ahmedabdelwahed/Medical_papers_title_and_abstract_NLP_dataset huggingface dataset:
Exploring the influence of COVID-19 on the spread of hand, foot, and mouth disease with an automatic machine learning prediction model

Hand, foot, and mouth disease (HFMD) is an important public health problem and has received concern worldwide. Moreover, the coronavirus disease 2019 (COVID-19) epidemic also increases the difficulty of understanding and predicting the prevalence of HFMD. The purpose is to prove the usability and applicability of the automatic machine learning (Auto-ML) algorithm in predicting the epidemic trend of HFMD and to explore the influence of COVID-19 on the spread of HFMD. The AutoML algorithm and the autoregressive integrated moving average (ARIMA) model were applied to construct and validate models, based on the monthly incidence numbers of HFMD and meteorological factors from May 2008 to December 2019 in Henan province, China. A total of four models were established, among which the Auto-ML model with meteorological factors had minimum RMSE and MAE in both the model constructing phase and forecasting phase (training set: RMSE = 1424.40 and MAE = 812.55; test set: RMSE = 2107.83, MAE = 1494.41), so this model has the best performance. The optimal model was used to further predict the incidence numbers of HFMD in 2020 and then compared with the reported cases. And, for analysis, 2020 was divided into two periods. The predicted incidence numbers followed the same trend as the reported cases of HFMD before the COVID-19 outbreak; while after the COVID-19 outbreak, the reported cases have been greatly reduced than expected, with an average of only about 103 cases per month, and the incidence peak has also been delayed, which has led to significant changes in the seasonality of HFMD. Overall, the AutoML algorithm is an applicable and ideal method to predict the epidemic trend of the HFMD. Furthermore, it was found that the countermeasures of COVID-19 have a certain influence on suppressing the spread of HFMD during the period of COVID-19. The findings are helpful to health administrative departments.
Ownership psychology as a cognitive adaptation: A minimalist model

Ownership is universal and ubiquitous in human societies, yet the psychology underpinning ownership intuitions is generally not described in a coherent and computationally tractable manner. Ownership intuitions are commonly assumed to derive from culturally transmitted social norms, or from a mentally represented implicit theory. While the social norms account is entirely ad hoc, the mental theory requires prior assumptions about possession and ownership that must be explained. Here I propose such an explanation, arguing that the intuitions result from the interaction of two cognitive systems. One of these handles competitive interactions for the possession of resources observed in many species including humans. The other handles mutually beneficial cooperation between agents, as observed in communal sharing, collective action and trade. Together, these systems attend to specific cues in the environment, and produce definite intuitions such as "this is hers", "that is not mine". This computational model provides an explanation for ownership intuitions, not just in straightforward cases of property, but also in disputed ownership (squatters, indigenous rights), historical changes (abolition of slavery), as well as apparently marginal cases, such as the questions, whether people own their seats on the bus, or their places in a queue, and how people understand "cultural appropriation" and slavery. In contrast to some previous theories, the model is empirically testable and free of ad hoc stipulations.
Efficient and High-Purity Sound Frequency Conversion with a Passive Linear Metasurface

Despite the significance for wave physics and potential applications, high-efficiency frequency conversion of low-frequency waves cannot be achieved with conventional nonlinearity-based mechanisms with poor mode purity, conversion efficiency, and real-time reconfigurability of the generated harmonic waves in both optics and acoustics. Rotational Doppler effect provides an intuitive paradigm to shifting the frequency in a linear system which, however, needs a spiral-phase change upon the wave propagation. Here a rotating passive linear vortex metasurface is numerically and experimentally presented with close-to-unity mode purity (>93%) and high conversion efficiency (>65%) in audible sound frequency as low as 3000 Hz. The topological charge of the transmitted sound is almost immune from the rotational speed and transmissivity, demonstrating the mechanical robustness and stability in adjusting the high-performance frequency conversion in situ. These features enable the researchers to cascade multiple vortex metasurfaces to further enlarge and diversify the extent of sound frequency conversion, which are experimentally verified. This strategy takes a step further toward the freewheeling sound manipulation at acoustic frequency domain, and may have far-researching impacts in various acoustic communications, signal processing, and contactless detection.
State of the art of osmotic membrane bioreactors for water reclamation

In the last few years, extensive research has been dedicated to development of a novel osmotic membrane bioreactor (OMBR), which combines high-retention osmotic separation and biological reactions in a single vessel. Although promising results have been reported in the literature, some challenges associated with applications of OMBR remain unresolved at the present stage of development, including lack of a high performance forward osmosis (FO) membrane, identification of an ideal draw solute and effect of salt accumulation on biological activity. Therefore, this paper attempts to provide a comprehensive review of state of the art of OMBR for water and wastewater reclamation. (C) 2012 Elsevier Ltd. All rights reserved.
Residue behavior and risk assessment of afidopyropen and its metabolite M440I007 in tea

Afidopyropen, a novel insecticide, is highly effective against piercing insects such as the tea leafhopper. The residual levels of afidopyropen and M440I007 in tea cultivation, processing, and brewing were studied. During tea cultivation, afidopyropen dissipated faster in fresh tea shoots in the rainy season (T1/2 of 1.2-2.5 d) than that in the dry season (T1/2 of 3.1-4.4 d); afidopyropen was metabolized into M440I007, the level of which peaked in 1 d, and degraded rapidly (over 90 %) afterward 3 d. The green tea processing steps had little effect on decreasing the afidopyropen residue (PF of 0.90-1.18). Low infusion rates of afidopyropen (16.7 %-17.7 %) and M440I007 (4.1 %-6.2 %) were observed from dry green tea to infusion; furthermore, the risk of ingesting afidopyropen from drinking tea was low, with the risk quotient values < 0.0001. This study can offer guidance on the rational application of afidopyropen in tea plants.
On Exploiting Concurrent Transmissions Through Discernible Interference Cancellation

This paper represents the design, feasibility evaluation, and performance validation of ICMR, a novel cross layer protocol that can maximize concurrent transmissions and avoid data frame interference in wireless networks, achieving a higher throughput comparing with the 802.11 standard and other state-of-the-art protocols. Observations on the 802.11 standard reveal that nodes around both the transmitter and receiver of the ongoing link waste concurrent transmission opportunities, degrading the network throughput dramatically. A state-of-the-art protocol IRMA is proposed to improve the network throughput through exploiting concurrent transmissions at the transmitter side. In this paper, a new ICMR protocol focuses on the receiver side to further improve the network throughput, through exploiting discernible interference cancellation, a physical layer mechanism that can successfully detect data frames when collided by control frames. We analyze the concurrent transmission opportunities of one link from the transmitter's transmission opportunities and the receiver's reception opportunities, then formulate the opportunities and give theoretical analysis to indicate that ICMR will have a higher opportunity over other protocols. Feasibility of the discernible interference cancellation mechanism is demonstrated through experiment results based on USRP2, and the throughput improvement of ICMR comparing with the other protocols is confirmed through simulations based on ns-2.
A 1.2-V Dynamic Bias Latch-Type Comparator in 65-nm CMOS With 0.4-mV Input Noise

A latch-type comparator with a dynamic bias pre-amplifier is implemented in a 65-nm CMOS process. The dynamic bias with a tail capacitor is simple to implement and ensures that the pre-amplifier output nodes are only partially discharged to reduce the energy consumption. The comparator is analyzed and compared to its prior art in terms of energy consumption and input referred noise voltage. First-order equations are presented that show how to optimize the pre-amplifier for low noise and high gain. Both the dynamic bias comparator and the prior art are implemented on the same die and measurements show that the dynamic bias can reduce the average energy consumption by about a factor 2.5 for the same input-equivalent noise at an input common-mode level of half the supply voltage.
Fusing Sorted Random Projections for Robust Texture and Material Classification

This paper presents a conceptually simple, and robust, yet highly effective, approach to both texture classification and material categorization. The proposed system is composed of three components: 1) local, highly discriminative, and robust features based on sorted random projections (RPs), built on the universal and information-preserving properties of RPs; 2) an effective bag-of-words global model; and 3) a novel approach for combining multiple features in a support vector machine classifier. The proposed approach encompasses the simplicity, broad applicability, and efficiency of the three methods. We have tested the proposed approach on eight popular texture databases, including Flickr Materials Database, a highly challenging materials database. We compare our method with 13 recent state-of-the-art methods, and the experimental results show that our texture classification system yields the best classification rates of which we are aware of 99.37% for Columbia-Utrecht, 97.16% for Brodatz, 99.30% for University of Maryland Database, and 99.29% for Kungliga Tekniska hogskolan-textures under varying illumination, pose, and scale. Moreover, the proposed approach significantly outperforms the current state-of-the-art approach in materials categorization, with an improvement to classification accuracy of 67%.
Do Appetite Traits Mediate the Link between Birth Weight and Later Child Weight in Low-Income Hispanic Families?

Background: Birth weight and appetite traits (ATs) are important early life determinants of child weight and obesity. Objectives: The aim of this study is to examine whether (1) birth weight-for-gestational age z-scores (BWGAzs) were associated with ATs at child age 2 years and (2) ATs mediated the link between BWGAzs and weight-for-age z-scores (WFAzs) at child ages 3 and 4 years among Hispanic children. Methods: We conducted a secondary longitudinal analysis of data from the Starting Early Program of low-income, Hispanic mother-child pairs. ATs were assessed using the Child Eating Behavior Questionnaire at age 2 years. Child birth weight was obtained from medical records. Birth weight, sex, and gestational age were used to generate BWGAzs with Fenton growth curves. WFAz was calculated based on the CDC 2000 growth charts. Regression and mediation analyses were used to explore associations between BWGAzs, ATs, and WFAzs. Results: Infants with higher BWGAzs had significantly lower Satiety Responsiveness (B = -0.10) and Food Fussiness (B = -0.13) scores at age 2 years and higher WFAzs at ages 3 (B = 0.44) and 4 (B = 0.34) years. Lower Satiety Responsiveness at age 2 years was associated with higher WFAzs at ages 3 (B = -0.11) and 4 (B = -0.34; all p < 0.01) years. Lower Satiety Responsiveness partially mediated the positive relationship between birth weight and child WFAzs at ages 3 and 4 years. Conclusions: Children with higher birth weight and lower Satiety Responsiveness scores may be at higher risk of developing obesity in childhood. Further research is needed to understand the mechanisms through which birth weight influences child appetite. Clinical Trial Registration: ClinicalTrials.gov: NCT01541761.
Secure Hashing-Based Verifiable Pattern Matching

Verifiable pattern matching is the problem of finding a given pattern verifiably from the outsourced textual data, which is resident in an untrusted remote server. This problem has drawn much attention due to a large number of applications. The state-of-the-art method for this problem suffers from low efficiency. To enable fast verifiable pattern matching, we propose a novel scheme in this paper. Our scheme is based on an ordered set accumulator data structure and a newly developed verifiable suffix array structure, which only involves fast cryptographic hash computations. Our scheme also supports fast multiple-occurrence pattern matching. A striking feature of our proposed scheme is that our scheme works even with no secret keys, which ensures public verifiability. We conduct extensive experiments to evaluate the proposed scheme using Java. The results show that our scheme is orders of magnitude faster than the state-of-the-art work. Specifically, our scheme with public verifiability only costs a preprocessing time of 47 s (merely one-time off-line cost during outsourcing), a search time of 30 mu s, a verification time of 149 mu s, and a proof size of 2760 bytes for a verifiable pattern matching query with pattern length 200 on 10-million long textual data which consists of sequences of two-byte, Unicode characters in Java.
Ganglioside Microdomains on Cellular and Intracellular Membranes Regulate Neuronal Cell Fate Determination

Gangliosides are sialylated glycosphingolipids (GSLs) with essential but enigmatic functions in brain activities and neural stem cell (NSC) maintenance. Our group has pioneered research on the importance of gangliosides for growth factor receptor signaling and epigenetic regulation of NSC activity and differentiation. The primary localization of gangliosides is on cell-surface microdomains and the drastic dose and composition changes during neural differentiation strongly suggest that they are not only important as biomarkers, but also are involved in modulating NSC fate determination. Ganglioside GD3 is the predominant species in NSCs and GD3-synthase knockout (GD3S-KO) revealed reduction of postnatal NSC pools with severe behavioral deficits. Exogenous administration of GD3 significantly restored the NSC pools and enhanced the stemness of NSCs with multipotency and self-renewal. Since morphological changes during neurogenesis require a huge amount of energy, mitochondrial functions are vital for neurogenesis. We discovered that a mitochondrial fission protein, the dynamin-related protein-1 (Drp1), as a novel GD3-binding protein, and GD3 regulates mitochondrial dynamics. Furthermore, we discovered that GM1 ganglioside promotes neuronal differentiation by an epigenetic regulatory mechanism. Nuclear GM1 binds with acetylated histones on the promoters of N-acetylgalactosaminyltransferase (GalNAcT; GM2 synthase) as well as on the NeuroD1 genes in differentiated neurons. In addition, epigenetic activation of the GalNAcT gene was detected as accompanied by an apparent induction of neuronal differentiation in NSCs responding to an exogenous supplement of GM1. GM1 is indeed localized in the nucleus where it can interact with transcriptionally active histones. Interestingly, GM1 could induce epigenetic activation of the tyrosine hydroxylase (TH) gene, with recruitment of nuclear receptor related 1 (Nurr1, also known as NR4A2), a dopaminergic neuron-associated transcription factor, to the TH promoter region. In this way, GM1 epigenetically regulates dopaminergic neuron specific gene expression. GM1 interacts with active chromatin via acetylated histones to recruit transcription factors at the nuclear periphery, resulting in changes in gene expression for neuronal differentiation. The significance is that multifunctional gangliosides modulate lipid microdomains to regulate functions of important molecules on multiple sites: the plasma membrane, mitochondrial membrane, and nuclear membrane. Versatile gangliosides could regulate functional neurons as well as sustain NSC functions via modulating protein and gene activities on ganglioside microdomains.
Adoptive Transfer of Renal Allograft Tolerance in a Large Animal Model

Our recent studies in an inbred swine model demonstrated that both peripheral and intra-graft regulatory cells were required for the adoptive transfer of tolerance to a second, naïve donor-matched kidney. Here, we have asked whether both peripheral and intra-graft regulatory elements are required for adoptive transfer of tolerance when only a long-term tolerant (LTT) kidney is transplanted. Nine highly-inbred swine underwent a tolerance-inducing regimen to prepare LTT kidney grafts which were then transplanted to histocompatible recipients, with or without the peripheral cell populations required for adoptive transfer of tolerance to a naïve kidney. In contrast to our previous studies, tolerance of the LTT kidney transplants alone was achieved without transfer of additional peripheral cells and without strategies to increase the number/potency of regulatory T cells in the donor. This tolerance was systemic, since most subsequent, donor-matched challenge kidney grafts were accepted. These results confirm the presence of a potent tolerance-inducing and/or tolerance-maintaining cell population within LTT renal allografts. They suggest further that additional peripheral tolerance mechanisms, required for adoptive transfer of tolerance to a naïve donor-matched kidney, depend on peripheral cells that, if not transferred with the LTT kidney, require time to develop in the adoptive host.
Retreatment of a recurrent giant aneurysm of the internal carotid artery after treatment with a flow-diverting stent

Flow-diverting stents (FDSs) have proven advantageous for the treatment of large, fusiform, and dissecting aneurysms that are otherwise difficult to treat. Retreatment strategies for recurrent large or giant aneurysms after FDSs are limited to overlapping implantation of an additional FDS or definitive occlusion of the parent vessel. We report a recurrent giant aneurysm that was initially treated with an FDS with coils and was successfully treated with an additional FDS. Visual symptoms due to the mass effect of the recurrent aneurysm were completely resolved, and follow-up digital subtraction angiography revealed complete obliteration of the aneurysm. Additional FDS implantation for the retreatment of incompletely occluded aneurysms after the initial FDS treatment may be feasible and safe. Further studies are required to validate these results.
D-galactose-induced cardiac ageing: A review of model establishment and potential interventions

Cardiovascular disease (CVD) is highly prevalent in an ageing society. The increased incidence and mortality rates of CVD are global issues endangering human health. There is an urgent requirement for understanding the aetiology and pathogenesis of CVD and developing possible interventions for preventing CVD in ageing hearts. It is necessary to select appropriate models and treatment methods. The D-galactose-induced cardiac ageing model possesses the advantages of low mortality, short time and low cost and has been increasingly used in the study of cardiovascular diseases in recent years. Therefore, understanding the latest progress in D-galactose-induced cardiac ageing is valuable. This review highlights the recent progress and potential therapeutic interventions used in D-galactose-induced cardiac ageing in recent years by providing a comprehensive summary of D-galactose-induced cardiac ageing in vivo and in vitro. This review may serve as reference literature for future research on age-related heart diseases.
Fast Retinomorphic Event-Driven Representations for Video Gameplay and Action Recognition

Good temporal representations are crucial for video understanding, and the state-of-the-art video recognition framework is based on two-stream networks. In such framework, besides the regular ConvNets responsible for RGB frame inputs, a second network is introduced to handle the temporal representation, usually the optical flow (OF). However, OF or other task-oriented flow is computationally costly, and is thus typically pre-computed. Critically, this prevents the two-stream approach from being applied to reinforcement learning (RL) applications such as video game playing, where the next state depends on current state and action choices. Inspired by the early vision systems of mammals and insects, we propose a fast event-driven representation (EDR) that models several major properties of early retinal circuits: (1) log-arithmic input response, (2) multi-timescale temporal smoothing to filter noise, and (3) bipolar (ON/OFF) pathways for primitive event detection. Trading off the directional information for fast speed (>9000 fps), EDR enables fast real-time inference/learning in video applications that require interaction between an agent and the world such as game-playing, virtual robotics, and domain adaptation. In this vein, we use EDR to demonstrate performance improvements over state-of-the-art reinforcement learning algorithms for Atari games, something that has not been possible with pre-computed OF. Moreover, with UCF-101 video action recognition experiments, we show that EDR performs near state-of-the-art in accuracy while achieving a 1,500x speedup in input representation processing, as compared to optical flow.
A Fast and Accurate Algorithm for Nuclei Instance Segmentation in Microscopy Images

Nuclei instance segmentation within microscopy images is a fundamental task in the pathology work-flow, based on that the meaningful nuclear features can be extracted and multiple biological related analysis can be performed. However, this task is still challenging because of the large variability among different types of nuclei. Although deep learning(DL) based methods have achieved state-of-the-art results in nuclei instance segmentation tasks, these methods are usually focus on improving the accuracy and require support of powerful computing resources. In this paper, we joint the detection and segmentation simultaneously, and propose a fast and accurate box-based nuclei instance segmentation method. Mainly, we employ a fusion module based on the feature pyramid network(FPN) to combine the complementary information of the shallow layers with deep layers for detection the nuclear location by bounding boxes. Subsequently, we crop the feature maps according to the bounding boxes and feed the cropped patches into an U-net architecture as a guide to separate clustered nuclei. The experiments show that the proposed approach outperforms prior state-of-the-art methods, not only on accuracy but also on speed. The source code will be released at: https://github.com/QUAPNH/Nucleiseg.