bluuebunny/crossref_metadata_2025 huggingface dataset:
OPTIMAL QUEUING STRATEGIES FOR AN M/G/1 RETRIAL QUEUE SYSTEM WITH RWV AND ISEV POLICIES
Authors: CHEN Z. , XU H. , HUO H.
Published: March 2024

Designing a reasonable M/G/1 retrial queue system that enhances service efficiency and reduces energy consumption is a challenging issue in Information and Communication Technology systems. This paper presents an M/G/1 retrial queue system incorporating random working vacation (RWV) and improved service efficiency during vacation (ISEV) policies, and examines its optimal queuing strategies. The RWV policy suggests that the server takes random working vacations during reserved idle periods, effectively reducing energy consumption. In contrast, the ISEV policy strives to augment service efficiency during regular working periods by updating, inspecting or maintaining the server on vacations. The system is transformed into a Cauchy problem to investigate its well-posedness and stability, employing operator semigroup theory. Based on the system’s stability, steady-state performance measures, such as service efficiency, energy consumption and expected costs, are quantified using the steady-state solution. The paper subsequently demonstrates the existence of optimal queuing strategies that achieve maximum efficiency and minimum expected costs. Finally, two numerical experiments are provided to illustrate the effectiveness of the system.
Oncogenic enhancers prime quiescent metastatic cells to escape NK immune surveillance by eliciting transcriptional memory
Authors: Michelatti Daniela , Beyes Sven , Bernardis Chiara , Negri Maria Luce , Morelli Leonardo , Bediaga Naiara Garcia , Poli Vittoria , Fagnocchi Luca , Lago Sara , D’Annunzio Sarah , Cona Nicole , Gaspardo Ilaria , Bianchi Aurora , Jovetic Jovana , Gianesello Matteo , Turdo Alice , D’Accardo Caterina , Gaggianesi Miriam , Dori Martina , Forcato Mattia , Crispatzu Giuliano , Rada-Iglesias Alvaro , Sosa Maria Soledad , Timmers H. T. Marc , Bicciato Silvio , Todaro Matilde , Tiberi Luca , Zippo Alessio
Published: March 2024

Metastasis arises from disseminated tumour cells (DTCs) that are characterized by intrinsic phenotypic plasticity and the capability of seeding to secondary organs. DTCs can remain latent for years before giving rise to symptomatic overt metastasis. In this context, DTCs fluctuate between a quiescent and proliferative state in response to systemic and microenvironmental signals including immune-mediated surveillance. Despite its relevance, how intrinsic mechanisms sustain DTCs plasticity has not been addressed. By interrogating the epigenetic state of metastatic cells, we find that tumour progression is coupled with the activation of oncogenic enhancers that are organized in variable interconnected chromatin domains. This spatial chromatin context leads to the activation of a robust transcriptional response upon repeated exposure to retinoic acid (RA). We show that this adaptive mechanism sustains the quiescence of DTCs through the activation of the master regulator SOX9. Finally, we determine that RA-stimulated transcriptional memory increases the fitness of metastatic cells by supporting the escape of quiescent DTCs from NK-mediated immune surveillance. Overall, these findings highlight the contribution of oncogenic enhancers in establishing transcriptional memories as an adaptive mechanism to reinforce cancer dormancy and immune escape, thus amenable for therapeutic intervention.
Blunted brain responses to neutral faces in healthy first-degree relatives of patients with schizophrenia: an image-based fMRI meta-analysis
Authors: Fiorito Anna M. , Blasi Giuseppe , Brunelin Jérôme , Chowdury Asadur , Diwadkar Vaibhav A. , Goghari Vina M. , Gur Ruben C. , Kwon Jun Soo , Quarto Tiziana , Rolland Benjamin , Spilka Michael J. , Wolf Daniel H. , Yun Je-Yeon , Fakra Eric , Sescousse Guillaume
Published: March 2024

Schizophrenia is characterized by the misattribution of emotional significance to neutral faces, accompanied by overactivations of the limbic system. To understand the disorder’s genetic and environmental contributors, investigating healthy first-degree relatives is crucial. However, inconsistent findings exist regarding their ability to recognize neutral faces, with limited research exploring the cerebral correlates of neutral face processing in this population. Thus, we here investigated brain responses to neutral face processing in healthy first-degree relatives through an image-based meta-analysis of functional magnetic resonance imaging studies. We included unthresholded group-level T-maps from 5 studies comprising a total of 120 first-degree relatives and 150 healthy controls. In sensitivity analyses, we ran a combined image- and coordinate-based meta-analysis including 7 studies (157 first-degree relatives, 207 healthy controls) aiming at testing the robustness of the results in a larger sample of studies. Our findings revealed a pattern of decreased brain responses to neutral faces in relatives compared with healthy controls, particularly in limbic areas such as the bilateral amygdala, hippocampus, and insula. The same pattern was observed in sensitivity analyses. These results contrast with the overactivations observed in patients, potentially suggesting that this trait could serve as a protective factor in healthy relatives. However, further research is necessary to test this hypothesis.
Multifunctional integrated pattern for enhancing fog harvesting water unidirectional transport in a heterogeneous pattern
Authors: Yang Daejeong , Ramu A. G. , Choi Dongjin
Published: March 2024

Solid surfaces with improved wettability as well as geometric structures can enhance capture and droplet removal, thereby improving fog harvesting. We fabricated Al wires by combining superhydrophilic (SHL), superhydrophobic (SHB), and oil-infused SHB (SHBO) surfaces into a pattern whose fog-harvesting efficiency could be measured. The SHL-SHBO-SHL pattern showed the highest promise of water droplet capture and mobility on a solid surface with 42% efficiency compared to the 34% efficiency of Bare. In order to identify the optimal efficiency features, two boundary conditions (boundary I: from SHL to SHBO and boundary II: from SHBO to SHL) were introduced, and the impact of the hydrophilic area was examined. Boundary I boosts capture efficiency whereas boundary II increases drain efficiency. Understanding the forces operating at the wettability gradient surface, as well as incorporating the area ratio of SHL and SHBO via wettability combinations, are key to designing effective fog harvesting systems.
Uncovering the hidden structure of small-world networks
Authors: Lachgar Ahmed , Achahbar Abdelfattah
Published: March 2024

The small-world (SW) network model introduced by Watts and Strogatz has significantly influenced the study of complex systems, spurring the development of network science as an interdisciplinary field. The Newman-Watts model is widely applied to analyze SW networks by adding several randomly placed shortcuts to a regular lattice. We meticulously examine related previous works and conclude that the scaling of various pertinent quantities lacks convincing evidence. We demonstrate that the SW property primarily stems from the existence of clusters of nodes linked by shortcuts rather than just the mean number of shortcuts. Introducing the mean degree of clusters linked by shortcuts as a new key parameter resolves the scaling ambiguity, yielding a more precise characterization of the network. Our findings provide a new framework for analyzing SW networks, highlighting the significance of considering emergent structures in complex systems. We also develop a phase diagram of the crossover transition from the small to the large world, offering profound insights into the nature of complex networks and highlighting the power of emergence in shaping their behavior.
Catalytic green synthesis of Tin(IV) oxide nanoparticles for phenolic compounds removal and molecular docking with EGFR tyrosine kinase
Authors: Alshahateet S. F. , Altarawneh R. M. , Al-Tawarh W. M. , Al-Trawneh S. A. , Al-Taweel S. , Azzaoui K. , Merzouki M. , Sabbahi R. , Hammouti B. , Hanbali G. , Jodeh S.
Published: March 2024

In this study, tin dioxide nanoparticles (SnO2 NPs) were successfully synthesized through an eco-friendly method using basil leaves extract. The fabricated SnO2 NPs demonstrated significant adsorption capabilities for phenol (PHE), p-nitrophenol (P-NP), and p-methoxyphenol (P-MP) from water matrices. Optimal conditions for maximum removal efficiency was determined for each phenolic compound, with PHE showing a remarkable 95% removal at a 3 ppm, 0.20 g of SnO2 NPs, pH 8, and 30 min of agitation at 35 °C. Molecular docking studies unveiled a potential anticancer mechanism, indicating the ability of SnO2 NPs to interact with the epidermal growth factor receptor tyrosine kinase domain and inhibit its activity. The adsorption processes followed pseudo-second order kinetics and Temkin isotherm model, revealing spontaneous, exothermic, and chemisorption-controlled mechanisms. This eco-friendly approach utilizing plant extracts was considered as a valuable tool for nano-sorbent production. The SnO2 NPs not only exhibit promise in water treatment and also demonstrate potential applications in cancer therapy. Characterization techniques including scanning electron microscopy, UV–visible spectroscopy, Fourier transform infrared spectroscopy, X-ray diffraction spectroscopy (XRD), and energy-dispersive X-ray spectroscopy (EDAX) provided comprehensive insights into the results.
An evolutionary model of personality traits related to cooperative behavior using a large language model
Authors: Suzuki Reiji , Arita Takaya
Published: March 2024

This study aims to demonstrate that Large Language Models (LLMs) can empower research on the evolution of human behavior, based on evolutionary game theory, by using an evolutionary model positing that instructing LLMs with high-level psychological and cognitive character descriptions enables the simulation of human behavior choices in game-theoretical scenarios. As a first step towards this objective, this paper proposes an evolutionary model of personality traits related to cooperative behavior using a large language model. In the model, linguistic descriptions of personality traits related to cooperative behavior are used as genes. The deterministic strategies extracted from LLM that make behavioral decisions based on these personality traits are used as behavioral traits. The population is evolved according to selection based on average payoff and mutation of genes by asking LLM to slightly modify the parent gene toward cooperative or selfish. Through experiments and analyses, we clarify that such a model can indeed exhibit evolution of cooperative behavior based on the diverse and higher-order representation of personality traits. We also observed repeated intrusion of cooperative and selfish personality traits through changes in the expression of personality traits. The words that emerged in the evolved genes reflected the behavioral tendencies of their associated personalities in terms of semantics, thereby influencing individual behavior and, consequently, the evolutionary dynamics.
Identification of expression profiles and prognostic value of RFCs in colorectal cancer
Authors: Misbah Md , Kumar Manoj , Najmi Abul Kalam , Akhtar Mymoona
Published: March 2024

Colorectal cancer (CRC) ranks among the most prevalent cancers globally, with its incidence closely tied to DNA damage. The Replication Factor C (RFC) complexes comprises five protein subunits: RFC1, RFC2, RFC3, RFC4, and RFC5. These RFC complexes play crucial roles in DNA replication, repair pathways, activities post DNA damage, and ATP-dependent processes during DNA synthesis. However, the impact of RFC complexes proteins on CRC prognosis remains unclear. To explore this, we employed a computational analysis approach, utilizing platforms such as the DepMap portal, GEPIA, DAVID Bioinformatics for KEGG pathway analysis, Human Protein Atlas (HPA), STRING, and TIMER. Our results indicate that the mRNA levels of RFC1 and RFC5 were the least expressed among CRC cell lines compared to other RFC complex subunits. Notably, low RFC1 and RFC5 expression was correlated with poor prognosis in terms of CRC patients' overall survival (OS). Immunohistochemical results from the Human Protein Atlas demonstrated medium staining for RFC1, RFC2, and RFC5 in CRC tissues. Furthermore, the low expression of RFC1 and RFC5 showed a significant correlation with high expression levels of miR-26a-5p and miR-636, impacting cell proliferation through mismatch repair, DNA replication, and the nucleotide excision repair pathway. Although the precise functions of RFC1 in cancer are still unknown, our findings suggest that the small-molecule single target, CHEMBL430483, and multiple target molecules could be potential treatments for CRC. In conclusion, the elevated expression of miR-26a-5p and miR-636 targeting RFC1 and RFC5 expression holds promise as a potential biomarker for early-stage CRC detection. These insights provide novel directions and strategies for CRC therapies.
Dynamic clustering via branched deep learning enhances personalization of stress prediction from mobile sensor data
Authors: Luo Yunfei , Deznabi Iman , Shaw Abhinav , Simsiri Natcha , Rahman Tauhidur , Fiterau Madalina
Published: March 2024

College students experience ever-increasing levels of stress, leading to a wide range of health problems. In this context, monitoring and predicting students’ stress levels is crucial and, fortunately, made possible by the growing support for data collection via mobile devices. However, predicting stress levels from mobile phone data remains a challenging task, and off-the-shelf deep learning models are inapplicable or inefficient due to data irregularity, inter-subject variability, and the “cold start problem”. To overcome these challenges, we developed a platform named Branched CALM-Net that aims to predict students’ stress levels through dynamic clustering in a personalized manner. This is thefirst platform that leverages the branching technique in a multitask setting to achieve personalization and continuous adaptation. Our method achieves state-of-the-art performance in predicting student stress from mobile sensor data collected as part of the Dartmouth StudentLife study, with a ROC AUC 37% higher and a PR AUC surpassing that of the nearest baseline models. In the cold-start online learning setting, Branched CALM-Net outperforms other models, attaining an average F1 score of 87% with just 1 week of training data for a new student, which shows it is reliable and effective at predicting stress levels from mobile data.
Larger wind turbines as a solution to reduce environmental impacts
Authors: Akhtar Naveed , Geyer Beate , Schrum Corinna
Published: March 2024

The EU aims for carbon neutrality by 2050, focusing on offshore wind energy. Investments in North Sea wind farms, with optimal wind resources, play a crucial role. We employed a high-resolution regional climate model, which incorporates a wind farm parametrization, to investigate and address potential mitigating impacts of large wind farms on power generation and air-sea fluxes. Specifically, we examined the effects of replacing 5 MW turbines with larger 15 MW turbines while maintaining total capacity. Our study found that substituting 15 MW turbines increases the capacity factor by 2–3%, enhancing efficiency. However, these turbines exhibit a slightly smaller impact on 10 m wind speed (1.2–1.5%) and near-surface kinetic energy (0.1–0.2%), leading to reduced effects on sea surface heat fluxes compared to 5 MW turbines. This was confirmed by a stronger reduction in net heat flux of about 0.6–1.3% in simulations with 5 MW compared to 15 MW wind turbines. Air-sea fluxes influence ocean dynamics and marine ecosystems; therefore, minimizing these impacts is crucial. Overall, deploying 15 MW turbines in offshore wind farms may offer advantages for ocean dynamics and marine ecosystems, supporting the EU's carbon–neutral objectives.
Whole genome discovery of regulatory genes responsible for the response of chicken to heat stress
Authors: Hosseinzadeh Sevda , Hasanpur Karim
Published: March 2024

Long noncoding RNAs (lncRNAs) are functional bridges connecting the genome with phenotypes by interacting with DNA, mRNA, and proteins. Using publically available acute heat stress (AHS)-related RNA-seq data, we discovered novel lncRNAs and tested their association with AHS along with ~ 8800 known lncRNAs and ~ 28,000 mRNA transcripts. Our pipeline discovered a total of 145 potentially novel-lncRNAs. One of them (Fishcomb_p-value = 0.06) along with another novel transcript (annotated as protein-coding; Fishcomb_p-value = 0.03) were identified as significantly associated with AHS. We found five known-lncRNAs and 134 mRNAs transcripts that were significantly associated with AHS. Four novel lncRNAs interact cis-regulated with 12 mRNA transcripts and are targeted by 11 miRNAs. Also six meta-lncRNAs associate with 134 meta-mRNAs through trans-acting co-expression, each targeted by 15 and 216 miRNAs, respectively. Three of the known-lncRNAs significantly co-expressed with almost 97 of the significant mRNAs (Pearson correlation p-value < 0.05). We report the mentioned three known-lncRNAs (ENSGALT00000099876, ENSGALT00000107573, and ENSGALT00000106323) as the most, significantly regulatory elements of AHS in chicken. It can be concluded that in order to alleviate the adverse effects of AHS on chicken, the manipulation of the three regulatory lncRNAs could lead to a more desirable result than the manipulation of the most significant mRNAs.
Effect of chronic vapor nicotine exposure on affective and cognitive behavior in male mice
Authors: Murdaugh Laura B. , Miliano Cristina , Chen Irene , Faunce Christine L. , Natividad Luis A. , Gregus Ann M. , Buczynski Matthew W.
Published: March 2024

Nicotine use is a leading cause of preventable deaths worldwide, and most of those who attempt to quit will relapse. While electronic cigarettes and other electronic nicotine delivery systems (ENDS) were presented as a safer alternative to traditional cigarettes and promoted as devices to help traditional tobacco smokers reduce or quit smoking, they have instead contributed to increasing nicotine use among youths. Despite this, ENDS also represent a useful tool to create novel preclinical animal models of nicotine exposure that more accurately represent human nicotine use. In this study, we validated a chronic, intermittent, ENDS-based passive vapor exposure model in mice, and then measured changes in multiple behaviors related to nicotine abstinence. First, we performed a behavioral dose curve to investigate the effects of different nicotine inter-vape intervals on various measures including body weight, locomotor activity, and pain hypersensitivity. Next, we performed a pharmacokinetic study to measure plasma levels of nicotine and cotinine following chronic exposure for each inter-vape interval. Finally, we utilized a behavior test battery at a single dosing regimen that produces blood levels equivalent to human smokers in order to characterize the effects of chronic nicotine, vehicle, or passive airflow and identified nicotine-induced impairments in cognitive behavior.
Role of plasma process gas on permeate flux augmentation of cellulose nitrate membrane for mud water treatment
Authors: Hazarika Tonmoi , Kakati Bharat , Pal Dipankar , Saikia Rimlee , Rawal Ankit , Mahanta Manoj Kumar , Biswas Subir
Published: March 2024

A comparative study between Nitrogen (N2) and Argon (Ar) plasma is carried out to investigate its effect on surface morphology, hydrophilicity, permeate flux and ageing of cellulose nitrate polymeric membranes in the present work. Langmuir probe and Optical Emission Spectroscopy are used to characterize the plasma. The SEM analysis reveals the noticeable macro-void creations and pore enlargement for both N2 and Ar plasma. The AFM analysis shows a higher surface roughness for Ar plasma treatment as compared to N2 plasma treatment. XPS analysis confirms the changes in the polymer matrix along with the incorporation of various functional groups on the membrane surface as a result of the plasma treatment. A better hydrophilic nature with prolonged plasma treatment is observed for Ar plasma as compared to N2 plasma treatment. The present results show a higher permeate flux with a high rejection rate for Ar plasma treatment in comparison to N2 plasma, which might be due to the pore size and pore area enlargement of the membrane. The hydrophobic recovery for both the plasma-treated membranes is found significant for the initial ageing period of 7 days and found almost stable in nature after 7 days. A diffusion-based theoretical model is developed to study the hydrophobic recovery of plasma-treated membranes. A strong alignment between experimental and theoretical results is observed in the present work. The Cake Filtration model, derived from the Hermia model, is identified as the most suitable model for describing the fouling mechanisms for the present work.
Dietary linoleic acid supplementation protects against obesity-induced microglial reactivity in mice
Authors: Jantzen Lucas , Dumontoy Stéphanie , Ramadan Bahrie , Houdayer Christophe , Haffen Emmanuel , Hichami Aziz , Khan Naim Akhtar , Van Waes Vincent , Cabeza Lidia
Published: March 2024

We investigated whether linoleic acid (LA) supplementation could modulate emotional behavior and microglia-related neuroinflammation. For that, male mice of C57BL/6J genetic background fed either a high-fat diet (HFD) or a standard diet (STD) for 12 weeks, were treated with a vehicle or LA solution for 5 weeks before being evaluated for emotional behavior using a battery of behavioral tests. The animals were subsequently sacrificed and their brains collected and processed for immunofluorescence staining, targeting microglia-specific calcium-binding proteins (IBA-1). Neuroinflammation severity was assessed in multiple hypothalamic, cortical and subcortical brain regions. We show an anxio-depressive-like effect of sustained HFD feeding that was neither alleviated nor worsened with LA supplementation. However, increased IBA-1 expression and microgliosis in the HFD group were largely attenuated by LA supplementation. These observations demonstrate that the anti-neuroinflammatory properties of LA are not restricted to hypothalamic areas but are also evident at the cortical and subcortical levels. This study discloses that neuroinflammation plays a role in the genesis of neuropsychiatric disorders in the context of obesity, and that LA supplementation is a useful dietary strategy to alleviate the impact of obesity-related neuroinflammation.
Risk factors for placenta accreta spectrum disorders in women with any prior cesarean and a placenta previa or low lying: a prospective population-based study
Authors: Kayem Gilles , Seco Aurélien , Vendittelli Francoise , Crenn Hebert Catherine , Dupont Corinne , Branger Bernard , Huissoud Cyril , Fresson Jeanne , Winer Norbert , Langer Bruno , Rozenberg Patrick , Morel Olivier , Bonnet Marie Pierre , Perrotin Franck , Azria Elie , Carbillon Lionel , Chiesa Coralie , Raynal Pierre , Rudigoz René Charles , Patrier Sophie , Beucher Gaël , Dreyfus Michel , Sentilhes Loïc , Deneux-Tharaux Catherine
Published: March 2024

This study aimed to identify the risk factors for placenta accreta spectrum (PAS) in women who had at least one previous cesarean delivery and a placenta previa or low-lying. The PACCRETA prospective population-based study took place in 12 regional perinatal networks from 2013 through 2015. All women with one or more prior cesareans and a placenta previa or low lying were included. Placenta accreta spectrum (PAS) was diagnosed at delivery according to standardized clinical and histological criteria. Of the 520,114 deliveries, 396 fulfilled inclusion criteria; 108 were classified with PAS at delivery. Combining the number of prior cesareans and the placental location yielded a rate ranging from 5% for one prior cesarean combined with a posterior low-lying placenta to 63% for three or more prior cesareans combined with placenta previa. The factors independently associated with PAS disorders were BMI ≥ 30, previous uterine surgery, previous postpartum hemorrhage, a higher number of prior cesareans, and a placenta previa. Finally, in this high-risk population, the rate of PAS disorders varies greatly, not only with the number of prior cesareans but also with the exact placental location and some of the women's individual characteristics. Risk stratification is thus possible in this population.
A generalisation of the method of regression calibration and comparison with Bayesian and frequentist model averaging methods
Authors: Little Mark P. , Hamada Nobuyuki , Zablotska Lydia B.
Published: March 2024

For many cancer sites low-dose risks are not known and must be extrapolated from those observed in groups exposed at much higher levels of dose. Measurement error can substantially alter the dose–response shape and hence the extrapolated risk. Even in studies with direct measurement of low-dose exposures measurement error could be substantial in relation to the size of the dose estimates and thereby distort population risk estimates. Recently, there has been considerable attention paid to methods of dealing with shared errors, which are common in many datasets, and particularly important in occupational and environmental settings. In this paper we test Bayesian model averaging (BMA) and frequentist model averaging (FMA) methods, the first of these similar to the so-called Bayesian two-dimensional Monte Carlo (2DMC) method, and both fairly recently proposed, against a very newly proposed modification of the regression calibration method, the extended regression calibration (ERC) method, which is particularly suited to studies in which there is a substantial amount of shared error, and in which there may also be curvature in the true dose response. The quasi-2DMC with BMA method performs well when a linear model is assumed, but very poorly when a linear-quadratic model is assumed, with coverage probabilities both for the linear and quadratic dose coefficients that are under 5% when the magnitude of shared Berkson error is large (50%). For the linear model the bias is generally under 10%. However, using a linear-quadratic model it produces substantially biased (by a factor of 10) estimates of both the linear and quadratic coefficients, with the linear coefficient overestimated and the quadratic coefficient underestimated. FMA performs as well as quasi-2DMC with BMA when a linear model is assumed, and generally much better with a linear-quadratic model, although the coverage probability for the quadratic coefficient is uniformly too high. However both linear and quadratic coefficients have pronounced upward bias, particularly when Berkson error is large. By comparison ERC yields coverage probabilities that are too low when shared and unshared Berkson errors are both large (50%), although otherwise it performs well, and coverage is generally better than the quasi-2DMC with BMA or FMA methods, particularly for the linear-quadratic model. The bias of the predicted relative risk at a variety of doses is generally smallest for ERC, and largest for the quasi-2DMC with BMA and FMA methods (apart from unadjusted regression), with standard regression calibration and Monte Carlo maximum likelihood exhibiting bias in predicted relative risk generally somewhat intermediate between ERC and the other two methods. In general ERC performs best in the scenarios presented, and should be the method of choice in situations where there may be substantial shared error, or suspected curvature in the dose response.
Simulant molten core–concrete interaction experiments in view of understanding Fukushima Daiichi Nuclear Power Station Cs-bearing particles generation mechanism
Authors: Laffolley Hugo , Journeau Christophe , Grambow Bernd
Published: March 2024

The Fukushima Daiichi accident resulted in the release of a novel form of radioactive Cs contamination into the environment, called Cs-bearing microparticles (CsMP). CsMPs constitute a substantial portion of the radioactive pollution near the nuclear power station and traveled beyond several hundred kilometers. Extensive characterization of the CsMPs revealed an amorphous silica matrix, along with Cs and other minor or trace elements such as Fe and Zn. This study explores the unclear generation mechanism of CsMPs by conducting experimental molten core concrete interactions (MCCI) as a source of Si and analyzing the resultant aerosols. The findings demonstrate that MCCI is in capacity to produce spherical submicronic and micronic particles, primarily composed of amorphous silica and incorporating elements akin to CsMPs. A humid atmosphere is found to favour an even closer chemical composition. Examination of the internal structure of the synthesized particles unveils pores and numerous crystalline nanoinclusions possibly serving as nucleation sites for CsMP formation through the condensation of Si-rich vapors.
The effect of a prosocial environment on health and well-being during the first COVID-19 lockdown and a year later
Authors: Trachtenberg Estherina , Ruzal Keren , Forkosh Oren , Ben-Ami Bartal Inbal
Published: March 2024

The outset of the COVID-19 pandemic was characterized by prolonged periods of chronic stress and social isolation. While studies have investigated the changes to well-being (WB) during this period, the impact of the social environment on long-term physical and mental health requires further study. This study aimed to assess the factors influencing WB and health outcomes, with the hypothesis that a positive social environment would play a significant immediate and long-term role in improving WB and preventing the effects of anxiety associated with the pandemic. At time point 1 (April 2020), an Israeli sample of 206 participants (84% female, mean age 31.5) responded to traditional questionnaires assessing mental health and social support. Factors affecting WB were assessed within subjects during the first COVID-19 lockdown for 6 weeks using a daily survey (Beiwe phone application). A year later, in May 2021, at time point 2, the initial questionnaires were readministered to a subset of the same participants (N = 94). We found that anxiety during the first lockdown adversely affected WB and predicted health and WB deterioration a year later. In contrast, a high quality of social relationships was associated with better short- and long-term WB, and mitigated the adverse effects of anxiety. Daily activities, including physical activity, meditation, and romantic relations, were also positively associated with WB during the first lockdown but did not have long-term effects. In summary, our study underscores the enduring health advantages of a positive social environment, particularly during stressful periods. These results have implications for health policymakers: programs which support individuals with high anxiety and low support, by integrating them into community-based interventions, promise to enhance well-being (WB) and health, as well as to fortify the community as a whole.
Suboptimal decision making and interpersonal problems in ADHD: longitudinal evidence from a laboratory task
Authors: Sørensen L. , Adolfsdottir S. , Kvadsheim E. , Eichele H. , Plessen K. J. , Sonuga-Barke E.
Published: March 2024

Over half of children with Attention-Deficit/Hyperactivity Disorder (ADHD) display interpersonal and social problems. Several lines of research suggest that suboptimal decision making, the ability to adjust choices to different risk-varying options, influences poorer choices made in social interactions. We thus measured decision making and its prediction of social problems longitudinally with the Cambridge Gambling Task in children with ADHD over four years. Children with ADHD had shown suboptimal decision making driven mainly by delay aversion at baseline and we expected this to be a stabile trait which would predict greater parent-reported social problems. From the baseline assessment (n = 70), 67% participated at the follow-up assessment, 21 from the ADHD group and 26 from the typically developing group. The mean age at the follow-up was 14.5 years old. The results confirmed our expectations that suboptimal decision making was a stabile trait in children and adolescents with ADHD. Although delay aversion did not differ from controls at follow-up it still proved to be the main longitudinal predictor for greater social problems. Our findings indicate that impulsivity in social interactions may be due to a motivational deficit in youth with ADHD.
Identifying American climate change free riders and motivating sustainable behavior
Authors: Magistro Beatrice , Abramson Cecilia , Ebanks Daniel , Debnath Ramit , Alvarez R. Michael
Published: March 2024

Free riders, who benefit from collective efforts to mitigate climate change but do not actively contribute, play a key role in shaping behavioral climate action. Using a sample of 2096 registered American voters, we explore the discrepancy between two groups of free riders: cynics, who recognize the significance of environmental issues but do not adopt sustainable behaviors, and doubters, who neither recognize the significance nor engage in such actions. Through statistical analyses, we show these two groups are different. Doubters are predominantly male, younger, with lower income and education, exhibit stronger conspiracy beliefs, lower altruism, and limited environmental knowledge, are more likely to have voted for Trump and lean towards conservative ideology. Cynics are younger, religious, higher in socioeconomic status, environmentally informed, liberal-leaning, and less likely to support Trump. Our research provides insights on who could be most effectively persuaded to make climate-sensitive lifestyle changes and provides recommendations to prompt involvement in individual sustainability behaviors. Our findings suggest that for doubters, incentivizing sustainability through positive incentives, such as financial rewards, may be particularly effective. Conversely, for cynics, we argue that engaging them in more community-driven and social influence initiatives could effectively translate their passive beliefs into active participation.
Antibiotics and probiotics-induced effects on the total fatty acid composition of feces in a rat model
Authors: Marosvölgyi Tamás , Mintál Kitti , Farkas Nelli , Sipos Zoltán , Makszin Lilla , Szabó Éva , Tóth Attila , Kocsis Béla , Kovács Krisztina , Hormay Edina , Lénárd László , Karádi Zoltán , Bufa Anita
Published: March 2024

Fatty acids (FAs) play important roles as membrane components and signal transduction molecules. Changes in short chain FA (SCFA) composition are associated with gut microbiota modifications. However, the effect of bacteria-driven changes on the detailed FA spectrum has not been explored yet. We investigated the effect of antibiotics (ABx) and/or probiotics, in four treatment groups on rat stool FA composition. Principal component analysis indicated that the chromatogram profiles of the treatment groups differ, which was also observed at different time points. Linear mixed effects models showed that in the parameters compared (sampling times, treatments. and their interactions), both the weight percentage and the concentration of FAs were affected by ABx and probiotic administration. This study found that the gut microbiome defines trans and branched saturated FAs, most saturated FAs, and unsaturated FAs with less carbon atoms. These results are among the first ones to demonstrate the restoring effects of a probiotic mixture on a substantial part of the altered total FA spectrum, and also revealed a previously unknown relationship between gut bacteria and a larger group of FAs. These findings suggest that intestinal bacteria produce not only SCFAs but also other FAs that may affect the host’s physiological processes.
Automated detection and recognition system for chewable food items using advanced deep learning models
Authors: Kumar Yogesh , Koul Apeksha , Kamini  , Woźniak Marcin , Shafi Jana , Ijaz Muhammad Fazal
Published: March 2024

Identifying and recognizing the food on the basis of its eating sounds is a challenging task, as it plays an important role in avoiding allergic foods, providing dietary preferences to people who are restricted to a particular diet, showcasing its cultural significance, etc. In this research paper, the aim is to design a novel methodology that helps to identify food items by analyzing their eating sounds using various deep learning models. To achieve this objective, a system has been proposed that extracts meaningful features from food-eating sounds with the help of signal processing techniques and deep learning models for classifying them into their respective food classes. Initially, 1200 audio files for 20 food items labeled have been collected and visualized to find relationships between the sound files of different food items. Later, to extract meaningful features, various techniques such as spectrograms, spectral rolloff, spectral bandwidth, and mel-frequency cepstral coefficients are used for the cleaning of audio files as well as to capture the unique characteristics of different food items. In the next phase, various deep learning models like GRU, LSTM, InceptionResNetV2, and the customized CNN model have been trained to learn spectral and temporal patterns in audio signals. Besides this, the models have also been hybridized i.e. Bidirectional LSTM + GRU and RNN + Bidirectional LSTM, and RNN + Bidirectional GRU to analyze their performance for the same labeled data in order to associate particular patterns of sound with their corresponding class of food item. During evaluation, the highest accuracy, precision,F1 score, and recall have been obtained by GRU with 99.28%, Bidirectional LSTM + GRU with 97.7% as well as 97.3%, and RNN + Bidirectional LSTM with 97.45%, respectively. The results of this study demonstrate that deep learning models have the potential to precisely identify foods on the basis of their sound by computing the best outcomes.
Investigation of deep learning model for predicting immune checkpoint inhibitor treatment efficacy on contrast-enhanced computed tomography images of hepatocellular carcinoma
Authors: Nakao Yasuhiko , Nishihara Takahito , Sasaki Ryu , Fukushima Masanori , Miuma Satoshi , Miyaaki Hisamitsu , Akazawa Yuko , Nakao Kazuhiko
Published: March 2024

Although the use of immune checkpoint inhibitors (ICIs)-targeted agents for unresectable hepatocellular carcinoma (HCC) is promising, individual response variability exists. Therefore, we developed an artificial intelligence (AI)-based model to predict treatment efficacy using pre-ICIs contrast-enhanced computed tomography (CT) imaging characteristics. We evaluated the efficacy of atezolizumab and bevacizumab in 43 patients at the Nagasaki University Hospital from 2020 to 2022 using the modified Response Evaluation Criteria in Solid Tumors. A total of 197 Progressive Disease (PD), 271 Partial Response (PR), and 342 Stable Disease (SD) contrast CT images of HCC were used for training. We used ResNet-18 as the Convolutional Neural Network (CNN) model and YOLOv5, YOLOv7, YOLOv8 as the You Only Look Once (YOLO) model with precision-recall curves and class activation maps (CAMs) for diagnostic performance evaluation and model interpretation, respectively. The 3D t-distributed Stochastic Neighbor Embedding was used for image feature analysis. The YOLOv7 model demonstrated Precision 53.7%, Recall 100%, F1 score 69.8%, mAP@0.5 99.5% for PD, providing accurate and clinically versatile predictions by identifying decisive points. The ResNet-18 model had Precision 100% and Recall 100% for PD. However, the CAMs sites did not align with the tumors, suggesting the CNN model is not predicting that a given CT slice is PD, PR, or SD, but that it accurately predicts Individual Patient's CT slices. Preparing substantial training data for tumor drug effect prediction models is challenging compared to general tumor diagnosis models; hence, large-scale validation using an efficient YOLO model is warranted.
Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI
Authors: Uus Alena U. , Hall Megan , Grigorescu Irina , Avena Zampieri Carla , Egloff Collado Alexia , Payette Kelly , Matthew Jacqueline , Kyriakopoulou Vanessa , Hajnal Joseph V. , Hutter Jana , Rutherford Mary A. , Deprez Maria , Story Lisa
Published: March 2024

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22–38 weeks gestational age range.
Normative values of motor performance and their relationship with BMI status in Hong Kong preschoolers
Authors: Yip Ka-Man , So Hung-Kwan , Tung Keith T. S. , Wong Rosa S. , Tso Winnie W. Y. , Wong Ian C. K. , Yam Jason C. , Kwan Mike Y. W. , Louie Lobo H. T. , Lee Albert , Wong Wilfred H. S. , Hui Lai-Ling , Nelson E. A. S. , Ip Patrick
Published: March 2024

This study aimed to establish sex- and age-specific reference values for motor performance (MP) in Hong Kong preschoolers aged 3–5 years old and examine the relationship between MP and BMI status. A cross-sectional study was conducted among 5579 preschoolers in Hong Kong. Three MP tests were administered, and height and weight information were collected. GAMLSS was used to compute the normative values of the motor tests. Boys outperformed girls in activities requiring muscle strength and power, while girls outperformed boys in activities requiring balance and coordination. The MP scores increased with age for both overarm beanbag throw and standing long jump for both sexes, while the one-leg balance scores showed larger differences between P50 and P95 in older preschoolers. Children with excessive weight performed worse in standing long jump and one-leg balance compared to their healthy weight peers. This study provides valuable information on the MP of preschoolers in Hong Kong, including sex- and age-specific reference values and the association between BMI status and MP scores. These findings can serve as a reference for future studies and clinical practice and highlight the importance of promoting motor skill development in preschoolers, particularly those who are overweight or obese.
Relationship between prediction accuracy and uncertainty in compound potency prediction using deep neural networks and control models
Authors: Roth Jannik P. , Bajorath Jürgen
Published: March 2024

The assessment of prediction variance or uncertainty contributes to the evaluation of machine learning models. In molecular machine learning, uncertainty quantification is an evolving area of research where currently no standard approaches or general guidelines are available. We have carried out a detailed analysis of deep neural network variants and simple control models for compound potency prediction to study relationships between prediction accuracy and uncertainty. For comparably accurate predictions obtained with models of different complexity, highly variable prediction uncertainties were detected using different metrics. Furthermore, a strong dependence of prediction characteristics and uncertainties on potency levels of test compounds was observed, often leading to over- or under-confident model decisions with respect to the expected variance of predictions. Moreover, neural network models responded very differently to training set modifications. Taken together, our findings indicate that there is only little, if any correlation between compound potency prediction accuracy and uncertainty, especially for deep neural network models, when predictions are assessed on the basis of currently used metrics for uncertainty quantification.
Dynamic multi-pinhole collimated brain SPECT of Parkinson’s disease by [123I]FP-CIT: a feasibility study of fSPECT
Authors: Fredensborg Filip L. H. , Thilsing-Hansen Kasper , Simonsen Jane A. , Grupe Peter , Farahani Ziba A. , Andersen Christian W. , Gjedde Albert , Hvidsten Svend
Published: March 2024

We investigated the feasibility of using a dopamine transporter (DaT) tracer ligand ([123I]FP-CIT) along with novel multi-pinhole brain collimators for dynamic brain single photon emission computed tomography (SPECT) in suspected Parkinson's disease patients. Thirteen patients underwent dynamic tracer acquisitions before standard imaging. Uptake values were corrected for partial volume effects. Specific binding ratio (SBRcalc) was calculated, reflecting binding potential relative to non-displaceable binding (BPND) in the cortex. Additional pharmacokinetic parameters (BPND, R1, k2) were estimated using the simplified reference tissue model, revealing differences between Kahraman low-score (LS) and high-score (HS) groups. Results showed increasing striatal tracer uptake until 100 min post-injection, with consistent values afterward. Uptake and SBRcalc ratios matched visual assessment. LS patients had lower putamen than caudate nucleus tracer uptake, decreased BPND values, while R1 and k2 values were comparable to HS patients. In conclusion, dynamic multi-pinhole SPECT using DaT tracer with the extraction of pharmacokinetic parameters is feasible and could help enable early differentiation of reduced and normal DaT values.