The Use of Chemistry of Garnets and Heavy Minerals Around Lalago Kimberlite Pipe in Deciphering Diamond and Non-Diamond Bearing Kimberlite Pipes in Tanzania

More than three hundred kimberlite pipes have been reported in Tanzania. Only a few are diamond–bearing. A prospecting criteria to outline the diamond and non-diamond bearing kimberlites has been proposed. Bulk rock chemical analyses and chemistry of garnets and black minerals (picroilmenite, magnetite, rutile and titanite) collected around one kimberlite pipe in Tanzania were studied using Atomic Absorption Spectrophotometer (AAS) and Electron Microprobe (EMP). Although chromite and zircons occur in kimberlite pipes, they were not used in this study because they also characterize other surrounding rocks. Electron microprobe analysis of heavy minerals indicate that the ilmenites (picroilmenite) are poor in MgO contents (0.03 – 0.6 wt.%); but are rich in MnO (9.94 – 12.27wt.%). The garnets are poor in Cr2O3 with pronounced almandine content which has led to the conclusion of having a barren kimberlite source. It is suggested that combination of the chemistry of garnet and heavy minerals may be used as an exploration tool for deciphering diamond and non-diamond bearing kimberlites.Keywords: Electron microprobe, black minerals, mineral and fluid inclusions, kimberlites, garnets

Elucidating the magma plumbing system of Ol Doinyo Lengai (Natron Rift, Tanzania) using satellite geodesy and numerical modeling

Ol Doinyo Lengai, located in the southern Eastern Branch of the East African Rift had several eruptive episodes with ash falls and lava flows (VEI 3) that caused damage to the nearby communities between 2007 and 2010. The volcano is remote and access is difficult. Although this volcano has been studied for decades, its plumbing system is still poorly understood, in part, because of the lack of precise observations of surface deformation during periods of quiet and unrest. This study investigates the volcanic plumbing system of Ol Doinyo Lengai and its surroundings using data from the network of permanent Global Navigation Satellite System (GNSS) sites monitoring the volcano (the TZVOLCANO network) around the flanks of the volcano and Interferometric Synthetic Aperture Radar (InSAR) observations. We constrain surface motions using 6 GNSS sites distributed around Ol Doinyo Lengai, operating between 2016 and 2021, and InSAR data covering nearly the same time period. Because of the complex local tectonics, the interpretation of the deformation pattern is not straightforward. We first invert the GNSS deformation and InSAR observations independently to infer potential deformation sources. Then we perform a joint inversion of both GNSS and InSAR datasets to verify our findings. We compare the results from the joint inversion with the results from inverting each dataset independently. The GNSS, InSAR, and joint inversion results point to a deflating source, located east of Ol Doinyo Lengai and southwest of the dormant volcano Gelai at a depth of 3.49 ± 0.03 km (GNSS inversion), 5.2 ± 1.2 km (InSAR inversion) and 3.49 ± 0.06 km (joint inversion) relative to the summit (vent) and with a volume change ΔV of -0.04 ± 0.05 × 106 m3 (GNSS inversion), -0.39 ± 0.29 × 106 m3 (InSAR inversion), and -0.04 ± 0.01 × 106 m3 (joint inversion). Although this is non-unique modeling of geodetic datasets with small signals, the inversion results suggest that Ol Doinyo Lengai could be fed by an offset multi-reservoir system that includes a shallow magma reservoir (<5 km) east of Ol Doinyo Lengai, possibly connected to a deeper magma reservoir

Variasi Komunitas Plankton dan Parameter Oseanografi di Daerah Penangkapan Ikan Pelagis di Perairan Malang Selatan, Jawa Timur

The South Malang water is a potential area as a fishing ground. The existence of various species of small pelagic fishes such as planktivores fishes are directly influenced by the growth of phytoplankton and zooplankton. Phytoplankton has an important role as the basis of the food chain in aquatic ecosystems, whereas zooplankton is its predator. The growth of phytoplankton and zooplankton is influenced by oceanography factors. This study aims to determine the variation of the plankton community and its relationship with oceanographic parameters, as well as the spatial distribution of plankton in the fishing ground at South Malang water. The purposive sampling method with zigzag technique at 10 sampling sites was used in data collection in this research. Sedwicgk rafter counting cells were used in plankton counting methods. The plankton samples were taken horizontally and vertically at depths of 1 and 15 m with a 20 μm planktonnet, while in situ oceanographic parameters were measured using aqua quality sensor AAQ type 1183 C. The results showed that phytoplankton abundance was 49.764 cells / m3, dominated by the genus Chaetoceros (Bacillariophyceae), and zooplankton abundance of 894 ind / m3, dominated by the genus Nauplius (Copepoda). The diversity index and uniformity index of phytoplankton and zooplankton could be categorized as the middle as 1.77-1.85 and 1.70-1.77; 0.58-0.59 and 0.77-0.79, respectively, while the dominance index was included in the low category that was 0.27-0.28 and 0.24-0,27, respectively. Principal Component Analysis (PCA) analysis showed that the most important major oceanographic parameters for plankton community variation were turbidity, chlorophyll-a, dissolved oxygen and phosphate. T test results show that the spatial distribution of phytoplankton abundance and zooplankton at depth of 1 m and 15 m are significantly different. This study provides important information on the plankton abundance and oceanography factors affected at fishing ground of South Malang water

Clostridium chauvoei, an evolutionary dead-end pathogen

Full genome sequences of 20 strains of Clostridium chauvoei, the etiological agent of blackleg of cattle and sheep, isolated from four different continents over a period of 64 years (1951–2015) were determined and analyzed. The study reveals that the genome of the species C. chauvoei is highly homogeneous compared to the closely related species C. perfringens, a widespread pathogen that affects human and many animal species. Analysis of the CRISPR locus is sufficient to differentiate most C. chauvoei strains and is the most heterogenous region in the genome, containing in total 187 different spacer elements that are distributed as 30 – 77 copies in the various strains. Some genetic differences are found in the 3 allelic variants of fliC1, fliC2 and fliC3 genes that encode structural flagellin proteins, and certain strains do only contain one or two alleles. However, the major virulence genes including the highly toxic C. chauvoei toxin A, the sialidase and the two hyaluronidases are fully conserved as are the metabolic and structural genes of C. chauvoei. These data indicate that C. chauvoei is a strict ruminant-associated pathogen that has reached a dead end in its evolution

End-stage renal disease in Canada: prevalence projections to 2005

BACKGROUND: The incidence and prevalence of end-stage renal disease (ESRD) have increased greatly in Canada over the last 2 decades. Because of the high cost of therapy, predicting numbers of patients who will require dialysis and transplantation is necessary for nephrologists and health care planners. METHODS: The authors projected ESRD incidence rates and therapy-specific prevalence by province to the year 2005 using 1981-1996 data obtained from the Canadian Organ Replacement Register. The model incorporated Poisson regression to project incidence rates, and a Markov model for patient follow-up. RESULTS: Continued large increases in ESRD incidence and prevalence were projected, particularly among people with diabetes mellitus. As of Dec. 31, 1996, there were 17,807 patients receiving renal replacement therapy in Canada. This number was projected to climb to 32,952 by the end of 2005, for a relative increase of 85% and a mean annual increase of 5.8%. The increased prevalence was projected to be greatest for peritoneal dialysis (6.0% annually), followed by hemodialysis (5.9%) and functioning kidney transplant (5.7%). The projected annual increases in prevalence by province ranged from 4.4%, in Saskatchewan, to 7.5%, in Alberta. INTERPRETATION: The projected increases are plausible when one considers that the incidence of ESRD per million population in the United States and other countries far exceeds that in Canada. The authors predict a continued and increasing short-fall in resources to accommodate the expected increased in ESRD prevalence

Using Smartphones and Machine Learning to Quantify Parkinson Disease Severity:The Mobile Parkinson Disease Score

IMPORTANCE: Current Parkinson disease (PD) measures are subjective, rater-dependent, and assessed in clinic. Smartphones can measure PD features, yet no smartphone-derived rating score exists to assess motor symptom severity in real-world settings. OBJECTIVES: To develop an objective measure of PD severity and test construct validity by evaluating the ability of the measure to capture intraday symptom fluctuations, correlate with current standard PD outcome measures, and respond to dopaminergic therapy. DESIGN, SETTING, AND PARTICIPANTS: This observational study assessed individuals with PD who remotely completed 5 tasks (voice, finger tapping, gait, balance, and reaction time) on the smartphone application. We used a novel machine-learning-based approach to generate a mobile Parkinson disease score (mPDS) that objectively weighs features derived from each smartphone activity (eg, stride length from the gait activity) and is scaled from 0 to 100 (where higher scores indicate greater severity). Individuals with and without PD additionally completed standard in-person assessments of PD with smartphone assessments during a period of 6 months. MAIN OUTCOMES AND MEASURES: Ability of the mPDS to detect intraday symptom fluctuations, the correlation between the mPDS and standard measures, and the ability of the mPDS to respond to dopaminergic medication. RESULTS: The mPDS was derived from 6148 smartphone activity assessments from 129 individuals (mean [SD] age, 58.7 [8.6] years; 56 [43.4%] women). Gait features contributed most to the total mPDS (33.4%). In addition, 23 individuals with PD (mean [SD] age, 64.6 [11.5] years; 11 [48%] women) and 17 without PD (mean [SD] age 54.2 [16.5] years; 12 [71%] women) completed in-clinic assessments. The mPDS detected symptom fluctuations with a mean (SD) intraday change of 13.9 (10.3) points on a scale of 0 to 100. The measure correlated well with the Movement Disorder Society Unified Parkinson Disease's Rating Scale total (r = 0.81; P < .001) and part III only (r = 0.88; P < .001), the Timed Up and Go assessment (r = 0.72; P = .002), and the Hoehn and Yahr stage (r = 0.91; P < .001). The mPDS improved by a mean (SD) of 16.3 (5.6) points in response to dopaminergic therapy. CONCLUSIONS AND RELEVANCE: Using a novel machine-learning approach, we created and demonstrated construct validity of an objective PD severity score derived from smartphone assessments. This score complements standard PD measures by providing frequent, objective, real-world assessments that could enhance clinical care and evaluation of novel therapeutics

Population-aware Hierarchical Bayesian Domain Adaptation via Multiple-component Invariant Learning

While machine learning is rapidly being developed and deployed in health settings such as influenza prediction, there are critical challenges in using data from one environment in another due to variability in features; even within disease labels there can be differences (e.g. "fever" may mean something different reported in a doctor's office versus in an online app). Moreover, models are often built on passive, observational data which contain different distributions of population subgroups (e.g. men or women). Thus, there are two forms of instability between environments in this observational transport problem. We first harness knowledge from health to conceptualize the underlying causal structure of this problem in a health outcome prediction task. Based on sources of stability in the model, we posit that for human-sourced data and health prediction tasks we can combine environment and population information in a novel population-aware hierarchical Bayesian domain adaptation framework that harnesses multiple invariant components through population attributes when needed. We study the conditions under which invariant learning fails, leading to reliance on the environment-specific attributes. Experimental results for an influenza prediction task on four datasets gathered from different contexts show the model can improve prediction in the case of largely unlabelled target data from a new environment and different constituent population, by harnessing both environment and population invariant information. This work represents a novel, principled way to address a critical challenge by blending domain (health) knowledge and algorithmic innovation. The proposed approach will have a significant impact in many social settings wherein who and where the data comes from matters