bluuebunny/crossref_metadata_2025 huggingface dataset:
A global relative similarity for inferring interactions of multi-agent systems
Authors: Gu Kongjing , Duan Xiaojun , Qi Mingze , Yan Liang
Published: October 2022

Interactions and dynamics are critical mechanisms for multi-agent systems to achieve complex intelligence through the cooperation of simple agents. Yet, inferring interactions of the multi-agent system is still a common and open problem. A new method named K-similarity is designed to measure the global relative similarities for inferring the interactions among multiple agents in this paper. K-similarity is defined to be a synthetic measure of relative similarity on each observation snapshot where regular distances are nonlinearly mapped into a network. Therefore, K-similarity contains the global relative similarity information, and the interaction topology can be inferred from the similarity matrix. It has the potential to transform into distance strictly and detect multi-scale information with various K strategies. Therefore, K-similarity can be flexibly applied to various synchronized dynamical systems with fixed, switching, and time-varying topologies. In the experiments, K-similarity outperforms four benchmark methods in accuracy in most scenarios on both simulated and real datasets, and shows strong stability towards outliers. Furthermore, according to the property of K-similarity we develop a Gaussian Mixture Model (GMM)-based threshold to select probable interactions. Our method contributes to not only similarity measurement in multi-agent systems, but also other global similarity measurement problems.
Identification and classification of water absorption profile of distributed optical fiber vibration signal based on XGBoost algorithm
Authors: Wang Yi , Li Moujie , Li Xincheng , Wang Huawei , Tian Longmei , Deng Rui
Published: October 2022

Aiming at the problem that the layered water injection cannot be accurately obtained in the process of oilfield development, this paper proposes an optical fiber vibration signal recognition and classification algorithm based on XGBoost integrated learning. Firstly, the optical fiber vibration signal collected by the optical fiber vibration sensor is denoised by variational mode decomposition and reconstruction. Then generate spectrograms corresponding to different water absorption layers at different times. A dataset containing 3000 spectral images is obtained. The data sets are imported into support vector machine, random forest classifier and XGBoost integrated classifier based on decision tree for recognition and classification. The parameters of the obtained model are optimized and the model is evaluated by cross validation. Finally, the obtained models are compared and tested. The experimental results show that the XGBoost ensemble learning algorithm with decision tree as the base classifier can effectively identify different vibration signals. This method has a certain significance for the identification of layered relative water absorption of water injection profile.
Mineralogical and Elemental Trends in Regolith on Historically Managed Sites in the southeastern United States Piedmont
Authors: Schroeder Paul A. , Austin Jason C. , Thompson Aaron , Richter Daniel D.
Published: October 2022

The deep regolith of the southeastern United States has undergone rapid erosion in the last two centuries due to intensive agricultural practices, which has altered the landscape and its inherent fertility. Parent material, landscape position, and land use are important factors in controlling the mineral and elemental composition of soil profiles. Independent quantitative X-ray diffraction (QXRD) and whole-rock chemical analysis of eight weathering profiles agreed well and allow mineral reaction pathways to be constrained as particles are conveyed in the subsurface. QXRD analysis of saprolite, argillic, and soil A-horizons in the profiles highlights the imprint of bedrock on the regolith, which includes Neoproterozoic meta-tonalitic to meta-granodioritic and Paleozoic meta-granitic to biotite- and amphibolite-gneissic lithologies. Also, aeolian input slightly influenced A-horizon composition. The clay mineral assemblage is dominated by kaolinite, but profiles differ in the amount of interstratified clay minerals, halloysite, hematite, goethite, and gibbsite. Rare-earth element totals vary between 30 and 1048 ppm and are generally correlated positively with clay and clay mineral content. Eu and Ce anomalies reflect parent rocks and subsequent hydrolysis and redox history, with trends depending upon landscape position and clay content in the weathering profile. Weathering profiles on a high-order interfluve and those that were actively cultivated have thick argillic horizons (as defined by clay mineral abundance) and are depleted in alkali and alkaline-earth elements. Profiles proximally developed on old-field pine and never-cultivated hardwood forest land do not show large differences in mineral composition trends, whereas profiles on old-field sites with ongoing cultivation exhibit assemblages enriched in clay minerals and (oxyhydr)oxides. Old-field pine sites that were historically eroded by previous cultivation tend to have shallower and thinner argillic horizons, which may well impact critical-zone processes involving gas and water fluxes. This study highlights that mineral compositions of deep regolith, saprolite, and shallow soil horizons are dependent on local geomorphology (i.e. watershed- and hillshed-orders). Quantifying soil and regolith compositional trends across the landscape is a prerequisite for determining rates of chemical and physical erosion on human and geologic time scales.
Head Injuries in Rock Climbing: A Scoping Review
Authors: Yoon Joo Hyung , Armstrong Wes , Philippopolous Eleni , Dilworth Neil , Cheng Ivy
Published: October 2022

Rock climbing was recognized as a sport at the 2020 Tokyo Olympics. Despite its increasing participation, there is no knowledge synthesis of head injuries (HIs), defined as any injury to the head, associated with climbing, making it challenging for clinicians to provide evidence-based care. Our aim was to synthesize HI literature within rock climbing and identify knowledge gaps. Six databases (Medline, Embase, Sports Medicine & Education Index, SPORTDiscus, CINAHL, and Cochrane) were searched. Two reviewers screened 345 studies and 31 studies were selected for data abstraction. We found the quality of individual studies mainly “fair” to “good.” Both HI and traumatic brain injury (TBI) had inconsistent definitions and categorization. The HIs represented between 0 to 36% of reported climbing injuries. Between 11 to 100% of HIs were TBIs, defined as an HI with permanent or temporary neurological sequelae. The most common causes of HIs were outdoor falls and falling objects. Climbing-specific factors associated with the causes were infrequently examined in the literature. Data sources of safety practices were incomplete. Overall, there was a lack of literature examining HIs, mechanisms of injury, and safety practices associated with climbing. To improve the tracking of HIs in climbing, we suggest the use of consistent reporting standards and the creation of a climbing injury surveillance system.
AN ABSTRACT ALGEBRAIC LOGIC STUDY OF DA COSTA’S LOGIC AND SOME OF ITS PARACONSISTENT EXTENSIONS
Authors: ALBUQUERQUE HUGO , CALEIRO CARLOS
Published: October 2022

Two famous negative results about da Costa’s paraconsistent logic
${\mathscr {C}}_1$
(the failure of the Lindenbaum–Tarski process [44] and its non-algebraizability [39]) have placed
${\mathscr {C}}_1$
seemingly as an exception to the scope of Abstract Algebraic Logic (AAL). In this paper we undertake a thorough AAL study of da Costa’s logic
${\mathscr {C}}_1$
. On the one hand, we strengthen the negative results about
${\mathscr {C}}_1$
by proving that it does not admit any algebraic semantics whatsoever in the sense of Blok and Pigozzi (a weaker notion than algebraizability also introduced in the monograph [6]). On the other hand,
${\mathscr {C}}_1$
is a protoalgebraic logic satisfying a Deduction-Detachment Theorem (DDT). We then extend our AAL study to some paraconsistent axiomatic extensions of
${\mathscr {C}}_1$
covered in the literature. We prove that for extensions
${\mathcal {S}}$
such as
${\mathcal {C}ilo}$
[26], every algebra in
${\mathsf {Alg}}^*({\mathcal {S}})$
contains a Boolean subalgebra, and for extensions
${\mathcal {S}}$
such as , , or [16, 53], every subdirectly irreducible algebra in
${\mathsf {Alg}}^*({\mathcal {S}})$
has cardinality at most 3. We also characterize the quasivariety
${\mathsf {Alg}}^*({\mathcal {S}})$
and the intrinsic variety
$\mathbb {V}({\mathcal {S}})$
, with , , and .
Marketing the Multinational in Shenbao, Shanghai, 1872–1889
Authors: Gibson Peter , Ville Simon
Published: October 2022

Foreign multinational enterprises (MNEs) operating in China, especially during the nineteenth century, have attracted less interest from historians than Chinese firms and expatriate merchant houses. However, in this period, MNEs shaped advertising in Shenbao, China’s most vital modern Chinese-language newspaper. Through our examination of the advertisements they placed during the newspaper’s first phase of publication, 1872–1889, we argue that MNEs were more significant to the history of business in China than heretofore recognized. We contend that they influenced Chinese print media advertising by pioneering product differentiation and branding in this newspaper. They did so, we suggest, because this approach to marketing, which differed from those used by most other foreign and Chinese domestic advertisers, provided a competitive advantage to overcome their liability of foreignness, and was facilitated by their global reach in the form of knowledge flows from offshore bases to onshore branches.
Betti maps, Pell equations in polynomials and almost-Belyi maps
Authors: Barroero Fabrizio , Capuano Laura , Zannier Umberto
Published: October 2022

We study the Betti map of a particular (but relevant) section of the family of Jacobians of hyperelliptic curves using the polynomial Pell equation$A^2-DB^2=1$, with$A,B,D\in \mathbb {C}[t]$and certain ramified covers$\mathbb {P}^1\to \mathbb {P}^1$arising from such equation and having heavy constrains on their ramification. In particular, we obtain a special case of a result of André, Corvaja and Zannier on the submersivity of the Betti map by studying the locus of the polynomialsDthat fit in a Pell equation inside the space of polynomials of fixed even degree. Moreover, Riemann existence theorem associates to the abovementioned covers certain permutation representations: We are able to characterize the representations corresponding to ‘primitive’ solutions of the Pell equation or to powers of solutions of lower degree and give a combinatorial description of these representations whenDhas degree 4. In turn, this characterization gives back some precise information about the rational values of the Betti map.
Data augmentation for disruption prediction via robust surrogate models
Authors: Rath Katharina , Rügamer David , Bischl Bernd , von Toussaint Udo , Rea Cristina , Maris Andrew , Granetz Robert , Albert Christopher G.
Published: October 2022

The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student

$t$

process regression. We apply Student

$t$

process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.
Birth weight and cardiometabolic risk factors: a discordant twin study in the UK Biobank
Authors: Wang Geng , Bond Tom A. , Warrington Nicole M. , Evans David M.
Published: October 2022

One of the longstanding debates in life-course epidemiology is whether an adverse intrauterine environment, often proxied by birth weight, causally increases the future risk of cardiometabolic disease. The use of a discordant twin study design, which controls for the influence of shared genetic and environmental confounding factors, may be useful to investigate whether this relationship is causal. We conducted a discordant twin study of 120 monozygotic (MZ) and 148 dizygotic (DZ) twin pairs from the UK Biobank to explore the potential causal relationships between birth weight and a broad spectrum of later-life cardiometabolic risk factors. We used a linear mixed model to investigate the association between birth weight and later-life cardiometabolic risk factors for twins, allowing for both within-pair differences and between-pair differences in birth weight. Of primary interest is the within-pair association between differences in birth weight and cardiometabolic risk factors, which could reflect an intrauterine effect on later-life risk factors. We found no strong evidence of association in MZ twins between the within-pair differences in birth weight and most cardiometabolic risk factors in later life, except for nominal associations with C-reactive protein and insulin-like growth factor 1. However, these associations were not replicated in DZ twin pairs. Our study provided no strong evidence for intrauterine effects on later-life cardiometabolic risk factors, which is consistent with previous large-scale studies of singletons testing the potential causal relationship. It does not support the hypothesis that adverse intrauterine environments increase the risk of cardiometabolic disease in later life.
GRAN-ONCO: „German Research Agenda for Nursing Oncology“
Authors: Zilezinski Max , Ritter-Herschbach Madeleine , Jahn Patrick
Published: October 2022

Zusammenfassung. Hintergrund: Die Etablierung von Forschungsprioritäten und Bündelung in einer Agenda dient dazu, die Weiterentwicklung des Forschungsfeldes zu systematisieren. In Deutschland liegt zum aktuellen Zeitpunkt keine Forschungsagenda für die onkologische Pflege vor. Zielstellung: Entwicklung einer Forschungsagenda der onkologischen Pflege im Rahmen eines diskursiven Prozesses unter Berücksichtigung verschiedener Perspektiven der an der Versorgung beteiligten Personengruppen. Methode: Mittels eines Mixed-Methods-Designs wurden Forschungsthemen mithilfe einer systematischen Literaturrecherche identifiziert und anhand einer Online-Befragung sowie einer qualitativen Erhebung im Rahmen von Expertengruppen durch Pflegefachpersonen der onkologischen Pflegepraxis und -forschung priorisiert und ergänzt. Ergebnisse: Die Synthese der Literatur umfasst 29 Veröffentlichungen mit 55 Themen. Auf Grundlage des Survey wurden hieraus 23 Themen als prioritär identifiziert. Aus den Survey-Rückmeldungen ( n = 174) und dem Feedback der Expert_innen ( n = 15) können folgende Themenbereiche für die Agenda formuliert werden: Krankheits- und therapiebedingte Auswirkungen und die damit einhergehenden Bedürfnisse, Veränderungen, Kommunikation/Information/Beratung und Edukation sowie Fragen nach Lebensqualität und Versorgung am Lebensende. Schlussfolgerung: Erstmals liegt für Deutschland eine Forschungsagenda für die onkologische Pflege vor. Sie ist ein wichtiger Schritt der Professionalisierung und bietet damit eine Orientierung für die wissenschaftliche Weiterentwicklung der Pflege in der Onkologie.
Daily Work Role Stressors and Dark Triad States
Authors: Nübold Annika , van Gils Suzanne , Zacher Hannes
Published: October 2022

Abstract. Organizational research on the dark triad has, so far, focused on individual differences in employees’ stable tendencies to act in manipulative, grandiose, or callous ways (i.e., dark triad traits). Research on momentary expressions of Machiavellianism, narcissism, and psychopathy (i.e., dark triad states) and the work situations that may trigger them is still in its infancy. Based on the conservation of resources theory, we hypothesized that daily role ambiguity and role conflict deplete employees’ daily self-control resources which, in turn, is related to the daily expression of dark triad states. To test our hypotheses, we conducted two daily diary studies across 5 and 10 workdays. Consistent with expectations, on days when employees experienced more role conflict than usual, they were more likely to express their darker side of personality. In contrast, hypotheses about the detrimental effects of daily role ambiguity and the mediating role of daily self-control depletion were not supported.
Melting Below the Pine Island Ice Shelf Minds the Gap
Authors: Derouin Sarah
Published: October 2022

New research shows that increased calving from West Antarctica’s Pine Island Ice Shelf will likely drive increased circulation of warm water—and melting—below the ice.
Seismological Evidence for Girdled Olivine Lattice‐Preferred Orientation in Oceanic Lithosphere and Implications for Mantle Deformation Processes During Seafloor Spreading
Authors: Russell J. B. , Gaherty J. B. , Mark H. F. , Hirth G. , Hansen L. N. , Lizarralde D. , Collins J. A. , Evans R. L.
Published: October 2022

Seismic anisotropy produced by aligned olivine in oceanic lithosphere offers a window into mid‐ocean ridge (MOR) dynamics. Yet, interpreting anisotropy in the context of grain‐scale deformation processes and strain observed in laboratory experiments and natural olivine samples has proven challenging due to incomplete seismological constraints and length scale differences spanning orders of magnitude. To bridge this observational gap, we estimate an in situ elastic tensor for oceanic lithosphere using co‐located compressional‐ and shear‐wavespeed anisotropy observations at the NoMelt experiment located on ∼70 Ma seafloor. The elastic model for the upper 7 km of the mantle, NoMelt_SPani7, is characterized by a fast azimuth parallel to the fossil‐spreading direction, consistent with corner‐flow deformation fabric. We compare this model with a database of 123 petrofabrics from the literature to infer olivine crystallographic orientations and shear strain accumulated within the lithosphere. Direct comparison to olivine deformation experiments indicates strain accumulation of 250%–400% in the shallow mantle. We find evidence for D‐type olivine lattice‐preferred orientation (LPO) with fast [100] parallel to the shear direction and girdled [010] and [001] crystallographic axes perpendicular to shear. D‐type LPO implies similar amounts of slip on the (010)[100] and (001)[100] easy slip systems during MOR spreading; we hypothesize that grain‐boundary sliding during dislocation creep relaxes strain compatibility, allowing D‐type LPO to develop in the shallow lithosphere. Deformation dominated by dislocation‐accommodated grain‐boundary sliding (disGBS) has implications for in situ stress and grain size during MOR spreading and implies grain‐size dependent deformation, in contrast to pure dislocation creep.
Hidden Tropical Pacific Sea Surface Temperature States Reveal Global Predictability for Monthly Precipitation for Sub‐Season to Annual Scales
Authors: Zhang Mengjie , Rojo‐Hernández Julián David , Yan Lei , Mesa Óscar José , Lall Upmanu
Published: October 2022

We present the first global precipitation predictability estimates corresponding to the recently discovered flavors of El Niño Southern Oscillation (ENSO) that are encoded in the hidden states of Tropical Pacific sea surface temperatures identified using a non‐homogeneous hidden Markov model. For each calendar month and for each hidden state, we assess future precipitation predictability through the conditional standardized anomaly of the average and the standard deviation of monthly precipitation, at 1, 3, 6, 9, and 12 months lead times. We find statistically significant potential predictive skill for key regions for each hidden state and calendar month, even for 12‐months in the future. We apply the algorithm sequentially over the period of record to identify regions that can be consistently predicted for different lead times and calendar months. The cross‐validated correlation skill is demonstrably superior to that of regression with an ENSO index used in the same way.
COVID-19 vaccine acceptance and perceived stigma in patients with depression: a network perspective
Authors: Cai Hong , Bai Wei , Du Xiangdong , Zhang Ling , Zhang Lan , Li Yu-Chen , Liu Huan-Zhong , Tang Yi-Lang , Jackson Todd , Cheung Teris , An Feng-Rong , Xiang Yu-Tao
Published: October 2022

The association between coronavirus disease (COVID-19) vaccine acceptance and perceived stigma of having a mental illness is not clear. This study examined the association between COVID-19 vaccine acceptance and perceived stigma among patients with recurrent depressive disorder (depression hereafter) using network analysis. Participants were 1149 depressed patients (842 men, 307 women) who completed survey measures of perceived stigma and COVID-19 vaccine attitudes. T-tests, chi-square tests, and Kruskal–Wallis tests were used to compare differences in demographic and clinical characteristics between depressed patients who indented to accepted vaccines and those who were hesitant. Hierarchical multiple regression analyses assessed the unique association between COVID-19 vaccine acceptance and perceived stigma, independent of depression severity. Network analysis examined item-level relations between COVID-19 vaccine acceptance and perceived stigma after controlling for depressive symptoms. Altogether, 617 depressed patients (53.7%, 95 confidence intervals (CI) %: 50.82–56.58%) reported they would accept future COVID-19 vaccination. Hierarchical multiple regression analyses indicated higher perceived stigma scores predicted lower levels of COVID-19 vaccination acceptance (β = −0.125, P < 0.001), even after controlling for depression severity. In the network model of COVID-19 vaccination acceptance and perceived stigma nodes, “Feel others avoid me because of my illness”, “Feel useless”, and “Feel less competent than I did before” were the most influential symptoms. Furthermore, “COVID-19 vaccination acceptance” had the strongest connections with illness stigma items reflecting social rejection or social isolation concerns (“Employers/co-workers have discriminated”, “Treated with less respect than usual”, “Sense of being unequal in my relationships with others”). Given that a substantial proportion of depressed patients reported hesitancy with accepting COVID-19 vaccines and experiences of mental illness stigma related to social rejection and social isolation, providers working with this group should provide interventions to reduce stigma concerns toward addressing reluctance in receiving COVID-19 vaccines.
Dimensions of childhood adversity differentially affect biological aging in major depression
Authors: Rampersaud Ryan , Protsenko Ekaterina , Yang Ruoting , Reus Victor , Hammamieh Rasha , Wu Gwyneth W. Y. , Epel Elissa , Jett Marti , Gautam Aarti , Mellon Synthia H. , Wolkowitz Owen M.
Published: October 2022

Adverse childhood experiences have been consistently linked with physical and mental health disorders in adulthood that may be mediated, in part, via the effects of such exposures on biological aging. Using recently developed “epigenetic clocks”, which provide an estimate of biological age, several studies have demonstrated a link between the cumulative exposure to childhood adversities and accelerated epigenetic aging. However, not all childhood adversities are equivalent and less is known about how distinct dimensions of childhood adversity relate to epigenetic aging metrics. Using two measures of childhood adversity exposure, we assess how the dimensions of Maltreatment and Household Dysfunction relate to epigenetic aging using two “second-generation” clocks, GrimAge and PhenoAge, in a cohort of unmedicated somatically healthy adults with moderate to severe major depression (n = 82). Our results demonstrate that the dimension of Maltreatment is associated with epigenetic age acceleration (EAA) using the PhenoAge but not the GrimAge clock. This association was observed using both the Childhood Trauma questionnaire (CTQ; β = 0.272, p = 0.013) and the Adverse Childhood Experiences (ACEs) questionnaire (β = 0.307, p = 0.005) and remained significant when adjusting for exposure to the dimension of Household Dysfunction (β = 0.322, p = 0.009). In contrast, the dimension of Household Dysfunction is associated with epigenetic age deceleration (β = −0.194, p = 0.083) which achieved significance after adjusting for exposure to the dimension of Maltreatment (β = −0.304, p = 0.022). This study is the first to investigate these effects among individuals with Major Depressive Disorder and suggests that these dimensions of adversity may be associated with disease via distinct biological mechanisms.
Endometrial stromal PRMT5 plays a crucial role in decidualization by regulating NF-κB signaling in endometriosis
Authors: Cai Xinyu , Xu Manlin , Zhang Hui , Zhang Mei , Wang Junxia , Mei Jie , Zhang Yang , Zhou Jidong , Zhen Xin , Kang Nannan , Yue Qiuling , Sun Haixiang , Jiang Ruiwei , Yan Guijun
Published: October 2022

Decidualization is a prerequisite for successful embryo implantation, in which elongated fibroblast-like endometrial stromal cells differentiate into more rounded decidual cells. Accumulating evidence has stressed the important role of the defective eutopic endometrium in infertility in endometriosis patients. However, the role of arginine methylation in the process of physiological decidualization and pathological decidualization defects is not clear. Here, we observed that the expression level of PRMT5, the main type II PRMT, was decreased in the endometrium of endometriosis patients, predominantly in stromal cells. Compared with the undecidualized state, PRMT5 was increased in the stromal cells of normal secretory endometrium in humans and in the decidua of normal pregnant mice or mice with artificially induced decidualization. The inhibition of PRMT5 resulted in a significant decrease in uterine weight and decidualization-related regulator expression, including FOXO1, HOXA10 and WNT4, in mice and IGFBP1 and prolactin levels in human endometrial stromal cells. Transcriptome analysis showed that decreased PRMT5 activity led to NF-κB signaling activation by inducing p65 translocation to the nucleus, which was also observed in endometriosis patients. Finally, overexpression of PRMT5 rescued the defective expression of IGFBP1 and prolactin in primary endometrial stromal cells from endometriosis patients. Our results indicate that promotion of PRMT5 may provide novel therapeutic strategies for the treatment of decidualization defects in infertile women, such as those with endometriosis.
Autophagy induction promoted by m6A reader YTHDF3 through translation upregulation of FOXO3 mRNA
Authors: Hao WeiChao , Dian MeiJuan , Zhou Ying , Zhong QiuLing , Pang WenQian , Li ZiJian , Zhao YaYan , Ma JiaCheng , Lin XiaoLin , Luo RenRu , Li YongLong , Jia JunShuang , Shen HongFen , Huang ShiHao , Dai GuanQi , Wang JiaHong , Sun Yan , Xiao Dong
Published: October 2022

Autophagy is crucial for maintaining cellular energy homeostasis and for cells to adapt to nutrient deficiency, and nutrient sensors regulating autophagy have been reported previously. However, the role of eiptranscriptomic modifications such as m6A in the regulation of starvation-induced autophagy is unclear. Here, we show that the m6A reader YTHDF3 is essential for autophagy induction. m6A modification is up-regulated to promote autophagosome formation and lysosomal degradation upon nutrient deficiency. METTL3 depletion leads to a loss of functional m6A modification and inhibits YTHDF3-mediated autophagy flux. YTHDF3 promotes autophagy by recognizing m6A modification sites around the stop codon of FOXO3 mRNA. YTHDF3 also recruits eIF3a and eIF4B to facilitate FOXO3 translation, subsequently initiating autophagy. Overall, our study demonstrates that the epitranscriptome regulator YTHDF3 functions as a nutrient responder, providing a glimpse into the post-transcriptional RNA modifications that regulate metabolic homeostasis.
Evolution of Br⋯Br contacts in enantioselective molecular recognition during chiral 2D crystallization
Authors: Yi Zhen-Yu , Yang Xue-Qing , Duan Jun-Jie , Zhou Xiong , Chen Ting , Wang Dong , Wan Li-Jun
Published: October 2022

Halogen-mediated interactions play an important role in molecular recognition and crystallization in many chemical and biological systems, whereas their effect on homochiral versus heterochiral recognition and crystallization has rarely been explored. Here we demonstrate the evolution of Br⋯Br contacts in chiral recognition during 2D crystallization. On Ag(100), type I contacts prevail at low coverage and lead to homochiral recognition and the formation of 2D conglomerates; whereas type II contacts mediating heterochiral recognition are suppressed at medium coverage and appear in the racemates induced by structural transitions at high coverage. On Ag(111), type I contacts dominate the 2D crystallization and generate 2D conglomerates exclusively. DFT calculations suggest that the energy difference between type I and type II contacts is reversed upon adsorption due to the substrate induced mismatch energy penalty. This result provides fundamental understanding of halogen-mediated interactions in molecular recognition and crystallization on surface.
Location bias contributes to functionally selective responses of biased CXCR3 agonists
Authors: Eiger Dylan Scott , Boldizsar Noelia , Honeycutt Christopher Cole , Gardner Julia , Kirchner Stephen , Hicks Chloe , Choi Issac , Pham Uyen , Zheng Kevin , Warman Anmol , Smith Jeffrey S. , Zhang Jennifer Y. , Rajagopal Sudarshan
Published: October 2022

Some G protein-coupled receptor (GPCR) ligands act as “biased agonists” that preferentially activate specific signaling transducers over others. Although GPCRs are primarily found at the plasma membrane, GPCRs can traffic to and signal from many subcellular compartments. Here, we determine that differential subcellular signaling contributes to the biased signaling generated by three endogenous ligands of the GPCR CXC chemokine receptor 3 (CXCR3). The signaling profile of CXCR3 changes as it traffics from the plasma membrane to endosomes in a ligand-specific manner. Endosomal signaling is critical for biased activation of G proteins, β-arrestins, and extracellular-signal-regulated kinase (ERK). In CD8 + T cells, the chemokines promote unique transcriptional responses predicted to regulate inflammatory pathways. In a mouse model of contact hypersensitivity, β-arrestin-biased CXCR3-mediated inflammation is dependent on receptor internalization. Our work demonstrates that differential subcellular signaling is critical to the overall biased response observed at CXCR3, which has important implications for drugs targeting chemokine receptors and other GPCRs.
Predictive control of selective secondary alcohol oxidation of glycerol on NiOOH
Authors: Goetz McKenna K. , Bender Michael T. , Choi Kyoung-Shin
Published: October 2022

Many biomass intermediates are polyols and selectively oxidizing only a primary or secondary alcohol group is beneficial for the valorization of these intermediates. For example, production of 1,3-dihydroxyacetone, a highly valuable oxidation product of glycerol, requires selective secondary alcohol oxidation. However, selective secondary alcohol oxidation is challenging due to its steric disadvantage. This study demonstrates that NiOOH, which oxidizes alcohols via two dehydrogenation mechanisms, hydrogen atom transfer and hydride transfer, can convert glycerol to 1,3-dihydroxyacetone with high selectivity when the conditions are controlled to promote hydrogen atom transfer, favoring secondary alcohol oxidation. This rational production of 1,3-dihydroxyacetone achieved by selectively enabling one desired dehydrogenation pathway, without requiring alteration of catalyst composition, demonstrates how comprehensive mechanistic understanding can enable predictive control over selectivity.
Fast hydrogen purification through graphitic carbon nitride nanosheet membranes
Authors: Zhou Yisa , Wu Ying , Wu Haoyu , Xue Jian , Ding Li , Wang Rui , Wang Haihui
Published: October 2022

Two-dimensional graphitic carbon nitride (g-C3N4) nanosheets are ideal candidates for membranes because of their intrinsic in-plane nanopores. However, non-selective defects formed by traditional top-down preparation and the unfavorable re-stacking hinder the application of these nanosheets in gas separation. Herein, we report lamellar g-C3N4 nanosheets as gas separation membranes with a disordered layer-stacking structure based on high quality g-C3N4 nanosheets through bottom-up synthesis. Thanks to fast and highly selective transport through the high-density sieving channels and the interlayer paths, the membranes, superior to state-of-the-art ones, exhibit high H2 permeance of 1.3 × 10−6 mol m−2 s−1 Pa−1 with excellent selectivity for multiple gas mixtures. Notably, these membranes show excellent stability under harsh practice-relevant environments, such as temperature swings, wet atmosphere and long-term operation of more than 200 days. Therefore, such lamellar membranes with high quality g-C3N4 nanosheets hold great promise for gas separation applications.
Deep learning models for histologic grading of breast cancer and association with disease prognosis
Authors: Jaroensri Ronnachai , Wulczyn Ellery , Hegde Narayan , Brown Trissia , Flament-Auvigne Isabelle , Tan Fraser , Cai Yuannan , Nagpal Kunal , Rakha Emad A. , Dabbs David J. , Olson Niels , Wren James H. , Thompson Elaine E. , Seetao Erik , Robinson Carrie , Miao Melissa , Beckers Fabien , Corrado Greg S. , Peng Lily H. , Mermel Craig H. , Liu Yun , Steiner David F. , Chen Po-Hsuan Cameron
Published: October 2022

Histologic grading of breast cancer involves review and scoring of three well-established morphologic features: mitotic count, nuclear pleomorphism, and tubule formation. Taken together, these features form the basis of the Nottingham Grading System which is used to inform breast cancer characterization and prognosis. In this study, we develop deep learning models to perform histologic scoring of all three components using digitized hematoxylin and eosin-stained slides containing invasive breast carcinoma. We first evaluate model performance using pathologist-based reference standards for each component. To complement this typical approach to evaluation, we further evaluate the deep learning models via prognostic analyses. The individual component models perform at or above published benchmarks for algorithm-based grading approaches, achieving high concordance rates with pathologist grading. Further, prognostic performance using deep learning-based grading is on par with that of pathologists performing review of matched slides. By providing scores for each component feature, the deep-learning based approach also provides the potential to identify the grading components contributing most to prognostic value. This may enable optimized prognostic models, opportunities to improve access to consistent grading, and approaches to better understand the links between histologic features and clinical outcomes in breast cancer.
Chiral superconductivity with enhanced quantized Hall responses in moiré transition metal dichalcogenides
Authors: Scherer Michael M. , Kennes Dante M. , Classen Laura
Published: October 2022

Experimental demonstrations of tunable correlation effects in magic-angle twisted bilayer graphene have put two-dimensional moiré quantum materials at the forefront of condensed-matter research. Other twisted few-layer graphitic structures, boron-nitride, and homo- or hetero-stacks of transition metal dichalcogenides (TMDs) have further enriched the opportunities for analysis and utilization of correlations in these systems. Recent experiments within the latter material class confirmed the relevance of many-body interactions and demonstrated the importance of their extended range. Since the interaction, its range, and the filling can be tuned experimentally by twist angle, substrate engineering and gating, we here explore Fermi surface instabilities and resulting phases of matter of hetero-bilayer TMDs. Using an unbiased renormalization group approach, we establish in particular that hetero-bilayer TMDs are platforms to realize topological superconductivity with winding number$$| {{{\mathcal{N}}}}| =4$$∣N∣=4. We show that this state reflects in pronounced experimental signatures, such as distinct quantum Hall features.
Comparing models of learning and relearning in large-scale cognitive training data sets
Authors: Kumar Aakriti , Benjamin Aaron S. , Heathcote Andrew , Steyvers Mark
Published: October 2022

Practice in real-world settings exhibits many idiosyncracies of scheduling and duration that can only be roughly approximated by laboratory research. Here we investigate 39,157 individuals’ performance on two cognitive games on the Lumosity platform over a span of 5 years. The large-scale nature of the data allows us to observe highly varied lengths of uncontrolled interruptions to practice and offers a unique view of learning in naturalistic settings. We enlist a suite of models that grow in the complexity of the mechanisms they postulate and conclude that long-term naturalistic learning is best described with a combination of long-term skill and task-set preparedness. We focus additionally on the nature and speed of relearning after breaks in practice and conclude that those components must operate interactively to produce the rapid relearning that is evident even at exceptionally long delays (over 2 years). Naturalistic learning over long time spans provides a strong test for the robustness of theoretical accounts of learning, and should be more broadly used in the learning sciences.
Large-scale audio dataset for emergency vehicle sirens and road noises
Authors: Asif Muhammad , Usaid Muhammad , Rashid Munaf , Rajab Tabarka , Hussain Samreen , Wasi Sarwar
Published: October 2022

Traffic congestion, accidents, and pollution are becoming a challenge for researchers. It is essential to develop new ideas to solve these problems, either by improving the infrastructure or applying the latest technology to use the existing infrastructure better. This research paper presents a high-resolution dataset that will help the research community to apply AI techniques to classify any emergency vehicle from traffic and road noises. Demand for such datasets is high as they can control traffic flow and reduce traffic congestion. It also improves emergency response time, especially for fire and health events. This work collects audio data using different methods, and pre-processed them  to develop a high-quality and clean dataset. The dataset is divided into two labelled classes one for emergency vehicle sirens and one for traffic noises. The developed dataset offers high quality and range of real-world traffic sounds and emergency vehicle sirens. The technical validity of the dataset is also established.
Effects of non-Darcy mixed convection over a horizontal cone with different convective boundary conditions incorporating gyrotactic microorganisms on dispersion
Authors: Ferdows M. , Alshuraiaan Bader , Nima Nayema Islam
Published: October 2022

This paper investigates the influence of dispersion impact on mixed convection flow over a horizontal cone within a non-Darcy porous medium. Multiple convective boundary conditions are applied to address the heat, mass and motile microorganism transfer phenomena. This paper incorporates the dispersion effect for gyrotactic microorganisms due to biological and environmental applications. By imposing appropriate similarity transformations, the nonlinear partial differential equations governing flow, temperature, concentration, and microbe fields are reduced to a system of ordinary differential equations & then solved using the MATLAB BVP4C function. The computation of grid independence test is analyzed for different flow profiles to show the precision of the points. In a few instances, our present numerical data is compared with previously published works, leading to excellent agreement. The non-Darcy effect, as well as mixed convection values from 0.1 to 0.9 and buoyancy parameters from 0.2 to 0.8, all significantly affects the velocity profile. The reduction in the microorganism profile is brought on by the increase in the bioconvection Lewis parameter and bio convection peclet number between 0.3 and 1. In the absence of dispersion, the variation of Biot numbers between 0.5 and 2, favor heat, mass, and motile microorganism transfer the most in the range of mixed convection parameter 0.5 to pure forced convection 1. Thermal, solutal and microorganism dispersion coefficients a, b, c that lie between $$\frac{1}{7}$$

1
7

and $$\frac{1}{3}$$

1
3

and higher values of modified peclet number ranges from 2 to 10 cause increased dispersion effects which lower flow transfer rates mostly in forced convection regime.
Iterative image segmentation of plant roots for high-throughput phenotyping
Authors: Seidenthal Kyle , Panjvani Karim , Chandnani Rahul , Kochian Leon , Eramian Mark
Published: October 2022

Accurate segmentation of root system architecture (RSA) from 2D images is an important step in studying phenotypic traits of root systems. Various approaches to image segmentation exist but many of them are not well suited to the thin and reticulated structures characteristic of root systems. The findings presented here describe an approach to RSA segmentation that takes advantage of the inherent structural properties of the root system, a segmentation network architecture we call ITErRoot. We have also generated a novel 2D root image dataset which utilizes an annotation tool developed for producing high quality ground truth segmentation of root systems. Our approach makes use of an iterative neural network architecture to leverage the thin and highly branched properties of root systems for accurate segmentation. Rigorous analysis of model properties was carried out to obtain a high-quality model for 2D root segmentation. Results show a significant improvement over other recent approaches to root segmentation. Validation results show that the model generalizes to plant species with fine and highly branched RSA’s, and performs particularly well in the presence of non-root objects.
An optimization model of tram timetables considering various signal priority strategies
Authors: He Jing , Xu Yuting , Li Yanhuan , Yang Jian , Long Sihui
Published: October 2022

Modern trams generally operate in a semi-independent Right Of Way that intersects with social vehicles at junctions. Typically, there are two signal priority strategies at junctions: active signal priority strategy and no-signal priority strategy. The active signal priority strategy is applied to improve the efficiency of the tram. However, it inevitably causes delays to social vehicles. The no-signal priority strategy could reduce the influence on social vehicles, but it will increase the tram travel time. Therefore, we develop a Mixed-Integer Linear Programming model to optimize the tram timetable and consider various signal priority strategies. In the model, the signal priority strategies of the tram are a set of decision variables that consider the traffic flow of social vehicles rather than fixed input parameters. The model considers minimizing the overall travel time of the tram and the negative utility of signal priority strategies. A numerical experiment is conducted to demonstrate the validity of the proposed model. The experimental results show that the proposed method can optimize the tram timetable and maximize the overall benefits of the junction. Moreover, we compare the experimental results of the proposed method with the approach of fixing the signal priority strategy for the tram at junctions. On the one hand, our proposed method can improve the operational efficiency of trams, i.e., the travel time decreases by 16.60%. On the other hand, the negative utility of signal priority for the comprehensive scheme proposed in this work reduces by 39.45%.
Species-specific transcriptomic changes upon respiratory syncytial virus infection in cotton rats
Authors: Strickland Britton A. , Rajagopala Seesandra V. , Kamali Arash , Shilts Meghan H. , Pakala Suman B. , Boukhvalova Marina S. , Yooseph Shibu , Blanco Jorge C. G. , Das Suman R.
Published: October 2022

The cotton rat (Sigmodon) is the gold standard pre-clinical small animal model for respiratory viral pathogens, especially for respiratory syncytial virus (RSV). However, without a reference genome or a published transcriptome, studies requiring gene expression analysis in cotton rats are severely limited. The aims of this study were to generate a comprehensive transcriptome from multiple tissues of two species of cotton rats that are commonly used as animal models (Sigmodon fulviventer and Sigmodon hispidus), and to compare and contrast gene expression changes and immune responses to RSV infection between the two species. Transcriptomes were assembled from lung, spleen, kidney, heart, and intestines for each species with a contig N50 > 1600. Annotation of contigs generated nearly 120,000 gene annotations for each species. The transcriptomes of S. fulviventer and S. hispidus were then used to assess immune response to RSV infection. We identified 238 unique genes that are significantly differentially expressed, including several genes implicated in RSV infection (e.g., Mx2, I27L2, LY6E, Viperin, Keratin 6A, ISG15, CXCL10, CXCL11, IRF9) as well as novel genes that have not previously described in RSV research (LG3BP, SYWC, ABEC1, IIGP1, CREB1). This study presents two comprehensive transcriptome references as resources for future gene expression analysis studies in the cotton rat model, as well as provides gene sequences for mechanistic characterization of molecular pathways. Overall, our results provide generalizable insights into the effect of host genetics on host-virus interactions, as well as identify new host therapeutic targets for RSV treatment and prevention.
Ecogenomics reveals viral communities across the Challenger Deep oceanic trench
Authors: Zhou Ying-Li , Mara Paraskevi , Vik Dean , Edgcomb Virginia P. , Sullivan Matthew B. , Wang Yong
Published: October 2022

Despite the environmental challenges and nutrient scarcity, the geographically isolated Challenger Deep in Mariana trench, is considered a dynamic hotspot of microbial activity. Hadal viruses are the least explored microorganisms in Challenger Deep, while their taxonomic and functional diversity and ecological impact on deep-sea biogeochemistry are poorly described. Here, we collect 13 sediment cores from slope and bottom-axis sites across the Challenger Deep (down to ~11 kilometers depth), and identify 1,628 previously undescribed viral operational taxonomic units at species level. Community-wide analyses reveals 1,299 viral genera and distinct viral diversity across the trench, which is significantly higher at the bottom-axis vs. slope sites of the trench. 77% of these viral genera have not been previously identified in soils, deep-sea sediments and other oceanic settings. Key prokaryotes involved in hadal carbon and nitrogen cycling are predicted to be potential hosts infected by these viruses. The detected putative auxiliary metabolic genes suggest that viruses at Challenger Deep could modulate the carbohydrate and sulfur metabolisms of their potential hosts, and stabilize host’s cell membranes under extreme hydrostatic pressures. Our results shed light on hadal viral metabolic capabilities, contribute to understanding deep sea ecology and on functional adaptions of hadal viruses for future research.