Miracle-dz/UniFAR-FTUs huggingface dataset:
Inhalation Exposure Analysis of Lung-Inhalable Particles in an Approximate Rat Central Airway

Rats have been widely used as surrogates for evaluating the adverse health effects of inhaled airborne particulate matter. This paper presents a computational fluid and particle dynamics (CFPD) study of particle transport and deposition in an approximate rat central airway model. The geometric model was constructed based on magnetic resonance (MR) imaging data sourced from previous study. Lung-inhalable particles covering a diameter range from 20 nm to 1.0 µm were passively released into the trachea, and the Lagrangian particle tracking approach was used to predict individual particle trajectories. Overall, regional and local deposition patterns in the central airway were analyzed in detail. A preliminary interspecies data comparison was made between present rat models and previously published human data. Results showed deposition “hot spots” were mainly concentrated at airway bifurcation apexes, and a gravitational effect should also be considered for inertia particles when using a rat as a laboratory animal. While for humans, this may not happen as the standing posture is completely different. Lastly, the preliminary interspecies data comparison confirms the deposition similarity in terms of deposition enhancement factors, which is a weighted deposition concentration parameter. This interspecies comparison confirms feasibility of extrapolating surrogate rat deposition data to humans using existing data extrapolation approach, which mostly relies on bulk anatomical differences as dose adjustment factors.
Interventions in live poultry markets for the control of avian influenza: A systematic review and meta-analysis.

BACKGROUND This review aimed to provide constructive suggestions for the control and management of avian influenza through quantitative and qualitative evaluation of the impact of different live poultry market (LPM) interventions. METHODS Both English and Chinese databases were searched for articles that were published on or before November 9, 2018. After extraction and assessment of the included literature, stata14.0 was applied to perform meta-analysis to explore the impacts of LPM interventions. RESULTS A total of 19 studies were identified. In total, 224 humans, 3550 poultry, and 13,773 environment samples were collected before the intervention; 181 humans, 4519 poultry, and 9562 environments were sampled after LPM interventions. Avian influenza virus (AIV) detection rates of the LPM environment (odds ratio [OR] = 0.393; 95% confidence interval [CI], 0.262-0.589) and the incidence of human avian influenza infection (OR = 0.045; 95% CI, 0.025-0.079) were significantly lower after LPM interventions, while LPM interventions on poultry were not significantly effective (OR = 0.803; 95% CI, 0.403-1.597). CONCLUSIONS LPM interventions can reduce human infections of avian influenza and the detection rate of AIV in market environments.
Amnion membrane hydrogel and amnion membrane powder accelerate wound healing in a full thickness porcine skin wound model

There is a need for effective wound treatments that retain the bioactivity of a cellular treatment, but without the high costs and complexities associated with manufacturing, storing, and applying living biological products. Previously, we developed an amnion membrane‐derived hydrogel and evaluated its wound healing properties using a mouse wound model. In this study, we used a full thickness porcine skin wound model to evaluate the wound‐healing efficacy of the amnion hydrogel and a less‐processed amnion product comprising a lyophilized amnion membrane powder. These products were compared with commercially available amnion and nonamnion wound healing products. We found that the amnion hydrogel and amnion powder treatments demonstrated significant and rapid wound healing, driven primarily by new epithelialization versus closure by contraction. Histological analysis demonstrated that these treatments promote the formation of a mature epidermis and dermis with similar composition to healthy skin. The positive skin regenerative outcomes using amnion hydrogel and amnion powder treatments in a large animal model further demonstrate their potential translational value for human wound treatments.
Influence of Timing of Insulin Initiation on Long-term Glycemic Control in Japanese Patients with Type 2 Diabetes: A Retrospective Cohort Study

Objective Delays in insulin initiation can lead to the development of complications in the management of type 2 diabetes. Methods In this study, the effects of the timing of insulin initiation on glycemic control in patients with type 2 diabetes were evaluated retrospectively. Changes in the HbA1c levels of 237 patients were analyzed after insulin initiation. Results The patients were divided into 4 groups according to the duration of diabetes at the time of insulin initiation: ≤3 years, 4 to 6 years, 7 to 9 years, or ≥10 years. Patients with a diabetes duration of ≤3 years were more frequently hospitalized at the time of insulin initiation, had a higher HbA1c level before insulin initiation and a lower HbA1c level at 1 year after insulin initiation and exhibited significant decreases in HbA1c at 1, 3, or 5 years after insulin initiation than those in the other 3 groups with longer durations of diabetes. In the group receiving 4 insulin injections per day, the reduction in HbA1c after 5 years of treatment was larger in patients with a diabetes duration at the time of insulin initiation of ≤3 years than in those with a duration of 7 to 9 years or ≥10 years. Conclusion Our results suggested that an earlier initiation of insulin therapy was crucial for sustaining glycemic control in Japanese patients with type 2 diabetes, particularly in those with a history of obesity or receiving multiple insulin injections daily.
Performance Analysis for DC-DC BuckConverter with Fuzzy and Robust Controller

The switch mode power supply (SMPS) has been achieved the high power density and high performance by developing the power semiconductor devices such as IGBT, BJT, MOSFET, and GTO etc. SMPS has the capacity to handle the variable loads and variable input voltage. The efficiency, weight and size of power supplies are a great area of concern for the power supply designers. This article introduces the method of intelligent regulation to control the Buck converter using the pulse width modulation switching by a fuzzy logic and robust controller. In this paper we use the buck circuit having power MOSFET as a switch and fuzzy logic and robust controller based PWM gate signals to the switch for controlling purpose. This paper describes the design of a fuzzy logic controller using output voltage of the converter as feedback for significantly improving the dynamic performance of buck dc-dc converter by using MATLAB/ SIMULINK. In the case of robust controller, the Buck converter output voltage remains constant irrespective of load and input voltage variations from 140V to 340V.
Environmental and Motivational Determinants of Physical Activity among Canadian Inuit in the Arctic

Background: Canadian Inuit have transited from a physically active hunter-gatherer subsistence lifestyle into sedentary ways of life. The purpose of the current study was to measure physical activity levels among Nunavut Inuit adults, and explore the socio-cognitive and environmental factors influencing the number of steps taken per day. Method: Inuit and non-Inuit adults (N = 272) in Nunavut participated in a seven-day pedometer study during summer and winter seasons. Participants were asked to complete the Neighbourhood Environmental Walkability Scale (NEWS) and Behavioral Regulation in Exercise Questionnaire (BREQ-3). Data analyses included descriptive statistics, hierarchical linear regression, and tests of mediation effects. Results: Participants had limited to low activity at a rate of 5027 ± 1799 and 4186 ± 1446 steps per day, during summer and winter, respectively. There were no seasonal and age effects on the number of steps. Gender effects and community differences were observed. Perceived infrastructure and safety as well as land use mix diversity were found to be positive environmental correlates of steps taken, which were partially mediated by identified motivational regulation. Conclusion: Physical activity levels among Nunavut adults are generally low, but can be promoted by improving the external physical environment and internal motivational regulation.
Text classification based on N-gram language model

Text classification has become a research focus in the field of natural language processing. After the review of traditional text classification models, a method using N-gram language models to classify Chinese text was presented. This model doesn′t present documents with bag of words, but regards documents as random observation sequences. With the bi-gram model, a text classifier based on word level was implemented. The performance of the N-gram model classifier was compared with that of the traditional models (Vector Space Model and Naive Bayes Model). Experiment result shows that the accuracy and the stability of the N-gram model classifier are better than others.
Sentence-Level Analysis of Sentiment Classification

There has been a large growth of online opinioned customer reviews in the recent years. Classifying such reviews into polarized ones would be beneficial in business intelligence and other application domains. Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. Sentiment analysis is the procedure by which information is extracted from the opinions, appraisals and emotions of people in regards to entities, events and their attributes. In decision making, the opinions of others have a significant effect on customers ease, making choices with regards to online shopping, choosing events, products, entities. The approaches of text sentiment analysis typically work at a particular level like phrase, sentence or document level. This paper aims at analyzing a solution for the sentiment classification at a fine-grained level, namely the sentence level in which polarity of the sentence can be given by three categories as positive, negative and neutral. Keywords - sentiment analysis, phrase level, sentence level, document level, polarity.
A Sensorless Rotational Speed-Based Control System for Continuous Flow Left Ventricular Assist Devices

Objective: Continuous Flow Left Ventricular Assist Devices (CFLVAD) are circulatory support devices that are implanted in patients with end-stage heart failure. We developed a novel control algorithm for CFLVAD to maintain physiologic perfusion while avoiding ventricular suction using only the intrinsic pump measurement of pump speed and without utilizing model-based estimation. Methods: The controller objective is to maintain a differential pump speed setpoint. A mathematical model of the circulatory system coupled with a model of a CFLVAD was used to test the control algorithm in silico. Robustness and efficacy were evaluated by comparing the proposed control algorithm to constant speed control, differential pump pressure control, mean aortic pressure control, and ventricular end diastolic pressure control during (1) rest and exercise conditions, (2) a rapid eight-fold increase in pulmonary vascular resistance under rest and exercise, (3) transitions from rest to exercise, and exercise to rest, (4) safe mode during left ventricular asystole, and (5) RPM measurement noise of 1% to 10% for (1) to (4). Results and conclusion: The control algorithm provided adequate perfusion while preventing ventricular suction for all test conditions. Performance did not deteriorate significantly with pump speed measurement noise of up to 6%. The safe mode successfully detected asystole and maintained adequate perfusion to sustain life even when the differential pump speed was low. Significance: Maintaining a constant differential pump speed can simultaneously achieve physiologic perfusion and suction prevention without needing unreliable, direct measurements of flow or pressure, or complex parameter or model-based estimation techniques.
A wavelet HD-video de-noising system with frame rate conversion

Real-time video de-noising is an active research topic in video surveillance application, aimed at improving the degraded video’s quality. In this paper, the key methods of frame rate conversion and wavelet de-noising during video processing are studied and a real-time wavelet HD-video (High Definition) de-noising system based on FPGA (Field Programmable Gate Array) platform is designed. With frame rate conversion module, 3-level wavelet transform, and BayesShrink adaptive threshold filtering, the proposed system achieves a real-time HD-video de-noising processing with better performance. Experiments show that this system can satisfy the requirements of varieties of input video source formats and real-time processing, and the peak signal to noise rate of the de-noising images is significantly improved.
Metric of a tidally perturbed spinning black hole

We explicitly construct the metric of a Kerr black hole that is tidally perturbed by the external universe in the slow-motion approximation. This approximation assumes that the external universe changes slowly relative to the rotation rate of the hole, thus allowing the parameterization of the Newman-Penrose scalar {psi}{sub 0} by time-dependent electric and magnetic tidal tensors. This approximation, however, does not constrain how big the spin of the background hole can be and, in principle, the perturbed metric can model rapidly spinning holes. We first generate a potential by acting with a differential operator on {psi}{sub 0}. From this potential we arrive at the metric perturbation by use of the Chrzanowski procedure in the ingoing radiation gauge. We provide explicit analytic formulas for this metric perturbation in Kerr coordinates, where the perturbation is finite at the horizon. This perturbation is parametrized by the mass and Kerr spin parameter of the background hole together with the electric and magnetic tidal tensors that describe the time evolution of the perturbation produced by the external universe. In order to make the metric accurate far away from the hole, these tidal tensors should be determined by asymptotically matching this metric to another one validmore » far from the hole. The tidally perturbed metric constructed here could be useful in initial data constructions to describe the metric near the horizons of a binary system of spinning holes. This perturbed metric could also be used to construct waveforms and study the absorption of mass and angular momentum by a Kerr black hole when external processes generate gravitational radiation. « less
An Established HPLC-MS/MS Method for Evaluation of the Influence of Salt Processing on Pharmacokinetics of Six Compounds in Cuscutae Semen

A sensitive and effective method was developed for clarifying the pharmacokinetic properties of six compounds (including hyperin, chlorogenic acid, neochlorogenic acid, p-coumaric acid, astragalin, and isoquercitrin) in two processed Cuscutae Semen samples by high performance liquid chromatography mass spectrometry (HPLC-MS/MS). The six compounds were separated by acetonitrile and 0.1% formic acid-water on an Agilent Eclipse plus C18 column (4.6 mm × 100 mm, 1.8 μm). All compounds were analyzed with negative ion mode in multiple reaction monitoring (MRM). The lower limits of quantification (LLOQ) of hyperin, astragalin, neochlorogenic acid, chlorogenic acid, isoquercitrin, and p-coumaric acid were 1, 0.1, 4, 0.1, 2, and 4 ng·mL−1, respectively. The validated approach was effectively used for the pharmacokinetics of six compounds of two processed Cuscutae Semen samples after oral administration to rat. The results indicated that salt processing could improve the adsorption and bioavailability of astragalin in Cuscutae Semen.
Construction of Pena’s DP2-Based Ordinal Synthetic Indicator When Partial Indicators are Rank Scores

The present study devises a computational scheme (and develops a FORTRAN 77 computer program) that may be appropriate to construct Pena’s DP2 (ordinal) synthetic indicator (Z) from the partial indicators (X) all of which are ordinal (ranking scores). An attempt has also been made to empirically apply the method (and the computer program) to obtain an ordinal synthetic indicator from a given ordinal data set.
Mechanoregulation of Bone Remodeling and Healing as Inspiration for Self-Repair in Materials

The material bone has attracted the attention of material scientists due to its fracture resistance and ability to self-repair. A mechanoregulated exchange of damaged bone using newly synthesized material avoids the accumulation of fatigue damage. This remodeling process is also the basis for structural adaptation to common loading conditions, thereby reducing the probability of material failure. In the case of fracture, an initial step of tissue formation is followed by a mechanobiological controlled restoration of the pre-fracture state. The present perspective focuses on these mechanobiological aspects of bone remodeling and healing. Specifically, the role of the control function is considered, which describes mechanoregulation as a link between mechanical stimulation and the local response of the material through changes in structure or material properties. Mechanical forces propagate over large distances leading to a complex non-local feedback between mechanical stimulation and material response. To better understand such phenomena, computer models are often employed. As expected from control theory, negative and positive feedback loops lead to entirely different time evolutions, corresponding to stable and unstable states of the material system. After some background information about bone remodeling and healing, we describe a few representative models, the corresponding control functions, and their consequences. The results are then discussed with respect to the potential design of synthetic materials with specific self-repair properties.
On-line Kernel Extreme Learning Machine Modelling based on Selective Update

On-line modeling method based on every new sample recursive update needs big compute consumption.The generally used intelligent modeling approaches have low learning speed.In order to effective update soft sensor model and improve on-line model's learning speed,a new on-line kernel extreme learning machine(KELM) modeling approach based on selective update is proposed in this paper.The approximate linear dependence(ALD) condition is used to judge the linear independent degree between the new sample and modeling samples.Only these samples that satisfy the ALD condition and contain interesting new information are used to update the soft sensor model.Thus,the on-line model's updating times is reduced.The KELM algorithm with the character of fast learning speed and strong generalization is used to construct soft sensor model,which overcome the randomization of ELM and the complexity solution process of SVM.Therefore,with the integration of ALD condition and KELM algorithm,the learning speed and the prediction performance of the on-line model are proposed.The simulation results base on synthetic data verifie the validity of the proposed method.
Estimating and testing the microbial causal mediation effect with high-dimensional and compositional microbiome data

Motivation Recent microbiome association studies have revealed important associations between microbiome and disease/health status. Such findings encourage scientists to dive deeper to uncover the causal role of microbiome in the underlying biological mechanism, and have led to applying statistical models to quantify causal microbiome effects and to identify the specific microbial agents. However, there are no existing causal mediation methods specifically designed to handle high dimensional and compositional microbiome data. Results We propose a rigorous Sparse Microbial Causal Mediation Model (SparseMCMM) specifically designed for the high dimensional and compositional microbiome data in a typical three-factor (treatment, microbiome and outcome) causal study design. In particular, linear log-contrast regression model and Dirichlet regression model are proposed to estimate the causal direct effect of treatment and the causal mediation effects of microbiome at both the community and individual taxon levels. Regularization techniques are used to perform the variable selection in the proposed model framework to identify signature causal microbes. Two hypothesis tests on the overall mediation effect are proposed and their statistical significance is estimated by permutation procedures. Extensive simulated scenarios show that SparseMCMM has excellent performance in estimation and hypothesis testing. Finally, we showcase the utility of the proposed SparseMCMM method in a study which the murine microbiome has been manipulated by providing a clear and sensible causal path among antibiotic treatment, microbiome composition and mouse weight.
Camshift Tracking Algorithm Based on LBP Feature

Traditional Camshift is a tracking algorithm based on H channel and can be disturbed by the illumination easily.The paper proposes a tracking algorithm based on Local Binary Pattern(LBP) feature which is combined with color feature.In the process of object tracking,H channel is separated from the HSV space and is converted to LBP image at first.Then Camshift operates on a back-projection image produced from object histogram model.The experiment result shows that the tracking algorithm proposed can track object stably and in real-time,regardless of the existence of covering,changed appearance and illumination changing.
Methodological Considerations on the Use of Different Spectral Decomposition Algorithms to Study Hippocampal Rhythms

Abstract Local field potential (LFP) oscillations are primarily shaped by the superposition of postsynaptic currents. Hippocampal LFP oscillations in the 25- to 50-Hz range (“slow γ”) are proposed to support memory retrieval independent of other frequencies. However, θ harmonics extend up to 48 Hz, necessitating a study to determine whether these oscillations are fundamentally the same. We compared the spectral analysis methods of wavelet, ensemble empirical-mode decomposition (EEMD), and Fourier transform. EEMD, as previously applied, failed to account for the θ harmonics. Depending on analytical parameters selected, wavelet may convolve over high-order θ harmonics due to the variable time-frequency atoms, creating the appearance of a broad 25- to 50-Hz rhythm. As an illustration of this issue, wavelet and EEMD depicted slow γ in a synthetic dataset that only contained θ and its harmonics. Oscillatory transience cannot explain the difference in approaches as Fourier decomposition identifies ripples triggered to epochs of high-power, 120- to 250-Hz events. When Fourier is applied to high power, 25- to 50-Hz events, only θ harmonics are resolved. This analysis challenges the identification of the slow γ rhythm as a unique fundamental hippocampal oscillation. While there may be instances in which slow γ is present in the rat hippocampus, the analysis presented here shows that unless care is exerted in the application of EEMD and wavelet techniques, the results may be misleading, in this case misrepresenting θ harmonics. Moreover, it is necessary to reconsider the characteristics that define a fundamental hippocampal oscillation as well as theories based on multiple independent γ bands.