Recommended Nomenclature for the Sapphirine and Surinamite Groups (Sapphirine Supergroup)

Minerals isostructural with sapphirine-1A, sapphirine-2M, and surinamite are closely related chain silicates that pose nomenclature problems because of the large number of sites and potential constituents, including several (Be, B, As, Sb) that are rare or absent in other chain silicates. Our recommended nomenclature for the sapphirine group (formerly-aenigmatite group) makes extensive use of precedent, but applies the rules to all known natural compositions, with flexibility to allow for yet undiscovered compositions such as those reported in synthetic materials. These minerals are part of a polysomatic series composed of pyroxene or pyroxene-like and spinel modules, and thus we recommend that the sapphirine supergroup should encompass the polysomatic series. The first level in the classification is based on polysome, i.e. each group within the supergroup Corresponds to a single polysome. At the second level, the sapphirine group is divided into subgroups according to the occupancy of the two largest M sites, namely, sapphirine (Mg), aenigmatite (Na), and rhonite (Ca). Classification at the third level is based on the occupancy of the smallest M site with most shared edges, M7, at which the dominant cation is most often Ti (aenigmatite, rhonite, makarochkinite), Fe(3+) (wilkinsonite, dorrite, hogtuvaite) or Al (sapphirine, khmaralite); much less common is Cr (krinovite) and Sb (welshite). At the fourth level, the two most polymerized T sites are considered together, e.g. ordering of Be at these sites distinguishes hogtuvaite, makarochkinite and khmaralite. Classification at the fifth level is based on X(Mg) = Mg/(Mg + Fe(2+)) at the M sites (excluding the two largest and M7). In principle, this criterion could be expanded to include other divalent cations at these sites, e.g. Mn. To date, most minerals have been found to be either Mg-dominant (X(mg) \u3e 0.5), or Fe(2+)-dominant (X(Mg) \u3c 0.5), at these M sites. However, X(mg) ranges from 1.00 to 0.03 in material described as rhonite, i.e. there are two species present, one Mg-dominant, the other Fe(2+)-dominant. Three other potentially new species are a Mg-dominant analogue of wilkinsonite, rhonite in the Allende meteorite, which is distinguished front rhonite and dorrite in that Mg rather than Ti or FC(3+) is dominant at M7, and an Al-dominant analogue of sapphirine, in which Al \u3e Si at the two most polymerized T sites vs. Al \u3c Si in sapphirine. Further splitting of the supergroup based on occupancies other than those specified above is not recommended

New minerals and nomenclature modifications approved in 2010

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A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction

Continuous vital signal monitoring is becoming more relevant in preventing diseases that afflict a large part of the world’s population; for this reason, healthcare equipment should be easy to wear and simple to use. Non-intrusive and non-invasive detection methods are a basic requirement for wearable medical devices, especially when these are used in sports applications or by the elderly for self-monitoring. Arterial blood pressure (ABP) is an essential physiological parameter for health monitoring. Most blood pressure measurement devices determine the systolic and diastolic arterial blood pressure through the inflation and the deflation of a cuff. This technique is uncomfortable for the user and may result in anxiety, and consequently affect the blood pressure and its measurement. The purpose of this paper is the continuous measurement of the ABP through a cuffless, non-intrusive approach. The approach of this paper is based on deep learning techniques where several neural networks are used to infer ABP, starting from photoplethysmogram (PPG) and electrocardiogram (ECG) signals. The ABP was predicted first by utilizing only PPG and then by using both PPG and ECG. Convolutional neural networks (ResNet and WaveNet) and recurrent neural networks (LSTM) were compared and analyzed for the regression task. Results show that the use of the ECG has resulted in improved performance for every proposed configuration. The best performing configuration was obtained with a ResNet followed by three LSTM layers: this led to a mean absolute error (MAE) of 4.118 mmHg on and 2.228 mmHg on systolic and diastolic blood pressures, respectively. The results comply with the American National Standards of the Association for the Advancement of Medical Instrumentation. ECG, PPG, and ABP measurements were extracted from the MIMIC database, which contains clinical signal data reflecting real measurements. The results were validated on a custom dataset created at Neuronica Lab, Politecnico di Torino

Tetrahedrite-(Hg), a new 'old' member of the tetrahedrite group

Tetrahedrite-(Hg), Cu6(Cu4Hg2)Sb4S13, has been approved as a new mineral species using samples from Buca della Vena mine (hereafter BdV), Italy, Jedová hora (Jh), Czech Republic and RoŽÅ 1/2ava (R), Slovakia. It occurs as anhedral grains or as tetrahedral crystals, black in colour, with metallic lustre. At BdV it is associated with cinnabar and chalcostibite in dolomite veins. At Jh, tetrahedrite-(Hg) is associated with baryte and chalcopyrite in quartz-siderite-dolomite veins; at R it is associated with quartz in siderite-quartz veins. Tetrahedrite-(Hg) is isotropic, greyish-white in colour, with creamy tints. Minimum and maximum reflectance data for Commission on Ore Mineralogy wavelengths in air (BdV sample), R in %) are 32.5 at 420 nm; 32.9 at 546 nm; 33.2 at 589 nm; and 30.9 at 650 nm. Chemical formulae of the samples studied, recalculated on the basis of 4 (As + Sb + Bi) atoms per formula unit, are: (Cu9.44Ag0.07)Σ9.51(Hg1.64Zn0.36Fe0.06)Σ2.06Sb4(S12.69Se0.01)Σ12.70 (BdV), Cu9.69(Hg1.75Fe0.25Zn0.06)Σ2.06(Sb3.94As0.06)S12.87 (Jh) and (Cu9.76Ag0.04) Σ9.80(Hg1.83Fe0.15Zn0.10)Σ2.08(Sb3.17As0.58Bi0.25)S13.01 (R). Tetrahedrite-(Hg) is cubic, I3m, with a = 10.5057(8) Å, V = 1159.5(3) Å3 and Z = 2 (BdV). Unit-cell parameters for the other two samples are a = 10.4939(1) Å and V = 1155.61(5) Å3 (Jh) and a = 10.4725(1) Å and V = 1148.55(6) Å3 (R). The crystal structure of tetrahedrite-(Hg) has been refined by single-crystal X-ray diffraction data to a final R1 = 0.019 on the basis of 335 reflections with Fo > 4σ(Fo) and 20 refined parameters. Tetrahedrite-(Hg) is isotypic with other members of the tetrahedrite group. Mercury is hosted at the tetrahedrally coordinated M(1) site, along with minor Zn and Fe. The occurrence of Hg at this position agrees both with the relatively large M(1)-S(1) bond distance (2.393 Å) and the refined site scattering. Previous occurrences of Hg-rich tetrahedrite and tetrahedrite-(Hg) are reviewed, and its relations with other Hg sulfosalts are discussed

IMA Commission on New Minerals, Nomenclature and Classification (CNMNC) – Newsletter 53

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Transformer-Based Approach to Melanoma Detection

Melanoma is a malignant cancer type which develops when DNA damage occurs (mainly due to environmental factors such as ultraviolet rays). Often, melanoma results in intense and aggressive cell growth that, if not caught in time, can bring one toward death. Thus, early identification at the initial stage is fundamental to stopping the spread of cancer. In this paper, a ViT-based architecture able to classify melanoma versus non-cancerous lesions is presented. The proposed predictive model is trained and tested on public skin cancer data from the ISIC challenge, and the obtained results are highly promising. Different classifier configurations are considered and analyzed in order to find the most discriminating one. The best one reached an accuracy of 0.948, sensitivity of 0.928, specificity of 0.967, and AUROC of 0.948