bluuebunny/crossref_metadata_2025_split huggingface dataset:
Smart Self‐Cleaning Materials—Lotus Effect Surfaces
Authors: Li Jun , Zhang Zhuqing , Xu Jianwen , Wong CP 
Published: November 2003

AbstractThe lotus effect represents superhydrophobicity and self‐cleaning properties of natural lotus leaves. Since it was first explained by Professor Wilhelm Barthlott in 1997, lotus effect has gained much attention, and various applications of lotus effect surfaces have been proposed. However, superhydrophobic surfaces have been prepared and studied since 1950s. In this work, the relationship between the two concepts, the lotus effect and superhydrophobicity, is discussed. We also briefly review the fundamental theories on the wettability of hydrophobic rough surfaces, recent modeling works on superhydrophobic and self‐cleaning properties, as well as the processing of lotus effect films. Based on the properties of lotus effect surfaces, different applications were introduced.
Drinking Water Distribution Systems: Controlling the Microbial Quality of Drinking Water
Authors: Geldreich Edwin E. 
Published: November 2003

AbstractBasic Concepts in Distributing Microbially Safe WaterMaintaining Distribution System IntegrityManaging Water QualityMicrobial Flora of Drinking WaterBiofilm and Water StabilityMonitoring Microbial QualityCase Studies on Pathogen Incursions
Rainwater Roof Catchment Systems, Microbial Quality of
Authors: Fujioka Roger S. 
Published: November 2003

AbstractUse and Status of Rainwater Roof Catchment SystemsTypes of RRCS Used Throughout the WorldCommon Components of RRCS and their Impact on Water QualityProfessional Organizations and Guidelines for RRCSVulnerability of RRCS for Microbial ContaminationAn Assessment of the Microbial and Hygienic Quality of Cistern WatersRecommendations for Microbial Contamination in RRCS
Protozoa: Occurrence in Spent Filter Backwash Water in Water Treatment Plants
Authors: Arora Harish , Le Chevallier Mark W. 
Published: November 2003

AbstractRegulation of Wash Water RecycleCurrent SFBW Recycle PracticesOccurrence ofCryptosporidiumandGiardiain SFBWOccurrence of other Water Quality ParametersTreatment Strategies for SFBW
Toxicity Testing in Soil, Use of Microbial and Enzymatic Tests
Authors: Pell Mikael , Torstensson Lennart 
Published: November 2003

AbstractThe Soil Microbial EcosystemSoil QualitySoil Test Data—A Comprehensive PictureMicrobial And Enzymatic TestsScreening and Testing of Chemicals and MetalsInternational GuidelinesTesting Effects On Soil QualityMonitoring Long‐Term Changes in Soil QualityImportant Remarks On Soil TestingConclusion
Compost: Biodegradation of Toxic Organic Compounds
Authors: Dosoretz Carlos G. , Mandelbaum Raphi , Hadar Yitzhak 
Published: November 2003

AbstractPrinciples of CompostingApplication of Compost for the Detoxification of Toxic Organic CompoundsDegradation of Petroleum ProductsDegradation of Halogenated CompoundsDegradation of PesticidesDegradation of ExplosivesThe Fate of Organic Pollutants During Composting
Human Caliciviruses: Basic Virology and Epidemiology
Authors: Meschke John S. , Sobsey Mark D. 
Published: November 2003

AbstractHistorical BackgroundGeneral Characterization of NLVs and Sapporo‐Like Viruses (SLVs)Genomic OrganizationTaxonomy of Human and other CaliciviridaeMethods of DetectionClinical Symptoms and DiagnosisPathogenesisImmunityControl and PreventionEpidemiology
Filamentous Bacteria in Activated Sludge: Current Taxonomic Status and Ecology
Authors: Kämpfer Peter , Wagner Michael 
Published: November 2003

AbstractMethods for the Detection of Filamentous Bacteria Involved in Sludge Bulking or FoamingIsolation and Cultivation of Filamentous Bacteria Involved in Sludge Bulking or FoamingTaxonomy of Filamentous Bacteria Causing Foaming and Bulking Problems in the Activated Sludge ProcessFilaments Affiliated with the β‐Subclass of the ProteobacteriaFilaments Affiliated with the γ‐Subclass of ProteobacteriaFlexibacter Cytophaga BacteroidesClusterChloroflexusSubdivisionOther Filamentous Organisms
Nosocomial Infections
Authors: Mota Patricia De Q. F. , Edberg Stephen C. 
Published: November 2003

AbstractDefinitionsIncidenceHistorical Background and Trends in Microbiology of Nosocomial InfectionsRisk Factors for Nosocomial InfectionsSpecial Patients and Nosocomial InfectionsPrognosisEnvironment and Nosocomial InfectionsPrevention and Control Measures
Salmonellain Aquatic Environments
Authors: Borrego Juan J. , Castro Dolores , Figueras Ma José 
Published: November 2003

AbstractTaxonomic Position ofSalmonellaThe Role ofSalmonellain Human InfectionImportance ofSalmonellain Water EnvironmentsWater Treatment andSalmonellaSurvival ofSalmonellain Aquatic EnvironmentsWaterborne Outbreaks Caused bySalmonellaSalmonellain ShellfishDetection and Enumeration Methods
Virus Survival in Soils
Authors: Yates Marylynn V. 
Published: November 2003

AbstractTemperatureMicrobial ActivitySoil Moisture ContentpHSalt Species and ConcentrationVirus Adsorption to SoilAggregation of Virus ParticlesSoil PropertiesVirus TypeOrganic MatterHydraulic ConditionsMechanisms of Virus SorptionThe Role of the Gas–Liquid Interface
Viable but not Culturable (VBNC) Microorganisms
Authors: Koch Arthur L. 
Published: November 2003

AbstractThe Concept of the Viable but non Culturable (VBNC) CellThe Reality of the VBNC PhenomenonOther Cases of Organisms ‘Choosing’ not to Grow MaximallyStill Other Kinds of Lethal ProcessesThe Different kinds of Bacterial Death
Insecticides, Microbial
Authors: Candas Mehmet , Bulla Lee A. 
Published: November 2003

AbstractProduction TechnologySafety and SpecificityEfficacyVirusesFungiProtozoaBacteriaBacillus ThuringiensisCry ToxinsCry Toxin ReceptorsInsect Resistance to Microbial InsecticidesSustainable Agriculture, Health, and Environment
Water Fungi as Decomposers in Freshwater Ecosystems
Authors: Gessner Mark O. , Van Ryckegem Gunther 
Published: November 2003

AbstractAn Overview of Fungal SystematicsDiversity, Distribution and DispersalThe Mycelial Growth FormSources of Organic MatterLeaf Litter Decomposers in StreamsWood Decomposers in StreamsDecomposers in Freshwater Marshes
Artificial Neural Networks for Physicians: A Technology Introduction
Authors: Rodvold David M. , McLeod David G. , Brandt Jeffrey M. , Snow Peter B. , Murphy Gerald P. 
Published: November 2003

Abstract
Artificial neural networks (ANNs) are the sole successfully deployed artificial intelligence (AI) applications that attempt to mimic the activities of the human brain and how it physically operates. The “artificial” in artificial neural nets is included to distinguish the computer‐based systems from their biological counterparts. ANNs have become very common in a broad set of problem domains including medical, industrial, and financial applications. Within urology, the 1994 seminal paper by Snow et al. established ANNs as a significant predictive technology in the prostate cancer arena. ANNs are well‐established computational models, with research dating to the 1940s.
Within AI, ANNs are generally contrasted with knowledge‐based systems (also known as expert systems). Expert systems use a somewhat contrived structure composed of explicit knowledge rules, while ANNs provide a reasonably faithful representation of biological neural systems. ANNs are generally easier to construct than knowledge‐based systems, but the simple rule structure of knowledge‐based systems provides a more traceable chain of logic from the input to output.
There are a large number of different types of ANNs that computer science researchers apply to various problem types. However, one particular type of ANN architecture has emerged as the de facto standard in practice—the so‐called “feedforward” network, which is described in detail below. Other types of networks that the reader may encounter will generally be some sort of a geometric construct, such as probabilistic neural networks or Kohonen networks. Probabilistic neural networks use a “nearest‐neighbor” approach, where prospective cases are associated with the closest case that was available during network construction. Kohonen networks are a clustering technique, where similar cases are identified as being members of the same group. This document does not address these more esoteric constructs, rather it focuses on the ubiquitous feedforward networks. An interested reader can find detailed information on various ANN paradigms in Lawrence or Wasserman.
Measuring the Accuracy of Prognostic Judgments
Authors: Mackillop William J. , Bartfay Emma , Groome Patti A. 
Published: November 2003

Abstract

The practical test of a true science is the power it confers of prevision, or of knowing now what will follow hereafter. When we can prognosticate with certainty, medicine will have become a science.

—H. Hartshorne
A system of medicine

Prognosis can be defined as, “a forecasting of the probable course and termination of an illness.” In the clinical practice of oncology the prognosis is important because it may determine the optimal choice of treatment, and because patients often want to know what is going to happen to them in order to prepare themselves for the future. Given that prognostic judgments are used as a basis for important decisions, we need to know how accurate they are. We may not yet be able to achieve the “certainty” in prognosis that Hartshorne envisaged as a characteristic of the scientific medicine, but we should at least be able to measure the predictive value of our prognostic judgments, and take this into account in our decision making. It is generally accepted today that we should not place reliance on the results of diagnostic tests without knowing their accuracy, and the same principle should apply to prognostic judgments. However, while modern textbooks of clinical epidemiology deal thoroughly with methods for measuring the accuracy of diagnostic tests, much less has been written about how to measure the accuracy of prognosis.
Although there is a very extensive literature about prognostic factors in cancer, there have been very few reports relating to the accuracy of prognostic judgments in practice in individual cases. In this chapter, we are concerned mainly with the quality of prognostic judgments at the level of the individual patient. This is much more of a challenge than predicting the outcome of groups of cases. Once the average outcome of a specific medical problem has been established in a large group of cases, the average outcome in another large group of similar cases may be predicted with great precision. However, a precise knowledge of the average outcome of an illness may have little or no predictive value in the individual case. For example, if a large group of patients with a specific stage of a specific cancer has been observed to have a 5‐year survival of 50% then we can be confident that if a large group of similar cases is managed in the same way in future, it will also have a 5‐year survival of about 50%. When we try to use this information to predict the outcome in the next case of this illness that we encounter, however, it translates into a probability of 5‐year survival of 0.5; In other words, it leaves us in a state of complete uncertainty.
The main objectives of this chapter are to describe methods that can be used to measure the accuracy of prognosis at the level of the individual case, and to review what little is known about the accuracy of prognostic judgments in oncology. We end by considering factors that may limit the degree of “certainty” that we can ever expect to achieve in prognosis in oncology.
Thyroid Cancer
Authors: Brierley James D. , Asa Sylvia L. 
Published: November 2003

Abstract
The thyroid gland is composed of follicular cells, parafollicular cells, and connective tissue with occasional lymphocytes. Each of these cell types can give rise to histologically distinct tumors that differ significantly in behavior, prognosis, and response to treatment. The commonest tumors by far are those derived from follicular epithelial cells; these tumors are classified as differentiated (papillary and follicular), poorly differentiated (insular), and undifferentiated (anaplastic). Medullary thyroid cancers are derived from parafollicular cells. Tumors derived from lymphocytes and connective tissue cells are similar to such lesions elsewhere in the body, but a detailed discussion of those entities is beyond the scope of this chapter.
In one of the earliest studies on prognostic factors in thyroid cancer, the multivariate analysis of the European Organization for Research and Treatment of Cancer (EORTC) Thyroid Cancer Co‐operative Group, all histologies were combined. The 5‐year survival ranged from 80% in differentiated carcinoma (papillary and follicular), to 55% in medullary thyroid carcinoma (MTC), to 10% in anaplastic carcinoma. Most subsequent studies of prognostic factors in thyroid cancer were developed for individual histologic types [differentiated papillary–follicular, poorly differentiated (insular), medullary, and anaplastic thyroid carcinomas] and they will be considered separately in this review. Some studies combine papillary and follicular, while others do not. The focus of this chapter is a review of the tumor‐, host‐ and environment‐related prognostic factors in differentiated thyroid cancer, with an additional discussion of prognostic factors in other thyroid histologies.
Extrahepatic Biliary Tract and the Ampulla ofVater Cancers
Authors: Henson Donald Earl , Ries Lynn G. , Albores‐Saavedra Jorge 
Published: November 2003

AbstractThis chapter reviews the most useful prognostic factors that are available for carcinomas arising in the gallbladder, extrahepatic bile ducts, or the ampulla of Vater. In considering these prognostic indicators, the complex anatomy of the extrahepatic biliary system and its relation to adjacent structures becomes critical. For this reason, the prognostic factors are described separately for the gallbladder, extrahepatic bile ducts, and ampulla. For all three sites, however, the single most important factor having the greatest effect on survival is the extent of tumor at the time of diagnosis. In preparing this chapter, data were reviewed from the Surveillance, Epidemiology, and End Results (SEER) program of the National Cancer Institute for the years 1973–1995. In the SEER Program, the extent of disease can be classified as “localized,” regional, and “distant” or by the standard TNM classification. A “localized” tumor is confined to the organ of origin without nodal involvement; “regional,” denotes regional lymph‐node involvement or direct extension to adjacent organs; and “distant” indicates that distant metastases have been found. The prognostic factors are presented as tumor‐related, host‐related, and environment‐related.
Cancer of the Pancreas
Authors: Roder Jürgen D. , Ott Katja 
Published: November 2003

AbstractPancreatic cancer remains an important cause of death in many nations. In most patients pancreatic adenocarcinoma is diagnosed at a stage of disease that is not curable by surgery and can only be little affected by multimodality treatment options (chemotherapy, radiotherapy, immunotherapy). Surgery is the only curative approach, but success with resection is low and the prognosis remains extremely poor, for example, patients with completely resected tumors have a 5‐year survival rate of 15–28%. The overall 5‐year survival remains extremely low despite a slight improvement during the last few years: figures of 1–3% for all, and 5–25% for resected patients are reported.More than 90–95% of carcinomas of the pancreas are exocrine tumors. Over 90% of these tumors, as referred to in the World Health Organization (WHO) classification, are ductal adenocarcinomas, including rare types, such as mucinous noncystic, adenosquamous, anaplastic, and osteoblast‐like giant‐cell carcinoma. The mucinous cystadenocarcinoma (about 1% of all cases or 5% of resected cases) had a relatively good prognosis and a 5‐year survival rate of 54.3% in a Japanese study. Also, the uncommon intraductal papillary mucinous adenocarcinoma has a better prognosis than the common ductal adenocarcinoma. These two tumor types, as well as the carcinoma in situ, are excluded in the following review of prognostic factors.The most important prognostic factor is the possibility of curative resection. The outcome of patients who undergo resection is definitely superior to those not undergoing resection. In the literature the resection rates for exocrine pancreatic carcinoma range from 2.6% to 30%. In contrast to patients without resection, who practically never show a long‐term survival rate, in specialized centers resected patients show a 5‐year survival rate of 20–25%. The median survival time for resected patients is 10–20 months. Therefore, the prognostic factors for those patients who have not been resected will be discussed separately in the section on treatment‐related prognostic factors.Due to the small number of resected patients and the poor prognosis, the importance of several prognostic factors still remains unclear, because most studies are based on univariate analysis. Only a few studies with more than 100 resected patients analyzed by multivariate analysis have been published.In the following analysis of prognostic factors, the relevant international literature will be considered. Problems in reviewing the actual literature are that often carcinomas of the head of the pancreas are not separated from those of the ampulla of Vater and the distal bile duct, which have a better prognosis. Furthermore, in 1997 the new International Union Against Cancer Tumors, Nodules, Metastases (UICC TNM) classification (5th ed.) was published this manual relates better to prognosis than does the old classification (4th ed.). Also, in the interpretation of prognostic factors, the reduction of perioperative mortality to less than 5% also has to be considered.
Lung Cancer
Authors: Brundage Michael D. , Mackillop William J. 
Published: November 2003

Abstract
Lung cancer is best conceptualized as a group of heterogeneous clinical entities with common molecular and cellular origins, but with different clinical behaviors, and hence, different prognoses. Lung cancer has a major impact on population health; taken together, lung cancers are the most common cause of cancer death in both males and females in North America. Overall, only 13% of patients survive more than 5 years after being diagnosed.
Determining the prognosis for an individual patient with lung cancer is difficult, in part, because the clinical course of the disease in the individual case can evolve along a seemingly infinite combination of branching pathways. Over 90% of lung cancer patients present with signs or symptoms of disease, and since no symptoms or signs are pathognomonic for lung cancer, the disease has a capacity clinically to mimic other cancers and other nonmalignant diseases with a constellation of possible presenting symptoms. These varying presentations, and potential clinical evolutions, are in turn due to the multiple potential manifestations of the primary tumor, metastatic sites of involvement, and paraneoplastic syndromes.
Despite the heterogeneity of the clinical manifestations of lung cancer, the prognosis for a population of patients with lung cancer is remarkably predictable. The overall mortality rates for lung cancer in North America over the last 15 years have remained unchanged. A population‐based study of over 12,000 patients with unresected non‐small‐cell lung cancer registered in seven Ontario Regional Cancer Centers (1982–1991) demonstrated no significant differences in patient survival either between Centers or over time. The predictability of population survival outcomes is of limited usefulness, however, due to the marked heterogeneity of patients making up the overall population. Prognostic factors are thus used to divide the overall population of lung cancer patients into subgroups in order to realize the benefits of prognostic stratification. A number of studies have addressed the identification and application of such factors.
Soft Tissue Sarcoma
Authors: Pisters Peter W.T. , O'Sullivan Brian , Pollock Raphael E. 
Published: November 2003

AbstractSignificant advances have been made over the past decade in the understanding of clinicopathologic prognostic factors for soft tissue sarcoma. Foremost among these advances is an improved ability to recognize the subset of patients at high risk for recurrent disease and tumor‐related death based on clinicopathologic data available at the time of initial presentation. Recent advances have also helped to elucidate specific molecular factors that have independent prognostic significance. This review summarizes the available data on traditional clinicopathologic, medical, and molecular prognostic factors for adult soft tissue sarcoma. Expected outcomes will be provided, drawing in particular from the results of a large series of patients managed at one center. Although there are many potential outcomes that could be assessed, the discussion will focus almost entirely on the traditional oncology outcomes of local control, metastatic risk, and survival. Other important outcomes, such as limb preservation; function, and quality of life will not be emphasized because of space limitations.The factors will be discussed in a framework that focuses on thetumor‐related,host‐related(or patient associated), andenvironment‐relatedbackground for these factors. The review will conclude with a summary tabulation identifying strata of the importance of factors to everyday clinical practice using the principles outlined in Chapter 2. These includeessentialfactors (needed for treatment decision‐making),additionalfactors (of benefit for describing a cohort of patients), andnew and promisingfactors that may of benefit in the future to exploit new treatment or diagnostic strategies.
Breast Cancer
Authors: Fitzgibbons Patrick L. 
Published: November 2003

Abstract
Breast cancer is a heterogeneous disease that exhibits a wide variety of clinical presentations, histologic types, and growth rates. Because of these variations, determining prognosis for an individual patient at the time of initial diagnosis requires careful assessment of multiple clinical and pathological parameters. This evaluation includes consideration of an expanding list of tumor‐ and patient‐related prognostic factors, which are especially important for decisions related to systemic treatment and predictions for overall survival.
Uterine Cervix Cancer
Authors: Pitson Graham , Fyles Anthony 
Published: November 2003

Abstract
Carcinoma of the cervix accounts for approximately 20% of all gynecologic cancers and 2% of all malignancies in women. Numerous prognostic factors have been studied in patients with cervical carcinoma, using both univariate and multivariate analysis. Differences in endpoints of analysis, whether survival (disease‐free or overall), relapse‐free rate, or local control rate, make comparison of such studies difficult. The failure to perform a multivariate analysis, or the use of different covariates in multivariate analyses, can further complicate comparisons between studies. Not all factors are relevant to all patients; for example, depth of tumor invasion and presence of vascular‐space invasion can only be reliably determined in patients treated with surgery, whereas hemoglobin level is important in patients treated with radiation. This review concentrates largely on those factors identified using multivariate techniques, such as log rank or Cox regression analysis, in order to account for interactions between various factors. Where available, estimates such as hazard ratios will be included in order to indicate the strengths of the individual variables.