Sensitivity analysis of marine Controlled-Source Electromagnetic data

Electromagnetic sounding methods represent one of the few geophysical techniques that can provide information about the state and the properties of deep continental crust and upper mantle. In particular, marine Controlled-Source Electromagnetic (mCSEM) method is being applied to offshore hydrocarbon exploration and providing encouraging results, as it can complement the information obtained from seismic elaborations, mainly the position of the elastic discontinuities, with a map of electrical conductivity, the principal "discriminator" between conductive water-bearing rocks and non-conductive hydrocarbon accumulations. The processing of mCSEM data can be problematic due to the non-uniqueness of the solution, the environmental and equipment noise, and the high computational power required when dealing with 3D inversion. This paper proposes a simplified procedure to study and rank the sensitivity of mCSEM in a canonical 1D scenario, with a single resistive anomaly embedded in a homogeneous background. We analyze the sensitivity of the data with respect to the most important test parameters, namely the frequency, target depth, thickness, and resistivity. In addition, this procedure is also utilized to validate the so-called T-equivalence theorem. The results of this study could assist the interpreter to highlight the reliability of the inverted parameters in a complex inversion environment

Deep Injective Prior for Inverse Scattering

In electromagnetic inverse scattering, the goal is to reconstruct object permittivity using scattered waves. While deep learning has shown promise as an alternative to iterative solvers, it is primarily used in supervised frameworks which are sensitive to distribution drift of the scattered fields, common in practice. Moreover, these methods typically provide a single estimate of the permittivity pattern, which may be inadequate or misleading due to noise and the ill-posedness of the problem. In this paper, we propose a data-driven framework for inverse scattering based on deep generative models. Our approach learns a low-dimensional manifold as a regularizer for recovering target permittivities. Unlike supervised methods that necessitate both scattered fields and target permittivities, our method only requires the target permittivities for training; it can then be used with any experimental setup. We also introduce a Bayesian framework for approximating the posterior distribution of the target permittivity, enabling multiple estimates and uncertainty quantification. Extensive experiments with synthetic and experimental data demonstrate that our framework outperforms traditional iterative solvers, particularly for strong scatterers, while achieving comparable reconstruction quality to state-of-the-art supervised learning methods like the U-Net.Comment: 13 pages, 11 figure

Increasing medicinal and phytochemical compounds of coneflower (Echinacea purpurea L.) as affected by NO3−/NH4+ ratio and perlite particle size in hydroponics

Medicinal plants are considered as one of the most important sources of chemical compounds, so preparing a suitable culture media for medicinal plant growth is a critical factor. The present study is aimed to improve the caffeic acid derivatives and alkylamides percentages of Echinacea purpurea root extract in hydroponic culture media with different perlite particle size and NO3−/NH4+ ratios. Perlite particle size in the growing media was varied as very coarse perlite (more than 2 mm), coarse perlite (1.5–2 mm), medium perlite (1–1.5 mm), fine perlite (0.5–1 mm), and very fine perlite (less than 0.5 mm) in different ratios to peat moss (including pure perlite, 50:50 v/v, 30:70 v/v, and pure peat moss). Two NO3−/NH4+ ratios (90:10 and 70:30) were tested in each growing media. All phytochemical analyses were performed according to standard methods using high performance liquid chromatography (HPLC). It was found that the E. purpurea grown in the medium containing very fine-grade perlite with 50:50 v/v perlite to peat moss ratio had the maximum caffeic acid derivatives, including chicoric acid (17 mg g−1 DW), caftaric acid (6.3 mg g−1 DW), chlorogenic acid (0.93 mg g−1 DW), cynarin (0.84 mg g−1 DW), and echinacoside (0.73 mg g−1 DW), as well as, alkylamides (54.21%). The percentages of these phytochemical compounds increased by decreasing perlite particle size and increasing of NO3−/NH4+ ratio. The major alkylamide in the E. purpurea root extract was dodeca-2E, 4E, 8Z-10 (E/Z)-tetraenoic acid isobutylamide in all treatments, ranging from 31.12 to 54.21% of total dry weight. It can be concluded that optimizing hydroponic culture media and nutrient solution has significant effects on E. purpurea chemical compounds

Virtual histological staining of unlabeled autopsy tissue

Histological examination is a crucial step in an autopsy; however, the traditional histochemical staining of post-mortem samples faces multiple challenges, including the inferior staining quality due to autolysis caused by delayed fixation of cadaver tissue, as well as the resource-intensive nature of chemical staining procedures covering large tissue areas, which demand substantial labor, cost, and time. These challenges can become more pronounced during global health crises when the availability of histopathology services is limited, resulting in further delays in tissue fixation and more severe staining artifacts. Here, we report the first demonstration of virtual staining of autopsy tissue and show that a trained neural network can rapidly transform autofluorescence images of label-free autopsy tissue sections into brightfield equivalent images that match hematoxylin and eosin (H&E) stained versions of the same samples, eliminating autolysis-induced severe staining artifacts inherent in traditional histochemical staining of autopsied tissue. Our virtual H&E model was trained using >0.7 TB of image data and a data-efficient collaboration scheme that integrates the virtual staining network with an image registration network. The trained model effectively accentuated nuclear, cytoplasmic and extracellular features in new autopsy tissue samples that experienced severe autolysis, such as COVID-19 samples never seen before, where the traditional histochemical staining failed to provide consistent staining quality. This virtual autopsy staining technique can also be extended to necrotic tissue, and can rapidly and cost-effectively generate artifact-free H&E stains despite severe autolysis and cell death, also reducing labor, cost and infrastructure requirements associated with the standard histochemical staining.Comment: 24 Pages, 7 Figure

Surface preparation of powder metallurgical tool steels by means of wire electrical discharge machining

The surface of two types of powder metallurgical (PM) tool steels (i.e., with and without nitrogen) was prepared using wire electrical discharge machining (WEDM). From each grade of tool steel, seven surfaces corresponding to one to seven passes of WEDM were prepared. The WEDM process was carried out using a brass wire as electrode and deionized water as dielectric. After eachWEDM pass the surface of the tool steels was thoroughly examined. Surface residual stresses were measured by the X-ray diffraction (XRD) technique. The measured stresses were found to be of tensile nature. The surface roughness of the WEDM specimens was measured using interference microscopy. The surface roughness as well as the residual stress measurements indicated an insignificant improvement of these parameters after four passes of WEDM. In addition, the formed recast layer was characterized by means of scanning electron microscopy (SEM), XRD, and X-ray photoelectron spectroscopy (XPS). The characterization investigation clearly shows diffusion of copper and zinc from the wire electrode into the work material, even after the final WEDM step. Finally, the importance of eliminating excessive WEDM steps is thoroughly discussed

Two novel human cytomegalovirus NK cell evasion functions target MICA for lysosomal degradation

NKG2D plays a major role in controlling immune responses through the regulation of natural killer (NK) cells, αβ and γδ T-cell function. This activating receptor recognizes eight distinct ligands (the MHC Class I polypeptide-related sequences (MIC) A andB, and UL16-binding proteins (ULBP)1–6) induced by cellular stress to promote recognition cells perturbed by malignant transformation or microbial infection. Studies into human cytomegalovirus (HCMV) have aided both the identification and characterization of NKG2D ligands (NKG2DLs). HCMV immediate early (IE) gene up regulates NKGDLs, and we now describe the differential activation of ULBP2 and MICA/B by IE1 and IE2 respectively. Despite activation by IE functions, HCMV effectively suppressed cell surface expression of NKGDLs through both the early and late phases of infection. The immune evasion functions UL16, UL142, and microRNA(miR)-UL112 are known to target NKG2DLs. While infection with a UL16 deletion mutant caused the expected increase in MICB and ULBP2 cell surface expression, deletion of UL142 did not have a similar impact on its target, MICA. We therefore performed a systematic screen of the viral genome to search of addition functions that targeted MICA. US18 and US20 were identified as novel NK cell evasion functions capable of acting independently to promote MICA degradation by lysosomal degradation. The most dramatic effect on MICA expression was achieved when US18 and US20 acted in concert. US18 and US20 are the first members of the US12 gene family to have been assigned a function. The US12 family has 10 members encoded sequentially through US12–US21; a genetic arrangement, which is suggestive of an ‘accordion’ expansion of an ancestral gene in response to a selective pressure. This expansion must have be an ancient event as the whole family is conserved across simian cytomegaloviruses from old world monkeys. The evolutionary benefit bestowed by the combinatorial effect of US18 and US20 on MICA may have contributed to sustaining the US12 gene family

Atypical teratoid/rhabdoid tumors (ATRTs) with SMARCA4 mutation are molecularly distinct from SMARCB1-deficient cases

Atypical teratoid/rhabdoid tumors (ATRTs) are very aggressive childhood malignancies of the central nervous system. The underlying genetic cause are inactivating bi-allelic mutations in SMARCB1 or (rarely) in SMARCA4. ATRT-SMARCA4 have been associated with a higher frequency of germline mutations, younger age, and an inferior prognosis in comparison to SMARCB1 mutated cases. Based on their DNA methylation profiles and transcriptomics, SMARCB1 mutated ATRTs have been divided into three distinct molecular subgroups: ATRT-TYR, ATRT-SHH, and ATRT-MYC. These subgroups differ in terms of age at diagnosis, tumor location, type of SMARCB1 alterations, and overall survival. ATRT-SMARCA4 are, however, less well understood, and it remains unknown, whether they belong to one of the described ATRT subgroups. Here, we examined 14 ATRT-SMARCA4 by global DNA methylation analyses. We show that they form a separate group segregating from SMARCB1 mutated ATRTs and from other SMARCA4-deficient tumors like small cell carcinoma of the ovary, hypercalcemic type (SCCOHT) or SMARCA4 mutated extra-cranial malignant rhabdoid tumors. In contrast, medulloblastoma (MB) samples with heterozygous SMARCA4 mutations do not group separately, but with established MB subgroups. RNA sequencing of ATRT-SMARCA4 confirmed the clustering results based on DNA methylation profiling and displayed an absence of typical signature genes upregulated in SMARCB1 deleted ATRT. In summary, our results suggest that, in line with previous clinical observations, ATRT-SMARCA4 should be regarded as a distinct molecular subgroup

Efficacy of a trivalent influenza vaccine against seasonal strains and against 2009 pandemic H1N1: a randomized, placebo-controlled trial

Background: Before pandemic H1N1 vaccines were available, the potential benefit of existing seasonal trivalent inactivated influenza vaccines (IIV3s) against influenza due to the 2009 pandemic H1N1 influenza strain was investigated, with conflicting results. This study assessed the efficacy of seasonal IIV3s against influenza due to 2008 and 2009 seasonal influenza strains and against the 2009 pandemic H1N1 strain. Methods: This observer-blind, randomized, placebo-controlled study enrolled adults aged 18–64 years during 2008 and 2009 in Australia and New Zealand. Participants were randomized 2:1 to receive IIV3 or placebo. The primary objective was to demonstrate the efficacy of IIV3 against laboratory-confirmed influenza. Participants reporting an influenza-like illness during the period from 14 days after vaccination until 30 November of each study year were tested for influenza by real-time reverse transcription polymerase chain reaction. Results: Over a study period of 2 years, 15,044 participants were enrolled (mean age ± standard deviation: 35.5 ± 14.7 years; 54.4% female). Vaccine efficacy of the 2008 and 2009 IIV3s against influenza due to any strain was 42% (95% confidence interval [CI]: 30%, 52%), whereas vaccine efficacy against influenza due to the vaccine-matched strains was 60% (95% CI: 44%, 72%). Vaccine efficacy of the 2009 IIV3 against influenza due to the 2009 pandemic H1N1 strain was 38% (95% CI: 19%, 53%). No vaccine-related deaths or serious adverse events were reported. Solicited local and systemic adverse events were more frequent in IIV3 recipients than placebo recipients (local: IIV3 74.6% vs placebo 20.4%, p < 0.001; systemic: IIV3 46.6% vs placebo 39.1%, p < 0.001). Conclusions: The 2008 and 2009 IIV3s were efficacious against influenza due to seasonal influenza strains and the 2009 IIV3 demonstrated moderate efficacy against influenza due to the 2009 pandemic H1N1 strain