Miracle-dz/UniFAR-FTUs huggingface dataset:
Change Point Detection on A Separable Model for Dynamic Networks

This paper studies change point detection in time series of networks, with the Separable Temporal Exponential-family Random Graph Model (STERGM). Dynamic network patterns can be inherently complex due to dyadic and temporal dependence. Detection of the change points can identify the discrepancies in the underlying data generating processes and facilitate downstream analysis. The STERGM that utilizes network statistics to represent the structural patterns is a flexible model to fit dynamic networks. We propose a new estimator derived from the Alternating Direction Method of Multipliers (ADMM) and Group Fused Lasso to simultaneously detect multiple time points, where the parameters of a time-heterogeneous STERGM have changed. We also provide a Bayesian information criterion for model selection and an R package CPDstergm to implement the proposed method. Experiments on simulated and real data show good performance of the proposed framework.
ConceptEVA: Concept-Based Interactive Exploration and Customization of Document Summaries

With the most advanced natural language processing and artificial intelligence approaches, effective summarization of long and multi-topic documents—such as academic papers—for readers from different domains still remains a challenge. To address this, we introduce ConceptEVA, a mixed-initiative approach to generate, evaluate, and customize summaries for long and multi-topic documents. ConceptEVA incorporates a custom multi-task longformer encoder decoder to summarize longer documents. Interactive visualizations of document concepts as a network reflecting both semantic relatedness and co-occurrence help users focus on concepts of interest. The user can select these concepts and automatically update the summary to emphasize them. We present two iterations of ConceptEVA evaluated through an expert review and a within-subjects study. We find that participants’ satisfaction with customized summaries through ConceptEVA is higher than their own manually-generated summary, while incorporating critique into the summaries proved challenging. Based on our findings, we make recommendations for designing summarization systems incorporating mixed-initiative interactions.
Establishing baselines and introducing TernaryMixOE for fine-grained out-of-distribution detection

Machine learning models deployed in the open world may encounter observations that they were not trained to recognize, and they risk misclassifying such observations with high confidence. Therefore, it is essential that these models are able to ascertain what is in-distribution (ID) and out-of-distribution (OOD), to avoid this misclassification. In recent years, huge strides have been made in creating models that are robust to this distinction. As a result, the current state-of-the-art has reached near perfect performance on relatively coarse-grained OOD detection tasks, such as distinguishing horses from trucks, while struggling with finer-grained classification, like differentiating models of commercial aircraft. In this paper, we describe a new theoretical framework for understanding fine- and coarse-grained OOD detection, we re-conceptualize fine grained classification into a three part problem, and we propose a new baseline task for OOD models on two fine-grained hierarchical data sets, two new evaluation methods to differentiate fine- and coarse-grained OOD performance, along with a new loss function for models in this task.
Impact of Treatment Sequencing on Overall Survival in Patients with Transplant-Ineligible Newly Diagnosed Myeloma

Abstract Background Because patients with newly diagnosed multiple myeloma (NDMM) do not always receive any treatment beyond first-line (1L) therapy, it is imperative that patients receive the best treatment in the 1L setting. However, the optimal initial treatment remains to be identified. We performed a clinical simulation to assess potential outcomes with different treatment sequences. Patients and Methods We used a partitioned survival model to compare overall survival (OS) with (1) daratumumab, lenalidomide, and dexamethasone (D-Rd) in 1L followed by a pomalidomide- or carfilzomib-based regimen in second line (2L) versus (2) bortezomib, lenalidomide, and dexamethasone (VRd) in 1L followed by a daratumumab-based regimen in 2L versus (3) lenalidomide and dexamethasone (Rd) in 1L followed by a daratumumab-based regimen in 2L. Probabilities of transition between health states (1L, 2L+, and death) were based on published clinical data and real-world data from the Flatiron Health database. The proportion of patients discontinuing treatment after 1L (attrition rates) in the base case was estimated with a binomial logistic model using data from the MAIA trial. Results Using D-Rd in 1L conferred a longer median OS compared with delaying daratumumab-based regimens until 2L after VRd or Rd, respectively (8.9 [95% CrI 7.58-10.42] vs. 6.92 [5.92-8.33] or 5.75 [4.50-7.25] years). Results of scenario analyses were consistent with the base case. Conclusion Our simulation, which incorporates clinically representative treatments and attrition rates, supports the use of D-Rd as initial therapy, rather than delaying the use of daratumumab until later lines of therapy, in patients with transplant-ineligible NDMM.
POTENTIALLY SINGULAR SOLUTIONS OF THE 3D INCOMPRESSIBLE

Whether the 3D incompressible Euler equations can develop a singularity in nite time from smooth initial data is one of the most challenging problems in mathematical uid dynamics. This work attempts to provide an armative answer to this long-standing open question from a numerical point of view, by presenting a class of potentially singular solutions to the Euler equations computed in axisymmetric geometries. The solutions satisfy a periodic boundary condition along the axial direction and no-ow boundary condition on the solid wall. The equations are discretized
Effects of tamoxifen and exemestane on cognitive function in postmenopausal patients with breast cancer

Abstract Background Cognitive effects of tamoxifen have been described. We augment data from a previous short-term (ST) follow-up study with long-term (LT) data to evaluate ST and LT cognitive effects of tamoxifen followed by exemestane and exemestane in breast cancer patients. Methods Patients from the Tamoxifen and Exemestane Adjuvant Multinational trial received 5 years exemestane (exemestane group, n = 114) or 2.5 years tamoxifen followed by 2.5 years exemestane (sequential group, n = 92). Neuropsychological performance was assessed pre-endocrine therapy, after 1 year (ST follow-up) and at 5 years (LT follow-up). A control group of healthy participants (n = 120) were assessed with parallel intervals. With random effects modeling we evaluated cognitive changes from baseline to ST and LT follow-up. Statistical tests were 2-sided. Results After controlling for age, intelligence quotient, attrition, menopausal symptoms, anxiety and/or depression, and/or fatigue, the sequential group showed ST and LT decline compared with control participants on verbal memory (effect size [ES] = 0.26, P = .01; ES = 0.34, P = .003) and executive function (ES = 0.27, P = .007; ES = 0.38, P = .002). Compared with the exemestane group, the sequential group demonstrated ST decline on information processing speed (ES = 0.33, P = .01) and executive function (ES = 0.32, P = .01) and LT decline on verbal memory (ES = 0.33, P = .02). The exemestane group showed no cognitive decline compared with control participants. Conclusion Cognitive adverse effects of tamoxifen alone and after switching to exemestane were observed, suggestive of a carryover effect of tamoxifen. Our results underline the need for well-controlled, prospective trials studying cognitive effects of endocrine therapy.
Soft X-Rays As A Tool To Investigate Radiation-Sensitive Sites In Mammalian Cells

It is now clear that the initial geometrical distribution of primary radiation products in irradiated biological matter is fundamental to the observed end-point (cell killing, mutation induction, chromosome aberrations, etc.) In recent years much evidence has accumulated, indicating that, for all radiations, physical quantities averaged over cellular dimensions (micrometers) are not good predictors of biological effect, and that energy deposition processes at the nanometer level are critical. Thus irradiation of cells with soft x rays whose secondary electrons have ranges of the order of nanometers is a unique tool for investigating different models for predicting the biological effects of radiation. We demonstrate techniques whereby the biological response of the cell, and the physical details of the energy deposition processes may be separated or factorized, so that given the response of a cellular system to, say soft x rays, the response of the cell to any other radiation may be predicted. The special advantages of soft x rays for eliciting this information and also information concerning the geometry of the radiation sensitive structures within the cell are discussed.
Transient mutation bias increases the predictability of evolution on an empirical genotype–phenotype landscape

Predicting how a population will likely navigate a genotype–phenotype landscape requires consideration of selection in combination with mutation bias, which can skew the likelihood of following a particular trajectory. Strong and persistent directional selection can drive populations to ascend toward a peak. However, with a greater number of peaks and more routes to reach them, adaptation inevitably becomes less predictable. Transient mutation bias, which operates only on one mutational step, can influence landscape navigability by biasing the mutational trajectory early in the adaptive walk. This sets an evolving population upon a particular path, constraining the number of accessible routes and making certain peaks and routes more likely to be realized than others. In this work, we employ a model system to investigate whether such transient mutation bias can reliably and predictably place populations on a mutational trajectory to the strongest selective phenotype or usher populations to realize inferior phenotypic outcomes. For this we use motile mutants evolved from ancestrally non-motile variants of the microbe Pseudomonas fluorescens SBW25, of which one trajectory exhibits significant mutation bias. Using this system, we elucidate an empirical genotype–phenotype landscape, where the hill-climbing process represents increasing strength of the motility phenotype, to reveal that transient mutation bias can facilitate rapid and predictable ascension to the strongest observed phenotype in place of equivalent and inferior trajectories. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
Mutation bias and the predictability of evolution

Predicting evolutionary outcomes is an important research goal in a diversity of contexts. The focus of evolutionary forecasting is usually on adaptive processes, and efforts to improve prediction typically focus on selection. However, adaptive processes often rely on new mutations, which can be strongly influenced by predictable biases in mutation. Here, we provide an overview of existing theory and evidence for such mutation-biased adaptation and consider the implications of these results for the problem of prediction, in regard to topics such as the evolution of infectious diseases, resistance to biochemical agents, as well as cancer and other kinds of somatic evolution. We argue that empirical knowledge of mutational biases is likely to improve in the near future, and that this knowledge is readily applicable to the challenges of short-term prediction. This article is part of the theme issue ‘Interdisciplinary approaches to predicting evolutionary biology’.
Nonlinear Feedback in a Five-Dimensional Lorenz Model

AbstractIn this study, based on the number of modes, the original three-dimensional Lorenz model (3DLM) is generalized with two additional modes [five-dimensional Lorenz model (5DLM)] to examine their role in the predictability of the numerical solutions and to understand the underlying processes that increase the solution stability. As a result of the simplicity of the 5DLM with respect to existing generalized Lorenz models (LMs), the author is able to obtain the analytical solutions of its critical points and identify the role of the major nonlinear term in the solution’s stability, which have previously not been documented in the literature. The nonlinear Jacobian terms of the governing equations are analyzed to highlight the importance of selecting new modes for extending the nonlinear feedback loop of the 3DLM and thus effectively increasing the degree of nonlinearity (i.e., the nonlinear mode–mode interactions) in the 5DLM. It is then shown that numerical solutions in the 5DLM require a larger norma.
Force-Freeness of Solar Magnetic Fields in the Photosphere

It is widely believed that solar magnetic fields are force-free in the solar corona but not in the solar photosphere at all. In order to examine the force-freeness of active region magnetic fields at the photospheric level, we have calculated the integrated magnetic forces for 12 vector magnetograms of three flare-productive active regions. The magnetic field vectors are derived from simultaneous Stokes profiles of the Fe I doublet λλ6301. 5 and 6302.5 obtained by the Haleakala Stokes Polarimeter of Mees Solar Observatory, with a nonlinear least-squares method adopted for field calibration. The resulting vertical Lorentz force normalized to the total magnetic pressure force |Fz/Fp| ranges from 0.06 to 0.32 with a median value of 0.13, which is smaller than the values (~0.4) obtained by Metcalf et al., who applied a weak field derivative method to the Stokes profiles of Na I λ5896. Our results indicate that the photospheric magnetic fields are not so far from force-free as conventionally regarded. As a good example of a linear force-free field, NOAA Active Region 5747 is examined. By applying three different methods (a most probable value method, a least-squares fitting method, and comparison with linear force-free solutions), we have derived relatively consistent linear force-free coefficients for NOAA AR 5747. It is found that the scaled downward Lorentz force (|Fz/Fp|) in the solar photosphere decreases with increasing |α|. Our results also show that the force-freeness of photospheric magnetic fields depends not only on the character of the active region but also on its evolutionary status.
Large-scale Annotation of Biochemically Relevant Pockets and Tunnels in Cognate Enzyme-Ligand Complexes

Tunnels in enzymes with buried active sites are key structural features allowing the entry of substrates and the release of products, thus contributing to the catalytic efficiency. Targeting the bottlenecks of protein tunnels is also a powerful protein engineering strategy. However, the identification of functional tunnels in multiple protein structures is a non-trivial task that can only be addressed computationally. We present a pipeline integrating automated structural analysis with an in-house machine-learning predictor for the annotation of protein pockets, followed by the calculation of the energetics of ligand transport via biochemically relevant tunnels. A thorough validation using eight distinct molecular systems revealed that CaverDock analysis of ligand un/binding is on par with time-consuming molecular dynamics simulations, but much faster. The optimized and validated pipeline was applied to annotate more than 17,000 cognate enzyme-ligand complexes. Analysis of ligand un/binding energetics indicates that the top priority tunnel has the most favourable energies in 75 % of cases. Moreover, energy profiles of cognate ligands revealed that a simple geometry analysis can correctly identify tunnel bottlenecks only in 50 % of cases. Our study provides essential information for the interpretation of results from tunnel calculation and energy profiling in mechanistic enzymology and protein engineering. We formulated several simple rules allowing identification of biochemically relevant tunnels based on the binding pockets, tunnel geometry, and ligand transport energy profiles.
Structure-preserving model order reduction based on Laguerre–SVD for coupled systems

In this paper, we present a model order reduction (MOR) method based on Laguerre polynomials and singular value decomposition (SVD) for coupled systems in the frequency domain. By constructing projection matrices from the global perspective and then blockdiagonalizing them, the reduced system is produced, which not only retains the structure of the original system, but also matches the first several Laguerre coefficients. In addition, the connection between our algorithm and the moment matching approximation is also discussed. The error estimation of our method is given as well. Besides, the stability of the reduced system is also studied. Finally, two numerical examples are provided to verify the effectiveness of our algorithm.
Diffusion lengths in solar cells from short‐circuit current measurements

The minority‐carrier diffusion length in the base region of a silicon solar cell has been determined by measuring the short‐circuit current as a function of the wavelength of incident light. The incident light intensity required to produce a given short‐circuit current is a linear function of the reciprical absorption coefficient for each wavelength, and the extrapolation of this linear relation to zero intensity yields the diffusion length. This method is similar to the surface photovoltage and open‐circuit voltage methods; however, the accuracy for high light bias levels appears to be greatly improved.
Targeted proteomics links virulence factor expression with clinical severity in staphylococcal pneumonia

Introduction The bacterial pathogen Staphylococcus aureus harbors numerous virulence factors that impact infection severity. Beyond virulence gene presence or absence, the expression level of virulence proteins is known to vary across S. aureus lineages and isolates. However, the impact of expression level on severity is poorly understood due to the lack of high-throughput quantification methods of virulence proteins. Methods We present a targeted proteomic approach able to monitor 42 staphylococcal proteins in a single experiment. Using this approach, we compared the quantitative virulomes of 136 S. aureus isolates from a nationwide cohort of French patients with severe community-acquired staphylococcal pneumonia, all requiring intensive care. We used multivariable regression models adjusted for patient baseline health (Charlson comorbidity score) to identify the virulence factors whose in vitro expression level predicted pneumonia severity markers, namely leukopenia and hemoptysis, as well as patient survival. Results We found that leukopenia was predicted by higher expression of HlgB, Nuc, and Tsst-1 and lower expression of BlaI and HlgC, while hemoptysis was predicted by higher expression of BlaZ and HlgB and lower expression of HlgC. Strikingly, mortality was independently predicted in a dose-dependent fashion by a single phage-encoded virulence factor, the Panton-Valentine leucocidin (PVL), both in logistic (OR 1.28; 95%CI[1.02;1.60]) and survival (HR 1.15; 95%CI[1.02;1.30]) regression models. Discussion These findings demonstrate that the in vitro expression level of virulence factors can be correlated with infection severity using targeted proteomics, a method that may be adapted to other bacterial pathogens.
Situational analysis on fluoroquinolones use and characterization of high-level ciprofloxacin-resistant Enterococcus faecalis by integrated broiler operations in South Korea

Fluoroquinolones are classified as “critically important antimicrobials for human medicine”; however, their extensive use in livestock poses a significant health risk to humans as it leads to the rapid spread of antimicrobial resistance. This study confirmed that 40.0%−71.4% of the farms in three of the five integrated broiler operations were administered ciprofloxacin (CIP). Moreover, preventive purposes (60.9%), veterinarian prescriptions (82.6%), drinking water route (100%), and 1 to 3 days (82.6%) of age were significantly highest (P < 0.05). 194 high-level ciprofloxacin-resistant (HLCR) Enterococcus faecalis (E. faecalis) were found in 65 of 74 farms, and of which, the prevalence of qnrA (63.9%), tetM (60.3%), ermB (64.9%), blaz (38.7%), and catA (34.0%) was significantly highest (P < 0.05). 154 (79.4%) isolates showed MDR, and the distribution of MDR was significantly differences among the operations (P < 0.05). All HLCR E. faecalis possessed double mutations in gyrA and parC, and S83I/S80I (90.7%) mutations were most commonly identified. Interestingly, the distribution of isolates with MICs ≥ 512 for both CIP and moxifloxacin was significantly higher in CIP–administered farms (56.5%) than in non-CIP–administered farms (41.4%) (P < 0.05). Also, the prevalence of strong or moderate biofilm formers in HLCR E. faecalis was significantly higher than that of weak and no biofilm formers (P < 0.05). HLCR E. faecalis were heavily distributed in the broiler farms in Korea; therefore, it is necessary to minimize the prevalence of resistant bacteria via structural management regulations such as cleaning and disinfection of farm environments.
Next-generation sequencing study on poorly differentiated carcinoma derived from a thirty-year-old epidermoid cyst: A case report

Although epidermoid cysts are frequently seen as benign lesions, they are highly uncommon to develop into cancerous lesions. A 36-year-old man with a cystic mass present on his left flank since childhood presented to our department. Based on the patient’s medical history and abdominal computed tomography scan, we excised the lesion under the suspicion of an epidermoid cyst. Histopathological evaluation revealed the presence of poorly differentiated carcinoma with squamoid and basaloid differentiation, which showed a strong possibility of carcinoma arising from an epidermal cyst. Next-generation sequencing using TruSight oncology 500 assay showed copy number variation of ATM and CHEK1 genes.
Children's internal migration and subjective wellbeing of older parents left behind: Spiritual or financial support?

Introduction Against the background of population aging and large-scale internal migration, this study uses an ordered logit with two-way fixed effects to examine the effect of children's internal migration on the subjective wellbeing of parents left behind. The study is based on the China Family Panel Studies database. Methods Data were obtained from CFPS (China Family Panel Studies), and ordered logit with two-way fixed effects was used to test the total effect of children's internal migration on subjective wellbeing of parents left behind, and KHB test was used to separate intergenerational spiritual support and intergenerational financial support to examine the intergenerational support preferences of parents left behind. Results The results show that children's internal migration has a significant negative effect on the subjective wellbeing of parents left behind, mainly through the reduction of intergenerational spiritual support. Furthermore, intergenerational financial support significantly mitigates this negative effect. There is heterogeneity in the direction of the total wellbeing effect across parents' preferences, as well as in the masking effect of financial support. However, the effect of financial support never fully offsets the effect of spiritual support. Discussion To cope with the negative effects of children's internal migration on parents, positive measures should be taken to change parental preferences.