Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning analysis of human skin by optoacoustic mesoscopy for automated extraction of psoriasis and aging biomarkers.

Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is a novel modality that has demonstrated unprecedented ability to visualize epidermal and dermal structures in-vivo. However, an automatic and quantitative analysis of three-dimensional RSOM datasets remains unexplored. In this work we present our framework: Deep Learning RSOM Analysis Pipeline (DeepRAP), to analyze and quantify morphological skin features recorded by RSOM and extract imaging biomarkers for disease characterization. DeepRAP uses a multi-network segmentation strategy based on convolutional neural networks with transfer learning. This strategy enabled the automatic recognition of skin layers and subsequent segmentation of dermal microvasculature with an accuracy equivalent to human assessment. DeepRAP was validated against manual segmentation on 25 psoriasis patients under treatment and our biomarker extraction was shown to characterize disease severity and progression well with a strong correlation to physician evaluation and histology. In a unique validation experiment, we applied DeepRAP in a time series sequence of occlusion-induced hyperemia from 10 healthy volunteers. We observe how the biomarkers decrease and recover during the occlusion and release process, demonstrating accurate performance and reproducibility of DeepRAP. Furthermore, we analyzed a cohort of 75 volunteers and defined a relationship between aging and microvascular features in-vivo. More precisely, this study revealed that fine microvascular features in the dermal layer have the strongest correlation to age. The ability of our newly developed framework to enable the rapid study of human skin morphology and microvasculature in-vivo promises to replace biopsy studies, increasing the translational potential of RSOM.
Epidemiology and Variability of Disease and Deoxynivalenol in Fusarium Head Blight of Wheat in Ohio

ii Dedication. v Acknowledgments. vi Vita. vii List of Figures. x List of Tables. xii Chapter 1: Introduction. 1 Chapter 2: Fusarium head blight development and deoxynivalenol accumulation in wheat as influenced by infection timing, cultivar resistance, and inoculum density . 19 Introduction. 19 Materials and Methods. 23 Results. 28 Discussion. 33 Chapter 3: Factors influencing post-anthesis infection and deoxynivalenol accumulation in wheat grain harvested from asymptomatic spikes. 55 Introduction. 55
Assembling spatial clustering framework for heterogeneous spatial transcriptomics data with GRAPHDeep

Abstract Motivation Spatial clustering is essential and challenging for spatial transcriptomics’ data analysis to unravel tissue microenvironment and biological function. Graph neural networks are promising to address gene expression profiles and spatial location information in spatial transcriptomics to generate latent representations. However, choosing an appropriate graph deep learning module and graph neural network necessitates further exploration and investigation. Results In this article, we present GRAPHDeep to assemble a spatial clustering framework for heterogeneous spatial transcriptomics data. Through integrating 2 graph deep learning modules and 20 graph neural networks, the most appropriate combination is decided for each dataset. The constructed spatial clustering method is compared with state-of-the-art algorithms to demonstrate its effectiveness and superiority. The significant new findings include: (i) the number of genes or proteins of spatial omics data is quite crucial in spatial clustering algorithms; (ii) the variational graph autoencoder is more suitable for spatial clustering tasks than deep graph infomax module; (iii) UniMP, SAGE, SuperGAT, GATv2, GCN, and TAG are the recommended graph neural networks for spatial clustering tasks; and (iv) the used graph neural network in the existent spatial clustering frameworks is not the best candidate. This study could be regarded as desirable guidance for choosing an appropriate graph neural network for spatial clustering. Availability and implementation The source code of GRAPHDeep is available at https://github.com/narutoten520/GRAPHDeep. The studied spatial omics data are available at https://zenodo.org/record/8141084.
Unsupervised Discriminative Feature Selection via Contrastive Graph Learning

Due to many unmarked data, there has been tremendous interest in developing unsupervised feature selection methods, among which graph-guided feature selection is one of the most representative techniques. However, the existing feature selection methods have the following limitations: (1) All of them only remove redundant features shared by all classes and neglect the class-specific properties; thus, the selected features cannot well characterize the discriminative structure of the data. (2) The existing methods only consider the relationship between the data and the corresponding neighbor points by Euclidean distance while neglecting the differences with other samples. Thus, existing methods cannot encode discriminative information well. (3) They adaptively learn the graph in the original or embedding space. Thus, the learned graph cannot characterize the data’s cluster structure. To solve these limitations, we present a novel unsupervised discriminative feature selection via contrastive graph learning, which integrates feature selection and graph learning into a uniform framework. Specifically, our model adaptively learns the affinity matrix, which helps characterize the data’s intrinsic and cluster structures in the original space and the contrastive learning. We minimize $\ell _{1,2}$ -norm regularization on the projection matrix to preserve class-specific features and remove redundant features shared by all classes. Thus, the selected features encode discriminative information well and characterize the discriminative structure of the data. Generous experiments indicate that our proposed model has state-of-the-art performance.
Chromosome-level genome assembly of the common chiton, Liolophura japonica (Lischke, 1873)

Chitons (Polyplacophora) are marine molluscs that can be found worldwide from cold waters to the tropics, and play important ecological roles in the environment. Nevertheless, there remains only two chiton genomes sequenced to date. The chiton Liolophura japonica (Lischke, 1873) is one of the most abundant polyplacophorans found throughout East Asia. Our PacBio HiFi reads and Omni-C sequencing data resulted in a high-quality near chromosome-level genome assembly of ∼609 Mb with a scaffold N50 length of 37.34 Mb (96.1% BUSCO). A total of 28,233 genes were predicted, including 28,010 protein-coding genes. The repeat content (27.89%) was similar to the other Chitonidae species and approximately three times lower than in the genome of the Hanleyidae chiton. The genomic resources provided in this work will help to expand our understanding of the evolution of molluscs and the ecological adaptation of chitons.
Vitruvius+: An Area-Eficient RISC-V Decoupled Vector Coprocessor for High Performance Computing Applications

FRANCESCO MINERVINI, Barcelona Supercomputing Center, Spain OSCAR PALOMAR and OSMAN UNSAL, Barcelona Supercomputing Center, Spain ENRICO REGGIANI and JOSUE QUIROGA, Barcelona Supercomputing Center, Spain JOAN MARIMON and CARLOS ROJAS, Barcelona Supercomputing Center, Spain ROGER FIGUERAS and ABRAHAM RUIZ, Barcelona Supercomputing Center, Spain ALBERTO GONZALEZ and JONNATAN MENDOZA, Barcelona Supercomputing Center, Spain IVAN VARGAS and CESAR HERNANDEZ, Barcelona Supercomputing Center, Spain JOAN CABRE and LINA KHOIRUNISYA, Barcelona Supercomputing Center, Spain MUSTAPHA BOUHALI and JULIAN PAVON, Barcelona Supercomputing Center, Spain FRANCESC MOLL and MAURO OLIVIERI, Barcelona Supercomputing Center, Spain MARIO KOVAC, MATE KOVAC, and LEON DRAGIC, University of Zagreb, FER, Croatia MATEO VALERO and ADRIAN CRISTAL, Barcelona Supercomputing Center, Spain
Long-term Follow-Up of Focal Cartilage Defects Repair with CartiMax ® Viable Cartilage Allograft in a Goat Model

CartiMax® VCA is a unique allogeneic graft matrix that offers putty-like handling properties and contains viable, functional chondrocytes. In this animal study, CartiMax grafts were implanted into chondral defects in goat knees and were demonstrated to be capable of providing a milieu for chondrogenesis and supporting remodeling of the grafts into hyaline cartilage that is well-integrated with the surrounding host tissue. Overall, these observations suggest that CartiMax is a safe and efficacious alternative to microfracture providing support for long-term cartilage repair and remodeling.
The First Resolved Gas-phase Metallicity Maps of Dust-obscured Star-forming Galaxies at z 4

We present the ﬁ rst spatially resolved maps of gas-phase metallicity for two dust-obscured star-forming galaxies at z ∼ 4, from the JWST TEMPLATES Early Release Science program, derived from NIRSpec integral ﬁ eld unit spectroscopy of the H α and [ N II ] emission lines. Empirical optical line calibrations are used to determine that the sources are globally enriched to near-solar levels. While one source shows elevated [ N II ] / H α ratios and broad H α emission consistent with the presence of an active galactic nucleus in a  1 kpc region, we argue that both systems have already undergone signi ﬁ cant metal enrichment as a result of their extremely high star formation rates. Utilizing Atacama Large Millimeter / submillimeter Array rest-frame 380 μ m continuum and [ C I ] ( 3 P 2 – 3 P 1 ) line maps we compare the spatial variation of the metallicity and gas-to-dust ratio in the two galaxies, ﬁ nding the two properties to be anticorrelated on highly resolved spatial scales, consistent with various literature studies of z ∼ 0 galaxies. The data are indicative of the enormous potential of JWST to probe the enrichment of the interstellar medium on ∼ kpc scales in extremely dust-obscured systems at z ∼ 4 and beyond.
Demographic and Socio-economic Determinants of Urban Youth Unemployment in West Hararghe Zone, Oromia National Regional State, Ethiopia

Youth unemployment has a serious impact on the economic development of a country. In Ethiopia, the intensity of urban youth unemployment problem is high. The persistent high urban unemployment in the country continues to be a cause of concern. The main objective of this study was to identify the demographic and socioeconomic determinants of urban youth unemployment in west Hararghe zone, Oromia national regional state of Ethiopia. To carry out the study, primary data were collected from 396 randomly selected sample youth respondents. To analyze the collected data, the study used logistic regression model. The findings revealed that migration status, marital status, health status, family support, educational level, access to training, work experience and monthly family income were the significant determinants of urban youth unemployment in the study area. The study highlights the importance of changing youth’s attitude towards work; capacitating youths through short term and long-term training; providing health care services; and improving the living conditions of urban households through expanding economic activity and employment opportunities in the area.
THE DWARF NOVA RZ LEONIS : PHOTOMETRIC PERIOD, ANTI-HUMPS AND NORMAL ALPHA DISC

We present results of differential photometry of the dwarf nova RZ Leonis spanning a 11-year baseline. The most striking feature of the light curve is a non-coherent periodic hump of variable amplitude. A seasonal time series analysis yields a photometric period of 0. d0756(12). In addition, low amplitude fluctuations of the mean magnitude in time scale of months are observed. We find that the hump’s amplitude is anti-correlated with the star’s mean magnitude and becomes “negative” (i.e. an absorption feature or “anti-hump”) when the system is very faint. Secondary humps and “anti-humps” are also observed. The transition from “anti-humps” to fully developed humps occurs on a time scale of 70 days. We interprete the observations as a rapid response of the accretion disk to the increase of mass transfer rate. In this case we deduce a viscosity parameter α ∼ 0.08, i.e. much larger than often claimed for WZ Sge-like stars. We note that the secondary star in RZ Leo is close to a main-sequence red dwarf and not a brown-dwarf like star as suggested for other long cycle-length SU UMa stars like WZ Sge and V592 Her. Our results indicate that large amplitude and long cycle length dwarf novae might not necessarily correspond to objects in the same evolutive stage.
Integrating online and residential master’s programs in education Integración de programas de máster online y presenciales en educación

: Online Principal Certification and Advanced Teacher Degree programs have taken up an incresingly larger share of graduate enrollments in U.S. colleges of education. This paper discusses how a major educational leadership department expanded its residence programs to incorporate online leadership degrees. Having started in 2008, the program now has current enrollments of over 80 students including students from outside the U.S. The paper discusses over-coming obstacles such as faculty resistance, meeting state licensing and accreditation requirements, staffing online courses, funding and marketing. Leadership for the program came from the department and college where online learning was increasingly seen as a priority.
The effect of consumers and mutualists of vaccinium The effect of consumers and mutualists of vaccinium membranaceum at Mount St. Helens: Dependence on membranaceum at Mount St. Helens: Dependence on successional context successional context

In contrast to secondary succession, studies of terrestrial primary succession largely ignore the role of biotic interactions, other than plant facilitation and competition, despite the expectation that simplified interaction webs and propagule-dependent demographics may amplify the effects of consumers and mutualists. We investigated whether successional context determined the impact of consumers and mutualists by quantifying their effects on reproduction by the shrub Vaccinium membranaceum in primary and secondary successional sites at Mount St. Helens (Washington, USA), and used simulations to explore the effects of these interactions on colonization. Species interactions differed substantially between sites, and the combined effect of consumers and mutualists was much more strongly negative for primary successional plants. Because greater local control of propagule pressure is expected to increase successional rates, we evaluated the role of dispersal in the context of these interactions. Our simulations showed that even a small local seed source greatly increases population growth rates, thereby balancing strong consumer pressure. The prevalence of strong negative interactions in the primary successional site is a reminder that successional communities will not exhibit the distribution of interaction strengths characteristic of stable communities, and suggests the potential utility of modeling succession as the consequence of interaction strengths.
Resilience of Montana's agroecosystems to economic and climatic change

Semiarid dryland agricultural systems in the western United States are faced with a highly uncertain production environment that complicates decision-making and makes static agronomic prescriptions unreliable for maintaining sustainability. The primary sources of uncertainty for farmers are weather, fluctuations in prices, and site-specific environmental and ecological variability, some of which may be amplified by climate change. To effectively respond to the risks posed by these uncertainties requires knowledge of the vulnerability of these agricultural systems. The aim of this dissertation was to meet this need for Montana by analyzing the economic resilience of the state‟s dryland agricultural systems at site-specific and county-wide scales. To begin, a framework was created to integrate weather, prices, nitrogen inputs, and spatial soil variability within a statistical model for site-specific crop responses and net returns. Simulations suggest that six crop years of simulated data collection and parameter tuning were required to derive an accurate model, suggesting that an extended period of observation and targeted nitrogen rate experimentation was required to optimize spatial fertilizer management. The framework was subsequently applied to a spatiotemporal precision agricultural dataset from a farm near Great Falls, MT, and was modified to account for several crop rotations and different farmer risk preferences. Regardless of farmers‟ level of risk aversion, winter wheat-pea rotations resulted in higher value (utility) for the farmer than winter wheat-fallow and continuous winter wheat rotations. For most levels of risk adversity, it was also optimal to apply no nitrogen fertilizer. Net returns at the field site were always threatened by drought. Subsequently, a qualitative analysis of farmer adaptability in Montana based on survey and interview data determined that farmers had few options for responding to drought but were more adaptable to high input prices. On-farm experimentation and crop rotations could greatly increase adaptability in the future. Finally, simulations of alternative price, precipitation, and crop rotation scenarios were completed. The most resilient agricultural systems were located in northeastern Montana where pulses have been more widely adopted; systems in north-central Montana were less resilient. Statewide, over 50% of dryland farmers may not be resilient to future economic or climatic variability.
Stochastic programming in master production scheduling: overcoming barriers to industry application

This paper stems from the observation that companies still rely on deterministic rolling-horizon planning despite a substantial body of literature on stochastic planning models. To foster practical applications, we identify barriers that limit the widespread application of stochastic programming in master production scheduling and develop a framework to overcome them. Our solutions include modelling uncertainty from available data, reﬂecting planning processes in the optimisation model and evaluating its performance accurately. A two-stage stochastic model with production recourse is introduced to improve planning ﬂexibility, stability and communicability. It is applied on a real-world case study with large product portfolio, complex production processes and uncertain seasonal demand. Out-of-sample rolling-horizon simulations show that well-deﬁned stochastic models can provide high demand satisfaction and low inventory costs while improving planning stability. In particular, planning nervousness can be reduced by 40% and raw-material nervousness by 80% compared to our industry partner’s current production scheduling solution.
replication study reveals a limit on probabilistic prediction in language

Do people routinely pre-activate the meaning and even the phonological form of upcoming words? The most acclaimed evidence for phonological prediction comes from a 2005 Nature Neuroscience publication by DeLong, Urbach and Kutas, who observed a graded modulation of electrical brain potentials (N400) to nouns and preceding articles by the probability that people use a word to continue the sentence fragment (‘cloze’). In our direct replication study spanning 9 laboratories ( N =334), pre-registered replication-analyses and exploratory Bayes factor analyses successfully replicated the noun-results but, crucially, not the article-results. Pre-registered single-trial analyses also yielded a statistically significant effect for the nouns but not the articles. Exploratory Bayesian single-trial analyses showed that the article-effect may be non-zero but is likely far smaller than originally reported and too small to observe without very large sample sizes. Our results do not support the view that readers routinely pre-activate the phonological form of predictable words.
FEMINIZATION OF AGRICULTURE AND COPING WITH CLIMATE VARIABILITY IN THE UPPER EAST REGION OF GHANA

The paper investigated the nexus between feminization of agriculture and coping with climate variability in five selected districts of the Upper East Region in Ghana. The districts were selected because there were women farming groups present in those districts. The study employed both quantitative and qualitative methods and data obtained were analysed using descriptive analysis. The result indicated that 25 (14.5%) of women farmers out of 173 would out-migrate because of climate related factors such as increasing temperature and shortening of rainy days. The study further saw that most people migrate because of decrease in food production, infertile land and poverty. For women farmers of the Upper East Region out-migration and planting trees to absorb the harsh effect of the sun are the mitigating efforts mostly adopted. As a result, women farmers resort to animal rearing (4.0%) and food processing (18.5%) to survive the dry season. Their challenge is lack of capital for agricultural inputs and land. It is recommended that families of migrant male farmers should support female farmers with farm-lands. The government and benevolent organizations must also give them financial support, climate smart measures and inputs.
: effects of two

Background We have previously reported on the design and efficacy of two cluster‑randomized multi‑level work‑ place interventions, attempting to decrease sedentary behavior (SED) or increase moderate to vigorous physical activity (MVPA) among office workers to improve mental health outcomes. The aim of this study was to investigate intervention effects on mental health outcomes, i.e., mental wellbeing, depression or anxiety symptoms, and stress immediately after the 6‑month intervention period. Methods Teams of 263 office workers were cluster‑randomized to one of two interventions or a waitlist control group. The PA intervention (iPA) focused on increasing MVPA and the SED intervention (iSED) on reducing SED. Both multi‑level interventions targeted individual office workers and their social, physical, and organizational work environ‑ ment, incorporating counseling based on cognitive behavioral therapy and motivational interviewing. Mental health outcomes were assessed using validated questionnaires before and immediately after the intervention. Intervention effects were analyzed using linear mixed effects models. Results Participants were mostly female and highly educated, with a mean age of 42 years and had favorable levels of mental health at baseline. Mental wellbeing improved for the iSED group (β = 8, 95% CI 1 to 15, p = 0.030) but not for the iPA group (β = 6, 95% CI ‑1 to 12, p = 0.072) compared to the control group. No effects were found for depression or anxiety symptoms or stress. Conclusions The multi‑level interventions improved mental wellbeing among this population of office workers, reaching statistical significance in the iSED group. The size of the effect can be regarded meaningful, considering favorable mental health and high PA level at baseline. Thus, workplace interventions that provide support on multiple levels appear to have potential for improving mental wellbeing, but not reducing ill‑health variables, among healthy office workers. More research is needed to understand the mechanisms through which such improvements can be achieved and to identify the most effective intervention components.