Miracle-dz/UniFAR-FTUs huggingface dataset:
The SNP-Based Profiling of Montecristo Feral Goat Populations Reveals a History of Isolation, Bottlenecks, and the Effects of Management

The Montecristo wild goat is an endangered feral population that has been on the homonymous island in the Tuscan Archipelago since ancient times. The origins of Montecristo goats are still debated, with authors dating their introduction either back to Neolithic times or between the 6th and 13th century of the Common Era. To investigate the evolutionary history and relationships of this population we assembled a 50K SNP dataset including 55 Mediterranean breeds and two nuclei of Montecristo goats sampled on the island and from an ex situ conservation project. Diversity levels, gene flow, population structure, and genetic relationships were assessed through multiple approaches. The insular population scored the lowest values of both observed and expected heterozygosity, highlighting reduced genetic variation, while the ex situ nucleus highlighted a less severe reduction. Multivariate statistics, network, and population structure analyses clearly separated the insular nucleus from all other breeds, including the population of Montecristo goats from the mainland. Moreover, admixture and gene flow analyses pinpointed possible genetic inputs received by the two Montecristo goat nuclei from different sources, while Runs of Homozygosity (ROHs) indicated an ancient bottleneck/founder effect in the insular population and recent extensive inbreeding in the ex situ one. Overall, our results suggest that Montecristo goats experienced several demographic fluctuations combined with admixture events over time and highlighted a noticeable differentiation between the two nuclei.
Performance, Well-Being, Motivation, and Identity in an Age of Abundant Data: Introduction to the “Well-Measured Life” Special Issue of Current Directions in Psychological Science

Our lives are being measured in rapidly increasing ways and frequency. These measurements have beneficial and deleterious effects at both individual and social levels. Behavioral measurement technologies offer the promise of helping us to know ourselves better and to improve our well-being by using personalized feedback and gamification. At the same time, they present threats to our privacy, self-esteem, and motivation. At the societal level, the potential benefits of reducing bias and decision variability by using objective and transparent assessments are offset by threats of systematic, algorithmic bias from invalid or flawed measurements. Considerable technological progress, careful foresight, and continuous scrutiny will be needed so that the positive impacts of behavioral measurement technologies far outweigh the negative ones.
Understanding Species–Habitat Associations: A Case Study with the World’s Bears

Habitat modeling is one of the most common practices in ecology today, aimed at understanding complex associations between species and an array of environmental, bioclimatic, and anthropogenic factors. This review of studies of seven species of terrestrial bears (Ursidae) occupying four continents examines how habitat models have been employed, and the functionality of their predictions for management and conservation. Bear occurrence data have been obtained at the population level, as presence points (e.g., sign surveys or camera trapping), or as locations of individual radio-collared animals. Radio-collars provide greater insights into how bears interact with their environment and variability within populations; they are more commonly used in North America and Europe than in South America and Asia. Salient problematic issues apparent from this review included: biases in presence data; predictor variables being poor surrogates of actual behavioral drivers; predictor variables applied at a biologically inappropriate scale; and over-use of data repositories that tend to detach investigators from the species. In several cases, multiple models in the same area yielded different predictions; new presence data occurred outside the range of predicted suitable habitat; and future range projections, based on where bears presently exist, underestimated their adaptability. Findings here are likely relevant to other taxa.
Equiauxetic Hinged Archimedean Tilings

There is increasing interest in two-dimensional and quasi-two-dimensional materials and metamaterials for applications in chemistry, physics and engineering. Some of these applications are driven by the possible auxetic properties of such materials. Auxetic frameworks expand along one direction when subjected to a perpendicular stretching force. An equiauxetic framework has a unique mechanism of expansion (an equiauxetic mode) where the symmetry forces a Poisson’s ratio of −1. Hinged tilings offer opportunities for the design of auxetic and equiauxetic frameworks in 2D, and generic auxetic behaviour can often be detected using a symmetry extension of the scalar counting rule for mobility of periodic body-bar systems. Hinged frameworks based on Archimedean tilings of the plane are considered here. It is known that the regular hexagonal tiling, {63}, leads to an equiauxetic framework for both single-link and double-link connections between the tiles. For single-link connections, three Archimedean tilings considered as hinged body-bar frameworks are found here to be equiauxetic: these are {3.122}, {4.6. 12}, and {4.82}. For double-link connections, three Archimedean tilings considered as hinged body-bar frameworks are found to be equiauxetic: these are {34.6}, {32.4. 3.4}, and {3.6. 3.6}.
Improving Operational Ocean Models for the Spanish Port Authorities: Assessment of the SAMOA Coastal Forecasting Service Upgrades

The Puertos del Estado SAMOA coastal and port ocean forecast service delivers operational ocean forecasts to the Spanish Port Authorities since 01/2017 (originally set-up for 9 ports). In its second development phase (2019–2021), the SAMOA service has been extended to 31 ports (practically, the whole Spanish Port System). Besides, the next generation of the SAMOA service is being developed. Research is being focused on (1) updating atmospheric forcing (by combining the AEMET HARMONIE 2.5 Km forecasts and the IFS-ECMWF ones), (2) upgrading the circulation model (ROMS), and (3) testing new methodologies to nest SAMOA systems in the Copernicus IBI-MFC regional solution (with emphasis on its 3D hourly dataset). Evaluation of specific model upgrades is here presented. Model sensitivity tests have been assessed using the available in-situ and remoted sensed (i.e., RadarHF) observations. The results show that SAMOA outperforms IBI-MFC in sea level forecasting at meso- and macro-tidal environments. Improvements by the herein proposed upgrades are incremental: some of these set-ups were used in the last SAMOA operational releases (i.e., the SAM_INI and the SAM_ADV ones; the later currently in operations), whereas the latest test (SAM_H3D) ensures more nesting consistency with the IBI-MFC and improves significantly surface currents and sea-surface temperature simulations.
Color-Dense Illumination Adjustment Network for Removing Haze and Smoke from Fire Scenario Images

The atmospheric particles and aerosols from burning usually cause visual artifacts in single images captured from fire scenarios. Most existing haze removal methods exploit the atmospheric scattering model (ASM) for visual enhancement, which inevitably leads to inaccurate estimation of the atmosphere light and transmission matrix of the smoky and hazy inputs. To solve these problems, we present a novel color-dense illumination adjustment network (CIANet) for joint recovery of transmission matrix, illumination intensity, and the dominant color of aerosols from a single image. Meanwhile, to improve the visual effects of the recovered images, the proposed CIANet jointly optimizes the transmission map, atmospheric optical value, the color of aerosol, and a preliminary recovered scene. Furthermore, we designed a reformulated ASM, called the aerosol scattering model (ESM), to smooth out the enhancement results while keeping the visual effects and the semantic information of different objects. Experimental results on both the proposed RFSIE and NTIRE’20 demonstrate our superior performance favorably against state-of-the-art dehazing methods regarding PSNR, SSIM and subjective visual quality. Furthermore, when concatenating CIANet with Faster R-CNN, we witness an improvement of the objection performance with a large margin.
Navigating the LGB Data Landscape: A Review of Appropriate Secondary Data Sources for Sexuality and Substance Use Research in the UK

Research has found that sexual minority individuals are more likely to experience health inequalities and have higher rates of substance use compared with their heterosexual counterparts. This association between sexuality and health outcomes is increasingly being explored using quantitative methodologies within the context of public health, psychology and health geography. Much of this research, however, has relied on primary data, despite the wide availability of secondary sources, mainly survey data, collecting information on sexuality and different types of health outcomes and health risk behaviours, such as substance use. This study reviewed recent surveys in the UK that are appropriate for exploring topics related to LGB populations and substance use behaviours. We carried out a narrative review of secondary data sources in the UK to assess the accessibility and suitability of secondary sources for sexuality and substance use research. We identified eight cross-sectional and two longitudinal surveys that contained both sexuality and substance use data. We summarised the possible applications of each survey and the scope of questions within sexuality and substance use research that could be addressed by each survey. The identification of appropriate surveys in this review can allow researchers to extend the use of secondary data sources in the UK to examine substance use inequalities between sexuality groups, further advancing this key topic.
Machine Learning and Deep Learning Applications in Multiple Myeloma Diagnosis, Prognosis, and Treatment Selection

Simple Summary Multiple myeloma is a malignant neoplasm of plasma cells with complex pathogenesis. With major progresses in multiple myeloma research, it is essential that we reconsider our methods for diagnosing and monitoring multiple myeloma disease. This fact needs the integration of serology, histology, radiology, and genetic data; therefore, multiple myeloma study has generated massive quantities of granular high-dimensional data exceeding human understanding. With improved computational techniques, artificial intelligence tools for data processing and analysis are becoming more and more relevant. Artificial intelligence represents a wide set of algorithms for which machine learning and deep learning are presently among the most impactful. This review focuses on artificial intelligence applications in multiple myeloma research, first illustrating machine learning and deep learning procedures and workflow, followed by how these algorithms are used for multiple myeloma diagnosis, prognosis, bone lesions identification, and evaluation of response to the treatment. Abstract Artificial intelligence has recently modified the panorama of oncology investigation thanks to the use of machine learning algorithms and deep learning strategies. Machine learning is a branch of artificial intelligence that involves algorithms that analyse information, learn from that information, and then employ their discoveries to make abreast choice, while deep learning is a field of machine learning basically represented by algorithms inspired by the organization and function of the brain, named artificial neural networks. In this review, we examine the possibility of the artificial intelligence applications in multiple myeloma evaluation, and we report the most significant experimentations with respect to the machine and deep learning procedures in the relevant field. Multiple myeloma is one of the most common haematological malignancies in the world, and among them, it is one of the most difficult ones to cure due to the high occurrence of relapse and chemoresistance. Machine learning- and deep learning-based studies are expected to be among the future strategies to challenge this negative-prognosis tumour via the detection of new markers for their prompt discovery and therapy selection and by a better evaluation of its relapse and survival.
Biological nutrient removal in SBR technology: from floccular to granular sludge

Biological nutrient removal has been studied and applied for decades in order to remove nitrogen and phosphorus from wastewater. However, more anthropogenic uses and the continued demand for water have forced the facilities to operate at their maximum capacity. Therefore, the goal of this thesis is to obtain more compact systems for nutrient removal from domestic wastewater. In this sense, optimization and long-term stabilization of high volume exchange ratios reactors, treating higher volumes of wastewater, have been investigated. With the same target, aerobic granular sludge was proposed as a reliable alternative to reduce space and increase loading rates in treatment plants. However, the low organic loading rate from low-strength influents (less than 1 Kg COD•m-3d-1) results in slower granular formation and a longer time to reach a steady state. Because of that, different methodologies and operational conditions were investigated in order to enhance granulation and nutrient removal from domestic wastewater.
The Impact of Institutional Creativity and Innovation Capability on Innovation Performance of Public Sector Organizations in Ghana

The public sector continues to be under intense pressure to improve its innovation performance. Consequently, stakeholders are calling for more empirical studies on the antecedents of innovation, especially from a developing country’s perspective. Motivated by this call, we investigate the impact of institutional creativity and institutional innovation capacity on public innovation performance in the context of Ghana. Key to our model is that, institutional creativity and institutional innovation capacity are also enabled by inter-agency collaborations, institutional leadership, and stakeholder pressure. The partial least squares structural equation model is employed to estimate the survey responses of 195 respondents from fifty public sector institutions. The empirical analysis confirms that organizational creativity has a positive and significant impact on innovation performance, innovation capacity has a negative and insignificant impact on innovation performance, and inter-agency collaboration and institutional leadership contribute positively to institutional creativity and innovation capacity. However, stakeholder pressure negatively impacts both institutional creativity and innovation performance. This study contributes to knowledge on the antecedents of public innovation performance from a developing country’s perspective. It also advances theories on public innovation performance.
Covid-19 and Reimbursement Mechanisms in Medical Economics; Retrospective(ffs) or Prospective(drg) Payment Healthcare Systems, Which One?

index. COVID-19 disease has economic and financial effects at both macro and micro levels. At the macro level, the economic effects of this pandemic disease have emerged as a shock or economic crisis, meaning that it has affected the financing and economic structure of countries, in other words, the economic pattern and turnover worldwide. At the micro-level, funding sources and health care reimbursement mechanisms in hospitals have been severely affected. In this study, health care repayment and financing systems in the health sector, especially hospitals, were studied. In addition, are retrospective health financing and repayment systems better or prospective repayment systems in times of crisis or economic shock caused by COVID-19 disease? The types of financing mechanisms and current repayment methods, depending on the type of health system as well as other conditions and requirements of each country, have strengths and weaknesses [1-3]. The results of a study (Ahangar et al ., 2021) at Iran University of Medical Sciences show that before and after COVID-19 disease, hospitals with prospective repayment systems (such as DRG, Global, Per Capita mechanisms) compared to hospitals with retrospective repayment systems were more efficient in controlling costs and resources, as well as better managing the quality of hospital care.
Implementation of Green Economy Program Based on Penta Helix Approach

Sustainable development needs to be proclaimed throughout the world, because the environment has begun to be degraded, especially natural resources that have begun to run out over time, this is because of continuous development without regard to the environmental sector, this is the concept of Green Economy. This study aims to determine how the application of the Green Economy concept in an independent village and the role of the five actors in it, namely the government, private sector, academics, and the press, commonly called the Penta Helix model to support development in Indonesia. The results showed that the application of the Green Economy principle in 3G villages had been carried out through the village conditions, programs that were made, and the activities that were there. In its development, the actors have also contributed in it, several universities in Malang, the banks, SOEs, and press friends such as the Kompas Post.
Language Policy and Multilingualism in the City of Osijek in a Diachronic Perspective

The city of Osijek (Croatia) has been for centuries a place of various contacts arising from direct or indirect social interactions of speakers reflecting their linguistic and non-linguistic (i.e. cultural, political and social) backgrounds. It has thus been the place of urban multilingual social inclusion and linguistic diversity. In this context, the paper aims at providing a theoretical and historical framework for language policy types that facilitated multilingualism in Osijek with reference to a particular German dialect of Osijek (Croatian: esekerski). The methodology applied in the paper is the primary sources analysis (applicable legislation, statistical demographic and historical data), interpretation of relevant LPP theories as well as a comparison regarding the language policy and languages spoken in the city of Osijek since the 19th century. In conclusion, the authors determine the role of language policy in maintaining the multilingual feature of the city of Osijek and give recommendations therefor.
Strategy as Vector and the Inertia of Coevolutionary Lock-in

To examine the consequences of a period of extraordinary success for the long-term adaptive capability of a firm's strategy-making process, this comparative longitudinal study of Andy Grove's tenure as Intel Corporation's chief executive officer (CEO) documents how he moved Intel's strategy-making process from an internal-ecology model to the classical rational-actor model during 1987-1998. His creation of a highly successful strategy vector pursued through an extremely focused induced-strategy process led to coevolutionary lock-in with the personal computer market segment, in which Intel's strategy making became increasingly tied to its existing product market. Intracompany analysis of four new business development cases highlights the inertial consequences of coevolutionary lock-in. The paper examines implications of coevolutionary lock-in in terms of its effect on balancing induced and autonomous strategy processes and exploitation and exploration in organizational learning.
A Novel Multi-Mode Thermal Therapy for Colorectal Cancer Liver Metastasis: A Pilot Study

A novel multi-mode thermal therapy was developed for local tumor ablation and the systemic stimulation of anti-tumor immunity, consisting of a rapid liquid nitrogen freezing, and followed by the radiofrequency heating of target tumor tissue. This pilot study aimed to compare the therapeutic effects of the new therapy with conventional radiofrequency ablation (RFA) on patients with colorectal cancer liver metastasis (CRCLM). From August 2016 to September 2019, thirty-one patients with CRCLM received either multi-mode thermal therapy (n = 17) or RFA (n = 14). Triphasic contrast-enhanced magnetic resonance imaging (MRI), routine blood tests, and peripheral blood immune responses were evaluated before the treatment and in 1, 3, 6, and 12 months after. Local tumor response and progression-free survival (PFS) were assessed using the Kaplan-Meier method, and pre- and post-treatment immune cell counts were analyzed using Mann-Whitney U and Wilcoxon tests. A significantly longer PFS was observed in the multi-mode thermal therapy group in comparison to that of the conventional RFA group (median, 11.4 versus 3.4 months, p = 0.022). It was found that multi-mode therapy induced the functional maturation of dendritic cells, promoted CD4+ T cell-mediated antitumor responses, and decreased regulatory T cells, contributing to better therapeutic efficacy in CRCLM patients.
Determinants of Dropout from and Variation in Adherence to an Exercise Intervention: The STRRIDE Randomized Trials

ABSTRACT Purpose This study aimed to characterize the timing and self-reported determinants of exercise dropout among sedentary adults with overweight or obesity. We also sought to explore variations in adherence among individuals who completed a 6- to 8-month structured exercise intervention. Methods A total of 947 adults with dyslipidemia (STRRIDE I, STRRIDE AT/RT) or prediabetes (STRRIDE-PD) were enrolled to either control or to 1 of 10 exercise interventions, ranging from doses of 8 to 23 kcal·kg−1·wk−1, intensities of 50% to 75% V˙O2 peak, and durations of 6 to 8 months. Two groups included resistance training, and one included dietary intervention (7% weight loss goal). Dropout was defined as an individual who withdrew from the study because of a variety of determinants. Timing of intervention dropout was defined as the last session attended and categorized into phases. Exercise training adherence was calculated by dividing weekly minutes or total sets of exercise completed by weekly minutes or total sets of exercise prescribed. General linear models were used to characterize the associations between timing of dropout and determinant category. Results Compared with exercise intervention completers (n = 652), participants who dropped out (n = 295) were on average non-White (98% vs 80%, P < 0.01), had higher body mass index (31.0 vs 30.2 kg·m−2, P < 0.01), and were less fit at baseline (25.0 vs 26.7 mL·kg−1·min−1, P < 0.01). Of those who dropped out, 67% did so before the start of or while ramping up to the prescribed exercise volume and intensity. The most commonly reported reason for dropout was lack of time (40%). Notably, among individuals who completed the ramp training period, subsequent exercise intervention adherence did not waiver over the ensuing 6–8 months of training. Conclusions These findings are some of the first to delineate associations between the timing of dropout and dropout determinants, providing guidance for future exercise interventions to better support individuals at risk for dropout.