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The complexity, scale, and uncertainty in regulatory networks (e.g., gene regulatory networks and microbial networks) regularly pose a huge uncertainty in their models. These uncertainties often cannot be entirely reduced using limited and costly data acquired from the normal condition of systems. Meanwhile, regulatory networks often suffer from the non-identifiability issue, which refers to scenarios where the true underlying network model cannot be clearly distinguished from other possible models. Perturbation or excitation is a well-known process in systems biology for acquiring targeted data to reveal the complex underlying mechanisms of regulatory networks and overcome the non-identifiability issue. We consider a general class of Boolean network models for capturing the activation and inactivation of components and their complex interactions. Assuming partial available knowledge about the interactions between components of the networks, this paper formulates the inference process through the maximum aposteriori (MAP) criterion. We develop a Bayesian lookahead policy that systematically perturbs regulatory networks to maximize the performance of MAP inference under the perturbed data. This is achieved by optimally formulating the perturbation process in a reinforcement learning context and deriving a scalable deep reinforcement learning perturbation policy to compute near-optimal Bayesian policy. The proposed method learns the perturbation policy through planning without the need for any real data. The high performance of the proposed approach is demonstrated by comprehensive numerical experiments using the well-known mammalian cell cycle and gut microbial community networks.

We aimed to assess the impact of hospital characteristics on the outcomes of detected possible brain-dead donors, in our organ procurement network in Iran. Data was collected through twice-daily calls with 57 hospitals' intensive care units and emergency departments over 1 year. The donation team got involved when there was suspicion of brain death before the hospital officially declared it. The data was categorized by hospital size, presence of neurosurgery/trauma departments, ownership, and referral site. Out of 813 possible donors, 315 were declared brain dead, and 203 were eligible for donation. After conducting family interviews (consent rate: 62.2%), 102 eligible donors became actual donors (conversion rate: 50.2%). While hospital ownership and the presence of trauma/neurosurgery care did not affect donation, early referral from the emergency department had a positive effect. Therefore, we strongly recommend prioritizing possible donor identification in emergency rooms and involving the organ donation team as early as possible. The use of twice-daily calls for donor identification likely contributed to the consistency in donation rates across hospitals, as this approach involves the donation team earlier and mitigates the impact of hospital characteristics. Early detection of possible donors from the emergency department is crucial in improving donation rates., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hasanzade, Nejatollahi, Mokhber Dezfouli, Hazrati, Sheikholeslami, Imani, Mohseni and Ghorbani.)

This paper focuses on joint state and parameter estimation in partially observed Boolean dynamical systems (POBDS), a hidden Markov model tailored for modeling complex networks with binary state variables. The majority of current techniques for parameter estimation rely on computationally expensive gradient-based methods, which become intractable in most practical applications with large size of networks. We propose a gradient-free approach that uses Gaussian processes to model the expensive log-likelihood function and utilizes Bayesian optimization for efficient likelihood search over parameter space. Joint state estimation is also achieved alongside parameter estimation using the Boolean Kalman filter. The performance of the proposed method is demonstrated using gene regulatory networks observed through synthetic gene-expression data. The numerical results demonstrate the scalability and effectiveness of the proposed method in the joint estimation of the model parameters and genes' states.

Objective: To assess the noninferiority of biosimilar aflibercept (P041, CinnaGen) to the originator aflibercept (AFL, Regeneron) in terms of efficacy, safety, and immunogenicity., Design: This was a phase Ш, 52-week, multicenter, randomized, double-masked, and active control trial involving eyes in a 1:1 ratio., Subjects: Patients with active subfoveal choroidal neovascularization secondary to age-related macular degeneration randomized into the 2 groups of P041 and AFL., Methods: Patients received an injection of aflibercept every 4 weeks for 3 doses, followed by administration every 8 weeks up to week 48., Main Outcome Measures: The primary outcome was the noninferiority analysis of eyes maintaining vision at week 52. Secondary outcomes included the changes in visual acuity and retinal thickness, safety evaluation, and immunogenicity during the study., Results: In total, 168 eyes of 168 patients were included. At week 52, the proportion of patients maintaining vision was 94.44% in the P041 group compared with 94.52% in the AFL group. The 95% confidence interval (CI) for the difference of maintaining vision from baseline did not exceed the predefined noninferiority margin of 10% (difference, -0.0008; 95% CI, -0.074 to 0.074; P = 0.98). Secondary outcomes indicated similar results in both arms (all P > 0.05). Safety measured outcomes and immunogenicity were similar between the 2 study groups., Conclusions: Biosimilar aflibercept was noninferior to AFL in eyes with neovascular age-related macular degeneration. Other efficacy and safety findings also indicated the similarity of 2 products., Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article., (Copyright © 2024 American Academy of Ophthalmology. Published by Elsevier Inc. All rights reserved.)

This paper focuses on inferring a general class of hidden Markov models (HMMs) using data acquired from experts. Expert-acquired data contain decisions/actions made by humans/users for various objectives, such as navigation data reflecting drivers' behavior, cybersecurity data carrying defender decisions, and biological data containing the biologist's actions (e.g., interventions and experiments). Conventional inference methods rely on temporal changes in data without accounting for expert knowledge. This paper incorporates expert knowledge into the inference of HMMs by modeling expert behavior as an imperfect reinforcement learning agent. The proposed method optimally quantifies experts' perceptions about the system model, which, alongside the temporal changes in data, contributes to the inference process. The proposed inference method is derived through a combination of dynamic programming and optimal recursive Bayesian estimation. The applicability of this method is demonstrated to a wide range of inference criteria, such as maximum likelihood and maximum a posteriori. The performance of the proposed method is investigated through a comprehensive numerical experiment using a benchmark problem and biological networks.

This study focuses on a rescue mission problem, particularly enabling agents/robots to navigate efficiently in unknown environments. Technological advances, including manufacturing, sensing, and communication systems, have raised interest in using robots or drones for rescue operations. Effective rescue operations require quick identification of changes in the environment and/or locating the victims/injuries as soon as possible. Several techniques have been developed in recent years for autonomy in rescue missions, including motion planning, adaptive control, and more recently, reinforcement learning techniques. These techniques rely on full knowledge of the environment or the availability of simulators that can represent real environments during rescue operations. However, in practice, agents might have little or no information about the environment or the number or locations of injuries, preventing/limiting the application of most existing techniques. This study provides a probabilistic/Bayesian representation of the unknown environment, which jointly models the stochasticity in the agent's navigation and the environment uncertainty into a vector called the belief state. This belief state allows offline learning of the optimal Bayesian policy in an unknown environment without the need for any real data/interactions, which guarantees taking actions that are optimal given all available information. To address the large size of belief space, deep reinforcement learning is developed for computing an approximate Bayesian planning policy. The numerical experiments using different maze problems demonstrate the high performance of the proposed policy., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Alali and Imani.)

The inaugural Canadian Conferences on Translational Geroscience were held as 2 complementary sessions in October and November 2023. The conferences explored the profound interplay between the biology of aging, social determinants of health, the potential societal impact of geroscience, and the maintenance of health in aging individuals. Although topics such as cellular senescence, molecular and genetic determinants of aging, and prevention of chronic disease were addressed, the conferences went on to emphasize practical applications for enhancing older people's quality of life. This article summarizes the proceeding and underscores the synergy between clinical and fundamental studies. Future directions highlight national and global collaborations and the crucial integration of early-career investigators. This work charts a course for a national framework for continued innovation and advancement in translational geroscience in Canada., (© The Author(s) 2024. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

Deep random forest (DRF), which combines deep learning and random forest, exhibits comparable accuracy, interpretability, low memory and computational overhead to deep neural networks (DNNs) in edge intelligence tasks. However, efficient DRF accelerator is lagging behind its DNN counterparts. The key to DRF acceleration lies in realizing the branch-split operation at decision nodes. In this work, we propose implementing DRF through associative searches realized with ferroelectric analog content addressable memory (ACAM). Utilizing only two ferroelectric field effect transistors (FeFETs), the ultra-compact ACAM cell performs energy-efficient branch-split operations by storing decision boundaries as analog polarization states in FeFETs. The DRF accelerator architecture and its model mapping to ACAM arrays are presented. The functionality, characteristics, and scalability of the FeFET ACAM DRF and its robustness against FeFET device non-idealities are validated in experiments and simulations. Evaluations show that the FeFET ACAM DRF accelerator achieves ∼10 6 ×/10× and ∼10 6 ×/2.5× improvements in energy and latency, respectively, compared to other DRF hardware implementations on state-of-the-art CPU/ReRAM.

Interventions in gene regulatory networks (GRNs) aim to restore normal functions of cells experiencing abnormal behavior, such as uncontrolled cell proliferation. The dynamic, uncertain, and complex nature of cellular processes poses significant challenges in determining the best interventions. Most existing intervention methods assume that cells are unresponsive to therapies, resulting in stationary and deterministic intervention solutions. However, cells in unhealthy conditions can dynamically respond to therapies through internal stimuli, leading to the recurrence of undesirable conditions. This paper proposes a Bayesian intervention policy that adaptively responds to cell dynamic responses according to the latest available information. The GRNs are modeled using a Boolean network with perturbation (BNp), and the fight between the cell and intervention is modeled as a two-player zero-sum game. Assuming an incomplete knowledge of cell stimuli, a recursive approach is developed to keep track of the posterior distribution of cell responses. The proposed Bayesian intervention policy takes action according to the posterior distribution and a set of Nash equilibrium policies associated with all possible cell responses. Analytical results demonstrate the superiority of the proposed intervention policy against several existing intervention techniques. Meanwhile, the performance of the proposed policy is investigated through comprehensive numerical experiments using the p53-MDM2 negative feedback loop regulatory network and melanoma network. The results demonstrate the empirical convergence of the proposed policy to the optimal Nash equilibrium policy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Objective: Belonging to the class II drugs according to the biopharmaceutics classification system, silibinin (SLB) benefits from high permeability but suffers poor solubility that negatively affects the development of any delivery system. This research aimed to improve SLB solubility by combined use of co-solvency and complexation phenomena., Methods: Solubility studies were performed using the phase solubility analysis according to the shake-flask method in the presence of ethanol and 2-hydroxypropyl-β-cyclodextrin (HP-β-CD) as a co-solvent and inclusion complexing agent, respectively. SLB release studies from chitosan nanoparticles were carried out in double-wall, diffusion cells using the optimized drug release medium., Results: SLB solubility was mathematically optimized constraining to using the lowest concentrations of ethanol and HP-β-CD. SLB solubility increased linearly with the increase of HP-β-CD concentration. The solubility in PBS-ethanol mixtures followed a log-linear model. SLB solubility in the presence of the ethanol co-solvent and HP-β-CD complexing agent was optimized by adopting a genetic algorithm suggesting the phosphate buffer saline solution supplemented by 6%v/v ethanol and 8 mM HP-β-CD as an optimized medium. The optimized solution was examined to study SLB release from chitosan nanoparticles (4.5 ± 0.2% drug loading) at 37 °C under static conditions. The sigmoidal release profile of SLB from the particles indicated a combination of erosion and diffusion mechanisms governing drug release from the nanoparticles., Conclusion: SLB solubility in a buffered solution supplemented by ethanol co-solvent and HP-β-CD complexing agent is a function of free drug present in the semi-aqueous media, the drug-ligand binary complex, and the drug/ligand/co-solvent ternary complex.

Introduction: Brain-inspired computing has become an emerging field, where a growing number of works focus on developing algorithms that bring machine learning closer to human brains at the functional level. As one of the promising directions, Hyperdimensional Computing (HDC) is centered around the idea of having holographic and high-dimensional representation as the neural activities in our brains. Such representation is the fundamental enabler for the efficiency and robustness of HDC. However, existing HDC-based algorithms suffer from limitations within the encoder. To some extent, they all rely on manually selected encoders, meaning that the resulting representation is never adapted to the tasks at hand., Methods: In this paper, we propose FLASH, a novel hyperdimensional learning method that incorporates an adaptive and learnable encoder design, aiming at better overall learning performance while maintaining good properties of HDC representation. Current HDC encoders leverage Random Fourier Features (RFF) for kernel correspondence and enable locality-preserving encoding. We propose to learn the encoder matrix distribution via gradient descent and effectively adapt the kernel for a more suitable HDC encoding., Results: Our experiments on various regression datasets show that tuning the HDC encoder can significantly boost the accuracy, surpassing the current HDC-based algorithm and providing faster inference than other baselines, including RFF-based kernel ridge regression., Discussion: The results indicate the importance of an adaptive encoder and customized high-dimensional representation in HDC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hernández-Cano, Ni, Zou, Zakeri and Imani.)

Suppression of the cGAS-STING pathway is an immune escape mechanism in cancer cells. The critical role of this pathway in gastric cancer (GC) is not fully understood. Herein, we evaluated the effect of the interferon-gamma (IFN-gamma), STING agonist, PD-1 immune checkpoint blockade, and their combination on the cGAS-STING pathway in GC. Expression of cGAS and STING in tumor tissue samples and adjacent normal tissue (ANT) biopsies of fifty new GC patients was evaluated by quantitative real-time PCR (qRT-PCR). Moreover, cGAS and STING expression levels were examined in Peripheral Blood Mononuclear Cells (PBMC) samples of forty GC patients and twenty-five healthy subjects. The apoptosis rate of cancer cells was analyzed by Annexin V-FITC/PI. Cell proliferation was measured by the BrdU assay. Also, IFN-β levels were evaluated in the supernatants of the treated groups. The cGAS expression was decreased in patients with distant metastasis. Co-cultures treated with IFN-gamma showed an elevated level of cGAS and STING expressions in PBMC and cancer cells. The rate of apoptosis increased in all the treatment groups. In addition, the rate of proliferation in PBMCs increased in different treated groups. The main role of PBMCs in cytotoxicity was determined by a comparative analysis of the viability of cells treated with all treatments, both with and without PBMCs. The production of IFN-β was elevated in all treated groups. The current study suggests that a combination therapy using IFN-gamma, STING agonist, and anti-PD-1 antibody can provide a promising approach to the treatment of GC., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Soil quality is one of the main limiting factor in the development of the food sector in arid areas, mainly due to its poor mechanics and lack of water retention. Soil's organic carbon is nearly absent in arid soils, though it is important for water and nutrient transport, to soil mechanics, to prevent erosion, and as a long-term carbon sink. In this study, we evaluate the potential benefits that are brought to inert sand by the incorporation of a range of, mainly, cellulosic networks in their polymeric or structured (fiber) forms, analogously to those found in healthy soils. We explore the impact of a wide range of nonfood polysaccharide-based amendments, including pulp fibers, nanocellulose, cellulose derivatives, and other readily available polysaccharide structures derived from arthropods (chitosan) or fruit peels (pectin) residues. A practical methodology is presented to form sand-polymer composites, which are evaluated for their soil mechanics as a function of humidity and the dynamics of their response to water. The mechanics are correlated to the network of polymers formed within the pores of the sandy soil, as observed by electron microscopy. The response to water is correlated to both the features of the network and the individual polysaccharides' physicochemical features. We expect this work to provide a rapid and reproducible methodology to benchmark sustainable organic amendments for arid soils.

Of major interest, especially in city environments, and increasingly inside vehicles or industrial plants, is the drive to reduce human exposure to nitrogen oxides (NO x ). This trend has drawn increasing attention to filtration, which has developed remarkably owing to the capabilities of recently developed mathematical models and novel filter concepts. This paper reports on the study of the kinetic modelling of adsorption of nitrogen dioxide (NO 2 ), collected from the tailpipe of a diesel engine, reacting to calcium nitrate salt (Ca(NO 3 ) 2 ) on a surface flow filter consisting of a coating of fine ground limestone or marble (CaCO 3 ) in combination with micro-nanofibrillated cellulose (MNFC) acting as binder and humectant applied onto a multiply recycled newsprint substrate. The coating and substrate are both porous, but on different pore size scales, with the coating having significantly lower permeability. To maximise gas-coating contact, therefore, the coating deposition is pixelated, achieved by pin coating. An axially dispersed gaseous plug flow model (dispersion model) was used to simulate the transport within the coating pore network structure, following earlier flow modelling studies, and a kinetic reaction model was used to examine NO 2 to NO 3 - conversion in correlation with experimental results. Modelling results indicate a 60.38% conversion of exposed NO 2 gas to Ca(NO 3 ) 2 under the specific conditions applied, with an absolute relative error between the predicted and experimentally estimated value being 0.81%. The model additionally enabled a prediction of effects of changing parameters over a limited perturbation range, thus assisting in predicting filter element consumption, with attention given to the active component CaCO 3 surface as a function of particle size in relation to the gas contact exchange, promoting the reaction over time. It is intended that the Ca(NO 3 ) 2 formed from the reaction can go on to be used as a value-added fertiliser, thus contributing to circular economy., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Despite the fact that studies indicate that earthquake trauma is associated with numerous psychological consequences, the mediating mechanisms leading to these outcomes have not been well-studied. Therefore, this study investigates the relationship between trauma exposure with substance use tendency, depression, and suicidal thoughts, with the mediating role of peritraumatic dissociation and experiential avoidance., Methods: The descriptive-correlational approach was employed in this study. The participants were people who had experienced the Kermanshah earthquake in 2017. A total of 324 people were selected by convenient sampling method. The Traumatic Exposure Severity Scale, the Peritraumatic Dissociative Experiences Questionnaire, the Acceptance and Action Questionnaire, the Iranian Addiction Potential Scale, Beck's Depression Inventory [BDI-II], and Beck's Suicidal Thoughts Scale were used to collect data. The gathered data was analyzed‌ using structural equation modeling in ‌SPSS Ver. 24 and LISREL Ver. 24., Results: The study findings indicated that the intensity of the trauma exposure is directly and significantly associated with depression symptoms, peritraumatic dissociation, and experiential avoidance. The severity of exposure to trauma had a significant indirect effect on the tendency to use substances through experiential avoidance. This is while the severity of the trauma experience did not directly correlate with substance use and suicidal thoughts. In addition, peritraumatic dissociation did not act as a mediator in the relationship between the severity of trauma exposure with substance use, depression, and suicidal thoughts., Conclusions: The severity of exposure to the earthquake was associated with symptoms of depression and these findings indicate the importance of experiential avoidance in predicting the tendency to use drugs. Hence, it is essential to design and implement psychological interventions that target experiential avoidance to prevent drug use tendencies and to establish policies that lower depression symptoms following natural disasters., (© 2024. The Author(s).)

Introduction: Understanding the neural code has been one of the central aims of neuroscience research for decades. Spikes are commonly referred to as the units of information transfer, but multi-unit activity (MUA) recordings are routinely analyzed in aggregate forms such as binned spike counts, peri-stimulus time histograms, firing rates, or population codes. Various forms of averaging also occur in the brain, from the spatial averaging of spikes within dendritic trees to their temporal averaging through synaptic dynamics. However, how these forms of averaging are related to each other or to the spatial and temporal units of information representation within the neural code has remained poorly understood., Materials and Methods: In this work we developed NeuroPixelHD, a symbolic hyperdimensional model of MUA, and used it to decode the spatial location and identity of static images shown to n = 9 mice in the Allen Institute Visual Coding-NeuroPixels dataset from large-scale MUA recordings. We parametrically varied the spatial and temporal resolutions of the MUA data provided to the model, and compared its resulting decoding accuracy., Results: For almost all subjects, we found 125ms temporal resolution to maximize decoding accuracy for both the spatial location of Gabor patches (81 classes for patches presented over a 9×9 grid) as well as the identity of natural images (118 classes corresponding to 118 images) across the whole brain. This optimal temporal resolution nevertheless varied greatly between different regions, followed a sensory-associate hierarchy, and was significantly modulated by the central frequency of theta-band oscillations across different regions. Spatially, the optimal resolution was at either of two mesoscale levels for almost all mice: the area level, where the spiking activity of all neurons within each brain area are combined, and the population level, where neuronal spikes within each area are combined across fast spiking (putatively inhibitory) and regular spiking (putatively excitatory) neurons, respectively. We also observed an expected interplay between optimal spatial and temporal resolutions, whereby increasing the amount of averaging across one dimension (space or time) decreases the amount of averaging that is optimal across the other dimension, and vice versa., Discussion: Our findings corroborate existing empirical practices of spatiotemporal binning and averaging in MUA data analysis, and provide a rigorous computational framework for optimizing the level of such aggregations. Our findings can also synthesize these empirical practices with existing knowledge of the various sources of biological averaging in the brain into a new theory of neural information processing in which the unit of information varies dynamically based on neuronal signal and noise correlations across space and time., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Samiei, Zou, Imani and Nozari.)

This paper addresses the inference challenges associated with a class of hidden Markov models with binary state variables, known as partially observed Boolean dynamical systems (POBDS). POBDS have demonstrated remarkable success in modeling the ON and OFF dynamics of genes, microbes, and bacteria in systems biology, as well as in network security to represent the propagation of attacks among interconnected elements. Despite existing optimal and approximate inference solutions for POBDS, scalability remains a significant issue due to the computational cost associated with likelihood evaluations and the exploration of extensive parameter spaces. To overcome these challenges, this paper proposes a kernel-based particle filtering approach for large-scale inference of POBDS. Our method employs a Gaussian process (GP) to efficiently represent the expensive-to-evaluate likelihood function across the parameter space. The likelihood evaluation is approximated using a particle filtering technique, enabling the GP to account for various sources of uncertainty, including limited likelihood evaluations. Leveraging the GP's predictive behavior, a Bayesian optimization strategy is derived for effectively seeking parameters yielding the highest likelihood, minimizing the overall computational burden while balancing exploration and exploitation. The proposed method's performance is demonstrated using two biological networks: the mammalian cell-cycle network and the T-cell large granular lymphocyte leukemia network.

Advances in technology have enabled the use of sensors with varied modalities to monitor different parts of systems, each providing diverse levels of information about the underlying system. However, resource limitations and computational power restrict the number of sensors/data that can be processed in real-time in most complex systems. These challenges necessitate the need for selecting/scheduling a subset of sensors to obtain measurements that guarantee the best monitoring objectives. This paper focuses on sensor scheduling for systems modeled by hidden Markov models. Despite the development of several sensor selection and scheduling methods, existing methods tend to be greedy and do not take into account the long-term impact of selected sensors on monitoring objectives. This paper formulates optimal sensor scheduling as a reinforcement learning problem defined over the posterior distribution of system states. Further, the paper derives a deep reinforcement learning policy for offline learning of the sensor scheduling policy, which can then be executed in real-time as new information unfolds. The proposed method applies to any monitoring objective that can be expressed in terms of the posterior distribution of the states (e.g., state estimation, information gain, etc.). The performance of the proposed method in terms of accuracy and robustness is investigated for monitoring the security of networked systems and the health monitoring of gene regulatory networks., Competing Interests: Disclosure Statement The authors declare no conflict of interest.

Dual-energy X-ray absorptiometry (DXA) scans are one of the most frequently used imaging techniques for calculating bone mineral density, yet calculating fracture risk using DXA image features is rarely performed. The objective of this study was to combine deep neural networks, together with DXA images and patient clinical information, to evaluate fracture risk in a cohort of adults with at least one known fall and age-matched healthy controls. DXA images of the entire body as, well as isolated images of the hip, forearm, and spine (1488 total), were obtained from 478 fallers and 48 non-faller controls. A modeling pipeline was developed for fracture risk prediction using the DXA images and clinical data. First, self-supervised pretraining of feature extractors was performed using a small vision transformer (ViT-S) and a convolutional neural network model (VGG-16 and Resnet-50). After pretraining, the feature extractors were then paired with a multilayer perceptron model, which was used for fracture risk classification. Classification was achieved with an average area under the receiver-operating characteristic curve (AUROC) score of 74.3%. This study demonstrates ViT-S as a promising neural network technique for fracture risk classification using DXA scans. The findings have future application as a fracture risk screening tool for older adults at risk of falls. © 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research., (© 2023 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.)

Purpose: To investigate foveal changes in infants with ROP not requiring treatment(nROP) and ROP infants needing treatment (tROP) using a handheld SD-OCT device., Method: We performed horizontal SD-OCT scans through the fovea in 156 eyes of 81 infants diagnosed with ROP. Foveal immaturity indices including the presence of inner retinal layers (IRL), absence of foveal outer nuclear layers widening (ONL) and attenuation of hyperreflective outer segment layers (OS), presence and type of cystoid macular edema (CME), epiretinal membrane (ERM), foveal pit depth (FPD), foveal pit width (FPW) and central foveal thickness (CFT) were calculated. The multivariate logistic regression model was used to predict the need for treatment based on OCT measurements., Results: The shape of the foveolar pit was not significantly different among tROP and nROP groups (P-value = 0.287, Chi-square test). IRL extrusion was incomplete in both tROP and nROP groups (P-value = 0.0.41, Chi-square test). Nevertheless, the presence of thicker IRL was more frequent in the nROP group in comparison with the tROP group (100% vs.64.8%, P-value = 0.001). CME was observed in 29% of eyes in the tROP group and 40% of eyes in the nROP group; however, this difference was not statistically significant (P-value = 0.32, Chi-square test). ERM was detected in 15 (75%) and 84 (65.6%) eyes in the nROP and tROP groups, respectively (P-value = 0.39, Chi-square test). Multivariate logistic regression analyses showed that the need for treatment was significantly associated with gestational age (GA), CFT and FPD (P-values 0.001 and 0.002 respectively)., Conclusions: This study demonstrated GA, foveal pit depth and the central foveal thickness could accurately predict the need for treatment with sensitivity, specificity, and diagnostic accuracy of 97%, 65% and 91.7% respectively., (© 2023. Brazilian Retina and Vitreous Society.)

Recently, interest in measuring the concentration of 220 Rn in air has increased greatly following the development of standards and the calibration of monitoring instruments. In this study, a 220 Rn calibration chamber was designed and developed at the Institute of Radiochemistry and Radioecology (RRI) based on the computational fluid dynamics (CFD) method implemented in ANSYS Fluent 2020 R1 code at the University of Pannonia in Hungary. The behavior of 220 Rn and its spatial distribution inside the 220 Rn calibration chamber at RRI were investigated at different flow rates. The 220 Rn concentration was close to homogeneous under higher flow regimes due to thorough mixing of the gas inside the chamber. Predictions based on CFD simulations were compared with experimentally measured transmission factors (C out /C in ). The spatial distribution of 220 Rn was dependent on the flow rate and the positions of the inlet and outlet. Our results clearly demonstrate the suitability of the 220 Rn calibration chamber at RRI for calibrating monitoring instruments. Furthermore, the CFD-based predictions were in good agreement with the results obtained at higher flow rates using experimental and analytical models according to the relative deviation, with a maximum of approximately 9%., (© 2023. Springer Nature Limited.)

The main purpose of the current study was to find suitable and optimum levels of protectants among chicken egg yolk plasma (CEYP) and low-density lipoproteins (LDLs), alone or supplemented with ewe or cow skim milk, for cryopreservation of ram semen. In Experiments 1 and 2, the CEYP (28%) freezing extender was enriched with ewe or cow milk (2.5%, 5%, 10%, or 20%; v/v), respectively. In Experiments 3 and 4, the semen extender was prepared by varying the amounts of fresh or lyophilized LDL (lyo-LDL), respectively. Finally, ewe or cow skim milk was added to the freshly extracted LDL extender and the quality of frozen-thawed semen was examined (Experiments 5 and 6). Kinematics of spermatozoa (assessed using a computer-assisted sperm analysis system), viability, functionality of the plasma membrane, and levels of malondialdehyde (MDA) and total antioxidant capacity (TAC) were evaluated. Results revealed that addition of ewe or cow skim milk (5%, 10%, or 20%; v/v) to the CEYP diluent enhanced kinematics, viability, and membrane integrity of spermatozoa compared with the control ( p  < 0.05). Moreover, fresh LDL diluent was more effective than lyo-LDL in the cryosurvival of ram spermatozoa. In addition, enrichment of fresh LDL diluent with ewe or cow skim milk improved different variables of spermatozoa compared with the control ( p  < 0.05). Levels of MDA and TAC were not affected by adding ewe or cow milk to the diluents ( p  > 0.05). In conclusion, enrichment of fresh LDL extenders with ewe or cow milk also is proposed as an approach to preserve ram semen quality against cold shock and cryodamage injuries.

Current genomics interventions have limitations in accounting for cell stimuli and the dynamic response to intervention. Although genomic sequencing and analysis have led to significant advances in personalized medicine, the complexity of cellular interactions and the dynamic nature of the cellular response to stimuli pose significant challenges. These limitations can lead to chronic disease recurrence and inefficient genomic interventions. Therefore, it is necessary to capture the full range of cellular responses to develop effective interventions. This paper presents a game-theoretic model of the fight between the cell and intervention, demonstrating analytically and numerically why current interventions become ineffective over time. The performance is analyzed using melanoma regulatory networks, and the role of artificial intelligence in deriving effective solutions is described.

Gene regulatory networks (GRNs) play crucial roles in various cellular processes, including stress response, DNA repair, and the mechanisms involved in complex diseases such as cancer. Biologists are involved in most biological analyses. Thus, quantifying their policies reflected in available biological data can significantly help us to better understand these complex systems. The primary challenges preventing the utilization of existing machine learning, particularly inverse reinforcement learning techniques, to quantify biologists' knowledge are the limitations and huge amount of uncertainty in biological data. This paper leverages the network-like structure of GRNs to define expert reward functions that contain exponentially fewer parameters than regular reward models. Numerical experiments using mammalian cell cycle and synthetic gene-expression data demonstrate the superior performance of the proposed method in quantifying biologists' policies.

Ulcerative colitis (UC) is a serious inflammatory disease of the colon. The pathogenic mechanisms of UC involve the activation of inflammatory and oxidative stress responses in the colon. Levetiracetam is an antiepileptic drug with anti-inflammatory and antioxidant effects. The aim of this study was to investigate the potential protective effect of levetiracetam against UC in a mouse model. UC was induced in mice by intrarectal administration of acetic acid and then mice were treated with levetiracetam (50 or 100 mg/kg/day, i.p.) for three days. The colonic tissue samples were dissected for biochemical, RT-PCR and immunofluorescence analysis. Results showed that levetiracetam treatment significantly decreased colonic mucosal injury as evidenced by the macroscopic and histopathological analysis. Levetiracetam induced Nrf2/HO-1 and antioxidants while reduced lipid peroxidation and myeloperoxidase activity in colon tissue. Levetiracetam treatment decreased NF-κB activity and the expression of proinflammatory mediators TNF-α, IL-6, IL-1β, IFN-γ, MCP-1 and ICAM-1. The colonic levels of anti-inflammatory cytokines IL-10 and TGF-β1 were increased by levetiracetam treatment. Furthermore, levetiracetam significantly diminished iNOS expression and NO production in colon tissue. These findings suggest that levetiracetam ameliorates the severity of UC in mice through the regulation of inflammatory and oxidative responses., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier B.V. All rights reserved.)

Human β-defensins (hBDs) are cationic peptides with an amphipathic spatial shape and a high cysteine content. The members of this peptide family have been found in the human body with various functions, including the human reproductive system. Of among β-defensins in the human body, β-defensin 1, β-defensin 2, and β-defensin 126 are known in the human reproductive system. Human β-defensin 1 interacts with chemokine receptor 6 (CCR6) in the male reproductive system to prevent bacterial infections. This peptide has a positive function in antitumor immunity by recruiting dendritic cells and memory T cells in prostate cancer. It is necessary for fertilization via facilitating capacitation and acrosome reaction in the female reproductive system. Human β-defensin 2 is another peptide with antibacterial action which can minimize infection in different parts of the female reproductive system such as the vagina by interacting with CCR6. Human β-defensin 2 could play a role in preventing cervical cancer via interactions with dendritic cells. Human β-defensin 126 is required for sperm motility and protecting the sperm against immune system factors. This study attempted to review the updated knowledge about the roles of β-defensin 1, β-defensin 2, and β-defensin 126 in both the male and female reproductive systems.

Gene regulatory networks (GRNs) consist of multiple interacting genes whose activities govern various cellular processes. The limitations in genomics data and the complexity of the interactions between components often pose huge uncertainties in the models of these biological systems. Meanwhile, inferring/estimating the interactions between components of the GRNs using data acquired from the normal condition of these biological systems is a challenging or, in some cases, an impossible task. Perturbation is a well-known genomics approach that aims to excite targeted components to gather useful data from these systems. This paper models GRNs using the Boolean network with perturbation, where the network uncertainty appears in terms of unknown interactions between genes. Unlike the existing heuristics and greedy data-acquiring methods, this paper provides an optimal Bayesian formulation of the data-acquiring process in the reinforcement learning context, where the actions are perturbations, and the reward measures step-wise improvement in the inference accuracy. We develop a semi-gradient reinforcement learning method with function approximation for learning near-optimal data-acquiring policy. The obtained policy yields near-exact Bayesian optimality with respect to the entire uncertainty in the regulatory network model, and allows learning the policy offline through planning. We demonstrate the performance of the proposed framework using the well-known p53-Mdm2 negative feedback loop gene regulatory network.

DNA-based analytical techniques have provided an advantageous sensing assay in the realm of biotechnology. Bio-inspired fluorescent nanodots are a novel type of biological staining agents with excellent optical properties widely used for cellular imaging and diagnostics. In the present research, we successfully synthesized bio-dots with excellent optical properties and high-quantum yield from DNA sodium salt through the hydrothermal method. We conjugated the bio-dots with 3' Eclipse Dark Quencher (Eclipse)-labeled single-strand oligodeoxyribonucleotide according to carbodiimide chemistry, to design a fluorescence resonance energy transfer (FRET) probe. The results confirmed the prosperous synthesis and surface functionalization of the bio-dot. Analysis of size, zeta potential, and FTIR spectroscopy verified successful bioconjugation of the bio-dots with probes. UV-visibility analysis and fluorescence intensity profile of the bio-dot and bio-dot@probes represented a concentration-dependent quenching of fluorescent signal of bio-dot by Eclipse after probe conjugation. The results demonstrated that TaqMan PCR was not feasible using the designed bio-dot@probes. Our results indicated that bio-dot can be used as an efficient fluorescent tag in the design of fluorescently labeled oligonucleotides with high biocompatibility and optical features., (© 2022 International Union of Biochemistry and Molecular Biology, Inc.)

In the present study, a new approach was introduced regarding the extracellular synthesis of selenium sulfide micro/nano-particles using Saccharomyces cerevisiae in different ammonium sulfate supplementation and in the presence of sodium selenosulfate precursors (S 1 ) and a blend of selenous acid and sodium sulfite (S 2 ). In S 1 , only cell supernatant exposed to ammonium sulfate was able to reduce sodium selenosulfate. Whereas, in S 2 , cell supernatant in both pre-conditions of with or without ammonium sulfate (S 2  + or S 2 -) were able to reduce selenous acid and sodium sulfite. Electron microscopy, also indicated that selenium sulfide NPs were successfully synthesized with average size of 288 and 332 nm for S 2  + and S 2 - in SEM and 268 and 305 nm in TEM. Additionally, elemental mapping by energy-dispersive x-ray analysis confirmed the presence of sulfur/selenium elements in the particles in a proportion of 24.50 and 23.31 for S 2 - and S 2  + , respectively. The mass spectrometry indicated the probability of Se 2 S 2 , SeS 1.1 , Se 2 , Se, SeS 5 , SeS 3 , Se 3 S 5 /Se 5 , Se 3 /SeS 5 , Se 6 , Se 4 /SeS 7 , Se 2.57 S 5.43 /Se 2 S 2 and Se 4 S/Se 2 S 6 molecules for S 2  + and of Se, Se 2 , Se 3 S 5 /Se 5 , Se 6 and Se 4 species for S 2 -. In FTIR spectra, primary (i.e. 1090-1020 and 1650-1580 cm -1 ) and secondary (1580-1490 cm -1 ) amine bands duly confirmed the protein corona around the NPs., (© 2023. The Author(s).)

Unlike established coating formulations, functional particulate coatings often demand the omission of polymer dispersant so as to retain surface functionality. This results in heterogeneous complex rheology. We take an example from a novel development for an NO x mitigation surface flow filter system, in which ground calcium carbonate (GCC), applied in a coating, reacts with NO 2 releasing CO 2 . Inclusion of mesoporous ancillary mineral acts to capture the CO 2 . The coating is applied as droplets to maximize gas-contact dynamic by forming a pixelated 2D array using a coating device consisting of protruding pins, which are loaded by submersion in the aqueous coating color such that the adhering droplets are transferred onto the substrate. The flow is driven by surface meniscus wetting causing lateral spread and bulk pore permeation. Filamentation occurs during the retraction of the pins. Stress-related viscoelastic and induced dilatancy in the suspension containing the ancillary mesoporous mineral disrupts processability. Adopting shear, oscillation and extensional rheometric methods, we show that the inclusion of an ancillary mineral that alone absorbs water, e.g., perlite (a naturally occurring porous volcanic glass), is rheologically preferable to one that in addition to absorbing water also immobilizes it on the mineral surface, e.g., sepiolite. When including micro-nanofibrillated cellulose (MNFC), critical for maintaining moisture to support NO 2 sorption, it is observed that it acts also as a flow modifier, enabling uniform coating transfer to be achieved, thus eliminating any possible detrimental effect on mineral surface activity by avoiding the use of soluble polymeric dispersant.

While sustainable mobility and decarbonization of transportation sector are among the most comprehensive solutions to the problem of climate change, electric vehicles (EV) are becoming increasingly popular as the future mode of transport. In this study, the integration of a solar carport canopy to a potential EV charging station is analyzed using various operating conditions. A detailed analysis has been provided for the carport located in southern Taiwan, Kaohsiung city, where electricity generation, emission impacts, and financial analysis of the solar EV charging station are discussed. The results of a case study showed a potential of 140 MWh/year of solar energy yield, which could provide solar electricity of more than 3000 vehicles per month with 1-h parking time, generating 94% lower total carbon dioxide emission than the electricity produced from traditional grid methods. Taken into account the impact of carbon tax implementation on driver economics, the results demonstrated the viability of such photovoltaic (PV)-based charging stations, particularly for possible higher carbon tax scenarios in the future. The presented results can be implemented on a larger scale, offering guidelines and tools for constructing solar-powered EV charging station infrastructure., (© 2023. The Author(s).)

Based on the European Union Basic Safety Standards to protect people against exposure to ionizing radiation, establishing and addressing the reference levels for indoor radon concentrations is necessary. Therefore, the indoor radon concentration should be monitored and control in dwelling and workplaces. However, proper ventilation and sustainability are the major factors that influence how healthy the environment in a building is for its occupants. In this paper, the indoor radon distribution in a typical naturally ventilated room under two scenarios (when the door is closed and open) using the computational fluid dynamics (CFD) technique was studied. The CFD code ANSYS Fluent 2020 R1 based on the finite volume method was employed before the simulation results were compared with analytical calculations as well as passive and active measurements. The average radon concentration from the CFD simulation was found to be between 70.21 and 66.25 Bq m -3 under closed and open-door conditions, respectively, at the desired ventilation rate of 1 ACH (Air Changes per Hour). Moreover, the highest concentrations of radon were measured close to the floor and the lowest values were recorded near to the inlet, resulting in the airflow velocity profile. The simulation results were in good agreement with the maxima of 19% and 7% compared to analytical calculations at different indoor air velocities in the open- and closed-door scenarios, respectively. The measured radon concentrations obtained by the active measurements also fitted well with the CFD results, for example, with a relative standard deviation of around 7% and 2% when measured by AlphaGUARD and RAD7 monitors at a height of 1.0 m above the ground in the open-door scenario. From the simulation results, the effective dose received by an individual from the indoor air of the workplace was also calculated., (© 2023. The Author(s).)

Background: People who inject drugs (PWID) continue to experience the highest burden of hepatitis C virus (HCV). We aimed to characterize HCV antibody prevalence, determinants of infection, and the cascade of engagement in HCV care among PWID in Iran., Methods: Participants were recruited in 11 cities of Iran using respondent-driven sampling. PWID underwent a structured interview capturing measures on socio-demographics, behaviors, and the HCV cascade of care. HCV and HIV were tested using antibody rapid tests. Multivariable logistic regression models identified characteristics associated with HCV seropositivity., Results: HCV antibody prevalence was 26.0% among 2684 PWID enrolled. Of 699 participants who were HCV antibody positive, 88 (12.6%) were aware of past infections. HCV antibody prevalence was associated with older age (adjusted odds ratio [aOR] 2.09; 95% CI 1.18, 3.71), lower education (aOR 1.31; 95% CI 1.02, 1.69), >10 years of injecting (aOR 6.03; 95% CI 4.10, 8.85), methamphetamine injection (aOR 1.46; 95% CI 1.07, 1.99), daily injection drug use (aOR 1.26; 95% CI 1.01, 1.58), needle/syringe sharing (aOR 2.04; 95% CI 1.24, 3.34), recent incarceration (aOR 1.74; 95% CI 1.30, 2.32), and HIV seropositivity (aOR 7.93; 95% CI 4.12, 15.24). Additionally, 12.0% had ever tested for HCV, 4.0% had previously tested reactive for HCV antibody, and 3.7% had received an HCV diagnosis. Of diagnosed cases, 44.4% were linked to care, 15.2% initiated treatment, and 3.0% achieved sustained virologic response., Conclusion: Our data show a high prevalence of HCV antibody and low engagement in HCV care, underscoring an unmet need for HCV prevention, screening, and treatment among PWID in Iran. HCV prevention and treatment programs tailored for PWID are needed to enhance harm reduction efforts and access to HCV care in Iran., Competing Interests: Conflict of interest The authors have confirmed they have no potential conflicts of interest to declare., (Copyright © 2023 Elsevier B.V. All rights reserved.)

To compare the functional and anatomical outcome of fluorescein angiography (FA) versus indocyanine green angiography (ICGA) guided photodynamic therapy (PDT) in the treatment of non-resolving central serous chorioretinopathy (CSCR). In this prospective interventional case series, all patients with non-resolving CSCR, defined as persistent SRF involving subfoveal area for at least three months, were nonrandomly assigned to receive either FA or ICGA-guided half dose PDT. Baseline and 4 months post-treatment data including best corrected visual acuity (BCVA), the status of foveal subretinal fluid, subfoveal choroidal thickness, choroidal vascularity index, pigment epithelial detachment area, treatment and PDT spot numbers were collected. Thirty-six eyes were included; 24 received ICGA-guided and 12 received FA-guided PDT. Overall, improvement in BCVA and choroidal parameters were observed in all patients. There was no significant difference in baseline parameters as well as follow-up measurements between groups. However, the mean total energy dose and spot number in the IGCA-guided PDT were significantly higher than the FA-guided PDT group (P = 0.001). Both FA-guided and ICGA-guided half-dose PDT were effective in the treatment of non-resolving CSCR, with favorable functional and anatomical outcome. In FA group, PDT with smaller spot sizes and fewer numbers of spots were applied., (© 2023. The Author(s).)

Cancer is one of the main reasons of mortality all over the world. Over the time, the major ways for cancer-therapy were based on radiotherapy, chemotherapy and surgery. These methods are not specific enough for that purpose, therefore, new ideas for design of new drugs with higher specificity are considered. Chimeric protein toxins are hybrid proteins consisting of a targeting portion and a toxic one which specifically bind and kill the target cancer cells. The main purpose of this study was designing a recombinant chimeric toxin with biding capability to one of the most key receptors namely claudin-4 which is over-expressed in almost all cancer cells. To design it, we utilized the last 30 C-terminal amino acids of Clostridium perfringens enterotoxin (CPE) as a binding module for claudin-4 and the toxic module which is the A-domain of Shiga toxin from Shigella dysenteriae . Using molecular modeling and docking methods, appropriate binding affinity of the recombinant chimeric toxin to its specific receptor was demonstrated. In the next step, the stability of this interaction was investigated by molecular dynamics simulation. Although partial instability was detected at some time points, however, sufficient stable situation of hydrogens bonds and high binding affinity between the chimeric toxin and receptor were observed in the in silico studies which in turn suggested that this complex could be formed successfully., Competing Interests: No conflicting interests and no funding in connection with this paper are applicable., (© 2023 Urmia University. All rights reserved.)

Accurate inference of biological systems, such as gene regulatory networks and microbial communities, is a key to a deep understanding of their underlying mechanisms. Despite several advances in the inference of regulatory networks in recent years, the existing techniques cannot incorporate expert knowledge into the inference process. Expert knowledge contains valuable biological information and is often reflected in available biological data, such as interventions made by biologists for treating diseases. Given the complexity of regulatory networks and the limitation of biological data, ignoring expert knowledge can lead to inaccuracy in the inference process. This paper models the regulatory networks using Boolean network with perturbation. We develop an expert-enabled inference method for inferring the unknown parameters of the network model using expert-acquired data. Given the availability of information about data-acquiring objectives and expert confidence, the proposed method optimally quantifies the expert knowledge along with the temporal changes in the data for the inference process. The numerical experiments investigate the performance of the proposed method using the well-known p53-MDM2 gene regulatory network.

The COVID-19 pandemic required a wide range of adaptations to the way that water sector operated globally. This paper looks into the impact of the COVID-19 pandemic on Brazilian water sector and evaluates the water sector's organisational resilience from the lens of water professionals. This study uses British Standard (BS 65000:2014)'s Resilience Maturity Scale method to evaluate organisational resilience in water sector under two defined scenarios of before and during the pandemic. For this purpose, the self-assessment framework developed by Southern Water in the United Kingdom (based on BS 65000:2014), comprising of the core resilience elements of Direction, Awareness, Alignment, Learning, Strengthening, and Assurance, are used for evaluations. A qualitative-quantitative surveying method is used for data collection. A total of 14 responses to the whole questionnaire were received from May 2021 to August 2021, each representing one water company in Brazil (four local companies and ten state-owned ones). The analyses identified COVID-19 as a threat multiplier particularly to already existing financial challenges due to the pre-existing threats in water sector. Bad debt and the COVID-19 emergency measures are recognised as the main challenges by 21 % and 14 % of the survey respondents. The state-owned and local companies scored an almost similar maturity level 3, 35 % and 34 % respectively, while the local companies scored much lower at maturity level 4 i.e., 26 % as opposed to 47 % in state-owned sector. This indicates that COVID-19 has a greater impact on local companies and the needs to increase preparedness. This study replicates an international experience to raise awareness on water sector's resiliency in Brazil and how it can be improved to withstand future external shocks. It sheds light on how and what existing challenges can be exacerbated facing a global shock and proposes opportunities for improvement of resilience maturity in water sector in Brazil., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Hyperdimensional computing (HDC) is a brain-inspired computational framework that relies on long hypervectors (HVs) for learning. In HDC, computational operations consist of simple manipulations of hypervectors and can be incredibly memory-intensive. In-memory computing (IMC) can greatly improve the efficiency of HDC by reducing data movement in the system. Most existing IMC implementations of HDC are limited to binary precision which inhibits the ability to match software-equivalent accuracies. Moreover, memory arrays used in IMC are restricted in size and cannot immediately support the direct associative search of large binary HVs (a ubiquitous operation, often over 10,000+ dimensions) required to achieve acceptable accuracies. We present a multi-bit IMC system for HDC using ferroelectric field-effect transistors (FeFETs) that simultaneously achieves software-equivalent-accuracies, reduces the dimensionality of the HDC system, and improves energy consumption by 826x and latency by 30x when compared to a GPU baseline. Furthermore, for the first time, we experimentally demonstrate multi-bit, array-level content-addressable memory (CAM) operations with FeFETs. We also present a scalable and efficient architecture based on CAMs which supports the associative search of large HVs. Furthermore, we study the effects of device, circuit, and architectural-level non-idealities on application-level accuracy with HDC., (© 2022. The Author(s).)

Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.

State-space models (SSMs) are a rich class of dynamical models with a wide range of applications in economics, healthcare, computational biology, robotics, and more. Proper analysis, control, learning, and decision-making in dynamical systems modeled by SSMs depend on the accuracy of the inferred/learned model. Most of the existing inference techniques for SSMs are capable of dealing with very small systems, unable to be applied to most of the large-scale practical problems. Toward this, this article introduces a two-stage Bayesian optimization (BO) framework for scalable and efficient inference in SSMs. The proposed framework maps the original large parameter space to a reduced space, containing a small linear combination of the original space. This reduced space, which captures the most variability in the inference function (e.g., log likelihood or log a posteriori), is obtained by eigenvalue decomposition of the covariance of gradients of the inference function approximated by a particle filtering scheme. Then, an exponential reduction in the search space of parameters during the inference process is achieved through the proposed two-stage BO policy, where the solution of the first-stage BO policy in the reduced space specifies the search space of the second-stage BO in the original space. The proposed framework's accuracy and speed are demonstrated through several experiments, including real metagenomics data from a gut microbial community.

Background: Retinopathy of prematurity (ROP) is a leading cause of irreversible blindness in infants. The Postnatal Growth and ROP (G-ROP) study proposed new screening criteria for ROP. This study aimed to validate the G-ROP screening criteria in a group of Iranian premature infants who were treated in the neonatal intensive care unit (NICU) for at least 40 days., Methods: In this retrospective study, we extracted the data pertaining to infants admitted to the NICU from January 2020 to December 2021. We screened all the included infants for ROP based on the Iranian national screening criteria. We applied the G-ROP criteria to our study population, and if no criterion was met, the infant was exempted from ROP screening. We determined the sensitivity and specificity of the G-ROP guidelines for ROP detection, along with its capacity for predicting the requirement for ROP treatment. Moreover, we compared the G-ROP guidelines with the Iranian and North American guidelines for ROP screening., Results: A total of 166 premature infants with complete datasets were included: 130 had ROP, of whom 61 were treated. There were 109 female infants (65.7%). The mean (standard deviation [SD]) birth weight and gestational age were 1080 (256) g and 28.28 (1.97) weeks, respectively. Applying the G-ROP criteria, 127 of 130 infants with ROP were identified (sensitivity, 97.69%; 95% confidence interval [CI], 95.11% - 100%), and of 36 infants without ROP, three were correctly excluded (specificity, 8.33%; 95% CI, 0% - 17.36%). The G-ROP criteria did not fail to identify infants who required treatment for ROP (sensitivity, 100%; 95% CI, 98.29 - 100) and had a specificity of 8.69% (95% CI, 2.04% - 15.34%). Although the Iranian and North American criteria had 100% sensitivity for infants with any stage of ROP, they could not detect infants without ROP (0% specificity)., Conclusions: The G-ROP screening criteria had a sensitivity of 100% in identifying infants requiring treatment for ROP in our high-risk group; however, specificity was not sufficiently high. Further studies with larger numbers of referred infants could confirm a decrease in the burden of retinal examinations using these criteria., Competing Interests: None, (© Author(s).)

The current study was designed to evaluate the possible protective effects of luteolin against β -cyfluthrin-mediated toxicity on the primary culture of rat hepatocytes (RHs). In the first step, the exposure of RHs to β -cyfluthrin (10, 20, 40, and 80  μ M) was assessed by MTT. Second, redox condition was evaluated in cotreatment of cells with luteolin (20, 40, and 60  μ M) and β -cyfluthrin (40  μ M) at both medium and intra levels. In comparison to control, viability was lower in 40 and 80  μ M β -cyfluthrin-treated groups at 24 h and all β -cyfluthrin-treated groups at 48 h ( P < 0.05). Cotreatment with 20 or 40  μ M luteolin + 40  μ M β -cyfluthrin resulted in a higher viability value compared to β -cyfluthrin alone at 24 and 48 h of incubation ( P < 0.05). Administration of 20 or 40  μ M luteolin with β -cyfluthrin led to the decrease of malondialdehyde and total nitrate/nitrite and the increase of total antioxidant capacity (TAC) values in both medium and intrahepatocyte levels compared to the β -cyfluthrin-treated group at 48 h ( P < 0.05). It seems that low and medium doses of luteolin possess the potential to reduce β -cyfluthrin-mediated hepatotoxicity via attenuation of peroxidative/nitrosative reactions and augmentation of TAC levels., Competing Interests: The authors declare that they have no conflicts of interest., (Copyright © 2022 Lachin Mousavi et al.)

Data in many practical problems are acquired according to decisions or actions made by users or experts to achieve specific goals. For instance, policies in the mind of biologists during the intervention process in genomics and metagenomics are often reflected in available data in these domains, or data in cyber-physical systems are often acquired according to actions/decisions made by experts/engineers for purposes, such as control or stabilization. Quantification of experts' policies through available data, which is also known as reward function learning, has been discussed extensively in the literature in the context of inverse reinforcement learning (IRL). However, most of the available techniques come short to deal with practical problems due to the following main reasons: 1) lack of scalability: arising from incapability or poor performance of existing techniques in dealing with large systems and 2) lack of reliability: coming from the incapability of the existing techniques to properly learn the optimal reward function during the learning process. Toward this, in this brief, we propose a multifidelity Bayesian optimization (MFBO) framework that significantly scales the learning process of a wide range of existing IRL techniques. The proposed framework enables the incorporation of multiple approximators and efficiently takes their uncertainty and computational costs into account to balance exploration and exploitation during the learning process. The proposed framework's high performance is demonstrated through genomics, metagenomics, and sets of random simulated problems.

Acrylamide is one of the undesirable compounds created in food, which leads to oxidative stress. Under normal conditions of the body, there is a balance between the production and elimination of free radicals. Imbalance in this process causes oxidative stress. Ascorbic acid has antioxidant properties and can be used to prevent oxidative stress damage. In this study, we considered the effect of acrylamide as a substance that causes oxidative stress and ascorbic acid as an antioxidant. In this experiment, 28 rats were separated into 4 groups (n = 7). Mice were fed orally with acrylamide (10 mg/kg), ascorbic acid (200 mg/kg), both acrylamide and ascorbic acid, and water as AR, AA, AR&AA, and control groups, respectively. Blood and ovarian tissue samples were collected after the final feeding and anesthesia for hematological tests. Blood cells, anti-oxidation status and relative expression of BAX (Bcl-2 Associated X-protein) , BCL-2 (B-cell lymphoma-2), Folliculogenesis Specific BHLH Transcription Factor (FIGLA), Follicle Stimulating Hormone Receptor (FSHR ), and Klotho (KL) genes were assessed and compared between the study groups. Despite no change in the levels of other factors, white blood cells were significantly different between all groups. The total oxidant and antioxidant index had significant changes in the AR group compared to controls. Glutathione Peroxidase showed the least concentration in the AR group than others although this change was not significant. Gene expression of BAX, BCL-2 , FIGLA, FSHR, and KL genes was not significant between the study groups ( P > 0.05 ). The most ratio of BAX to BCL-2 belonged to the AR group compared to other groups. In conclusion, AR probably induces ovarian dysfunction by increasing the proportions of apoptosis-related genes, and the usage of antioxidants like AA can minimize its hazardous effects. However, more research is needed to uncover effective ways to limit AR exposure., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 The Authors.)

Brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Hyper-Dimensional Computing (HDC) has shown promising results in enabling efficient and robust cognitive learning. In this study, we exploit HDC as an alternative computational model that mimics important brain functionalities toward high-efficiency and noise-tolerant neuromorphic computing. We present EventHD, an end-to-end learning framework based on HDC for robust, efficient learning from neuromorphic sensors. We first introduce a spatial and temporal encoding scheme to map event-based neuromorphic data into high-dimensional space. Then, we leverage HDC mathematics to support learning and cognitive tasks over encoded data, such as information association and memorization. EventHD also provides a notion of confidence for each prediction, thus enabling self-learning from unlabeled data. We evaluate EventHD efficiency over data collected from Dynamic Vision Sensor (DVS) sensors. Our results indicate that EventHD can provide online learning and cognitive support while operating over raw DVS data without using the costly preprocessing step. In terms of efficiency, EventHD provides 14.2× faster and 19.8× higher energy efficiency than state-of-the-art learning algorithms while improving the computational robustness by 5.9×., Competing Interests: NS was employed by the company Intal Labs. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Zou, Alimohamadi, Kim, Najafi, Srinivasa and Imani.)

Cellulose nanofibrils (CNFs) have emerged as sustainable options for a wide range of applications. However, the high aspect ratio and biopersistence of CNFs raise concerns about potential health effects. Here, we evaluated the in vivo pulmonary and systemic toxicity of unmodified (U-CNF), carboxymethylated (C-CNF), and TEMPO (2,2,6,6-tetramethyl-piperidin-1-oxyl)-oxidized (T-CNF) CNFs, fibrillated in the same way and administered to mice by repeated (3×) pharyngeal aspiration (14, 28, and 56 μg/mouse/aspiration). Toxic effects were assessed up to 90 days after the last administration. Some mice were treated with T-CNF samples spiked with lipopolysaccharide (LPS; 0.02-50 ng/mouse/aspiration) to assess the role of endotoxin contamination. The CNFs induced an acute inflammatory reaction that subsided within 90 days, except for T-CNF. At 90 days post-administration, an increased DNA damage was observed in bronchoalveolar lavage and hepatic cells after exposure to T-CNF and C-CNF, respectively. Besides, LPS contamination dose-dependently increased the hepatic genotoxic effects of T-CNF.

Background: MS is a demyelinating disease that can result in significant disability. Along with physical complications, this disease is associated with significant psychological complications, including cognitive decline. Therefore, this study aimed to determine the efficacy of mindfulness-based cognitive therapy in combination with rTMS on information processing and working memory in patients with MS., Methods: The current study used a single-case experimental design and included a follow-up (A-B-A). The statistical population of the present study was all MS patients in Tehran who referred to Imam Khomeini Hospital in Tehran in 2020. The present study sample consisted of 5 MS patients selected by the sampling methods available . Subjects were assessed three times before, during, and after the intervention using the Zahlen-Verbindongs and n-back tests in the two-back position. Subjects received cognitive therapy based on mindfulness and rTMS at a frequency of 10 Hz. Visual and graphical recovery percentage and effect size methods were used to analyze the data., Results: The current study's findings indicate that combining mindfulness with rTMS has a beneficial effect on the information processing and working memory of MS patients. Overall, 67.24% recovered following the intervention stage, 53.64% recovered following the follow-up for information processing, 104.04% recovered following the intervention stage, and 76.98% recovered following the follow-up for working memory., Conclusion: The study shows the effect of mindfulness combined with rTMS on cognitive problems in MS patients. Significant improvements in MS patients' information processing, working memory, and therapeutic outcomes were observed throughout the follow-up period., Competing Interests: The authors declare that there is no conflict of interest among them.

Purpose: The objective of this study was to compare the effects of 12 weeks of resistance training combined with either 5:2 intermittent fasting or continuous energy restriction on body composition, muscle size and quality, and upper and lower body strength., Methods: Untrained individuals undertook 12 weeks of resistance training plus either continuous energy restriction [20% daily energy restriction (CERT)] or 5:2 intermittent fasting [~ 70% energy restriction 2 days/week, euenergetic consumption 5 days/week (IFT)], with both groups prescribed a mean of ≥ 1.4 g of protein per kilogram of body weight per day. Participants completed 2 supervised resistance and 1 unsupervised aerobic/resistance training combination session per week. Changes in lean body mass (LBM), thigh muscle size and quality, strength and dietary intake were assessed., Results: Thirty-four participants completed the study (CERT = 17, IFT = 17). LBM was significantly increased (+ 3.7%, p < 0.001) and body weight (- 4.6%, p < 0.001) and fat (- 24.1%, p < 0.001) were significantly reduced with no significant difference between groups, though results differed by sex. Both groups showed improvements in thigh muscle size and quality, and reduced intramuscular and subcutaneous fat assessed by ultrasonography and peripheral quantitative computed tomography (pQCT), respectively. The CERT group demonstrated a significant increase in muscle surface area assessed by pQCT compared to the IFT group. Similar gains in upper and lower body strength and muscular endurance were observed between groups., Conclusion: When combined with resistance training and moderate protein intake, continuous energy restriction and 5:2 intermittent fasting resulted in similar improvements in body composition, muscle quality, and strength., Actrn: ACTRN12620000920998, September 2020, retrospectively registered., (© 2022. The Author(s).)

Purpose: To present the success rate of nonsurgical management of full-thickness inflammatory macular hole (IMH)., Method: Retrospective case series of five patients with IMH., Result: Five eyes from five patients with IMH enrolled in the current case series. All five eyes had successful closure with corticosteroid in the form of topical, periocular, or intravitreal injections. Systemic immunomodulatory treatment was employed for two patients, in addition to local therapy. For local therapy, one patient received topical eye drops, subtenon injection of corticosteroid, and intravitreal injection of combination of corticosteroid and anti-VEGF was performed in two patients. The closed macular hole reopened in one patient after two years, which required pars plana vitrectomy and anatomical and visual success achieved., Conclusion: Inflammatory macular holes can be closed with non-surgical interventions, although reopening may occur which requires surgery.

Recently, brain-inspired computing models have shown great potential to outperform today's deep learning solutions in terms of robustness and energy efficiency. Particularly, Spiking Neural Networks (SNNs) and HyperDimensional Computing (HDC) have shown promising results in enabling efficient and robust cognitive learning. Despite the success, these two brain-inspired models have different strengths. While SNN mimics the physical properties of the human brain, HDC models the brain on a more abstract and functional level. Their design philosophies demonstrate complementary patterns that motivate their combination. With the help of the classical psychological model on memory, we propose SpikeHD, the first framework that fundamentally combines Spiking neural network and hyperdimensional computing. SpikeHD generates a scalable and strong cognitive learning system that better mimics brain functionality. SpikeHD exploits spiking neural networks to extract low-level features by preserving the spatial and temporal correlation of raw event-based spike data. Then, it utilizes HDC to operate over SNN output by mapping the signal into high-dimensional space, learning the abstract information, and classifying the data. Our extensive evaluation on a set of benchmark classification problems shows that SpikeHD provides the following benefit compared to SNN architecture: (1) significantly enhance learning capability by exploiting two-stage information processing, (2) enables substantial robustness to noise and failure, and (3) reduces the network size and required parameters to learn complex information., (© 2022. The Author(s).)