Miracle-dz/UniFAR-FTUs huggingface dataset:
Above‐ground plant metabolomic responses to plant–soil feedbacks and herbivory

Understanding the causes of variation in foliar plant metabolomes is essential for our understanding of ecological interactions between plants and other organisms. It is well‐accepted that foliar herbivory alters metabolites in leaves. However, soil (micro)organisms can also induce such changes. We generated plant‐specific soil legacies by growing 12 plant species individually in a common starting soil. Then we planted all plant species in all soils and exposed a subset to foliar herbivory. We then used 1H nuclear magnetic resonance to analyse the shoot metabolomes of all responding plants. Above‐ground herbivory and soil legacies altered shoot metabolomes. In most plant species, soil legacy more strongly affected shoot metabolomes than foliar herbivory. Synthesis. Our results show that plant‐induced changes in soil alter metabolomes of plants that grow later in those soils. Such below‐ground legacy effects can have far‐stretching consequences for above‐ground multitrophic interactions as these often depend on the plant chemical composition. Recently, plant–soil feedbacks have received considerable attention in ecological studies, and our study now highlights that these feedbacks can be an important determinant of the often unexplained intraspecific variation in chemical composition among plants.
Modeling and Building IoT Data Platforms with Actor-Oriented Databases

Vast amounts of data are being generated daily with the adoption of Internet-of-Things (IoT) solutions in an ever-increasing number of application domains. There are problems associated with all stages of the lifecycle of these data (e.g., capture, cura-tion and preservation). Moreover, the volume, variety, dynam-icity and ubiquity of IoT data present additional challenges to their usability, prompting the need for constructing scalable data-intensive IoT data management and processing platforms. This paper presents a novel approach to model and build IoT data platforms based on the characteristics of an Actor-Oriented Database (AODB). We take advantage of two complementary case studies – in structural health monitoring and beef cattle tracking and tracing – to describe novel software requirements introduced by IoT data processing. Our investigation illustrates the challenges and benefits provided by AODB to meet these requirements in terms of modeling and IoT-based systems implementation. Obtained results reveal the advantages of using AODB in IoT scenarios and lead to principles on how to effectively use an actor model to design and implement IoT data platforms.
Development and Piloting of an Algorithm to Select Older Patients for Different Types of Medication Review

Aim: To develop and pilot an algorithm to select older people for different types of medication review based on their case complexity. Methods: Experts rated complexity of patient cases through a Delphi-consensus method. The case characteristics were included in a regression model predicting complexity to develop a criteria-based algorithm. The algorithm was piloted in four community pharmacies with 38 patients of high and low complexity. Pharmacists conducted medication reviews according to their personal judgment and rated the patients' complexity. Time needed for reviewing and number of interventions (proposed and implemented) were assessed. Feasibility was evaluated with in-depth interviews. Results: We developed the algorithm with 75 cases proceeding from patients in average 79 years old and using 10 prescribed medications. The regression model (adjusted R2 = 0.726, P < 0.0001) resulted in the following criteria for the algorithm: “number of medications” × 1 + “number of prescribers” × 3 + “recent fall incident” × 7 + “does not collect own medication” × 4. The pharmacists performed advanced medication reviews with all patients. The time needed to perform the medication review did not differ significantly according to case complexity (76.9 min for high complexity; 66.1 min for low complexity). Agreement between the algorithm scores and the pharmacists' ratings on complexity degree was slight to moderate (Kappa 0.16–0.42). The pharmacists had mixed opinions about the feasibility of applying the algorithm, particularly regarding the criterion “fall incidents. ” Conclusion: We developed an algorithm with four criteria that distinguished between high and low complexity patients as rated by experts. Additional validation steps are needed before implementation.
Temperature-stable black phosphorus field-effect transistors through effective phonon scattering suppression on atomic layer deposited aluminum nitride

Abstract Black phosphorus (BP) shows great potential in electronic and optoelectronic applications; however, maintaining the stable performance of BP devices over temperature is still challenging. Here, a novel BP field-effect transistor (FET) fabricated on the atomic layer deposited AlN/SiO2/Si substrate is demonstrated. Electrical measurement results show that BP FETs on the AlN substrate possess superior electrical performance compared with those fabricated on the conventional SiO2/Si substrate. It exhibits a large on-off current ratio of 5 × 108, a low subthreshold swing of <0.26 V/dec, and a high normalized field-effect carrier mobility of 1071 cm2 V−1 s−1 in the temperature range from 77 to 400 K. However, these stable electrical performances are not found in the BP FETs on SiO2/Si substrate when the temperature increases up to 400 K; instead, the electrical performance of BP FETs on the SiO2/Si substrate degrades drastically. Furthermore, to gain a physical understanding on the stable performance of BP FETs on the AlN substrate, low-frequency noise analysis was performed, and it revealed that the AlN film plays a significant role in suppressing the lattice scattering and charge trapping effects at high temperatures.
Performance Comparison of Probabilistically Shaped QAM Formats and Hybrid Shaped APSK Formats With Coded Modulation

We study and compare performance of probabilistically shaped (PS)-64QAM formats (with spectral efficiency ranging from 3.6 to 8.6 b/s/Hz) and coded modulation (CM)-64/16APSK formats with PS or geometric shaping (GS) or hybrid shaping (HS) with spectral efficiency ranging from 4.8 to 8.8 b/s/Hz. We show that a well-designed CM-64/16APSK format could exceed the performance of PS-64QAM formats by a small margin both in back-to-back and transmission. This was verified for transmission distances from 4,435 to 22,176 km using laboratory hardware and our implemented FEC. We show similar nonlinearity compensation (NLC) benefit for both PS and CM formats using 3-stage NLC schemes. The NLC benefit increases from 1.2 to 1.6 dB as the transmission distance increases from 4,435 to 9,504 km and then nearly saturates at ∼1.6 dB after ∼10,000 km. Furthermore, we experimentally demonstrate 71.6 Tb/s (7.36 b/s/Hz) using a CM-64/16APSK format and 70.3 Tb/s (7.22 b/s/Hz) with a PS-64QAM format over 6,970 km using C+L band EDFA testbed.
Evolution of Governance in a Collaborative University–Industry Program

This article presents the emergence and evolution of actors and institutions in a collaborative program from the strategic planning to the execution and delivery phase. The ethnographic study of a collaboration revealed that, while formal regulations are prerequisites for initiating collaborations, further drivers of the interactions between program participants are oriented by path dependency, cultural familiarities, and goal sharing. This article shows that many microdynamics exist among actors and institutions inside the governance structure, co-evolving in parallel with the transition through the program’s phases. There are, however, two main drivers orienting these dynamics: formal regulations and trust.
The relative effects of physical and verbal discipline and gender variations on adolescents’ bonds to parents: a case of urban China

ABSTRACT This study examines the relative effects of physical and verbal discipline on Chinese adolescents’ bonds to parents and whether the relationship between parental discipline and adolescent–parent bonds may be gender- and domain-specific. Data drawn from over 500 middle school students from an urban city in China yielded results generally consistent with the hypotheses. Harsh discipline is significantly associated with a reduction in adolescent–parent bonds and verbal discipline exerts stronger overall effects than physical punishment. Furthermore, the effects of harsh discipline depend on the gender of adolescents and parents as well as domains of parental harshness. Father’s physical discipline is associated with a reduction in father–son closeness, whereas mother’s physical punishment is linked to the declined mother–daughter relationship. Meanwhile, father’s verbal discipline lowers girls’ bonds to father, whereas mother’s verbal harshness is associated with lower bonds of boys to both parents and girls’ bonds to mother only. Additionally, interactive effects are observed. Father’s verbal discipline is associated with a stronger detachment of girls to father and father’s physical discipline is related to higher bonds of girls to mother when mother applies low verbal or physical discipline. Lastly, physical and verbal discipline rendered by mother jointly reduces girls’ bonds to father.
Cancer-Associated Fibroblasts and Desmoplastic Reactions Related to Cancer Invasiveness in Patients With Colorectal Cancer

Purpose We evaluated the relationship of cancer-associated fibroblasts (CAFs) and desmoplastic reactions with cancer invasiveness and long-term outcomes in patients with colorectal cancer (CRC). Methods Histologic evaluation of mature CAFs and desmoplasia was performed by observing the collagen fiber structure and fibroblast cytomorphology in the intratumoral stroma and invasive front of CRC tissues. Cancer-cell invasiveness was evaluated using lymphatic invasion, vascular invasion, perineural invasion, tumor budding, and tumor growth patterns. Overall survival and systemic recurrence were analyzed. A network analysis was performed between CAF maturation, desmoplastic reaction, and cancer invasiveness. Results The proportions of mature CAFs in the intratumoral stroma and the invasive front were 57.6% and 60.3%, respectively. Epidermal growth factor receptor (EGFR) overexpression was significantly higher in the mature CAFs in the invasive front as compared to immature CAFs. Lymphatic invasion increased as the number of mature fibroblasts in the intratumoral stroma increased. Tumor budding was observed in almost half of both mature and immature stroma samples and occurred more frequently in infiltrating tumors. On network analysis, well-connected islands were identified that was associated with EGFR overexpression, CAF maturation, and infiltrating tumor growth patterns leading to tumor budding. Conclusion The maturity of CAFs and desmoplastic reactions were associated with cancer invasion. However, the cytomorphologic characteristics of CAFs were insufficient as an independent prognostic factor for patients with CRC.
Comparison of Measures to Predict Mortality and Length of Stay in Hospitalized Patients

Background Patient risk adjustment is critical for hospital benchmarking and allocation of healthcare resources. However, considerable heterogeneity exists among measures. Objectives The performance of five measures was compared to predict mortality and length of stay (LOS) in hospitalized adults using claims data; these include three comorbidity composite scores (Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, and V W Elixhauser age-comorbidity score), 3 M risk of mortality (3 M ROM), and 3 M severity of illness (3 M SOI) subclasses. Methods Binary logistic and zero-truncated negative binomial regression models were applied to a 2-year retrospective dataset (2013–2014) with 123,641 adult inpatient admissions from a large hospital system in New York City. Results All five measures demonstrated good to strong model fit for predicting in-hospital mortality, with C-statistics of 0.74 (95% confidence interval [CI] [0.74, 0.75]), 0.80 (95% CI [0.80, 0.81]), 0.81(95% CI [0.81, 0.82]), 0.94 (95% CI [0.93, 0.94]), and 0.90 (95% CI [0.90, 0.91]) for Charlson/Deyo age-comorbidity score, V W Elixhauser comorbidity score, V W Elixhauser age-comorbidity score, 3 M ROM, and 3 M SOI, respectively. The model fit statistics to predict hospital LOS measured by the likelihood ratio index were 0.3%, 1.2%, 1.1%, 6.2%, and 4.3%, respectively. Discussion The measures tested in this study can guide nurse managers in the assignment of nursing care and coordination of needed patient services and administrators to effectively and efficiently support optimal nursing care.
Scan registration for underwater mechanical scanning imaging sonar using symmetrical Kullback–Leibler divergence

Abstract. Due to its advantages in size and energy consumption, mechanical scanning imaging sonar (MSIS) has been widely used in portable and economic underwater robots to observe the turbid and noisy underwater environment. However, handicapped by the coarseness in spatial and temporal resolution, it is difficult to stitch the scan pieces together into a panoramic map for global understanding. A registration method named symmetrical Kullback–Leibler divergence (SKLD)-distribution-to-distribution (D2D), which models each scan as a Gaussian mixture model (GMM) and evaluates the similarity between two GMMs in a D2D way with the measure defined by SKLD, is proposed to register the scans collected by MSIS. SKLD not only weights the difference between distributions with the prior probability but also increases the numerical stability with the symmetrical constraint in distance measure. Moreover, an approximation strategy is designed to derive a tractable solution for the KLD between two GMMs. Experimental results on the scans that were collected from the realistic underwater environment demonstrate that SKLD-D2D dramatically reduces the computational cost without compromising the estimation precision.
Preliminary evaluation of voluntary event cancellation as a countermeasure against the COVID-19 outbreak in Japan as of 11 March, 2020

Background: To control COVID−19 outbreak in Japan, sports and entertainment events were canceled in Japan for two weeks from 26 February to 11 March. It has been designated as voluntary event cancellation (VEC). Object: This study predicts the effectiveness of VEC enduring and after its implementation. Method: We applied a simple susceptible−infected−recovery model to data of patients with symptoms in Japan during 14 January to VEC introduction and after VEC introduction to 8 March. We adjusted the reporting delay in the latest few days. Results: Results suggest that the basic reproduction number, R0, before VEC introduced as 2.50 with a 95% confidence interval (CI) was [2.43, 2.55] and the effective reproduction number, Rv, after VEC introduced as 1. 88; its 95% CI was [1.68,2.02]. Discussion and Conclusion: Results demonstrated that VEC can reduce COVID−19 infectiousness by 35%, but R0 remains higher than one.
Platelet-Rich Plasma as a Treatment for Androgenetic Alopecia.

BACKGROUND Platelet-rich plasma (PRP) treatment may encourage hair growth by promoting cellular maturation, differentiation, and proliferation. OBJECTIVE The objective of this study was to evaluate the effectiveness of PRP as a treatment for androgenetic alopecia (AGA). MATERIALS AND METHODS A literature search combined with meta-analysis was used to calculate the overall standardized mean difference (SMD) in hair density in patients treated with PRP injections in comparison with baseline and placebo treatment. Chi squared analysis and Fisher exact test were used to investigate variation in protocols. RESULTS The overall SMD in hair density was 0.58 (95% confidence interval [CI]: 0.35-0.80) and 0.51 (95% CI: 0.23-0.80, p < .0004) in favor of PRP treatment when compared with baseline and placebo treatment, respectively. CONCLUSION Platelet-rich plasma is beneficial in the treatment of AGA. It is recommended that 3 monthly sessions of PRP (once monthly ×3 treatments) be used followed by a 3- to 6-month maintenance period.
Building diverse careers in clinical and translational research: Evaluation of a certificate program in translational research

Introduction The Certificate Program in Translational Research (CPTR) at the Georgia Clinical and Translational Science Alliance provides Ph.D. students, postdoctoral fellows and faculty with didactic, mentored, and experiential training in clinical and translational research. Methods Quantitative evaluation includes tracking trainee competency, publications, grants and careers in clinical and translational research. Qualitative evaluation includes interviews with trainees about program experiences. Results The CPTR provided knowledge and skills in clinical and translational research through coursework, clinical rotations, and collaboration with interdisciplinary scientists. Trainees reported increased confidence in 22 program competencies. Trainees have published more than 290 peer-reviewed articles and received over $4 million in grants from the NIH, over $15 million from the U.S. Department of Defense, and more than $300,000 from foundations. Trainees who completed the program remained in clinical and translational research. Conclusions Programs like the CPTR are needed to train investigators to advance biomedical discoveries into population health.
Simulating the Impact of Elevated Levels of Interleukin-6 on the Pharmacokinetics of Various CYP450 Substrates in Patients with Neuromyelitis Optica or Neuromyelitis Optica Spectrum Disorders in Different Ethnic Populations

A physiologically based pharmacokinetic (PBPK) model was used to simulate the impact of elevated levels of interleukin (IL)-6 on the exposure of several orally administered cytochrome P450 (CYP) probe substrates (caffeine, S-warfarin, omeprazole, dextromethorphan, midazolam, and simvastatin). The changes in exposure of these substrates in subjects with rheumatoid arthritis (and hence elevated IL-6 levels) compared with healthy subjects were predicted with a reasonable degree of accuracy. The PBPK model was then used to simulate the change in oral exposure of the probe substrates in North European Caucasian, Chinese, and Japanese population of patients with neuromyelitis optica (NMO) or NMO spectrum disorder with elevated plasma IL-6 levels (up to 100 pg/mL). Moderate interactions [mean AUC fold change, ≤ 2.08 (midazolam) or 2.36 (simvastatin)] was predicted for CYP3A4 probe substrates and weak interactions (mean AUC fold change, ≤ 1.29–1.97) were predicted for CYP2C19, CYP2C9, and CYP2D6 substrates. No notable interaction was predicted with CYP1A2. Although ethnic differences led to differences in simulated exposure for some of the probe substrates, there were no marked differences in the predicted magnitude of the change in exposure following IL-6–mediated suppression of CYPs. Decreased levels of serum albumin (as reported in NMO patients) had little impact on the magnitude of the simulated IL-6–mediated drug interactions. This PBPK modeling approach allowed us to leverage knowledge from different disease and ethnic populations to make predictions of cytokine-related DDIs in a rare disease population where actual clinical studies would otherwise be difficult to conduct.
Optimization of an In Vitro Transcription/Translation System Based on Sulfolobus solfataricus Cell Lysate

A system is described which permits the efficient synthesis of proteins in vitro at high temperature. It is based on the use of an unfractionated cell lysate (S30) from Sulfolobus solfataricus previously well characterized in our laboratory for translation of pretranscribed mRNAs, and now adapted to perform coupled transcription and translation. The essential element in this expression system is a strong promoter derived from the S. solfataricus 16S/23S rRNA-encoding gene, from which specific mRNAs may be transcribed with high efficiency. The synthesis of two different proteins is reported, including the S. solfataricus DNA-alkylguanine-DNA-alkyl-transferase protein (SsOGT), which is shown to be successfully labeled with appropriate fluorescent substrates and visualized in cell extracts. The simplicity of the experimental procedure and specific activity of the proteins offer a number of possibilities for the study of structure-function relationships of proteins.
An Instance-Specific Algorithm for Learning the Structure of Causal Bayesian Networks Containing Latent Variables

Almost all of the existing algorithms for learning a causal Bayesian network structure (CBN) from observational data recover a structure that models the causal relationships that are shared by the instances in a population . Although learning such population-wide CBNs accurately is useful, it is important to learn CBNs that are speciﬁc to each instance in domains in which diﬀerent instances may have varying causal structures, such as in human biology. For example, a breast cancer tumor in a patient (instance) is often a composite of causal mechanisms, where each of these individual causal mechanisms may appear relatively frequently in breast-cancer tumors of other patients, but the particular combination of mechanisms is unique to the current tumor. Therefore, it is critical to discover the speciﬁc set of causal mechanisms that are operating in each patient to understand and treat that particular patient eﬀectively. We previously introduced an instance-speciﬁc CBN structure learning method that builds a causal model for a given instance T from the features we know about T and from a training set of data on many other instances [12]. However, that method assumes that there are no latent (hidden) confounders, that is, there are no latent variables that cause two or more of the measured variables. Unfortunately, this assumption rarely holds in practice. In the current paper, we introduce a novel instance-speciﬁc causal structure learning algorithm that uses partial ancestral graphs (PAGs) to model latent confounders. Simulations support that the proposed instance-speciﬁc method improves structure-discovery performance compared to an existing PAG-learning method called GFCI, which is not instance-speciﬁc. We also report results that provide support for instance-speciﬁc causal relationships existing in real-world datasets.
Extending Cross-Domain Knowledge Bases with Long Tail Entities using Web Table Data

Cross-domain knowledge bases such as YAGO, DBpedia, or the Google Knowledge Graph are being used as background knowledge within an increasing range of applications including web search, data integration, natural language understanding, and question answering. The usefulness of a knowledge base for these applications depends on its completeness. Relational HTML tables from the Web cover a wide range of topics and describe very specific long tail entities, such as small villages, less-known football players, or obscure songs. This systems and applications paper explores the potential of web table data for the task of completing cross-domain knowledge bases with descriptions of formerly unknown entities. We present the first system that handles all steps that are necessary for this task: schema matching, row clustering, entity creation, and new detection. The evaluation of the system using a manually labeled gold standard shows that it can construct formerly unknown instances and their descriptions from table data with an average F1 score of 0.80. In a second experiment, we apply the system to a large corpus of web tables extracted from the Common Crawl. This experiment allows us to get an overall impression of the potential of web tables for augmenting knowledge bases with long tail entities. The experiment shows that we can augment the DBpedia knowledge base with descriptions of 14 thousand new football players as well as 187 thousand new songs. The accuracy of the facts describing these instances is 0.90.