Scientific Community Research Articles

Comparing The Mediterranean and The Japanese Dietary Pattern in Relation to Longevity - A Narrative Review.

The Mediterranean dietary pattern (MDP) and Japanese dietary pattern (JDP) have received increasing attention from the scientific community and media, predominantly due to their association with increased longevity and health. Although similarities between the two dietary patterns are evident, a detailed comparison between them is still relatively unexplored. This narrative review aimed to explore the similarities and differences between the MDP and JDP in terms of longevity while also reflecting on the adoption of these diets by other populations outside their regions of origin. Both dietary patterns are plant-based, minimally processed, and sustainable for their respective regions and have been shown to significantly prolong life expectancy in different populations. Nevertheless, these dietary patterns also differ in terms of macronutrient ratios, food preparation and consumption and individual cultural characteristics of each population. Additionally, both dietary patterns are part of broader lifestyle patterns, which include other behaviors, such as abstaining from smoking, engaging in regular physical activity, having low stress levels and a sense of community, spirituality/religiousness and purpose. The promotion of these two dietary patterns should be implemented in other regions after considering cultural and socio-economical characteristics.

Viruses in the era of microplastics and plastispheres: Analytical methods, advances and future directions.

Research on microplastics and plastispheres now incorporates the study of viruses to evaluate their effects on the environment and human health. Sharing these new discoveries with the scientific community is crucial to fostering further research and collaborations. However, the current research and methodologies used are fragmented. To address this issue, this paper reviews the literature and the use of methodology developments in each study, identifying four emerging research areas: (1) viral interactions with microplastics; (2) viral population, diversity, and function in plastispheres; (3) the effects of viruses and plastic particles in host-associated environments; and (4) the impacts of viruses within plastispheres. To that end, the article is structured to streamline navigation and help readers easily access existing approaches, recent advancements, key findings, challenges, and opportunities in these areas. Our synthesis reveals that research methods include biochemical assays, omics techniques, spectroscopic analysis, and molecular and bioinformatic tools. Various mechanisms enable viruses to attach to microplastics and plastispheres, leading to widespread distribution and contributing to toxic effects and gene transfer. While the growing evidence is intriguing, there is still much to uncover about their ecological interactions, functions, and impacts.

Network Anomaly Detection using Autoencoder on Various Datasets: A Comprehensive Review

Abstract: The scientific community is currently very concerned about information and communication technology security because any assault or network anomaly can have a remarkable collision on a number of areas, including national security, the storage of private data, social welfare, economic concerns, and more. As a result, many strategies and approaches for this goal have been developed over time, making the anomaly detection domain a large research subject. The primary concern of this study is to review the most crucial elements relating to anomaly detection, including an overview of background analysis and a core study on the most important approaches, procedures, and systems in the field. To make the structure of this survey easier to understand, the domain of anomaly detection was examined along with five dimensions: Detection methods in network traffic, objectives of the paper, various datasets used, accuracy, and open issues/ gaps. The gap which has been identified after the survey can be extended as a future scope might be helpful for the researcher.

Uptake, removal and trophic transfer of fluorescent polyethylene microplastics by freshwater model organisms: the impact of particle size and food availability.

As an emerging contaminant, microplastics (MPs) are widely distributed in freshwater ecosystems and pose potential threats to aquatic organisms, attracting significant attention from both the scientific community and the general public. However, there is still uncertainty regarding the mechanisms of MPs transfer within aquatic biota and how particle size and food availability influence their transport patterns. In this study, zebrafish (Danio rerio) were selected as a model organism to investigate the uptake and elimination of fluorescent polyethylene (PE) MPs under different exposure scenarios (waterborne or trophic transfer, with or without food) and varying particle sizes (ranging from 10-300 μm at concentrations of 0.1, 2, and 300 mg/L). Additionally, water fleas (Daphnia magna) were provided as prey for the fish. The dynamic accumulation of PE-MPs sized between 10-20 μm at a concentration of 25 mg/L by daphnia was also determined along with its impact on animal feeding behavior. The results demonstrated that both organisms were capable of ingesting PE-MPs during exposures lasting up to 24 hours for daphnia and up to 72 hours for zebrafish. Furthermore, rapid elimination rates were observed within just 30 minutes for daphnia and between 6-12 hours for zebrafish. The presence of food reduced MPs uptake and removal by daphnia but significantly increased MP elimination by fish. Zebrafish showed a preference for ingesting larger-sized MPs that they could easily recognize; however, trophic transfer from daphnia to fish was found to be the primary route of ingestion specifically for PE-MPs sized between 10-20 μm. The findings suggest that while fish directly ingest fewer invisible MPs from the water column, they still accumulate these particles through predation on contaminated prey organisms. Therefore, it is imperative to prioritize the ecological risks associated with the transfer of MPs from zooplankton to fish.

Optimized deep neural network based vulnerability detection enabled secured testing for cloud SaaS

Based on the information technology service model, an on-demand services towards user becomes cost effective, which is provided with cloud computing. The network attack is detected with research community that pays huge interest. The novel proposed framework is intended with the combination of mitigation and detection of attack. While enormous traffic is obtainable, extract the relevant fields decide with Software-as-a-service (SaaS) provider. According to the network vulnerability and mitigation procedure, perform deep learning-based attack detection model. The golf optimization algorithm (GOA) done the selection of features followed by deep neural network (DNN) detect the attacks from the selected features. The correntropy variational features validates the level of risk and performs vulnerability assessment. Perform the process of bait-oriented mitigation during the phase of attack mitigation. The proposed approach demonstrates 0.97kbps throughput with 0.2% packet loss ratio than traditional methods.

Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimization of lead compounds, which has been made more accessible by the introduction of computational methods, including deep learning (DL) techniques. Diverse DL model architectures have been put forward to learn the vast landscape of interaction between proteins and ligands and predict their affinity, helping in the identification of lead compounds. This survey fills a gap in previous research by comprehensively analyzing the most commonly used datasets and discussing their quality and limitations. It also offers a comprehensive classification of the most recent DL methods in the context of protein-ligand binding affinity prediction (BAP), providing a fresh perspective on this evolving field. We thoroughly examine commonly used datasets for BAP and their inherent characteristics. Our exploration extends to various preprocessing steps and DL techniques, including graph neural networks, convolutional neural networks, and transformers, which are found in the literature. We conducted extensive literature research to ensure that the most recent deep learning approaches for BAP were included by the time of writing this manuscript. The systematic approach used for the present study highlighted inherent challenges to BAP via DL, such as data quality, model interpretability, and explainability, and proposed considerations for future research directions. We present valuable insights to accelerate the development of more effective and reliable DL models for BAP within the research community. The present study can considerably enhance future research on predicting affinity between protein and ligand molecules, hence further improving the overall drug development process.

Surface Reconstruction From Point Clouds: A Survey and a Benchmark.

Reconstruction of a continuous surface of two-dimensional manifold from its raw, discrete point cloud observation is a long-standing problem in computer vision and graphics research. The problem is technically ill-posed, and becomes more difficult considering that various sensing imperfections would appear in the point clouds obtained by practical depth scanning. In literature, a rich set of methods has been proposed, and reviews of existing methods are also provided. However, existing reviews are short of thorough investigations on a common benchmark. The present paper aims to review and benchmark existing methods in the new era of deep learning surface reconstruction. To this end, we contribute a large-scale benchmarking dataset consisting of both synthetic and real-scanned data; the benchmark includes object- and scene-level surfaces and takes into account various sensing imperfections that are commonly encountered in practical depth scanning. We conduct thorough empirical studies by comparing existing methods on the constructed benchmark, and pay special attention on robustness of existing methods against various scanning imperfections; we also study how different methods generalize in terms of reconstructing complex surface shapes. Our studies help identity the best conditions under which different methods work, and suggest some empirical findings. For example, while deep learning methods are increasingly popular in the research community, our systematic studies suggest that, surprisingly, a few classical methods perform even better in terms of both robustness and generalization; our studies also suggest that the practical challenges of misalignment of point sets from multi-view scanning, missing of surface points, and point outliers remain unsolved by all the existing surface reconstruction methods. We expect that the benchmark and our studies would be valuable both for practitioners and as a guidance for new innovations in future research.

The human dimensions of harmful algal blooms: An evolving research agenda

Harmful algal blooms (HABs) are a global phenomenon occurring in a range of aquatic environments, with the potential to have diverse environmental, social and economic impacts. There is growing interest in the human dimensions of HABs particularly as a result of the increased occurrence of HABs linked to anthropogenic activities. However, to date this has focused more on the economic and health implications of HABs events rather than broader human dimensions. This paper presents a series of research priorities that could strengthen social science research on HABs, highlighting topics related to public awareness and attitudes, community science, governance and co-development of solutions, links to the blue economy agenda, impacts on marine culture and heritage, and impacts on health and wellbeing. Finally, the paper presents a series of recommendations developing a transdisciplinary research agenda in order for the ongoing challenges of HABs to be addressed.

Development of a Scale to Measure Planned Behavior in Inclusive Science Communication: Validity Evidence in Undergraduate STEM Students.

Science communication has historically been inequitable, with certain voices and perspectives holding the power and dominant ways of knowing being promoted over others. Recently, there has been a push toward inclusive science communication, which values diverse perspectives and ways of knowing in collaborative conversations to solve complex socioscientific issues. However, there is a lack of both trainings in inclusive science communication for undergraduate science, technology, engineering, and mathematics (STEM) students as well as established ways to evaluate the efficacy of these trainings. To address this need, we designed a new multifactorial survey based on the Theory of Planned Behavior to assess students' attitudes/norms, self-efficacy, behavioral intents, and behaviors in inclusive science communication, which we termed the Planned Behaviors in Inclusive Science Communication (PB-ISC) Scale. We utilized expert review, exploratory factor analysis, confirmatory factor analysis, cognitive interviews, and quantitative measures to gather evidence of validity supporting the proposed use of the final 4-factor, 26-item survey. This survey can be used as a tool by science communication educators and researchers to assess students' planned behavior in inclusive science communication in response to trainings or experiences in science communication or related topics like socioscientific issues, civic engagement, and citizen science.

On the design and behavior of axially-compressed square concrete-filled double-skin tubular short columns for ocean engineering applications

Deep-sea offshore wind technology development is now deemed a strategic national need in China. At the same time, research and engineering communities have a substantial increase in intertest on the design and behavior of novel arrangements for double-skin composite columns. Accordingly, concrete-filled double-skin steel tubular (CFDST) columns have recently gained significant importance in wind farm support structures as well as tall buildings with heavy loads. To predict the axial compressive resistance of square CFDST short columns with square inner tubes (SS-CFDST) or circular inner tubes (SC-CFDST), a new design resistance model has been proposed in this paper. First, test data for axially-loaded short columns of SS-CFDST and SC-CFDST configurations were extracted from literature studies. Then, available design resistances in the European and American specifications, as well as from previous studies, were examined, and a range of accuracy results were displayed. Subsequently, new formulae for determining the lateral confining pressure (frp) supplied by outer and inner tubes in different types of square CFDST columns have been developed using the data gathered from the experimental tests. A new design resistance model has then been proposed to calculate the axial compressive resistance of SS-CFDST and SC-CFDST short columns, based on the proposed formulae for frp in addition to the confined concrete strength. This has been followed by a finite element (FE) modeling using ABAQUS program that has been well validated, generated to increase the resistance data of these columns. The design formula was then verified through the extensive parametric studies generated by the FE modeling. According to the comparisons between numerical and proposed resistances, the design proposal was found to yield more accurate and reliable results in terms of the average resistance ratio, accuracy across the whole range of the relative tube slenderness ratio and the calculated reliability index.

To stay, switch, or leave: A four-year longitudinal study of the situated and stable social influences on women’s STEM major choices

Research on situational antecedents for women’s persistence is critical to advancing gender equity in science, technology, engineering, and mathematics (STEM) fields. To disentangle the influences of stable and situated aspects of motivational antecedents, we used survival analysis to predict if, when, and to where undergraduate women change majors (i.e., staying in, switching across, or out of STEM) from between-person average and within-person fluctuations in Tripartite Integration Model of Social Influence (TIMSI) motivational constructs (science self-efficacy, identity, and community values) and stereotype threat. Women (N = 413) STEM majors in their first or second year of college were recruited from nine U.S. universities and followed over four years. Women were most likely to leave STEM in the first year of college and were most likely to change STEM majors within the first two years. Major change was predicted by (a) between-person average and within-person fluctuations in science identity, (b) within-person fluctuations in stereotype threat, and (c) an interaction between average stereotype threat and fluctuations in science identity. These findings emphasize the importance of distinguishing between-person and within-person aspects of motivational antecedents of STEM choices and developing tailored motivational interventions for short- and longer-term periods.

Advances in the study of LNPs for mRNA delivery and clinical applications.

Messenger ribonucleic acid (mRNA) was discovered in 1961 as an intermediary for transferring genetic information from DNA to ribosomes for protein synthesis. The COVID-19 pandemic brought worldwide attention to mRNA vaccines. The emergency use authorization of two COVID-19 mRNA vaccines, BNT162b2 and mRNA-1273, were major achievements in the history of vaccine development. Lipid nanoparticles (LNPs), one of the most superior non-viral delivery vectors available, have made many exciting advances in clinical translation as part of the COVID-19 vaccine and therefore has the potential to accelerate the clinical translation of many gene drugs. In addition, due to these small size, biocompatibility and excellent biodegradability, LNPs can efficiently deliver nucleic acids into cells, which is particularly important for current mRNA therapeutic regimens. LNPs are composed cationic or pH-dependent ionizable lipid bilayer, polyethylene glycol (PEG), phospholipids, and cholesterol, represents an advanced system for the delivery of mRNA vaccines. Furthermore, optimization of these four components constituting the LNPs have demonstrated enhanced vaccine efficacy and diminished adverse effects. The incorporation of biodegradable lipids enhance the biocompatibility of LNPs, thereby improving its potential as an efficacious therapeutic approach for a wide range of challenging and intricate diseases, encompassing infectious diseases, liver disorders, cancer, cardiovascular diseases, cerebrovascular conditions, among others. Consequently, this review aims to furnish the scientific community with the most up-to-date information regarding mRNA vaccines and LNP delivery systems.

A primer: peer review process for Advances in Physiology Education.

The dissemination of discipline-focused educational scholarship advances theory and stimulates pedagogical application. The aim of Advances in Physiology Education is to publish manuscripts that advance knowledge and inform educators in the field. This primer is tailored for individuals new to manuscript reviewing, early in their careers, or experienced in reviewing research but not educational manuscripts. Peer reviewing for basic and applied science is akin to evaluating research questions and rigor in teaching and learning studies, with differences in approach and analysis similar to those between biophysics and molecular physiology or cell and integrated physiology. Our purpose is to provide an overview of the review process and expectations. The submission and peer review process involves several steps: authors submit a manuscript and the Editor assigns an Associate Editor, who then assigns peer Reviewers. Reviewers are contacted via email and can accept or decline the invitation. Reviewers evaluate the work's strengths and weaknesses and then independently submit comments and recommendations to the Associate Editor. After review, the Associate Editor collects and weighs Reviewers' comments, sometimes garners additional reviews and input, to make a recommendation to the Editor. The Editor reviews the process, comments, and recommendations to render a final decision. Both authors and Reviewers receive an email with the decision. The editorial staff assists with communication and helps track the overall process. Peer review is integral to scientific publishing, ensuring quality and rigor, and reviewing is both a privilege and a responsibility of all in the scientific community.NEW & NOTEWORTHY This mini-review offers a comprehensive and current overview of the peer review process and the qualifications required to serve as a journal reviewer for Advances in Physiology Education. The guidelines are specifically designed for early career professionals new to manuscript reviewing, as well as seasoned research manuscript reviewers who are new to educational manuscript evaluation. Peer review is a cornerstone of scientific publishing, ensuring both quality and rigor. It is both a privilege and a responsibility for all members of the scientific community.

Playing for 3D Human Recovery.

Image- and video-based 3D human recovery (i.e., pose and shape estimation) have achieved substantial progress. However, due to the prohibitive cost of motion capture, existing datasets are often limited in scale and diversity. In this work, we obtain massive human sequences by playing the video game with automatically annotated 3D ground truths. Specifically, we contribute GTA-Human, a large-scale 3D human dataset generated with the GTA-V game engine, featuring a highly diverse set of subjects, actions, and scenarios. More importantly, we study the use of game-playing data and obtain five major insights. First, game-playing data is surprisingly effective. A simple frame-based baseline trained on GTA-Human outperforms more sophisticated methods by a large margin. For video-based methods, GTA-Human is even on par with the in-domain training set. Second, we discover that synthetic data provides critical complements to the real data that is typically collected indoor. We highlight that our investigation into domain gap provides explanations for our data mixture strategies that are simple yet useful, which offers new insights to the research community. Third, the scale of the dataset matters. The performance boost is closely related to the additional data available. A systematic study on multiple key factors (such as camera angle and body pose) reveals that the model performance is sensitive to data density. Fourth, the effectiveness of GTA-Human is also attributed to the rich collection of strong supervision labels (SMPL parameters), which are otherwise expensive to acquire in real datasets. Fifth, the benefits of synthetic data extend to larger models such as deeper convolutional neural networks (CNNs) and Transformers, for which a significant impact is also observed. We hope our work could pave the way for scaling up 3D human recovery to the real world.

Investigation of the sintering behavior of nanoparticulate SiC by molecular dynamics simulation

Sintering is a valuable processing technique utilized to create bulk materials from powder compacts. In recent years, sintering has a garnered significant attention in the research community due to its versatility and applicability in various fields, including additive manufacturing. Herein, we investigate the sintering kinetics and mechanism of SiC nanoparticulate using molecular dynamics simulations. The surface morphology, microstructure, radial distribution function, mean square displacement, atomic displacement distribution, structural transformation, and diffusion coefficient are analyzed in detail. The free surface surrounding the neck undergoes a reconstruction to eventually reach an equilibrium dihedral angle. The neck rapidly grows and aligns with the surface, causing the SiC particle to transform from an original spherical shape into a twists shape. The concentration of shear stress results in a change of the local lattice structure from the face-centered cubic phase to amorphous phase in the surface. With an increasing sintering time, the dominant diffusion mechanism gradually shifts from surface diffusion to interface diffusion. This trend leads to a rise in the atomic migration, the consumption of interface energy, and the degree of particle fusion. Ultimately, the sintered SiC particles have the spherical shape, low surface energy, and stable structure. Additionally, the high mobility of melted atom causes a gradual dissolution of SiC particle into the large particle from the surface towards the interior. This process leads to high-altitude concentration structures that facilitate atomic migration. These results shed light on an atomic sintering mechanism in SiC nanoparticulate for applications in the preparation of nanostructured materials, and additive manufacturing.

MultiADE: A Multi-domain benchmark for Adverse Drug Event extraction

Objective:Active adverse event surveillance monitors Adverse Drug Events (ADE) from different data sources, such as electronic health records, medical literature, social media and search engine logs. Over the years, many datasets have been created, and shared tasks have been organised to facilitate active adverse event surveillance. However, most—if not all—datasets or shared tasks focus on extracting ADEs from a particular type of text. Domain generalisation—the ability of a machine learning model to perform well on new, unseen domains (text types)—is under-explored. Given the rapid advancements in natural language processing, one unanswered question is how far we are from having a single ADE extraction model that is effective on various types of text, such as scientific literature and social media posts. Methods:We contribute to answering this question by building a multi-domain benchmark for adverse drug event extraction, which we named MultiADE. The new benchmark comprises several existing datasets sampled from different text types and our newly created dataset—CADECv2, which is an extension of CADEC (Karimi et al., 2015), covering online posts regarding more diverse drugs than CADEC. Our new dataset is carefully annotated by human annotators following detailed annotation guidelines. Conclusion:Our benchmark results show that the generalisation of the trained models is far from perfect, making it infeasible to be deployed to process different types of text. In addition, although intermediate transfer learning is a promising approach to utilising existing resources, further investigation is needed on methods of domain adaptation, particularly cost-effective methods to select useful training instances.The newly created CADECv2 and the scripts for building the benchmark are publicly available at CSIRO’s Data Portal (https://data.csiro.au/collection/csiro:62387). These resources enable the research community to further information extraction, leading to more effective active adverse drug event surveillance.

Centralizing an ecological sport psychology through science-practice dialectics

There has been considerable discussion for more than 50 years of how scientists and practitioners in elite level sport can work collaboratively to ensure that evidence-based practice augments the sport performance and human development of elite amateur and professional athletes. The bridging of these two, often disparate competencies, science and practice, though considered at the conceptual level, continues to be scarcely evidenced within the international sport science community. Much of the research that frames the experiences of elite athletes and their consequent needs, is heavily influenced by scientists, often without direct reciprocity to bridge science, theory, and applied context. The knowledge influencing these interventions has derived from qualitative methods, such as semi-structured interviews, surveys, and focus groups, as well as a breadth of psychometric assessments. Though these approaches to gathering robust data are a necessary part of inquiry, they often produce decontextualized data collection strategies and results, which can lead to generalized, ineffective practices in sport performance environments. Within this submission, the first author cooperated with an international team of scientist-practitioners who are well versed in elite sport to delineate ecologically sound science-practice reciprocity. The authors consider the strengths and weaknesses of conventional qualitative research strategies in terms of their utility and the parlance of evidence into intervention and world-class performance. Two emerging, context driven approaches to inquiry are proposed; arts-based methods and an idiosyncratic approach to ethnography to encourage the reader toward an expanded selection of inquiry approaches from which better understanding and intervention can be generated. This contribution conclude with summary points to open further possibilities for innovative science to practice approaches.

Assessment of permafrost-related hazards in China: based on Chinese literature

Abstract. High Mountain Asia (HMA) is undergoing unprecedented warming, affecting the cryosphere – including permafrost (frozen ground) – and leading to various hazards. However, understanding the prevalence, distribution, and dynamics of these hazards and how they respond to a changing climate is challenging. Permafrost is extensive in HMA, and China makes up a significant portion of this. The permafrost area in China is about 1.6×106 km2, 66 % of which is on the Qinghai–Tibet Plateau. However, most of the scientific literature concerning permafrost in China is published in Chinese and, hence, remains largely unnoticed by the non-Chinese-speaking scientific communities. In this article, we used a systematic review to evaluate the Chinese scientific literature on permafrost-related hazards and found that the studied areas are concentrated in certain areas, especially on the Qinghai–Tibet Engineering Corridor (QTEC). The increasing amount of literature on permafrost hazards reflects the increased impact of climate warming on infrastructure built on permafrost. Not only is permafrost affecting infrastructure; these anthropogenic disturbances themselves also have amplified the occurrence of hazards around settlements and infrastructure. The literature shows the strong relationship between latitude and elevation with permafrost thickness. The permafrost classification system and nomenclature used by Chinese scientists is different to that used elsewhere, which is a potential source of confusion and deserves attention.

UniProt: the Universal Protein Knowledgebase in 2025

Abstract The aim of the UniProt Knowledgebase (UniProtKB; https://www.uniprot.org/) is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this publication, we describe ongoing changes to our production pipeline to limit the sequences available in UniProtKB to high-quality, non-redundant reference proteomes. We continue to manually curate the scientific literature to add the latest functional data and use machine learning techniques. We also encourage community curation to ensure key publications are not missed. We provide an update on the automatic annotation methods used by UniProtKB to predict information for unreviewed entries describing unstudied proteins. Finally, updates to the UniProt website are described, including a new tab linking protein to genomic information. In recognition of its value to the scientific community, the UniProt database has been awarded Global Core Biodata Resource status.

Towards routine proteome profiling of FFPE tissue: insights from a 1,220-case pan-cancer study

AbstractProteome profiling of formalin-fixed paraffin-embedded (FFPE) specimens has gained traction for the analysis of cancer tissue for the discovery of molecular biomarkers. However, reports so far focused on single cancer entities, comprised relatively few cases and did not assess the long-term performance of experimental workflows. In this study, we analyze 1220 tumors from six cancer entities processed over the course of three years. Key findings include the need for a new normalization method ensuring equal and reproducible sample loading for LC-MS/MS analysis across cohorts, showing that tumors can, on average, be profiled to a depth of >4000 proteins and discovering that current software fails to process such large ion mobility-based online fractionated datasets. We report the first comprehensive pan-cancer proteome expression resource for FFPE material comprising 11,000 proteins which is of immediate utility to the scientific community, and can be explored via a web resource. It enables a range of analyses including quantitative comparisons of proteins between patients and cohorts, the discovery of protein fingerprints representing the tissue of origin or proteins enriched in certain cancer entities.