows to conclude, under a smallness assumption on the data, the existence of a unique solution. The discrete analysis is conducted in a similar manner, utilizing the Brouwer and Banach theorems to demonstrate both the existence and uniqueness of the discrete solution. The rates of convergence of the resulting Galerkin method are then presented. Finally, a number of numerical tests are shown to validate the aforementioned statement and demonstrate the good performance of the method.
Chemolithoautotrophic bacteria flourish at dark water–ice interfaces of an emerged Arctic cold seep
Authors: Delpech Lisa-Marie , Tveit Alexander T , Hodson Andrew J , Hand Kevin P , Kalenitchenko Dimitri
Published: September 2024

Below their ice shells, icy moons may offer a source of chemical energy that could support microbial life in the absence of light. In the Arctic, past and present glacial retreat leads to isostatic uplift of sediments through which cold and methane-saturated groundwater travels. This fluid reaches the surface and freezes as hill-shaped icings during winter, producing dark ice–water interfaces above water ponds containing chemical energy sources. In one such system characterized by elevated methane concentrations — the Lagoon Pingo in Adventdalen, Svalbard, Norway (~10 mg/L CH4, <0.3 mg/L O2, −0.25°C, pH 7.9), we studied amplicons of the bacterial and archaeal (microbial) 16S rRNA gene and transcripts in the water pond and overlaying ice. We found that active chemolithoautotrophic sulfur-oxidizing microorganisms (Sulfurimonas, Thiomicrorhabdus) dominate a niche at the bottom of the ice that is in contact with the anoxic water reservoir. There, the growing ice offers surfaces that interface with water and hosts favorable physico-chemical conditions for sulfide oxidation. The detection of anaerobic methanotrophs further suggests that throughout the winter, a steady-state dark and cold methane sink occurs under the ice in two steps: first, methane is oxidized to carbon dioxide and sulfates are concomitantly reduced to sulfides by the activity of anaerobic methanotrophs (ANME) ANME-1a and sulfate-reducing bacteria (SRB) SEEP-SRB1 consortia; and second, energy from sulfides is used by sulfur-oxidizing microorganisms to fix carbon dioxide into organic carbon. Our results underscore that ice-covered and dark ecosystems are hitherto overlooked oases of microbial life and emphasize the need to study microbial communities in icy habitats.
Antiviral effect of Evusheld in COVID-19 hospitalized patients infected with pre-Omicron or Omicron variants: a modelling analysis of the randomized DisCoVeRy trial
Authors: Beaulieu Maxime , Gaymard Alexandre , Massonnaud Clément , Peiffer-Smadja Nathan , Bouscambert-Duchamp Maude , Carcelain Guislaine , Lingas Guillaume , Mentré France , Ader Florence , Hites Maya , Poignard Pascal , Guedj Jérémie
Published: September 2024

Background

The antiviral efficacy of Evusheld (AZD7442) in patients hospitalized for SARS-CoV-2 is unknown.

Methods

We analysed the evolution of both the nasopharyngeal viral load and the serum neutralization activity against the variant of infection in 199 hospitalized patients (109 treated with Evusheld, 90 treated with placebo) infected with the SARS-CoV-2 virus and included in the randomized, double-blind, trial DisCoVeRy (NCT04315948). Using a mechanistic mathematical model, we reconstructed the trajectories of viral kinetics and how they are modulated by the increase in serum neutralization activity during Evusheld treatment.

Results

Our model identified that the neutralization activity was associated with viral kinetics. Reflecting the variant-dependent neutralization activity of Evusheld, the antiviral activity of Evusheld was larger in patients infected with pre-Omicron or Omicron BA.2 variants than in patients infected with Omicron BA.1 variant. More specifically, the model predicted that Evusheld reduced the median time to viral clearance compared with placebo-treated patients by more than 5 days in patients infected by pre-Omicron (median: 5.9; 80% PI: 2.1–13.6) or Omicron BA.2 (median: 5.4; 80% PI: 2.0–12.4), respectively. The effect was more modest in patients infected by the Omicron BA.1 variant, reducing the median time to viral clearance by 2 days (median: 2.2; 80% PI: 0.4–8.9).

Conclusions

Hospitalized patients treated with Evusheld had a shorter median time to SARS-CoV-2 viral clearance. As Evusheld antiviral activity is mediated by the level of neutralization activity, its impact on viral clearance varies largely according to the variant of infection.
Emergence of high-level aztreonam–avibactam and cefiderocol resistance following treatment of an NDM-producing Escherichia coli bloodstream isolate exhibiting reduced susceptibility to both agents at baseline
Authors: Haidar Ghady , Kline Ellen G , Kitsios Georgios D , Wang Xiaohong , Kwak Eun Jeong , Newbrough Anthony , Friday Kelly , Hughes Kramer Kailey , Shields Ryan K
Published: September 2024

Background

Cefiderocol (FDC) or ceftazidime-avibactam with aztreonam (CZA-ATM) are frontline agents for New Delhi metallo-β-lactamase (NDM)-producing Enterobacterales; however, clinical data are scarce, and mechanisms of treatment-emergent resistance are ill-defined. Our objectives were to characterize serial isolates and stool microbiota from a liver transplant recipient with NDM-producing Escherichia coli bacteraemia.

Methods

Isolates collected pre- and post-CZA–ATM treatment underwent broth microdilution susceptibility testing and whole-genome sequencing. Longitudinal stool collected during CZA–ATM therapy underwent metagenomic sequencing (Nanopore MinION).

Results

The baseline isolate exhibited elevated MICs for ATM–AVI (16/4 µg/mL) and FDC (8 µg/mL). Posttreatment, a rectal surveillance isolate exhibited high-level resistance to ATM–AVI (> 128/4 µg/mL) and FDC (32 µg/mL). Both isolates belonged to ST361 and harboured WT blaNDM-5. The baseline isolate contained wild type (WT) blaCMY-145 and mutations in ftsI (which encodes PBP3), including a YRIN insertion at residue 338 and the non-synonymous substitutions Q227H, E353K and I536L. The posttreatment isolate harboured new mutations in ftsI (A417 V) and blaCMY-145 (L139R and N366Y). Analysis of four stool samples collected during CZA–ATM treatment revealed high E. coli abundance. E. coli relative abundance increased from 34.5% (first sample) to 61.9% (last sample).

Conclusions

Baseline mutations in ftsI were associated with reduced susceptibility to ATM–AVI and FDC in an ST361 NDM-5-producing E. coli bloodstream isolate. High-level resistance was selected after CZA–ATM treatment, resulting in new ftsl and blaCMY-145 mutations. These findings underscore the need for ATM–AVI susceptibility testing for NDM producers, and the potential for PBP3 mutations to confer cross-resistance to ATM–AVI and FDC, which can emerge after CZA–ATM treatment.