Miracle-dz/UniFAR-FTUs huggingface dataset:
One-Step Purification of Recombinant Cutinase from an E. coli Extract Using a Stabilizing Triazine-Scaffolded Synthetic Affinity Ligand

Cutinase from Fusarium solani pisi is an enzyme that bridges functional properties between lipases and esterases, with applications in detergents, food processing, and the synthesis of fine chemicals. The purification procedure of recombinant cutinase from E. coil extracts is a well-established but time-consuming process, which involves a sequence of two anionic exchange chromatography steps followed by dialysis. Affinity chromatography is the most efficient method for protein purification, the major limitation of its use being often the availability of a ligand selective for a given target protein. Synthetic affinity ligands that specifically recognize certain sites on the surface of proteins are highly desirable for affinity processes due to their cost-effectiveness, durability, and reusability across multiple cycles. Additionally, these ligands establish moderate affinity interactions with the target protein, making it possible to purify proteins under gentle conditions while maintaining high levels of activity recovery. This study aimed to develop a new method for purifying cutinase, utilizing triazine-scaffolded biomimetic affinity ligands. These ligands were previously screened from a biased-combinatorial library to ensure their binding ability to cutinase without compromising its biological function. A lead ligand, designated as 11/3′, [4-({4-chloro-6-[(2-methylbutyl)amino]-1,3,5-triazin-2-yl}amino)benzoic acid], was chosen and directly synthesized onto agarose. Experiments conducted at different scales demonstrated that this ligand (with an affinity constant Ka ≈ 104 M−1) exhibited selectivity towards cutinase, enabling the purification of the enzyme from an E. coli crude production medium in a single step. Under optimized conditions, the protein and activity yields reached 25% and 90%, respectively, with a resulting cutinase purity of 85%.
Development of AI-based Intrusion Detection System with Real-Time Flow Feature Extraction

Artificial intelligence (AI) based intrusion detection system (IDS) leverages machine learning and deep learning algorithms to analyze and identify patterns that may indicate malicious behavior. Achieving real-time operation in AI-based IDS involves a tradeoff between inspection performance and the complexity of feature extraction, influenced by the duration of traffic flow observation. To reduce the delay in feature extraction, we developed an AI-based IDS with parallel traffic feature processes utilizing off-the-shelf computing devices. The proposed AI-based IDS can process traffic flows and conduct inspections at a rate of 2,800 flows per second.
Comprehensive Analysis of 1-Year-Old Female Apolipoprotein E-Deficient Mice Reveals Advanced Atherosclerosis with Vulnerable Plaque Characteristics

Apolipoprotein E-knockout (Apoe-/-) mice constitute the most widely employed animal model of atherosclerosis. Deletion of Apoe induces profound hypercholesterolemia and promotes the development of atherosclerosis. However, despite its widespread use, the Apoe-/- mouse model remains incompletely characterized, especially at late time points and advanced disease stages. Thus, it is unclear how late atherosclerotic plaques compare to earlier ones in terms of lipid deposition, calcification, macrophage accumulation, smooth muscle cell presence, or plaque necrosis. Additionally, it is unknown how cardiac function and hemodynamic parameters are affected at late disease stages. Here, we used a comprehensive analysis based on histology, fluorescence microscopy, and Doppler ultrasonography to show that in normal chow diet-fed Apoe-/- mice, atherosclerotic lesions at the level of the aortic valve evolve from a more cellular macrophage-rich phenotype at 26 weeks to an acellular, lipid-rich, and more necrotic phenotype at 52 weeks of age, also marked by enhanced lipid deposition and calcification. Coronary artery atherosclerotic lesions are sparse at 26 weeks but ubiquitous and extensive at 52 weeks; yet, left ventricular function was not significantly affected. These findings demonstrate that atherosclerosis in Apoe-/- mice is a highly dynamic process, with atherosclerotic plaques evolving over time. At late disease stages, histopathological characteristics of increased plaque vulnerability predominate in combination with frequent and extensive coronary artery lesions, which nevertheless may not necessarily result in impaired cardiac function.
Text mining using clinical terms in electronic records of annual falls of patients in home community care

The number of health informatics in text in electronic health records (EHRs) has increased. In parallel, the text mining of health informatics in clinical notes has also increased in the attempt to utilize big data in EHRs for better patient care. However, little is known on how to utilize the word frequencies of text in clinical notes in EHRs to investigate patient care over a per annual basis.Our methods used a tool called NimbleMiner (NM) that utilized RStudio and generate a relatively “naïve” native lexicon of Simclins (i.e., SIMilar CLINical terms) via the applications with special emphasis on patient falls, which are important events that can lead to decreased health outcomes. The identified Simclins’ frequencies in the file were then determined using a basic Python script for 1-gram words (e.g., “home”) and using Voyant Tools for n+1-gram words (e.g., “living room” or “intensive care unit”). The most frequent words (over an annual basis) in the training corpus were determined to be: “mg” (15,726); “patient” (12,945); “tablet” (10,992); “po” or per os or by mouth (8,904); “blood” (8,824). The Simclins were identified for each domain of interest, the initial prompts for the first search iteration of NM, as well as ChatGPT3.5 output for comparison purposes. There were 15,252 Simclin word frequency related to home and 2,421 related to clinical settings over one year, respectively. Simclin frequencies per year by domain were “fall” (514), “setting” (17,673), computerized provider order entry – CPOE (3,408), and medications (62,317). Furthermore, the significantly larger text of 33,769 on medications (i.e., mg, tablet, medications, aspirin, coumadin, dose, doses, prescribed, and unit_ml_solution) of the total of 46,530 or 72.6% of the corpus.The use of NM and other tools proved that text mining of the clinical notes can provide operational information of important clinical events based on word Simclin frequencies in the clinical notes over an annual basis. The results showed importance of lower frequency words like “fall” as well as higher frequency terms such as “medications” and “prescriptions”. More investigation is needed in terms using Simclins across the health big data of clinical notes of EHRs to better understand the use of text in patient care.
Drug Authentication and Counterfeit Drug Detection Using Blockchain

Drug counterfeiting is a worldwide phenomenon which affects practically every industry involved with the healthcare sector in both developed and developing countries. This widespread smuggling and usage of counterfeit drugs has serious economic, health, and safety impacts on any country. In addition to this, the credibility of the healthcare system can be questioned and harmed if the counterfeit drugs find a way into the market and harm the public opinion about the pharmaceutical chain. An annual analysis done by the US Trade Representative office on rights protection in 2019 stated that up to 20% of the pharmaceutical supply sold in India were counterfeit and this has only increased since the pandemic. In an estimate done between 2020 to 2021, it was found that incidents of counterfeit and fake drugs had shot up to about 47 percent. Traditional methods of drug authentication have proven to be ineffective in detecting counterfeit drugs. Blockchain technology provides a promising solution to the problem of counterfeit drugs. The proposed system for detecting counterfeit drugs using blockchain technology consists of a blockchain network, a web application, and a database. The proposed system aims to provide an efficient and secure method of detecting counterfeit drugs using blockchain technology, which can enhance trust in the healthcare system and reduce the cost of drug authentication.
Novel attentional gait index reveals a cognitive ability-related decline in gait automaticity during dual-task walking

Introduction Gait automaticity refers to the ability to walk with minimal recruitment of attentional networks typically mediated through the prefrontal cortex (PFC). Reduced gait automaticity (i.e., greater use of attentional resources during walking) is common with aging, contributing to an increased risk of falls and reduced quality of life. A common assessment of gait automaticity involves examining PFC activation using near-infrared spectroscopy (fNIRS) during dual-task (DT) paradigms, such as walking while performing a cognitive task. However, neither PFC activity nor task performance in isolation measures automaticity accurately. For example, greater PFC activation could be interpreted as worse gait automaticity when accompanied by poorer DT performance, but when accompanied by better DT performance, it could be seen as successful compensation. Thus, there is a need to incorporate behavioral performance and PFC measurements for a more comprehensive evaluation of gait automaticity. To address this need, we propose a novel attentional gait index as an analytical approach that combines changes in PFC activity with changes in DT performance to quantify automaticity, where a reduction in automaticity will be reflected as an increased need for attentional gait control (i.e., larger index). Methods The index was validated in 173 participants (≥65 y/o) who completed DTs with two levels of difficulty while PFC activation was recorded with fNIRS. The two DTs consisted of reciting every other letter of the alphabet while walking over either an even or uneven surface. Results As DT difficulty increases, more participants showed the anticipated increase in the attentional control of gait (i.e., less automaticity) as measured by the novel index compared to PFC activation. Furthermore, when comparing across individuals, lower cognitive function was related to higher attentional gait index, but not PFC activation or DT performance. Conclusion The proposed index better quantified the differences in attentional control of gait between tasks and individuals by providing a unified measure that includes both brain activation and performance. This new approach opens exciting possibilities to assess participant-specific deficits and compare rehabilitation outcomes from gait automaticity interventions.
Design and Analysis of Koch fractal antenna for UHF Detection

Ultra-high frequency can capture the electromagnetic waves that are generated due to partial discharge. In wireless communication the technology has been over-rising day by day due to this there is an introduction of compact multiband antenna. In this paper we will discuss about the Koch fractal antenna. Fractal antennas are the antennas which have self-similar properties. There are used to increase the effective length and perimeter of a material. A Koch fractal antenna has been developed for multiband application. Fractal antennas are systematic in comparison to the conventional antenna. The principle of Koch fractal antenna is its self-similar properties that make the antenna to work in multiband operation. Miniaturing of antenna can be obtained by space filling property of fractals. As the iterations of Koch fractal antenna increases, the resonate frequency also increases. The maximum range of Koch fractal antenna is 2-30GHz.
Acupuncture in circadian rhythm sleep–wake disorders and its potential neurochemical mechanisms

Circadian rhythm sleep–wake disorders (CRSWDs) are becoming increasingly common in modern societies due to lifestyle changes. The detrimental effects of CRSWDs on sleep and psychological health have attracted considerable attention recently. Alternative remedies for the treatment of CRSWDs have also gained attention in recent years owing to the limitations of medications. Several in vivo and clinical investigations have shown that acupuncture, one of the most important components of traditional Chinese medicine (TCM), has been shown to modulate sleep-related circadian rhythms. Owing to the lack of research on the mechanism and effectiveness of acupuncture in treating CRSWDs, clinical applications of acupuncture have not gained popularity. This paper reviews the acupuncture methods, acupoint selection, and biochemical indicators supplied by in vivo and clinical studies to explore the effectiveness of acupuncture, and summarizes the circadian rhythm mechanisms and the acupuncture characteristics on circadian rhythm. The neurochemical mechanisms linked to acupuncture in treating CRSWDs are also outlined from the perspective of the central and peripheral biological clocks. Lastly, the inadequacy of previous studies on CRSWDs and conflicting results regarding acupuncture are explored and future research directions are envisioned.
Landscape Pattern and Eco-hydrological Process

There is tight connection and interaction between landscape patterns and eco-hydrological processes,especially in the arid and semi-arid regions.The complex interaction between them is one of the research frontiers.Scale,as an important impact factor on both of landscape patterns and eco-hydrological processes,plays an important role in their relationship.Interaction mechanisms may be distinct in different scales.In this article,we reviewed the domestic and foreign research of landscape patterns and eco-hydrological processes in the semi-arid regions in different scales,including the patch scale,the slope scale and the watershed scale.We also discussed the interaction mechanisms,the main influential factors,the effects of disturbance,especially the human beings′ disturbance,and the main models.Finally,the problems,insufficiencies and tendency of the present studies are put forward,including further study in physical mechanism,recovery mechanism after disturbance and simulation models.At the same time,we should pay more attention to the interactions in the watershed scale and the scale conversion.
Prescriptive Theorizing in Management Research: A New Impetus for Addressing Grand Challenges

Although management research has a rich tradition of both descriptive and prescriptive theorizing, the latter is often (and erroneously) viewed as unscientific, purely practice‐oriented, or simply a corollary of descriptive analysis. Prescriptive theorizing concerns how things should be and how they can be achieved, as opposed to descriptive theorizing, which focuses on why or how things are (interrelated). Accordingly, prescriptive theorizing has strong normative and instrumental properties, which are especially relevant when addressing pressing societal, ecological, and ethical concerns, also referred to as grand challenges, that demand a re‐evaluation of established norms and behavioural patterns. However, this opportunity is currently underutilized in the management literature, and there is a lack of guidance on how to leverage the principles of prescriptive theorizing. Therefore, I clarify its main characteristics, outline how scholars can construct rigorous prescriptive arguments, and show how normative and instrumental reasoning can promote positive social change. Embracing prescriptive theorizing as a vital complement to descriptive theorizing in management research provides scholars with an intellectual toolkit to actively engage in the urgent discourse on grand challenges and develop compelling new and impactful theories.
Deconvolution of Astronomical Images using Wiener Deconvolution: Mitigating Ground-Based Telescope Effects and Cosmic Noise

This study focuses on the deconvolution of astronomical images employing the Wiener deconvolution method to overcome the challenges posed by the ground-based telescope’s seeing effect and cosmic noise. Utilizing the Hubble Space Telescope, which is free from seeing effects, we intentionally introduce a simulated seeing effect through Airy Point Spread Function (PSF) convolution. Additionally, Gaussian noise is incorporated to emulate cosmic noise commonly encountered in astronomical observations. The proposed deconvolution method is validated using key metrics such as Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Mean Squared Error (MSE). The Hubble Space Telescope’s unique characteristics, combined with the intentional introduction of seeing effects and cosmic noise, contribute to a comprehensive evaluation of the deconvolution process. Our results showcase the efficacy of the Wiener deconvolution method in enhancing the resolution of astronomical images affected by ground-based telescope limitations and cosmic noise. The validation metrics demonstrate the capability of the proposed approach to restore details and improve the overall quality of astronomical imagery. This research offers valuable insights for astronomers and researchers seeking to optimize image processing techniques for enhanced astronomical observations and analyses.
A Review of IoT Firmware Vulnerabilities and Auditing Techniques

In recent years, the Internet of Things (IoT) paradigm has been widely applied across a variety of industrial and consumer areas to facilitate greater automation and increase productivity. Higher dependability on connected devices led to a growing range of cyber security threats targeting IoT-enabled platforms, specifically device firmware vulnerabilities, often overlooked during development and deployment. A comprehensive security strategy aiming to mitigate IoT firmware vulnerabilities would entail auditing the IoT device firmware environment, from software components, storage, and configuration, to delivery, maintenance, and updating, as well as understanding the efficacy of tools and techniques available for this purpose. To this effect, this paper reviews the state-of-the-art technology in IoT firmware vulnerability assessment from a holistic perspective. To help with the process, the IoT ecosystem is divided into eight categories: system properties, access controls, hardware and software re-use, network interfacing, image management, user awareness, regulatory compliance, and adversarial vectors. Following the review of individual areas, the paper further investigates the efficiency and scalability of auditing techniques for detecting firmware vulnerabilities. Beyond the technical aspects, state-of-the-art IoT firmware architectures and respective evaluation platforms are also reviewed according to their technical, regulatory, and standardization challenges. The discussion is accompanied also by a review of the existing auditing tools, the vulnerabilities addressed, the analysis method used, and their abilities to scale and detect unknown attacks. The review also proposes a taxonomy of vulnerabilities and maps them with their exploitation vectors and with the auditing tools that could help in identifying them. Given the current interest in analysis automation, the paper explores the feasibility and impact of evolving machine learning and blockchain applications in securing IoT firmware. The paper concludes with a summary of ongoing and future research challenges in IoT firmware to facilitate and support secure IoT development.
Engaging Attackers with a Highly Interactive Honeypot System Using ChatGPT

Cyber-attacks are becoming more sophisticated and frequent[2] in today's environment of growing connectivity. To safeguard networks and systems, it is essential to establish efficient and effective security solutions[4]. This project intends to develop a highly interactive honeypot system, utilizing the ChatGPT API, to engage attackers and gather data on their behaviour. In order to gain knowledge of the attacker's strategies and tactics, the system will react to commands by imitating a weak system. The ultimate objective is to enhance our comprehension of attacker behaviour and support the creation of more effective security solutions. This honeypot system's implementation and evaluation will make significant contributions to the field of computer security.
Image Recognition of Maize Pests and Diseases Based on Convolutional Neural Network Algorithm

Crop disease recognition techniques based on convolutional neural networks, although accurate, are difficult to be extended in complex environments due to the limitations of sample collection and research objects. In this paper, an improved convolutional neural network approach is proposed as an example of maize disease identification, focusing on the identification and classification of pests and diseases affecting maize crops through image analysis using improved convolutional neural networks (CNNs). The factors affecting the application effect of the dataset were investigated by processing the dataset with magnification, scaling, background removal and local fine segmentation, and the optimised recognition model was used to design a disease recognition system based on mobile terminals and validated in the field. By comparing the recognition of four diseases and pests, including round spot, grey leaf spot and rust of soil maize, the results show that the recognition accuracy of the model can reach 0.9742 and the misclassification rate is 0.0258, which is improved compared with other models.
A GRU-Based Model for Detecting Common Accidents of Construction Workers

Fall accidents in the construction industry have been studied over several decades and identified as a common hazard and the leading cause of fatalities. Inertial sensors have recently been used to detect accidents of workers in construction sites, such as falls or trips. IMU-based systems for detecting fall-related accidents have been developed and have yielded satisfactory accuracy in laboratory settings. Nevertheless, the existing systems fail to uphold consistent accuracy and produce a significant number of false alarms when deployed in real-world settings, primarily due to the intricate nature of the working environments and the behaviors of the workers. In this research, the authors redesign the aforementioned laboratory experiment to target situations that are prone to false alarms based on the feedback obtained from workers in real construction sites. In addition, a new algorithm based on recurrent neural networks was developed to reduce the frequencies of various types of false alarms. The proposed model outperforms the existing benchmark model (i.e., hierarchical threshold model) with higher sensitivities and fewer false alarms in detecting stumble (100% sensitivity vs. 40%) and fall (95% sensitivity vs. 65%) events. However, the model did not outperform the hierarchical model in detecting coma events in terms of sensitivity (70% vs. 100%), but it did generate fewer false alarms (5 false alarms vs. 13).
Optimized Meditative EEG Channel Estimation for Efficient Impact Study of Mindfulness for Sustainable Health

Meditation is a disciplined mindfulness practice in which the practitioners direct their attention towards a specific object, action, or thought. To comprehensively investigate the impact of meditation on electroencephalography (EEG) signals, it is crucial to develop a technique that illustrates the interactions among various EEG channels during diverse cognitive activities. In this work, factor analysis (FA) is utilized to examine the connections and fluctuations between EEG channels during different meditational tasks. By employing FA, we can effectively reduce the extensive dimensionality of the EEG channel. Significantly, the application of FA revealed three distinct factors that collectively accounted for an impressive 92.9% of the total variance in the EEG channels. This dimensionality reduction enables a focused exploration of the fundamental aspects of brain activity associated with meditation. Additionally, by organizing electrodes based on their maximum absolute loading values, the study uncovered intricate patterns of interaction among different brain channels.