Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting V-Gene Bias for Rapid, High-Throughput Monoclonal Antibody Isolation from Horses

Horses and humans share a close relationship that includes both species’ viromes. Many emerging infectious diseases can be transmitted between horses and humans and can exhibit mortality rates as high as 90% in both populations. Antibody biologics represents an emerging field of rapidly discoverable and potent antiviral therapeutics. These biologics can be used to provide passive immunity, as well as blueprints for the rational design of novel active vaccine antigens. Here, we exploit the limited diversity of immunoglobulin variable genes used by horses to develop a rapid, high-throughput monoclonal antibody discovery pipeline. The antibodies isolated from two horses in this study were developed with near exclusivity from a few highly related germline genes within a single IgHV and IgλV gene family and could be recovered for cloning with just three primer pairs. This variable gene pairing was compatible with both horse and human immunoglobulin G isotypes, confirming the suitability of an equine antibody discovery pipeline for developing novel therapeutics to meet the One Health approach to infectious diseases.
FIGAT: Accurately Classify Individual Crime Risks With Multi-Information Fusion

Crime prediction plays a vital role in public security. Existing studies infer crime locations or crime groups without considering individual GPS trajectory data. They ignore joint influence on crime patterns coming from the internal relationship between criminals, locations, and time. In this study, we propose Fusion Information Graph Attention Networks (FIGAT), which classifies individuals into high and low risks with personal movement time series and location trajectories. To solve the independence of individual crime behavior and the fusion information loss problem, FIGAT proposes Multi-dimension Fusion Information Graph to combine semantic correlation features with conventional person basic features, time features, and location features. FIGAT constructs a multi-relation graph attention layer, which utilizes the semantic relationship and node information to accurately classify individuals into high and low risks. We evaluate FIGAT with 14,625,884 GPS trajectories from 1038 individuals collected by a real-world public safety department. The results demonstrate that FIGAT improves F1 score by 41%, 32%, and 23% compared with legacy machine learning, RNN-based deep learning, and graph neural network SOTA methods, respectively. T-SNE results and ablation experiments further prove the effectiveness of FIGAT.
Tidal streams from axion miniclusters and direct axion searches

In some axion dark matter models a dominant fraction of axions resides in dense small-scale substructures, axion miniclusters. A fraction of these substructures is disrupted and forms tidal streams where the axion density may still be an order of magnitude larger than the average. We discuss implications of these streams for the direct axion searches. We estimate the fraction of disrupted miniclusters and the parameters of the resulting streams, and find that stream-crossing events would occur at a rate of about 1/(20 yr) for 2–3 days, during which the signal in axion detectors would be amplified by a factor ∼ 10. These estimates suggest that the effect of the tidal disruption of axion miniclusters may be important for direct axion searches and deserves a more thorough study.
Full Coverage Estimation of the PM Concentration Across China Based on an Adaptive Spatiotemporal Approach

Particulate pollution threatens the ecological environment, air quality, and public health. Therefore, it has become an increasing concern for the public and governments in recent decades. In this study, a full coverage PM2.5 (aerodynamic diameter of less than 2.5 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>) estimation strategy is proposed based on spatiotemporal machine learning approaches, including the convolutional neural network with long short-term memory (CNN-LSTM) and random forest (RF). The RF estimates PM2.5 by considering the features of a single pixel, while the introduction of the CNN-LSTM (size of 7 <inline-formula> <tex-math notation="LaTeX">$\times7\,\,\times4$ </tex-math></inline-formula>) assists in exploiting the spatiotemporal correlation of surrounding pixel features. Compared with linear models and empirical spatiotemporal weight methods, our CNN-LSTM+RF avoids the uncertainty and complexity owing to actual measurements of the surrounding sites. In addition, full coverage is achieved using both satellite data and reanalysis data. Results showed that the root mean square error (RMSE) and coefficient of determination (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) of the CNN-LSTM+RF were 12.790 <inline-formula> <tex-math notation="LaTeX">$\mu \text{g}/\text{m}^{3}$ </tex-math></inline-formula> and 0.910, respectively, in sample-based cross-validation (CV). From the perspective of the season, the best performance of the CNN-LSTM+RF was found in autumn (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.915), and the lowest was in summer (<inline-formula> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula> of 0.848). In the meantime, for the different regions of China, the CNN-LSTM+RF also showed stable performance. The proposed method can generate high-precision continuous PM2.5 distribution maps that provide beneficial support for improving environmental and public health, and provide a reference for using deeper networks.
Understanding the Paths and Patterns of App-Switching Experiences in Mobile Searches

Mobile searches have become the main channel for people to search for information, and mobile searches have received attention in the field of information-seeking behavior. Especially as users use various apps to search network information, the app-switching behaviors in mobile searches have also attracted scholars’ attention in recent years. Research on app-switching behaviors in mobile searches can help to further understand users’ search motivations, evaluate search results, and improve users’ mobile search experiences. This study recruited participants (n = 30) and conducted a 15-day user experiment. This study collected all participants’ mobile phone log data during the experiment and identified the app-switching behaviors in mobile searches through a log collection tool. This study aimed to discover the app-switching behavior paths and patterns in mobile searches. Firstly, the basic characteristics of app-switching behaviors in mobile searches were analyzed, as were the app-switching paths in mobile searches from the perspective of switching probability between apps. Then, the different behaviors in mobile search sessions were identified and app-switching behavior patterns were put forward. These behavior patterns summarize user behavior changes in mobile search sessions. This paper focused on analyzing app-switching behavior paths under different patterns and found apparent differences in app-switching behavior paths. This study examined mobile search behavior from the perspective of app-switching. The research of this paper can help to better understand the relationship between users’ mobile search behaviors and app interactions and is an excellent supplement to the analysis of mobile search behaviors.
MRD Codes: Constructions and Connections

This preprint is of a chapter to appear in {\it Combinatorics and finite fields: Difference sets, polynomials, pseudorandomness and applications. Radon Series on Computational and Applied Mathematics}, K. -U. Schmidt and A. Winterhof (eds.). Rank-metric codes are codes consisting of matrices with entries in a finite field, with the distance between two matrices being the rank of their difference. Codes with maximum size for a fixed minimum distance are called Maximum Rank Distance (MRD) codes. Such codes were constructed and studied independently by Delsarte (1978), Gabidulin (1985), Roth (1991), and Cooperstein (1998). Rank-metric codes have seen renewed interest in recent years due to their applications in random linear network coding. MRD codes also have interesting connections to other topics such as semifields (finite nonassociative division algebras), finite geometry, linearized polynomials, and cryptography. In this chapter we will survey the known constructions and applications of MRD codes, and present some open problems.
Experimental Investigation on Water Seepage through Transparent Synthetic Rough-Walled Fractures

One of the impacts of climate changes nowadays is the increase in the frequency of high-intensity rainfall events alternating with extreme dry periods, which affect the components of the hydrologic cycle, such as runoff, infiltration, and aquifer recharge. Several experimental investigations and theoretical studies have demonstrated that infiltration flow in fractured media can develop along preferential pathways. However, the prediction of infiltration phenomena in fractured media still remains an open issue. This, together with erratic rainfall patterns due to climate changes, affects the prediction of aquifer recharge and contaminant transport in fractured aquifers. The present work contributes to reducing this research gap by means of experimental investigation and forecast analysis, with a focus on the geometrical properties of single fractures and their influence on flow patterns. Several fracture surfaces based on different fractal dimensions, standard deviations, and mismatch lengths were designed using the SynFrac model and were generated by 3D printing technology. The results revealed that the fracture’s fractal dimension has a significant impact on the number of flow paths, while the fracture inclination only increases the number of intermediate preferential channels, and, hence, modifies the flow rate distribution over the fracture outlet. Additionally, the change in the inclination angle of the dry fracture from 55 to 65 degrees resulted in an 8% reduction in the mean width of first flow path. A sensitivity analysis using an M5 tree indicates that there is a linear relationship between flow rate and the exponential form of the fractal dimension. The location of flow channels is a function of fracture fractal dimension, and the influence of mismatch length on their location is negligible. Finally, an accurate prediction algorithm with a Nash value of 0.81 was developed using Wavelet transform in order to estimate the time series of periodic flow rates over the fracture outlet.
SIMULATIONS OF THE SYMBIOTIC RECURRENT NOVA V407 CYG. I. ACCRETION AND SHOCK EVOLUTIONS

The shock interaction and evolution of nova ejecta with wind from a red giant (RG) star in a symbiotic binary system are investigated via three-dimensional hydrodynamics simulations. We specifically model the 2010 March outburst of the symbiotic recurrent nova V407 Cygni from its quiescent phase to its eruption phase. The circumstellar density enhancement due to wind–white-dwarf interaction is studied in detail. It is found that the density-enhancement efficiency depends on the ratio of the orbital speed to the RG wind speed. Unlike another recurrent nova, RS Ophiuchi, we do not observe a strong disk-like density enhancement, but instead observe an aspherical density distribution with ∼20% higher density in the equatorial plane than at the poles. To model the 2010 outburst, we consider several physical parameters, including the RG mass-loss rate, nova eruption energy, and ejecta mass. A detailed study of the shock interaction and evolution reveals that the interaction of shocks with the RG wind generates strong Rayleigh–Taylor instabilities. In addition, the presence of the companion and circumstellar density enhancement greatly alter the shock evolution during the nova phase. Depending on the model, the ejecta speed after sweeping out most of the circumstellar medium decreases to ∼100–300 km s−1, which is consistent with the observed extended redward emission in [N ii] lines in 2011 April.
Monitoring 15-year land use/land cover change in the Vietnamese Mekong Delta

Although land use/land cover (LULC) information plays a critical role in the maintenance of living standards with a balance among the environment, development, and sustainability, it remains little comprehensive understanding in the entire Vietnamese Mekong Delta (VMD). In this study, we used the random forest algorithm, Landsat images, ancillary and empirical reference data to carefully analyse the 15 years’ spatiotemporal changes (2005 - 2020) of LULC in the VMD region. Results show that agriculture has been the most dominant land, accounting for approximately half of the whole region during the fifteen years. Remarkably, most of the LULC categories have undergone dramatic transformation with the proportion of the wetland area decreasing from about 16% in 2005 to 5% in 2020, whereas that of the aquaculture area sharply increased from about 12% to 19% over the same period. Meanwhile, there was a marked increase in the area of perennial crops and built-up lands. These results of LULC maps and change detection helps understand the impact of past policies and the role of several factors such as socio-economic trends and environmental changes in this region.
A dual filtering scheme for nonlinear active noise control

Active noise control problems are often affected by nonlinear effects such as distortion and saturation of measurement and actuation devices, which call for suitable nonlinear models and algorithms. The active noise control problem can be interpreted as an indirect model identification problem, due to the secondary path dynamics that follow the control filter block. This complicates the weight update mechanism in the nonlinear case, in that the error gradient depends on the secondary path gradient through nonlinear recursions. A simpler and computationally less demanding approach is here proposed that employs the updating scheme of the standard filtered‐x least mean squares (LMS) or filtered‐u LMS algorithm. As in those schemes, the calculation of the error gradient requires a signal filtering through an auxiliary system, here obtained through a secondary adaptation loop. The resulting dual filtering LMS algorithm performs the adaptation of the controller parameters in a direct identification mode and can therefore be easily coupled with adaptive model structure selection schemes to provide online tuning of the model structure, for improved model robustness. Copyright © 2013 John Wiley & Sons, Ltd.
Synthesis, Characterization and Biological Investigation of New N-Modified Spinorphin Analogs

The emergence of diverse peptide derivatives has been due to constant efforts to find a specific peptide with pronounced biological activity for effective application as a therapeutic. Spinorphin-peptide products have been reported to possess various applications and properties. In the present study, spinorphin peptides with a rhodamine residue and a modification in the amino acid backbone were synthesized by a solid-phase method using Fmoc chemistry. The results obtained from the spectral and electrochemical techniques used: Scanning electron microscopy (SEM), UV-vis, fluorescence, infrared spectroscopy (IR), and voltammetry were used to elucidate the structural characteristics and some physicochemical properties to gain insight into their behavior in the solid state and in aqueous solutions with different pHs. Both Rh-S5 and Rh-S6 had compound anticonvulsant effect comparable to Rh-S against psychomotor seizures at the highest dose of 20 μg. Furthermore, Rh-S6 showed a strong ability to inhibit seizure propagation and had a similar threshold to Rh-S against the intravenous pentylenetetrazol induced clonic seizure in mice; one of the three hybrid spinorphin analogs tested when screened for anticonvulsant activity. Biological tests against several bacterial pathogens such as Staphylococcus aureus, Escherichia coli, and Bacillus cereus showed similar results to negative control of the new peptide derivatives. The compounds also showed weak activity against Candida albicans fungus. The antioxidant testing results revealed more than 50% activity by reviewing the radical deterrence capabilities of 2,2-diphenyl-1-picrylhydrazyl (DPPH). The results are indicative of the ongoing search for universal antimicrobial agents with pronounced synergism when used simultaneously as anticonvulsant, antibacterial, and antifungal agents.
A high quality and phonetic balanced speech corpus for Vietnamese

This paper presents a high quality Vietnamese speech corpus that can be used for analyzing Vietnamese speech characteristic as well as building speech synthesis models. The corpus consists of 5400 clean-speech utterances spoken by 12 speakers including 6 males and 6 females. The corpus is designed with phonetic balanced in mind so that it can be used for speech synthesis, especially, speech adaptation approaches. Specifically, all speakers utter a common dataset contains 250 phonetic balanced sentences. To increase the variety of speech context, each speaker also utters another 200 non-shared, phonetic-balanced sentences. The speakers are selected to cover a wide range of age and come from different regions of the North of Vietnam. The audios are recorded in a soundproof studio room, they are sampling at 48 kHz, 16 bits PCM, mono channel.
Characterizing gate operations near the sweet spot of an exchange-only qubit

Optimal working points or "sweet spots" have arisen as an important tool for mitigating charge noise in quantum dot logical spin qubits. The exchange-only qubit provides an ideal system for studying this effect because $Z$ rotations are performed directly at the sweet spot, while $X$ rotations are not. Here for the first time we quantify the ability of the sweet spot to mitigate charge noise by treating $X$ and $Z$ rotations on an equal footing. Specifically, we optimize $X$ rotations and determine an upper bound on their fidelity. We find that sweet spots offer a fidelity improvement factor of at least 20 for typical GaAs devices, and more for Si devices.
Insights into Muscle Contraction Derived from the Effects of Small-Molecular Actomyosin-Modulating Compounds

Bottom-up mechanokinetic models predict ensemble function of actin and myosin based on parameter values derived from studies using isolated proteins. To be generally useful, e.g., to analyze disease effects, such models must also be able to predict ensemble function when actomyosin interaction kinetics are modified differently from normal. Here, we test this capability for a model recently shown to predict several physiological phenomena along with the effects of the small molecular compound blebbistatin. We demonstrate that this model also qualitatively predicts effects of other well-characterized drugs as well as varied concentrations of MgATP. However, the effects of one compound, amrinone, are not well accounted for quantitatively. We therefore systematically varied key model parameters to address this issue, leading to the increased amplitude of the second sub-stroke of the power stroke from 1 nm to 2.2 nm, an unchanged first sub-stroke (5.3–5.5 nm), and an effective cross-bridge attachment rate that more than doubled. In addition to better accounting for the effects of amrinone, the modified model also accounts well for normal physiological ensemble function. Moreover, a Monte Carlo simulation-based version of the model was used to evaluate force–velocity data from small myosin ensembles. We discuss our findings in relation to key aspects of actin–myosin operation mechanisms causing a non-hyperbolic shape of the force–velocity relationship at high loads. We also discuss remaining limitations of the model, including uncertainty of whether the cross-bridge elasticity is linear or not, the capability to account for contractile properties of very small actomyosin ensembles (<20 myosin heads), and the mechanism for requirements of a higher cross-bridge attachment rate during shortening compared to during isometric contraction.
Relationship between Eye Frailty and Physical, Social, and Psychological/Cognitive Weaknesses among Community-Dwelling Older Adults in Japan

This study investigated the relationship between eye frailty and physical, social, and psychological/cognitive weaknesses among older adults in Japan. The participants were 192 community-dwelling older adult women. We measured handgrip strength, walking speed, and skeletal muscle mass; additionally, their physical, social, and psychological/cognitive frailties were surveyed using questionnaires. Eye frailty self-checks were used to assess eye frailty. Exploratory and confirmatory factor analyses were employed to verify the validity of the eye frailty self-checks. Eye frailty prevalence and related factors were investigated by conducting a binomial logistic regression analysis, with eye frailty as the dependent variable. The factor analysis results showed that a model could be constructed with the two factors of visual acuity or contrast and visual field. The model’s goodness of fit was acceptable, supporting the validity of the self-checking construct. The Kihon checklist was the only variable with a significant relationship to eye frailty. Regarding the relationship between eye frailty and subordinate items of the Kihon checklist, social withdrawal [odds ratio (OR) 2.437, 95% confidence interval 1.145–5.188], cognitive function (OR 2.047, 95%CI 1.051–3.984), and depressed mood (OR 1.820, 95%CI 1.163–2.848) were significant. Eye frailty can be considered a factor reflecting the existence of social and psychological/cognitive frailties.
Displacement for development: The nature of oil-induced displacement of households in Turkana County

This study sought to examine the displacement of households in the Lokichar-Kochodin basin due to the discovery and mining of oil in Turkana County. Guided by Anthony Giddens’ “Structuration Theory” in exploring the social processes associated with oil-induced displacement, a cross-sectional survey research design was adopted. A mixed method approach was further employed to collect data using questionnaires, focus group discussion (FGD) guides, and key informant interview (KII) guides. Both quantitative and qualitative analysis was performed through Statistical Package for Social Sciences (SPSS) Version 21 and Nvivo, respectively. The study found that the displaced households experienced loss of land for cultivation, and grazing, loss of social infrastructure, loss of residences, and overall loss of their livelihoods. The study concludes that oil-induced displacement, as a category or cause of development-induced displacement, is associated with adverse environmental, human-social, economic, and physical livelihood vulnerabilities for the affected populations. Therefore it is recommended that the government of Kenya should contemplate passing a law that necessitates compensation for affected victims in the line of development.
Cross-Kerr effect on an optomechanical system

We study the cross-Kerr (CK) effect on an optomechanical system driven by two-tone fields. We show that in the presence of the CK effect, a bistable behavior of the mean photon number in the cavity becomes more robust against the fluctuations of the frequency detuning between the cavity mode and the control field. The bistability can also be turned into a tristability within the experimentally accessible range of the system parameters. Also, we find that the symmetric profile of the optomechanically induced transparency is broken and the zero-absorption point is shifted in the presence of the CK effect. This shift can be used to measure the strength of the CK effect and the asymmetric absorption profiles can be employed to engineer a high quality factor of the cavity.