Miracle-dz/UniFAR-FTUs huggingface dataset:
Community Health Needs Predict Population Health Partnerships Among U.S. Children’s Hospitals

Cross-sector collaboration is critical to improving population health, but data on partnership activities by children’s hospitals are limited, and there is a need to identify service delivery gaps for families. The aim of this study is to use public community benefit reports for all children’s hospitals in the United States to assess the extent to which children’s hospitals partner with external organizations to address five key health needs: health care access, chronic disease, social needs, mental health, and substance abuse. Strategies that involved partnering with community organizations were most common in addressing social needs and substance abuse. When adjusted for institutional and community characteristics hospitals in a multilevel regression model, hospitals had higher odds of partnering to address chronic illness and social needs. To encourage hospital engagement with complex social and behavioral health needs and promote health equity, support should be provided to help hospitals establish local population health networks.
Size‐dependent dendritic maladaptations of hypoglossal motor neurons in SOD1G93A mice

The total motor neuron (MN) somato‐dendritic surface area is correlated with motor unit type. MNs with smaller surface areas innervate slow (S) and fast fatigue‐resistant (FR) motor units, while MNs with larger surface areas innervate fast fatigue‐intermediate (FInt) and fast fatigable (FF) motor units. Differences in MN surface area (equivalent to membrane capacitance) underpin the intrinsic excitability of MNs and are consistent with the orderly recruitment of motor units (S > FR > FInt > FF) via the Size Principle. In amyotrophic lateral sclerosis (ALS), large MNs controlling FInt and FF motor units exhibit earlier denervation and death, compared to smaller and more resilient MNs of type S and FR motor units that are spared until late in ALS. Abnormal dendritic morphologies in MNs precede neuronal death in human ALS and in rodent models. We employed Golgi‐Cox methods to investigate somal size‐dependent changes in the dendritic morphology of hypoglossal MNs in wildtype and SOD1G93A mice (a model of ALS), at postnatal (P) day ~30 (pre‐symptomatic), ~P60 (onset), and ~P120 (mid‐disease) stages. In wildtype hypoglossal MNs, increased MN somal size correlated with increased dendritic length and spines in a linear fashion. By contrast, in SOD1G93A mice, significant deviations from this linear correlation were restricted to the larger vulnerable MNs at pre‐symptomatic (maladaptive) and mid‐disease (degenerative) stages. These findings are consistent with excitability changes observed in ALS patients and in rodent models. Our results suggest that intrinsic or synaptic increases in MN excitability are likely to contribute to ALS pathogenesis, not compensate for it.
When the Good Turns Ugly: Speculating Next Steps for Digital Wellbeing Tools

Concerns surrounding technology use in society has led to the HCI community creating tools for ‘digital wellbeing’. These aim to improve users’ relationships with technology, but these positively motivated tools may initiate further negative impacts for users e.g. on their privacy or autonomy. Using Pierce’s speculative design concepts of ‘foot-in-the-door’ technologies and focusing on three common digital wellbeing features (time limits and prompts, social ‘do not disturb’ modes, app and service blocking), I highlight how these tools are a small step away from being used to manipulate users which could enact slow shifts in users accepting such manipulation. Through this and the discussion, I accentuate that positively motivated designs may not explicitly lead to positive interactions by default. I hope this paper will facilitate speculative design and discussion in the digital wellbeing community, to ensure that our designs continue to mitigate negative impacts from technology now and in the future.
Chemical Composition of Essential Oil in Mosla Chinensis Maxim Cv. Jiangxiangru and its Inhibitory Effect on Staphylococcus Aureus Biofilm Formation

Abstract The essential oil of Mosla chinensis Maxim cv. Jiangxiangru is known for its antibacterial ability. This study aimed to investigate the chemical composition of Jiangxiangru essential oil and its inhibitory effect on Staphylococcus aureus biofilm formation. Gas chromatography/mass spectrometry (GC–MS) was used to determine the chemical composition of Jiangxiangru essential oil. Subsequently, the eight major chemical components were quantitatively analyzed using GC– MS, and their minimum inhibitory concentration (MIC) values against S. aureus were tested. Biofilm formation was detected by crystal violet semi-quantitative method and silver staining. Of the 59 peaks detected, 29 were identified by GC–MS. Of these peaks, thymol, carvacrol, p-cymene, γ-terpinene, thymol acetate, α-caryophyllene, 3-carene, and carvacryl acetate were present at a relatively higher concentration. The results of the quantitative test showed that thymol, carvacrol, p-cymene, and γ-terpinene were the major components of the essential oil. Among the eight reference substances, only thymol, carvacrol, and thymol acetate had lower MICs compared with the essential oil. Essential oil, carvacrol, carvacryl acetate, α-caryophyllene, and 3-carene showed the better inhibition of S. aureus biofilm formation. When one fourth of the MIC concentrations were used for these substances (0.0625 mg/mL for essential oil, 0.0305 mg/mL for carvacrol, 1.458 mg/mL for carvacryl acetate, 0.1268 mg/mL for α-caryophyllene, and 2.5975 mg/mL for 3-carene), the inhibition rates were over 80%. However, thymol, γ-terpinene, thymol acetate, and p-cymene showed a relatively poor inhibition of S. aureus biofilm formation. When 1× MIC concentrations of these substances were used, the inhibition rates were less than 50%. In conclusion, Jiangxiangru essential oil and its major components, carvacrol, carvacryl acetate, α-caryophyllene, and 3-carene, strongly inhibited biofilm formation in S. aureus.
Pilot Cluster Randomized Controlled Trial of Integrative Nutritional Counseling Versus Standard Diabetes Self-Management Education for Chinese Americans with Type 2 Diabetes

Purpose: Chinese Americans (CAs) with diabetes and limited English proficiency often struggle to adhere to standard diabetes diets focused on food measurement/restriction. Chinese medicine principles commonly inform food choices among CAs but are rarely acknowledged in nutritional interventions. We developed and tested feasibility of a theoretically informed integrative nutritional counseling (INC) program that combines Chinese medicine principles with biomedical nutrition standards. Methods: We randomized diabetes self-management education (DSME) classes to include either: (1) usual nutrition curriculum based on American Diabetes Association (ADA) recommendations delivered by a diabetes educator (control) or (2) INC curriculum based on a combination of ADA recommendations and Chinese medicine principles delivered by a diabetes educator and a licensed acupuncturist (intervention). All DSME enrollees were invited to participate in research entailing data collection at three time points: baseline, after the DSME nutrition class, and at 6-month follow-up. Using validated measures, we collected dietary self-efficacy, diabetes distress, diet satisfaction, and dietary adherence. We also measured weight and glycemic control. Results: Study participants were 18 Cantonese-speaking patients with diabetes who were predominantly female and older, with low levels of income and acculturation. Intervention and control groups were similar at baseline. INC performed similarly to usual DSME with 100% of participants reporting the INC booklet helped their learning. Dietary adherence significantly improved in participants who received the INC curriculum. Conclusion: INC is feasible to implement as part of DSME classes and shows promise as a complementary culturally sensitive addition to usual diabetes nutrition education for CA patients.
A Purely Regular Approach to Non-Regular Core Spanners

The regular spanners (characterised by vset-automata) are closed under the algebraic operations of union, join and projection, and have desirable algorithmic properties. The core spanners (introduced by Fagin, Kimelfeld, Reiss, and Vansummeren (PODS 2013, JACM 2015) as a formalisation of the core functionality of the query language AQL used in IBM's SystemT) additionally need string equality selections and it has been shown by Freydenberger and Holldack (ICDT 2016, Theory of Computing Systems 2018) that this leads to high complexity and even undecidability of the typical problems in static analysis and query evaluation. We propose an alternative approach to core spanners: by incorporating the string-equality selections directly into the regular language that represents the underlying regular spanner (instead of treating it as an algebraic operation on the table extracted by the regular spanner), we obtain a fragment of core spanners that, while having slightly weaker expressive power than the full class of core spanners, arguably still covers the intuitive applications of string equality selections for information extraction and has much better upper complexity bounds of the typical problems in static analysis and query evaluation.
The planted $k$-factor problem

We consider the problem of recovering an unknown $k$-factor, hidden in a weighted random graph. For $k=1$ this is the planted matching problem, while the $k=2$ case is closely related to the planted travelling salesman problem. The inference problem is solved by exploiting the information arising from the use of two different distributions for the weights on the edges inside and outside the planted sub-graph. We argue that, in the large size limit, a phase transition can appear between a full and a partial recovery phase as function of the signal-to-noise ratio. We give a criterion for the location of the transition.
Did You Ask a Good Question? A Cross-Domain Question Intention Classification Benchmark for Text-to-SQL

Neural models have achieved significant results on the text-to-SQL task, in which most current work assumes all the input questions are legal and generates a SQL query for any input. However, in the real scenario, users can input any text that may not be able to be answered by a SQL query. In this work, we propose TriageSQL, the first cross-domain text-to-SQL question intention classification benchmark that requires models to distinguish four types of unanswerable questions from answerable questions. The baseline RoBERTa model achieves a 60% F1 score on the test set, demonstrating the need for further improvement on this task. Our dataset is available at https://github.com/chatc/TriageSQL.
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is attracting much attention to mitigate data scarcity, but OOS detection becomes even more challenging. In this paper, we present a simple yet effective approach, discriminative nearest neighbor classification with deep self-attention. Unlike softmax classifiers, we leverage BERT-style pairwise encoding to train a binary classifier that estimates the best matched training example for a user input. We propose to boost the discriminative ability by transferring a natural language inference (NLI) model. Our extensive experiments on a large-scale multi-domain intent detection task show that our method achieves more stable and accurate in-domain and OOS detection accuracy than RoBERTa-based classifiers and embedding-based nearest neighbor approaches. More notably, the NLI transfer enables our 10-shot model to perform competitively with 50-shot or even full-shot classifiers, while we can keep the inference time constant by leveraging a faster embedding retrieval model.
A General Approach for Simulation-based Bias Correction in High Dimensional Settings.

An important challenge in statistical analysis lies in the control of bias of estimators due to the ever-increasing data size and model complexity. Approximate numerical methods and data features like censoring and misclassification often result in analytical and/or computational challenges when implementing standard estimators. As a consequence, consistent estimators may be difficult to obtain, especially in complex models such as nonlinear models, and in high dimensional settings where the number of parameters $p$ diverges with the sample size $n$. In this paper, we study the properties of a general simulation-based estimation framework based on biased (possibly inconsistent) estimators. This bias correction approach allows to construct bias corrected consistent estimators in high dimensional settings, and we formally show that the considered approach enjoys from desirable properties compared to alternative methods. In particular, under suitable conditions, it is possible to construct unbiased estimators with sufficiently large (but finite) $n$, generally enjoying from a reduced mean squared error in finite samples. We study the computational efficiency of the considered approach, and provide a simple and general framework that is straightforward to implement. In particular, the proposed method allow to construct consistent estimators in situations, for example, where no alternative consistent estimators are available due to the analytical and/or numerical challenges entailed. These theoretical results are highlighted with simulation studies of various commonly used models, including the logistic regression (with and without misclassification errors) and the negative binomial regression (with and without censoring). Additional numerical illustrations are provided in the appendices.
A one-dimensional morphoelastic model for burn injuries: stability, numerical validation, and biological interpretation

To deal with permanent deformations and residual stresses, we consider a morphoelastic model for the scar formation as the result of wound healing after a skin trauma. Next to the mechanical components such as strain and displacements, the model accounts for biological parameters such as the concentration of chemokines, the cellular densities of fibroblasts and myofibroblasts, and the density of collagen. We present separate stability constraints for the mechanical part of the one-dimensional morphoelastic model and stability constraints for the chemical part of the one-dimensional morphoelastic model. Stability results are developed for both the continuous and (semi-)discrete problems. For the mechanical part, we distinguish between elasticity and plasticity. We perform numerical validation to these constraints and provide a biological interpretation of the (in)stability. For the mechanical model, the results show that the components reach equilibria in a (non) monotonic way, depending on the value of the viscosity. Furthermore, the results show that the parameters of the chemical model need to meet the stability constraint to avoid unrealistic results.
Evaluating tool support for embedded operating system security: an experience feedback

Embedded systems are more and more connected to a variety of networks, which increases their attack surface. At the same time, more and more objects are augmented with embedded systems, which increases the potential impact of attacks. Cybersecurity must therefore be taken into account while designing and developing embedded software systems. While there are multiple complementary facets to the security of such systems, we focus on embedded operating system security, which is critical to build secure applications. In order to evaluate the applicability of the many available cybersecurity techniques and tools, we need to define a relevant case study. Given that a system's inputs are especially vulnerable, we have specified a fictive device driver, which we have both modeled in UML and implemented in C. We report here on the initial application of a couple of techniques to analyse the security of this device driver at the model and code levels.
Overcoming Conflicting Data for Model Updates

In this paper, we explore how to use a small amount of new data to update a model when the desired output for some examples has changed. When making updates in this way, one potential problem that arises is the presence of conflicting data, or out-of-date labels in the original training set. To evaluate the impact of this problem, we propose an experimental setup for simulating changes to a neural semantic parser. We show that the presence of conflicting data greatly hinders learning of an update, then explore several methods to mitigate its effect. Our methods lead to large improvements in model accuracy compared to a naive mixing strategy, and our best method closes 86% of the accuracy gap between this baseline and an oracle upper bound.
Manner salience and translation: A case study based on a multilingual corpus of graphic novels

Abstract This paper examines the salience of Manner-of-motion and its translation in a multilingual corpus of graphic novels, with the dual aim of further investigating the role of visual language in Slobin’s Thinking-for-translating hypothesis and identifying the relevant translation techniques. Many studies that draw on the hypothesis have shown, for instance, that, in the translation process from a satellite-framed language (e. g. German, English) into a verb-framed language (e. g. Spanish, French), Manner-of-motion is usually omitted, whereas in the translation process between languages belonging to the same typological group, it is generally transferred, although some intratypological variation has also been identified in the literature. The corpus studied allows both inter- and intratypological analyses: it is composed of two graphic novels by the Austrian cartoonist Ulli Lust and their corresponding translations into Spanish, French and English. The resulting data were compared with previous research in the field. The paper concludes that, although visual language minimizes the consequences of Thinking-for-translating, the conventions and restrictions of graphic novels deserve greater attention within this framework.
Soil Organic Matter Quality in Soils With Different Levels of Manure Fertilisation

Abstract Soil organic matter (SOM) significantly affects the growth, development and yield of cultivated plants. In the era of increasing agriculture intensification and adverse changes in the crop structure as well as climate change, it is important to know the processes affecting the accumulation and degradation of SOM. This paper aims to assess the quality of organic matter in Stagnic Luvisol fertilised with bovine manure to varying degrees. Soil samples were taken in spring from topsoil after pre-sowing treatments. Three soil profiles were selected: soil fertilised with manure annually at a dose of 30 t ha−1, soil fertilised with manure every 4 years at the same dose and soil deprived of organic fertilisation in the past several years. The following laboratory analyses were performed (soil pH, total carbon content). Sequential fractionation of organic matter was carried out based on the Schnitzer method. In humic acid preparations, the elemental composition (CHNO and H/C ratio) was determined and spectrophotometric properties were measured. The conducted tests revealed a significant impact of manure fertilisation on the quantity and quality of organic matter. In the soil fertilised with manure annually, a much larger amount of SOM was found with a greater share of labile forms. In addition, humic acids extracted from the soil were characterised by lower maturity as well as molecular weight and condensation of the aromatic part of their structure.
The Economics of the Joint Management of Water Resources and Aquatic Species in the United States

The health of many marine, coastal, freshwater, and other aquatic ecosystems is inextricably linked to decisions about the management of water quality and quantity. In this article we review the economic literature aimed at quantifying the impacts of water resource management on aquatic species in the United States and the potential welfare gains of managing water and aquatic species systems jointly. Existing studies consider multiple water uses, such as agricultural irrigation and hydropower generation, as well as different societal benefits from aquatic species, such as commercial and recreational fishing and endangered species preservation. These studies use a variety of methodologies, including stated and revealed preference techniques, bioeconomic modeling, and reduced-form econometrics. We conclude with a discussion of future directions for research that could increase understanding of the trade-offs between water and aquatic species management outcomes and identify gains from the joint management of water resources and aquatic species in the United States.
On the Role of Fibrocytes and the Extracellular Matrix in the Physiology and Pathophysiology of the Spiral Ligament

The spiral ligament in the cochlea has been suggested to play a significant role in the pathophysiology of different etiologies of strial hearing loss. Spiral ligament fibrocytes (SLFs), the main cell type in the lateral wall, are crucial in maintaining the endocochlear potential and regulating blood flow. SLF dysfunction can therefore cause cochlear dysfunction and thus hearing impairment. Recent studies have highlighted the role of SLFs in the immune response of the cochlea. In contrast to sensory cells in the inner ear, SLFs (more specifically type III fibrocytes) have also demonstrated the ability to regenerate after different types of trauma such as drug toxicity and noise. SLFs are responsible for producing proteins, such as collagen and cochlin, that create an adequate extracellular matrix to thrive in. Any dysfunction of SLFs or structural changes to the extracellular matrix can significantly impact hearing function. However, SLFs may prove useful in restoring hearing by their potential to regenerate cells in the spiral ligament.
Producing circle patterns via configurations

This paper studies circle patterns from the viewpoint of configurations. By using the topological degree theory, we extend the Koebe-Andreev-Thurston Theorem to include circle patterns with obtuse exterior intersection angles. As a consequence, we obtain a generalized Andreev's Theorem which allows obtuse dihedral angles.