Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Modality Enables New Evidence-Based Individual Risk Stratification That Can Potentially Lead to Decisive Management and Treatment Decisions in Prostate Cancer

A key step in providing management/treatment options to men with suspected prostate cancer (PCa) is categorizing the risk in terms of the presence of benign, low-risk, intermediate-risk, or high-risk disease. Our novel modality brings new evidence, based on the long-known hallmark characteristic of PCa—decreased zinc (Zn), which is the most direct metabolic sign of malignancy and its aggressiveness. To date, this approach has not been adopted for clinical use for a number of reasons that are described in this article, and which have been addressed by our approach. Zn has to be measured on fresh samples, prior to fixating in formalin; therefore, samples have to be scanned during the biopsy session. As Zn depletion occurs in the glands where the tumors develop, estimation of the glands’ levels in the scanned tissue, along with their compactness, are essential for accurate diagnosis. Combined with the Zn depletion, this facilitates a reliable assessment of disease aggressiveness. Data gathered in the clinical study described here indicate that, in addition to improving the biopsy quality by real-time interactive guidance, a malignancy score can now be established for the entire prostate, allowing higher granularity personalized risk stratification and more decisive treatment decisions for all PCa patients.
Global QBO Circulation Derived from UKMO Stratospheric Analyses

Global circulation anomalies associated with the stratospheric quasi-biennial oscillation (QBO) are analyzed based on U.K. Meteorological Office (UKMO) assimilated wind and temperature fields. Zonal winds and temperatures from the assimilation are compared with Singapore rawinsonde data (the standard QBO reference time series), showing reasonable agreement but an underestimate of maxima in the UKMO analyses. Global structure of the QBO in zonal wind, temperature, and residual mean meridional circulation (derived from thermodynamic balance and mass continuity) is isolated, showing coherent tropical and midlatitude components. Important aspects of the QBO revealed in these data include 1) out of phase maxima in temperature (and vertical velocity) between the lower and upper stratosphere, and 2) strong seasonal synchronization of midlatitude anomalies. These characteristics are also evident in long records of satellite radiance measurements.
The interactive effects of soil fertility and tree mycorrhizal association explain spatial variation of diversity–biomass relationships in a subtropical forest

Observed biodiversity–ecosystem function (BEF) relationships are highly variable, particularly in natural forests. However, our understanding of the factors that generate these often contradictory patterns, especially the role of different mycorrhizal associations, is still limited. By relating tree species richness and above‐ground biomass (AGB) in a fully mapped 24‐ha subtropical forest dynamics plot, we evaluated the impacts of soil fertility and tree mycorrhizal type in mediating BEF relationships at multiple spatial scales. Our results demonstrate a highly positive total richness effect on AGB for arbuscular mycorrhizal (AM) trees but a negative effect on AGB for ectomycorrhizal (EcM) trees, and their relationships were highly spatial scale dependent. However, the observed BEF relationships turned into positive at small spatial scales (i.e. 10 and 20 m) after controlling for other confounding factors (i.e. topography, soil fertility and AM proportion). In addition, we found significant interactions between soil fertility and species richness on AGB. Specifically, the positive effect of total species richness on AGB for major mycorrhizal types gradually weakened with increasing soil fertility, while the positive effect of EcM species richness on AM AGB gradually enhanced at small spatial scales, suggesting the observed diversity effects can be largely attributed to resource niche complementarity and the role of EcM fungi. Synthesis. We conclude that the variable BEF relationships among forest communities could be explained by spatial variation in abiotic environments and community mycorrhizal composition because different types of symbionts perform different nutrient uptake strategies and ability in protection from antagonists. Our findings provide novel insights into the understanding of the variation in the shape of BEF relationships in natural forests, which is critical for forest management, conservation and restoration in a changing world.
Skeleton-Based Action Recognition Through Contrasting Two-Stream Spatial-Temporal Networks

For pursuing accurate skeleton-based action recognition, most prior methods combine Graph Convolution Networks (GCNs) with attention-based methods in a serial way. However, they regard the human skeleton as a complete graph, resulting in less variations between different actions (e.g., the connection between the elbow and head in action “clapping hands”). For this, we propose a novel Contrastive GCN-Transformer Network (ConGT) which fuses the spatial and temporal modules in a parallel way. The ConGT involves two parallel streams: Spatial-Temporal Graph Convolution stream (STG) and Spatial-Temporal Transformer stream (STT). STG is designed to obtain action representations maintaining the natural topology structure of the human skeleton. STT is devised to acquire action representations containing the global relationships among joints. Since the action representations produced from these two streams contain different characteristics, and each of them knows little information of the other, we introduce the contrastive learning paradigm to guide their output representations of the same sample to be as close as possible in a self-supervised manner. Through the contrastive learning, they can learn information from each other to enrich the action features by maximizing the mutual information between the two types of action representations. To further improve action recognition accuracy, we introduce the Cyclical Focal Loss (CFL) which can focus on confident training samples in early training epochs, with an increasing focus on hard samples during the middle epochs. We conduct experiments on three benchmark datasets, which demonstrate that our model achieves state-of-the-art performance in action recognition.
Housing wealth distribution, inequality and residential satisfaction

ABSTRACT This research investigates the relationship between housing wealth and residential satisfaction. Using household panel survey data from the UK, we find that individuals’ asymmetric responses to changes in housing wealth distribution, that is, loss aversion experienced by the worse-off group, could offset the gain from an increase in housing wealth at the aggregate level. Consequently, housing wealth growth does not necessarily improve residential satisfaction for society as a whole if it leads to housing wealth inequality. Given the significant impact of housing wealth distribution on residential satisfaction, it is important to consider housing wealth inequality in making public policy decisions.
A derivative‐free projection method for nonlinear equations with non‐Lipschitz operator: Application to LASSO problem

In this paper, we introduce a derivative‐free iterative method for finding the solutions of convex constrained nonlinear equations (CCNE) using the projection strategy. The new approach is free from gradient evaluations at each iteration. Also, the search direction generated by the proposed method satisfies the sufficient descent property, which is independent of the line search. Compared with traditional methods for solving CCNE that assumes Lipschitz continuity and monotonicity to establish the global convergence result, an advantage of our proposed method is that the global convergence result does not require the assumption of Lipschitz continuity. Moreover, the underlying operator is assumed to be pseudomonotone, which is a milder condition than monotonicity. As an applications, we solve the LASSO problem in compressed sensing. Numerical experiments illustrate the performances of our proposed algorithm and provide a comparison with related algorithms.
Engineering promising A-π-D type molecules for efficient organic-based material solar cells

ABSTRACT Within this work, to promote the efficiency of organic-based solar cells, a series of novel A-π-D type small molecules were scrutinised. The acceptors which we designed had a moiety of N, N-dimethylaniline as the donor and catechol moiety as the acceptor linked through various conjugated π-linkers. We performed DFT (B3LYP) as well as TD-DFT (CAM-B3LYP) computations using 6-31G (d,p) for scrutinising the impact of various π-linkers upon optoelectronic characteristics, stability, and rate of charge transport. In comparison with the reference molecule, various π-linkers led to a smaller HOMO–LUMO energy gap. Compared to the reference molecule, there was a considerable red shift in the molecules under study (A1–A4). Therefore, based on the analysis of energy level, A4 and A3 were shown to be promising non-fullerene acceptors with the designed donors for applications in solar cells. It is hoped that the current study would provide theoretical insights into the design and amplification of optoelectronic characteristics of suggested frameworks on a grand scale in comparison with the reference molecules.
On the stability of nonlinear cold plasma waves. Part 2

In part 1 (Infeld 1972), it was shown that a nonlinear stationary wave in a cold plasma is marginally stable to all perturbations that are functions of the spatial direction parallel to the direction of propagation only. The stability analysis is extended here in part 2 to include general spatial variations. Within the limitations of the treatment, it is found that, for wave amplitudes up to maximal value (onset of trapping), the wave is marginally stable. Hence one would expect these large-amplitude cold plasma waves to exist in practice. The one-dimensional analysis of part 1 is also extended to include waves with relativistic velocities, and such waves are again found to be marginally stable.
Cell Factory for Phenylnaphthacenoid Polyketide Production

Covering 2009–2022. Phenylnaphthacenoid polyketides have gained significant interest in recent years owing to their potent antibacterial and anticancer activities. Notably, more than 100 members of this class of natural products have been discovered from various Streptomyces species by different research groups including ours over the last 13 years. This review summarizes the current knowledge of the discovery, chemical diversity, and bioactivity of phenylnaphthacenoid polyketides. The current review also highlights the cell factory for phenylnaphthacenoid production: (1) native strains, (2) mutant strains, (3) heterologous expression, and (4) biocatalytic halogenations. Furthermore, current challenges and future opportunities are also presented as a guide for researchers to explore them more purposefully.
Learning path for Construction 4.0 based on tinkering and STEAM

This article describes the outcomes of a completed study of practice in civil engineering education. The study is aimed at infusing Construction 4.0 content to a Bachelor degree on Civil Engineering. A set of STEAM-rich activities are created in the form of an individual learning path. These activities are conceived with a threefold perspective: i) Construction 4.0-related, ii) STEAM vision by-design and iii) hardware-software independent (open-source, accessible, affordable). Cornerstone and capstone projects as well as a set of workshops represent some demonstrators of these activities. All these demonstrators are knitted together in a single path in that is pegged to the traditional curriculum The STEAM perspective provides completeness to the whole development. During the last two years, design, development and implementation of several demonstrators have been completed. Some results related to the application of some activities are already available. In the years to come, it is expected that an improved systematic deployment of such activities will allow assessing the evolution between tools, pedagogies and the needs of the sector. In this paper, the description of the activities together with the discussion on the potential of tinkering on Construction 4.0-based curriculum is addressed.
Quantifying the Aerodynamic Roughness Length of Snow Surfaces With Time‐Lapse Structure‐From‐Motion

The aerodynamic roughness length (z0m) of snow surfaces plays an essential role in estimating turbulent fluxes of latent and sensible heat (LE and H). Constant z0m has been widely used in previous studies due to the difficulty in monitoring dynamic z0m. This leads to large uncertainties in simulating LE and H fluxes. In the current study we used a time‐lapse structure‐from‐motion (SfM) photogrammetric method to study temporal variation in z0m of snow surfaces. The study site was located at the edge of the August‐one ice cap in the Qilian Mountains of China. The study ran from August 2019 to September 2020. We measured aerodynamic roughness length over time (z0m_t) using SfM‐based micro‐topographic methods and validated our results by comparing z0m_t with z0m_prof (profile‐based) and z0m_EC (EC‐based) data. The validated dynamic z0m_t was then used to estimate LE and H fluxes using a bulk aerodynamic method. These results were then compared with LE and H measured by eddy covariance (EC) and with manual sublimation/condensation measurements to indirectly validate z0m_t. We found that z0m_t overall agreed with profile‐based z0m_prof and EC‐based z0m_EC. Indeed, our estimates of bulk LE and H fluxes using dynamic z0m_t agreed slightly better with EC and manual sublimation/condensation measurements than did static EC‐based z0m_EC. Micro‐topographic z0m_t can be helpful in precisely estimating varying LE and H fluxes over the course of a year.
Enabling Artificial Intelligent Virtual Sensors in an IoT Environment

The demands for a large number of sensors increase as the proliferation of Internet of Things (IoT) and smart cities applications are continuing at a rapid pace. This also increases the cost of the infrastructure and the installation and maintenance overhead and creates significant performance degradation in the end-to-end communication, monitoring, and orchestration of the various connected devices. In order to solve the problem of increasing sensor demands, this paper suggests replacing physical sensors with machine learning (ML) models. These software-based artificial intelligence models are called virtual sensors. Extensive research and simulation comparisons between fourteen ML models provide a solid ground decision when it comes to the selection of the most accurate model to replace physical sensors, such as temperature and humidity sensors. In this problem at hand, the virtual and physical sensors are designed to be scattered in a smart home, while being connected and run on the same IoT platform. Thus, this paper also introduces a custom lightweight IoT platform that runs on a Raspberry Pi equipped with physical temperature and humidity sensors, which may also execute the virtual sensors. The evaluation results of the devised virtual sensors in a smart home scenario are promising and corroborate the applicability of the proposed methodology.
Using LMS Log Data to Explore Student Engagement with Coursework Videos

Driven by the increased availability of Learning Management System data, this study explored its value and sought understanding of student behaviour through the information contained in activity level log data. Specifically, this study examined analytics data to understand students’ engagement with online videos. Learning analytics data from the MoodleTM and Vimeo® platforms were compared. The research also examined the impact of video length on engagement, and how engagement with videos changed over the course of a semester when multiple video resources were used in a course. The comparison in platform learning analytics showed differences in metrics thus offering a caution to users relying on unidimensional metrics. While the results support the notion that log data do provide educators with an opportunity for review, the time and expertise in extracting, handling, and using the data may stifle its widespread adoption.
Calculation of the quantum entanglement measure of bipartite states, based on relative entropy, using genetic algorithms

Quantum entanglement is an enigmatic add powerful property that has attracted much attention, since it provides new means of communication, such as quantum cryptography, quantum teleportation and superdense coding, and quantum computation. Therefore, the measure of entanglement of composite systems becomes crucial. For pure bipartite states the von Neumann entropy is a good measure and it is easily calculated; however, to quantify the entanglement of mixed states is a harder task. Some measures have been proposed, among them one based on relative entropy. This measure needs a minimization procedure. In this paper, we present an algorithm to calculate the quantum entanglement measure based on relative entropy, in which the required minimization procedure is done using a genetic algorithm. The main advantage of using a genetic algorithm is it is easier to implement than gradient methods are when the number of parameters is very large.
SARS-CoV-2 Nsp2 Contributes to Inflammation by Activating NF-κB

COVID-19 is associated with robust inflammation and partially impaired antiviral responses. The modulation of inflammatory gene expression by SARS-CoV-2 is not completely understood. In this study, we characterized the inflammatory and antiviral responses mounted during SARS-CoV-2 infection. K18-hACE2 mice were infected with a Wuhan-like strain of SARS-CoV-2, and the transcriptional and translational expression interferons (IFNs), cytokines, and chemokines were analyzed in mouse lung homogenates. Our results show that the infection of mice with SARS-CoV-2 induces the expression of several pro-inflammatory CC and CXC chemokines activated through NF-κB but weakly IL1β and IL18 whose expression are more characteristic of inflammasome formation. We also observed the downregulation of several inflammasome effectors. The modulation of innate response, following expressions of non-structural protein 2 (Nsp2) and SARS-CoV-2 infection, was assessed by measuring IFNβ expression and NF-κB modulation in human pulmonary cells. A robust activation of the NF-κB p65 subunit was induced following the infection of human cells with the corresponding NF-κB-driven inflammatory signature. We identified that Nsp2 expression induced the activation of the IFNβ promoter through its NF-κB regulatory domain as well as activation of p65 subunit phosphorylation. The present studies suggest that SARS-CoV-2 skews the antiviral response in favor of an NF-κB-driven inflammatory response, a hallmark of acute COVID-19 and for which Nsp2 should be considered an important contributor.
Activating Three Languages Simultaneously in Mental Lexicon: An Investigation of Nepali – English – Sanskritparallel Language Activation

This is the first study investigating the nature of trilingual parallel language activation in Nepal-English-Sanskrit triad. Twenty-four participants have been divided into groups of 8 based on the dominance of the three language use to understand the nature of activation in three groups. Stimuli consisted of audio-visual bimodal in nature. An emerging novel technology Mouse Tracker that can study continuous unfolding of cognitive processing has been used to design the experiment and collect the software generated data. The data has been analyzed using SPSS and visualized using R. Results which showed when three languages are activated simultaneously, the phonological similarity plays a facilitating role. The results are explained in line with BLINCS (Bilingual Language Interaction Network for Comprehension of Speech) model.
Prevalence and implementation of small-sided games in rugby union: a preliminary survey study

Received: 20.01. 2022 Accepted: 30.06. 2022 Online: 08.01. 2023 Small-sided games (SSG) are popular in team sports training because they are thought to promote physiological, kinematic, and tactical benefits. Researchers in the football codes often use this as a rationale for conducting SSG-related investigations. However, the prevalence of SSG has not been scientifically documented in rugby union (RU). Therefore, the purpose of this study was to examine the prevalence of SSG in RU and develop understanding of how SSG are implemented in RU practice. An anonymous, multiple-choice electronic survey including a proposed consensus definition was dispersed globally through email and several social media platforms. The survey was launched and re-distributed six times at regular intervals over a one-year period. One hundred and fifteen responses were collected from various RU coaching strata. The respondents ratified the proposed definition, identifying SSG as modifiable methods representative of the full game, which can be used to target various training outcomes. Ninety-nine percent of survey participants used SSG. The largest cohort was characterised by ≥5 years of coaching experience (n = 76; 66% of sample) in New Zealand (n = 89; 77%), fulfilling the role of head coach (n = 61; 53%) to male RU players (n = 79; 69%). Eighty-five percent of coaches (n = 98; 85%) reported using SSG every 1 to 3 sessions. 3v3 (top 1) and 5v5 (top 3) were the most popular formats. These formats were used to improve technique (n = 30; 26%) and promote fun (n = 29; 25%). SSG are widespread and frequently used by New Zealand RU coaches through involving different numbers of players. RU staff have a uniform conceptual understanding of SSG. The implementation of SSG is related to coaching roles. Meaningful differences in the implementation of SSG are plausible in RU practice depending on playing levels, coaching experience, player sex, and geographic location.
Search for the SM Higgs boson decaying to bb in associated production with a Z boson decaying in the invisible channel

A search for the Standard Model (SM) Higgs boson decaying into two b jets using associated produc- tion with a Z boson decaying into a pair of neutrinos is presented at LHCP. The CMS pp collisions data-samples of 4.7 =fb of at the center-of-mass energy of 7 TeV and 19.0 =fb at the energy of 8 TeV have been analyzed. The techniques employed to discriminate signal from background are explained. An upper limit of 2.3 times the SM Higgs cross section at 95% of confidence level has been observed. The signal strength for mH = 125 GeV is 1:0 0:8 times the SM prediction.
Control environment and integrity management in educational system in Greece

The purpose of the research: First, to recognize and analyze the types of unethical behavior among the educational staff of any level of public education units in Greece. Second, to identify elements of control environment that are in place and promote ethical conduct and set the tone at the educational unit level. Our results could provide the basis for targeted enhancements of the control environment of the various educational units and facilitate the drawing of centralized strategies that will promote integrity through principled-based codes of conduct and clear lines of responsibility, etc.