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.

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.

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.

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.

Bora-diaza-indacene based fluorescent probes for simultaneous visualisation of lipid droplets and endoplasmic reticulum.

Organelle selective fluorescent probes, especially those capable of concurrent detection of specific organelles, are of benefit to the research community in delineating the interplay between various organelles and the impact of such interaction in maintaining cellular homeostasis and its disruption in the diseased state. Although very useful, such probes are synthetically challenging to design due to the stringent lipophilicity requirement posed by different organelles, and hence, the lack of such probes being reported so far. This work details the synthesis, photophysical properties, and cellular imaging studies of two bora-diaza-indacene based fluorescent probes that can specifically and simultaneously visualise lipid droplets and endoplasmic reticulum; two organelles suggested having close interactions and implicated in stress-induced cellular dysfunction and disease progression.

Arabidopsis transcriptome responses to low water potential using high-throughput plate assays.

Soil-free assays that induce water stress are routinely used to investigate drought responses in the plant Arabidopsis thaliana. Due to their ease of use, the research community often relies on polyethylene glycol (PEG), mannitol, and salt (NaCl) treatments to reduce the water potential of agar media, and thus induce drought conditions in the laboratory. However, while these types of stress can create phenotypes that resemble those of water deficit experienced by soil-grown plants, it remains unclear how these treatments compare at the transcriptional level. Here, we demonstrate that these different methods of lowering water potential elicit both shared and distinct transcriptional responses in Arabidopsis shoot and root tissue. When we compared these transcriptional responses to those found in Arabidopsis roots subject to vermiculite drying, we discovered many genes induced by vermiculite drying were repressed by low water potential treatments on agar plates (and vice versa). Additionally, we also tested another method for lowering water potential of agar media. By increasing the nutrient content and tensile strength of agar, we show the 'hard agar' (HA) treatment can be leveraged as a high-throughput assay to investigate natural variation in Arabidopsis growth responses to low water potential.

Socio-political dynamics in clean energy transition

A rapid and effective transition to low-carbon energy production is essential to limit climate change impacts. While the scientific community has mostly focused on research and development and techno-economic aspects, quantifying the role of public acceptability and policy in shaping emission trajectories has been much more elusive. This study investigates the coupled dynamics of nonlinear socio-political acceptance and anthropogenic CO2 emissions, with implications for climate policies and clean energy investments. Our findings show that a top-down policy approach alone may not be sufficient for effective emission cuts, highlighting the need for a multi-level strategy that combines top-down and bottom-up approaches. Additionally, opinion polarization can trigger detrimental CO2 emission oscillations when governments decide to take heavy-handed policy interventions in highly polarized socio-political systems. Delayed perception of climate change damage or abrupt reactions to extreme weather events may also significantly affect emission reduction efforts, although in the opposite direction. Integrating these socio-political dynamics into climate models can enhance our understanding of the complex interplay between human and natural systems, enabling the development of more effective and resilient mitigation strategies.

Integrating Python data analysis in an existing introductory laboratory course

In this article we describe how we successfully incorporated data analysis in Python in a first year laboratory course without significantly altering the course structure and without overburdening students. We show how we created and used carefully designed Jupyter Notebooks with exercises and physics application examples that allow students to master data analysis programming in the laboratory course. We use these Notebooks to guide students through the fundamentals of data handling and analysis in Python while performing simple experiments. We present our teaching approach and the developed materials. We discuss the effectiveness of our intervention based on the results from pre- and post-course questionnaires and students’ group work. The results presented give insights about advantages and challenges of introducing computation at the early stage of the curriculum in a laboratory course setting and are informative for other instructors and the physics education research community.

Crop Type Classification using Sentinel 2A-Derived Normalized Difference Red Edge Index (NDRE) and Machine Learning Approach

Satellite remote sensing (RS) enables the extraction of vital information on land cover and crop type. Land cover and crop type classification using RS data and machine learning (ML) techniques have recently gained considerable attention in the scientific community. This study aimed to enhance remote sensing research using high-resolution satellite imagery and a ML approach. To achieve this objective, ML algorithms were employed to demonstrate whether it was possible to accurately classify various crop types within agricultural areas using the Sentinel 2A-derived Normalized Difference Red Edge Index (NDRE). Five ML classifiers, namely Support Vector Machines (SVM), Random Forest (RF), Decision Tree (DT), K-Nearest Neighbors (KNN), and Multi-Layer Perceptron (MLP), were implemented using Python programming on Google Colaboratory. The target land cover classes included cereals, fallow, forage, fruits, grassland-pasture, legumes, maize, sugar beet, onion-garlic, sunflower, and watermelon-melon. The classification models exhibited strong performance, evidenced by their robust overall accuracy (OA). The RF model outperformed, with an OA rate of 95% and a Kappa score of 92%. It was followed by DT (88%), KNN (87%), SVM (85%), and MLP (82%). These findings showed the possibility of achieving high classification accuracy using NDRE from a few Sentinel 2A images. This study demonstrated the potential enhancement of the application of high-resolution satellite RS data and ML for crop type classification in regions that have received less attention in previous studies.

Recurrent Appearance Flow for Occlusion-Free Virtual Try-On

Image-based virtual try-on aims at transferring a target in-shop garment onto a reference person, and has garnered significant attention from the research communities recently. However, previous methods have faced severe challenges in handling occlusion problems. To address this limitation, we classify occlusion problems into three types based on the reference person’s arm postures: single-arm occlusion , two-arm non-crossed occlusion , and two-arm crossed occlusion . Specifically, we propose a novel Occlusion-Free Virtual Try-On Network (OF-VTON) that effectively overcomes these occlusion challenges. The OF-VTON framework consists of two core components: (i) a new Recurrent Appearance Flow based Deformation (RAFD) model that robustly aligns the in-shop garment to the reference person by adopting a multi-task learning strategy . This model jointly produces the dense appearance flow to warp the garment and predicts a human segmentation map to provide semantic guidance for the subsequent image synthesis model. (ii) a powerful Multi-mask Image SynthesiS (MISS) model that generates photo-realistic try-on results by introducing a new mask generation and selection mechanism . Experimental results demonstrate that our proposed OF-VTON significantly outperforms existing state-of-the-art methods by mitigating the impact of occlusion problems. Our code is available at https://github.com/gxl-groups/OF-VTON .

A Paradigm for the missing middle

ABSTRACT This commentary highlights what I see as the most important of Walter Kintsch’s many contributions to cognitive psychology and especially text comprehension. His work filled the great void between the top and the bottom in research on comprehension. The state of the field at the time was (a) strong research foundation on word reading, (b) detail-focused research on syntactic processes by a vibrant sentence processing research community, and (c) demonstration research on the importance of higher-level top down influences. The void between (b) and (c) meant there was no possibility of a comprehension science that was both rigorous, like (a) and (b), and reflective of the role of outside-the-text knowledge, like (c). Beyond the specific empirical contributions of Kintsch’s work is that he showed that it was possible to create a paradigm for the missing middle, a characterization of how word-driven and knowledge-driven processes yield a coherent meaning-based understanding of a text a few pieces at a time. This allowed the flourishing of work on the incremental processes that result in a reader’s (often) coherent representation of text meaning.

Expanded trade: tripartite interactions in the mycorrhizosphere.

Interactions between arbuscular mycorrhizal fungi (AMF), plants, and the soil microbial community have the potential to increase the availability and uptake of phosphorus (P) and nitrogen (N) in agricultural systems. Nutrient exchange between plant roots, AMF, and the adjacent soil microbes occurs at the interface between roots colonized by mycorrhizal fungi and soil, referred to as the mycorrhizosphere. Research on the P exchange focuses on plant-AMF or AMF-microbe interactions, lacking a holistic view of P exchange between the plants, AMF, and other microbes. Recently, N exchange at both interfaces revealed the synergistic role of AMF and bacterial community in N uptake by the host plant. Here, we highlight work carried out on each interface and build upon it by emphasizing research involving all members of the tripartite network. Both nutrient systems are challenging to study due to the complex chemical and biological nature of the mycorrhizosphere. We discuss some of the effective methods to identify important nutrient processes and the tripartite members involved in these processes. The extrapolation of in vitro studies into the field is often fraught with contradiction and noise. Therefore, we also suggest some approaches that can potentially bridge the gap between laboratory-generated data and their extrapolation to the field, improving the applicability and contextual relevance of data within the field of mycorrhizosphere interactions. Overall, we argue that the research community needs to adopt a holistic tripartite approach and that we have the means to increase the applicability and accuracy of in vitro data in the field.

CHEOPS in-flight performance. A comprehensive look at the first 3.5 years of operations

Since the discovery of the first exoplanet almost three decades ago, the number of known exoplanets has increased dramatically. By beginning of the 2000s it was clear that dedicated facilities to advance our studies in this field were needed. The CHaracterising ExOPlanet Satellite ( is a space telescope specifically designed to monitor transiting exoplanets orbiting bright stars. In September 2023 completed its nominal mission duration of 3.5 years and remains in excellent operational conditions. As a testament to this, the mission has been extended until the end of 2026. Scientific and instrumental data have been collected throughout in-orbit commissioning and nominal operations, enabling a comprehensive analysis of the mission's performance. In this article, we present the results of this analysis with a twofold goal. First, we aim to inform the scientific community about the present status of the mission and what can be expected as the instrument ages. Secondly, we intend for this publication to serve as a legacy document for future missions, providing insights and lessons learned from the successful operation of CHEOPS. To evaluate the instrument performance in flight, we developed a comprehensive monitoring and characterisation (M C) programme. It consists of dedicated observations that allow us to characterise the instrument's response and continuously monitor its behaviour. In addition to the standard collection of nominal science and housekeeping data, these observations provide valuable input for detecting, modelling, and correcting instrument systematics, discovering and addressing anomalies, and comparing the instrument's actual performance with expectations. The precision of the measurements has enabled the mission objectives to be met and exceeded. The satellite's performance remains stable and reliable, ensuring accurate data collection throughout its operational life. Careful modelling of the instrumental systematics allows the data quality to be significantly improved during the light curve analysis phase, resulting in more precise scientific measurements. is compliant with the driving scientific requirements of the mission. Although visible, the ageing of the instrument has not affected the mission's performance. The satellite's capabilities remain robust, and we are confident that we will continue to acquire high-quality data during the mission extension.

The human malaria-Aotus monkey model: a historical perspective in antimalarial chemotherapy research at the Gorgas Memorial Laboratory-Panama.

The human malaria-Aotus monkey model has served the malaria research community since its inception in 1966 at the Gorgas Memorial Laboratory (GML) in Panama. Spanning over five decades, this model has been instrumental in evaluating the in vivo efficacy and pharmacokinetics of a wide array of candidate antimalarial drugs, whether used singly or in combination. The animal model could be infected with drug-resistant and susceptible Plasmodium falciparum and Plasmodium vivax strains that follow a characteristic and reproducible course of infection, remarkably like human untreated and treated infections. Over the years, the model has enabled the evaluation of several synthetic and semisynthetic endoperoxides, for instance, artelinic acid, artesunate, artemether, arteether, and artemisone. These compounds have been evaluated alone and in combination with long-acting partner drugs, commonly referred to as artemisinin-based combination therapies, which are recommended as first-line treatment against uncomplicated malaria. Further, the model has also supported the evaluation of the primaquine analog tafenoquine against blood stages of P. vivax, contributing to its progression to clinical trials and eventual approval. Besides, the P. falciparum/Aotus model at GML has also played a pivotal role in exploring the biology, immunology, and pathogenesis of malaria and in the characterization of drug-resistant P. falciparum and P. vivax strains. This minireview offers a historical overview of the most significant contributions made by the Panamanian owl monkey (Aotus lemurinus lemurinus) to malaria chemotherapy research.

Global cancer stigma research:a U.S. National Cancer Institute workshop report.

Stigma is a social process characterized by negative beliefs, attitudes, and stereotypes associated with a specific attribute or characteristic that leads to discrimination and social exclusion. Stigma manifests across the cancer control continuum and remains a key challenge for cancer prevention and control worldwide. In this commentary, we provide an overview of the U.S. National Cancer Institute's (NCI) Global Cancer Stigma Research Workshop, a multi-disciplinary international conference held virtually in September 2022, which focused on the intersection of cancer and stigma. The meeting was unique in its convening of researchers, advocates, clinicians, and non-governmental and governmental organizations, who-as a collective-provided overarching topics, cross-cutting considerations, and future directions for the cancer stigma research community to consider, which we describe herein. In summary, studying cancer stigma comprehensively requires a holistic, adaptive, and multifaceted approach-and should consider interrelated factors and their intersection within diverse cultural and social contexts worldwide. Collectively, there was a call for: an inclusive approach, encouraging researchers and practitioners to identify and measure cancer stigma as a driver for cancer health inequities globally; an expansion of existing research methodology to include diversity of experiences, contexts, and perspectives; and collaborations among diverse stakeholders to develop more effective strategies for reducing stigma and improving cancer outcomes. Such efforts are essential to cultivating effective and equitable approaches to preventing and treating cancer worldwide.

Investigation of fast and efficient lossless compression algorithms for macromolecular crystallography experiments.

Structural biology experiments benefit significantly from state-of-the-art synchrotron data collection. One can acquire macromolecular crystallography (MX) diffraction data on large-area photon-counting pixel-array detectors at framing rates exceeding 1000 frames per second, using 200 Gbps network connectivity, or higher when available. In extreme cases this represents a raw data throughput of about 25 GB s-1, which is nearly impossible to deliver at reasonable cost without compression. Our field has used lossless compression for decades to make such data collection manageable. Many MX beamlines are now fitted with DECTRIS Eiger detectors, all of which are delivered with optimized compression algorithms by default, and they perform well with current framing rates and typical diffraction data. However, better lossless compression algorithms have been developed and are now available to the research community. Here one of the latest and most promising lossless compression algorithms is investigated on a variety of diffraction data like those routinely acquired at state-of-the-art MX beamlines.

Lethal Carbon Monoxide Generated from Small Internal Combustion Engines.

Fuel-burning small engines have the potential to emit dangerous and potentially lethal concentrations of carbon monoxide when used in poorly ventilated environments. The North Carolina Office of the Chief Medical Examiner investigated seven cases from 2013 - 2020 involving lethal carbon monoxide from small internal combustion engines. Evaluation of percent carboxyhemoglobin saturation was determined in these case studies as ratios of carboxyhemoglobin to reduced hemoglobin, using HP 8453 and Agilent 8454 UV-Visible Spectrophotometers (Agilent Technologies, Santa Clara, CA). Sources of carbon monoxide included a pressure washer, a propane-powered forklift, an inboard engine boat, a motorcycle, propane and kerosene heaters, and home-use generators. It was demonstrated during one death investigation that the Dräger X-am 2000 electrochemical gas monitor often used by first responders, falsely reacted to acetylene gas, initially misleading investigators to the source of the carbon monoxide. Educating first responders about not only the hazards of these unexpected carbon monoxide sources, but the limitations of their equipment, is a valuable goal of disseminating complete medical examiner case information. The details of these cases will educate first responders, the forensic science community, and public health leaders on potential small engine sources of carbon monoxide in death investigations, responder safety, and the limitations of portable air quality monitoring equipment during death investigation.

Alternatives to β-Lactams as Agents for the Management of Dentoalveolar Abscess.

Dentoalveolar abscesses are localized infections within the tooth or the surrounding alveolar bone, often resulting from untreated dental caries or dental trauma causing alveolar bone resorption or even loss. Serious consequences arising from the spread of a dental abscess can often lead to significant morbidity and mortality. The acute dentoalveolar abscess is a polymicro-bial infection comprising strict anaerobes, such as anaerobic cocci i.e., Prevotella fusobacterium species, and facultative anaerobes i.e., Streptococci viridians and Streptococcus anginosus. Moreover, inappropriately managed dental infections can progress to severe submandibular space infections with associated serious complications, such as sepsis and airway obstruction. An audit of the Hull Royal Infirmary between 1999 and 2004 showed an increase in the number of patients presenting to oral and maxillofacial surgery services with dental sepsis. Thus, the scientific com-munity is forced to focus on treatment strategies for the management of dentoalveolar abscess (DAA) and other related dental problems. The current treatment includes antibiotic therapy, including β-lactams and non-β- lactams drugs, but it leads to the development of resistant micro-organisms due to improper and wide usage. Furthermore, the currently used β-lactam therapeutics is non-specific and easily hydrolyzed by the β-lactamase enzymes. Thus, the research focused on the non-β-lactams that can be the potential pharmacophore and helpful in the management of DAA, as the appropriate use and choice of antibiotics in dentistry plays an important role in antibiotic stewardship. The newer target for the choice is NLRP inflammasome, which is the major chemical mediator involved in dental problems. This review focused on pathogenesis and current therapeutics for the treatment of dentoalveolar abscesses.