Susceptibility of enamel to initial erosion in relation to tooth type, tooth surface and enamel depth

This study aimed at assessing the susceptibility of different tooth types (molar/premolar), surfaces (buccal/lingual) and enamel depths (100, 200, 400 and 600 μm) to initial erosion measured by surface microhardness loss (ΔSMH) and calcium (Ca) release. Twenty molars and 20 premolars were divided into experimental and control groups, cut into lingual/ buccal halves, and ground/polished, removing 100 μm of enamel. The initial surface microhardness (SMH 0 ) was measured on all halves. The experimental group was subjected to 3 consecutive erosive challenges (30 ml/tooth of 1% citric acid, pH 3.6, 25 ° C, 1 min). After each challenge, ΔSMH and Ca release were measured. The same teeth were consecutively ground to 200, 400 and 600 μm depths, and the experimental group underwent 3 erosive challenges at each depth. No difference was found in SMH 0 between experimental and control groups. Multivariate nonparametric ANOVA showed no significant differences between lingual and buccal surfaces in ΔSMH (p = 0.801) or Ca release (p = 0.370). ΔSMH was significantly greater in premolars than in molars (p < 0.05), but not different with respect to enamel depth. Ca release decreased significantly with increasing depth. Regression between Ca release and ΔSMH at 100 μm depth showed lower slope and r 2 value, associated with greater Ca release values. At 200-600 μm depths, moderately large r 2 values were observed (0.651-0.830). In conclusion, different teeth and enamel depths have different susceptibility to erosion, so when Ca release is used to measure erosion, the depth of the test facet in enamel should be standardized, whereas this is less important if ΔSMH is used.

Prognostic value of PCSK9 levels in patients with acute coronary syndromes.

AIMS Proprotein convertase subtilisin kexin 9 (PCSK9) is an emerging target for the treatment of hypercholesterolaemia, but the clinical utility of PCSK9 levels to guide treatment is unknown. We aimed to prospectively assess the prognostic value of plasma PCSK9 levels in patients with acute coronary syndromes (ACS). METHODS AND RESULTS Plasma PCSK9 levels were measured in 2030 ACS patients undergoing coronary angiography in a Swiss prospective cohort. At 1 year, the association between PCSK9 tertiles and all-cause death was assessed adjusting for the Global Registry of Acute Coronary Events (GRACE) variables, as well as the achievement of LDL cholesterol targets of <1.8 mmol/L. Patients with higher PCSK9 levels at angiography were more likely to have clinical familial hypercholesterolaemia (rate ratio, RR 1.21, 95% confidence interval, CI 1.09-1.53), be treated with lipid-lowering therapy (RR 1.46, 95% CI 1.30-1.63), present with longer time interval of chest pain (RR 1.29, 95% CI 1.09-1.53) and higher C-reactive protein levels (RR 1.22, 95% CI 1.16-1.30). PCSK9 increased 12-24 h after ACS (374 ± 149 vs. 323 ± 134 ng/mL, P < 0.001). At 1 year follow-up, HRs for upper vs. lower PCSK9-level tertiles were 1.13 (95% CI 0.69-1.85) for all-cause death and remained similar after adjustment for the GRACE score. Patients with higher PCSK9 levels were less likely to reach the recommended LDL cholesterol targets (RR 0.81, 95% CI 0.66-0.99). CONCLUSION In ACS patients, high initial PCSK9 plasma levels were associated with inflammation in the acute phase and hypercholesterolaemia, but did not predict mortality at 1 year.

Detection of haemorrhagic shock: Are vital signs obsolete? A commentary by Dr M. Luginbühl

The ATLS program by the American college of surgeons is probably the most important globally active training organization dedicated to improve trauma management. Detection of acute haemorrhagic shock belongs to the key issues in clinical practice and thus also in medical teaching. (In this issue of the journal William Schulz and Ian McConachrie critically review the ATLS shock classification Table 1), which has been criticized after several attempts of validation have failed [1]. The main problem is that distinct ranges of heart rate are related to ranges of uncompensated blood loss and that the heart rate decrease observed in severe haemorrhagic shock is ignored [2]. Table 1. Estimated blood loos based on patient's initial presentation (ATLS Students Course Manual, 9th Edition, American College of Surgeons 2012). Class I Class II Class III Class IV Blood loss ml Up to 750 750–1500 1500–2000 >2000 Blood loss (% blood volume) Up to 15% 15–30% 30–40% >40% Pulse rate (BPM) <100 100–120 120–140 >140 Systolic blood pressure Normal Normal Decreased Decreased Pulse pressure Normal or ↑ Decreased Decreased Decreased Respiratory rate 14–20 20–30 30–40 >35 Urine output (ml/h) >30 20–30 5–15 negligible CNS/mental status Slightly anxious Mildly anxious Anxious, confused Confused, lethargic Initial fluid replacement Crystalloid Crystalloid Crystalloid and blood Crystalloid and blood Table options In a retrospective evaluation of the Trauma Audit and Research Network (TARN) database blood loss was estimated according to the injuries in nearly 165,000 adult trauma patients and each patient was allocated to one of the four ATLS shock classes [3]. Although heart rate increased and systolic blood pressure decreased from class I to class IV, respiratory rate and GCS were similar. The median heart rate in class IV patients was substantially lower than the value of 140 min−1 postulated by ATLS. Moreover deterioration of the different parameters does not necessarily go parallel as suggested in the ATLS shock classification [4] and [5]. In all these studies injury severity score (ISS) and mortality increased with in increasing shock class [3] and with increasing heart rate and decreasing blood pressure [4] and [5]. This supports the general concept that the higher heart rate and the lower blood pressure, the sicker is the patient. A prospective study attempted to validate a shock classification derived from the ATLS shock classes [6]. The authors used a combination of heart rate, blood pressure, clinically estimated blood loss and response to fluid resuscitation to classify trauma patients (Table 2) [6]. In their initial assessment of 715 predominantly blunt trauma patients 78% were classified as normal (Class 0), 14% as Class I, 6% as Class II and only 1% as Class III and Class IV respectively. This corresponds to the results from the previous retrospective studies [4] and [5]. The main endpoint used in the prospective study was therefore presence or absence of significant haemorrhage, defined as chest tube drainage >500 ml, evidence of >500 ml of blood loss in peritoneum, retroperitoneum or pelvic cavity on CT scan or requirement of any blood transfusion >2000 ml of crystalloid. Because of the low prevalence of class II or higher grades statistical evaluation was limited to a comparison between Class 0 and Class I–IV combined. As in the retrospective studies, Lawton did not find a statistical difference of heart rate and blood pressure among the five groups either, although there was a tendency to a higher heart rate in Class II patients. Apparently classification during primary survey did not rely on vital signs but considered the rather soft criterion of “clinical estimation of blood loss” and requirement of fluid substitution. This suggests that allocation of an individual patient to a shock classification was probably more an intuitive decision than an objective calculation the shock classification. Nevertheless it was a significant predictor of ISS [6]. Table 2. Shock grade categories in prospective validation study (Lawton, 2014) [6]. Normal No haemorrhage Class I Mild Class II Moderate Class III Severe Class IV Moribund Vitals Normal Normal HR > 100 with SBP >90 mmHg SBP < 90 mmHg SBP < 90 mmHg or imminent arrest Response to fluid bolus (1000 ml) NA Yes, no further fluid required Yes, no further fluid required Requires repeated fluid boluses Declining SBP despite fluid boluses Estimated blood loss (ml) None Up to 750 750–1500 1500–2000 >2000 Table options What does this mean for clinical practice and medical teaching? All these studies illustrate the difficulty to validate a useful and accepted physiologic general concept of the response of the organism to fluid loss: Decrease of cardiac output, increase of heart rate, decrease of pulse pressure occurring first and hypotension and bradycardia occurring only later. Increasing heart rate, increasing diastolic blood pressure or decreasing systolic blood pressure should make any clinician consider hypovolaemia first, because it is treatable and deterioration of the patient is preventable. This is true for the patient on the ward, the sedated patient in the intensive care unit or the anesthetized patients in the OR. We will therefore continue to teach this typical pattern but will continue to mention the exceptions and pitfalls on a second stage. The shock classification of ATLS is primarily used to illustrate the typical pattern of acute haemorrhagic shock (tachycardia and hypotension) as opposed to the Cushing reflex (bradycardia and hypertension) in severe head injury and intracranial hypertension or to the neurogenic shock in acute tetraplegia or high paraplegia (relative bradycardia and hypotension). Schulz and McConachrie nicely summarize the various confounders and exceptions from the general pattern and explain why in clinical reality patients often do not present with the “typical” pictures of our textbooks [1]. ATLS refers to the pitfalls in the signs of acute haemorrhage as well: Advanced age, athletes, pregnancy, medications and pace makers and explicitly state that individual subjects may not follow the general pattern. Obviously the ATLS shock classification which is the basis for a number of questions in the written test of the ATLS students course and which has been used for decades probably needs modification and cannot be literally applied in clinical practice. The European Trauma Course, another important Trauma training program uses the same parameters to estimate blood loss together with clinical exam and laboratory findings (e.g. base deficit and lactate) but does not use a shock classification related to absolute values. In conclusion the typical physiologic response to haemorrhage as illustrated by the ATLS shock classes remains an important issue in clinical practice and in teaching. The estimation of the severity haemorrhage in the initial assessment trauma patients is (and was never) solely based on vital signs only but includes the pattern of injuries, the requirement of fluid substitution and potential confounders. Vital signs are not obsolete especially in the course of treatment but must be interpreted in view of the clinical context. Conflict of interest None declared. Member of Swiss national ATLS core faculty.

Bayesian and survival model for tumor relapse status and disease-specific survival, with applications to breast cancer

Breast cancer is the most common non-skin cancer and the second leading cause of cancer-related death in women in the United States. Studies on ipsilateral breast tumor relapse (IBTR) status and disease-specific survival will help guide clinic treatment and predict patient prognosis.^ After breast conservation therapy, patients with breast cancer may experience breast tumor relapse. This relapse is classified into two distinct types: true local recurrence (TR) and new ipsilateral primary tumor (NP). However, the methods used to classify the relapse types are imperfect and are prone to misclassification. In addition, some observed survival data (e.g., time to relapse and time from relapse to death)are strongly correlated with relapse types. The first part of this dissertation presents a Bayesian approach to (1) modeling the potentially misclassified relapse status and the correlated survival information, (2) estimating the sensitivity and specificity of the diagnostic methods, and (3) quantify the covariate effects on event probabilities. A shared frailty was used to account for the within-subject correlation between survival times. The inference was conducted using a Bayesian framework via Markov Chain Monte Carlo simulation implemented in softwareWinBUGS. Simulation was used to validate the Bayesian method and assess its frequentist properties. The new model has two important innovations: (1) it utilizes the additional survival times correlated with the relapse status to improve the parameter estimation, and (2) it provides tools to address the correlation between the two diagnostic methods conditional to the true relapse types.^ Prediction of patients at highest risk for IBTR after local excision of ductal carcinoma in situ (DCIS) remains a clinical concern. The goals of the second part of this dissertation were to evaluate a published nomogram from Memorial Sloan-Kettering Cancer Center, to determine the risk of IBTR in patients with DCIS treated with local excision, and to determine whether there is a subset of patients at low risk of IBTR. Patients who had undergone local excision from 1990 through 2007 at MD Anderson Cancer Center with a final diagnosis of DCIS (n=794) were included in this part. Clinicopathologic factors and the performance of the Memorial Sloan-Kettering Cancer Center nomogram for prediction of IBTR were assessed for 734 patients with complete data. Nomogram for prediction of 5- and 10-year IBTR probabilities were found to demonstrate imperfect calibration and discrimination, with an area under the receiver operating characteristic curve of .63 and a concordance index of .63. In conclusion, predictive models for IBTR in DCIS patients treated with local excision are imperfect. Our current ability to accurately predict recurrence based on clinical parameters is limited.^ The American Joint Committee on Cancer (AJCC) staging of breast cancer is widely used to determine prognosis, yet survival within each AJCC stage shows wide variation and remains unpredictable. For the third part of this dissertation, biologic markers were hypothesized to be responsible for some of this variation, and the addition of biologic markers to current AJCC staging were examined for possibly provide improved prognostication. The initial cohort included patients treated with surgery as first intervention at MDACC from 1997 to 2006. Cox proportional hazards models were used to create prognostic scoring systems. AJCC pathologic staging parameters and biologic tumor markers were investigated to devise the scoring systems. Surveillance Epidemiology and End Results (SEER) data was used as the external cohort to validate the scoring systems. Binary indicators for pathologic stage (PS), estrogen receptor status (E), and tumor grade (G) were summed to create PS+EG scoring systems devised to predict 5-year patient outcomes. These scoring systems facilitated separation of the study population into more refined subgroups than the current AJCC staging system. The ability of the PS+EG score to stratify outcomes was confirmed in both internal and external validation cohorts. The current study proposes and validates a new staging system by incorporating tumor grade and ER status into current AJCC staging. We recommend that biologic markers be incorporating into revised versions of the AJCC staging system for patients receiving surgery as the first intervention.^ Chapter 1 focuses on developing a Bayesian method to solve misclassified relapse status and application to breast cancer data. Chapter 2 focuses on evaluation of a breast cancer nomogram for predicting risk of IBTR in patients with DCIS after local excision gives the statement of the problem in the clinical research. Chapter 3 focuses on validation of a novel staging system for disease-specific survival in patients with breast cancer treated with surgery as the first intervention. ^

TIME SERIES ANALYSIS AS INPUT FOR PREDICTIVE MODELING: PREDICTING CARDIAC ARREST IN A PEDIATRIC INTENSIVE CARE UNIT

The first manuscript, entitled "Time-Series Analysis as Input for Clinical Predictive Modeling: Modeling Cardiac Arrest in a Pediatric ICU" lays out the theoretical background for the project. There are several core concepts presented in this paper. First, traditional multivariate models (where each variable is represented by only one value) provide single point-in-time snapshots of patient status: they are incapable of characterizing deterioration. Since deterioration is consistently identified as a precursor to cardiac arrests, we maintain that the traditional multivariate paradigm is insufficient for predicting arrests. We identify time series analysis as a method capable of characterizing deterioration in an objective, mathematical fashion, and describe how to build a general foundation for predictive modeling using time series analysis results as latent variables. Building a solid foundation for any given modeling task involves addressing a number of issues during the design phase. These include selecting the proper candidate features on which to base the model, and selecting the most appropriate tool to measure them. We also identified several unique design issues that are introduced when time series data elements are added to the set of candidate features. One such issue is in defining the duration and resolution of time series elements required to sufficiently characterize the time series phenomena being considered as candidate features for the predictive model. Once the duration and resolution are established, there must also be explicit mathematical or statistical operations that produce the time series analysis result to be used as a latent candidate feature. In synthesizing the comprehensive framework for building a predictive model based on time series data elements, we identified at least four classes of data that can be used in the model design. The first two classes are shared with traditional multivariate models: multivariate data and clinical latent features. Multivariate data is represented by the standard one value per variable paradigm and is widely employed in a host of clinical models and tools. These are often represented by a number present in a given cell of a table. Clinical latent features derived, rather than directly measured, data elements that more accurately represent a particular clinical phenomenon than any of the directly measured data elements in isolation. The second two classes are unique to the time series data elements. The first of these is the raw data elements. These are represented by multiple values per variable, and constitute the measured observations that are typically available to end users when they review time series data. These are often represented as dots on a graph. The final class of data results from performing time series analysis. This class of data represents the fundamental concept on which our hypothesis is based. The specific statistical or mathematical operations are up to the modeler to determine, but we generally recommend that a variety of analyses be performed in order to maximize the likelihood that a representation of the time series data elements is produced that is able to distinguish between two or more classes of outcomes. The second manuscript, entitled "Building Clinical Prediction Models Using Time Series Data: Modeling Cardiac Arrest in a Pediatric ICU" provides a detailed description, start to finish, of the methods required to prepare the data, build, and validate a predictive model that uses the time series data elements determined in the first paper. One of the fundamental tenets of the second paper is that manual implementations of time series based models are unfeasible due to the relatively large number of data elements and the complexity of preprocessing that must occur before data can be presented to the model. Each of the seventeen steps is analyzed from the perspective of how it may be automated, when necessary. We identify the general objectives and available strategies of each of the steps, and we present our rationale for choosing a specific strategy for each step in the case of predicting cardiac arrest in a pediatric intensive care unit. Another issue brought to light by the second paper is that the individual steps required to use time series data for predictive modeling are more numerous and more complex than those used for modeling with traditional multivariate data. Even after complexities attributable to the design phase (addressed in our first paper) have been accounted for, the management and manipulation of the time series elements (the preprocessing steps in particular) are issues that are not present in a traditional multivariate modeling paradigm. In our methods, we present the issues that arise from the time series data elements: defining a reference time; imputing and reducing time series data in order to conform to a predefined structure that was specified during the design phase; and normalizing variable families rather than individual variable instances. The final manuscript, entitled: "Using Time-Series Analysis to Predict Cardiac Arrest in a Pediatric Intensive Care Unit" presents the results that were obtained by applying the theoretical construct and its associated methods (detailed in the first two papers) to the case of cardiac arrest prediction in a pediatric intensive care unit. Our results showed that utilizing the trend analysis from the time series data elements reduced the number of classification errors by 73%. The area under the Receiver Operating Characteristic curve increased from a baseline of 87% to 98% by including the trend analysis. In addition to the performance measures, we were also able to demonstrate that adding raw time series data elements without their associated trend analyses improved classification accuracy as compared to the baseline multivariate model, but diminished classification accuracy as compared to when just the trend analysis features were added (ie, without adding the raw time series data elements). We believe this phenomenon was largely attributable to overfitting, which is known to increase as the ratio of candidate features to class examples rises. Furthermore, although we employed several feature reduction strategies to counteract the overfitting problem, they failed to improve the performance beyond that which was achieved by exclusion of the raw time series elements. Finally, our data demonstrated that pulse oximetry and systolic blood pressure readings tend to start diminishing about 10-20 minutes before an arrest, whereas heart rates tend to diminish rapidly less than 5 minutes before an arrest.

Measuring value in cancer-related health care: A case study in head and neck cancer

In the current climate of escalating health care costs, defining value and accurately measuring it are two critical issues affecting not only the future of cancer care in particular but also the future of health care in general. Specifically, measuring and improving value in cancer-related health care are critical for continued advancements in research, management, and overall delivery of care. However, in oncology, most of this research has focused on value as it relates to insurance industry and payment reform, with little attention paid to value as the output of clinical interventions that encompass integrated clinical teams focusing on the entire cycle of care and measuring objective outcomes that are most relevant to patients. ^ In this study, patient-centered value was defined as health outcomes achieved per dollar spent, and calculated using objective functional outcomes and total care costs. The analytic sample comprised patients diagnosed with three common head and neck cancers—cancer of the larynx, oral cavity, and oropharynx—who were treated in an integrated tertiary care center over an approximately 10-year period. The results of this study provide initial empirical data that can be used to assess and ultimately to help improve the quality and value of head and neck cancer care, and more importantly they can be used by patients and clinicians to make better-informed decisions about care, particularly what therapeutic services and outcomes matter the most to patients.^

Factors associated with the retention of HIV-infected children in care in Botswana

Lost to follow up (LTFU) in the care and treatment of HIV/AIDS represents a particularly problematic aspect when evaluating the success of treatment programs. Identifying modifiable factors that lead to LTFU would be important if we are to design effective retention interventions. The purpose of this study was to identify the challenges faced by children seeking care and treatment at a large HIV Clinic in Botswana. In order to identify those factors, we used mixed methods from different sources of information available at the Baylor Clinic. The first method involved a case-control study through which we interviewed a select representation of children 1-18 years who, at some point in time, have attended clinic at Baylor Clinic in Gaborone, Botswana. We document this in detail using the first journal article. We defined LTFU as patients who had not attended clinic for more than 6 months at the onset of the study; the comparison group was recruited from among those who have attended clinic at any point in the 6 months leading to the start of study. Factors were compared between the cases and controls. The second methodology involved conducting in-depth interviews with health providers to elicit their opinions and experiences dealing with patients at the at the Baylor clinic in general and the LTFU patients in particular. We document this methodology and its findings in the second journal article. ^ We found that most patients that are LTFU failed to engage with the clinic. Most of the LTFU made only one visit to the clinic (47.66%) as compared to less than 1% in the control group (P<0.01, 2-tailed Fisher's exact test). Among the interviewed patients, psychosocial factors such as stigma, religious beliefs, child rebellion and disclosure of HIV status concerns were characteristic of the LTFU population, but psychosocial issues were not cited among the comparison group. We also found that these psychosocial aspects of the patients point towards a bigger problem of mental health that needs to be addressed. Socioeconomic factors such as lack of transport, school-related activities and forgetting check-up dates were cited predominantly by the controls than cases. ^ From these findings, there is need to target interventions towards engaging pediatric patients at their initial clinic visit. Such interventions would focus on psychosocial support, as well as involving faith-based organizations in planning joint responses.^

(Appendix) Characteristic directions at DSDP Hole 76-534A

Kimmeridgian-Tithonian red marly limestones and Berriasian white limestones were recovered at Site 534 of DSDP Leg 76 in the western North Atlantic. These yielded a well-defined magnetostratigraphy with the characteristic magnetization carried by hematite in red sediments and magnetite in white sediments. The polarity sequence is correlated to the magnetostratigraphy of Kimmeridgian-Tithonian-Berriasian pelagic carbonates of northern Italy and southern Spain, allowing precise biostratigraphic age correlations. The Berriasian/Tithonian boundary occurs within the upper half of Core 90, the late Tithonian/early Tithonian boundary at the base of Core 96, and the Tithonian/Kimmeridgian boundary at the top of Core 102. Correlations are also made to M-16 through M-22 of the marine magnetic anomaly M-sequence. Poor recovery and irregular magnetic properties of the underlying Kimmeridgian-Oxfordian-Callovian marls and claystones prevented determination of a polarity sequence, but the entire interval has mixed polarity. Valanginian gray marly limestones have very weak magnetizations, and preliminary results are inadequate to determine the polarity pattern.

Grain size investigations of sediments off the coast of Istria

Composition, grain-size distribution, and areal extent of Recent sediments from the Northern Adriatic Sea along the Istrian coast have been studied. Thirty one stations in four sections vertical to the coast were investigated; for comparison 58 samples from five small bays were also analyzed. Biogenic carbonate sediments are deposited on the shallow North Adriatic shelf off the Istrian coast. Only at a greater distance from the coast are these carbonate sediments being mixed with siliceous material brought in by the Alpine rivers Po, Adige, and Brenta. Graphical analysis of grain-size distribution curves shows a sediment composition of normally three, and only in the most seaward area, of four major constituents. Constituent 1 represents the washed-in terrestrial material of clay size (Terra Rossa) from the Istrian coastal area. Constituent 2 consists of fine to medium sand. Constituent 3 contains the heterogeneous biogenic material. Crushing by organisms and by sediment eaters reduces the coarse biogenic material into small pieces generating constituent 2. Between these two constituents there is a dynamic equilibrium. Depending upon where the equilibrium is, between the extremes of production and crushing, the resulting constituent 2 is finer or coarser. Constituent 4 is composed of the fine sandy material from the Alpine rivers. In the most seaward area constituents 2 and 4 are mixed. The total carbonate content of the samples depends on the distance from the coast. In the near coastal area in high energy environments, the carbonate content is about 80 %. At a distance of 2 to 3 km from the coast there is a carbonate minimum because of the higher rate of sedimentation of clay-sized terrestrial, noncarbonate material at extremely low energy environments. In an area between 5 and 20 km off the coast, the carbonate content is about 75 %. More than 20 km from the shore, the carbonate content diminishes rapidly to values of about 30 % through mixing with siliceous material from the Alpine rivers. The carbonate content of the individual fractions increases with increasing grain-size to a maximum of about 90 % within the coarse sand fractions. Beyond 20 km from the coast the samples show a carbonate minimum of about 13 % within the sand-size classes from 1.5 to 0.7 zeta¬? through mixing with siliceous material from the alpine rivers. By means of grain-size distribution and carbonate content, four sediment zones parallel to the coast were separated. Genetically they are closely connected with the zonation of the benthic fauna. Two cores show a characteristic vertical distribution of the sediment. The surface zone is inversely graded, that means the coarse fractions are at the top and the fine fractions are at the bottom. This is the effect of crushing of the biogenic material produced at the surface by predatory organisms and by sediment eaters. lt is proposed that at a depth of about 30 cm a chemical solution process begins which leads to diminution of the original sediment from a fine to medium sand to a silt. The carbonate content decreases from about 75 % at the surface to 65 % at a depth of 100 cm. The increase of the noncarbonate components by 10 % corresponds to a decrease in the initial amount of sediment (CaC03=75 %) by roughly 30 % through solution. With increasing depth the carbonate content of the individual fractions becomes more and more uniform. At the surface the variation is from 30 % to 90 %, at the bottom it varies only between 50 % and 75 %. Comparable investigations of small-bay sediments showed a c1ear dependence of sediment/faunal zonation from the energy of the environment. The investigations show that the composition and three-dimensional distribution of the Istrian coastal sediments can not be predicted only from one or a few measurable factors. Sedimentation and syngenetic changes must be considered as a complex interaction between external factors and the actions of producing and destroying organisms that are in dynamic equilibrium. The results obtained from investigations of these recent sediments may be of value for interpreting fossil sediments only with strong limitations.

Descriptions, isotopes and minerals of sediments at DSDP Leg 64 Holes

Mineralogical and oxygen isotopic analyses of samples from Deep Sea Drilling Project Sites 477, 481, and 477 in the Guaymas Basin indicate the existence of two distinct hydrothermal systems. In the first, at Sites 481 and 478, hot dolerite sills intruded into highly porous hemipelagic siliceous mudstones that were moderately rich in organic matter, thermally altered the adjacent sediments, and expelled hydrothermal pore fluids. The second, at Site 477 and active at present, is most probably caused by a recent igneous intrusion forming a magma chamber at shallow depth. In the first hydrothermal system, the main thermal reactions above and below the sills are dissolution of opal-A and formation of quartz, either directly or through opal-CT; formation of smectite; formation of analcime only above the sills; dissolution and recrystallization of calcite and occasional formation of dolomite or protodolomite. The d18O values of the hydrothermally altered sediments range from 9.9 to 12.2 per mil (SMOW). The d18O values of recrystallized calcites above the first sill complex, Site 481, indicate temperatures of 140° to 170°C. No fluid recharge is required in this system. The thickness of the sill complexes and the sequence and depth of intrusion into the sediment column determine the thickness of the alteration zones, which ranges from 2 or 3 to approximately 50 meters. Generally, the hydrothermally altered zone is thicker above than below the sill. In the second type, the sediments are extensively recrystallized. The characteristic greenschist-facies mineral assemblage of quartz-albite-chlorite-epidote predominates. Considerable amounts of pyrite, pyrrhotite, and sphene are also present. The lowest d18O value of the greenschist facies rocks is 6.6 per mil, and the highest d18O value of the associated pore fluids is +1.38 per mil (SMOW). The paragenesis and the oxygen isotopes of individual phases indicate alteration temperatures of 300 ± 50°C. On the basis of the oxygen isotopes of the solids and associated fluids, it is concluded that recharge of fluids is required. The water/rock ratio in wt.% is moderate, approximately 2/1 to 3/1 - higher than the calculated water/rock ratio of the hydrothermal system at the East Pacific Rise, 21 °N.

40Ar-39Ar dating of rocks drilled at sites 60-458 and 60-459

40Ar-39Ar dating of a high-MgO bronzite andesite from near the top of basement drilled at Site 458 shows the characteristic symptoms of artificially disturbed samples - i.e., an inverse staircase-type age spectrum, approximate linearity on an isochron plot, and concordance between total fusion age and isochron age. From conclusions based on other artificially disturbed samples (Ozima et al., 1979), we suggest that the reference isochron age (33.6 Ma) approximates the age of the sample. A basalt from deeper in Hole 458 gives an isochron age of 19.1 ± 0.2 Ma, which is slightly younger than the plateau age of 21.4 ± 1.0 Ma. Both ages are, however, considerably younger than the age of fossils in the overlying sediments (30 - 34 Ma). The age discrepancy may be explained if the 40Ar-39Ar age represents the age of secondary minerals, which formed later. No useful age data were obtained from a basalt sample recovered from Hole 459B.