PnpProbs: A better multiple sequence alignment tool by better handling of guide trees

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Laser spectroscopy of iridium monoboride

High resolution laser induced fluorescence spectrum of IrB in the spectral region between 545 and 610 nm has been recorded and analyzed. Reacting laser-ablated iridium atoms with 1% B2 H6 seeded in argon produced the IrB molecule. This is the first experimental observation of the IrB molecule. Four vibronic transition bands, (v,0) with v=0-3 of an electronic transition system, have been observed. Spectra of all four isotopic molecules, Ir191 B10, Ir193 B10, Ir191 B11, and Ir193 B11, were recorded. Isotopic relationships confirmed the carrier of the spectra and the vibrational quantum number assignment. Preliminary analysis of rotational lines showed that these vibronic bands are with ′ =2 and ″ =3. The electronic transition identified is assigned as the [16.5] Π23 -X Δ33 system. Partially resolved hyperfine structure which conforms to the Hund's case aΒ coupling scheme has been observed and analyzed. The bond length r0 of the lower X Δ33 state of IrB was determined to be 1.7675 Å. © 2008 American Institute of Physics.published_or_final_versio

GLProbs: Aligning multiple sequences adaptively

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Solving ill-posed bilevel programs

This paper deals with ill-posed bilevel programs, i.e., problems admitting multiple lower-level solutions for some upper-level parameters. Many publications have been devoted to the standard optimistic case of this problem, where the difficulty is essentially moved from the objective function to the feasible set. This new problem is simpler but there is no guaranty to obtain local optimal solutions for the original optimistic problem by this process. Considering the intrinsic non-convexity of bilevel programs, computing local optimal solutions is the best one can hope to get in most cases. To achieve this goal, we start by establishing an equivalence between the original optimistic problem an a certain set-valued optimization problem. Next, we develop optimality conditions for the latter problem and show that they generalize all the results currently known in the literature on optimistic bilevel optimization. Our approach is then extended to multiobjective bilevel optimization, and completely new results are derived for problems with vector-valued upper- and lower-level objective functions. Numerical implementations of the results of this paper are provided on some examples, in order to demonstrate how the original optimistic problem can be solved in practice, by means of a special set-valued optimization problem

Evidence for genetic association of TBX21 and IFNG with systemic lupus erythematosus in a Chinese Han population

TBX21 recode T-bet which is an important transcription factor that drives the Th1 immune response primarily by promoting expression of the interferon-gamma (IFNG) gene. Recent studies have shown that genetic variants in TBX21 and IFNG are connected with risk of systemic lupus erythematosus (SLE). The aim of the present study was to replicate these genetic associations with SLE in Anhui Chinese population. Genotyping of 3 variants (rs4794067 in TBX21, rs2069705 and rs2069718 in IFNG) was performed. A total of 3732 subjects were included in the final analysis. The study only identified the association of rs2069705 with SLE susceptibility (T vs. C: odds ratio [OR] = 1.12, 95% confidence interval [CI] = 1.00-1.26, P = 0.046). Combined analysis with Hong Kong GWAS showed that the OR for rs2069705 was 1.10 (95% CI: 1.01-1.21, P = 0.027). Further pooled analysis with Korean populations involving 10498 subjects showed a more significant association between rs2069705 and SLE (T vs. C: OR = 1.11, 95%CI = 1.04-1.19, P = 0.002; TT + TC vs. CC: OR = 1.11, 95%CI = 1.02-1.21, P = 0.012; TT vs. TC + CC: OR = 1.28, 95%CI = 1.07-1.54, P = 0.008; TT vs. CC: OR = 1.33, 95%CI = 1.10-1.60, P = 0.003). In addition, we also identified a significant genetic interaction between rs2069705 and rs4794067 in Anhui Chinese population. Our study suggests that IFNG and IFNG-TBX21 interaction are involved in SLE susceptibility.published_or_final_versio

Safety of two-dose COVID-19 vaccination (BNT162b2 and CoronaVac) in adults with cancer: a territory-wide cohort study

BACKGROUND: The World Health Organization has defined a list of adverse events of special interest (AESI) for safety surveillance of vaccines. AESI have not been adequately assessed following COVID-19 vaccination in patients with cancer contributing to vaccine hesitancy in this population. We aimed to evaluate the association between BNT162b2 and CoronaVac vaccines and the risk of AESI in adults with active cancer or a history of cancer. PATIENTS AND METHODS: We conducted a territory-wide cohort study using electronic health records managed by the Hong Kong Hospital Authority and vaccination records provided by the Department of Health. Patients with a cancer diagnosis between January 1, 2018, and September 30, 2021, were included and stratified into two cohorts: active cancer and history of cancer. Within each cohort, patients who received two doses of BNT162b2 or CoronaVac were 1:1 matched to unvaccinated patients using the propensity score. Cox proportional hazards regression was used to estimate hazard ratios (HR) and 95% confidence intervals (CIs) for AESI 28 days after the second vaccine dose. RESULTS: A total of 74,878 patients with cancer were included (vaccinated: 25,789 [34%]; unvaccinated: 49,089 [66%]). Among patients with active cancer, the incidence of AESI was 0.31 and 1.02 per 10,000 person-days with BNT162b2 versus unvaccinated patients and 0.13 and 0.88 per 10,000 person-days with CoronaVac versus unvaccinated patients. Among patients with history of cancer, the incidence was 0.55 and 0.89 per 10,000 person-days with BNT162b2 versus unvaccinated patients and 0.42 and 0.93 per 10,000 person-days with CoronaVac versus unvaccinated patients. Neither vaccine was associated with a higher risk of AESI for patients with active cancer (BNT162b2: HR 0.30, 95% CI 0.08-1.09; CoronaVac: 0.14, 95% CI 0.02-1.18) or patients with history of cancer (BNT162b2: 0.62, 95% CI 0.30-1.28; CoronaVac: 0.45, 95% CI 0.21-1.00). CONCLUSIONS: In this territory-wide cohort study of patients with cancer, the incidence of AESI following vaccination with two doses of either BNT162b2 or CoronaVac vaccines was low. The findings of this study can reassure clinicians and patients with cancer about the overall safety of BNT162b2 and CoronaVac in patients with cancer, which could increase the COVID-19 vaccination rate in this vulnerable group of patients

Soil bio-cementation treatment strategies: state-of-the-art review

Bio-cementation is a new sustainable approach that has gained popularity due to its low energy and carbon footprint compared to existing technologies for geotechnical and geoenvironmental engineering applications. Bio-cementation is a soil improvement technique that involves binding the pore space of soil particles with calcium carbonate minerals by microbially induced carbonate precipitation (MICP) and filling the soil pore space. The purpose of this article is to present a current state-of-the-art and comprehensive discussion on the development of bio-cementation for soil improvement/reinforcement. Premixing, injection, immersing, and surface percolation are identified as four distinct bio-cementation treatment techniques. Furthermore, scholars have reported employing ureolytic bacteria such as Sporosarcina pasteurii, Bacillus sphaericus, and Lysinibacillus sphaericus) isolated from corals, limestone caves, soils, waste materials, seawaters, and other sources to accomplish effective bio-cementation Some of the major issues (bacterial cultivation costs and ammonium production) that impede its industrial potential and promising remedial techniques were also discussed. This state-of-the-art review also discussed the benefits and drawbacks of bio-cementation compared to traditional approaches. The significance of enzyme-induced carbonate precipitation as a soil bio-cementation alternative to MICP was also highlighted. Finally, the sustainable procedure, bio-cementation principles, and future implications are discussed

Insight into the Stability of Cross-β Amyloid Fibril from VEALYL Short Peptide with Molecular Dynamics Simulation

Amyloid fibrils are found in many fatal neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, type II diabetes, and prion disease. The VEALYL short peptide from insulin has been confirmed to aggregate amyloid-like fibrils. However, the aggregation mechanism of amyloid fibril is poorly understood. Here, we utilized molecular dynamics simulation to analyse the stability of VEALYL hexamer. The statistical results indicate that hydrophobic residues play key roles in stabilizing VEALYL hexamer. Single point and two linkage mutants confirmed that Val1, Leu4, and Tyr5 of VEALYL are key residues. The consistency of the results for the VEALYL oligomer suggests that the intermediate states might be trimer (3-0) and pentamer(3-2). These results can help us to obtain an insight into the aggregation mechanism of amyloid fibril. These methods can be used to study the stability of amyloid fibril from other short peptides

A Wide-angle Multi-Octave Broadband Waveplate Based on Field Transformation Approach

J.Z. acknowledge the support from the National Nature Science Foundation of China (61571218, 61571216, 61301017, 61371034, 61101011), and the Ph.D. Programs Foundation of Ministry of Education of China (20120091110032, 20110091120052). Y. H. acknowledge the support from the UK EPSRC under the QUEST Programme Grant (EP/I034548/1)

Classification of Camellia (Theaceae) Species Using Leaf Architecture Variations and Pattern Recognition Techniques

Leaf characters have been successfully utilized to classify Camellia (Theaceae) species; however, leaf characters combined with supervised pattern recognition techniques have not been previously explored. We present results of using leaf morphological and venation characters of 93 species from five sections of genus Camellia to assess the effectiveness of several supervised pattern recognition techniques for classifications and compare their accuracy. Clustering approach, Learning Vector Quantization neural network (LVQ-ANN), Dynamic Architecture for Artificial Neural Networks (DAN2), and C-support vector machines (SVM) are used to discriminate 93 species from five sections of genus Camellia (11 in sect. Furfuracea, 16 in sect. Paracamellia, 12 in sect. Tuberculata, 34 in sect. Camellia, and 20 in sect. Theopsis). DAN2 and SVM show excellent classification results for genus Camellia with DAN2's accuracy of 97.92% and 91.11% for training and testing data sets respectively. The RBF-SVM results of 97.92% and 97.78% for training and testing offer the best classification accuracy. A hierarchical dendrogram based on leaf architecture data has confirmed the morphological classification of the five sections as previously proposed. The overall results suggest that leaf architecture-based data analysis using supervised pattern recognition techniques, especially DAN2 and SVM discrimination methods, is excellent for identification of Camellia species