Brief Report: A Cross-Sectional Study of Anxiety Levels and Concerns of Chinese Families of Children With Special Educational Needs and Disabilities Post-first-wave of COVID-19

The COVID-19 pandemic has a multifaceted impact on mental health due to ill health, restrictions and lockdowns, and loss of employment and institutional support. COVID-19 may disproportionally impact families with special educational needs and disabilities (SEND) due to the already higher prevalence of mental health conditions in children with SEND and their parents. Therefore, it is essential to determine the short-term impact of the pandemic on the mental health of families with SEND in order to identify their ongoing health support needs. The current study aims to examine the anxiety level and concerns of children with SEND and their parents living in China. The sample consisted of 271 parents of children with SEND aged between 6 and 17 years (M age = 8.37; SD age = 2.76). Parents completed an online survey between 10 April to 8 June 2020. Both child and parental anxiety levels and various concerns increased after the initial wave of COVID-19 when compared with retrospective pre-COVID-19 levels. Parental anxiety and concern levels were significantly higher for those living in rural areas compared to urban areas. In addition, parental and child anxiety and concern levels were significantly correlated with each other. Parental anxiety at the lowest level made a unique and significant statistical contribution to children's anxiety levels. The implications of the study findings are discussed

Using biomarkers to predict TB treatment duration (Predict TB): a prospective, randomized, noninferiority, treatment shortening clinical trial

Background : By the early 1980s, tuberculosis treatment was shortened from 24 to 6 months, maintaining relapse rates of 1-2%. Subsequent trials attempting shorter durations have failed, with 4-month arms consistently having relapse rates of 15-20%. One trial shortened treatment only among those without baseline cavity on chest x-ray and whose month 2 sputum culture converted to negative. The 4-month arm relapse rate decreased to 7% but was still significantly worse than the 6-month arm (1.6%, P<0.01).  We hypothesize that PET/CT characteristics at baseline, PET/CT changes at one month, and markers of residual bacterial load will identify patients with tuberculosis who can be cured with 4 months (16 weeks) of standard treatment.Methods: This is a prospective, multicenter, randomized, phase 2b, noninferiority clinical trial of pulmonary tuberculosis participants. Those eligible start standard of care treatment. PET/CT scans are done at weeks 0, 4, and 16 or 24. Participants who do not meet early treatment completion criteria (baseline radiologic severity, radiologic response at one month, and GeneXpert-detectable bacilli at four months) are placed in Arm A (24 weeks of standard therapy). Those who meet the early treatment completion criteria are randomized at week 16 to continue treatment to week 24 (Arm B) or complete treatment at week 16 (Arm C). The primary endpoint compares the treatment success rate at 18 months between Arms B and C.Discussion: Multiple biomarkers have been assessed to predict TB treatment outcomes. This study uses PET/CT scans and GeneXpert (Xpert) cycle threshold to risk stratify participants. PET/CT scans are not applicable to global public health but could be used in clinical trials to stratify participants and possibly become a surrogate endpoint. If the Predict TB trial is successful, other immunological biomarkers or transcriptional signatures that correlate with treatment outcome may be identified. TRIAL REGISTRATION: NCT02821832

Resonances in J/ψ→ϕπ+π−J/\psi \to \phi \pi ^+\pi ^- and ϕK+K−\phi K^+K^-

A partial wave analysis is presented of $J/\psi \to \phi \pi ^+\pi ^-$ and $\phi K^+K^-$ from a sample of 58M $J/\psi$ events in the BES II detector. The $f_0(980)$ is observed clearly in both sets of data, and parameters of the Flatt\' e formula are determined accurately: $M = 965 \pm 8$ (stat) $\pm 6$ (syst) MeV/c$^2$, $g_1 = 165 \pm 10 \pm 15$ MeV/c$^2$, $g_2/g_1 = 4.21 \pm 0.25 \pm 0.21$. The $\phi \pi \pi$ data also exhibit a strong $\pi \pi$ peak centred at $M = 1335$ MeV/c$^2$. It may be fitted with $f_2(1270)$ and a dominant $0^+$ signal made from $f_0(1370)$ interfering with a smaller $f_0(1500)$ component. There is evidence that the $f_0(1370)$ signal is resonant, from interference with $f_2(1270)$. There is also a state in $\pi \pi$ with $M = 1790 ^{+40}_{-30}$ MeV/c$^2$ and $\Gamma = 270 ^{+60}_{-30}$ MeV/c$^2$; spin 0 is preferred over spin 2. This state, $f_0(1790)$, is distinct from $f_0(1710)$. The $\phi K\bar K$ data contain a strong peak due to $f_2'(1525)$. A shoulder on its upper side may be fitted by interference between $f_0(1500)$ and $f_0(1710)$.Comment: 17 pages, 6 figures, 1 table. Submitted to Phys. Lett.

Measurement of the Branching Fraction of J/psi --> pi+ pi- pi0

Using 58 million J/psi and 14 million psi' decays obtained by the BESII experiment, the branching fraction of J/psi --> pi+ pi- pi0 is determined. The result is (2.10+/-0.12)X10^{-2}, which is significantly higher than previous measurements.Comment: 9 pages, 8 figures, RevTex

Search for K_S K_L in psi'' decays

K_S K_L from psi'' decays is searched for using the psi'' data collected by BESII at BEPC, the upper limit of the branching fraction is determined to be B(psi''--> K_S K_L) < 2.1\times 10^{-4} at 90% C. L. The measurement is compared with the prediction of the S- and D-wave mixing model of the charmonia, based on the measurements of the branching fractions of J/psi-->K_S K_L and psi'-->K_S K_L.Comment: 5 pages, 1 figur

First observation of psi(2S)-->K_S K_L

The decay psi(2S)-->K_S K_L is observed for the first time using psi(2S) data collected with the Beijing Spectrometer (BESII) at the Beijing Electron Positron Collider (BEPC); the branching ratio is determined to be B(psi(2S)-->K_S K_L) = (5.24\pm 0.47 \pm 0.48)\times 10^{-5}. Compared with J/psi-->K_S K_L, the psi(2S) branching ratio is enhanced relative to the prediction of the perturbative QCD ``12%'' rule. The result, together with the branching ratios of psi(2S) decays to other pseudoscalar meson pairs (\pi^+\pi^- and K^+K^-), is used to investigate the relative phase between the three-gluon and the one-photon annihilation amplitudes of psi(2S) decays.Comment: 5 pages, 4 figures, 2 tables, submitted to Phys. Rev. Let

Study of psi(2S) decays to X J/psi

Using J/psi -> mu^+ mu^- decays from a sample of approximately 4 million psi(2S) events collected with the BESI detector, the branching fractions of psi(2S) -> eta J/psi, pi^0 pi^0 J/psi, and anything J/psi normalized to that of psi(2S) -> pi^+ pi^- J/psi are measured. The results are B(psi(2S) -> eta J/psi)/B(psi(2S) -> pi^+ pi^- J/psi) = 0.098 \pm 0.005 \pm 0.010, B(psi(2S) -> pi^0 pi^0 J/psi)/B(psi(2S) -> pi^+ pi^- J/psi) = 0.570 \pm 0.009 \pm 0.026, and B(psi(2S) -> anything J/psi)/B(psi(2S) -> pi^+ pi^- J/psi) = 1.867 \pm 0.026 \pm 0.055.Comment: 13 pages, 8 figure

First Measurements of eta_c Decaying into K^+K^-2(pi^+pi^-) and 3(pi^+pi^-)

The decays of eta_c to K^+K^-2(pi^+pi^-) and 3(pi^+pi^-) are observed for the first time using a sample of 5.8X10^7 J/\psi events collected by the BESII detector. The product branching fractions are determined to be B(J/\psi-->gamma eta_c)*B(eta_c-->K^+K^-pi^+pi^-pi^+pi^-)=(1.21+-0.32+- 0.23)X10^{-4}$,B(J/\psi-->gamma eta_c)*B(eta_c-->K^{*0}\bar{K}^{*0}pi^+pi^-)= (1.29+-0.43+-0.32)X10^{-4}$, and (J/\psi-->gamma eta_c)* B(eta_c-->pi^+pi^-pi^+pi^-pi^+pi^-)= (2.59+-0.32+-0.48)X10^{-4}. The upper limit for eta_c-->phi pi^+pi^-pi^+pi^- is also obtained as B(J/\psi-->gamma eta_c)*B(eta_c--> phi pi^+pi^-pi^+pi^-)< 6.03 X10^{-5} at the 90% confidence level.Comment: 11 pages, 4 figure

Accurate Prediction of Protein Structural Class

Because of the increasing gap between the data from sequencing and structural genomics, the accurate prediction of the structural class of a protein domain solely from the primary sequence has remained a challenging problem in structural biology. Traditional sequence-based predictors generally select several sequence features and then feed them directly into a classification program to identify the structural class. The current best sequence-based predictor achieved an overall accuracy of 74.1% when tested on a widely used, non-homologous benchmark dataset 25PDB. In the present work, we built a multiple linear regression (MLR) model to convert the 440-dimensional (440D) sequence feature vector extracted from the Position Specific Scoring Matrix (PSSM) of a protein domain to a 4-dimensinal (4D) structural feature vector, which could then be used to predict the four major structural classes. We performed 10-fold cross-validation and jackknife tests of the method on a large non-homologous dataset containing 8,244 domains distributed among the four major classes. The performance of our approach outperformed all of the existing sequence-based methods and had an overall accuracy of 83.1%, which is even higher than the results of those predicted secondary structure-based methods

Phenotypic Variation and Bistable Switching in Bacteria

Microbial research generally focuses on clonal populations. However, bacterial cells with identical genotypes frequently display different phenotypes under identical conditions. This microbial cell individuality is receiving increasing attention in the literature because of its impact on cellular differentiation, survival under selective conditions, and the interaction of pathogens with their hosts. It is becoming clear that stochasticity in gene expression in conjunction with the architecture of the gene network that underlies the cellular processes can generate phenotypic variation. An important regulatory mechanism is the so-called positive feedback, in which a system reinforces its own response, for instance by stimulating the production of an activator. Bistability is an interesting and relevant phenomenon, in which two distinct subpopulations of cells showing discrete levels of gene expression coexist in a single culture. In this chapter, we address techniques and approaches used to establish phenotypic variation, and relate three well-characterized examples of bistability to the molecular mechanisms that govern these processes, with a focus on positive feedback.