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Abstract Electron self-injection in laser wakefield accelerators (LWFAs) is an important determinator of electron beam parameters. Controllable and adjustable LWFA beams are essential for applications. Controlled injection by capturing sheath electrons can be achieved using plasma density down-ramps or bumps, which perturb the LWFA bubble phase velocity by varying the plasma frequency and by affecting relativistic self-focussing of the laser. We report on a comprehensive study, using particle-in-cell simulations, of the effect of laser pulse evolution on injection on density perturbations. We show how the LWFA can be optimised to make it suitable for use in a wide range of applications, in particular those requiring short duration, low slice-emittance and low energy spread, and high-charge electron bunches.

Abstract We study the cosmological implications of an effective field theory model derived within a configuration of D7 brane stacks in the framework of type-IIB string theory. We consider a suitable geometric setup where the Kähler moduli fields are stabilized and the parametric space is constrained so that a de Sitter vacuum is ensured. In addition to the moduli fields we also take into account the usual Higgs and matter fields included in the effective field theory. In this background, we implement the standard hybrid inflation scenario with a singlet scalar field acting as the inflaton and the Higgs states serving as waterfall fields. Radiative corrections and soft supersymmetry breaking terms play an essential role in the realization of a successful inflationary scenario consistent with the present cosmological data. Small tensor-to-scalar ratio values are predicted, which can be probed in future planned experiments. Further constraints on the model’s parameters are derived from bounds on dark radiation which is measured as a contribution to the effective number of neutrino species N eff. In particular, we find an excess of ∆N eff ≤ 0.95 at 2σ confidence level with natural values of the involved couplings.

Vertebral osteomyelitis is a well-documented disease entity in literature with various known etiologies. However, vertebral diskitis-osteomyelitis secondary to an infected aortic aneurysm is an uncommon and life-threatening complication. We present the case of a 65-year-old male patient who presented with chronic low back pain that acutely worsened for 1 to 1.5 months and was diagnosed with vertebral diskitis-osteomyelitis secondary to a contiguous infection from an adjacent mycotic aortic aneurysm. To our knowledge, this is one of the few cases reported of vertebral diskitis-osteomyelitis secondary to mycotic aortic aneurysm. We discuss the findings on CT and MRI, as well as the value of imaging in guiding management.

Abstract Background The burden of diarrhoea morbidity and mortality among children under 5 years is higher in developing countries than in developed regions. Residents of informal settlements in many developing lower‐and‐middle‐income countries (LMICs) have a relatively high prevalence of diarrhoea and diarrhoea‐related diseases. Mothers are the burden bearers during children's diarrhoea episodes. Therefore, maternal knowledge and management skills are important to minimise the effects of morbidity and mortality associated with diarrhoeal diseases. Methods This cross‐sectional study was conducted among mothers residing in an informal settlement to assess their knowledge of diarrhoea prevention and home‐based management practices. An interviewer‐based questionnaire was used to gather information, imported into Microsoft Excel (Microsoft) and exported to STATA software version 13.1 (Stata) for data analysis. We performed descriptive multivariable analysis to find out participants knowledge of diarrhoea prevention and home‐based management practices and associated factors. Results A total of 428 participants participated in this study, with the majority, 6 64.5% (276/428) having good knowledge of prevention and home‐based management of diarrhoea among children under 5 years. Multivariable logistic regression analysis, participants who were multiparous and completed tertiary were 4.01 [confidence interval (CI) = 3.23–7.15; p =

The field of wearable technology is undergoing a transformative shift with the integration of IoT-enabled, AI-assisted sensors into sportswear, bringing sophisticated performance analysis within reach for a broader audience. Despite advancements in stitch strain sensors, there is a significant research gap in developing high-performance, stable, and IoT-connected complete stitch strain wearable sensor systems. These advanced sensors are essential for enhancing wearable technology and meeting the growing demands for improved user experience and functionality. In this study by utilizing conductive sewing threads and innovative stitching techniques, we fabricated textile strain sensors that offer high sensitivity, flexibility, and durability. Extensive calibration tests revealed strong linear relationships between resistance and tensile strain, with highest sensor sensitivity (2.08 Ω/mm during loading and 2.72 Ω/mm during unloading). These sensors demonstrate reliable performance with minimal hysteresis, particularly in shorter lengths. The wearable sportswear device, tested in gym environments, demonstrated the sensor's capability to provide real-time feedback on knee flexing/bending and body movement during exercise. This functionality supports injury prevention and enhances workout efficiency. The sensor system is IoT-enabled, powered by an ESP8266 microcontroller, facilitating data transmission to the Thing Speak platform for advanced analysis and remote monitoring. This comprehensive approach not only advances wearable technology but also opens new avenues for remote health monitoring and smart e-textiles, contributing significantly to the fields of sports science and fitness technology.

Abstract Insufficient functional β-cell mass causes diabetes; however, an effective cell replacement therapy for curing diabetes is currently not available. Reprogramming of acinar cells toward functional insulin-producing cells would offer an abundant and autologous source of insulin-producing cells. Our lineage tracing studies along with transcriptomic characterization demonstrate that treatment of adult mice with a small molecule that specifically inhibits kinase activity of focal adhesion kinase results in trans-differentiation of a subset of peri-islet acinar cells into insulin producing β-like cells. The acinar-derived insulin-producing cells infiltrate the pre-existing endocrine islets, partially restore β-cell mass, and significantly improve glucose homeostasis in diabetic mice. These findings provide evidence that inhibition of the kinase activity of focal adhesion kinase can convert acinar cells into insulin-producing cells and could offer a promising strategy for treating diabetes.

The aim of this study was to evaluate the effects of two different methods of controlling ovine reproduction (ram effect and a combination of eco-friendly substances) in three Greek indigenous sheep breeds, namely Florina, Karagouniko, and Chios. 180 ewes, aged 2–3 years old, were used, equally divided between the three Greek sheep breeds. Each breed was divided in two equal experimental groups: In group ECO (combination of eco-friendly substances), a dose of Gonadotropin-releasing hormone (GnRH) was administered at day 0, followed by an intramuscular injection of prostaglandin seven days later and another dose of GnRH at day 9. In group ME, sexually active males were introduced to the females that have been isolated for 3 months. The experiment was conducted in three consecutive years: 2020, 2021, and 2022. Average estrus expression and fecundity in all three examined breeds was comparable, with the exemption of Florina ewes treated with the eco-friendly substances in the first year, a result that could be attributed to the strong seasonal pattern in estrus expression of the Florina breed. The average length to estrus expression in days was significantly lower in the eco-friendly method than the procedure using male effect and the prolificacy was higher for the Chios sheep than the Florina and the Karagouniko breeds. Overall, both methods for the induction and synchronization of estrus (male effect and PGF2a administration) could be used in sheep reproduction with promising results. Nevertheless, the male effect seems to be more efficient in indigenous breeds with long anestrus periods.

Abstract Recycling vanadium from alternative sources is essential due to its expanding demand, depletion in natural sources, and environmental issues with terrestrial mining. Here, we present a complexation-precipitation method to selectively recover pentavalent vanadium ions, V(V), from complex metal ion mixtures, using an acid-stable metal binding agent, the cyclic imidedioxime, naphthalimidedioxime (H2CIDIII). H2CIDIII showed high extraction capacity and fast binding towards V(V) with crystal structures showing a 1:1 M:L dimer, [V2(O)3(C12H6N3O2)2]2−, 1, and 1:2 M:L non-oxido, [V(C12H6N3O2)2] ̶ complex, 2. Complexation selectivity studies showed only 1 and 2 were anionic, allowing facile separation of the V(V) complexes by pH-controlled precipitation, removing the need for solid support. The tandem complexation-precipitation technique achieved high recovery selectivity for V(V) with a selectivity coefficient above 3 × 105 from synthetic mixed metal solutions and real oil sand tailings. Zebrafish toxicity assay confirmed the non-toxicity of 1 and 2, highlighting H2CIDIII’s potential for practical and large-scale V(V) recovery.

Abstract We study supersymmetric (SUSY) flipped SU(5) × U(1) unification, focussing on its predictions for proton decay, fermion masses and gravitational waves. We performed a two-loop renormalisation group analysis and showed that the SUSY flipped SU(5) model predicts a high GUT scale M GUT > 1016 GeV. We also investigated the restrictions on the M B−L scale which is associated with the U(1) χ breaking scale. We found that the M B−L scale can vary in a broad region with negligible or little effect on the value of M GUT. Proton decay in this model is induced by dimension-6 operators only. The dimension-5 operator induced by SUSY contribution is suppressed due to the missing partner mechanism. We found that the partial decay width p → π 0 e + is high suppressed, being at least one order of magnitude lower than the future Hyper-K sensitivity. We also studied fermion (including neutrino) masses and mixings which can also influence proton decay. We presented two scenarios of flavour textures to check the consistency of the results with fermion masses and mixing. The B − L gauge breaking leads to the generation of cosmic strings. The B − L scale here is not constrained by gauge coupling unification. If this scale is very close that of GUT breaking, strings can be unstable due to the decay to monopole-antimonople pair. Such metastable strings can be used to explain the NANOGrav signals of stochastic gravitational wave background, which may be interpreted here as resulting from the decay of metastable cosmic strings.

Scaling complexity and appropriate data sets availability for training current Computer Vision (CV) applications poses major challenges. We tackle these challenges finding inspiration in biology and introducing a Self-supervised (SS) active foveated approach for CV. In this paper we present our solution to achieve portability and reproducibility by means of containerization utilizing Singularity. We also show the parallelization scheme used to run our models on ThetaGPU–an Argonne Leadership Computing Facility (ALCF) machine of 24 NVIDIA DGX A100 nodes. We describe how to use mpi4py to provide DistributedDataParallel (DDP) with all the needed information about world size as well as global and local ranks. We also show our dual pipe implementation of a foveator using NVIDIA Data Loading Library (DALI). Finally we conduct a series of strong scaling tests on up to 16 ThetaGPU nodes (128 GPUs), and show some variability trends in parallel scaling efficiency.

Background The risk of recurrence after nephrectomy for primary clear cell renal cell carcinoma (ccRCC) is estimated in daily practice solely based on clinical criteria. The aim of this study was to assess the prognostic relevance of common somatic mutations with respect to tumor aggressiveness and outcomes of ccRCC patients after definitive treatment. Methods Primary tumors from 37 patients with ccRCC who underwent radical nephrectomy were analyzed for presence of somatic mutations using a 15-gene targeted next-generation sequencing (NGS) panel. Associations to histopathologic characteristics and outcomes were investigated in the study cohort (n=37) and validated in The Cancer Genome Atlas (TCGA) ccRCC cohort (n=451). Results VHL was the most frequently mutated gene (51%), followed by PBRM1 (27%), BAP1 (13%), SETD2 (13%), KDM5C (5%), ATM (5%), MTOR (5%), and PTEN (3%). One-third of patients did not have any somatic mutations within the 15-gene panel. The vast majority of tumors harboring no mutations at all or VHL-only mutations (51%) were more frequently of smaller size (pT1-2) and earlier stage (I/II), whereas presence of any other gene mutations in various combinations with or without VHL was enriched in larger (pT3) and higher stage tumors (III) (p=0.02). No recurrences were noted in patients with unmutated tumors or VHL-only mutations as opposed to three relapses in patients with non-VHL somatic mutations (p=0.06). Presence of somatic mutations in PBRM1, BAP1, SETD2, KDM5C, ATM, MTOR, or PTEN genes in 451 TCGA ccRCC patients was associated with a significantly shorter disease-free survival (DFS) compared to those with unaltered tumors (q=0.01). Conclusions Preliminary findings from this ongoing study support the prognostic value of non-VHL mutations including PBRM1, BAP1, SETD2, KDM5C, ATM, MTOR, and PTEN in primary ccRCC tumors as surrogates of earlier recurrence and potential selection for adjuvant immune checkpoint inhibition.

Background Socioeconomic factors may lead to a disproportionate impact on health care usage and death among individuals with congenital heart defects (CHD) by race, ethnicity, and socioeconomic factors. How neighborhood poverty affects racial and ethnic disparities in health care usage and death among individuals with CHD across the life span is not well described. Methods and Results Individuals aged 1 to 64 years, with at least 1 CHD‐related International Classification of Diseases, Ninth Revision, Clinical Modification (ICD‐9‐CM) code were identified from health care encounters between January 1, 2011, and December 31, 2013, from 4 US sites. Residence was classified into lower‐ or higher‐poverty neighborhoods on the basis of zip code tabulation area from the 2014 American Community Survey 5‐year estimates. Multivariable logistic regression models, adjusting for site, sex, CHD anatomic severity, and insurance‐evaluated associations between race and ethnicity, and health care usage and death, stratified by neighborhood poverty. Of 31 542 individuals, 22.2% were non‐Hispanic Black and 17.0% Hispanic. In high‐poverty neighborhoods, non‐Hispanic Black (44.4%) and Hispanic (47.7%) individuals, respectively, were more likely to be hospitalized (adjusted odds ratio [aOR], 1.2 [95% CI, 1.1–1.3]; and aOR, 1.3 [95% CI, 1.2–1.5]) and have emergency department visits (aOR, 1.3 [95% CI, 1.2–1.5] and aOR, 1.8 [95% CI, 1.5–2.0]) compared with non‐Hispanic White individuals. In high poverty neighborhoods, non‐Hispanic Black individuals with CHD had 1.7 times the odds of death compared with non‐Hispanic White individuals in high‐poverty neighborhoods (95% CI, 1.1–2.7). Racial and ethnic disparities in health care usage were similar in low‐poverty neighborhoods, but disparities in death were attenuated (aOR for non‐Hispanic Black, 1.2 [95% CI=0.9–1.7]). Conclusions Racial and ethnic disparities in health care usage were found among individuals with CHD in low‐ and high‐poverty neighborhoods, but mortality disparities were larger in high‐poverty neighborhoods. Understanding individual‐ and community‐level social determinants of health, including access to health care, may help address racial and ethnic inequities in health care usage and death among individuals with CHD.

Abstract Background Evolutionary models of breast cancer progression differ on the extent to which metastatic potential is pre-encoded within primary tumors. Although metastatic recurrences often harbor putative driver mutations that are not detected in their antecedent primary tumor using standard sequencing technologies, whether these mutations were acquired before or after dissemination remains unclear. Methods To ascertain whether putative metastatic driver mutations initially deemed specific to the metastasis by whole exome sequencing were, in actuality, present within rare ancestral subclones of the primary tumors from which they arose, we employed error-controlled ultra-deep sequencing (UDS-UMI) coupled with FFPE artifact mitigation by uracil-DNA glycosylase (UDG) to assess the presence of 132 “metastasis-specific” mutations within antecedent primary tumors from 21 patients. Maximum mutation detection sensitivity was ~1% of primary tumor cells. A conceptual framework was developed to estimate relative likelihoods of alternative models of mutation acquisition. Results The ancestral primary tumor subclone responsible for seeding the metastasis was identified in 29% of patients, implicating several putative drivers in metastatic seeding including LRP5 A65V and PEAK1 K140Q. Despite this, 93% of metastasis-specific mutations in putative metastatic driver genes remained undetected within primary tumors, as did 96% of metastasis-specific mutations in known breast cancer drivers, including ERRB2 V777L, ESR1 D538G, and AKT1 D323H. Strikingly, even in those cases in which the rare ancestral subclone was identified, 87% of metastasis-specific putative driver mutations remained undetected. Modeling indicated that the sequential acquisition of multiple metastasis-specific driver or passenger mutations within the same rare subclonal lineage of the primary tumor was highly improbable. Conclusions Our results strongly suggest that metastatic driver mutations are sequentially acquired and selected within the same clonal lineage both before, but more commonly after, dissemination from the primary tumor, and that these mutations are biologically consequential. Despite inherent limitations in sampling archival primary tumors, our findings indicate that tumor cells in most patients continue to undergo clinically relevant genomic evolution after their dissemination from the primary tumor. This provides further evidence that metastatic recurrence is a multi-step, mutation-driven process that extends beyond primary tumor dissemination and underscores the importance of longitudinal tumor assessment to help guide clinical decisions.

This paper presents an aeronautical-grade payload unit designed for real-time execution of visual-inertial-LiDAR odometry and mapping (VILOAM) algorithms. The payload offers platform interchangeability between full-scale aircraft (e.g., Bell 412 helicopter), small-scale drones (e.g., DJI M600), and ground platforms. The use of small-scale drones renders a convenient option for the research and development of VILOAM algorithms due to the reduced resource demand and simplified pilot training, while full-scale aircraft experiments provide important operationally relevant datasets to test navigation algorithm performance for field deployment. The payload unit consists of two monocular cameras, an inertial measurement unit (IMU), a light detection and ranging (LiDAR) sensor, and a real-time kinematic (RTK) enabled global navigation satellite system (GNSS) receiver. A portable GPU interfaces with these sensors to capture hardware time-synchronized sensing data and perform real-time VILOAM, including support for AI modules for obstacle detection, emergency landing zone detection that typically occurs in field robotic applications such as last-mile goods delivery, surveillance and search and rescue flights. Field validation results for the payload unit are provided by running the developed VILOAM algorithm, as well as state-of-the-art VILOAM algorithms, and evaluating their performance in real-time localization and mapping on both platforms.

Continuous and unobtrusive monitoring of daily human activities in homes can potentially improve the quality of life and prolong independent living for the elderly and people with chronic diseases by recognizing normal daily activities and detecting gradual changes in their conditions. However, existing human activity recognition (HAR) solutions employ wearable and video-based sensors, which either require dedicated devices to be carried by the user or raise privacy concerns. Radar sensors enable non-intrusive long-term monitoring, while they can exploit existing communication systems, e.g., Wi-Fi, as illuminators of opportunity. This survey provides an overview of passive radar system architectures, signal processing techniques, feature extraction, and machine learning's role in HAR applications. Moreover, it points out challenges in wireless human activity sensing research like robustness, privacy, and multiple user activity sensing and suggests possible future directions, including the coexistence of sensing and communications and the construction of open datasets.

Over-the-air (OTA) computing has emerged as a promising technique that utilizes the superposition property of the wireless multiple access channel (MAC) as a means for computation. In this work, we propose a deep learning-based mechanism that approximates the pre- and post-processing functions of OTA computing, with the ultimate goal of approximating any desired target function. Specifically, we adopt a centralized training-decentralized execution approach that allows independent execution of deep neural networks (DNNs) on both devices and server to interpret the pre- and post-processing functions. The analysis is extended to the case of representing the pre- and post-processing functions to a higher dimensional space, further facilitating the reconstruction of the target function. To evaluate the effectiveness of the proposed method, we introduce a benchmark that serves as a lower bound on the computational distortion, i.e., the average mean square error (MSE) between the target function and the OTA computing estimation, which is described by closed-form solutions. It is noteworthy that the considered benchmark can serve as a reference point for any OTA computing-based application with any target function. Furthermore, the performance of the proposed decentralized DNN over-the-air computing execution (DOTACE) is evaluated through simulations, demonstrating its potential.

The prevalence of unforeseen floods has heightened the need for more accurate flood simulation and forecasting models. Even though forecast stations are expanding across the United States, the coverage is usually limited to major rivers and urban areas. Most rural and sub-urban areas, including recreational areas such as the Window Cliffs State Natural Area, do not have such forecast stations and as such, are prone to the dire effects of unforeseen flooding. In this study, four machine learning model architectures were developed based on the long short-term memory, random forest, and support vector regression techniques to forecast water depths at the Window Cliffs State Natural Area, located within the Cane Creek watershed in Putnam County, Tennessee. Historic upstream and downstream water levels and absolute pressure were used to forecast the future water levels downstream of the Cane Creek watershed. The models were tested with lead times of 3, 4, 5, and 6 h, revealing that the model performances reduced with an increase in lead time. Even though the models yielded low errors of 0.063–0.368 ft MAE, there was an apparent delay in predicting the peak water depths. However, including rainfall data in the forecast showed a promising improvement in the models’ performance. Tests conducted on the Cumberland River in Tennessee showed a promising improvement in model performance when trained with larger data.

The structure of fibrous assemblies is highly complex, being both random and regular at the same time, which leads to the complexity of its mechanical behaviour. Using algorithms such as machine learning to process complex mechanical property data requires consideration and understanding of its information principles. There are many different methods and instruments for measuring flexible material mechanics, and many different mechanics models exist. There is a need for an evaluation method to determine how close the results they obtain are to the real material mechanical behaviours. This paper considers and investigates measurements, data, models and simulations of fabric’s low-stress mechanics from an information perspective. The simplification of measurements and models will lead to a loss of information and, ultimately, a loss of authenticity in the results. Kolmogorov complexity is used as a tool to analyse and evaluate the algorithmic information content of multivariate nonlinear relationships of fabric stress and strain. The loss of algorithmic information content resulting from simplified approaches to various material measurements, models and simulations is also evaluated. For example, ignoring the friction hysteresis component in the material mechanical data can cause the model and simulation to lose more than 50% of the algorithm information, whilst the average loss of information using uniaxial measurement data can be as high as 75%. The results of this evaluation can be used to determine the authenticity of measurements and models and to identify the direction for new measurement instrument development and material mechanics modelling. It has been shown that a vast number of models, which use unary relationships to describe fabric behaviour and ignore the presence of frictional hysteresis, are inaccurate because they hold less than 12% of real fabric mechanics data. The paper also explores the possibility of compressing the measurement data of fabric mechanical properties.

The deployment of maritime communication networks (MCNs) enables Internet-of-Things (IoT) applications, related to autonomous navigation, offshore facilities and smart ports. Still, the majority of maritime nodes, residing in MCNs lacks reliable connectivity. Towards this end, integrating unmanned aerial vehicles (UAVs) in sixth generation (6G) MCN topologies results in the formation of an aerial segment, complementing shore base stations that may offer insufficient coverage, and satellite communication, characterized by increased delays. In this study, we focus on an MCN where the direct links towards a shore BS are not available, due to excessive fading conditions. For this case, we use a UAV swarm to provide improved wireless connectivity, adopting non-orthogonal multiple access (NOMA) for high resource efficiency. In downlink communication, UAVs take into consideration the desired service rate and the channel quality of their links towards the maritime nodes. In the uplink, UAVs employ dynamic decoding ordering to enhance the performance of successive interference cancellation, avoiding fixed ordering of the maritime nodes’ signals. Moreover, to ensure highly flexible UAV selection, UAVs are equipped with buffers to store data. Performance comparisons show that the UAV swarm-aided MCN enjoys increased average sum-rate by relying on multi-criteria-based interference cancellation and buffer-aided UAVs, over other benchmark schemes in the downlink and uplink. Finally, the delay-aware nature of the proposed algorithms where the UAV-destination links are prioritized, leads to reduced average delay.

The requirement of the upcoming sixth-generation (6G) wireless communication systems to significantly elevate the services of enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC) necessitates the design and investigation of appropriate multiple access schemes. This paper investigates the impact of random source deployment on the performance of uplink systems, emphasizing the implications of non-orthogonality in contention-free and contention-based access schemes. For the latter scheme, we combine the strengths of slotted ALOHA and successive interference cancellation. Considering the advantages of breaking orthogonality in scenarios with random source deployment, we propose distinct policies tailored for mMTC, eMBB, and hybrid mMTC-eMBB scenarios by splitting the cell into rings. Furthermore, we derive closed-form expressions for the outage probability, which play a pivotal role in extracting the throughput of the sources in the considered scenarios. The paper offers a comprehensive analysis of the proposed approach, corroborated through simulations, shedding light on the potential of such protocols in future 6G wireless communication systems.

The Solar wind Magnetosphere Ionosphere Link Explorer (SMILE) is a joint mission of the European Space Agency (ESA) and the Chinese Academy of Sciences (CAS). Primary goals are investigating the dynamic response of the Earth's magnetosphere to the solar wind (SW) impact via simultaneous in situ magnetosheath plasma and magnetic field measurements, X-Ray images of the magnetosheath and magnetic cusps, and UV images of global auroral distributions. Magnetopause deformations associated with magnetosheath high speed jets (HSJs) under a quasi-parallel interplanetary magnetic field condition are studied using a three-dimensional (3-D) global hybrid simulation. Soft X-ray intensity calculated based on both physical quantities of solar wind proton and oxygen ions is compared. We obtain key findings concerning deformations at the magnetopause: (1) Magnetopause deformations are highly coherent with the magnetosheath HSJs generated at the quasi-parallel region of the bow shock, (2) X-ray intensities estimated using solar wind \begin{document}${\rm{H}}^+$\end{document} and self-consistent \begin{document}${\rm{O}}^{7+}$\end{document} ions are consistent with each other, (3) Visual spacecraft are employed to check the discrimination ability for capturing magnetopause deformations on Lunar and polar orbits, respectively. The SMILE spacecraft on the polar orbit could be expected to provide opportunities for capturing the global geometry of the magnetopause in the equatorial plane. A striking point is that SMILE has the potential to capture small-scale magnetopause deformations and magnetosheath transients, such as HSJs, at medium altitudes on its orbit. Simulation results also demonstrate that a lunar based imager (e.g., Lunar Environment heliospheric X-ray Imager, LEXI) is expected to observe a localized brightening of the magnetosheath during HSJ events in the meridian plane. These preliminary results might contribute to the pre-studies for the SMILE and LEXI missions by providing qualitative and quantitative soft X-ray estimates of dayside kinetic processes.

Epilepsy is a common neurological disorder that affects millions of people worldwide, disrupting brain networks and causing recurrent seizures. In this regard, investigating the distinctive characteristics of brain connectivity is crucial to understanding the underlying neural processes of epilepsy. However, the various graph-theory frameworks and different estimation measures may yield significant variability among the results of different studies. On this premise, this study investigates the brain network topological variations between patients with temporal lobe epilepsy (TLE) and extratemporal lobe epilepsy (ETLE) using both directed and undirected network connectivity methods as well as different graph-theory metrics. Our results reveal distinct topological differences in connectivity graphs between the two epilepsy groups, with TLE patients displaying more disassortative graphs at lower density levels compared to ETLE patients. Moreover, we highlight the variations in the hub regions across different network metrics, underscoring the importance of considering various centrality measures for a comprehensive understanding of brain network dynamics in epilepsy. Our findings suggest that the differences in brain network organization between TLE and ETLE patients could be attributed to the unique characteristics of each epilepsy type, offering insights into potential biomarkers for type-specific epilepsy diagnosis and treatment.

Objective: Northern Finland has a unique distribution of clefts compared to the rest of Europe and Finland. This may reflect the need for orthognathic surgery among Northern Finland’s patient pool. The aim of this study was to compare previously operated patients aged 18 years or older with cleft lip, cleft lip and alveolus, cleft lip and palate, cleft palate and to evaluate the need for orthognathic surgery in order to achieve a stable and functional occlusion. Materials and methods: The study group consisted of all 18-years-old cleft patients treated in the Oulu Cleft Center. The total amount of patients was 110. The patients were compared retrospectively using patients’ hospital records. The majority of patients did not have any cleft-associated syndrome. The need for maxillary or bimaxillary orthognathic or corrective-jaw surgery was evaluated by the Oulu Cleft Team. A descriptive and statistical analysis was used to determine the need for orthognathic surgery according to sex and cleft type. Results: There were nineteen patients of the total of 110 patients who met the criteria requiring corrective-jaw surgery (17,3%). In total 12 males (25,0%) and 7 females (11,3%) were evaluated for the need of orthognathic surgery. Sixteen of the 19 patients had palatal involvement of the cleft. Conclusions: The need for orthognathic surgery was greater in the cleft lip palate and cleft palate patient groups compared to cleft lip alveolus or cleft lip groups. This study also found that males from Northern Finland need surgery more often than females.

Purpose: To a large extent, the recent global instability is responsible for the rapid structural mutation of the career landscape, thereby affecting individuals’ career development, especially expatriates who are subjected to an acculturation process. Even though the cultural element exists in the career development literature, the implications of expatriate acculturation (EA) on individual expatriates’ career competence (CC) and career sustainability (CS) remain understudied. Therefore, this study proposes to fill this gap in literature by exploring the intrinsic role EA plays in regulating the CC–CS interactions. Design/methodology/approach: For a more comprehensive analysis, we attempt to identify the effect on each dimension of CC, thus Reflective, Social, and Behavioural competencies (RC, SC, and BC), in the case of Chinese University postgraduate alumni from Africa. We conducted a double-layer analysis with 389 respondents’ data samples. Findings/results: Results reveal that, at the aggregate level, the positive relationship between CC and CS is weakened by EA in the case of African postgraduates who studied at Chinese universities. The detailed analyses reveal that the negative effect of EA significantly undermines the RC–CS mechanism, but significantly strengthens the BC–CS interaction. Besides, the SC–CS mechanism is not significantly moderated by EA despite the negative acculturation tendencies. Practical implications: From the perspective of international interdependence, this research elucidates the magnitude and diversity of expatriates’ contribution to the host country’s career ecosystem. Originality/value: Investigating from the career construction theory (CCT) perspective, this article aims to fill this gap by finding the moderating effect of EA on the CC–CS mechanisms.

Abstract Genetic variation in Arctic species is often influenced by vicariance during the Pleistocene, as ice sheets fragmented the landscape and displaced populations to low‐ and high‐latitude refugia. The formation of secondary contact or suture zones during periods of ice sheet retraction has important consequences on genetic diversity by facilitating genetic connectivity between formerly isolated populations. Brant geese (Branta bernicla) are a maritime migratory waterfowl (Anseriformes) species that almost exclusively uses coastal habitats. Within North America, brant geese are characterized by two phenotypically distinct subspecies that utilize disjunct breeding and wintering areas in the northern Pacific and Atlantic. In the Western High Arctic of Canada, brant geese consist of individuals with an intermediate phenotype that are rarely observed nesting outside this region. We examined the genetic structure of brant geese populations from each subspecies and areas consisting of intermediate phenotypes using mitochondrial DNA (mtDNA) control region sequence data and microsatellite loci. We found a strong east–west partition in both marker types consistent with refugial populations. Within subspecies, structure was also observed at mtDNA while microsatellite data suggested the presence of only two distinct genetic clusters. The Western High Arctic (WHA) appears to be a secondary contact zone for both Atlantic and Pacific lineages as mtDNA and nuclear genotypes were assigned to both subspecies, and admixed individuals were observed in this region. The mtDNA sequence data outside WHA suggests no or very restricted intermixing between Atlantic and Pacific wintering populations which is consistent with published banding and telemetry data. Our study indicates that, although brant geese in the WHA are not a genetically distinct lineage, this region may act as a reservoir of genetic diversity and may be an area of high conservation value given the potential of low reproductive output in this species.

Abstract Background In sub-Saharan Africa, more women than men access HIV testing and treatment and may have better viral load suppression (VLS). We utilized routinely reported aggregated HIV program data from 21 sub-Saharan African countries to examine sex differences in VLS and death rates within antiretroviral therapy (ART) programs supported by the United States President's Emergency Plan for AIDS Relief (PEPFAR). Methods We included VLS and reported death data for persons aged 15 + years on ART from October–December 2020 disaggregated by sex and age for each subnational unit (SNU). We used linear mixed-model regression to estimate VLS proportion and negative binomial mixed-model regression to estimate the rates of death and death plus interruptions in treatment (IIT). All models were weighted for SNU-level ART population size and adjusted for sex, age, HIV/tuberculosis coinfection, country, and SNU; models for reported deaths and deaths plus IIT were also adjusted for SNU-level VLS. Results Mean VLS proportion was higher among women than men (93.0% vs. 92.0%, p-value

Cerebellar, hippocampal, and basal nuclei transient edema with restricted diffusion (CHANTER) syndrome is a constellation of specific imaging findings characterized by cytotoxic edema in the bilateral hippocampi, cerebellar cortices, and basal ganglia in patients presenting with altered mental status in the setting of substance intoxication. Previous case reports have demonstrated a strong correlation between CHANTER syndrome and polysubstance abuse, particularly with opioid intoxication. The patient we present in this case was found unresponsive following opioid use and demonstrated a constellation of findings on initial and follow-up imaging, consistent with CHANTER syndrome. While cases of irreversible brain damage or death during hospitalization have been reported in the literature, our patient demonstrated near-full recovery a few days after admission to the hospital. We aim to highlight the presentation and progression of CHANTER syndrome and alert clinicians and radiologists to include this entity in their diagnostic checklist for patients with polysubstance abuse and altered mental status.

Mental fatigue during driving poses significant risks to road safety, necessitating accurate assessment methods to mitigate potential hazards. This study explores the impact of individual variability in brain networks on driving fatigue assessment, hypothesizing that subject-specific connectivity patterns play a pivotal role in understanding fatigue dynamics. By conducting a linear regression analysis of subject-specific brain networks in different frequency bands, this research aims to elucidate the relationships between frequency-specific connectivity patterns and driving fatigue. As such, an EEG sustained driving simulation experiment was carried out, estimating individuals’ brain networks using the Phase Lag Index (PLI) to capture shared connectivity patterns. The results unveiled notable variability in connectivity patterns across frequency bands, with the alpha band exhibiting heightened sensitivity to driving fatigue. Individualized connectivity analysis underscored the complexity of fatigue assessment and the potential for personalized approaches. These findings emphasize the importance of subject-specific brain networks in comprehending fatigue dynamics, while providing sensor space minimization, advocating for the development of efficient mobile sensor applications for real-time fatigue detection in driving scenarios.

Abstract Despite the widespread interest in electrospinning technology, very few simulation studies have been conducted. Thus, the current research produced a system for providing a sustainable and effective electrospinning process by combining the design of experiments with machine learning prediction models. Specifically, in order to estimate the diameter of the electrospun nanofiber membrane, we developed a locally weighted kernel partial least squares regression (LW-KPLSR) model based on a response surface methodology (RSM). The accuracy of the model's predictions was evaluated based on its root mean square error (RMSE), its mean absolute error (MAE), and its coefficient of determination (R 2). In addition to principal component regression (PCR), locally weighted partial least squares regression (LW-PLSR), partial least square regression (PLSR), and least square support vector regression model (LSSVR), some of the other types of regression models used to verify and compare the results were fuzzy modelling and least square support vector regression model (LSSVR). According to the results of our research, the LW-KPLSR model performed far better than other competing models when attempting to forecast the membrane's diameter. This is made clear by the much lower RMSE and MAE values of the LW-KPLSR model. In addition, it offered the highest R2 values that could be achieved, reaching 0.9989.

Background & Aims: Metabolic dysfunction-associated steatotic liver disease (MASLD) has become the most prevalent chronic liver disorder, with no approved treatment. Our previous work demonstrated the efficacy of a pan-ErbB inhibitor, Canertinib, in reducing steatosis and fibrosis in a murine fast-food diet (FFD) model of MASLD. The current study explores the effects of hepatocyte-specific ErbB1 (ie, epidermal growth factor receptor [EGFR]) deletion in the FFD model. Methods: EGFRflox/flox mice, treated with AAV8-TBG-CRE to delete EGFR specifically in hepatocytes (EGFR-KO), were fed either a chow-diet or FFD for 2 or 5 months. Results: Hepatocyte-specific EGFR deletion reduced serum triglyceride levels but did not prevent steatosis. Surprisingly, hepatic fibrosis was increased in EGFR-KO mice in the long-term study, which correlated with activation of transforming growth factor-β/fibrosis signaling pathways. Further, nuclear levels of some of the major MASLD regulating transcription factors (SREBP1, PPARγ, PPARα, and HNF4α) were altered in FFD-fed EGFR-KO mice. Transcriptomic analysis revealed significant alteration of lipid metabolism pathways in EGFR-KO mice with changes in several relevant genes, including downregulation of fatty-acid synthase and induction of lipolysis gene, Pnpla2, without impacting overall steatosis. Interestingly, EGFR downstream signaling mediators, including AKT, remain activated in EGFR-KO mice, which correlated with increased activity pattern of other receptor tyrosine kinases, including ErbB3/MET, in transcriptomic analysis. Lastly, Canertinib treatment in EGFR-KO mice, which inhibits all ErbB receptors, successfully reduced steatosis, suggesting the compensatory roles of other ErbB receptors in supporting MASLD without EGFR. Conclusions: Hepatocyte-specific EGFR-KO did not impact steatosis, but enhanced fibrosis in the FFD model of MASLD. Gene networks associated with lipid metabolism were greatly altered in EGFR-KO, but phenotypic effects might be compensated by alternate signaling pathways.

Abstract The evaporation of particle‐free silver ink droplets on heated substrates directly impacts the morphology of the resultant silver particles and electrically conductive inkjet‐printed films. In this work, COMSOL Multiphysics simulations of the droplet evaporation process are used to help explain the fluidic processes responsible for the experimental observations. The results show that the silver particle morphology depends on its location within the evaporating droplet where large particles (3 to 5 µm) accumulate at the center and smaller ones (1 to 2 µm) in the area beyond the central region. The smallest particles (≈500 nm) are near the contact line. Without the fluoro‐surfactant FS‐31, an inner ring with 3 µm particles also appears within the evaporating droplet. The large particles at the central region originate from the fluid near the droplet's apex due to the accumulation of polyacrylamide in the concentrated ethylene glycol solvent near the flow‐abundant apex. Furthermore, the capillary flow along the fluid–air interface transports silver ions and particles throughout the droplet, causing the larger particles to fall onto the substrate at the central vortex, forming the inner ring. The simulations also demonstrate that the evaporating droplet heterogeneously accumulates ethylene glycol in a sandhill formation at the bottom center.

ABSTRACTAntibody-mediated effector functions are widely considered to unfold according to an associative model of IgG-Fcγ receptor (FcγR) interactions. The associative model presupposes that Fc receptors cannot discriminate antigen-bound IgG from free IgG in solution and have equivalent affinities for each. Therefore, the clustering of Fcγ receptors (FcγR) in the cell membrane, cross-activation of intracellular signaling domains, and the formation of the immune synapse are all the result of avid interactions between the Fc region of IgG and FcγRs that collectively overcome the individually weak, transient interactions between binding partners. Antibody allostery, specifically conformational allostery, is a competing model in which antigen-bound antibody molecules undergo a physical rearrangement that causes them to stand out from the background of free IgG by virtue of greater FcγR affinity. Various evidence exists in support of this model of antibody allostery, but it remains controversial. We report observations from multiplexed, label-free kinetic experiments in which the affinity values of FcγR were characterized for covalently immobilized, captured, and antigen-bound IgG. Across the strategies tested, receptors had greater affinity for the antigen-bound mode of IgG presentation. This phenomenon was observed across multiple FcγRs and generalized to multiple antigens, antibody specificities, and subclasses. Furthermore, the thermodynamic signatures of FcγR binding to free or immune-complexed IgG in solution differed when measured by an orthogonal label-free method, but the failure to recapitulate the trend in overall affinity leaves open questions as to what additional factors may be at play.

Saffron is a spice derived from the flower of Crocus sativus used as a flavoring and coloring agent in the food industry which also possesses medicinal properties. In the current study, the optimum Solid Phase Extraction (SPE) conditions for separating picrocrocin and crocins from aqueous saffron extracts were investigated, using Rotatable-Central Composite Design (RCCD) in combination with Response Surface Methodology (RSM). The optimized factors were volume of saffron extract (3 mL), elution solvent (15% v/v ACN/water for picrocrocin; and 50% v/v ACN/water for crocins), and volume of elution solvent (ACN/water) (10 mL). The response factor measured was the UV-Vis absorbance. The presence of picrocrocin and crocins in the solutions obtained from SPE was confirmed using Ultra-Performance Liquid Chromatography Quadrupole Time-of-Flight Mass Spectrometry (UPLC-QToF-MS). The above optimized SPE procedure provides economy in reagents and consumables.

This study aimed to examine whether we could use the discrepancy consistency method on CAS-2: Brief data collected in Cyprus. A total of 438 Grade 6 children (201 boys, 237 girls, Mage = 135.75 months, SD = 4.05 months) from Cyprus were assessed on the Cognitive Assessment System-2: Brief that is used to operationalize four neurocognitive processes, namely Planning, Attention, Simultaneous, and Successive (PASS) processing. They were also assessed on two measures of reading (Wordchains and CBM-Maze) and mathematics (Mathematics Achievement Test and Mathematics Reasoning Test). The results showed that 31.5% of our sample had a PASS disorder, and 8% to 10% of our sample had both a PASS disorder and an academic disorder. These numbers are similar to those reported in previous studies that used DCM in North America and suggest that the method can be used to inform instruction, particularly in places where no screening for learning disabilities is available.

Background/Aim: Activin A is involved in the pathogenesis of human liver diseases, but its therapeutic targeting is not fully explored. Here, we tested the effect of novel, highly specific small-molecule-based activin A antagonists (NUCC-474/555) in improving liver regeneration following partial hepatectomy and halting fibrosis progression in models of chronic liver diseases (CLDs). Methods: Cell toxicity of antagonists was determined in rat hepatocytes and Huh-7 cells using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide assay. Hepatocytes and hepatic stellate cells (HSCs) were treated with activin A and NUCC-555 and analyzed by reverse transcription–polymerase chain reaction and immunohistochemistry. Partial hepatectomized Fisher (F)344 rats were treated with NUCC-555, and bromodeoxyuridine (BrdU) incorporation was determined at 18/24/36/120/240 h. NUCC-555 was administered into thioacetamide- or carbon tetrachloride-treated F344 rats or C57BL/6 mice, and the fibrosis progression was studied. Results: NUCC-474 showed higher cytotoxicity in cultured hepatic cells; therefore, NUCC-555 was used in subsequent studies. Activin A-stimulated overexpression of cell cycle-/senescence-related genes (e.g., p15INK4b, DEC1, Glb1) was near-completely reversed by NUCC-555 in hepatocytes. Activin A-mediated HSC activation was blocked by NUCC-555. In partial hepatectomized rats, antagonizing activin A signaling resulted in a 1.9-fold and 2.3-fold increase in BrdU+ cells at 18 and 24 h, respectively. Administration of NUCC-555 in rats and mice with progressing fibrosis significantly reduced collagen accumulation (7.9-fold), HSC activation indicated by reduced alpha smooth muscle actin+ and vimentin+ cells, and serum aminotransferase activity. Conclusions: Our studies demonstrate that activin A antagonist NUCC-555 promotes liver regeneration and halts fibrosis progression in CLD models, suggesting that blocking activin A signaling may represent a new approach to treating people with CLD.

Small ruminants, such as sheep (Ovisaries) and goats (Capra hircus), contribute to approximately 475 million metric tons of carbon dioxide equivalent (MtCO2e) greenhouse gas (GHG) emissions, accounting for approximately 6.5% of the global emissions in the agriculture sector. Crop residues, silage, grasses, hay, browse, plant leaves, shrubs, agro-industrial by-products, poultry litter, and other alternative feed sources are frequently utilized for small ruminant production. The use of these valuable alternative feeds can significantly improve animal productivity and reduce carbon footprints and GHG fluxes, making it both environmentally friendly and cost-effective. Additionally, these alternative feeds possess antioxidant, antimicrobial, and antiseptic properties that can enhance the quality of the meat and milk produced. By impacting the bacteria involved in ruminal biohydrogenation, alternative feeds can reduce methane emissions and contribute to a decrease in the carbon footprint. Overall, the use of alternative feed sources for small ruminants generally improves their apparent nutrient digestibility and productivity, and has an impact on the production of greenhouse gases, especially methane. Finally, this review recommends evaluating the economic analysis of reducing methane emissions in small ruminants by utilizing different feed sources and feeding techniques.

Indoor communication and positioning are significant fields of applications for indoor Internet of Things (IoT) given the rapid growth of IoT in smart cities, smart grids, and smart industries. Visible light positioning (VLP) has become more and more attractive for researchers to provide indoor location-aware IoT services. Additionally, artificial intelligence (AI) has attracted considerable research effort to address the challenges in visible-light communication (VLC) systems. This is an emerging technology in next-generation wireless networks. However, despite the rapid progress, the use of AI in localization, navigation, and position estimation is still underexplored in VLC systems, and various research challenges are still open. This methodological review summarizes the research efforts regarding the use of AI in the field of VLP, to improve the position estimation accuracy in both two-dimensional (2D) and three-dimensional (3D) indoor IoT applications. This treatise also presents open issues and potential future directions for motivating further research in the field. Various databases were utilized in this paper: Scopus, Google Scholar, and IEEE Xplore; obtained 88 papers from 2017 to early 2023. Most (68%) of the AI articles in VLP systems are machine learning (ML) methods applied for localization and position estimation in VLC systems, while the other 32% of the research articles focussed on evolutionary algorithms. ML and evolutionary models may present limitations in terms of complexity and time-consuming nature but offer highly accurate, robust, reliable, and cost-effective results in terms of position estimation over conventional approaches.

The rapidly growing demand for vast numbers of Internet of Things (IoT), in both urban and rural areas, necessitates their ceaseless and automatic energy supply. This is particularly vital in cases where the IoT sensors are deployed in distant or dangerous locations outside human reach. In this direction, unmanned aerial vehicles (UAVs) with wireless power transfer (WPT) capabilities can address this issue, due to their flexible deployment. To this end, we devise an architecture in which a UAV swarm covers the energy demand of an IoT network, while concurrently, the UAVs fulfil their energy needs through a charging station (CS) infrastructure. A practical energy model is considered, which takes into account the UAVs’ battery level, energy consumption due to transition to different locations, hovering, and WPT. Also, to capture the UAV-CS interaction, an economic model is introduced. The UAVs aim to maximize their profit by transferring energy to the IoT, while the CSs aim to maximize their profit by recharging the UAVs. To ensure a profit-wise stable CS-UAV association, while providing energy coverage to the IoT, we formulate a many-to-one matching game. Due to inter-dependencies between UAVs’ utilities, i.e., externalities, a matching algorithm with two-sided exchange-stability is proposed. To further evaluate the considered system, we design an optimization scheme which performs the UAV-CS assignment towards maximizing the energy coverage of the IDs. Numerical results showcase the matching algorithm’s ability to provide near-optimal energy coverage to the IDs, while balancing fairness among the competing agents’ profit, compared to the optimization scheme.

Owing to the breakthrough of artificial meta-materials, the emergence and evolution of reconfigurable intelligent surface (RIS) have drawn a novel blueprint for the development of the sixth-generation (6G) wireless networks, through the creation of smart radio environment to meet the requirement of ubiquitous connectivity. Meanwhile, the vision for the 6G illuminates the integration of sensing and communication, bringing out a demand for both high-quality communication and high-precision localization. Therefore, regarding the future wireless localization, the potential of RIS to tag the signals provides additional degrees of freedom. To this end, to fulfil the increasing demands for positioning, RIS has drawn a growing amount of research both from academia and industry. In this paper, we first introduce the emerging investigations and development of RIS, and we give a concise summary of the localization principles. Then we explain the potential of RIS in localization, and provide a comprehensive survey of the current state of research on RIS-assisted localization. Finally, we discuss the most significant research challenges to tackle for RIS-aided localization in the future.

Recently, there has been an ever-growing demand for virtual reality (VR) and 360° video applications. Different from conventional 2D videos, 360° videos take users into an immersive experience by providing them with a navigable panoramic view. However, achieving adequate quality of experience (QoE) levels poses significant network challenges, especially in mobile delivery setups. Despite the tremendous improvements offered by 5G and beyond mobile networks, streaming 360° videos in a similar fashion to 2D videos is suboptimal, while scaling at high numbers questions the feasibility of the endeavor. This paper explores the utilization of caching and multicasting solutions for the mobile delivery of VR and 360° videos. First, an overview of immersive technologies and their distinctive characteristics is provided. Then, we discuss the network challenges associated with 360° videos and the role of implementing robust caching and multicasting schemes that exploit the unique features of 360° videos and capitalize on the correlations among end-users’ viewports. Having established the foundations and challenges of 360° video streaming, we continue with a comparison of the state-of-the-art literature, while focusing on video streaming optimization aspects. We conclude our work by discussing the status and future research directions.

Digital twin-aided (DT) edge computing is investigated, where users utilize grant-free random access with adaptive rate to offload their tasks to the edge server. A novel, with lower implementation complexity, probabilistic partial offloading scheme is introduced, while each device is assumed to have an infinite buffer to store its tasks. The aim of the proposed work is to minimize the average delay of the partial offloading. To that end, the average delay of waiting in the queue, the delay of offloading, and the local computation delay are extracted by using queuing theory tools. Then, the non-convex problem of minimizing the average delay of all clients is formulated, while taking into account DT imperfections. Successive convex approximation (SCA), alternating optimization (AO), and various algebraic manipulations are utilized to transform the problem into an equivalent convex problem with tractable solution. Finally, simulation results showcase the value of the proposed analysis and offer important insights for the proposed DT-aided edge network. Specifically, the proposed partial offloading scheme is shown to be more delay efficient compared to both local computing and full offloading, particularly, for greater task generation rates at the users. Also, the impact of the DT imperfections at the average delay is shown to be more notable as the number of users, or the tasks’ size, increases.

In this work, we talk about the problem of joint power allocation and user association based on quality-of-service for non-orthogonal multiple access (NOMA) to downlink networks. The problem is especially difficult due to its non-convex form and the large number of optimization variables, which are solved using two different nature-inspired algorithms with low complexity. We investigate the effect of different network parameters on increasing users. Numerical results show that, for a growing number of users, the problem is becoming increasingly difficult, which indicates the increasing network resources required to solve it. The results of the simulations show that using evolutionary algorithms is a fast and effective way to solve this kind of problem. Moreover, the NOMA advantage over OMA becomes clear as the number of users increases. Evolutionary techniques outperform randomly generated solutions, as expected.

Extreme learning machine (ELM) uses a simple machine learning (ML) architecture that allows its implementation for smart IoT network operations. However, some inaccuracy lies between the predicted and the actual data during the ELM training, which can be caused due to the limitation of the modeling representation. This paper thus investigates a residual compensation-based ELM (RC-ELM) for its application in designing a receiver for MIMO-NOMA aided IoT systems. In RC-ELM, the base ELM layers determine the relationship between the transmitted and received data and the additional layers of the RC-ELM attempt to compensate the error introduced during the training mechanism. The analysis of the appropriate number of compensation layers for training error minimization is conducted on the basis of the bit error rate (BER) and the error vector magnitude (EVM) performances of the RC-ELM training. A minimum BER improvement of 5% for user 1 and 18% for user 2 is shown with with the aid of RC-ELM for a two-IoT user instance. The EVM is marginally increased by 0.0008% for user 1 and 1.61% for user 2 in training stage. Besides, the RC-ELM receiver is also compared to the minimum mean square error (MMSE), the classic ELM and the trained multilayer perceptron (MLP) receivers in terms of BER and EVM. Both of the ELM receivers show improved performances with respect to the other receivers.

Abstract Background Breast cancer mortality is principally due to recurrent disease that becomes resistant to therapy. We recently identified copy number (CN) gain of the putative membrane progesterone receptor PAQR8 as one of four focal CN alterations that preferentially occurred in recurrent metastatic tumors compared to primary tumors in breast cancer patients. Whether PAQR8 plays a functional role in cancer is unknown. Notably, PAQR8 CN gain in recurrent tumors was mutually exclusive with activating ESR1 mutations in patients treated with anti-estrogen therapies and occurred in > 50% of both patients treated with anti-estrogen therapies and those treated with chemotherapy or anti-Her2 agents. Methods We used orthotopic mouse models to determine whether PAQR8 overexpression or deletion alters breast cancer dormancy or recurrence following therapy. In vitro studies, including assays for colony formation, cell viability, and relative cell fitness, were employed to identify effects of PAQR8 in the context of therapy. Cell survival and proliferation were quantified by immunofluorescence staining for markers of apoptosis and proliferation. Sphingolipids were quantified by liquid chromatography-high resolution mass spectrometry. Results We show that PAQR8 is necessary and sufficient for efficient mammary tumor recurrence in mice, spontaneously upregulated and CN gained in recurrent tumors that arise following therapy in multiple mouse models, and associated with poor survival following recurrence as well as poor overall survival in breast cancer patients. PAQR8 promoted resistance to therapy by enhancing tumor cell survival following estrogen receptor pathway inhibition by fulvestrant or estrogen deprivation, Her2 pathway blockade by lapatinib or Her2 downregulation, and treatment with chemotherapeutic agents. Pro-survival effects of PAQR8 were mediated by a Gi protein-dependent reduction in cAMP levels, did not require progesterone, and involved a PAQR8-dependent decrease in ceramide levels and increase in sphingosine-1-phosphate levels, suggesting that PAQR8 may possess ceramidase activity. Conclusions Our data provide in vivo evidence that PAQR8 plays a functional role in cancer, implicate PAQR8, cAMP, and ceramide metabolism in breast cancer recurrence, and identify a novel mechanism that may commonly contribute to the acquisition of treatment resistance in breast cancer patients.

Muscular dystrophies are a group of rare and severe inherited disorders mainly affecting the muscle tissue. Duchene Muscular Dystrophy, Myotonic Dystrophy types 1 and 2, Limb Girdle Muscular Dystrophy and Facioscapulohumeral Muscular Dystrophy are some of the members of this family of disorders. In addition to the current diagnostic tools, there is an increasing interest for the development of novel non-invasive biomarkers for the diagnosis and monitoring of these diseases. miRNAs are small RNA molecules characterized by high stability in blood thus making them ideal biomarker candidates for various diseases. In this study, we present the first genome-wide next-generation small RNA sequencing in serum samples of five different types of muscular dystrophy patients and healthy individuals. We identified many small RNAs including miRNAs, lncRNAs, tRNAs, snoRNAs and snRNAs, that differentially discriminate the muscular dystrophy patients from the healthy individuals. Further analysis of the identified miRNAs showed that some miRNAs can distinguish the muscular dystrophy patients from controls and other miRNAs are specific to the type of muscular dystrophy. Bioinformatics analysis of the target genes for the most significant miRNAs and the biological role of these genes revealed different pathways that the dysregulated miRNAs are involved in each type of muscular dystrophy investigated. In conclusion, this study shows unique signatures of small RNAs circulating in five types of muscular dystrophy patients and provides a useful resource for future studies for the development of miRNA biomarkers in muscular dystrophies and for their involvement in the pathogenesis of the disorders.

As the pioneer in the intelligent construction technologies (ICT) of transportation infrastructure, intelligent compaction (IC) has been applied in the infrastructure construction of various countries. It is currently the technology that best reflects the intelligence of engineering construction. This article overviews the latest developments and trends in IC. Firstly, the basic meaning of ICT is defined based on the essential characteristics of intelligent construction of transportation infrastructure, “perception, analysis, decision-making, execution” (PADE). The concept of intelligent compaction technology classification is also introduced. The PADE requirements that intelligent compaction should meet are proposed. Secondly, according to the sequence of “perception, analysis, decision-making, execution,” the workflow and key technologies of intelligent compaction are analyzed, and the mechanism of using the response of the roller to solve the modulus is given and verified. On this basis, The IC feasibility test methods, including compaction degree, compaction stability, and compaction uniformity, are briefly described. The implementation scheme of feedback control is given. Then, the use of artificial neural networks (ANN), hybrid expert systems, and reinforcement learning in intelligent compaction are briefly introduced. Finally, several extended applications of intelligent compaction are expounded, including the development ideas of intelligent road rollers and the role of intelligent compaction in virtual construction, the layer-specific mechanical parameters of fillers, etc.

Membrane materials might be used for face protection because they can decontaminate the inhaled air from particle pollution and viruses like the SARS-Cov0-2 which damages our respiration system. In this study, plyethersulfone membranes (PES) were synthesized with green solvent at room temperature and its filtration effectiveness was investigated against nano-bacteria (size 0.05 to 0.2 µm) by measuring their Bacterial Filtration Efficiency (BFE) and micro aerosol size (0.3 µm), and Particulate Filtration Efficiency (PFE). The average SARS-CoV-2 diameters are between 50 nm to 160 nm. A series of experiments were performed to accomplish between 0.03 to 0.21 µm PES sponge like diameters so that can be used for SARS-CoV-2 filtration. Results showed that nanofiltration/ultrafiltration could filter 99.9% of bacteria and aerosol from contaminated air the size of the Covid-19 molecule.