To maximize the impact of precision medicine approaches, it is critical to accurately identify genetic variants in cancer-associated genes with functional consequences. Yet, our knowledge of rare variants conferring clinically relevant phenotypes and the mechanisms through which they act remains highly limited. A tumor suppressor gene exemplifying the challenge of variant interpretation is VHL. VHL encodes an E3 ubiquitin ligase that regulates the cellular response to hypoxia. Germline pathogenic variants in VHL predispose patients to tumors including clear cell renal cell carcinoma (ccRCC) and pheochromocytoma, and somatic VHL mutations are frequently observed in sporadic renal cancer. Here, we optimize and apply Saturation Genome Editing (SGE) to assay nearly all possible single nucleotide variants (SNVs) across VHL’s coding sequence. To delineate mechanisms, we quantify mRNA dosage effects over time and compare effects in isogenic cell lines. Function scores for 2,268 VHL SNVs identify a core set of pathogenic alleles driving ccRCC with perfect accuracy, inform differential risk across tumor types, and reveal novel mechanisms by which variants impact function. These results have immediate utility for classifying VHL variants encountered in both germline testing and tumor profiling and illustrate how precise functional measurements can resolve pleiotropic and dosage-dependent genotype-phenotype relationships across complete genes.

In this paper, we report on teachers’ and principals’ shared perceptions regarding beliefs, rules, trust, and encouragement of new initiatives. Collectively, these are aspects of leadership for learning (LFL) describing an overall shared climate in schools. We demonstrate how these perceptions on school climate differ across teachers and principals within and across countries. Moreover, we report how different perceptions of school climate are associated with leadership style. We analyze data from 37 countries that participated in the last cycle of the Teaching and Learning International Survey (TALIS) in 2018. To build the measurement model, we employ multigroup multilevel confirmatory factor analysis, whereas multivariate linear regression is used to inspect associations. Overall, principals and teachers differ in their views of school climate. In the majority of the countries, principals report stronger school climate than teachers. We further confirm these perceptual differences between teachers and principals by separately studying the relationships between teacher perceived school climate and principal perceived school climate with relevant leadership variables. In the entire sample, we find that principals’ perceptions of school climate are more strongly and consistently associated with leadership in schools. This relationship is particularly stable for distributed leadership. In the entire sample, leadership styles are weakly positively correlated with teacher perceptions of school climate too; however, this association is less pronounced and less stable within individual countries. The analyses conducted within countries revealed that the distributed leadership rather than instructional leadership shapes teachers’ perceptions of school climate. More discussion is presented on the need for alignment between different perceptions of school climate and leadership styles in the overall organizational quality.

The automotive industry requires ultra-reliable low-latency connectivity for its vehicles, and as such, it is one of the promising customers of 5G ecosystems and their orchestrated network infrastructure. In particular, Multi-Access Edge Computing (MEC) provides moving vehicles with localized low-latency access to service instances. However, given the mobility of vehicles, and various resource demand patterns at the distributed MEC nodes, challenges such as fast reconfiguration of the distributed deployment according to mobility pattern and associated service and resource demand need to be mitigated. In this paper, we present the orchestrated edges platform, which is a solution for orchestrating distributed edges in complex cross-border network environments, tailored to Connected, Cooperative, and Automated Mobility (CCAM) use cases within a 5G ecosystem. The proposed solution enables collaboration between orchestrators that belong to different tiers, and various federated edge domains, with the goal to enable service continuity for vehicles traversing cross-border corridors. The paper presents the prototype that we built for the H2020 5G-CARMEN trials, including the validation of the orchestration design choices, followed by the promising results that span both orchestration (orchestration latency) and application performance-related metrics (client-to-edge and edge-to-edge service data plane latencies).

Financial literacy is a critical life skill that is essential for achieving financial security and individual well-being, economic growth and overall sustainable development. Based on the analysis of research on financial literacy, we aim to provide a balance sheet of current research and a starting point for future research with the focus on identifying significant predictors of financial literacy, as well as variables that are affected by financial literacy. The main methods of our research are a systematic literature review, and bibliometric and bibliographical analysis. We establish a chronological path of the financial literacy topic in the scientific research. Based on the analysis of the most cited articles, we develop a comprehensive conceptual framework for mapping financial literacy. We identified a large number of predictors of financial literacy starting with education, gender, age, knowledge, etc. Financial literacy also affects variables such as retirement planning, financial inclusion, return on wealth, risk diversification, etc. We discuss in detail the main trends and topics in financial literacy research by involving financial literacy of the youth, financial literacy from the gender perspective, financial inclusion, retirement planning, digital finance and digital financial literacy. Our research can help policymakers in their pursuit of improving the levels of individual financial literacy by enabling individuals to make better financial decisions, avoid financial stress and achieve their financial goals. It can also help governments in their efforts in achieving sustainable development goals (SDGs).

ABSTRACT High-risk Human Papillomaviruses (HPVs) and Epstein – Barr virus (EBV) are present and involved in several types of human carcinomas, including cervical and, head and neck cancers. Nevertheless, their presence and association in the pathogenesis of colorectal cancer is still nascent. The current study explored the association between the high-risk HPVs and EBV and tumor phenotype in colorectal cancers (CRCs) in the Qatari population. We found that high-risk HPVs and EBV are present in 69/100 and 21/100 cases, respectively. Additionally, 17% of the cases showed a copresence of high-risk HPVs and EBV, with a significant correlation only between the HPV45 subtype and EBV (p = .004). While the copresence did not significantly associate with clinicopathological characteristics, we identified that coinfection with more than two subtypes of HPV is a strong predictor of advanced stage CRC, and the confounding effect of the copresence of EBV in such cases strengthens this association. Our results indicate that high-risk HPVs and EBV can co-present in human CRCs in the Qatari population where they could plausibly play a specific role in human colorectal carcinogenesis. However, future studies are essential to confirm their copresence and synergistic role in developing CRCs.

Leveraging second-order information at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to medium-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via an efficient and simple-to-implement error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression \emph{before} it is fed into the preconditioner, feeding the compression error back into future iterations. Extensive experiments on deep neural networks for vision show that this approach can compress full-matrix preconditioners by up to two orders of magnitude without impact on accuracy, effectively removing the memory overhead of full-matrix preconditioning for implementations of full-matrix Adagrad (GGT) and natural gradient (M-FAC). Our code is available at https://github.com/IST-DASLab/EFCP.

Leveraging second-order information about the loss at the scale of deep networks is one of the main lines of approach for improving the performance of current optimizers for deep learning. Yet, existing approaches for accurate full-matrix preconditioning, such as Full-Matrix Adagrad (GGT) or Matrix-Free Approximate Curvature (M-FAC) suffer from massive storage costs when applied even to small-scale models, as they must store a sliding window of gradients, whose memory requirements are multiplicative in the model dimension. In this paper, we address this issue via a novel and efficient error-feedback technique that can be applied to compress preconditioners by up to two orders of magnitude in practice, without loss of convergence. Specifically, our approach compresses the gradient information via sparsification or low-rank compression \emph{before} it is fed into the preconditioner, feeding the compression error back into future iterations. Experiments on deep neural networks show that this approach can compress full-matrix preconditioners to up to 99\% sparsity without accuracy loss, effectively removing the memory overhead of full-matrix preconditioners such as GGT and M-FAC. Our code is available at \url{https://github.com/IST-DASLab/EFCP}.

OBJECTIVES Devices such as mobile phones and smart speakers could be useful to remotely identify voice alterations associated with alcohol intoxication, which could be used to deliver just-in-time interventions, but data to support such approaches for the English language are lacking. In this controlled lab study, we compare how well English spectrographic voice features identify alcohol intoxication. METHODS 18 participants (72% male, aged 21-62 y) read a different randomly-assigned tongue twister prior to drinking and each hour for up to 7 hours after drinking a weight-based dose of alcohol. Vocal segments were cleaned and split into 1 second windows. We built support vector machine models for detecting alcohol intoxication, defined as breath alcohol concentration [BrAC] >0.08%, comparing the baseline voice spectrographic signature to each subsequent timepoint and present ensemble examine accuracy with 95% confidence intervals (CIs). RESULTS Alcohol intoxication was predicted with an accuracy of 98% (95% CI 97.1 to 98.6); mean sensitivity = .98; specificity = .97; positive predictive value = .97; and negative predictive value = .98. CONCLUSIONS In this small controlled lab study, voice spectrographic signatures collected from brief recorded English segments were useful in identifying alcohol intoxication. Larger studies using varied voice samples are needed to validate and expand models.

The Variational Autoencoder (VAE) is a seminal approach in deep generative modeling with latent variables. Interpreting its reconstruction process as a nonlinear transformation of samples from the latent posterior distribution, we apply the Unscented Transform (UT) -- a well-known distribution approximation used in the Unscented Kalman Filter (UKF) from the field of filtering. A finite set of statistics called sigma points, sampled deterministically, provides a more informative and lower-variance posterior representation than the ubiquitous noise-scaling of the reparameterization trick, while ensuring higher-quality reconstruction. We further boost the performance by replacing the Kullback-Leibler (KL) divergence with the Wasserstein distribution metric that allows for a sharper posterior. Inspired by the two components, we derive a novel, deterministic-sampling flavor of the VAE, the Unscented Autoencoder (UAE), trained purely with regularization-like terms on the per-sample posterior. We empirically show competitive performance in Fr\'echet Inception Distance (FID) scores over closely-related models, in addition to a lower training variance than the VAE.

Over the last 14 years, ichthyological and ecological parameters have been monitored in the Labudovo okno Ramsar site. This area is important for its biodiversity as it is home to many rare and endangered plants and animal species. A total of 3861 fish specimens were sampled and measured at six sampling sites four times during the sampling period. An analysis of biodiversity indexes, relative biomass (kg/ha), and relative annual production (kg/ha) was carried out to assess the effectiveness of existing conservation measures. The results obtained show a trend decline in biodiversity, relative biomass, and relative annual production. This indicates a biodiversity conservation problem that should be addressed through other mechanisms in addition to the principles of the Ramsar Convention.

One of the most important challenges when building road infrastructure is the selection of appropriate mechanization, on which the efficiency of construction and the life of exploitation depends largely. As construction machinery, pavers occupy a significant place in civil engineering projects, so their selection, depending on a road category, is a very important activity. The objective of this paper is to develop an intelligent Fuzzy MCDM (Multi-Criteria Decision-Making) model, which consists of the integration of D and Z numbers for the selection of construction machinery. The IMF D-SWARA (Improved Fuzzy D Step-Wise Weight Assessment Ratio Analysis) method was used to determine weighting coefficients. A novel Fuzzy ARAS-Z (Additive Ratio Assessment) method has been developed to determine an adequate paver for a lower category of roads (asphalt width up to 5 m), which represents an important contribution and novelty of the paper. A total of 10 alternatives were evaluated based on 16 criteria which were classified into 4 main groups. The results have shown that the alternative A8—SUPER 1300-3 represents a paver with the best characteristics for the considered set of parameters. After that, verification tests were calculated, and they include a comparative analysis with four other MCDM methods based on Z numbers, a change in the normalization procedure, and the impact of changing the size of an initial fuzzy matrix. The tests showed the stability of the developed model with negligible deviations.

BACKGROUND The efficacy of stromal vascular fraction (SVF) treatment, ie, stem cells, directly depends on the SVF cell count and their viability. The SVF cell count and viability are in direct correlation with adipose tissue harvesting site which yields SVF cells, thus making contribution to developing Tissue Guidance. OBJECTIVES To investigate the importance of harvesting subcutaneous adipose tissue-derived SVF cells on the concentration and viability of SVF. METHODS Adipose tissue was collected by vibration assisted liposuction, from the regions of the upper and lower abdomen, lumbar region and inner thigh region. Using the semi-automatic UNISTATION 2nd Version system (NeoGenesis, Seoul, South Korea), the obtained fat was chemically processed (collagenase enzyme) and a concentrate of SVF cells was obtained by centrifugation. These samples were then analyzed using the Luna-Stem Counter device (Logos Biosystems; Gyeonggi-do, South Korea) to measure the number and viability of SVF cells. RESULTS The highest concentration of SVF, comparing the regions of the upper abdomen, lower abdomen, lumbar, and inner thigh, was found in the lumbar region, specifically at 97498.00 per 1.0 ml of concentrate. The lowest concentration was found in the upper abdomen region. By ranking the viability values, the highest cell viability of SVF was observed in the lumbar region, measuring 36.6200%. The lowest viability was found in the upper abdomen region, measuring 24.4967%. CONCLUSIONS By comparing the upper and lower abdomen regions, lumbar and upper thigh regions, the authors have come to the conclusion that, on average, the largest number of cells and their viability was obtained from the lumbar region.

Climate change is recognized as a global threat that negatively impacts biodiversity and forest resources. The use of existing indicators for sustainable forest management (SFM) related to biodiversity and climate change, as well as the development of new indicators, will help assess how forest management practices impact biodiversity enhancement and climate change mitigation. A Pan-European set of criteria and indicators has been developed as a policy instrument for monitoring, evaluating, and reporting on the progress in implementing SFM. In Bosnia and Herzegovina and Western Balkans in general, the Pan-European set of criteria and indicators is an insufficiently researched topic and there is a lack of scientific research conducted regarding their development and implementation. Through the analysis of the current situation in forestry of the Federation of Bosnia and Herzegovina (FBiH), regarding the compliance and importance of the Pan-European criteria for SFM, this paper aims to explain how the international process of development and application of the Pan-European criteria for SFM can contribute to the improvement of the situation in forestry and the creation of a consistent forest policy in FBiH. The survey among forestry professionals (n=360), from the public forest administration and public forest companies in FBiH, included the sets of questions related to socio-demographic characteristics, assessment of compliance and importance of six criteria of SFM. Research results revealed that forestry professionals are mainly males, on average 41 years old, with 13 years of working experience. The majority of forestry professionals in FBiH are not familiar with Pan-European criteria for SFM, and have a low level of their understanding. On average, forestry professionals indicated that the Pan-European criteria for SFM were of high importance, while compliance with current forest management activities were rated lower on average. The large differences between responses regarding the average rating of compliance and importance of the Pan-European criteria for SFM indicate their low level of implementation in FBiH forest management activities. Accordingly, the results indicate that there is a need to organize educational lifelong learning programs in FBiH forestry sector, involving forestry professionals and other interested parties, to generate knowledge related to the Pan-European criteria for SFM and the concept of SFM in general.