Miracle-dz/UniFAR-FTUs huggingface dataset:
A Deep Neural Network Two-part Model and Feature Importance Test for Semi-continuous Data

Semi-continuous data frequently arise in clinical practice. For example, while many surgical patients suffer from varying degrees of acute postoperative pain (POP) post surgery (i.e., POP score > 0), others experience none (i.e., POP score = 0), indicating the existence of two distinct data processes at play. Existing parametric or semi-parametric two-part modeling methods for this type of semicontinuous data can fail to appropriately model these two underlying data processes as such methods rely heavily on (generalized) linear additive assumptions. However, many factors may interact to jointly influence the experience of POP non-additively and non-linearly. Motivated by this challenge and inspired by the flexibility of deep neural networks (DNN) to accurately approximate complex functions universally, we derive a DNN-based two-part model by adapting the conventional DNN methods by adding two additional components: a bootstrapping procedure along with a filtering algorithm to boost the stability of the conventional DNN, an approach we denote as sDNN. To improve the interpretability and transparency of sDNN, we further derive a feature importance testing procedure to identify important features contributing to the outcome measurements of the two data processes, denoting this approach fsDNN. We show that fsDNN not only offers a valid feature importance test but also that using the identified features can further improve the predictive performance of sDNN. The proposed sDNN- and fsDNN-based twopart models are applied to the analysis of real data from a POP study, in which application they clearly demonstrate advantages over the existing parametric and semi-parametric two-part models. Further, we conduct extensive numerical studies to demonstrate that sDNN and fsDNN consistently outperform the existing two-part models regardless of the data complexity. An R package implementing the proposed methods has been developed and deposited on GitHub (https://github.com/SkadiEye/fsDNN).
Dysfunctional T Follicular Helper Cells Cause Intestinal and Hepatic Inflammation in NASH

Nonalcoholic steatohepatitis (NASH), characterized by hepatic inflammation and cellular damage, is the most severe form of nonalcoholic fatty liver disease and the fastest-growing indication for a liver transplant. The intestinal immune system is a central modulator of local and systemic inflammation. In particular, Peyer’s patches (PPs) contain T follicular helper (Tfh) cells that support germinal center (GC) responses required for the generation of high-affinity intestinal IgA and the maintenance of intestinal homeostasis. However, our understanding of the mechanisms regulating mucosal immunity during the pathogenesis of NASH is incomplete. Here, using a preclinical mouse model that resembles the key features of human disease, we discovered an essential role for Tfh cells in the pathogenesis of NASH. We have found that mice fed a high-fat high-carbohydrate (HFHC) diet have an inflamed intestinal microenvironment, characterized by enlarged PPs with an expansion of Tfh cells. Surprisingly, the Tfh cells in the PPs of NASH mice showed evidence of dysfunction, along with defective GC responses and reduced IgA+ B cells. Tfh-deficient mice fed the HFHC diet showed compromised intestinal permeability, increased hepatic inflammation, and aggravated NASH, suggesting a fundamental role for Tfh cells in maintaining gut-liver homeostasis. Mechanistically, HFHC diet feeding leads to an aberrant increase in the expression of the transcription factor KLF2 in Tfh cells which inhibits its function. Thus, transgenic mice with reduced KLF2 expression in CD4 T cells displayed improved Tfh cell function and ameliorated NASH, including hepatic steatosis, inflammation, and fibrosis after HFHC feeding. Overall, these findings highlight Tfh cells as key intestinal immune cells involved in the regulation of inflammation in the gut-liver axis during NASH.
Development of cell-free transcription-translation systems in three soil Pseudomonads

In vitro transcription-translation (TX-TL) can enable faster engineering of biological systems. This speed-up can be significant, especially in difficult-to-transform chassis. This work shows the successful development of TX-TL systems using three soil-derived wild-type Pseudomonads known to promote plant growth: Pseudomonas synxantha, Pseudomonas chlororaphis, and Pseudomonas aureofaciens. One, P. synxantha, was further characterized. A lysate test of P. synxantha showed a maximum protein yield of 2.5 μM at 125 proteins per DNA template and a maximum protein synthesis rate of 20 nM/min. A set of different constitutive promoters driving mNeonGreen expression were tested in TX-TL and integrated into the genome, showing similar normalized strengths for in vivo and in vitro fluorescence. This correspondence between the TX-TL derived promoter strength and the in vivo promoter strength indicates these lysate-based cell-free systems can be used to characterize and engineer biological parts without genome integration, enabling a faster designbuild-test cycle.
On Batch Teaching Without Collusion

Formal models of learning from teachers need to respect certain criteria to avoid collusion. The most commonly accepted notion of collusion-avoidance was proposed by Goldman and Mathias (1996), and various teaching models obeying their criterion have been studied. For each model M and each concept class C, a parameter M -TD(C) refers to the teaching dimension of concept class C in model M—defined to be the number of examples required for teaching a concept, in the worst case over all concepts in C. This paper introduces a new model of teaching, called no-clash teaching, together with the corresponding parameter NCTD(C). No-clash teaching is provably optimal in the strong sense that, given any concept class C and any model M obeying Goldman and Mathias’s collusionavoidance criterion, one obtains NCTD(C) ≤ M -TD(C). We also study a corresponding notion NCTD for the case of learning from positive data only, establish useful bounds on NCTD and NCTD, and discuss relations of these parameters to other complexity parameters of interest in computational learning theory. We further argue that Goldman and Mathias’s collusion-avoidance criterion may in some settings be too weak in that it admits certain forms of interaction between teacher and learner that could be considered collusion in practice. Therefore, we introduce a strictly stronger notion of collusion-avoidance and demonstrate that the well-studied notion of Preference-based Teaching is optimal among all teaching schemes that are strongly collusion-avoiding on all finite subsets of a given concept class.
Classification of crystal defects in multicrystalline silicon solar cells and wafer using spectrally and spatially resolved photoluminescence

In this contribution, spectral photoluminescence (SPL) imaging detecting both the spectral distribution and the lateral position is applied on recombination active defects in multicrystalline silicon solar cells and wafers. The result is analysed by a Multivariate Curve Resolution (MCR) algorithm using the spectral photoluminescence response and their positions. (i) Without any pre-assumptions made, the algorithm distinguishes four different recombination active defect types. Looking at the spatial distribution, it is shown that two of these defect types coincide with two defect types that have been distinguished on solar cell level using an analysis of forward and reverse biased electroluminescence (denoted as Type-A and -B) previously. (ii) Using SPL, all previously classified defects can also be distinguished at the wafer level. Therefore, the defects limiting the solar cell efficiency are already present in the wafer material and not introduced by the solar cell process. This is of particular interest.
Concentration analysis of multivariate elliptic diffusions

We prove concentration inequalities and associated PAC bounds for both continuous- and discrete-time additive functionals for possibly unbounded functions of multivariate, nonreversible diﬀusion processes. Our analysis relies on an approach via the Poisson equation allowing us to consider a very broad class of subexponentially ergodic, multivariate diﬀusion processes. These results add to existing concentration inequalities for additive functionals of diﬀusion processes which have so far been only available for either bounded functions or for unbounded functions of processes from a signiﬁcantly smaller class. We demonstrate the power of these exponential inequalities by two examples of very diﬀerent areas. Considering a possibly high-dimensional, parametric, nonlinear drift model under sparsity constraints we apply the continuous-time concentration results to validate the restricted eigenvalue condition for Lasso estimation, which is fundamental for the derivation of oracle inequalities. The results for discrete additive functionals are applied for an investigation of the unadjusted Langevin MCMC algorithm for sampling of moderately heavy tailed densities π . In particular, we provide PAC bounds for the sample Monte Carlo estimator of integrals π ( f ) for polynomially growing functions f that quantify suﬃcient sample and step sizes for approximation within a prescribed margin with high probability.
Evaluation of new robust silk fibroin hydrogels for posterior scleral reinforcement in rabbits

Background: Currently, there is no ideal material available for posterior scleral reinforcement (PSR) to prevent the progression of high myopia. In this study, we investigated robust regenerated silk fibroin (RSF) hydrogels as potential grafts for PSR in animal experiments to evaluate their safety and biological reactions. Methods: PSR surgery was performed on the right eye of twenty-eight adult New Zealand white rabbits, with the left eye serving as a self-control. Ten rabbits were observed for 3 months, while 18 rabbits were observed for 6 months. The rabbits were evaluated using intraocular pressure (IOP), anterior segment and fundus photography, A- and B-ultrasound, optical coherence tomography (OCT), histology, and biomechanical tests. Results: No complications such as significant IOP fluctuation, anterior chamber inflammation, vitreous opacity, retinal lesion, infection, or material exposure were observed. Furthermore, no evidence of pathological changes in the optic nerve and retina, or structural abnormalities on OCT, were found. The RSF grafts were appropriately located at the posterior sclera and enclosed in fibrous capsules. The scleral thickness and collagen fiber content of the treated eyes increased after surgery. The ultimate stress of the reinforced sclera increased by 30.7%, and the elastic modulus increased by 33.0% compared to those of the control eyes at 6 months after surgery. Conclusion: Robust RSF hydrogels exhibited good biocompatibility and promoted the formation of fibrous capsules at the posterior sclera in vivo. The biomechanical properties of the reinforced sclera were strengthened. These findings suggest that RSF hydrogel is a potential material for PSR.
A new Bayesian-based brittleness prediction method for the Pingdiquan formation in the Eastern Shiqiantan depression of the East Junggar Basin

This paper presents a novel Bayesian-based method for predicting brittleness. The method involves synthesizing petrophysical data from multiple well cores to establish a joint Gaussian distribution function for shale facies and non-shale facies. Furthermore, Bayesian facies classification is applied to seismic data. The proposed method combines non-shale facies data with Rickman brittleness data to obtain a new brittleness index. The joint Gaussian distribution function and Bayesian classification are utilized to enhance the differentiation of brittleness among different geological bodies. Practical data analysis demonstrates that the new brittleness index effectively increases the contrast in brittleness values between various geological bodies, highlighting target areas of interest. The presented method offers a promising approach for brittleness prediction, leveraging the integration of petrophysical and seismic data through Bayesian techniques. The results suggest its potential applicability in enhancing the characterization and understanding of geological formations.
Increased plasma renin by vasodilators promotes the progression of abdominal aortic aneurysm

Background: It is well-accepted that antihypertensive therapy is the cornerstone of treatment for abdominal aortic aneurysm (AAA) patients with hypertension. Direct-acting vasodilators were used in the treatment of hypertension by directly relaxing vascular smooth muscle but may have destructive effects on the aortic wall by activating the renin–angiotensin system axis. Their roles in AAA disease remain to be elucidated. In this study, we used hydralazine and minoxidil, two classical direct-acting vasodilators, to investigate their influence and potential mechanisms on AAA disease. Methods and results: In this study, we investigated the plasma renin level and plasma renin activity in AAA patients. Simultaneously, age and gender ratio-matched patients diagnosed with peripheral artery disease and varicose veins were selected as the control group using a ratio of 1:1:1. Our regression analysis suggested both the plasma renin level and plasma renin activity are positively associated with AAA development. In view of the well-established relationship between direct-acting vasodilators and increased plasma renin concentration, we established a porcine pancreatic elastase-infused AAA mouse model, followed by oral administration of hydralazine (250 mg/L) and minoxidil (120 mg/L) to investigate effects of direct-acting vasodilators on AAA disease. Our results suggested both hydralazine and minoxidil promoted the progression of AAA with increased aortic degeneration. Mechanistically, the vasodilators aggravated aortic inflammation by increased leukocyte infiltration and inflammatory cytokine secretion. Conclusion and relevance: The plasma renin level and plasma renin activity are positively associated with AAA development. Direct vasodilators aggravated experimental AAA progression, which raised cautionary concerns about their applications in AAA disease.
Editorial: Freshwater science in Africa

Human activities and global climate change affect ecosystems, species, and human services and resources (Dudgeon et al., 2006; Reid et al., 2019). Freshwater ecosystems are the most fragile habitats, hosting roughly 7% of the world’s biodiversity on 1% of land and 0.01% of water (Gleick, 1996; Reid et al., 2019). Human population growth exacerbates climate change, pollution, water abstraction, impoundment of streams and rivers, nutrient and organic matter loading, invasive species, wetland degradation and loss, and other stressors (Dudgeon et al., 2006; Vörösmarty et al., 2010; Cazzolla Gatti, 2016). Knowledge of freshwater ecosystem structure and function helps us understand how they react to different pressures. This knowledge guides freshwater ecosystem conservation, management, and decision-support tools. Africa’s diverse and large freshwater systems include iconic rivers that fostered the earliest civilisations, the Great Lakes that hold 25% of the world’s unfrozen fresh water, and extensive wetlands, floodplains, and deltas that support unique species. Fisheries, irrigation, and livestock production from these freshwater ecosystems feed millions of people. Due to rapid population growth, land use change, urbanisation, energy consumption, and water harvesting are threatening freshwater ecosystems (UNEP-WCMC, 2016; Fouchy et al., 2019; Birk et al., 2020). Studies have expressed serious concerns about the continent’s freshwater fauna and ecological processes (Darwall et al., 2005; Thieme et al., 2005; Sayer et al., 2018; Masese et al., 2021). Freshwater impacts are reducing the continent’s ecological services and human welfare (IPBES, 2018). There is a growing need to investigate the condition of freshwater ecosystems to document their conservation status, and the threats they face, and to devise management and conservation measures. This Research Topic explores Africa’s freshwater science potential and distinct freshwater ecosystems and communities. This Research Topic brings together expert research on African freshwater science by African and non-African scientists to highlight the continent’s contribution to the global freshwater community. Articles in OPEN ACCESS
Acute stroke CDS: automatic retrieval of thrombolysis contraindications from unstructured clinical letters

Introduction Thrombolysis treatment for acute ischaemic stroke can lead to better outcomes if administered early enough. However, contraindications exist which put the patient at greater risk of a bleed (e.g. recent major surgery, anticoagulant medication). Therefore, clinicians must check a patient's past medical history before proceeding with treatment. In this work we present a machine learning approach for accurate automatic detection of this information in unstructured text documents such as discharge letters or referral letters, to support the clinician in making a decision about whether to administer thrombolysis. Methods We consulted local and national guidelines for thrombolysis eligibility, identifying 86 entities which are relevant to the thrombolysis decision. A total of 8,067 documents from 2,912 patients were manually annotated with these entities by medical students and clinicians. Using this data, we trained and validated several transformer-based named entity recognition (NER) models, focusing on transformer models which have been pre-trained on a biomedical corpus as these have shown most promise in the biomedical NER literature. Results Our best model was a PubMedBERT-based approach, which obtained a lenient micro/macro F1 score of 0.829/0.723. Ensembling 5 variants of this model gave a significant boost to precision, obtaining micro/macro F1 of 0.846/0.734 which approaches the human annotator performance of 0.847/0.839. We further propose numeric definitions for the concepts of name regularity (similarity of all spans which refer to an entity) and context regularity (similarity of all context surrounding mentions of an entity), using these to analyse the types of errors made by the system and finding that the name regularity of an entity is a stronger predictor of model performance than raw training set frequency. Discussion Overall, this work shows the potential of machine learning to provide clinical decision support (CDS) for the time-critical decision of thrombolysis administration in ischaemic stroke by quickly surfacing relevant information, leading to prompt treatment and hence to better patient outcomes.
Antibacterial effect of essential oils and their components against Xanthomonas arboricola pv. pruni revealed by microdilution and direct bioautographic assays

Bacterial spot of stone fruits caused by Xanthomonas arboricola pv. pruni (Xap) is one of the most significant diseases of several Prunus species. Disease outbreaks can result in severe economic losses while the control options are limited. Antibacterial efficacy of essential oils (EOs) of thyme, cinnamon, clove, rosemary, tea tree, eucalyptus, lemon grass, citronella grass, and lemon balm was assessed against two Hungarian Xap isolates. The minimal inhibitory concentration (MIC) was determined by broth microdilution assay and for the identification of active EOs’ components a newly introduced high-performance thin-layer chromatography (HPTLC)-Xap (direct bioautography) method combined with solid-phase microextraction-gas chromatography/mass spectrometry (SPME-GC/MS) was applied. All EOs inhibited both bacterium isolates, but cinnamon proved to be the most effective EO with MIC values of 31.25 µg/mL and 62.5 µg/mL, respectively. Compounds in the antibacterial HPTLC zones were identified as thymol in thyme, trans-cinnamaldehyde in cinnamon, eugenol in clove, borneol in rosemary, terpinen-4-ol in tea tree, citral (neral and geranial) in lemon grass and lemon balm, and citronellal and nerol in citronella grass. Regarding active compounds, thymol had the highest efficiency with a MIC value of 50 µg/mL. Antibacterial effects of EOs have already been proven for several Xanthomonas species, but to our knowledge, the studied EOs, except for lemon grass and eucalyptus, were tested for the first time against Xap. Furthermore, in case of Xap, this is the first report demonstrating that direct bioautography is a fast and suitable method for screening anti-Xap components of complex matrices, like EOs.
Combined analysis of transcriptomics and metabolomics on the cumulative effect of nano-titanium dioxide on mulberry seedlings

Introduction Titanium dioxide nanoparticles (TiO2 NPs) are among the most widely used inorganic nanomaterials in industry, medicine and food additives. There are increasing concerns regarding their potential risks to plants and the environment. Mulberry trees are widely grown in China due to their high survival rate and ability to aid ecological recovery. Methods Herein, the effects of TiO2 NPs with different concentrations (100, 200, 400 and 800 mg/L) on the growth and physiology of the mulberry tree were systematically evaluated in aspects of physiology, transcriptomics and metabolomics. Results Results showed that TiO2 NPs could be absorbed by the mulberry sapling root system and be transferred to the plant shoot. This results in the destruction of mulberry sapling root and leaf tissue. Furthermore, the number of chloroplasts and their pigment contents were reduced and the homeostasis of metal ions was disrupted. The toxic effects of TiO2 NPs attenuated the mulberry sapling’s stress resistance, the contents of malondialdehyde in 100 mg/L, 200 mg/L 400 mg/L and 800 mg/L treatment groups increased by 87.70%, 91.36%, 96.57% and 192.19% respectively compared with the control group. The transcriptomic data showed that TiO2 NPs treatment mainly affected the expression of genes related to energy synthesis and transport, protein metabolism, and response to stress. Meanwhile, the results of metabolomics showed that 42 metabolites produced significant differences in mulberry, of which 26 differential metabolites were up-regulated in expression and 16 differential metabolites were down-regulated, mainly including metabolic pathways such as secondary metabolite biosynthesis, citric acid cycle, and tricarboxylic acid cycle, and was not conducive to the seed germination and or growth of the mulberry sapling. Discussion This study enriches the understanding of the effects of TiO2 NPs on plants and provides a reference for the comprehensive scientific assessment of the potential risks of nanomaterials on plants.