Automatic discrimination between front and back ensemble locations in HRTF-convolved binaural recordings of music

One of the greatest challenges in the development of binaural machine audition systems is the disambiguation between front and back audio sources, particularly in complex spatial audio scenes. The goal of this work was to develop a method for discriminating between front and back located ensembles in binaural recordings of music. To this end, 22, 496 binaural excerpts, representing either front or back located ensembles, were synthesized by convolving multi-track music recordings with 74 sets of head-related transfer functions (HRTF). The discrimination method was developed based on the traditional approach, involving hand-engineering of features, as well as using a deep learning technique incorporating the convolutional neural network (CNN). According to the results obtained under HRTF-dependent test conditions, CNN showed a very high discrimination accuracy (99.4%), slightly outperforming the traditional method. However, under the HRTF-independent test scenario, CNN performed worse than the traditional algorithm, highlighting the importance of testing the algorithms under HRTF-independent conditions and indicating that the traditional method might be more generalizable than CNN. A minimum of 20 HRTFs are required to achieve a satisfactory generalization performance for the traditional algorithm and 30 HRTFs for CNN. The minimum duration of audio excerpts required by both the traditional and CNN-based methods was assessed as 3 s. Feature importance analysis, based on a gradient attribution mapping technique, revealed that for both the traditional and the deep learning methods, a frequency band between 5 and 6 kHz is particularly important in terms of the discrimination between front and back ensemble locations. Linear-frequency cepstral coefficients, interaural level differences, and audio bandwidth were identified as the key descriptors facilitating the discrimination process using the traditional approach.

Worry from contracting COVID-19 infection and its stigma among Egyptian health care providers

Backgrounds Healthcare providers (HCPs) in COVID-19 epidemic face stressful workload of disease management, shortage of protective equipment and high risk of infection and mortality. These stressors affect greatly their mental health. The aim is to identify working conditions among Egyptian HCPs during COVID-19 epidemic as well as stigma and worry perceptions from contracting COVID-19 infection and their predictors. Methods A cross-sectional study was conducted among 565 HCPs. Data was collected through Google online self-administered questionnaire comprised seven parts: demographics characteristics, knowledge and attitude of COVID-19, working condition, worry of contracting COVID-19 at work, discrimination intention at work for COVID-19 patients, stigma assessment using impact stigma, and internalized shame scales. Results The vast majority of HCPs (94.7%) were worried from contracting COVID-19 at work. Risk factors for perceiving severe worry from contracting COVID-19 were expecting infection as a severe illness, believing that infection will not be successfully controlled, improbability to continue working during the pandemic even if in a well/fit health, high discrimination intention and impact stigma scales. Significantly high impact stigma scores were detected among those aged < 30 years, females, workers primarily in sites susceptible for contracting COVID-19 infection, those had severe worry from contracting infection at work, and high internalized shame scale. The risk factors for perceiving higher internalized shame scores were not having a previous experience in working during a pandemic, high discrimination intention towards COVID-19 patients and high impact stigma scale. Conclusions Considerable levels of worry and stigma were detected among Egyptian HCPs during COVID-19 outbreak. The psychological aspect of health care providers should not be overlooked during epidemic; appropriate institutional mental health support should be provided especially for young HCPs, those without previous work experience in epidemic and those who work in high-risk units. Raising the community awareness about contribution of HCPs in fighting the epidemic might decrease stigmatization action toward HCPs.

The Clinical Significance of Forkhead Box M1 in Esophageal Squamous Cell Carcinoma Tissues: A Study Based on In-house Immunohistochemistry, RNA-sequencing, and Data Mining

BackgroundEsophageal squamous cell carcinoma (ESCC) ranks the sixth in mortality rates in cancers due to a lack of a specific target of diagnosis and treatment in the early stages. Although Forkhead box M1 (FOXM1) has been reported to be differentially expressed in ESCC, its clinical role and function in ESCC remained unclarified.MethodsData from our hospital and public databases (n = 1906) were combined to estimate how FOXM1 overexpression showed its discriminatory ability between ESCC and non-ESCC esophageal tissues. Downstream targets of FOXM1 were predicted by using Cistrome database. Functional enrichment analyses were performed to explore the potential signaling pathways related to FOXM1 in ESCC. Based on the available clinical parameters, we investigated the prognosis potential of FOXM1 and its targets.ResultsThe pooled standard mean difference (SMD) for FOXM1 is 2.62 (95% CI: 2.08–3.16), indicating that FOXM1 is upregulated in ESCC. FOXM1 has an extremely high discrimination potential in ESCC because the area under the curve (AUC) of the summary receiver operating characteristic curve (sROC) is 0.99 (95% CI: 0.97–0.99). A total of 168 downstream targets were identified, and nine hub genes were screened from them. We found that FOXM1 and its targets were significantly enriched in the cell cycle. Additionally, the correlation between FOXM1 and clinical parameters had not been observed, except for age.ConclusionsFOXM1 is upregulated in ESCC and has an extremely high discrimination potential in ESCC.

Intersectional Discrimination Attributions and Health Outcomes Among American Older Adults: A Latent Class Analysis

Guided by an intersectionality framework, this study examined intersectional discrimination attributions and their associations with health outcomes. Older respondents (aged ≥50) from the Health and Retirement Study in 2014-2015 were included ( N = 6286). Their reasons for discrimination (age, gender, sexual orientation, race, national origin, religion, financial status, weight, physical appearance, disability, and others) were examined. Latent class analysis examined the subgroup profiles. Six classes were identified: class 1 (54.52% of the sample) had no/minimal discrimination; Class 2 (21.89%) experienced primarily ageism; class 3 (8.81%) reported discrimination based on age/gender/national origin/race; class 4 (7.99%) attributed discrimination to financial/other reasons; class 5 (5.87%) experienced discrimination based on age/weight/physical appearance/disability; and class 6 (0.92%) perceived high discrimination. Intersectional discrimination was associated with poorer self-rated health and higher depressive symptoms compared to the no/minimal discrimination group. Multiple marginalized identities co-occur and contribute to discrimination. An intersectional approach is recommended to understand discrimination in later life.

Development and Validation of A Long-Term Incident Heart Failure Risk Model

Background: Average lifetime risk for heart failure (HF) is high, but differs significantly across and within sex-race groups. No models for estimating long-term risk for HF exist, which would allow for earlier identification and interventions in high-risk subsets. The authors aim to derive 30-year HF risk equations. Methods: Adults between the ages of 20 to 59 years and free of cardiovascular disease at baseline from 5 population-based cohorts were included. Among 24,838 participants (55% women, 25% Black based on self-report), follow-up consisted of 599,551 person-years. Sex- and race-specific 30-year HF risk equations were derived and validated accounting for competing risk of non-HF death. HF was based on a clinical diagnosis. Model discrimation and calibration were assessed using 10-fold cross-validation. Finally, the model was applied to varying risk factor patterns for systematic examination. Results: The rate of incident HF was 4.0 per 1000 person-years. Harrell's c statistics were 0.82 (0.80-0.83) and 0.84 (0.82-0.85) in White and Black men, and 0.84 (0.82-0.85) and 0.85 (0.83-0.87) in White and Black women, respectively. Hosmer-Lemeshow calibration was acceptable, with x 2 <30 in all subgroups. Risk estimation varied across sex-race groups: for example, in an average 40-year-old non-smoker with an untreated systolic blood pressure of 140 mm Hg and body mass index of 30 kg/m 2 , risk was estimated to be 22.8% in a Black man, 13.7% in a White man, 13.0% in a Black woman, and 12.1% in a White woman. Conclusions: Sex- and race-specific equations for prediction of long-term risk of HF demonstrated high discrimination and adequate calibration.

Investigation of Mosaicism Detected in STR Typing

Short tandem repeats (STRs) are the most popular markers for human identification in forensics. These markers can be easily analyzed through a multiplex polymerase chain reaction and electrophoresis and provide high discrimination power. However, in STR analysis, several atypical phenomena can be observed such as allelic dropouts, drop-ins, or imbalance, which may be due to DNA polymerase slippage or DNA degradation effects. The observed atypical STR profiles can also provide information for mixed DNA samples or chromosomal abnormalities. In this study, we report a case of mosaicism detected in routine casework of paternity testing. Hair samples from a phenotypically normal male were tested, and the result presented a typical STR profile of a female for the amelogenin gene (XX). Through chromosome analysis using peripheral blood, it was found that 45,X/46,XY mosaicism resulted in the discrepancy between the genotype and the phenotype. In addition, the amount of Y chromosome detected was particularly low in hair compared to that in blood. This study shows that mosaicism can make interpretation difficult during STR analysis and suggests that sample types and repeated analysis should be considered even for routine STR testing.

E-Cadherin, Integrin Alpha2 (Cd49b), and Transferrin Receptor-1 (Tfr1) Are Promising Immunohistochemical Markers of Selected Adverse Pathological Features in Patients Treated with Radical Prostatectomy

In patients treated for prostate cancer (PCa) with radical prostatectomy (RP), determining the risk of extraprostatic extension (EPE) and nodal involvement (NI) remains crucial for planning nerve-sparing and extended lymphadenectomy. The study aimed to determine proteins that could serve as immunohistochemical markers of locally advanced PCa. To select candidate proteins associated with adverse pathologic features (APF) reverse-phase protein array data of 498 patients was retrieved from The Cancer Genome Atlas. The analysis yielded 6 proteins which were then validated as predictors of APF utilizing immunohistochemistry in a randomly selected retrospective cohort of 53 patients. For univariate and multivariate analysis, logistic regression was used. Positive expression of TfR1 (OR 13.74; p = 0.015), reduced expression of CD49b (OR 10.15; p = 0.013), and PSA (OR 1.29; p = 0.013) constituted independent predictors of EPE, whereas reduced expression of e-cadherin (OR 10.22; p = 0.005), reduced expression of CD49b (OR 24.44; p = 0.017), and PSA (OR 1.18; p = 0.002) were independently associated with NI. Both models achieved high discrimination (AUROC 0.879 and 0.888, respectively). Immunohistochemistry constitutes a straightforward tool that might be easily utilized before RP. Expression of TfR1 and CD49b is associated with EPE, whereas expression of e-cadherin and CD49b is associated with NI. Since following immunohistochemical markers predicts respective APFs independently from PSA, in the future they might supplement existing preoperative nomograms or be implemented in novel tools.

Sterols and Triterpene Diols in Virgin Olive Oil: A Comprehensive Review on Their Properties and Significance, with a Special Emphasis on the Influence of Variety and Ripening Degree

Olive oil is considered one of the most valuable vegetable oils and is highly appreciated by consumers for its specific and distinguishable taste and aroma, as well as its nutritional value. Sterols and triterpene diols are important carriers of bioactive properties of olive oil and are responsible for some of the beneficial effects of its consumption on human health, such as lowering serum LDL-cholesterol levels and significantly reducing the risk of cardiovascular diseases. The concentration of total sterols and the proportions of particular sterols and triterpene diols are among the parameters used to verify and prove the authenticity of olive oil in accordance with the EU and other countries’ regulations. Finally, their composition has been shown to have high discrimination potential for ensuring traceability with respect to variety, geographical origin, harvest date, and other factors. For these reasons, the research on sterols and triterpene diols in olive oil is an ever-growing field of scientific interest with great practical importance. This review focuses on all the important aspects of sterols and triterpene diols in olive oil, from their chemical structure, biosynthesis, occurrence and role in plants, health benefits, and their use in official controls of olive oil purity and authenticity, to a conclusive survey on the recent findings about the effects of different factors of influence on their content and composition, with a detailed comparative analysis of studies that investigated the effects of the two most important factors, variety and ripening degree.

Garnet major-element composition as an indicator of host-rock type: a machine learning approach using the random forest classifier

The major-element chemical composition of garnet provides valuable petrogenetic information, particularly in metamorphic rocks. When facing detrital garnet, information about the bulk-rock composition and mineral paragenesis of the initial garnet-bearing host-rock is absent. This prevents the application of chemical thermo-barometric techniques and calls for quantitative empirical approaches. Here we present a garnet host-rock discrimination scheme that is based on a random forest machine-learning algorithm trained on a large dataset of 13,615 chemical analyses of garnet that covers a wide variety of garnet-bearing lithologies. Considering the out-of-bag error, the scheme correctly predicts the original garnet host-rock in (i) > 95% concerning the setting, that is either mantle, metamorphic, igneous, or metasomatic; (ii) > 84% concerning the metamorphic facies, that is either blueschist/greenschist, amphibolite, granulite, or eclogite/ultrahigh-pressure; and (iii) > 93% concerning the host-rock bulk composition, that is either intermediate–felsic/metasedimentary, mafic, ultramafic, alkaline, or calc–silicate. The wide coverage of potential host rocks, the detailed prediction classes, the high discrimination rates, and the successfully tested real-case applications demonstrate that the introduced scheme overcomes many issues related to previous schemes. This highlights the potential of transferring the applied discrimination strategy to the broad range of detrital minerals beyond garnet. For easy and quick usage, a freely accessible web app is provided that guides the user in five steps from garnet composition to prediction results including data visualization.

Discrimination of the SARS–CoV-2 strains using of coloured s-LASCA-imaging of GB-speckles, developed for the gene “S” nucleotide sequences

Background: A recent bioinformatics technique involves changing nucleotide sequences into 2D speckles. This technique produces speckles called GB-speckles (Gene Based speckles). All classical strategies of speckle-optics, namely speckle-interferometry, subtraction of speckle-images as well as speckle-correlometry have been inferred for processing of GB-speckles. This indicates the considerable improvement in the present tools of bioinformatics. Methods: Colour s-LASCA imaging of virtual laser GB-speckles, a new method of high discrimination and typing of pathogenic viruses, has been developed. This method has been adapted to the detecting of natural mutations in nucleotide sequences, related to the spike glycoprotein (coding the gene «S») of SARS–CoV-2 gene as the molecular target. Results: The rate of the colouring images of virtual laser GB-speckles generated by s-LASCA can be described by the specific value of R. If the nucleotide sequences compared utilizing this approach the relevant images are completely identical, then the three components of the resulting colour image will be identical, and therefore the value of R will be equal to zero. However, if there are at least minimal differences in the matched nucleotide sequences, then the value of R will be positive. Conclusion: The high effectiveness of an application of the colour images of GB-speckles that were generated by s-LASCA- has been demonstrated for discrimination between different variants of the SARS–CoV-2 spike glycoprotein gene.