Using canopy greenness index to identify leaf ecophysiological traits during the foliar senescence in an oak forest

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Ecosphere 9 (2018): e02337, doi:10.1002/ecs2.2337.Camera‐based observation of forest canopies allows for low‐cost, continuous, high temporal‐spatial resolutions of plant phenology and seasonality of functional traits. In this study, we extracted canopy color index (green chromatic coordinate, Gcc) from the time‐series canopy images provided by a digital camera in a deciduous forest in Massachusetts, USA. We also measured leaf‐level photosynthetic activities and leaf area index (LAI) development in the field during the growing season, and corresponding leaf chlorophyll concentrations in the laboratory. We used the Bayesian change point (BCP) approach to analyze Gcc. Our results showed that (1) the date of starting decline of LAI (DOY 263), defined as the start of senescence, could be mathematically identified from the autumn Gcc pattern by analyzing change points of the Gcc curve, and Gcc is highly correlated with LAI after the first change point when LAI was decreasing (R2 = 0.88, LAI < 2.5 m2/m2); (2) the second change point of Gcc (DOY 289) started a more rapid decline of Gcc when chlorophyll concentration and photosynthesis rates were relatively low (13.4 ± 10.0% and 23.7 ± 13.4% of their maximum values, respectively) and continuously reducing; and (3) the third change point of Gcc (DOY 295) marked the end of growing season, defined by the termination of photosynthetic activities, two weeks earlier than the end of Gcc curve decline. Our results suggested that with the change point analysis, camera‐based phenology observation can effectively quantify the dynamic pattern of the start of senescence (with declining LAI) and the end of senescence (when photosynthetic activities terminated) in the deciduous forest.Priority Academic Program Development of Jiangsu Higher Education Institutions in Discipline of Environmental Science and Engineer in Nanjing Forest University; China Scholarship Council Grant Number: 201506190095; Brown University Seed Funds for International Research Projects on the Environmen

Less is More? An Empirical Study on Configuration Issues in Python PyPI Ecosystem

Python is widely used in the open-source community, largely owing to the extensive support from diverse third-party libraries within the PyPI ecosystem. Nevertheless, the utilization of third-party libraries can potentially lead to conflicts in dependencies, prompting researchers to develop dependency conflict detectors. Moreover, endeavors have been made to automatically infer dependencies. These approaches focus on version-level checks and inference, based on the assumption that configurations of libraries in the PyPI ecosystem are correct. However, our study reveals that this assumption is not universally valid, and relying solely on version-level checks proves inadequate in ensuring compatible run-time environments. In this paper, we conduct an empirical study to comprehensively study the configuration issues in the PyPI ecosystem. Specifically, we propose PyCon, a source-level detector, for detecting potential configuration issues. PyCon employs three distinct checks, targeting the setup, packing, and usage stages of libraries, respectively. To evaluate the effectiveness of the current automatic dependency inference approaches, we build a benchmark called VLibs, comprising library releases that pass all three checks of PyCon. We identify 15 kinds of configuration issues and find that 183,864 library releases suffer from potential configuration issues. Remarkably, 68% of these issues can only be detected via the source-level check. Our experiment results show that the most advanced automatic dependency inference approach, PyEGo, can successfully infer dependencies for only 65% of library releases. The primary failures stem from dependency conflicts and the absence of required libraries in the generated configurations. Based on the empirical results, we derive six findings and draw two implications for open-source developers and future research in automatic dependency inference.Comment: This paper has been accepted by ICSE 202

The penC mutation conferring antibiotic resistance in Neisseria gonorrhoeae arises from a mutation in the PilQ secretin that interferes with multimer stability: GonococcalpilQmutants with increased antibiotic resistance

The penC resistance gene was previously characterized in a FA19 penA mtrR penB gonococcal strain (PR100) as a spontaneous mutation that increased resistance to penicillin and tetracycline. We show here that antibiotic resistance mediated by penC is the result of a Glu-666 to Lys missense mutation in the pilQ gene that interferes with the formation of the SDS-resistant high-molecular-mass PilQ secretin complex, disrupts piliation, and decreases transformation frequency by 50-fold. Deletion of pilQ in PR100 confers the same level of antibiotic resistance as the penC mutation, but increased resistance was observed only in strains containing the mtrR and penB resistance determinants. Site-saturation mutagenesis of Glu-666 revealed that only acidic or amidated amino acids at this position preserved PilQ function. Consistent with early studies suggesting the importance of cysteine residues on stability of the PilQ multimer, mutation of either of the two cysteine residues in FA19 PilQ led to a similar phenotype as penC: increased antibiotic resistance, loss of piliation, intermediate levels of transformation competence, and absence of SDS-resistant PilQ oligomers. These data show that a functional secretin complex can enhance the entry of antibiotics into the cell and suggest that the PilQ oligomer forms a pore in the outer membrane through which antibiotics diffuse into the periplasm

LLM-Mini-CEX: Automatic Evaluation of Large Language Model for Diagnostic Conversation

There is an increasing interest in developing LLMs for medical diagnosis to improve diagnosis efficiency. Despite their alluring technological potential, there is no unified and comprehensive evaluation criterion, leading to the inability to evaluate the quality and potential risks of medical LLMs, further hindering the application of LLMs in medical treatment scenarios. Besides, current evaluations heavily rely on labor-intensive interactions with LLMs to obtain diagnostic dialogues and human evaluation on the quality of diagnosis dialogue. To tackle the lack of unified and comprehensive evaluation criterion, we first initially establish an evaluation criterion, termed LLM-specific Mini-CEX to assess the diagnostic capabilities of LLMs effectively, based on original Mini-CEX. To address the labor-intensive interaction problem, we develop a patient simulator to engage in automatic conversations with LLMs, and utilize ChatGPT for evaluating diagnosis dialogues automatically. Experimental results show that the LLM-specific Mini-CEX is adequate and necessary to evaluate medical diagnosis dialogue. Besides, ChatGPT can replace manual evaluation on the metrics of humanistic qualities and provides reproducible and automated comparisons between different LLMs

Proteomic analysis of spinal cord tissue in a rat model of cancer-induced bone pain

BackgroundCancer-induced bone pain (CIBP) is a moderate to severe pain and seriously affects patients’ quality of life. Spinal cord plays critical roles in pain generation and maintenance. Identifying differentially expressed proteins (DEPs) in spinal cord is essential to elucidate the mechanisms of cancer pain.MethodsCIBP rat model was established by the intratibial inoculation of MRMT-1 cells. Positron emission tomography (PET) scan and transmission electron microscopy (TEM) were used to measure the stats of spinal cord in rats. Label free Liquid Chromatography with tandem mass spectrometry (LC-MS-MS) were used to analyze the whole proteins from the lumbar spinal cord. Differentially expressed proteins (DEPs) were performed using Gene Ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) enrichment analysis, and verified using Western blot and immunofluorescence assay.ResultsIn the current study, CIBP rats exhibited bone damage, spontaneous pain, mechanical hyperalgesia, and impaired motor ability. In spinal cord, an hypermetabolism and functional abnormality were revealed on CIBP rats. An increase of synaptic vesicles density in active zone and a disruption of mitochondrial structure in spinal cord of CIBP rats were observed. Meanwhile, 422 DEPs, consisting of 167 up-regulated and 255 down-regulated proteins, were identified among total 1539 proteins. GO enrichment analysis indicated that the DEPs were mainly involved in catabolic process, synaptic function, and enzymic activity. KEGG pathway enrichment analysis indicated a series of pathways, including nervous system disease, hormonal signaling pathways and amino acid metabolism, were involved. Expression change of synaptic and mitochondrial related protein, such as complexin 1 (CPLX1), synaptosomal-associated protein 25 (SNAP25), synaptotagmin 1 (SYT1), aldehyde dehydrogenase isoform 1B1 (ALDH1B1), Glycine amidinotransferase (GATM) and NADH:ubiquinone oxidoreductase subunit A11 (NDUFA11), were further validated using immunofluorescence and Western blot analysis.ConclusionThis study provides valuable information for understanding the mechanisms of CIBP, and supplies potential therapeutic targets for cancer pain

Genetic causal relationship between gut microbiome and psoriatic arthritis: a bidirectional two-sample Mendelian randomization study

BackgroundSeveral observational studies have suggested a potential relationship between gut microbiome and psoriatic arthritis (PsA). However, the causality of this relationship still remains unclear. We aim to explore if the specific gut microbiome is causally associated with PsA at the genetic level and offer valuable insights into the etiology of PsA.MethodsIn this study, we employed a bidirectional two-sample Mendelian randomization (MR) analysis to investigate the causal effects of the gut microbiome on PsA. Publicly accessible genome-wide association study summary data of gut microbiome were obtained from the MiBioGen consortium (n = 14,306), while the summary statistics of psoriatic arthropathies were sourced from the FinnGen consortium R8 release data (2,776 cases and 221,323 controls). The primary analytical method employed was inverse variance weighted (IVW), complemented by supplementary methods including MR-Egger, weighted median, weighted mode, maximum likelihood, MR-PRESSO, and cML-MA. Reverse MR analysis was performed on the bacteria that were found to be causally associated with PsA in forward MR analysis. Cochran’s IVW Q statistic was utilized to assess the heterogeneity of instrumental variables among the selected single nucleotide polymorphisms.ResultsIVW estimates revealed that Ruminococcaceae_UCG-002 (odds ratio (OR) = 0.792, 95% confidence interval (CI), 0.643–0.977, p = 0.029) exhibited a protective effect on PsA. Conversely, Blautia (OR = 1.362, 95% CI, 1.008–1.842, p = 0.044), Eubacterium_fissicatena_group (OR = 1.28, 95% CI, 1.075–1.524, p = 0.006), and Methanobrevibacter (OR = 1.31, 95% CI, 1.059–1.621, p = 0.013) showed a positive correlation with the risk of PsA. No significant heterogeneity, horizontal pleiotropy, or outliers were observed, and the results of the MR analysis remained unaffected by any single nucleotide polymorphisms. According to the results of reverse MR analysis, no significant causal effect of PsA was found on gut microbiome.ConclusionThis study establishes for the first time a causal relationship between the gut microbiome and PsA, providing potential valuable strategies for the prevention and treatment of PsA. Further randomized controlled trials are urgently warranted to support the targeted protective mechanisms of probiotics on PsA

Measurement Invariance of the Depression Anxiety Stress Scales-21 Across Gender in a Sample of Chinese University Students

The Depression Anxiety Stress Scales-21 (DASS-21) has three 7-item subscales (depression, anxiety, and stress). The current study aims assess the gender-based measurement invariance of the DASS-21 questionnaire in a Chinese university student sample from five different cities. The sample was composed of 13208 participants (62.3% female, mean age of 19.7 years, and SD age = 1.8). Multi-group confirmatory factor analysis supported full measurement invariance for the three subscales. The findings support the measurement invariance of DASS-21 scores across gender. Future research on the DASS should include additional validation across ethnicities and testing of all versions of the DASS