Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comprehensive Review of Surface Acoustic Wave-Enabled Acoustic Droplet Ejection Technology and Its Applications

This review focuses on the development of surface acoustic wave-enabled acoustic drop ejection (SAW-ADE) technology, which utilizes surface acoustic waves to eject droplets from liquids without touching the sample. The technology offers advantages such as high throughput, high precision, non-contact, and integration with automated systems while saving samples and reagents. The article first provides an overview of the SAW-ADE technology, including its basic theory, simulation verification, and comparison with other types of acoustic drop ejection technology. The influencing factors of SAW-ADE technology are classified into four categories: fluid properties, device configuration, presence of channels or chambers, and driving signals. The influencing factors discussed in detail from various aspects, such as the volume, viscosity, and surface tension of the liquid; the type of substrate material, interdigital transducers, and the driving waveform; sessile droplets and fluid in channels/chambers; and the power, frequency, and modulation of the input signal. The ejection performance of droplets is influenced by various factors, and their optimization can be achieved by taking into account all of the above factors and designing appropriate configurations. Additionally, the article briefly introduces the application scenarios of SAW-ADE technology in bioprinters and chemical analyses and provides prospects for future development. The article contributes to the field of microfluidics and lab-on-a-chip technology and may help researchers to design and optimize SAW-ADE systems for specific applications.
Luteolin regulates the distribution and function of organelles by controlling SIRT1 activity during postovulatory oocyte aging

The quality of oocytes determines their development competence, which will be rapidly lost if the oocytes are not fertilized at the proper time after ovulation. SIRT1, one of the sirtuin family members, has been proven to protect the quality of oocytes during postovulatory oocyte aging. However, evidence of the effect of SIRT1 on the activity of organelles including the mitochondria, the endoplasmic reticulum (ER), the Golgi apparatus, and the lysosomes in postovulatory aging oocyte is lacking. In this study, we investigated the distribution and function of organelles in postovulatory aged oocytes and discovered abnormalities. Luteolin, which is a natural flavonoid contained in vegetables and fruits, is an activator of SIRT1. When the oocytes were treated with luteolin, the abnormal distribution of mitochondria, ER, and Golgi complex were restored during postovulatory oocyte aging. The ER stress protein GRP78 and the lysosome protein LAMP1 increased, while the mitochondrial membrane potential and the Golgi complex protein GOLPH3 decreased in aged oocytes, and these were restored by luteolin treatment. EX-527, an inhibitor of SIRT1, disrupted the luteolin-mediated normal distribution and function of mitochondria, ER, Golgi apparatus, and lysosomes. In conclusion, we demonstrate that luteolin regulates the distribution and function of mitochondria, ER, Golgi apparatus, and lysosomes during postovulatory oocyte aging by activating SIRT1.
Intelligent Resource Allocation for V2V Communication with Spectrum–Energy Efficiency Maximization

Aiming to address the limitations of traditional resource allocation algorithms in the Internet of Vehicles (IoV), whereby they cannot meet the stringent demands for ultra-low latency and high reliability in vehicle-to-vehicle (V2V) communication, this paper proposes a wireless resource allocation algorithm for V2V communication based on the multi-agent deep Q-network (MDQN). The system model utilizes 5G network slicing technology as its fundamental feature and maximizes the weighted spectrum–energy efficiency (SEE) while satisfying reliability and latency constraints. In this approach, each V2V link is treated as an agent, and the state space, action, and reward function of MDQN are specifically designed. Through centralized training, the neural network parameters of MDQN are determined, and the optimal resource allocation strategy is achieved through distributed execution. Simulation results demonstrate the effectiveness of the proposed scheme in significantly improving the SEE of the network while maintaining a certain success rate for V2V link load transmission.
Low Complexity State Metric Memory Reduction for Turbo Decoding With Stochastic Quantization

The size of the state metrics cache (SMC) has a predominant impact on the overall hardware consumption of the Turbo decoder. This brief presents a low complexity SMC reduction algorithm based on the proposed stochastic quantization (SQ) technique, which reduces the size of the SMC by randomly quantizing the state metrics to different small bit-width numbers. The selection of the random source and the updating method of the extrinsic information are further explored to minimize the performance loss caused by bit-width reduction. The simulation and synthesis results show that the proposed algorithm can achieve the best bit error rate (BER) performance with the lowest hardware consumption among the compared SMC reduction algorithms.
The Impact of City Lockdown and Air Pollution
on the Spread of Covid-19

Considering the high variability of Covid-19 and unknown sequelae of rebound infections. We may be caught in a long-term struggle against the virus. The evaluation and summary of past anti-epidemic policies highlight profound practical significance for either decision makers or individuals. This paper applies both theoretical and empirical approaches to study the impact of city lockdown and air pollution on Covid-19. First, we apply an extended Susceptible-Infected-Removed (SIR) model to identify the relationship between lockdown, air pollution, and Covid-19 cases. Second, we apply the Differences-in-Differences (DID) model and Poisson Pseudo-Maximum Likelihood (PPML) model to test the impact of lockdown on Covid-19, by sorting out lockdown policies implemented by cities during the epidemic. Third, we empirically analyze the impact of air pollution on Covid-19, based on daily data from 257 cities in China. Finally, we examine the mechanism by which lockdown impacts Covid-19. Newly confirmed Covid-19 cases are reduced by 31.1% nationwide (excluding Wuhan) during the lockdown period. Regions with lower air pollution experience fewer Covid-19 cases. Air pollution aggravates Covid-19, with the pollutants PM2.5, PM10, NO2, CO, and O3 having the greatest impact. The results indicate that an increase in one unit of air quality index (AQI) concentrations is associated with 1.723 more newly confirmed cases. The effects of air pollution on the spread of Covid-19 diminish as the population size increases. Lockdown can inhibit the spread of Covid-19 by reducing air pollution and population movement.
Video Anomaly Detection via Visual Cloze Tests

Although great progress has been sparked in video anomaly detection (VAD) by deep neural networks (DNNs), existing solutions still fall short in two aspects: 1) The extraction of video events cannot be both precise and comprehensive. 2) The semantics and temporal context are under-explored. To tackle above issues, we are inspired by cloze tests in language education and propose a novel approach named Visual Cloze Completion (VCC), which conducts VAD by completing visual cloze tests (VCTs). Specifically, VCC first localizes each video event and encloses it into a spatio-temporal cube (STC). To realize both precise and comprehensive event extraction, appearance and motion are used as complementary cues to mark the object region associated with each event. For each marked region, a normalized patch sequence is extracted from several neighboring frames and stacked into a STC. With each patch and the patch sequence of a STC regarded as a visual “word” and “sentence” respectively, we deliberately erase a certain “word” (patch) to yield a VCT. Then, the VCT is completed by training DNNs to infer the erased patch and its optical flow via video semantics. Meanwhile, VCC fully exploits temporal context by alternatively erasing each patch in temporal context and creating multiple VCTs. Furthermore, we propose localization-level, event-level, model-level and decision-level solutions to enhance VCC, which can further exploit VCC’s potential and produce significant VAD performance improvement. Extensive experiments demonstrate that VCC achieves highly competitive VAD performance.
Analysis of Fecal Microbial Changes in Young Calves Following Bovine Rotavirus Infection

Simple Summary We conducted a metataxonomic analysis to compare the taxonomic composition and functional profile of the fecal microbiota in healthy and diarrheic calves across three clinical periods, aiming to gain a better understanding of the changes. The findings here might provide information about pathogenic microbiota in diarrheic calves, which were found to be more enriched than in healthy calves. Abstract The objective of the present study was to identify changes in fecal microbiota and predict the functional features of healthy calves and those infected with rotavirus over time. Six Holstein calves (average body weight 43.63 ± 1.19 kg, age-matched within 5–7 d) were randomly selected and distributed into two groups which contained three calves each. Fecal samples were taken 3 days before inoculation and on days 1 and 7 post-inoculation. The 16S rRNA gene amplicon sequencing was performed. Bacterial diversity tended to decrease in the rota group, as indicated by the alpha (evenness, p = 0.074 and Shannon, p = 0.055) and beta (Bray–Curtis dissimilarity, p = 0.099) diversity at 1 day post-inoculation. Differences in the bacterial taxa between healthy and rota-infected calves were detected using a linear discriminant analysis effect size (LDA > 2.0, p < 0.05). Rota calves had a higher abundance of certain bacterial taxa, such as Enterococcus, Streptococcus, and Escherichia-Shigella, and a lower abundance of bacteria that contribute to the production of short-chain fatty acids, such as Alistipes, Faecalibacterium, Pseudoflavonifractor, Subdoligranulum, Alloprevotella, Butyricicoccus, and Ruminococcus, compared to the healthy calves. The observed changes in the fecal microbiota of the rota-infected group compared to the healthy group indicated potential dysbiosis. This was further supported by significant differences in the predicted functional metagenomic profiles of these microbial communities. We suggest that calves infected with bovine rotavirus had bacterial dysbiosis, which was characterized by lower diversity and fewer observed genera than the fecal microbiota of healthy calves.
Ways to Assess and Regulate the Performance of a Bi-Mechanism-Induced Borneol-Based In Situ Forming Matrix

As an alternative to the traditional polymeric-based system, it is now possible to use an in situ forming system that is based on small molecules. Borneol was used as matrix formation in this study. While triacetin was incorporated into the formulation for prolonging the drug release. The objective of this study is to understand the initial period of the solvent exchange mechanism at the molecular level, which would provide a basis for explaining the matrix formation and drug release phenomena. The evaluation of basic physical properties, matrix formation, in vitro drug release, and molecular dynamics (MD) simulation of borneol-based in situ forming matrixes (ISM) was conducted in this study. The proportion of triacetin was found to determine the increase in density and viscosity. The density value was found to be related to viscosity which could be used for the purpose of prediction. Slow self-assembly of ISM upon the addition of triacetin was associated with higher viscosity and lower surface tension. This phenomenon enabled the regulation of solvent exchange and led to sustaining the drug release. In MD simulation using AMBER Tools, the free movement of the drug and the rapid approach to equilibrium of both solvent and water molecule in a solvent exchange mechanism in borneol-free ISM was observed, supporting that sustained release would not occur. Water infiltration was slowed down and NMP movement was restricted by the addition of borneol and triacetin. In addition, the increased proportion of triacetin promoted the diminished down of all substances’ movement because of the viscosity. The diffusion constant of relevant molecules decreased with the addition of borneol and/or triacetin. Although the addition of triacetin tended to slow down the solvent exchange and molecular movement from computation modelling results, it may not guarantee to imply the best drug release control. The Low triacetin-incorporated (5%) borneol-based ISM showed the highest ability to sustain the drug release due to its self-assembly and has proper solvent exchange. MD simulation addressed the details of the mechanism at the beginning of the process. Therefore, both MD and classical methods contribute to a clearer understanding of solvent exchange from the molecular to macroscopic level and from the first nanosecond of the formulation contact with water to the 10-day of drug release. These would be beneficial for subsequent research and development efforts in small molecule-based in situ forming systems.
Variétés analytiques réelles et variétés analytiques complexes

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Alphas and Surface Backgrounds in Liquid Argon Dark Matter Detectors

Current observations from astrophysics indicate the presence of dark matter, an invisible form of matter that makes up a large part of the mass of the universe. One of the leading theories for dark matter is that it is made up of Weakly Interacting Massive Particles (WIMPs). One of the ways we try to discover WIMPs is by directly detecting their interaction with regular matter. This can be done through the use of a scintillator such as liquid argon, which gives off light when a particle interacts with it. Liquid argon (LAr) is a favorable means of detecting WIMPs because it has an inherent property that enables a technique called pulse-shape discrimination (PSD). PSD could distinguish a WIMP signal from the constant background of electromagnetic signals from other sources, like gamma rays. However, there are other background signals that PSD is not as capable of rejecting, such as those caused by alpha decays on the interior surfaces of the detector. Radioactive elements that undergo alpha decay are introduced to detector surfaces during construction by radon gas that is naturally present in the air, as well as other means. When these surface isotopes undergo alpha decay they can produce WIMPlike signals in the detector. We present here two LAr experiments. The first (RaDOSE) discovered a property of an organic compound that led to a technique for rejecting surface alpha decays in LAr detectors with high efficiency. The second (DarkSide-50) is a dark matter experiment operated at LNGS in Italy and is the work of an international collaboration. A detailed look is given into alpha decays and surface backgrounds present in the detector, and projections are made of alpha-related backgrounds for 500 live days of data. The technique developed with RaDOSE is applied to DarkSide-50 to determine its effectiveness in practice. It is projected to suppress the surface background in DarkSide-50 by more than a factor of 1000. iii
Presence of p25alpha-Domain in Seed Plants (Spermatophyta): Microbial/Animal Contaminations and/or Orthologs

Genome and transcriptome assembly data often contain DNA and RNA contaminations from external organisms, introduced during nucleotide extraction or sequencing. In this study, contamination of seed plant (Spermatophyta) transcriptomes/genomes with p25alpha domain encoding RNA/DNA was systematically investigated. This domain only occurs in organisms possessing a eukaryotic flagellum (cilium), which seed plants usually do not have. Nucleotide sequences available at the National Center for Biotechnology Information website, including transcriptome shotgun assemblies (TSAs), whole-genome shotgun contigs (WGSs), and expressed sequence tags (ESTs), were searched for sequences containing a p25alpha domain in Spermatophyta. Despite the lack of proteins containing the p25alpha domain, such fragments or complete mRNAs in some EST and TSA databases were found. A phylogenetic analysis showed that these were contaminations whose possible sources were microorganisms (flagellated fungi, protists) and arthropods/worms; however, there were cases where it cannot be excluded that the sequences found were genuine hits and not of external origin.
High-Order Mode Cavity Filtering Antenna Array Based on Wavefront Phase Compensation

In order to solve the problem of large sidelobes in traditional high-order mode cavity slot antenna design, an effective method based on wavefront phase compensation is proposed in this article. Two compensation methodologies, wavefront phase advance and wavefront phase lag, are given and analyzed in detail. By combining the two compensation methods, the wavefronts of adjacent elements with opposite phases can be transformed into those with the same phase, achieving a broadside beam. In addition, the relationship between the directivity and radiation quality factor of the radiation structure is discussed in detail, which is used to solve the problem of different gains of the radiation elements in the array. For verification, a first-order filtering antenna array with five elements and a fifth-order filtering antenna array with ten elements are designed using the TE501 mode cavity. The fifth-order filtering antenna array is fabricated and measured. The measured gain in the passband is stable and the maximum gain is 17.3 dBi. The simulated results are in good agreement with the measured ones, which indicates that the proposed method can be used to design a filtering cavity slot antenna array with high gain and low sidelobes.
Assessment of udder skin surface temperature changes in relation to mastitis in crossbred dairy cows in different farms in Kerala

The aim of the present study was to assess the infrared thermographic profile in relation to mastitis in lactating dairy cows. In this study, 20 lactating crossbred dairy cows each were monitored for a period of 21 days in four dairy farms. The udder skin surface temperature (USST) was recorded using infrared thermography (IRT) during the study period at diurnal time intervals. Milk samples were collected at the time of afternoon milking from each quarter and tested for California Mastitis Test (CMT). It was observed that higher USST was noticed in udders with clinical mastitis followed by non-mastitis and subclinical mastitis, and the skin surface temperature of non-mastitis and clinical mastitis udders were significantly higher than subclinical mastitis affected udders. The results of this study indicate the limitations in the use of IRT of udders for early detection of mastitis across all regions.
Impulse response constrained LS-SVM modelling for MIMO Hammerstein system identification

ABSTRACT In this paper, a new methodology for identifying multiple inputs multiple outputs Hammerstein systems is presented. The proposed method aims at incorporating the impulse response of the system into a least-squares support vector machine (LS-SVM) formulation and therefore the regularisation capabilities of LS-SVM are applied to the system as a whole. One of the main advantages of this method comes from the fact that it is flexible concerning the class of problems it can model and that no previous knowledge about the underlying non-linearities is required except for very mild assumptions. Also, it naturally adapts to handle different numbers of inputs/outputs and performs well in the presence of white Gaussian noise. Finally, the method incorporates information about the structure of the system but still the solution of the model follows from a linear system of equations. The performance of the proposed methodology is shown through three simulation examples and compared with other methods in the literature.
Effect of Inflammatory Microenvironment on the Regenerative Capacity of Adipose-Derived Mesenchymal Stem Cells

Adipose-derived mesenchymal stem cells are increasingly being used in regenerative medicine as cell therapy targets, including in the treatment of burns and ulcers. The regenerative potential of AD-MSCs and some of their immunological properties are known from in vitro studies; however, in clinical applications, cells are used in non-ideal conditions and can behave differently in inflammatory environments, affecting the efficacy and outcome of therapy. Our aim was to investigate and map the pathways that the inflammatory microenvironment can induce in these cells. High-throughput gene expression assays were performed on AD-MSCs activated with LPS and TNFα. Analysis of RNA-Seq data showed that control, LPS-treated and TNFα-treated samples exhibited distinct gene expression patterns. LPS treatment increased the expression of 926 genes and decreased the expression of 770 genes involved in cell division, DNA repair, the cell cycle, and several metabolic processes. TNFα treatment increased the expression of 174 genes and decreased the expression of 383 genes, which are related to cell division, the immune response, cell proliferation, and differentiation. We also map the biological pathways by further investigating the most altered genes using the Gene Ontology and KEGG databases. Secreted cytokines, which are important in the immunological response, were also examined at the protein level, and a functional assay was performed to assess wound healing. Activated AD-MSC increased the secretion of IL-6, IL-8 and CXCL-10, and also the closure of wounds. AD-MSCs presented accelerated wound healing under inflammation conditions, suggesting that we could use this cell in clinical application.
Optimal Allocation and Sizing of Decentralized Solar Photovoltaic Generators Using Unit Financial Impact Indicator

A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty.