Comparative study of alkali roasting and leaching of chromite ores and titaniferous minerals

Extraction of titanium and chromium oxides may be achieved via roasting the respective minerals with alkali at high temperatures, followed by water and organic acid leaching. In this study, sodium and potassium hydroxides are used as alkali for roasting of chromite ores and ilmenite mineral concentrates. The thermodynamic analysis of the roasting process is discussed in terms of designing the process. Samples of chromite and titaniferous minerals were roasted with NaOH and KOH in a temperature range of 400 °C–1000 °C in an oxidising atmosphere. The roasted chromite and ilmenite samples were further processed in order to extract water-soluble Na2CrO4 from the reacted chromite and purify titanium dioxide from titaniferous minerals, respectively. The TiO2 purity obtained after roasting at 400 °C with NaOH and double leaching was 49.2 wt.%, whereas when using KOH the purity was 54.5 wt.%. The highest TiO2 purity obtained after roasting at 1000 °C for 2 h and double leaching with water and organic acids was 84 wt.%. At low temperature (400 °C) the recovery of chromium was higher for chromite roasted with KOH than for chromite roasted with NaOH. However, at high temperatures (700 °C and 1000 °C) chromium recoveries were similar when roasting with both hydroxides. Around 95% chromium extraction yield was achieved when chromite was roasted with sodium and potassium hydroxides at 1000 °C for 2 h and water leached

Sporadic Creutzfeldt-Jakob disease VM1: phenotypic and molecular characterization of a novel subtype of human prion disease

The methionine (M)-valine (V) polymorphic codon 129 of the prion protein gene (PRNP) plays a central role in both susceptibility and phenotypic expression of sporadic Creutzfeldt-Jakob diseases (sCJD). Experimental transmissions of sCJD in humanized transgenic mice led to the isolation of five prion strains, named M1, M2C, M2T, V2, and V1, based on two major conformations of the pathological prion protein (PrPSc, type 1 and type 2), and the codon 129 genotype determining susceptibility and propagation efficiency. While the most frequent sCJD strains have been described in codon 129 homozygosis (MM1, MM2C, VV2) and heterozygosis (MV1, MV2K, and MV2C), the V1 strain has only been found in patients carrying VV. We identified six sCJD cases, 4 in Catalonia and 2 in Italy, carrying MV at PRNP codon 129 in combination with PrPSc type 1 and a new clinical and neuropathological profile reminiscent of the VV1 sCJD subtype rather than typical MM1/MV1. All patients had a relatively long duration (mean of 20.5 vs. 3.5 months of MM1/MV1 patients) and lacked electroencephalographic periodic sharp-wave complexes at diagnosis. Distinctive histopathological features included the spongiform change with vacuoles of larger size than those seen in sCJD MM1/MV1, the lesion profile with prominent cortical and striatal involvement, and the pattern of PrPSc deposition characterized by a dissociation between florid spongiform change and mild synaptic deposits associated with coarse, patch-like deposits in the cerebellar molecular layer. Western blot analysis of brain homogenates revealed a PrPSc type 1 profile with physicochemical properties reminiscent of the type 1 protein linked to the VV1 sCJD subtype. In summary, we have identified a new subtype of sCJD with distinctive clinicopathological features significantly overlapping with those of the VV1 subtype, possibly representing the missing evidence of V1 sCJD strain propagation in the 129MV host genotype

Gene-environment interaction analysis of redox-related metals and genetic variants with plasma metabolic patterns in a general population from Spain: The Hortega Study

Background: Limited studies have evaluated the joint influence of redox-related metals and genetic variation on metabolic pathways. We analyzed the association of 11 metals with metabolic patterns, and the interacting role of candidate genetic variants, in 1145 participants from the Hortega Study, a population-based sample from Spain. Methods: Urine antimony (Sb), arsenic, barium (Ba), cadmium (Cd), chromium (Cr), cobalt (Co), molybdenum (Mo) and vanadium (V), and plasma copper (Cu), selenium (Se) and zinc (Zn) were measured by ICP-MS and AAS, respectively. We summarized 54 plasma metabolites, measured with targeted NMR, by estimating metabolic principal components (mPC). Redox-related SNPs (N = 291) were measured by oligo-ligation assay. Results: In our study, the association with metabolic principal component (mPC) 1 (reflecting non-essential and essential amino acids, including branched chain, and bacterial co-metabolism versus fatty acids and VLDL subclasses) was positive for Se and Zn, but inverse for Cu, arsenobetaine-corrected arsenic (As) and Sb. The association with mPC2 (reflecting essential amino acids, including aromatic, and bacterial co-metabolism) was inverse for Se, Zn and Cd. The association with mPC3 (reflecting LDL subclasses) was positive for Cu, Se and Zn, but inverse for Co. The association for mPC4 (reflecting HDL subclasses) was positive for Sb, but inverse for plasma Zn. These associations were mainly driven by Cu and Sb for mPC1; Se, Zn and Cd for mPC2; Co, Se and Zn for mPC3; and Zn for mPC4. The most SNP-metal interacting genes were NOX1, GSR, GCLC, AGT and REN. Co and Zn showed the highest number of interactions with genetic variants associated to enriched endocrine, cardiovascular and neurological pathways. Conclusions: Exposures to Co, Cu, Se, Zn, As, Cd and Sb were associated with several metabolic patterns involved in chronic disease. Carriers of redox-related variants may have differential susceptibility to metabolic alterations associated to excessive exposure to metals.This work was supported by the Strategic Action for Research in Health sciences [CP12/03080, PI15/00071, PI10/0082, PI13/01848, PI14/00874, PI16/01402, PI21/00506 and PI11/00726], CIBER Fisio patología Obesidad y Nutrición (CIBEROBN) (CIBER-02-08-2009, CB06/03 and CB12/03/30,016), the State Agency for Research (PID2019-108973RB- C21 and C22), the Valencia Government (GRUPOS 03/101; PROMETEO/2009/029 and ACOMP/2013/039, IDI FEDER/2021/072 and GRISOLIAP/2021/119), the Castilla-Leon Government (GRS/279/A/08) and European Network of Excellence Ingenious Hypercare (EPSS-037093) from the European Commission. The Strategic Action for Research in Health sciences, CIBERDEM and CIBEROBN are initiatives from Carlos III Health Institute Madrid and cofunded with European Funds for Regional Development (FEDER). The State Agency for Research and Carlos III Health Institute belong to the Spanish Ministry of Science and Innovation. ADR received the support of a fellowship from “la Caixa” Foundation (ID 100010434) (fellowship code “LCF/BQ/DR19/11740016”). MGP received the support of a fellowship from “la Caixa” Foundation (ID 100010434, fellowship code LCFLCF/BQ/DI18/11660001). The funding bodies had no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.S

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector.

The development and operation of liquid-argon time-projection chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies

Determination of muon momentum in the MicroBooNE LArTPC using an improved model of multiple Coulomb scattering

We discuss a technique for measuring a charged particle's momentum by means of multiple Coulomb scattering (MCS) in the MicroBooNE liquid argon time projection chamber (LArTPC). This method does not require the full particle ionization track to be contained inside of the detector volume as other track momentum reconstruction methods do (range-based momentum reconstruction and calorimetric momentum reconstruction). We motivate use of this technique, describe a tuning of the underlying phenomenological formula, quantify its performance on fully contained beam-neutrino-induced muon tracks both in simulation and in data, and quantify its performance on exiting muon tracks in simulation. Using simulation, we have shown that the standard Highland formula should be re-tuned specifically for scattering in liquid argon, which significantly improves the bias and resolution of the momentum measurement. With the tuned formula, we find agreement between data and simulation for contained tracks, with a small bias in the momentum reconstruction and with resolutions that vary as a function of track length, improving from about 10% for the shortest (one meter long) tracks to 5% for longer (several meter) tracks. For simulated exiting muons with at least one meter of track contained, we find a similarly small bias, and a resolution which is less than 15% for muons with momentum below 2 GeV/c. Above 2 GeV/c, results are given as a first estimate of the MCS momentum measurement capabilities of MicroBooNE for high momentum exiting tracks

Convolutional Neural Networks Applied to Neutrino Events in a Liquid Argon Time Projection Chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

The Pandora multi-algorithm approach to automated pattern recognition of cosmic-ray muon and neutrino events in the MicroBooNE detector

The development and operation of Liquid-Argon Time-Projection Chambers for neutrino physics has created a need for new approaches to pattern recognition in order to fully exploit the imaging capabilities offered by this technology. Whereas the human brain can excel at identifying features in the recorded events, it is a significant challenge to develop an automated, algorithmic solution. The Pandora Software Development Kit provides functionality to aid the design and implementation of pattern-recognition algorithms. It promotes the use of a multi-algorithm approach to pattern recognition, in which individual algorithms each address a specific task in a particular topology. Many tens of algorithms then carefully build up a picture of the event and, together, provide a robust automated pattern-recognition solution. This paper describes details of the chain of over one hundred Pandora algorithms and tools used to reconstruct cosmic-ray muon and neutrino events in the MicroBooNE detector. Metrics that assess the current pattern-recognition performance are presented for simulated MicroBooNE events, using a selection of final-state event topologies.Comment: Preprint to be submitted to The European Physical Journal

Measurement of cosmic-ray reconstruction efficiencies in the MicroBooNE LArTPC using a small external cosmic-ray counter

The MicroBooNE detector is a liquid argon time projection chamber at Fermilab designed to study short-baseline neutrino oscillations and neutrino-argon interaction cross-section. Due to its location near the surface, a good understanding of cosmic muons as a source of backgrounds is of fundamental importance for the experiment. We present a method of using an external 0.5 m (L) x 0.5 m (W) muon counter stack, installed above the main detector, to determine the cosmic-ray reconstruction efficiency in MicroBooNE. Data are acquired with this external muon counter stack placed in three different positions, corresponding to cosmic rays intersecting different parts of the detector. The data reconstruction efficiency of tracks in the detector is found to be $\epsilon_{\mathrm{data}}=(97.1\pm0.1~(\mathrm{stat}) \pm 1.4~(\mathrm{sys}))\%$, in good agreement with the Monte Carlo reconstruction efficiency $\epsilon_{\mathrm{MC}} = (97.4\pm0.1)\%$. This analysis represents a small-scale demonstration of the method that can be used with future data coming from a recently installed cosmic-ray tagger system, which will be able to tag $\approx80\%$ of the cosmic rays passing through the MicroBooNE detector.Comment: 19 pages, 12 figure

Noise Characterization and Filtering in the MicroBooNE Liquid Argon TPC

The low-noise operation of readout electronics in a liquid argon time projection chamber (LArTPC) is critical to properly extract the distribution of ionization charge deposited on the wire planes of the TPC, especially for the induction planes. This paper describes the characteristics and mitigation of the observed noise in the MicroBooNE detector. The MicroBooNE's single-phase LArTPC comprises two induction planes and one collection sense wire plane with a total of 8256 wires. Current induced on each TPC wire is amplified and shaped by custom low-power, low-noise ASICs immersed in the liquid argon. The digitization of the signal waveform occurs outside the cryostat. Using data from the first year of MicroBooNE operations, several excess noise sources in the TPC were identified and mitigated. The residual equivalent noise charge (ENC) after noise filtering varies with wire length and is found to be below 400 electrons for the longest wires (4.7 m). The response is consistent with the cold electronics design expectations and is found to be stable with time and uniform over the functioning channels. This noise level is significantly lower than previous experiments utilizing warm front-end electronics.Comment: 36 pages, 20 figure