Unusual Activity Research Articles

Insights into the Unusual Activity of a Novel Homospermidine Synthase with a Promising Application to Produce Spermidine.

Spermidine is a naturally occurring polyamine with multiple biological activities and potential food and agricultural applications. However, sustainable and scalable spermidine production has not yet been attained. In this study, a homospermidine synthase (HSS) from Pseudomonas frederiksbergensis (PfHSS) capable of catalyzing the synthesis of spermidine from 1,3-diaminopropane and putrescine was identified based on multiple sequence alignment using Blastochloris viridis HSS (BvHSS) as a template. The optimal reaction pH and temperature for purified PfHSS were determined to be 8.5 and 45 °C, respectively, and K+ was able to promote the enzyme activity. Further analysis of the structural and functional relationships through molecular docking and molecular dynamics simulation indicates that glutamic acid at position 359 is the essential residue for the enzyme-catalyzed synthesis of spermidine. The whole-cell catalytic reaction yielded 1321.4 mg/L spermidine and 678.2 mg/L of homospermidine. This study presents a novel, promising, and sustainable biological method for producing spermidine.

A Cost-Effective, Nanoporous, High-Entropy Oxide Electrode for Electrocatalytic Water Splitting

High-entropy materials have attracted extensive attention as emerging electrode materials in various energy applications due to their flexible tunability, unusual outstanding activities, and cost-effectiveness using multiple earth-abundant elements. We introduce a novel high-entropy composite oxide with the five elements of Cu, Ni, Co, Fe, and Cr (HEO-3CNF) for use in the oxygen evolution reaction (OER) in electrocatalytic water splitting. HEO-3CNF is composed of two phases with a non-equimolar, deficient high-entropy spinel oxide of (Cu0.2−xNi0.2Co0.2Fe0.2Cr0.2)3O4 and monoclinic copper oxide (CuO). Electrochemical impedance spectroscopy (EIS) with distribution of relaxation times (DRT) analysis validates that the HEO-3CNF-based electrode exhibits faster charge transfer than benchmark CuO. It results in improved OER performance with a lower overpotential at 10 mA/cm2 and a Tafel slope than CuO (518.1 mV and 119.7 mV/dec versus 615.9 mV and 131.7 mV/dec, respectively) in alkaline conditions. This work may provide a general strategy for preparing novel, cost-effective, high-entropy electrodes for water splitting.

Curcuphenol possesses an unusual histone deacetylase enhancing activity that counters immune escape in metastatic tumours.

Curcuphenol, a common component of the culinary spices, naturally found in marine invertebrates and plants, has been identified as a novel candidate for reversing immune escape by restoring expression of the antigen presentation machinery (APM) in invasive cancers, thereby resurrecting the immune recognition of metastatic tumours. Two synthetic curcuphenol analogues, were prepared by informed design that demonstrated consistent induction of APM expression in metastatic prostate and lung carcinoma cells. Both analogues were subsequently found to possess a previously undescribed histone deacetylase (HDAC)-enhancing activity. Remarkably, the H3K27ac ChIPseq analysis of curcuphenol-treated cells reveals that the induced epigenomic marks closely resemble the changes in genome-wide pattern observed with interferon-γ, a cytokine instrumental for orchestrating innate and adaptive immunity. These observations link dietary components to modifying epigenetic programs that modulate gene expression guiding poised immunity.

The detection and management of attempted fraud during an online randomised trial

BackgroundOnline studies offer an efficient method of recruiting participants and collecting data. Whilst delivering an online randomised trial, we detected unusual recruitment activity. We describe our approach to detecting and managing suspected fraud and share lessons for researchers.MethodsOur trial investigated the single and combined effects of different ways of presenting clinical audit and feedback. Clinicians and managers who received feedback from one of five United Kingdom national clinical audit programmes were emailed invitations that contained a link to the trial website. After providing consent and selecting their relevant audit, participants were randomised automatically to different feedback versions. Immediately after viewing their assigned feedback, participants completed a questionnaire and could request a financial voucher by entering an email address. Email addresses were not linked to trial data to preserve participant anonymity. We actively monitored participant numbers, questionnaire completions, and voucher claims.ResultsFollowing a rapid increase in trial participation, we identified 268 new voucher claims from three email addresses that we had reason to believe were linked. Further scrutiny revealed duplicate trial completions and voucher requests from 24 email addresses. We immediately suspended the trial, improved security measures, and went on to successfully complete the study.We found a peak in questionnaires completed in less than 20 seconds during a likely contamination period. Given that study and personal data were not linked, we could not directly identify the trial data from the 268 duplicate entries within the 603 randomisations occurring during the same period. We therefore excluded all 603 randomisations from the primary analysis, which was consequently based on 638 randomisations. A sensitivity analysis, including all 961 randomisations over the entire study except for questionnaire completions of less than 20 seconds, found only minor differences from the primary analysis.ConclusionOnline studies offering incentives for participation are at risk of attempted fraud. Systematic monitoring and analysis can help detect such activity. Measures to protect study integrity include linking participant identifiers to study data, balancing study security and ease of participation, and safeguarding the allocation of participant incentives.Trial registrationInternational Standard Randomised Controlled Trial Number: ISRCTN41584028. Registration date is August 17, 2017.

An adaptive combination algorithm based on deep learning and genetic algorithm for anomalous events detection

One of the most widely used human behavior detection methods is anomaly detection, which this article covers. Ensuring a person's safety is a crucial task in every community today due to the ever-increasing actions that can be dangerous, from planned crime to harm from an accident. Classic closed-circuit television is insufficient since a person must always be awake and available to monitor the cameras, which is costly. Also, someone's attention tends to decrease after a certain period of time. Due to these reasons, a surveillance system that is automated and able to detect unusual activities in real-time and give sufferers prompt aid is necessary. It should be noted that the identification process must be completed swiftly and correctly. In this paper, we employ a model based on mixes the machine learning (ML) model, namely genetic algorithms with deep learning (DL). In this study's experimentation, the UCF-Crime dataset was employed. The detection accuracy on the testing sample dataset was equal to 89.90%, while the area under the curve (AUC) was equal to 94.58%. The developed models have demonstrated reliability and the ability to achieve the greatest accuracy when compared to models that have already been designed.

Boosting the antimicrobial and Azo dye mineralization activities of ZnO ceramics by enhancing the light-harvesting and charge transport properties

Herein, we report the wet-chemical synthesis of a ferromagnetic nickel-doped ZnO (Zn1-xNixO) nanocatalyst as a novel and visible-light-driven photocatalyst. Through X-ray diffraction, UV/Vis absorption, electronic studies, and current-voltage experiments, the effect of the ferromagnetic nickel dopant on the structural, optical, morphological, and electrical properties of the synthesized Zn1-xNixO nanocatalyst was studied. The Ni-doping introduced the structural variation in the Zn1-xNixO nanocatalyst, exhibiting a visible light-triggered optical band gap of 2.96 eV and an excellent current conductivity of 6.3 × 10−4 Sm−1. Moreover, the synthesis of the Zn1-xNixO catalyst at the nanoscale enhanced its surface energy, showing a robust affinity to stick with the dye and pathogenic microbes. The synergistic effects of all the mentioned features enable our Zn1-xNixO nanocatalyst to efficiently generate and transport reactive oxygen species (ROS) under visible light illumination. Regarding antibacterial action, the as-synthesized Zn1-xNixO nanocatalyst showed 1.7% higher activity against E. coli than that of the drug Ciprofloxacin. In addition, doped nanocatalysts mineralize almost 97% of the Allura red dye in just 80 min with a constant rate value of 0.036 min−1. The impedance study and post-application XRD proposed that our Zn1-xNixO nanocatalyst has good conductivity and structural stability. Applications studies show the unusual photocatalytic activity of as-synthesized Zn1-xNixO nanocatalysts, which makes it a suitable candidate for industrial discharge treatment applications at the expense of solar light.

Abnormal Activity Recognition in Private Places Using Deep Learning: A Survey

Abstract: The method of analysing human motion using computer and machine vision technologies is known as "human activity recognition," or HAR. One of the applications of human activity recognition in security systems is anomaly detection. Surveillance cameras have been widely placed as the foundation for video analysis as the demand for security has grown. Identifying aberrant behaviour necessitates considerable human effort, which is one of the major challenges in surveillance video analysis. It is important to set up video recording in order to detect unusual activity automatically. Our intelligent video surveillance system can detect an abnormality in a video using deep learning technologies. Real-time detection of activities is also conceivable, and these video frames will be saved in the system as images for the user to study. The proposed Abnormal Activity Recognition system was designed with the purpose of finding and detecting anomalies in the financial industry, especially in an ATM context, using a live stream. The first part of the research focuses on the use of image deep learning algorithms to recognise different products and detect anomalous behaviour utilizing ATM monitoring systems

Guarding Networks: Understanding the Intrusion Detection System (IDS)

An Intrusion Detection System (IDS) maintains network traffic looks for unusual activity and sends alerts when it occurs. The main duties of an Intrusion Detection System (IDS) are anomaly detection and reporting; however, certain Intrusion Detection Systems can act when malicious activity or unusual traffic is discovered. In this article, we will discuss every point about the Intrusion Detection System.

The confidence interval analysis for stocks with “Unusual Market Activity”

In order to protect investors, the capital market authorities actively monitor stock price movements. Unusual movements have resulted in the capital market authorities announcing unusual market activity (UMA). This study uses firms that obtain UMA status in the announcement period from March to April 2023. To analyze UMA, the study uses confidence interval estimation. The results of this study indicate that UMA status tends to be proven by paying attention to the classification of critical values ​​and the range of data

Whistleblowing, fraud prevention, and fraud awareness: Evidence from the Palembang Local Government of Indonesia

Fraud is an enormous obstacle to a country’s development because it can cause failure in achieving goals. The most significant source of fraud in Palembang Local Government of Indonesia arises from the procurement of goods and services, which reached 49.1% in 2020. Therefore, this study aims to examine how fraud awareness and good government governance affect fraud prevention. This paper targets one of the Regional Government areas of Palembang City, Indonesia. One hundred twenty-two respondents comprised the head of budget staff, budget user authority staff, and commitment-making committee staff. Data were obtained using a questionnaire with a Likert scale. The study employed two endogenous variables (fraud prevention and fraud awareness), one intervening (fraud awareness), and two exogenous variables (whistleblowing and good government governance). Following this, structural equation models were used to examine the relationships between each variable. The study results show a significant positive effect of whistleblowing and good governance on fraud awareness. In contrast, fraud awareness had no significant impact on fraud prevention. The obtained results found that fraud awareness cannot mediate the whistleblowing system through fraud prevention; also, fraud awareness is not a moderating variable to influence good governance through fraud prevention. The paper contributed empirical evidence to explore fraud prevention approaches by maintaining consistency in the implementation of the whistleblowing system, anti-corruption culture, and supervision to identify unusual activities.

Diversification of services at the central libraries of RF constituent territories in the circumstances of service economy

The study goal is to specify the nomenclature of services types as presented at the library official websites. The authors characterize the library services as the subject of multi-aspect analysis and evaluation. They emphasize that the concept “library services” and “services by libraries” are not identical. The service economy affects diversification of services provided by the libraries. The authors complement the publications theoretical analysis with the findings of the largescale empirical study of services represented at the official websites of 77 central libraries of the RF constituent units (republican, regional territorial universal scientific libraries). The scale of diversification is demonstrated. The authors argue that the libraries have to undertake some unusual activities and to provide services foreign to their nature and social mission. The findings of the study demonstrate that services production in the libraries corresponds to 30 different types of professional activities. Many services provided by the libraries call for other than library, information, or bibliographic competences. Therefore, services quality assessment is needed; certification and licensing of the libraries providing them may be needed. The authors conclude that diversification as manifested in services inappropriate to the libraries is forced by the transfer to the market economy, development of service economy, and the focus on service-oriented government.

Teraryl Braces in Macrocycles: Synthesis and Conformational Landscape Remodeling of Peptides.

The three-dimensional structure of medium-sized cyclic peptides accounts for their biological activity and other important physiochemical properties. Despite significant advances in the past few decades, chemists' ability to fine-tune the structure, in particular, the backbone conformation, of short peptides made of canonical amino acids is still quite limited. Nature has shown that cross-linking the aromatic side chains of linear peptide precursors via enzyme catalysis can generate cyclophane-braced products with unusual structures and diverse activities. However, the biosynthetic path to these natural products is challenging to replicate in the synthetic laboratory using practical chemical modifications of peptides. Herein, we report a broadly applicable strategy to remodel the structure of homodetic peptides by cross-linking the aromatic side chains of Trp, His, and Tyr residues with various aryl linkers. The aryl linkers can be easily installed via copper-catalyzed double heteroatom-arylation reactions of peptides with aryl diiodides. These aromatic side chains and aryl linkers can be combined to form a large variety of assemblies of heteroatom-linked multi-aryl units. The assemblies can serve as tension-bearable multijoint braces to modulate the backbone conformation of peptides as an entry to previously inaccessible conformational space.

Deep Learning Driven Instinctive Surveillance

It is a very boring and laborious job providing observation security. In order to determine whether the exercises that were caught were unusual or suspicious, a labour force is needed. Here, we’ll put together a structure to automate the task of reviewing video reconnaissance. We will regularly review the camera feed to look for any unusual activities like surprising or suspicious ones. and an automatic acknowledgment will be sent to the user with an alert email along with the suspicious frames and SMS to mobile number. Deep learning computations for deep reconnaissance have improved from earlier encounters. These developments have revealed a key pattern in thorough reconnaissance and promise a significant increase in efficacy. Deep observation is typically used for things like identifying evidence of burglary, finding violence, and recognising explosion potential. We will propose a spatio-temporal auto-encoder for this project that relies on a 3D convolutional brain structure. The decoder then reproduces the edges after the encoder section has removed the spatial and transient data. By recording the recreation misfortune using the Euclidean distance between the original and replicated batch, the odd occurrences are distinguished.

A lightweight unsupervised approach for adverse health detection and digital biomarker discovery in people living with dementia

AbstractBackgroundSensor‐based remote health monitoring of persons living with dementia (PLwD) can be used to gain insights into their health and monitor the progression of their condition, with minimal intrusion. This helps minimize preventable hospital admissions, while allowing researchers to improve their understanding of dementia. Existing approaches for detecting activity and behavioural anomalies in PLwD are challenged by noise in data, lack of annotated datasets, multivariate data, scalability, data drift and explainability.MethodWe propose and evaluate a solution based on the Matrix Profile, an exact, ultra‐fast distance‐based anomaly detection algorithm, specifically the Contextual Matrix Profile (CMP), to detect anomalies that may indicate unusual activity and onset of UTI. Daily household movement data collected via passive infrared (PIR) sensors are used to generate CMPs from location‐wise sensor counts, duration and change in hourly movement patterns. We create CMP‐based multivariate anomaly detection models to generate a single daily normalized anomaly score for each patient. We discover digital biomarkers of anomalies and evaluate our method vs. three state‐of‐the‐art algorithms.ResultCMP‐based models yield up to 85% recall with only a 5% alert rate, when evaluated on a subset of 9363 days from 15 participant households with 41 clinically validated incidences of urinary tract infections (UTI) and hospitalization, collected by the UK Dementia Research Institute between August 2019 and July 2021. Our multidimensional CMP model offers the best balance of recall vs. anomalies raised, with excellent generalisation. We discover that bathroom early AM activity (midnight to 6 am) is the prime cross‐patient digital biomarker of anomalies. This validates findings in literature that unusual bathroom activity is a clinically significant feature in UTI for dementia. We also demonstrate a cross‐patient view of anomaly patterns.ConclusionWe address the need for anomaly detection and scoring using multivariate time series sensor data in remote health monitoring. The CMP allows configurability, ability to denoise and detect patterns, and explainability to clinical practitioners. With higher sensitivity, fewer alerts and better overall performance than state‐of‐the‐art methods, and the ability to discover digital biomarkers of anomalies, the CMP is a clinically meaningful unsupervised anomaly detection technique for dementia and beyond.

Detecting abnormal behavior in megastore for intelligent surveillance through 3D deep convolutional model

Abstract The use of neural networks in a range of academic and scientific pursuits has introduced a great interest in modeling human behavior and activity patterns to recognize particular events. Various methods have so far been proposed for building expert vision systems to understand the scene and draw true semantic inferences from the observed dynamics. However, classifying abnormal or unusual activities in real-time video sequences is still challenging, as the details in video sequences have a time continuity constraint. A cost-effective approach is still demanding and so this work presents an advanced three-dimensional convolutional network (A3DConvNet) for detecting abnormal behavior of persons by analyzing their actions. The network proposed is 15 layers deep that uses 18 convolutional operations to effectively analyze the video contents and produces spatiotemporal features. The integrated dense layer uses these features for the efficient learning process and the softmax layer is used as the output layer for labeling the sequences. Additionally, we have created a dataset that carries video clips to represent abnormal behaviors of humans in megastores/shops, which is a consequent contribution of this paper. The dataset includes five complicated activities in the shops/megastores: normal, shoplifting, drinking, eating, and damaging. By analyzing human actions, the proposed algorithm produces an alert if anything like abnormalities is found. The extensive experiments performed on the synthesized dataset demonstrate the effectiveness of our method, with achieved accuracy of up to 90.90%.

From the bottom up – attributes of small vesper bats’ activity sites in an upland ecosystem in Chiapas, México

We studied bats in a natural protected area and the influence zone in Chiapas, México. Focusing on small vesper bats (Vespertilionidae: Myotinae) in water sinkholes, flooded surfaces, and forested locations (sampling sites), our objectives were to: 1) differentiate the sampling sites based on abiotic and biotic variables, 2) compare the relative activity between the contrasting sampling sites, and 3) determine the relative importance of surrounding land covers amounts on the relative activity. We expected the following: 1) that sampling sites would be differentiable based on an interaction of environmental conditions and nocturnal flying insects’ biomass, 2) to find a significantly higher relative activity associated with water bodies as a reflection of potential drinking and feeding supplies, and 3) that forest cover would have a significant association in a positive direction with the relative activity as a reflection of roosting and commuting opportunities. We obtained weather data with a portable station, collected flying nocturnal insects with a passive trap, and recorded bats with ultrasonic detectors. We also used satellite imagery to calculate land covers amounts around the sampling sites. We performed a multivariate analysis to compare sampling sites, and elaborated correlation models of relative activity against surrounding land covers amounts. The only significant variable for differentiating sampling sites was wind speed; in water sinkholes, with lower mean value compared to flooded surfaces and forested locations, along with a comparatively higher percentage of dipterans in the samples. The mean relative activity of bats was significantly higher in water sinkholes, with maximum values reaching 95 %, which we can relate to environmental conditions and resources available. We found associations in a positive direction with forest cover, secondary forest and water surface, and negative with agricultural land and human development. Finally, we argue that some of the water sinkholes meet the criteria of small natural features supported by the unusual activity of the studied bats, and that targeted conservation actions will complement other strategies implemented in the area.

Dual-Mode Paper Biosensing Platform Based on Oxidase-Like CoFeMn Nanozymes for Point-of-Care Detection of Glutathione

A paper-based biosensing platform is a point-of-care (POC) analysis toolkit for detection of various biomarkers. Nonetheless, its lack of high sensitivity, selectivity, and multi-signal readout poses a high degree of challenge. Herein, we introduce an interesting colorimetric-temperature dual-signal readout biosensor based on multifunctional CoFeMn biomimetic oxidase. Due to the cost-effectiveness, no additional detector requirement, dual-signal readout capacity, and darker signal change, CoFeMn hydroxide is innovatively fabricated by a facile hydrothermal method using Mn as an active center to construct a multifunctional signal tag for mimicking the structure of oxidase. This strategy allows CoFeMn hydroxide to exhibit an unusual oxidase-like activity, transforming O2 into ·OH in the absence of H2O2 and resulting in the generation of a colorimetric temperature dual signal by oxidizing 3,3′,5,5′-tetramethylbenzidine (TMB). We used glutathione (GSH) as the analyst and successfully applied this biosensor to determine GSH, which showed excellent analytical performance of rapidity, high specificity, sensitivity, and accuracy. Employing the colorimetric and temperature test strips, these toolkits are designed to visualize GSH. Benefiting from a relatively independent signal transduction pathway, the constructed test strip POC devices display satisfactory coupling efficiency and are further employed to monitor GSH in tumor cells. This research will be conducive to the rational design of a mimetic oxidase-based dual-response nanoprobe in paper-based POC devices, the exploration of more nanomaterial-derived mimetic oxidase, and the extension of their applications in medical diagnosis.

On the Geomorphic, Meteorological, and Hydroclimatic Drivers of the Unusual 2018 Early Summer Salt Dust Storms in Central Asia

AbstractA series of synoptically forced dust events, including an extreme salt storm from the Aralkum desert, stuck large parts of Central Asia during early summer 2018, causing flight cancellation, widespread haze, and salt deposition on croplands. This manuscript explores the geomorphic, meteorological, and hydroclimatic drivers of the unusual dust activity based on the three prerequisite factors of dust outbreak: sediment supply, sediment availability, and dust‐uplifting winds. Dry lake beds and lower river floodplains are identified as the source area of frequent dust episodes likely due to the abundant supply of fine‐grain sediments. Whereas, minimal dust activity was detected at the region's major sandy deserts. The salt dust storms were triggered by cold intrusion and strong postfrontal winds at the southeast flank of a rapidly developing anticyclone over western Russia which was enhanced by the Scandinavian blocking upstream. The 2018 early summer dust events in Central Asia were identified as part of spatially compounding weather hazards linked to a slow‐moving synoptic wave pattern across the Northern Hemisphere midlatitudes. Finally, the aeolian sediment availability at the dust source areas was enhanced by below‐average soil moisture and vegetation during summer 2018 linked to reduced precipitation in the preceding winter. The 2017/18 winter precipitation deficit was driven by a double‐dip, Central Pacific La Niña episode associated with large‐scale precipitation‐suppressing subsidence and enhanced moisture flux divergence over Central Asia.

Random ionic polymers: Salt-triggered reversible vesicular self-assembly in water

NaCl salt as an external stimulus triggers the self-assembly of ionic polymers and offers a convenient route to structuring vesicular reversible polymeric nanoparticles. In this contribution, amphiphilic random imidazolium ionic polymers are synthesized by roping the partial anion exchange and the choice of N-substituent. These polymers are evaluated for their self-assembly behavior in water, and it is strengthened by dye encapsulation application in water through self-assembly. The effects of salt and pH study demonstrated the reversibility of self-assembly. The self-assembled nano-aggregates are thermoresponsive at the upper critical solution temperature (UCST) (Cp 47 °C). The synergic balance between hydrogen bonding and ionic self-assembly is attributed to the constituents in the macromolecular structure, making these polyionic liquids (PILs) a “smart” responsive system in water upon the addition of external stimuli, such as NaCl and KPF6. This self-assembly is acquired with partial anion exchange and salt balance in the water. The unusual interfacial activity of polymer to form a regular shape eventually to surfaces organic dye molecule serving as a type of stabilizer or scavenger evidenced by nano-size and vesicular morphology analysis.

Fluid-driven seismic swarms in the Gripp valley (Haute-Pyrénées, France)

SUMMARY An unusual seismic activity has recently occurred in the Gripp valley, located in the central part of the French Pyrenees. Since spring 2020, two new swarms appeared, clearly outside the usual location of the seismicity in this area. On 20 September 2020, almost concomitantly with the activation of the second seismic swarm, a hole suddenly opened in the bed of a local river, the Adour de Payolle. This hole drained the water from the river, which dried up over 500 m. We follow and study the spatial and temporal evolution of these clusters, using four temporary stations deployed a few days or months after the beginning of the crisis to complete the regional network. These additional data lead to the construction of a comprehensive catalogue of more than 4900 earthquakes, using both a template matching approach and a deep-learning based phase picking method to complete and improve the initial catalogue available from the French seismological agency. This allows highlighting a slow and clear migration of the seismicity during 1 yr. Precise absolute and relative event locations reveal a dipping faulting structure, confirmed by the focal mechanism estimated for the highest magnitude event of the sequence (ML 3). We propose to explain the observed migration of the seismicity by deep fluids going up through a newly discovered faulting structure.