Contemporary Methods Research Articles

Review of contemporary fluorescence correlation spectroscopy method in diverse solution studies.

Fluorescence correlation spectroscopy (FCS) is a well-known and established non-invasive method for quantification of physical parameters that preside over molecular mechanisms and dynamics. It combines maximum sensitivity and statistical confidence for the analysis of speed, size, and number of fluorescent molecules and interactions with surrounding molecules by time-averaging fluctuation analysis in a well-defined volume element. The narrow compass of this study is to acquaint the basic principle of diffusion and the FCS method in general regarding variable magnitudes and standardization adjustment. In this review, we give a theoretical introduction, examples of experimental applications, and utensils in solution systems with future perspectives.

Contemporary Methods and Novel Approaches in the Identification and Quantification of Foodborne Pathogens

Foodborne pathogens are a continuous problem attracting the attention of public health institutions, microbiologists and food producers globally. The scientific community has therefore been focused for years on finding “new” and “better” methods, all in an effort to detect foodborne pathogens in the timeliest manner possible. It should be emphasized that the development of molecular genetics – the use of genetic information of biological macromolecules in routine testing in particular – has led to a revolutionary turn in biological science research. Almost all procedures detect different bacterial genetic footprint or biomarkers, which makes bacterial isolation unnecessary. However, these recent studies of “fast microbiological methods”, which require only a fewhours, rather than a few days, have not supplanted the classic way of microbiological food testing, i.e. the classic microbiological methods and the gold standard. The new methods are therefore faced with high requirements such as great detection limits, speed of analysis, relatively low costs and high sensitivity to identify various foodborne pathogens. Some of them provide the possibility of monitoring the pathogens, which is an important part of establishing a pathogen control network in the agri-food production chain at the international level. In this comprehensive literature review the authors present and review identification and quantification methodswith emphasis on the most important EU foodborne pathogens (Salmonella, Campylobacter, Escherichiacoli – STEC, Yersinia and Listeria). Methods have been divided into two large groups – Contemporary methods (Immunoassays and Polymerase Chain Reaction) and Novel approach methods (Biosensors, Bacterial typing, and Omics), and assessed by their functionality, advantages and disadvantages.

An event tree-based distance transform algorithm for simultaneously determining mountain railway alignments and station locations

The concurrent optimization of railway alignments and station locations (RA&SL) is a complex task, especially for the main railway lines in mountainous regions. The intricate coupling constraints between RA and SL are very challenging for the conventional RA&SL search methods, which consider limited solution types to generate an optimized RA&SL solution. To solve this problem, an event tree-based distance transform (ET-DT) algorithm is proposed. The study area is first divided into grid cells, followed by a preprocessing step to identify infeasible areas for station locations. Next, an event tree (ET) is constructed to represent all the possible solution types during the RA&SL design process. Afterward, in the ET-DT method, a total of eight types of RA&SL search operators are integrated to generate diverse RA&SL alternatives. Ultimately, the proposed method is applied to a realistic case with significantly-undulating terrain. Results show that ET-DT generates 2.8 times more solutions than a contemporary DT-based method (PreDT) and further improves solution quality through an 8.7% reduction in construction cost compared to the best manually-designed alignment, while PreDT only achieves a 5.7% reduction.

Exploring Contemporary Teaching Methods: A Self-Reflective Study from an Educator Without a Formal Teaching Degree

Exploring Contemporary Teaching Methods: A Self-Reflective Study from an Educator Without a Formal Teaching Degree

Changing Landscape of Randomized Clinical Trials in Stroke: Explaining Contemporary Trial Designs and Methods.

Evidence generated from randomized clinical trials (RCTs) plays an indispensable role in advancing clinical stroke care. Although the number of stroke-related RCTs published every year has grown exponentially over the past 25 years, the execution and completion of RCTs, particularly those conducted in a hyperacute setting, have grown more complicated and challenging over the years. In addition to the practical challenges associated with conducting a clinical trial, like obtaining human subjects approval, identifying clinical sites, training trial personnel, and enrolling the target number of patients within the available funding and timeline, the complexity of contemporary RCT designs and analyses has become much more exacting. It is no longer sufficient to have a decent understanding of the 2-arm, placebo-controlled RCT, combined with a rudimentary grasp of the P value; things are now much more complicated. Innovations in trial design and analysis, including adaptive, Bayesian, platform, and noninferiority designs, have occurred to address the problems of poor trial efficiency. However, these advances require the end user to have a much greater level of understanding regarding the rationale, conduct, analysis, and interpretation of each design. While these newer designs seek greater efficiency, there are inevitably tradeoffs that need to be understood. In this month's edition of Stroke, we introduce a new series designed to help fill in these knowledge gaps. Over the next few months, 4 papers will be published that address major design innovations (adaptive, Bayesian, platform, and noninferiority) with the aim of illustrating how these approaches can make trials more efficient (where efficiency is defined as getting to the right answer, sooner, with a potentially lower sample size). In addition to introducing this series, this current article also reviews traditional hypothesis testing and the common misinterpretations of the P value; fortunately, new philosophical schools of inference are beginning to vanquish the overreliance on the P value. We are excited about the opportunity to educate the Stroke readership about these new trial designs and the profound implications that they bring.

Harnessing the Link between Climate Change and Traditional Food Preservation Methods: Implications for Global Food Security and Public Health

Climate change is a global issue, which impacts the different geographical zones in different levels. It is also linked to numerous human health issues, which are a concern to human health. The impact of climate change on traditional methods of food preservation in Nigeria and the consequences for food security and health are examined in this research. Information from the Nigerian Meteorological Agency (NIMET) and the National Emergency Management Agency (NEMA) show that average temperatures are increasing and rainfall is decreasing, which are effects of climate change that affect agriculture, water resources, and traditional food preservation methods that are detrimental to public health.Traditional food preservation methods are critical for understanding the link between climate change and food security.This paper uses a case study of Nigeria to examine the effects of climate change on traditional food preservation practices and public health. The research focuses on the shifts in the conventional preservation techniques, the impact of these shifts on nutrition, and policy interventions for enhancing sustainable preservation of food. Climate change indicators such as temperature and rainfall were used as surrogates for climate change, food insecurity such as undernourishment rates were used for food security and nutritional health outcomes such as child stunting and micronutrient malnutrition. The study shows that climate change has a negative effect on food security since it reduces crop yields and enhances post-harvest losses. Sun-drying and smoking are the common preservation techniques that are now less efficient because of high humidity and unpredictable weather. As such, people are experiencing increased incidences of food wastage, nutritional imbalance, and food borne diseases especially with those people in rural regions. Furthermore, the study confirms the relationship between climate change and food insecurity, which has a negative impact on the health of pregnant women and children, especially under five years, and high under-five mortality despite a decrease in child mortality in the last decade.The results provide important regional information relevant to the entire Sub-Saharan Africa and useful to the international community in matters of climate change, food security and health. This study calls for the use of innovation that combines the conventional and contemporary methods of food preservation in order to mitigate the impacts of climate change on food security systems. Furthermore, it underlines the need for the policy interventions that may prevent food borne illness and other diseases. These insights are in line with the United Nations Sustainable Development Goals (UN-SDGs) 2, 3, and 13, namely; Zero Hunger, Good Health and Well-being, and Climate Action, respectively.This research addresses a significant gap in the literature by linking climate change effects with food preservation practices and offering recommendations for improving food system vulnerability to climate change. It provides a clear picture of the prospects and constraints of mitigating climate change impacts on food insecurity and enhancing food security and health in the world.

Exploring the potential of contemporary deep learning methods in purifying polluted information

Abstract Accurately predicting chaotic dynamical systems is a crucial task in various fields, and recent advancements have leveraged deep learning for this purpose. However, in the era of big data, the inevitable challenge of data contamination caused by invalid information from other interfering systems becomes increasingly prominent and complicates accurate predictions. Although contemporary deep learning methods have shown their potential, very few studies have focused on developing algorithms specifically designed to address the problem posed by such data contamination. Thus, exploring the ability of contemporary deep learning methods to purify polluted information fills an important gap in the current body of research. This study explores the performance and stability of several modern deep learning techniques for predicting chaotic systems using datasets polluted by invalid information. Our findings reveal that while most of the state-of-the-art deep learning methods exhibit reduced and unstable predictive performance owing to such contamination, the dynamical system deep learning (DSDL) method stands out, remaining unaffected by any interference. This breakthrough illustrates DSDL's unique ability to purify invalid data and uncover the inherent rules of chaotic systems. As we move forward, DSDL paves the way for a more reliable and interpretable model, ensuring that we can confidently predict chaotic systems with precision even in the most challenging environments.

Selective pH-Responsive Conjugation between a Pair of De Novo Discovered Peptides.

There is a demand for site-selective peptide/protein conjugation chemistry that is fully reversible in a stimulus-responsive manner. The contemporary methods for site-selective protein modification enable the preparation of homogeneous protein-small molecule conjugates, which are indispensable for drug delivery and chemical biology purposes, but such chemistries are usually irreversible. In contrast, the existing reversible protein labeling techniques are generally not site-selective. Here, we report an mRNA display-enabled de novo discovery of a pair of peptides which selectively react with each other to form a conjugate that is stable under neutral conditions (pH 7.5) but rapidly dissociates into the constituents at pH 10. A Cys thiol of peptide CP1 rapidly reacts (k1 = 340 M-1·s-1) with the isothiocyanate moiety in partner ITC6 to furnish a stable dithiocarbamate (t1/2 = 6 h at pH 7.5). We show that the pH-responsive nature of the reaction (conjugate's t1/2 = 5 min at pH 10) can be leveraged to utilize ITC6 (1) as a pull-down handle to selectively isolate CP1 from cell lysates and (2) as a temporary protecting group to protect CP1 from nonspecific Cys labeling reagents such as iodoacetamide. Altogether, the chemistry developed here complements the existing approaches and is applicable in a variety of chemical biology settings where selective reversible reactions are needed.

Electroinduced Reductive and Dearomative Alkene-Aldehyde Coupling.

The direct coupling of alkene feedstocks with aldehydes represents an expedient approach to the generation of new and structurally diverse C(sp3)-hybridized alcohols that are primed for elaboration into privileged architectures. Despite their abundance, current disconnection strategies enabling the direct coupling of carbon-carbon π-bonds and aldehydes remain challenging because contemporary methods are often limited by substrate or functional group tolerance and compatibility in complex molecular environments. Here, we report a coupling between simple alkenes, heteroarenes and unactivated aliphatic aldehydes via an electrochemically induced reductive activation of C-C π-bonds. The cornerstone of this approach is the discovery of rapid alternating polarity (rAP) electrolysis to access and direct highly reactive radical anion intermediates derived from conjugated alkenes and heterocyclic compounds. Our developed catalyst-free protocol enables direct access to new and structurally diverse C(sp3)-hybridized alcohol products. This is achieved by the controlled reduction of conjugated alkenes and the C2-C3 π-bond in heteroarenes via an unprecedented reductive dearomative functionalization for heterocyclic compounds. Experimental mechanistic studies demonstrate a kinetically biased single-electron reduction of C-C π-bonds over aldehydes. Application of rAP enables chemoselective generation of olefinic radical anion intermediates and avoids undesired saturative overreduction. Overall, this technology provides a versatile approach to the reductive coupling of olefin and heterocycle feedstocks with aliphatic aldehydes, offering straightforward access to diverse C(sp3)-rich oxygenated scaffolds.

Key Technologies and Typical Applications in Deepfake Detection

As deepfaking technology advances, it increasingly facilitates the seamless splicing of a person's voice, facial expressions, and body movements into source images or videos to create hyper-realistic virtual content. This growing technological sophistication, paired with the open-source nature of neural network algorithms, has unfortunately led to the exploitation of deepfake technology by individuals for illicit activities, seeking financial gain. This situation highlights the urgent necessity to thoroughly understand and vigilantly monitor the evolution of deepfake technology. This paper delves into the fundamental concepts and operational mechanisms underlying contemporary deepfake detection methods. It aims to augment the comprehension of these technologies and assesses the strengths and weaknesses of current detection approaches. This evaluation provides critical insights into their operational efficacy and reliability in differentiating genuine from manipulated content. Furthermore, the paper explores the challenges that current technologies face, such as the rapid evolution of deepfake methods that may outpace detection capabilities. By thoroughly analyzing these technologies, the paper endeavors to offer a comprehensive overview and projects future trends in deepfake detection. It anticipates developments that could significantly enhance the efficacy of countermeasures and thus bolster the fight against digital identity fraud and misinformation, safeguarding digital media integrity in an increasingly synthetic landscape.

Current State and Challenges of Tissue and Organ Cryopreservation in Biobanking

Recent years have witnessed significant advancements in the cryopreservation of various tissues and cells, yet several challenges persist. This review evaluates the current state of cryopreservation, focusing on contemporary methods, notable achievements, and ongoing difficulties. Techniques such as slow freezing and vitrification have enabled the successful preservation of diverse biological materials, including embryos and ovarian tissue, marking substantial progress in reproductive medicine and regenerative therapies. These achievements highlight improved post-thaw survival and functionality of cryopreserved samples. However, there are remaining challenges such as ice crystal formation, which can lead to cell damage, and the cryopreservation of larger, more complex tissues and organs. This review also explores the role of cryoprotectants and the importance of optimizing both cooling and warming rates to enhance preservation outcomes. Future research priorities include developing new cryoprotective agents, elucidating the mechanisms of cryoinjury, and refining protocols for preserving complex tissues and organs. This comprehensive overview underscores the transformative potential of cryopreservation in biomedicine, while emphasizing the necessity for ongoing innovation to address existing challenges.

Vision-augmented robotic fabrication (V-aRF): systematic review on contemporary approaches and computational methods in architectural fabrication and assembly using machine vision

Vision-augmented robotic fabrication (V-aRF): systematic review on contemporary approaches and computational methods in architectural fabrication and assembly using machine vision

The Microbiome and Atopic Dermatitis

: The microbiome consists of a collection of microorganisms, their genetic material, and their interactions within a specific biological environment. Atopic dermatitis is a complex, long-lasting inflammatory skin condition that causes dryness and itchiness. It is often associated with other conditions like allergic rhino conjunctivitis and asthma. Contemporary methods that do not require culturing have been developed to identify microorganisms and their genetic makeup, which can have both positive and negative effects on their host. There are indications that microorganisms in the gut and on the skin could potentially influence the progression of atopic dermatitis. Although antiseptic treatments have been used for many years, conventional diagnostic methods and genomics are now beginning to help us understand that targeted interventions for imbalanced microbial communities might eventually be incorporated into a comprehensive therapeutic approach.

Invasive Fungal Infections in Immunocompromised Conditions: Emphasis on COVID-19.

The COVID-19 pandemic caused death of 6 million lives globally, primarily from respiratory failure, but also a significant number from invasive fungal co-infections in these patients, owing to the immune dysfunction in hospitalized patients. Such complications occurred more often in critically ill, hospitalized patients particularly those admitted in intensive care units and were reported as the major reason associated with a high mortality rate worldwide. Fungal pathogens most commonly associated with COVID-19 patients comprise members of the Mucorales (such as Rhizopus, Mucor, and Lichtheimia), as well as genera Aspergillus and Candida. In India, the prevalence rate of mucormycosis is relatively high than aspergillosis and candidiasis, and the predisposing risk factors associated with such infections included uncontrolled diabetes, underlying lung disease, leukopenia, neutropenia, malignancies and prolonged steroid therapy. However, co-infection with other fungi, including Alternariaand Scedosporium was also sporadically reported. These devastating invasive fungal infections are associated with differential mortality (high-low) and morbidity rates even after active management. The diagnosis of such infections is often challenging due to lack of sensitivity in contemporary diagnostic methods and poses an enormous challenge to healthcare experts. Thus, the role of early and accurate diagnosis, and management of such fungal infections, is vital in preventing life-threatening situations. Hence, this review focusses primarily on the epidemiology, predisposing risk factors, host environment, diagnosis and treatment of the most common medically important invasive fungal infections in immunocompromised conditions associated with COVID-19.

New Extracted Features to Recognize Faces Effected by Occlusions and Common Variations

Face recognition from non-identical face photos is a prominent area of research in pattern recognition and computer vision. Existing face recognition systems struggle with diverse changes like lighting conditions, expressions, and facial occlusions. This paper proposes a new Face Recognition (FR) approach that combines the Elastic Bunch Graph Matching (EBGM) approach with the greedy algorithm to automatically identify face landmarks. The proposed approach independently selects each optimal landmark of face image from different corresponding face images where the corresponding landmark of corresponding face image which achieves the best similarity is used rather than using one or at most two corresponding face images and computing the average between both. The locations of corresponding landmarks can be displaced to achieve maximum similarity with optimal landmarks. This proposed approach demonstrates improved recognition performance compared to contemporary face recognition methods. It effectively handles changing ratios of face parts and can recognize faces even with increasing occlusion sizes.

Advanced Cooperative Formation Control in Variable-Sweep Wing UAVs via the MADDPG–VSC Algorithm

UAV technology is advancing rapidly, and variable-sweep wing UAVs are increasingly valuable because they can adapt to different flight conditions. However, conventional control methods often struggle with managing continuous action spaces and responding to dynamic environments, making them inadequate for complex multi-UAV cooperative formation control tasks. To address these challenges, this study presents an innovative framework that integrates dynamic modeling with morphing control, optimized by the multi-agent deep deterministic policy gradient for two-sweep control (MADDPG–VSC) algorithm. This approach enables real-time sweep angle adjustments based on current flight states, significantly enhancing aerodynamic efficiency and overall UAV performance. The precise motion state model for wing morphing developed in this study underpins the MADDPG–VSC algorithm’s implementation. The algorithm not only optimizes multi-UAV formation control efficiency but also improves obstacle avoidance, attitude stability, and decision-making speed. Extensive simulations and real-world experiments consistently demonstrate that the proposed algorithm outperforms contemporary methods in multiple aspects, underscoring its practical applicability in complex aerial systems. This study advances control technologies for morphing-wing UAV formation and offers new insights into multi-agent cooperative control, with substantial potential for real-world applications.

Enhancing IoT intrusion detection through machine learning with AN-SFS: a novel approach to high performing adaptive feature selection

The vast volume of redundant and irrelevant network traffic data poses significant hurdles for intrusion detection. Effective feature selection is crucial for eliminating irrelevant information. Presently, most filtering and embedded methods rely on fixed thresholds or ratios, necessitating prior knowledge. Conversely, wrapper methods are computationally intensive, and individual feature selection methods may introduce biases in evaluation. To address these challenges, this study introduces Adaptive Neighborhood based Feature Selection (AN-SFS), a dynamic feature selection approach that adapts to local statistical properties of the data. Unlike traditional methods, AN-SFS adjusts its threshold based on the characteristics of the current feature subset and incorporates statistical measures of neighboring features, capturing subtle relationships and dependencies. This adaptability enables AN-SFS to achieve robust and effective feature selection outcomes. Using NSL-KDD and UNSW-NB15 datasets, our model demonstrates superiority over conventional ML classifiers in detection rate, precision, and recall, achieving outstanding accuracy rates of 99.3% on NSL-KDD and 97.5% on UNSW-NB15, significantly outperforming contemporary methods. To further demonstrate the effectiveness of our feature selection approach, we conducted a series of comparisons with other statistical methods, including ANOVA, Pearson correlation, and the chi-square test. In each comparison, our approach consistently outperformed these alternatives, underscoring its superior efficacy.

Bat box monitoring: comparing multiple contemporary methods

ABSTRACT Bat boxes can be employed to provide mitigation for the loss of trees with bat roost potential. Eighty bat boxes were deployed by the Hamilton City Council as part of the bat mitigation strategy for the Southern Links Project in southern Hamilton. Several contemporary methods were employed to determine bat box occupancy to ensure effective monitoring. This included visual scoring assessments (e.g. staining, wetness), endoscope surveys, thermal imaging, and eDNA. Emergence survey data were provided by the University of Waikato which provided further insight into the accuracy of the methods. Jointly the methods confirmed the use of 34 Kent style boxes (n = 70; 49%). Indicating a high success rate of box utilisation over a relatively short period (two years). Of the methods, eDNA confirmed the largest percentage of bat box use. Comparatively, thermal imaging provided an alternative and less intrusive means of surveying on-the-day bat presence. However, there were instances where survey conditions (e.g. terrain, vegetation clutter, solar irradiance, rainfall) influenced the reliability of thermal imaging. While visual assessment and thermal imaging cannot be solely relied on, they provide a supportive tool for other monitoring techniques. They can help guide the application of more definitive methods such as eDNA.

Shedding light on the DICER1 mutational spectrum of uncertain significance in malignant neoplasms.

The Dicer protein is an indispensable player in such fundamental cell pathways as miRNA biogenesis and regulation of protein expression in a cell. Most recently, both germline and somatic mutations in DICER1 have been identified in diverse types of cancers, which suggests Dicer mutations can lead to cancer progression. In addition to well-known hotspot mutations in RNAase III domains, DICER1 is characterized by a wide spectrum of variants in all the functional domains; most are of uncertain significance and unstated clinical effects. Moreover, various new somatic DICER1 mutations continuously appear in cancer genome sequencing. The latest contemporary methods of variant effect prediction utilize machine learning algorithms on bulk data, yielding suboptimal correlation with biological data. Consequently, such analysis should be conducted based on the functional and structural characteristics of each protein, using a well-grounded targeted dataset rather than relying on large amounts of unsupervised data. Domains are the functional and evolutionary units of a protein; the analysis of the whole protein should be based on separate and independent examinations of each domain by their evolutionary reconstruction. Dicer represents a hallmark example of a multidomain protein, and we confirmed the phylogenetic multidomain approach being beneficial for the clinical effect prediction of Dicer variants. Because Dicer was suggested to have a putative role in hematological malignancies, we examined variants of DICER1 occurring outside the well-known hotspots of the RNase III domain in this type of cancer using phylogenetic reconstruction of individual domain history. Examined substitutions might disrupt the Dicer function, which was demonstrated by molecular dynamic simulation, where distinct structural alterations were observed for each mutation. Our approach can be utilized to study other multidomain proteins and to improve clinical effect evaluation.

How to manage a dry socket?- review of the latest treatment methods

Introduction Alveolar osteitis is the most common postoperative complication of tooth extraction. This medical condition has been recognized for decades, however its treatment approach is diverse with varying outcome. Aim of the study The purpose of this review is to examine and discuss contemporary methods of treating dry socket. Through a synthesis of available studies, the paper aims to identify the most beneficial methods to deal with this post-extraction complication. Materials and methods An analysis of scientific articles available in Pubmed and Google Scholar databases was conducted. The study used publications from 2017 to 2024. The search process included the use of the following keywords: “dry socket”, “alveolar osteitis”, “alveolitis sicca dolorosa”, “socket pain treatment”. Results Thirty-five articles were selected and reviewed carefully and independently by all authors. The agents used in the treatment of dry socket were classified as: remedies, drugs and medications, blood products, and technologies. Each group of agents is described with a discussion of their therapeutic efficacy. Summary Dry socket is a common dental problem. Currently, non-invasive methods of treating this complication are preferred. Modern solutions can provide quick and effective therapy. The treatment should relieve discomfort and promote tissue regeneration. Despite various methods, there is still no clear opinion on the best way to treat dry socket.