Literature Citations Research Articles

Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging.

Since its introduction in 2015, the U-Net architecture used in Deep Learning has played a crucial role in medical imaging. Recognized for its ability to accurately discriminate small structures, the U-Net has received more than 2600 citations in academic literature, which motivated continuous enhancements to its architecture. In hospitals, chest radiography is the primary diagnostic method for pulmonary disorders, however, accurate lung segmentation in chest X-ray images remains a challenging task, primarily due to the significant variations in lung shapes and the presence of intense opacities caused by various diseases. This article introduces a new approach for the segmentation of lung X-ray images. Traditional max-pooling operations, commonly employed in conventional U-Net++ models, were replaced with the discrete wavelet transform (DWT), offering a more accurate down-sampling technique that potentially captures detailed features of lung structures. Additionally, we used attention gate (AG) mechanisms that enable the model to focus on specific regions in the input image, which improves the accuracy of the segmentation process. When compared with current techniques like Atrous Convolutions, Improved FCN, Improved SegNet, U-Net, and U-Net++, our method (U-Net++-DWT) showed remarkable efficacy, particularly on the Japanese Society of Radiological Technology dataset, achieving an accuracy of 99.1%, specificity of 98.9%, sensitivity of 97.8%, Dice Coefficient of 97.2%, and Jaccard Index of 96.3%. Its performance on the Montgomery County dataset further demonstrated its consistent effectiveness. Moreover, when applied to additional datasets of Chest X-ray Masks and Labels and COVID-19, our method maintained high performance levels, achieving up to 99.3% accuracy, thereby underscoring its adaptability and potential for broad applications in medical imaging diagnostics.

Three-dimensional conformal radiation therapy with concurrent chemotherapy for stage III non-small cell lung cancer: protocol for a systematic review and meta-analysis

IntroductionLung cancer continues to be a common form of cancer worldwide and a primary contributor to cancer-related fatalities. Non-small cell lung cancer (NSCLC) is the most prevalent form, making up...

Prognostic Value of TGF-β Expression in Bladder Cancer: A Systematic Review and Meta-analysis.

Transforming growth factor beta (TGF-β) is a member of the growth factor superfamily that clinical studies address its association with bladder cancer invasion, progression, and metastasis. The present systematic review and meta-analysis aimed to explore the prognostic significance of TGF-β expression in bladder cancer patients. The major international databases, including PubMed, Web of Science, Embase, and Scopus, were searched for full-text literature citations. The hazard ratio (HR) with a 95% CI as the effect size was applied as the appropriate summarized statistic. We used a random-effects model using the DerSimonian and Laird method to estimate the pooled effect size. To assess the heterogeneity among trials, the I-square (I 2 ) statistic and Cochran's Q test were used. Forest and funnel plots were drawn to respectively demonstrate the findings and detect any existing publication bias. This meta-analysis included 3 studies that met the criteria and included 535 patients. The combined HR for the selected studies was 2.250 (95% CI=(1.411, 3.586), P< .001) and no significant heterogeneity was detected between trials (I 2=58.63, P=.089). Furthermore, no severe asymmetry was seen within the funnel plot, indicating a lack of potential publication bias. Our findings suggest that TGF-β expression can remarkably predict a worse prognosis in patients with bladder cancer. The results of the present meta-analysis may be validated through further updated reviews and additional relevant investigations in future studies.

Grey Literature Citations by Social Science Researchers in India: A Study

Grey literature has a pivotal role in the research study. It offers valuable insights that are often overlooked in traditional academic publications. Non conventional published materials such as reports, theses, and conference proceedings are examples of grey literature. This study investigates the utilization of grey literature by social science students in India, and the findings reveal that it is widely utilized due to its accessibility, relevance to local contexts, and provision of current information. The recent publication of grey literature is used more than the earlier published literature. Researchers frequently rely on government reports and NGO publications. This study encourages researchers to cite grey literature in their research.

Enhancing linguistic research through AI-powered reference management: A proposal for a voice-controlled academic assistant

In today’s digital age, the amount of available research literature is growing exponentially, making it more challenging for researchers to efficiently manage and cite relevant sources. This issue is particularly pronounced in fields like linguistics, where scholars must contend with interdisciplinary sources spanning linguistics, psychology, and computer science. This paper proposes the development of an AI-powered, voice-controlled academic assistant aimed at enhancing the research experience for linguists by streamlining the literature review and citation process. The assistant would use natural language processing (NLP) and machine learning to allow researchers to search for, retrieve, and cite references using voice commands, thereby eliminating many of the tedious aspects of academic research. This proposal outlines the current state of voice-controlled technology, discusses how these technologies can be implemented in academic workflows, and presents a roadmap for the development of a linguist-friendly reference management system. By addressing key technical, ethical, and practical concerns, this paper offers a compelling vision for the future of linguistic research, powered by AI.

Bibliometric analysis of research trends on factors affecting older adults with mild cognitive impairment.

In recent years, the problem of cognitive impairment in the elderly has become increasingly prominent. Understanding the research trend of influencing factors of mild cognitive impairment, and provide reference for medical staff to early screening of the elderly with mild cognitive impairment. Through the visual analysis of the influence factors of the elderly with mild cognitive impairment, the current research status was discussed. The relevant literature in the field of influencing factors of mild cognitive impairment in the elderly included in the Web of Science core collection database from 2013 to 2022 was searched. Using software such as Cite Space and VOS viewer to visually analyze literature citations, country, keywords, and development trends. A total of 547 relevant literatures were included, and the number of publications showed an increasing trend in the past ten years. The United States ranked first in both the number of published papers (157) and centrality (0.34), and the United States and China had a greater influence on the influencing factors of mild cognitive impairment. Alzheimer's disease, cognitive decline, the elderly, risk factors, are the research hotspot in this field. Cognitive decline will affect the autonomy of the elderly. Cognitive frailty, MRI is the forefront of MCI research, to understand the research hotspots and frontiers in this field, to conduct early screening and intervention guidance for people with mild cognitive impairment, so as to delay the occurrence of Alzheimer's disease, and reduce the pressure on family caregivers and society.

The Comparative Performance of Large Language Models on the Hand Surgery Self-Assessment Examination.

Generative artificial intelligence (AI) models have emerged as capable of producing human-like responses and have showcased their potential in general medical specialties. This study explores the performance of AI systems on the American Society for Surgery of the Hand (ASSH) Self-Assessment Exams (SAE). ChatGPT 4.0 and Bing AI were evaluated on a set of multiple-choice questions drawn from the ASSH SAE online question bank spanning 5 years (2019-2023). Each system was evaluated with 999 questions. Images and video links were inserted into question prompts to allow for complete AI interpretation. The performance of both systems was standardized using the May 2023 version of ChatGPT 4.0 and Microsoft Bing AI, both of which had web browsing and image capabilities. ChatGPT 4.0 scored an average of 66.5% on the ASSH questions. Bing AI scored higher, with an average of 75.3%. Bing AI outperformed ChatGPT 4.0 by an average of 8.8%. As a benchmark, a minimum passing score of 50% was required for continuing medical education credit. Both ChatGPT 4.0 and Bing AI had poorer performance on video-type and image-type questions on analysis of variance testing. Responses from both models contained elements from sources such as PubMed, Journal of Hand Surgery, and American Academy of Orthopedic Surgeons. ChatGPT 4.0 with browsing and Bing AI can both be anticipated to achieve passing scores on the ASSH SAE. Generative AI, with its ability to provide logical responses and literature citations, presents a convincing argument for use as an interactive learning aid and educational tool.

Overview of Tools used in Molecular Docking

The creative process of developing novel drugs based on the identification of a biological target is known as “drug design.” Drug design, in its most basic form, is the design of molecules that complement the molecular target that they interact and bind to in terms of charge and shape. The process of locating chemical entities with the potential to become medicinal agents is known as drug discovery. The identification of novel molecular entities that could prove valuable in treating illnesses that meet the criteria for unmet medical needs is a primary objective of drug discovery initiatives. In response to this demand for greater quality, the drug discovery process has incorporated absorption, distribution, metabolism, and elimination studies as well as, more and more, safety data. Examine the chemical structure drawing method and use the ChemSketch app to draw. The world’s largest publicly available chemical information database is called PubChem. Chemicals can be found using their names, structures, molecular formulas, and other identifiers. Discover information about biological activity, safety and toxicity, chemical and physical qualities, patents, literature citations, and more. Large biological molecules like proteins and nucleic acids have three-dimensional structural data stored in a database called the Protein Data Bank (PDB). The process of docking involves predicting a ligand’s preferred orientation towards a target when they are bonded together to create a stable complex. Using the ChemSketch, PubChem, and pdb databases in the autodock tool, molecular docking is accomplished make additional adjustments by selecting and swapping out words. Amantadine, initially showcased as an antiviral specialist for flu A, is Adamantan-1amine. 1-Adamantylamine may be a synthetic tricyclic amine, and it may be a part of the lesson of adamantanes. Amantadine, initially promoted as an antiviral operator for flu A, is an elective pharmaceutical for antipsychotic-induced parkinsonism and dodges the antagonistic central anxious framework and fringe impacts of anticholinergic solutions. Its component of activity is vague but shows up to Include presynaptic dopamine (DA) reuptake barricade, help of DA discharge, postsynaptic DA agonism, and receptor tweak.

Associations between food insecurity in high-income countries and pregnancy outcomes: A systematic review and meta-analysis.

Maternal nutrition is crucial for health in pregnancy and across the generations. Experiencing food insecurity during pregnancy is a driver of inequalities in maternal diet with potential maternal and infant health consequences. This systematic review explored associations between food insecurity in pregnancy and maternal and infant health outcomes. Searches included 8 databases (MEDLINE, Embase, Scopus, Web of Science, PsychInfo, ASSIA, SSPC in ProQuest, and CINAHL), grey literature, forwards and backwards citation chaining, and contacting authors. Studies in high-income countries (HICs) reporting data on food insecurity in pregnancy and maternal or infant health, from January 1, 2008 to November 21, 2023 were included. Screening, data extraction, and quality assessment were carried out independently in duplicate. Random effects meta-analysis was performed when data were suitable for pooling, otherwise narrative synthesis was conducted. The protocol was registered on PROSPERO (CRD42022311669), reported with PRISMA checklist (S1 File). Searches identified 24,223 results and 25 studies (n = 93,871 women) were included: 23 from North America and 2 from Europe. Meta-analysis showed that food insecurity was associated with high stress level (OR 4.07, 95% CI [1.22, 13.55], I2 96.40%), mood disorder (OR 2.53, 95% CI [1.46, 4.39], I2 55.62%), gestational diabetes (OR 1.64, 95% CI [1.37, 1.95], I2 0.00%), but not cesarean delivery (OR 1.42, 95% CI [0.78, 2.60], I2 56.35%), birth weight (MD -58.26 g, 95% CI [-128.02, 11.50], I2 38.41%), small-for-gestational-age (OR 1.20, 95%, CI [0.88, 1.63], I2 44.66%), large-for-gestational-age (OR 0.88, 95% CI [0.70, 1.12] I2 11.93%), preterm delivery (OR 1.18, 95% CI [0.98, 1.42], I2 0.00%), or neonatal intensive care (OR 2.01, 95% CI [0.85, 4.78], I2 70.48%). Narrative synthesis showed food insecurity was significantly associated with dental problems, depression, anxiety, and maternal serum concentration of perfluoro-octane sulfonate. There were no significant associations with other organohalogen chemicals, assisted delivery, postpartum haemorrhage, hospital admissions, length of stay, congenital anomalies, or neonatal morbidity. Mixed associations were reported for preeclampsia, hypertension, and community/resilience measures. Maternal food insecurity is associated with some adverse pregnancy outcomes, particularly mental health and gestational diabetes. Most included studies were conducted in North America, primarily the United States of America, highlighting a research gap across other contexts. Further research in other HICs is needed to understand these associations within varied contexts, such as those without embedded interventions in place, to help inform policy and care requirements.

Large Language Models to Help Appeal Denied Radiotherapy Services

PURPOSE Large language model (LLM) artificial intelligences may help physicians appeal insurer denials of prescribed medical services, a task that delays patient care and contributes to burnout. We evaluated LLM performance at this task for denials of radiotherapy services. METHODS We evaluated generative pretrained transformer 3.5 (GPT-3.5; OpenAI, San Francisco, CA), GPT-4, GPT-4 with internet search functionality (GPT-4web), and GPT-3.5ft. The latter was developed by fine-tuning GPT-3.5 via an OpenAI application programming interface with 53 examples of appeal letters written by radiation oncologists. Twenty test prompts with simulated patient histories were programmatically presented to the LLMs, and output appeal letters were scored by three blinded radiation oncologists for language representation, clinical detail inclusion, clinical reasoning validity, literature citations, and overall readiness for insurer submission. RESULTS Interobserver agreement between radiation oncologists' scores was moderate or better for all domains (Cohen's kappa coefficients: 0.41-0.91). GPT-3.5, GPT-4, and GPT-4web wrote letters that were on average linguistically clear, summarized provided clinical histories without confabulation, reasoned appropriately, and were scored useful to expedite the insurance appeal process. GPT-4 and GPT-4web letters demonstrated superior clinical reasoning and were readier for submission than GPT-3.5 letters ( P &lt; .001). Fine-tuning increased GPT-3.5ft confabulation and compromised performance compared with other LLMs across all domains ( P &lt; .001). All LLMs, including GPT-4web, were poor at supporting clinical assertions with existing, relevant, and appropriately cited primary literature. CONCLUSION When prompted appropriately, three commercially available LLMs drafted letters that physicians deemed would expedite appealing insurer denials of radiotherapy services. LLMs may decrease this task's clerical workload on providers. However, LLM performance worsened when fine-tuned with a task-specific, small training data set.

The transformative impact of large language models on medical writing and publishing: current applications, challenges and future directions.

Large language models (LLMs) are rapidly transforming medical writing and publishing. This review article focuses on experimental evidence to provide a comprehensive overview of the current applications, challenges, and future implications of LLMs in various stages of academic research and publishing process. Global surveys reveal a high prevalence of LLM usage in scientific writing, with both potential benefits and challenges associated with its adoption. LLMs have been successfully applied in literature search, research design, writing assistance, quality assessment, citation generation, and data analysis. LLMs have also been used in peer review and publication processes, including manuscript screening, generating review comments, and identifying potential biases. To ensure the integrity and quality of scholarly work in the era of LLM-assisted research, responsible artificial intelligence (AI) use is crucial. Researchers should prioritize verifying the accuracy and reliability of AI-generated content, maintain transparency in the use of LLMs, and develop collaborative human-AI workflows. Reviewers should focus on higher-order reviewing skills and be aware of the potential use of LLMs in manuscripts. Editorial offices should develop clear policies and guidelines on AI use and foster open dialogue within the academic community. Future directions include addressing the limitations and biases of current LLMs, exploring innovative applications, and continuously updating policies and practices in response to technological advancements. Collaborative efforts among stakeholders are necessary to harness the transformative potential of LLMs while maintaining the integrity of medical writing and publishing.

The safety and effectiveness of music medicine as an intervention for depression: A systematic evaluation and re-evaluation.

As the methodological quality and evidence level of the existing systematic reviews (SRs) on music as an intervention for depression have not been thoroughly evaluated, a systematic evaluation and re-evaluation (SERE) was conducted. Multiple databases including PubMed, Web of Science, Embase, China National Knowledge Infrastructure, SinoMed, Wanfang, and the VIP database were searched for SRs and meta-analyses (MAs) on the effectiveness of music as an intervention for depression. The literature screening, evaluation of methodological quality, and assessment of evidence level were carried out by a team of researchers. The methodological quality was evaluated using the Assessment of Multiple Systematic Reviews 2 (AMSTAR 2) scale in accordance with the 2020 Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) criteria were utilized to assess the level of evidence. A total of 18 SRs were included in the analysis. The 2020 PRISMA guidelines were utilized to evaluate various aspects such as search terms, funding sources, statistical methods for missing values, subgroup and sensitivity analyses, certainty assessment, excluded literature citations, assessment of publication bias, protocol information, conflicts of interest, and data availability, which were rarely reported. The evaluation of the studies using the AMSTAR 2 scale revealed that one article was rated as high quality, six were rated as low quality, and 11 were rated as very low quality. Based on the GRADE criteria evaluation, the quality of the evidence was found to be inconsistent, with reports primarily consisting of medium-quality evidence. The methodological quality of SRs/MAs of music as an intervention in depression is generally poor, and the level of evidence is generally low.

Police Violence Exposure and Associated Health Outcomes Among Latinx Adults in the United States: An Integrative Review.

Police violence exposure among Latinx adults in the United States is increasingly concerning due to its prevalence and associated risks for adverse mental, behavioral, and physical health outcomes. This integrative review appraised studies published from 2003 to 2023 that examine the relationship between police violence exposure and negative health outcomes in Latinx adults. Using structured search terms, articles were identified in APA PsycInfo and PubMed databases, supplemented by a gray literature search and citation mining. Eleven studies met the inclusion criteria. Findings indicate a significant positive association between police violence exposure and adverse mental (n = 9), behavioral (n = 2), and physical (n = 1) health outcomes. The results underscore the mental, behavioral, and physical health consequences of police violence exposure for Latinx adults. Given the increasing Latinx population in the United States, further research is needed to better understand this relationship and inform interventions.

Advances in the Digitization and Mobilization of Natural History Collections in Guatemala

Since its development in 2020, the Guatemala Biodiversity Portal (Fig. 1), generated with Symbiota (Gries et al. 2014, Gilbert et al. 2020, Symbiota Support Hub 2021), has turned into an outstanding resource to facilitate the digitization, mobilization, and use of specimen data from local natural history collections (Orellana et al. 2023). More than 50,000 records and several thousands of images from Guatemalan zoological, botanical, mycological, and paleontological collections have been shared online for the first time in this portal. An integrated Symbiota installation allows further data mobilization to the Global Biodiversity Information Facility (GBIF.org 2024a), amplifying its impact on biodiversity research at an international level. To date, more than 42,000 records have been shared from the Guatemala Biodiversity Portal (GBIF.org 2024b), promoting visibility and recognition for national collections and researchers as shown by more than 300 citations in scientific literature, and the presence of at least 50 local collectors and taxonomists in the platform, Bionomia (Shorthouse 2020). Personalized training, documentation, and technical support have been key to the growing success of the portal (Orellana et al. 2022a, Portal de Biodiversidad de Guatemala 2023). Continuous leadership provided by portal managers and the Symbiota team has additionally amplified the active participation of the Guatemalan collections community. Engagement with undergraduate students has also been crucial to increasing the volume of specimen digitization. Outreach and crowdsourcing activities, as well as guided assignments and temporary internships in research projects, have contributed to transcribing most of the available data. Careful supervision by collection and portal managers, integrated data cleaning tools (Pearson 2021), and a curated taxonomic thesaurus (Orellana et al. 2022b) help maintain the accuracy of the digital records in the portal. Furthermore, the improving accessibility to specimen data from Guatemalan collections and tailored resources in a bilingual Spanish-English interface (e.g., interactive checklists, Pearson and Walker 2021), have made the portal an important source of information, enabling local researchers to develop and share studies about the natural diversity of the country (Prado et al. 2023, Vásquez-Almazán 2023). Additional outcomes, strategies, and reflections about the dynamics of the community will be shared during the presentation.

Bibliometric analysis for artificial intelligence in the internet of medical things: mapping and performance analysis.

The development of computer technology has revolutionized how people live and interact in society. The Internet of Things (IoT) has enabled the development of the Internet of Medical Things (IoMT) to transform healthcare delivery. Artificial intelligence has been used to improve the IoMT. Despite the significance of bibliometric analysis in a research area, to the best of the authors' knowledge, based on searches conducted in academic databases, no bibliometric analysis on artificial intelligence (AI) for the IoMT has been conducted. To address this gap, this study proposes performing a comprehensive bibliometric analysis of AI applications in the IoMT. A bibliometric analysis of top literature sources, main disciplines, countries, prolific authors, trending topics, authorship, citations, author-keywords, and co-keywords was conducted. In addition, the structural development of AI in the IoMT highlights its growing popularity. This study found that security and privacy issues are serious concerns hindering the massive adoption of the IoMT. Future research directions on the IoMT, including perspectives on artificial general intelligence, generative artificial intelligence, and explainable artificial intelligence, have been outlined and discussed.

Comparison of the Usability and Reliability of Answers to Clinical Questions: AI-Generated ChatGPT versus a Human-Authored Resource.

Our aim was to compare the usability and reliability of answers to clinical questions posed of Chat-Generative Pre-Trained Transformer (ChatGPT) compared to those of a human-authored Web source (www.Pearls4Peers.com) in response to "real-world" clinical questions raised during the care of patients. Two domains of clinical information quality were studied: usability, based on organization/readability, relevance, and usefulness, and reliability, based on clarity, accuracy, and thoroughness. The top 36 most viewed real-world questions from a human-authored Web site (www.Pearls4Peers.com [P4P]) were posed to ChatGPT 3.5. Anonymized answers by ChatGPT and P4P (without literature citations) were separately assessed for usability by 18 practicing physicians ("clinician users") in triplicate and for reliability by 21 expert providers ("content experts") on a Likert scale ("definitely yes," "generally yes," or "no") in duplicate or triplicate. Participants also directly compared the usability and reliability of paired answers. The usability and reliability of ChatGPT answers varied widely depending on the question posed. ChatGPT answers were not considered useful or accurate in 13.9% and 13.1% of cases, respectively. In within-individual rankings for usability, ChatGPT was inferior to P4P in organization/readability, relevance, and usefulness in 29.6%, 28.3%, and 29.6% of cases, respectively, and for reliability, inferior to P4P in clarity, accuracy, and thoroughness in 38.1%, 34.5%, and 31% of cases, respectively. The quality of ChatGPT responses to real-world clinical questions varied widely, with nearly one-third or more answers considered inferior to a human-authored source in several aspects of usability and reliability. Caution is advised when using ChatGPT in clinical decision making.

SMAFIRA: a literature-based web tool to assist researchers with retrieval of 3R-relevant information.

The search for 3R-relevant information is a prerequisite for any planned experimental approach considering animal use. Such a literature search includes all methods to replace, reduce and refine (3Rs) animal testing with the aim of improving animal welfare, and requires an intensive screening of literature databases reflecting the current state of knowledge in experimental biomedicine. We developed SMAFIRA, a freely available online tool to facilitate the screening of PubMed/MEDLINE for possible alternatives to animal testing. SMAFIRA employs state-of-the-art language models from the field of deep learning, and provides relevant literature citations in a ranked order, classified according to the experimental model used. By using this classification, the search for alternative methods in the biomedical literature will become much more efficient. The tool is available at https://smafira.bf3r.de.

Checklist of oribatid mites (Acari: Oribatida) from China

The species checklist is the basic data that reflects the species diversity in a country or region. In order to commemorate the 100th anniversary of the taxonomic study of oribatid mites in China and the 60th anniversary of the Special Committee of Acarology of the Entomological Society of China, and to explore the China's biodiversity, and scientifically answer “how many species and what species are there in China”, we compiled a checklist of Chinese oribatid mites, including a total of 1000 species and 35 subspecies in 311 genera and 158 subgenera representing 112 families. In addition, we provided a brief history of taxonomic research on oribatid mites in China in the past 100 years, Chinese name, literature citations and provincial distribution information of each species and subspecies, and a list of species nomina nuda. This work will support China's actions to implement the “Convention on Biological Diversity”, and promote biodiversity research and protection.

Synthesis and structural characterization of a new dinuclear platinum(III) complex, [Pt2Cl4(NH3)2{μ-HN=C(O)But}2], and Synthesis and structure of two novel trans-platinum complexes. Addenda and errata

This article reports changes to the authorship of two papers by Vinci &amp; Chateigner [(2022). Acta Cryst. B78, 835–841; (2023). Acta Cryst. B79, 213–219], as well as providing additional literature citations.

Aquatic Plants of Northern and Central Europe Including Great Britain and Ireland

The introduction takes the reader back to early explorers of river systems and aquatic habits in the 1800s for the geographical areas highlighted in the book. Pages are filled with color photographs illustrating plant morphological examples along with taxonomic key couplets. Each species is profusely illustrated with line drawings and color photographs along with distribution maps. There is an illustrated glossary (pp. 728–733) that aids in interpreting the species descriptions. A literature citation section (pp. 734–738) is organized by topical headings, e.g., Species Identification and Biology. The Index of Latin Names locates the species by page numbers. I found this book easy to use because the authors have focused their attention on organization, function, and usability for the public, as well as aquatic taxonomists. Everything about this book is first class! The size and weight will limit its use in the field and will be more appropriate for in house laboratory or classroom use. The design, layout, printing, binding, and overall quality of the text is of exceptional high quality. I highly recommend this book for botanists interested in European aquatic habitats at a bargain price.