Scientific Community Research Articles

Multiplexed in situ RNA imaging by combFISH.

Multiplexed in situ RNA imaging offers new opportunities for gene expression profiling by providing high-throughput spatial information. In this work, we present a cyclic combinatorial fluorescent in situ hybridization (combFISH) assay to achieve multiplexed detection of RNA in cell cultures and tissues. Specifically, multiplexing is achieved through cyclic interrogation of barcode sequences on the rolling circle amplicons generated from the padlock probe assay by using sets of combinatorial detection probes. Theoretically, combFISH can detect 64 genes in three hybridization cycles by combinatorial barcoding using 12 fluorescently labeled detection probes. Our method eliminates sequencing-by-ligation (SBL) chemistry in the in situ sequencing protocol and directly uses RNA as targets for ligation, making it more straightforward. We showed that our method works in fresh-frozen and formalin-fixed paraffin-embedded tissue sections. With its straightforward protocols, we expect our method to be adopted by the scientific community and extended to clinical settings.

I still know it's you! On Challenges in Anonymizing Source Code

The source code of a program not only defines its semantics but also contains subtle clues that can identify its author. Several studies have shown that these clues can be automatically extracted using machine learning and allow for determining a program's author among hundreds of programmers. This attribution poses a significant threat to developers of anti-censorship and privacy-enhancing technologies, as they become identifiable and may be prosecuted. An ideal protection from this threat would be the anonymization of source code. However, neither theoretical nor practical principles of such an anonymization have been explored so far. In this paper, we tackle this problem and develop a framework for reasoning about code anonymization. We prove that the task of generating a 𝑘-anonymous program—a program that cannot be attributed to one of 𝑘 authors—is not computable in the general case. As a remedy, we introduce a relaxed concept called 𝑘-uncertainty, which enables us to measure the protection of developers. Based on this concept, we empirically study candidate techniques for anonymization, such as code normalization, coding style imitation, and code obfuscation. We find that none of the techniques provides sufficient protection when the attacker is aware of the anonymization. While we observe a notable reduction in attribution performance on real-world code, a reliable protection is not achieved for all developers. We conclude that code anonymization is a hard problem that requires further attention from the research community.

The Search for Antidotes Against Ricin.

The castor plant (Ricinus communis) is primarily known for its seeds, which contain a unique fatty acid called ricinoleic acid with several industrial and commercial applications. Castor seeds also contain ricin, a toxin considered a chemical and biological warfare agent. Despite years of investigation, there is still no effective antidote or vaccine available. However, some progress has been made, and the development of an effective treatment may be on the horizon. To provide an updated overview of this issue, we have conducted a comprehensive review of the literature on the current state of research in the fight against ricin. This mini-review is based on the reported research and aims to address the challenges faced by researchers, as well as highlight the most successful cases achieved thus far. Our goal is to encourage the scientific community to continue their efforts in this critical search.

Comparing Leach protocol and its descendants on transferring scalar data

In the last years, The CMOS was developed and miniaturized rapidly, which, made sensors very fast, small and accurate. Hence, the creation of wireless sensor network (WSN) which are a network of nodes that exchange the data between them until it reaches the sink (base station). It is responsible for treating the data and transfer them to other servers linked to the internet for further treatment or storage. Therefore, everything related to WSN is a big topic of research for scientific community, especially transferring scalar data. In fact, many factors enter into account when it comes to send data like a radio, range of transmission, energy consumption and routing protocol. Routing protocols are very important in transferring data. They also have a big impact on energy consumption by nodes. Many categories of routing protocols exist: planning and level routing. Each type has its strength and weakness points. So, using a routing protocol in high-density environments is very challenging in energy consumption and data delivery. In addition, since level routing protocols like Leach are known for their energy efficiency. We choose three level routing protocol (Leach, MLD-Leach and MRE-Leach) to put them in a harsh environment to test their energy consumption and data transferring. We found that MLD-Leach has better energy consumption and data delivery.

Neoteric Role Of Quercetin In Visual Disorders.

Flavonoids are a family of secondary metabolites found in plants and fungi that exhibit strong antioxidant properties and low toxicity, making them potential candidates for medicinal use. Quercetin, a flavonoid present in various plant-based foods, has gained attention for its numerous biological benefits, including anti-inflammatory, anti-fibrosis, and antioxidant properties. The ocular surface research community has recently focused on quercetin's therapeutic potential for managing ocular diseases, such as dry eye, keratoconus, corneal inflammation, and neovascularization. In this paper, we discuss the role of quercetin for ocular disease prevention, highlighting its fundamental characteristics, common biological properties, and recent applications. By reviewing the latest research conducted in the last 10 years which was focused on novel herbal formulations for ocular diseases, we aim to provide insights into the role of quercetin in managing ocular diseases and offer perspectives on its potential as a therapeutic agent.

Addressing the Challenges of Traditional, Complementary, and Integrative Medicine Research: An International Perspective and Proposed Strategies Moving Forward

Traditional, complementary, and integrative medicine (TCIM) encompasses a broad range of healthcare practices beyond conventional Western medicine. Despite its use globally and increased research, many TCIM research challenges persist impeding its progress and integration into clinical practice. Key challenges involve financial constraints, insufficient research training and educational support, and the methodological barriers which arise from a lack of standardization. Financial limitations hinder investment into crucial research limiting both the quantity and quality of TCIM research. Inadequate training in research and educational support limit the development of TCIM research, hindering growth and recognition of TCIM in academic and clinical settings. The inherent dynamic nature of TCIM therapies poses additional challenges for applying standardized biomedical research models. These challenges not only impede the advancement of TCIM research but also perpetuate negative attitudes and biases within the healthcare and research communities. To overcome these challenges, a comprehensive strategy is necessary to increase funding, improve literacy, and the promotion of open science practices in TCIM. Addressing these confounding factors will enable well-informed TCIM research literacy and the development of TCIM skills and facilitate the integration of evidence based TCIM therapies into a more inclusive healthcare domain, ultimately reducing negative attitudes and biases towards TCIM.

Sex as a Determinant of Age-Related Changes in the Brain

Sex as a Determinant of Age-Related Changes in the Brain

Advanced White-Box Heuristics for Search-Based Fuzzing of REST APIs

Due to its importance and widespread use in industry, automated testing of REST APIs has attracted major interest from the research community in the last few years. However, most of the work in the literature has been focused on black-box fuzzing. Although existing fuzzers have been used to automatically find many faults in existing APIs, there are still several open research challenges that hinder the achievement of better results (e.g., in terms of code coverage and fault finding). For example, under-specified schemas are a major issue for black-box fuzzers. Currently, EvoMaster is the only existing tool that supports white-box fuzzing of REST APIs. In this paper, we provide a series of novel white-box heuristics, including for example how to deal with under-specified constrains in API schemas, as well as under-specified schemas in SQL databases. Our novel techniques are implemented as an extension to our open-source, search-based fuzzer EvoMaster . An empirical study on 14 APIs from the EMB corpus, plus one industrial API, shows clear improvements of the results in some of these APIs.

É Astrologia MA non É Astrologo: John Aubrey's Brief Lives and Astrology

This article explores John Aubrey's (1627–1697) extensive use of astrology in his biographical collection Brief Lives (1680–1681), which has been widely celebrated for its vivid portrayal of seventeenth-century English culture and society. Aubrey's life writing was not only informed by his antiquarian pursuits, the vibrant coffee house culture of his time and his lively scientific community, but also by the practice of astrology. Through Aubrey's horoscope collection, Collectio geniturarum (1674–1690), and his correspondence with a wide astrological network, this article traces his deep engagement with the practice. In squarely focusing on these two sources, I argue that Aubrey greatly relied on astrological methods to inform his life writing. In making his collection of horoscopes, Aubrey drew on a long tradition of astrological data gathering. Aubrey's careful collation and calculation of data to verify birth times, identify major life events and determine ‘ingenious’ traits also contributed to a wider effort to reform astrology considering the nascent experimental philosophy. By examining Aubrey's multifaceted astrological writings, this article clarifies one of Aubrey's key techniques used in his early sketches of Brief Lives . More broadly, it further complicates the historical narrative that astrology was largely marginalized in late seventeenth-century English intellectual and scientific communities.

Hydrazone-Linked Covalent Organic Frameworks.

Hydrazone-linked covalent organic frameworks (COFs) with structural flexibility, heteroatomic sites, post-modification ability and high hydrolytic stability have attracted great attention from scientific community. Hydrazone-linked COFs, as a subclass of Schiff-base COFs, was firstly reported in 2011 by Yaghi's group and later witnessed prosperous development in various aspects. Their adjustable structures, precise pore channels and plentiful heteroatomic sites of hydrazone-linked structures possess much potential in diverse applications, for example, adsorption/separation, chemical sensing, catalysis and energy storage, etc. Up to date, the systematic reviews about the reported hydrazone-linked COFs are still rare. Therefore, in this review, we will summarize their preparation methods, characteristics and related applications, and discuss the opportunity or challenge of hydrazone-linked COFs. We hope this review could provide new insights about hydrazone-linked COFs for exploring more appealing functions or applications.

Uncovering Art's Vanishing Hues with Surface-Enhanced Raman Scattering: Drawing Inspiration from the Past for the Future.

The aesthetic and historical significance of art is well recognized; art can stoke emotions, invite close inquiry, and connect us to the past. However, works of art are also complex material objects that present unique challenges and opportunities for the scientific community. Identifying "fugitive" organic pigments in traditional oil paintings, for example, presents a particularly complex analytical challenge that is critical to address for their conservation and long-term preservation. In this Perspective, we discuss the benefits and technical challenges of applying surface-enhanced Raman scattering (SERS) spectroscopy to the ultrasensitive identification of fugitive pigments in paintings as well as future developments in SERS we envision that are inspired by the past.

Proteomics Meets Dose-Response: A New Paradigm for Deciphering Drug Effects.

In a groundbreaking study, Eckert and colleagues introduce DecryptE, an innovative approach to dose-resolved proteomics that significantly advances our understanding of drug effects at the proteomic level. This method integrates cutting-edge sample preparation and mass spectrometry technologies, establishing a robust platform for high-throughput proteome analysis. DecryptE enables the quantification of over 7,000 proteins per hour and was employed to study 144 clinical drugs and research compounds, generating over 1 million dose-response curves using Jurkat acute T cell leukemia cells as a model system. The platform demonstrates outstanding reproducibility, ensuring reliable and consistent results across multiple experiments. By providing detailed information on drug potency and efficacy, DecryptE allows the identification of subtle changes in protein expression and facilitates the clustering of drugs based on their proteomic profiles. This study not only reveals novel drug mechanisms but also creates a comprehensive resource that can be utilized by the broader research community. Furthermore, it highlights the potential of integrating proteomics-and potentially other omics modalities in the future-with dose-response analysis to advance pharmacological research and improve therapeutic strategies.

Artificial intelligence and personalized diagnostics in periodontology: A narrative review.

Periodontal diseases pose a significant global health burden, requiring early detection and personalized treatment approaches. Traditional diagnostic approaches in periodontology often rely on a "one size fits all" approach, which may overlook the unique variations in disease progression and response to treatment among individuals. This narrative review explores the role of artificial intelligence (AI) and personalized diagnostics in periodontology, emphasizing the potential for tailored diagnostic strategies to enhance precision medicine in periodontal care. The review begins by elucidating the limitations of conventional diagnostic techniques. Subsequently, it delves into the application of AI models in analyzing diverse data sets, such as clinical records, imaging, and molecular information, and its role in periodontal training. Furthermore, the review also discusses the role of research community and policymakers in integrating personalized diagnostics in periodontal care. Challenges and ethical considerations associated with adopting AI-based personalized diagnostic tools are also explored, emphasizing the need for transparent algorithms, data safety and privacy, ongoing multidisciplinary collaboration, and patient involvement. In conclusion, this narrative review underscores the transformative potential of AI in advancing periodontal diagnostics toward a personalized paradigm, and their integration into clinical practice holds the promise of ushering in a new era of precision medicine for periodontal care.

In situ mass spectrometric investigation to probe GeSn growth dynamics and mechanisms in the chemical vapor deposition processes

GeSn has attracted increasing attention due to its tunable bandgap from indirect to direct resulting in unique electronic and optoelectronic capabilities. Chemical vapor deposition (CVD) is well acknowledged as an advanced growth method for GeSn, demonstrating its capability with grown materials for infrared lasers and detector development. As an in situ diagnostics of the CVD process, a residual gas analyzer (RGA) could enable the detection of all gaseous species during growth, thus probing the growth dynamics and mechanism. Therefore, it is highly desirable but is lacking in the (Si)GeSn research community. This work utilized an RGA equipped with a specialized differential vacuum pumping system to analyze the mass spectra of the GeSn CVD precursors of SnCl4, GeH4, and their combination. The spectra of SnCl4 collected from gas samples at a standard base pressure of 0.3 Torr display consistent fragments with central mass-to-charge ratios (m/z) of 78, 120, 155, 225, and 260, corresponding to Sn+, SnCl2+, SnCl+, SnCl2+, SnCl3+, and SnCl4+, respectively. These profiles closely resemble those observed in the National Institute of Standards and Technology and Wiley and Matsumoto et al. The simultaneous introduction of SnCl4 and GeH4 at a combined pressure of several tens of Torr yields spectra, indicating a chemical reaction that produces GeCl4 at room temperature in the vacuum chamber and tubing. Utilizing Gaussian16 and ORCA codes, the ab initio and density functional thermochemistry computations were employed to predict potential essential reactions and validate the experimental findings.

Haunting Messages: Online Videos and Public Belief in Paranormal Phenomena.

Despite broad skepticism within the scientific community regarding paranormal phenomena such as ghosts and unidentified flying objects (UFOs), many members of the public believe in these phenomena. Previous studies have argued that viewing paranormal-themed television-including documentary, reality, and news programming-can cultivate such beliefs. In addition, recent research suggests that the online video-sharing platform YouTube may serve as an important source of messages about fringe phenomena. With that in mind, this study builds on theoretical accounts of genre-specific cultivation and social media affordances to examine how YouTube videos present paranormal topics and whether YouTube use predicts belief in the paranormal. A content analysis of 50 highly viewed YouTube videos about paranormal topics (25 about hauntings and 25 about UFOs) showed that a large majority of these videos featured paranormal claims and that a majority included purported footage of paranormal phenomena. Scientific sources appeared in only a quarter of UFO videos and almost no haunting videos, while government sources appeared in most UFO videos, but few haunting videos. An analysis of data from a survey of the U.S. public (n = 1,035), in turn, found that paranormal documentary or reality television viewing, paranormal news use, and YouTube use predicted belief in hauntings, controlling for a range of other media use variables and background factors. These results highlight the potential for video-sharing platforms such as YouTube to reinforce paranormal beliefs among the public.

Commercial Small-Scale Horizontal and Vertical Wind Turbines: A Comprehensive Review of Geometry, Materials, Costs and Performance

The effective exploitation of renewable energy sources is one of the most effective solutions to counter the energy, environmental and economic problems associated with the use of fossil fuels. Small-scale wind turbines (converting wind energy into electric energy with a power output lower than 50 kW) have received tremendous attention over the past few decades thanks to their reduced environmental impact, high efficiency, low maintenance cost, high reliability, wide wind operation range, self-starting capability at low wind speed, limited installation space, reduced dependence on grid-connected power and long transmission lines, low capital costs, as well as the possibility to be installed in some urban settings. However, there are significant challenges and drawbacks associated with this technology from many different perspectives, including the significant discrepancy between theoretical performance data provided by the manufacturers and real field operation, that need to be investigated in greater depth in order to enable a more widespread deployment of small-scale wind turbines. In this review, a complete and updated list of more than 200 commercially available small-scale horizontal and vertical wind turbine models is provided and analysed, detailing the corresponding characteristics in terms of the number and material of blades, start-up wind speed, cut-in wind speed, cut-out wind speed, survival wind speed, maximum power, noise level, rotor diameter, turbine length, tower height, and specific capital cost. In addition, several scientific papers focusing on the experimental assessment of field performance of commercially available small-scale horizontal and vertical wind turbines have been reviewed and the corresponding measured data have been compared with the rated performance derived from the manufacturers’ datasheets in order to underline the discrepancies. This review represents an opportunity for the scientific community to have a clear and up-to-date picture of small-scale horizontal as well as vertical wind turbines on the market today, with a precise summary of their geometric, performance, and economic characteristics, which can enable a more accurate and informed choice of the wind turbine to be used depending on the application. It also describes the differences between theoretical and in-situ performance, emphasizing the need for further experimental research and highlighting the direction in which future studies should be directed for more efficient design and use of building-integrated small-scale wind turbines.

A Systematic Review on Artificial Intelligence (AI) Assisted Drug & Vaccine Development against SARS-CoV-2

Background: Adaptation and application of Artificial Intelligence (AI) technology for the development of drugs against the deadly and continuously mutating Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) virus has been extremely beneficial, cost-effective, and time saving for the scientific community. A systematic review is necessary for complete picturization of the overall AI assistance in developing drugs and vaccines against SARS-CoV-2. Materials and Methods: A systematic analysis and review of the research literature available on the application of AI in the development of drugs and vaccines against SARS-CoV-2 from various online platforms has been performed, and relevant full papers have been selected on certain selection criteria and have been used for this review. Results: Utilization of AI tools has enabled the selection, modification, evaluation, and prediction of the effectiveness of drug formulations against coronavirus disease (COVID-19) in a very rapid and efficient manner. Vaccine development against the deadly SARS-CoV-2 virus has also been aided and benefited immensely by using AI tools and techniques. Discussion: Thousands of studies regarding the development of effective drugs and vaccines against the constantly evolving, mutating, and prevailing SARS-CoV-2 virus have been conducted, and several thousands are still being conducted around the world. Conclusion: AI is a powerful tool, and its application has been highly beneficial in developing effective drugs and vaccines against the deadly SARS-CoV-2 in a cost-effective and time-effective frame. This systematic review briefs the findings and achievements till the date of writing this article in the field of AI-assisted drug and vaccine development against COVID-19.

Modelling Cellular Response to Ionizing Radiation: Mechanistic, Semi-Mechanistic, and Phenomenological Approaches - A Historical Perspective.

Radiation research is a multidisciplinary field, and among its many branches, mathematical and computational modelers have played a significant role in advancing boundaries of knowledge. A fundamental contribution is modelling cellular response to ionizing radiation as that is the key to not only understanding how radiation can kill cancer cells, but also cause cancer and other health issues. The invention of microdosimetry in the 1950s by Harold Rossi paved the way for brilliant scientists to study the mechanism of radiation at cellular and sub-cellular scales. This paper reviews some snippets of ingenious mathematical and computational models published in microdosimetry symposium proceedings and publications of the radiation research community. Among these are simulations of radiation tracks at atomic and molecular levels using Monte Carlo methods, models of cell survival, quantification of the amount of energy required to create a single strand break, and models of DNA-damage-repair. These models can broadly be categorized into mechanistic, semi-mechanistic, and phenomenological approaches, and this review seeks to provide historical context of their development. We salute pioneers of the field and great teachers who supported and educated the younger members of the community and showed them how to build upon their work.

10-year progress on Forest Carbon Research in Canada

In 2012 <i>A Blueprint for Forest Carbon Science in Canada:2012–2020</i> was published to guide policy and research and to support Canada’s obligations for climate change mitigation and adaptation, sustainable forest management, and international reporting. Over the past decade, the body of scientific research focused on forest carbon dynamics in Canada has significantly evolved, providing crucial insights into the intricate interplay between various forest ecosystems and the global carbon cycle. This comprehensive review synthesizes key findings from this period, highlighting improvements to Canada’s estimates of current forest greenhouse gas emissions and removals, the effects of global changes on Canada’s future forest carbon, and forest contributions to mitigate climate change. Collaborating with the forest carbon science community, we identified 426 peer-reviewed published articles on landscape-scale forest carbon research for Canada from 2012 to 2021. The review emphasizes anthropogenic influences and natural disturbances in contemporary GHG emissions and removals for managed forests in the national GHG inventory, highlights the existing dichotomy between management and research models, and stresses the need for integrating disturbance and climate effects for comprehensive GHG estimates. Despite significant progress in estimating and tracking forest carbon using modelled, remotely sensed and ground-based observations, challenges remain in reducing uncertainties, particularly regarding climate impacts on forest growth, decay, and disturbances. Furthermore, the review showcases recent advancements in climate change mitigation strategies and the use of a systems approach that includes forest ecosystem emissions and removals, wood product emissions and substitution benefits of avoided fossil emissions. Through comprehensive scenario analyses, the review underscores regional variations in assessing GHG reductions and notes a shift towards a more holistic approach that considers environmental, economic, and social values. By providing a nuanced understanding of the complexities underlying forest carbon dynamics in Canada, this review sets the stage for future research and policy initiatives aimed at fostering sustainable forest management practices and mitigating the impact of climate change on these vital ecosystems.

Highly precise community science annotations of video camera‐trapped fauna in challenging environments

AbstractAs camera trapping grows in popularity and application, some analytical limitations persist including processing time and accuracy of data annotation. Typically images are recorded by camera traps although videos are becoming increasingly collected even though they require much more time for annotation. To overcome limitations with image annotation, camera trap studies are increasingly linked to community science (CS) platforms. Here, we extend previous work on CS image annotations to camera trap videos from a challenging environment; a dense tropical forest with low visibility and high occlusion due to thick canopy cover and bushy undergrowth at the camera level. Using the CS platform Chimp&See, established for classification of 599 956 video clips from tropical Africa, we assess annotation precision and accuracy by comparing classification of 13 531 1‐min video clips by a professional ecologist (PE) with output from 1744 registered, as well as unregistered, Chimp&See community scientists. We considered 29 classification categories, including 17 species and 12 higher‐level categories, in which phenotypically similar species were grouped. Overall, annotation precision was 95.4%, which increased to 98.2% when aggregating similar species groups together. Our findings demonstrate the competence of community scientists working with camera trap videos from even challenging environments and hold great promise for future studies on animal behaviour, species interaction dynamics and population monitoring.