Miracle-dz/UniFAR-FTUs huggingface dataset:
Turning Red without Feeling Embarrassed—Xanthenium-Based Photocages for Red-Light-Activated Phototherapeutics

Herein, we present high-yielding, concise access to a set of xanthenium-derived, water-soluble, low-molecular-weight photocages allowing light-controlled cargo release in the green to red region. Very importantly, these new photocages allow installation of various payloads through ester, carbamate, or carbonate linkages even at the last stage of the synthesis. Payloads were uncaged with high efficiency upon green, orange, or red light irradiation, leading to the release of carboxylic acids, phenols, and amines. The near-ideal properties of a carboxanthenium derivative were further evaluated in the context of light-controlled drug release using a camptothecin-derived chemotherapeutic drug, SN38. Notably, the caged drug showed orders of magnitude lower efficiency in cellulo, which was reinstated after red light irradiation. The presented photocages offer properties that facilitate the translation of photoactivated chemotherapy toward clinical applications.
Star formation and jet-induced coronal lines near the core of NGC 1068 High angular resolution near-IR spectroscopy with NaCo

Context. Active galactic nuclei (AGNs) are very luminous galaxies from the ultraviolet (UV) to the far infrared (FIR). To study regions near the core, which are dominated by dust, IR is the perfect spectral range because of the lower optical depth of the dust. However, these galaxies are usually distant, and the structures near the core are faint compared to the central source. High resolution and high contrast are mandatory to study the inner structures of AGNs and better understand the interaction between the central core and its surroundings. Aims. Our goal is to propose an explanation for the coronal lines in our spectra of NGC 1068 at a far distance (few tens of parsec) from the central source inside wave-like structures that we call nodules. Methods. NGC 1068 was observed with NaCo at ESO/VLT, using long-slit spectroscopy at 2.2 μm and 3.5 μm. Reduced spectra were compared to the output of Cloudy, using various ionizing continua: the continuum of the central source, young stars, and radiative shocks caused by the jet. Results. For the first time, thanks to the resolution provided by NaCo, near-IR coronal emission can be precisely associated to structures observable in the IR broad-band images. We found that coronal lines observed in the nodules cannot be caused by photoionization by the central source but are instead caused by a local jet-induced ionizing continuum. In addition, studying the Brackett-γ line (Brγ) and CO 2-0 band head, we deduced a star density within the four nodules (∼800 000 stars) compatible with super star cluster (hereafter SSC) models.
Baseline characteristics of the North American prodromal Synucleinopathy cohort

Rapid eye movement (REM) sleep behavior disorder (RBD) is widely considered a prodromal synucleinopathy, as most with RBD develop overt synucleinopathy within ~10 years. Accordingly, RBD offers an opportunity to test potential treatments at the earliest stages of synucleinopathy. The North American Prodromal Synucleinopathy (NAPS) Consortium has created a multisite RBD participant, primarily clinic‐based cohort to better understand characteristics at diagnosis, and in future work, identify predictors of phenoconversion, develop synucleinopathy biomarkers, and enable early stage clinical trial enrollment.
Nonparametric kernel estimation methods for discrete conditional functions in econometrics

This thesis studies the mixed data types kernel estimation framework for the models of discrete dependent variables, which are known as kernel discrete conditional functions. The conventional parametric multinomial logit MNL model is compared with the mixed data types kernel conditional density estimator in Chapter (2). A new kernel estimator for discrete time single state hazard models is developed in Chapter (3), and named as the discrete time ? external kernel hazard? estimator. The discrete time (mixed) proportional hazard estimators are then compared with the discrete time external kernel hazard estimator empirically in Chapter (4). The work in Chapter (2) attempts to estimate a labour force participation decision model using a cross-section data from the UK labour force survey in 2007. The work in Chapter (4) estimates a hazard rate for job-vacancies in weeks, using data from Lancashire Careers Service (LCS) between the period from March 1988 to June 1992. The evidences from the vast literature regarding female labour force participation and the job-market random matching theory are used to examine the empirical results of the estimators.The parametric estimator are tighten by the restrictive assumption regarding the link function of the discrete dependent variable and the dummy variables of the discrete covariates. Adding interaction terms improves the performance of the parametric models but encounters other risks like generating multicollinearity problem, increasing the singularity of the data matrix and complicates the computation of the ML function. On the other hand, the mixed data types kernel estimation framework shows an outstanding performance compared with the conventional parametric estimation methods. The kernel functions that are used for the discrete variables, including the dependent variable, in the mixed data types estimation framework, have substantially improved the performance of the kernel estimators. The kernel framework uses very few assumptions about the functional form of the variables in the model, and relay on the right choice of the kernel functions in the estimator.The outcomes of the kernel conditional density shows that female education level and fertility have high impact on females propensity to work and be in the labour force. The kernel conditional density estimator captures more heterogeneity among the females in the sample than the MNL model due to the restrictive parametric assumptions in the later. The (mixed) proportional hazard framework, on the other hand, missed to capture the effect of the job-market tightness in the job-vacancies hazard rate and produce inconsistent results when the assumptions regarding the distribution of the unobserved heterogeneity are changed. The external kernel hazard estimator overcomes those problems and produce results that consistent with the job market random matching theory. The results in this thesis are useful for nonparametric estimation research in econometrics and in labour economics research.
Importance of conserving large and old trees to continuity of tree‐related microhabitats

Protecting structural features, such as tree‐related microhabitats (TreMs), is a cost‐effective tool crucial for biodiversity conservation applicable to large forested landscapes. Although the development of TreMs is influenced by tree diameter, species, and vitality, the relationships between tree age and TreM profile remain poorly understood. Using a tree‐ring‐based approach and a large data set of 8038 trees, we modeled the effects of tree age, diameter, and site characteristics on TreM richness and occurrence across some of the most intact primary temperate forests in Europe, including mixed beech and spruce forests. We observed an overall increase in TreM richness on old and large trees in both forest types. The occurrence of specific TreM groups was variably related to tree age and diameter, but some TreM groups (e.g., epiphytes) had a stronger positive relationship with tree species and elevation. Although many TreM groups were positively associated with tree age and diameter, only two TreM groups in spruce stands reacted exclusively to tree age (insect galleries and exposed sapwood) without responding to diameter. Thus, the retention of trees for conservation purposes based on tree diameter appears to be a generally feasible approach with a rather low risk of underrepresentation of TreMs. Because greater tree age and diameter positively affected TreM development, placing a greater emphasis on conserving large trees and allowing them to reach older ages, for example, through the establishment of conservation reserves, would better maintain the continuity of TreM resource and associated biodiversity. However, this approach may be difficult due to the widespread intensification of forest management and global climate change.
Training cessation and subsequent retraining of a world‐class female Olympic sailor after Tokyo 2020: A case study

Olympic sailing is a complex sport where sailors are required to predict and interpret weather conditions while facing high physical and physiological demands. While it is essential for sailors to develop physical and physiological capabilities toward major competition, monitoring training status following the competition is equally important to minimize the magnitude of detraining and facilitate retraining. Despite its long history in the modern Olympics, reports on world‐class sailors' training status and performance characteristics across different periodization phases are currently lacking. This case study aimed to determine the influence of training cessation and subsequent retraining on performance parameters in a world‐class female sailor. A 31‐year old female sailor, seventh in the Women's Sailing 470 medal race in Tokyo 2020, completely stopped training for 4 weeks following the Olympics, and resumed low‐intensity training for 3 weeks. Over these 7 weeks, 12.7 and 5.3% reductions were observed in 6 s peak cycling power output and jump height, respectively, with a 4.7% decrease in maximal aerobic power output. Seven weeks of training cessation‐retraining period induced clear reductions in explosive power production capacities but less prominent decreases in aerobic capacity. The current findings are likely attributed to the sailor's training characteristics during the retraining period.
The Effects of Dimension and Size for a Compartmental Model of Repression

The bifurcation of time periodic solutions in a compartmental reaction-diffusion model of a eukaryotic cell is studied as a function of various physical parameters including the size of the cell and the delays due to transcription and translation. The analytical results suggest a possible mechanism for the triggering of mitosis which is in qualitative agreement with observed behavior. These results are based on both detailed analyses of specific cases and on numerical solutions of the model equations.
The rapid anxiety screening study—pilot data

Anxiety disorders are the most common mental health condition in the United States. Medical providers often have many active issues when treating a patient, and do not always have time for a thorough psychiatric evaluation. The Patient Health Questionnaire 2, a two-question binary (yes/no) screening tool, has been developed to address these issues in the identification of unipolar depression. No similar binary screening tool has been developed to comprehensively screen for anxiety disorders. Our study seeks to develop a three-question, comprehensive, binary screening tool for anxiety and related disorders such that these patients can be easily identified and connected to treatment. Our pilot data shows promising results for our three-question binary Rapid Anxiety Screen (the RAS). Future studies will look to confirm these preliminary data and determine the sensitivities and specificities of the RAS with a larger data set.
Genetic diversity and IUCN Red List status

The International Union for Conservation of Nature (IUCN) Red List is an important and widely used tool for conservation assessment. The IUCN uses information about a species’ range, population size, habitat quality and fragmentation levels, and trends in abundance to assess extinction risk. Genetic diversity is not considered, although it affects extinction risk. Declining populations are more strongly affected by genetic drift and higher rates of inbreeding, which can reduce the efficiency of selection, lead to fitness declines, and hinder species’ capacities to adapt to environmental change. Given the importance of conserving genetic diversity, attempts have been made to find relationships between red‐list status and genetic diversity. Yet, there is still no consensus on whether genetic diversity is captured by the current IUCN Red List categories in a way that is informative for conservation. To assess the predictive power of correlations between genetic diversity and IUCN Red List status in vertebrates, we synthesized previous work and reanalyzed data sets based on 3 types of genetic data: mitochondrial DNA, microsatellites, and whole genomes. Consistent with previous work, species with higher extinction risk status tended to have lower genetic diversity for all marker types, but these relationships were weak and varied across taxa. Regardless of marker type, genetic diversity did not accurately identify threatened species for any taxonomic group. Our results indicate that red‐list status is not a useful metric for informing species‐specific decisions about the protection of genetic diversity and that genetic data cannot be used to identify threat status in the absence of demographic data. Thus, there is a need to develop and assess metrics specifically designed to assess genetic diversity and inform conservation policy, including policies recently adopted by the UN's Convention on Biological Diversity Kunming‐Montreal Global Biodiversity Framework.
Effects of climate change and anthropogenic activity on ranges of vertebrate species endemic to the Qinghai–Tibet Plateau over 40 years

Over the past 40 years, the climate has been changing and human disturbance has increased in the vast Qinghai–Tibet Plateau (QTP). These 2 factors are expected to affect the distribution of a large number of endemic vertebrate species. However, quantitative relationships between range shifts and climate change and human disturbance of these species in the QTP have rarely been evaluated. We used occurrence records of 19 terrestrial vertebrate species (birds, mammals, amphibians, and reptiles) occurring in the QTP from 1980 to 2020 to quantify the effects of climate change and anthropogenic impacts on the distribution of these 4 taxonomic groups and estimated species range changes in each species. The trend in distribution changes differed among the taxonomic groups, although, generally, ranges shifted to central QTP. Climate change contributed more to range variation than human disturbance (the sum of the 4 climatic variables contributed more than the sum of the 4 human disturbance variables for all 4 taxonomic groups). Suitable geographic range increased for most mammals, amphibians, and reptiles (+27.6%, +18.4%, and +27.8% on average, respectively), whereas for birds range decreased on average by 0.9%. Quantitative evidence for climate change and human disturbance associations with range changes for endemic vertebrate species in the QTP can provide useful insights into biodiversity conservation under changing environments.
Combinations of medicines in patients with polypharmacy aged 65–100 in primary care: Large variability in risks of adverse drug related and emergency hospital admissions

Background Polypharmacy can be a consequence of overprescribing that is prevalent in older adults with multimorbidity. Polypharmacy can cause adverse reactions and result in hospital admission. This study predicted risks of adverse drug reaction (ADR)-related and emergency hospital admissions by medicine classes. Methods We used electronic health record data from general practices of Clinical Practice Research Datalink (CPRD GOLD) and Aurum. Older patients who received at least five medicines were included. Medicines were classified using the British National Formulary sections. Hospital admission cases were propensity-matched to controls by age, sex, and propensity for specific diseases. The matched data were used to develop and validate random forest (RF) models to predict the risk of ADR-related and emergency hospital admissions. Shapley Additive eXplanation (SHAP) values were calculated to explain the predictions. Results In total, 89,235 cases with polypharmacy and hospitalised with an ADR-related admission were matched to 443,497 controls. There were over 112,000 different combinations of the 50 medicine classes most implicated in ADR-related hospital admission in the RF models, with the most important medicine classes being loop diuretics, domperidone and/or metoclopramide, medicines for iron-deficiency anaemias and for hypoplastic/haemolytic/renal anaemias, and sulfonamides and/or trimethoprim. The RF models strongly predicted risks of ADR-related and emergency hospital admission. The observed Odds Ratio in the highest RF decile was 7.16 (95% CI 6.65–7.72) in the validation dataset. The C-statistics for ADR-related hospital admissions were 0.58 for age and sex and 0.66 for RF probabilities. Conclusions Polypharmacy involves a very large number of different combinations of medicines, with substantial differences in risks of ADR-related and emergency hospital admissions. Although the medicines may not be causally related to increased risks, RF model predictions may be useful in prioritising medication reviews. Simple tools based on few medicine classes may not be effective in identifying high risk patients.
The post‐arteriole transitional zone: a specialized capillary region that regulates blood flow within the CNS microvasculature

The brain is an energy hog, consuming available energy supplies at a rate out of all proportion to its relatively small size. This outsized demand, largely reflecting the unique computational activity of the brain, is met by an ensemble of neurovascular coupling mechanisms that link neuronal activity with local increases in blood delivery. This just‐in‐time replenishment strategy, made necessary by the limited energy‐storage capacity of neurons, complicates the nutrient‐delivery task of the cerebral vasculature, layering on a temporo‐spatial requirement that invites ‐ and challenges ‐ mechanistic interpretation. The centre of gravity of research efforts to disentangle these mechanisms has shifted from an initial emphasis on astrocyte‐arteriole‐level processes to mechanisms that operate on the capillary level, a shift that has brought into sharp focus questions regarding the fine control of blood distribution to active neurons. As these investigations have drilled down into finer reaches of the microvasculature, they have revealed an arteriole‐proximate subregion of CNS capillary networks that serves a regulatory function in directing blood flow into and within downstream capillaries. They have also illuminated differences in researchers’ perspectives on the vascular structures and identity of mural cells in this region that impart the vasomodulatory effects that control blood distribution. In this review, we highlight the regulatory role of a variably named region of the microvasculature, referred to here as the post‐arteriole transition zone, in channeling blood flow within CNS capillary networks, and underscore the contribution of dynamically contractile perivascular mural cell – generally, but not universally, recognized as pericytes – to this function.
DaTeR: error-correcting phylogenetic chronograms using relative time constraints

Abstract Motivation A chronogram is a dated phylogenetic tree whose branch lengths have been scaled to represent time. Such chronograms are computed based on available date estimates (e.g. from dated fossils), which provide absolute time constraints for one or more nodes of an input undated phylogeny, coupled with an appropriate underlying model for evolutionary rates variation along the branches of the phylogeny. However, traditional methods for phylogenetic dating cannot take into account relative time constraints, such as those provided by inferred horizontal transfer events. In many cases, chronograms computed using only absolute time constraints are inconsistent with known relative time constraints. Results In this work, we introduce a new approach, Dating Trees using Relative constraints (DaTeR), for phylogenetic dating that can take into account both absolute and relative time constraints. The key idea is to use existing Bayesian approaches for phylogenetic dating to sample posterior chronograms satisfying desired absolute time constraints, minimally adjust or ‘error-correct’ these sampled chronograms to satisfy all given relative time constraints, and aggregate across all error-corrected chronograms. DaTeR uses a constrained optimization framework for the error-correction step, finding minimal deviations from previously assigned dates or branch lengths. We applied DaTeR to a biological dataset of 170 Cyanobacterial taxa and a reliable set of 24 transfer-based relative constraints, under six different molecular dating models. Our extensive analysis of this dataset demonstrates that DaTeR is both highly effective and scalable and that its application can significantly improve estimated chronograms. Availability and implementation Freely available from https://compbio.engr. uconn.edu/software/dater/ Supplementary information Supplementary data are available at Bioinformatics online.
Sample-efficient Multi-objective Molecular Optimization with GFlowNets

Many crucial scientific problems involve designing novel molecules with desired properties, which can be formulated as a black-box optimization problem over the discrete chemical space. In practice, multiple conflicting objectives and costly evaluations (e.g., wet-lab experiments) make the diversity of candidates paramount. Computational methods have achieved initial success but still struggle with considering diversity in both objective and search space. To fill this gap, we propose a multi-objective Bayesian optimization (MOBO) algorithm leveraging the hypernetwork-based GFlowNets (HN-GFN) as an acquisition function optimizer, with the purpose of sampling a diverse batch of candidate molecular graphs from an approximate Pareto front. Using a single preference-conditioned hypernetwork, HN-GFN learns to explore various trade-offs between objectives. We further propose a hindsight-like off-policy strategy to share high-performing molecules among different preferences in order to speed up learning for HN-GFN. We empirically illustrate that HN-GFN has adequate capacity to generalize over preferences. Moreover, experiments in various real-world MOBO settings demonstrate that our framework predominantly outperforms existing methods in terms of candidate quality and sample efficiency. The code is available at https://github.com/violet-sto/HN-GFN.
Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning

Deep reinforcement learning policies, despite their outstanding efficiency in simulated visual control tasks, have shown disappointing ability to generalize across disturbances in the input training images. Changes in image statistics or distracting background elements are pitfalls that prevent generalization and real-world applicability of such control policies. We elaborate on the intuition that a good visual policy should be able to identify which pixels are important for its decision, and preserve this identification of important sources of information across images. This implies that training of a policy with small generalization gap should focus on such important pixels and ignore the others. This leads to the introduction of saliency-guided Q-networks (SGQN), a generic method for visual reinforcement learning, that is compatible with any value function learning method. SGQN vastly improves the generalization capability of Soft Actor-Critic agents and outperforms existing stateof-the-art methods on the Deepmind Control Generalization benchmark, setting a new reference in terms of training efficiency, generalization gap, and policy interpretability.