bluuebunny/crossref_metadata_2025 huggingface dataset:
Horizontal and vertical crossover of sine cosine algorithm with quick moves for optimization and feature selection
Authors: Hu Hanyu , Shan Weifeng , Tang Yixiang , Heidari Ali Asghar , Chen Huiling , Liu Haijun , Wang Maofa , Escorcia-Gutierrez José , Mansour Romany F , Chen Jun
Published: November 2022

The sine cosine algorithm (SCA) is a metaheuristic algorithm proposed in recent years that does not resort to nature-related metaphors but explores and exploits the search space with the help of two simple mathematical functions of sine and cosine. SCA has fewer parameters and a simple structure and is widely used in various fields. However, it tends to fall into local optimality because it does not have a well-balanced exploitation and exploration phase. Therefore, in this paper, a new, improved SCA algorithm (QCSCA) is proposed to improve the performance of the algorithm by introducing a quick move mechanism and a crisscross mechanism to SCA and adaptively improving one of the parameters. To verify the effectiveness of QCSCA, comparison experiments with some conventional metaheuristic algorithms, advanced metaheuristic algorithms, and SCA variants are conducted on IEEE CEC2017 and CEC2013. The experimental results show a significant improvement in the convergence speed and the ability to jump out of the local optimum of the QCSCA. The scalability of the algorithm is verified in the benchmark function. In addition, QCSCA is applied to 14 real-world datasets from the UCI machine learning database for selecting a subset of near-optimal features, and the experimental results show that QCSCA is still very competitive in feature selection (FS) compared to similar algorithms. Our experimental results and analysis show that QCSCA is an effective method for solving global optimization problems and FS problems.
A complicated case of terminal ileitis post-COVID-19 infection requiring bowel resection
Authors: Tong Chai Wei , Jiwane Ashish
Published: November 2022

Since the beginning of the COVID-19 pandemic, there have been reports of children developing systemic hyperinflammatory response to COVID-19 infection, known as Paediatric Inflammatory Multisystem Syndrome Temporally associated with SARS-COV-2 (PIMS-TS). Here we would like to discuss a case of a 15-year-old male with PIMS-TS presenting as complicated terminal ileitis, requiring ileocaecal resection. Histopathologic findings of the ileocaecal specimen revealed thickened bowel mucosa, with features of granulomatous inflammation similar to Crohn’s disease, without features of intestinal vasculitis or viral particles. His post-operative course was complicated by fresh rectal bleeding, requiring urgent blood transfusion and endoscopy. Source of bleeding was not identified and his bleeding self-resolved. Gut biopsies of the remaining bowel taken during endoscopy were normal. Although this case suggests that the inflammatory process is focal to the terminal ileum with a good short-term prognosis, further follow-up is required to assess for long-term effects and risks of bowel disease.
Machine learning methods to estimate observational properties of galaxy clusters in large volume cosmological N-body simulations
Authors: de Andres Daniel , Yepes Gustavo , Sembolini Federico , Martínez-Muñoz Gonzalo , Cui Weiguang , Robledo Francisco , Chuang Chia-Hsun , Rasia Elena
Published: November 2022

In this paper, we study the applicability of a set of supervised machine learning (ML) models specifically trained to infer observed related properties of the baryonic component (stars and gas) from a set of features of dark matter (DM)-only cluster-size haloes. The training set is built from the three hundred project that consists of a series of zoomed hydrodynamical simulations of cluster-size regions extracted from the 1 Gpc volume MultiDark DM-only simulation (MDPL2). We use as target variables a set of baryonic properties for the intracluster gas and stars derived from the hydrodynamical simulations and correlate them with the properties of the DM haloes from the MDPL2 N-body simulation. The different ML models are trained from this data base and subsequently used to infer the same baryonic properties for the whole range of cluster-size haloes identified in the MDPL2. We also test the robustness of the predictions of the models against mass resolution of the DM haloes and conclude that their inferred baryonic properties are rather insensitive to their DM properties that are resolved with almost an order of magnitude smaller number of particles. We conclude that the ML models presented in this paper can be used as an accurate and computationally efficient tool for populating cluster-size haloes with observational related baryonic properties in large volume N-body simulations making them more valuable for comparison with full sky galaxy cluster surveys at different wavelengths. We make the best ML trained model publicly available.
Optical and X-ray follow-up to a globular cluster ultraluminous X-ray source in NGC 4472
Authors: Athukoralalage Wasundara Ranhari , Dage Kristen C , Zepf Stephen E , Bahramian Arash , Cackett Edward M , Kundu Arunav , Maccarone Thomas J
Published: November 2022

NGC 4472 is home to five ultraluminous X-ray sources hosted by globular clusters. These sources have been suggested as good black hole candidates in extragalactic globular clusters—a highly sought after population that may provide observational information regarding the progenitors of merging black hole binaries. In this body of work, we present X-ray and optical follow-up to one of these sources, CXOUJ1229410+075744 (GCU1). We find no evidence of [OIII] optical emission in GCU1 which indicates a lack of significant evidence for super-Eddington outflows, unlike what is seen in a handful of ULXs in extragalactic GCs. X-ray monitoring from 2019 to 2021 shows no detected X-ray emission above a few × 1038 erg/s. Comparisons of the multiwavelength properties to disc-dominated, near Eddington Galactic black hole low-mass X-ray binaries (GRS 1915+105 and XTEJ1817-330) suggests that GCU1 may show similar behaviour to GRS 1915+105 in terms of X-ray variability and similar relationships between LX and kT, with GCU1 showing maximum X-ray luminosities on order of higher magnitude.
Rotational spectra of interstellar N- and CN-PAHs: pyrene and coronene
Authors: Vats Akant , Pathak Amit
Published: November 2022

The detection of benzonitrile (C6H5CN), 1- and 2-cyano-naphthalene (C10H7CN) in the cold, dark molecular cloud TMC-1 at centimetre (cm) wavelengths has opened up prospects for the detection of other N- and CN-containing polycyclic aromatic hydrocarbons (PAHs). In this light, the pure rotational spectra of N-pyrene (C15H9N), CN-pyrene (C15H9CN), N-coronene (C23H11N), and CN-coronene (C23H11CN) are reported here for the first time. The B3LYP/6-311 + G (d, p) level of theory, in the density functional theory (DFT) calculations, achieves the best performance for calculating the spectroscopic parameters and simulating the rotational spectra. The large permanent dipole moment of CN-PAHs makes them the most suitable PAH species for detection in the interstellar medium. Additionally, pyrene’s smaller partition function makes CN-pyrene a prime candidate to be discovered in cold, dark molecular clouds such as the TMC-1. The present work sets a benchmark for theoretical rotational spectra of N- and CN-containing PAHs and may act as a guide for laboratory experiments and observational searches.
Temperature properties in magnetized and radiatively cooled two-temperature accretion flows on to a black hole
Authors: Dihingia Indu K , Mizuno Yosuke , Fromm Christian M , Rezzolla Luciano
Published: November 2022

Simplified assumptions about the thermodynamics of the electrons are normally employed in general-relativistic magnetohydrodynamic (GRMHD) simulations of accretion on to black holes. To counter this, we have developed a self-consistent approach to study magnetized and radiatively cooled two-temperature accretion flows around a Kerr black hole in two spatial dimensions. The approach includes several heating processes, radiative cooling, and a coupling between the electrons and the ions via Coulomb interaction. We test our approach by performing axisymmetric GRMHD simulations of magnetized tori accreting on to a Kerr black hole under various astrophysical scenarios. In this way, we find that the inclusion of the Coulomb interaction and the radiative cooling impacts the thermodynamical properties of both the ions and electrons, changing significantly the temperature distribution of the latter, and underlining the importance of a two-temperature approach when imaging these flows. In addition, we find that the accretion rate influences the bulk properties of the flow as well as the thermodynamics of the electrons and ions. Interestingly, we observe qualitatively distinct temperature properties for SANE and MAD accretion modes while maintaining the same accretion rates, which could help distinguishing MAD and SANE accretion flows via observations. Finally, we propose two new relations for the temperature ratios of the electrons, ions, and of the gas in terms of the plasma-β parameter. The new relations represent a simple and effective approach to treat two-temperature accretion flows on supermassive black holes such as Sgr A* and M 87*.
The merger fraction of ultramassive white dwarfs
Authors: Kilic Mukremin , Moss Adam G , Kosakowski Alekzander , Bergeron P , Conly Annamarie A , Brown Warren R , Toonen Silvia , Williams Kurtis A , Dufour P
Published: November 2022

We search for merger products among the 25 most massive white dwarfs in the Montreal White Dwarf Database 100 pc sample through follow-up spectroscopy and high-cadence photometry. We find an unusually high fraction, 40 per cent, of magnetic white dwarfs among this population. In addition, we identify four outliers in transverse velocity and detect rapid rotation in five objects. Our results show that $56^{+9}_{-10}$ per cent of the $M\approx 1.3\, {\rm M}_{\odot }$ ultramassive white dwarfs form through mergers. This fraction is significantly higher than expected from the default binary population synthesis calculations using the α prescription (with αλ = 2), and provides further support for efficient orbital shrinkage, such as with low values of the common-envelope efficiency.
eIF3j inhibits translation of a subset of circular RNAs in eukaryotic cells
Authors: Song Zhenxing , Lin Jiamei , Su Rui , Ji Yu , Jia Ruirui , Li Shi , Shan Ge , Huang Chuan
Published: November 2022

Increasing studies have revealed that a subset of circular RNAs (circRNAs) harbor an open reading frame and can act as protein-coding templates to generate functional proteins that are closely associated with multiple physiological and disease-relevant processes, and thus proper regulation of synthesis of these circRNA-derived proteins is a fundamental cellular process required for homeostasis maintenance. However, how circRNA translation initiation is coordinated by different trans-acting factors remains poorly understood. In particular, the impact of different eukaryotic translation initiation factors (eIFs) on circRNA translation and the physiological relevance of this distinct regulation have not yet been characterized. In this study, we screened all 43 Drosophila eIFs and revealed the conflicting functions of eIF3 subunits in the translational control of the translatable circRNA circSfl: eIF3 is indispensable for circSfl translation, while the eIF3-associated factor eIF3j is the most potent inhibitor. Mechanistically, the binding of eIF3j to circSfl promotes the disassociation of eIF3. The C-terminus of eIF3j and an RNA regulon within the circSfl untranslated region (UTR) are essential for the inhibitory effect of eIF3j. Moreover, we revealed the physiological relevance of eIF3j-mediated circSfl translation repression in response to heat shock. Finally, additional translatable circRNAs were identified to be similarly regulated in an eIF3j-dependent manner. Altogether, our study provides a significant insight into the field of cap-independent translational regulation and undiscovered functions of eIF3.
Politicizing Europe
Authors: Costa Lobo Marina
Published: November 2022

This chapter focuses on the way Europe has become an issue in Portuguese politics from the perspective of political parties and voters, since membership. It begins by introducing the main milestones which have characterized Portugal’s relationship with the EU. An overview of parties’ positioning regarding the EU is provided alongside data from the Chapel Hill expert data on parties to understand the evolution of parties stance on this topic. The Great Recession does not seem to have catapulted the European Union (EU) issue as a major factor in party polarization or voting since 2009. While austerity did become a major political issue, Eurozone policy or EU membership did not. Yet, the Eurozone crisis led to a major change in party system dynamics, namely the socialists’ parliamentary coalition with the left parties in 2015. This paradoxically contributed to the depoliticization of the EU as a political issue, as the communists and the Left Block parties, the traditional bulwarks of Euroscepticism, became quasi government parties for the first time since democratization. At voter level, Euroscepticism continues to be predictive of a vote for the Communists as well as for the centre-right PSD in 2019.
Semi-Presidentialism in Portugal: Academic Quarrels amidst Institutional Stability
Authors: Amorim Neto Octavio
Published: November 2022

Portugal’s experience with semi-presidentialism has been relatively stable, particularly after 1982. However, the very definition of the country’s system of government has been contested. To demonstrate this stability and map the accompanying academic debate, this chapter will first define the main systems of government found around the world. It will then go on to discuss why Portugal chose the presidential-parliamentary variant of semi-presidential system, in 1976, later adopting the premier-presidential version, in 1982. Next, it identifies the conditions that led to the consolidation and stability of the latter system, focusing on the relationships between the president, the electorate, and the prime minister and the cabinet. Subsequently, the chapter briefly evaluates how the country’s executive type has fared under the 2011–2014 financial crisis and the COVID-19 pandemic. The final section presents a conclusion and lays out an agenda for future research.
Cytokinin signaling regulates two-stage inflorescence arrest in Arabidopsis
Authors: Walker Catriona H , Ware Alexander , Šimura Jan , Ljung Karin , Wilson Zoe , Bennett Tom
Published: November 2022

To maximize reproductive success, flowering plants must correctly time entry and exit from the reproductive phase. While much is known about mechanisms that regulate initiation of flowering, end-of-flowering remains largely uncharacterized. End-of-flowering in Arabidopsis (Arabidopsis thaliana) consists of quasi-synchronous arrest of inflorescences, but it is unclear how arrest is correctly timed with respect to environmental stimuli and reproductive success. Here, we showed that Arabidopsis inflorescence arrest is a complex developmental phenomenon, which includes the arrest of the inflorescence meristem (IM), coupled with a separable “floral arrest” of all unopened floral primordia; these events occur well before visible inflorescence arrest. We showed that global inflorescence removal delays both IM and floral arrest, but that local fruit removal only delays floral arrest, emphasizing their separability. We tested whether cytokinin regulates inflorescence arrest, and found that cytokinin signaling dynamics mirror IM activity, while cytokinin treatment can delay both IM and floral arrest. We further showed that gain-of-function cytokinin receptor mutants can delay IM and floral arrest; conversely, loss-of-function mutants prevented the extension of flowering in response to inflorescence removal. Collectively, our data suggest that the dilution of cytokinin among an increasing number of sink organs leads to end-of-flowering in Arabidopsis by triggering IM and floral arrest.
Sleep restriction reduces positive social emotions and desire to connect with others
Authors: Palmer Cara A , John-Henderson Neha A , Bawden Hannah , Massey Adrielle , Powell Suzanna L , Hilton Ashleigh , Carter Jason R
Published: November 2022

Feeling connected with others and experiencing positive interpersonal interactions is associated with physical health and psychological functioning. Despite the importance of social experiences, experimental studies investigating how sleep impacts social connections and positive social experiences are limited. The current study sought to examine how sleep loss impacted social motivation and emotions. Healthy emerging adults (N = 53; 83% female, ages 18–28 years) were randomly assigned to one night of sleep restriction (4h time in bed) or typical sleep (8 h time in bed). Following the experimental night, participants reported on their desire to pursue social connections, and completed a reflection task where they wrote about something generous someone did for them. After the reflection, participants reported on their positive and negative social emotions (gratitude, connectedness, guilt, indebtedness). Coding of the reflections was conducted to extract emotional tone and social words used. Sleep restricted participants reported reduced motivation to pursue social connections, and less gratitude and feelings of connectedness after the reflection compared to the control condition. Sleep restricted participants also used fewer socially-oriented words (i.e. words focused on other people) when reflecting on this interpersonal event. No differences emerged in guilt or indebtedness or emotional tone of the reflection. Findings suggest that sleep loss may decrease desire to engage in social interactions and reduces positive social emotions. These findings expand the limited body of research on sleep and social functioning by examining the impact of partial sleep restriction on social motivation, and on the experience of social emotions within a positive interpersonal context.
Evolution of the Collaborative Pediatric Critical Care Research Network
Authors: Dean J. Michael ,   for the Collaborative Pediatric Critical Care Research Network (CPCCRN) Investigators
Published: November 2022

The Collaborative Pediatric Critical Care Research Network (CPCCRN) was established by the Eunice Kennedy Shriver National Institute of Child Health and Human Development in May 2005 to develop an infrastructure for collaborative clinical trials and meaningful descriptive studies in pediatric critical care. This article describes the history of CPCCRN, discusses its financial and organizational structure, illustrates how funds were efficiently used to carry out studies, and describes CPCCRN public use datasets and future directions, concluding with the development of the PeRsonalizEd Immunomodulation in PediatriC SepsIS-InducEd MODS study.
Community Engagement in Nonprofit Hospital Community Health Needs Assessments and Implementation Plans
Authors: Burns Ashlyn , Yeager Valerie A. , Cronin Cory E. , Franz Berkeley
Published: November 2022

Context:

Nonprofit hospitals are required to provide community benefits in exchange for their tax-exempt status. This includes a community health needs assessment (CHNA) to identify community needs and an implementation plan (IP) with strategies to address top needs every 3 years. In addition, hospitals are required to engage community members in these assessments.

Objective:

The objective of this study was to explore community engagement and representation in CHNAs and IPs.

Design:

The researchers conducted a content analysis of CHNAs and IPs from a nationally representative sample of 503 nonprofit hospitals between 2018 and 2021.

Main Outcome Measures:

For CHNAs, a coding sheet was used to record the types of community members engaged by hospitals. For IPs, the team coded whether community engagement was reported at all and then performed an in-depth analysis to identify categories of community members, engagement methods used, and roles of community input. Finally, frequencies of categories across IPs were quantified.

Results:

Eighty-nine percent of hospitals (n = 449) engaged community members in their CHNA, but only 14% (n = 71) engaged community members in their IP. An in-depth look at these IPs found that hospitals engaged underserved/minority populations, low-income populations, high school students, public health experts, and stakeholder organizations. Community members were involved in multiple steps, including brainstorming ideas, narrowing down needs, developing strategies, and reacting to proposed strategies.

Conclusions:

Although IPs are intended to benefit the community, there is a lack of community involvement reported in IPs. Hospitals may need incentives, resources, and personnel support to ensure representation of community members throughout the entire CHNA and IP process.
Investigating the Severity of Complications following Nipple-sparing Mastectomy and Immediate Prepectoral Implant-based vs. Autologous Reconstruction – A Single-Surgeon Experience
Authors: Pedreira Rachel , Tevlin Ruth , Griffin Michelle , Wan Derrick , Momeni Arash
Published: November 2022

Background:

Several clinical studies have reported autologous breast reconstruction (ABR) to be associated with a higher postoperative complication rate; however, few have investigated the impact of reconstructive modality on complication severity. This study examines the impact of reconstructive modality on complication severity in a matched cohort of patients who underwent ABR versus implant-based breast reconstruction (IBR).

Methods:

A retrospective study of patients who underwent nipple-sparing mastectomy with immediate reconstruction was performed. Propensity score matching (PSM) ensured adequate matching of patients who underwent ABR and staged prepectoral IBR, respectively. Patient demographics, breast measurements and postoperative outcomes, including the incidence and severity of complications were analyzed. Multivariable logistic regression analysis was performed. A P‐value of <0.05 was considered significant.

Results:

128 patients (214 breast reconstructions) were included for analysis (ABR n = 64; IBR n = 64). No difference in overall complication rate was noted (p = 0.61). However, a significant association of IBR with major complications was noted (p = 0.02). In contrast, minor complications were significantly more frequent following ABR (p = 0.04).

Conclusions:

While the reconstructive modality did not appear to have an effect on the overall complication rate, it did significantly affect the severity of postoperative complications with major and minor complications being associated with IBR and ABR, respectively. These findings are relevant to patient-centered decision-making as they provide further granularity regarding postoperative complications and address the issue of complication severity.
Genetic characterization of Escherichia coli and Klebsiella spp. from Humans and Poultry in Nigeria
Authors: Jesumirhewe Christiana , CABAL-ROSEL Adriana , ALLERBERGER Franz , SPRINGER Burkhard , RUPPITSCH Werner
Published: November 2022

The emergence of antibiotic resistance in food producing animals is of major public health significance arising from the risk of these bacteria entering the food chain. In this study, the genetic characteristics of antibiotic resistant Escherichia coli and Klebsiella spp. isolates from humans and poultry in Edo state, Nigeria were investigated. In April 2017, 45 Klebsiella spp. and 46 Escherichia coli isolates were obtained from urine, clinical wounds, nasal and chicken fecal samples. Isolates were recovered and identified as previously described. Species identification was achieved by matrix-assisted laser desorption ionization-time of flight (MALDI-TOF) mass spectrometry and ribosomal multilocus sequence typing. Antimicrobial susceptibility testing was carried out using the Kirby-Bauer method for 12 antibiotics. Double disc synergy test was used to screen for ESBL production. Whole genome sequencing was performed for strain characterization of the isolates. Thirteen Klebsiella spp. isolates yielded positive results by the ESBL phenotypic test and harbored ESBL genes. Out of the 46 E. coli isolates, 21 human and 13 poultry isolates were resistant to at least one of the tested antibiotics. Four human E. coli isolates harbored ESBL genes and revealed positive results when applying ESBL double disc synergy tests. ESBL genes in the Klebsiella spp. and E. coli isolates include bla CTX-M-15 and bla SHV-28 . Whole genome based cgMLST of the Klebsiella spp. and E. coli isolates revealed a close relatedness among the isolates. An integrated “One Health” surveillance system is required to monitor transmission of antimicrobial resistance in Nigeria.
LiMAP-curvature: a simple model-free approach for analysing dose-finding studies
Authors: Han Linxi , Deng Qiqi , He Zhangyi , Yu Feng
Published: November 2022

MCP-Mod (Multiple Comparison Procedure-Modelling) is an efficient statistical method for the analysis of Phase II dose-finding trials, although it requires specialised expertise to pre-specify plausible candidate models along with model parameters. This can be problematic given limited knowledge of the agent/compound being studied, and misspecification of candidate models and model parameters can severely degrade its performance. To circumvent this challenge, in the work, we introduce LiMAP-curvature, a Bayesian model-free approach for the detection of the dose-response signal in Phase II dose-finding trials. LiMAP-curvature is built upon a Bayesian hierarchical framework incorporating information about the total curvature of the dose-response curve. Through extensive simulations, we show that LiMAP-curvature has comparable performance to MCP-Mod if the true underlying dose-response model is included in the candidate model set of MCP-Mod. Otherwise, LiMAP-curvature can achieve performance superior to that of MCP-Mod, especially when the true dose-response model drastically deviates from candidate models in MCP-Mod.
Refined Matrix Completion for Spectrum Estimation of Heart Rate Variability
Authors: Lu Lei , Zhu Tingting , Tan Ying , Zhou Jiandong , Clifton Lei , Zhang Yuan-Ting , Clifton David A.
Published: November 2022

Heart rate variability (HRV) is the reflection of physiological effects modulating heart rhythm. In particular, spectral HRV metrics provide valuable information to investigate activities of the cardiac autonomic nervous system. However, uncertainties and artifacts from measurements can reduce signal quality and therefore affect the evaluation of HRV measures. In this paper, we propose a new method for HRV spectrum estimation with measurement uncertainties using matrix completion (MC). We show that missing values of HRV spectrum can be efficiently estimated using the MC method by leveraging the low rank property of the spectrum matrix. In addition, we proposed a refined matrix completion (RMC) method to improve the estimation accuracy and computational efficiency by introducing model information for the HRV spectrum. Experimental studies on five public benchmark datasets show the effectiveness and robustness of the developed RMC method for estimating missing entries for HRV spectrum with different masking ratios. Furthermore, our developed RMC method is compared with five deep learning models and the traditional MC method; the results of this comparison study demonstrate that our developed RMC method obtains the least estimation error with the minimal computation cost, indicating the advantages of our developed method for HRV spectrum estimation.
Network Occlusion Sensitivity Analysis Identifies Regional Contributions to Brain Age Prediction
Authors: He Lingfei , Chen Cheng , Wang Yaping , Fan Qingcheng , Chu Congying , Xu Junhai , Fan Lingzhong
Published: November 2022

Deep learning frameworks utilizing convolutional neural networks (CNNs) have frequently been used for brain age prediction and have achieved outstanding performance. Nevertheless, deep learning remains a black box as it is hard to interpret which brain parts contribute significantly to the predictions. To tackle this challenge, we first trained a lightweight, fully CNN model for brain age estimation on a large sample data set (N= 3054, age range = [8,80 years]) and tested it on an independent data set (N= 555, mean absolute error (MAE) = 4.45 years,r= 0.96). We then developed an interpretable scheme combining network occlusion sensitivity analysis (NOSA) with a fine-grained human brain atlas to uncover the learned invariance of the model. Our findings show that the dorsolateral, dorsomedial frontal cortex, anterior cingulate cortex, and thalamus had the highest contributions to age prediction across the lifespan. More interestingly, we observed that different regions showed divergent patterns in their predictions for specific age groups and that the bilateral hemispheres contributed differently to the predictions. Regions in the frontal lobe were essential predictors in both the developmental and aging stages with the thalamus remaining relatively stable and saliently correlated with other regional changes throughout the lifespan. The lateral and medial temporal brain regions gradually became involved during the aging phase. At the network level, the frontoparietal and the default mode networks show an inverted U-shape contribution from the developmental to the aging stages. The framework could identify regional contributions to the brain age prediction model, which could help increase the model interpretability when serving as an aging biomarker.
COVID-19 and non-Hodgkin’s lymphoma: a common susceptibility pattern?
Authors: Sara De Matteis , Minelli Cosetta , Broccia Giorgio , Vineis Paolo , Cocco Pierluigi
Published: November 2022

Objectiv

To explore the link between COVID-19 incidence, socio-economic covariates, and NHL incidence.

Design

Ecological study design.

Setting

Sardinia, Italy.

Participants

We used official reports on the total cases of COVID-19 in 2020, published data on NHL incidence, and socio-economic indicators by administrative unit, covering the whole regional population.

Main outcomes and measures

We used multivariable regression analysis to explore the association between the natural logarithm (ln) of the 2020 cumulative incidence of COVID-19 and the ln-transformed NHL incidence in 1974-2003, weighing by population size and adjusting by socioeconomic deprivation and other covariates.

Results

The cumulative incidence of COVID-19 increased in relation to past incidence of NHL (p< 0.001), socioeconomic deprivation (p= 0.006), and proportion of elderly residents (p< 0.001) and decreased with urban residency (p= 0.001). Several sensitivity analyses confirmed the finding of an association between COVID-19 and NHL.

Conclusion

This ecological study found an ecological association between NHL and COVID-19. If further investigation would confirm our findings, shared susceptibility factors should be investigated among the plausible underlying mechanisms.

Strengths and limitations of this study

This study exploited the availability of incidence data for non-Hodgkin’s lymphoma over a 30-year time frame and the 2020 cumulative incidence data for COVID-19 available for all administrative units in the Sardinia region, Italy.

Using an ecological study design, we observed that COVID-19 occurrence increased with past incidence of non-Hodgkin’s lymphoma.

Limitations of the ecological study design require further investigation for confirmation and for identification of susceptibility factors possibly shared between the two diseases.
Development and validation of an fMRI-informed EEG model of reward-related ventral striatum activation
Authors: Singer Neomi , Poker Gilad , Dunsky Netta , Nemni Shlomi , Doron Maayan , Baker Travis , Dagher Alain , Zatorre Robert J , Hendler Talma
Published: November 2022

Reward processing is essential for our mental-health and well-being. Here, we present the development and validation of a scalable fMRI-informed EEG model related to reward processing in the ventral-striatum (VS); a central reward circuit node. Simultaneous EEG/fMRI data were acquired from 17 healthy individuals listening to pleasurable music, and used to construct a one-class regression model for predicting the reward-related VS-BOLD signal using spectro-temporal features from the EEG. Validation analyses, applied on EEG/fMRI data from a different group (N=14), revealed that the EEG model predicted VS-BOLD activation from the simultaneous EEG to a greater extent than a model derived from another anatomical region. The VS-EEG-model was also modulated by musical pleasure and predictive of the VS-BOLD during a monetary reward task, further indicating it functional relevance. These findings provide compelling evidence for the use of a scalable yet precise EEG-only probe of VS-originated reward processing, which could serve for process specific neruo-monitoring and -modulation.
Post-Stimulus Encoding of Decision Confidence in EEG: Toward a Brain-Computer Interface for Decision Making
Authors: Sadras Nitin , Sani Omid G. , Ahmadipour Parima , Shanechi Maryam M.
Published: November 2022

When making decisions, humans can evaluate how likely they are to be correct. If this subjective confidence could be reliably decoded from brain activity, it would be possible to build a brain-computer interface (BCI) that improves decision performance by automatically providing more information to the user if needed based on their confidence. But this possibility depends on whether confidence can be decoded right after stimulus presentation and before the response so that a corrective action can be taken in time. Although prior work has shown that decision confidence is represented in brain signals, it is unclear if the representation is stimulus-locked or response-locked, and whether stimulus-locked pre-response decoding is sufficiently accurate for enabling such a BCI. We investigate the neural correlates of confidence by collecting high-density EEG during a perceptual decision task with realistic stimuli. Importantly, we design our task to include a post-stimulus gap that prevents the confounding of stimulus-locked activity by response-locked activity and vice versa, and then compare with a task without this gap. We perform event-related potential (ERP) and source-localization analyses. Our analyses suggest that the neural correlates of confidence are stimulus-locked, and that an absence of a post-stimulus gap could cause these correlates to incorrectly appear as response-locked. By preventing response-related activity to confound stimulus-locked activity, we then show that confidence can be reliably decoded from single-trial stimulus-locked pre-response EEG alone. We also identify a high-performance classification algorithm by comparing a battery of algorithms. Lastly, we design a simulated BCI framework to show that the EEG classification is accurate enough to build a BCI and that the decoded confidence could be used to improve decision making performance particularly when the task difficulty and cost of errors are high. Our results show feasibility of non-invasive EEG-based BCIs to improve human decision making.
Estimating the gendered impact of COVID-19
Authors: Singh Samer , Singh Rakesh Kumar
Published: November 2022

The extent of gendered COVID-19 impact remains undetermined for the lack of sex-disaggregated data. The prevailing view puts males nearly twice as impacted as females. Globally, access to resources and their usage are gendered- mostly favoring males. Gender gaps widen during natural/man-made calamities and pandemics. Modeling estimates of impact for top 70 countries reporting >300 sex-disaggregated COVID-19 deaths (>80% of total), indicates average mortality sex (male:female) ratio (COVID-MSR) of 1.37±0.30 (95% confidence interval:1.30-1.44; range:0.85-2.47) against prevalent pre-pandemic MSR of 1.79±0.41 (1.70-1.89; range:0.93-2.99). Contrary to the prevailing view, widened gender gaps globally increased female mortality by 19.57±21.16% (14.62%-24.88%; range: -22.46 to +68.50%) causing an estimated 22.03% excess deaths (360 thousand by 30 December 2021). Identification of factors favoring gendered impacts is needed for equitable pandemic management.

One-Sentence Summary

‘Missing Females’ in COVID-19?
The effects of maternal mental health in pregnancy on neonatal adversity: role of physical health and familial factors
Authors: Lin Emma , Wilson Elah , Kodesh Arad , Levine Stephen Z. , Fox Nathan , Reichenberg Abraham , Khachadourian Vahe , Janecka Magdalena
Published: November 2022

Background

There exists substantial evidence of the effects of maternal mental health on birth outcomes. However, the roles of (1) comorbidity between mental and physical health, and (2) familial confounding underlying this association, remain unclear.

Methods

This cohort study included a random sample of 19.5% children born between January 1, 1997, through December 31, 2008, within a health maintenance organization (HMO) in Israel, as well as their parents and siblings born within the cohort years. Outcomes were ICD-9 diagnoses of neonatal adversity (birth complications and congenital anomalies), and exposure was parental mental health diagnosis – all ascertained through the HMO records. Odds ratios (ORs) and their 95% CIs for the associations between the maternal mental health diagnosis and neonatal adversity were calculated using logistic regression, iteratively adjusting for covariates (maternal age, child’s year of birth, socioeconomic status, number of physical health diagnoses). We also examined potential familial confounding using a negative control approach based on paternal exposure.

Results

In our sample of 74,533 children, 6,674 (9.1%) were born after birth complications and 14,569 (19.9%) with a congenital anomaly. Maternal mental health diagnosis around pregnancy was significantly associated with these measures of neonatal adversity, adjusting for demographic and socioeconomic factors (birth complications: OR=1.3 (1.2-1.4); p<0.001;congenital anomalies:OR=1.2 (1.1-1.3); p<0.001). These associations became attenuated and non-significant after further adjustment for the number of physical health diagnoses. In a joint model, maternal and paternal diagnosis of mental health disorder were independently associated with measures of neonatal adversity (birth complications: ORmat=1.3 (1.1-1.4); p < 0.001; ORpat=1.2 (1.1-1.3); p=0.004;congenital anomalies: ORmat=1.2 (1.1-1.3); p<0.001; ORpat=1.1 (1.0-1.2); p=0.01).

Conclusions

Physical health comorbidities and familial factors play a role in the associations between maternal mental health and neonatal adversity.
Epicardial reservoir-enabled multidose delivery of exogenous FSTL1 leads to improved cardiac function, healing, and angiogenesis
Authors: Varela Claudia E. , Monahan David S. , Islam Shahrin , Whyte William , Bonnemain Jean , Ngoy Souen , Fisch Sudeshna , Duffy Garry P. , Roche Ellen T.
Published: November 2022

Epicardial delivery of human follistatin-like 1 protein (FSTL1) induces significant cardiac benefit following a myocardial infarction (MI). However, the optimal dosing regimen for maximal therapeutic benefit has not yet been elucidated. To investigate the impact of multiple FSTL1 doses, without the confounding effects of multiple surgical procedures for multidose delivery, alternative delivery strategies are needed. Here, we use an epicardial reservoir that allows non-invasive delivery of additional doses after implantation to investigate the impact of single, double, and triple FSTL1 dose regimens in a rat model of MI. Multidose delivery of FSTL1 improves ejection fraction (3 doses), fractional shortening (1, 2 and 3 doses), and chamber stiffness (2 doses) 28 days after MI. Histologically, multiple FSTL1 doses increase ventricular wall thickness (2 and 3 doses) and reduce infarct size (1, 2, and 3 doses). We also demonstrate a dose-dependent increase in blood vessel number and density in the infarct zone, with three FSTL1 doses leading to the highest improvements. This study shows that multidose delivery of FSTL1 improves cardiac function, healing, and angiogenesis following MI. The epicardial delivery platform used here may be essential in optimizing dosing regimens of various bioagent combinations for a range of clinical indications.
Equipment-Free Personal Protective Equipment (PPE) Fabrication from Bacterial Cellulose-Derived Biomaterials via Waste-to-Wealth Conversion
Authors: Veerubhotla Ramya , Bandopadhyay Aditya , Chakraborty Suman
Published: November 2022

The recent COVID-19 crisis necessitated the universal use of Personal Protection Equipment (PPE) kits, generating tons of plastic wastes that inevitably lead to environmental damage. Circumventing the challenges stemming from such undesirable non-degradability on disposal, here we present an eco-friendly, robust, yet inexpensive and equipment-free method of growing biodegradable PPE fabrics by the fermentation of locally-sourced organic feed stocks in a rural livelihood. Using a pre-acclimatized symbiotic culture, we report the production of a high yield (up to 3.2 g fabric/g substrate) of bacterial cellulose, a biopolymer matrix, obtained by bacterial weaving. This membrane has an intricate, self-assembled, nano-porous 3D architecture formed by randomly oriented cellulose fibres. Scanning electron microscopy reveals that the pore size of the membrane turns out to be in the tune of 140 nanometers on the average, indicating that it can filter out viruses effectively. In-vitro results demonstrate assured breathability through the membrane for a filter thickness of approximately 5 microns. When subjected to soil degradation, the fabrics are seen to disintegrate rapidly and fully decompose within 15 days. With a favourable cost proposition of less than 1 US$ per meter square of the developed fabric unit, our approach stands out in providing a unique sustainable, and production-ready alternative to synthetic PPE fabrics, solving community healthcare and environmental crisis, and opening up new avenues sustainable under-served livelihood at the same time.

Graphical abstract
Controls of spatial grain size and environmental variables on observed beta diversity of molluscan assemblage at a regional scale
Authors: Bhattacherjee Madhura , Chattopadhyay Devapriya
Published: November 2022

Beta diversity, which quantifies the compositional variation among communities, is one of the fundamental partitions of biodiversity and is associated with abiotic and biotic drivers. Unveiling these drivers is essential for understanding various ecological processes in past and recent faunal communities. Although the quantification of measures of beta diversity has improved over the years, the potential dependence of beta diversity on methodological choices are relatively understudied. Here, we investigate the effect of the variable scale of sampling on different measures of beta diversity at a regional scale. The west coast of India bordering the eastern margin of the Arabian sea, presents a coastal stretch of approximately 6100km from 8–21°N. We used marine bivalve distribution data, consisting of live occurrence data from literature reports and abundance data from death assemblages collected from localities representing latitude bins. We tested if the observed variation in beta diversity is explained by variable sampling scales due to differences in bin sizes and unequal coastline length. We developed a null model to generate a beta diversity pattern with an increase in spatial scale of sampling by increasing the spatial grain size along the 14 latitude bins progressively. Our null model demonstrates that for the both live and dead dataset, the total beta diversity measured by Bray-Curtis, Whittaker and Sorenson indices decreases with increasing sampling scale. The species replacement (turnover) evaluated by Simpson index decreases and the species loss (nestedness) measured by Sorenson index increases with increasing sampling scale. A comparison between the simulated and observed beta diversity distribution using K-S test demonstrated that the observed pattern of beta diversity is significantly different from the pattern generated from the null model in both live and death assemblages. This implies that sampling alone does not generate the spatial variation in beta diversity in this region. The results show that environmental parameters such as salinity, productivity, and cyclones play a significant role in shaping the regional beta diversity along the west coast. Our study provides an approach for evaluating the effect of variable sampling scale on comparing regional beta diversity. It also highlights the importance of spatial standardization while inferring about processes driving spatial diversity changes.
Multi-model order spatially constrained ICA reveals highly replicable group differences and consistent predictive results from fMRI data
Authors: Meng Xing , Iraji Armin , Fu Zening , Kochunov Peter , Belger Aysenil , Ford Judy M. , McEwen Sara , Mathalon Daniel H. , Mueller Bryon A. , Pearlson Godfrey , Potkin Steven G. , Preda Adrian , Turner Jessica , van Erp Theo G.M. , Sui Jing , Calhoun Vince D.
Published: November 2022

Brain functional networks identified from resting fMRI data have the potential to reveal biomarkers for brain disorders, but studies of complex mental illnesses such as schizophrenia (SZ) often yield mixed results across replication studies. This is likely due in part to the complexity of the disorder, the short data acquisition time, and the limited ability of the approaches for brain imaging data mining. Therefore, the use of analytic approaches which can both capture individual variability while offering comparability across analyses is highly preferred. Fully blind data-driven approaches such as independent component analysis (ICA) are hard to compare across studies, and approaches that use fixed atlas-based regions can have limited sensitivity to individual sensitivity. By contrast, spatially constrained ICA (scICA) provides a hybrid, fully automated solution that can incorporate spatial network priors while also adapting to new subjects. However, scICA has thus far only been used with a single spatial scale. In this work, we present an approach using scICA to extract subject-specific intrinsic connectivity networks (ICNs) from fMRI data at multiple spatial scales (ICA model orders), which also enables us to study interactions across spatial scales. We evaluate this approach using a large N (N>1,600) study of schizophrenia divided into separate validation and replication sets. A multi-scale ICN template was estimated and labeled, then used as input into spatially constrained ICA which was computed on an individual subject level. We then performed a subsequent analysis of multiscale functional network connectivity (msFNC) to evaluate the patient data, including group differences and classification. Results showed highly consistent group differences in msFNC in regions including cerebellum, thalamus, and motor/auditory networks. Importantly, multiple msFNC pairs linking different spatial scales were implicated. We also used the msFNC features as input to a classification model in cross-validated hold-out data and also in an independent test data. Visualization of predictive features was performed by evaluating their feature weights. Finally, we evaluated the relationship of the identified patterns to positive symptoms and found consistent results across datasets. The results verified the robustness of our framework in evaluating brain functional connectivity of schizophrenia at multiple spatial scales, implicated consistent and replicable brain networks, and highlighted a promising approach for leveraging resting fMRI data for brain biomarker development.