Search

Parents of children with autism spectrum disorder (ASD) experience higher stress levels than those of typically developing children. The goal of the current study was to examine whether a mentalization-based intervention would enhance parental cognitive reappraisal, an adaptive form of emotion regulation associated with lower levels of stress. Findings from 27 parents who completed a short training indicated an improvement in cognitive reappraisal. In exploratory analyses, two different types of reappraisal were examined. The intervention-related improvement was found mainly with one type of reappraisal, namely reflective reappraisal that consist of cognitive reappraisal with mentalization characteristics. In light of the evidence indicating that high cognitive reappraisal and high reflective functioning are associated with quality caregiving, findings from the current study suggesting that a brief mentalization-based intervention supports ASD parents' cognitive reappraisal with mentalization characteristics are promising and warrant further investigation.

Novel organometallic complexes Mn(benzene-1,2-diamine)(CO)3Br, Mn-1, Mn(3-methylbenzene-1,2-diamine)(CO)3Br, Mn-2, and Re(benzene-1,2-diamine)(CO)3Cl, Re-1, have been synthesized and characterized by IR, UV/Vis, 1H-NMR, EA and HRMS. The structures of Mn-2 and Re-1 were confirmed by X-ray crystallography. The three novel compounds were studied for their electrocatalytic reduction of carbon dioxide to carbon monoxide using cyclic voltammetry in acetonitrile solutions. Controlled potential electrolysis was used to obtain information on faradaic yield, with product formation being confirmed by GC. Using earth-abundant manganese, compounds Mn-1 and Mn-2 display turnover frequencies of 108 s−1 and 82 s−1, respectively, amid selective production of carbon monoxide (faradaic yields ~85%), with minimal co-production of dihydrogen (Re-1, displays no activity as an electrocatalyst for carbon dioxide reduction under identical conditions.

The many stressors associated with teaching can take a toll, resulting in high levels of burnout among teachers and reduced motivation and academic performance among students. This is especially true in the context of science, technology, engineering, and mathematics (STEM) subjects. Despite the efficacy of emotion regulation interventions in pedagogical settings in general and in STEM teaching in particular, there is a lack of suitable interventions. We applied the process model of emotion regulation to STEM teaching and proposed a framework, STEM-Model of EmotioN regulation: Teachers’ Opportunities and Responsibilities (STEM-MENTOR), to elucidate how the high demands of STEM teaching and contextual factors (e.g., culture, reforms, teacher-student interactions) may lead to intensified negative emotions and deficits in executive functioning and emotion regulation implementation. Teacher emotions, in turn, shape students’ STEM-related achievements and epistemic emotions. Thus, teachers’ emotion regulation skills have pervasive effects on teaching outcomes for both teachers and students. We illustrate how at each level of our framework, steps could be taken to improve teachers’ emotional trajectory. Our proposed STEM-MENTOR framework has implications for theoretical understanding and may help to shape future interventions that focus on cognitive-emotional processes in STEM education.

A compound was observed using airborne proton transfer reaction time-of-flight mass spectrometry (PTR-TOF-MS) measurements in the emission plumes from the Daesan petrochemical facility in South Korea. The compound was detected at m/z 43.018 on the PTR-TOF-MS and was tentatively identified as ketene, a rarely measured reactive volatile organic compound (VOC). Estimated ketene mixing ratios as high as ∼ 50 ppb (parts per billion) were observed in the emission plumes. Emission rates of ketene from the facility were estimated using a horizontal advective flux approach and ranged from 84–316 kg h−1. These emission rates were compared to the emission rates of major known VOCs such as benzene, toluene, and acetaldehyde. Significant correlations (r2 > 0.7) of ketene with methanol, acetaldehyde, benzene, and toluene were observed for the peak emissions, indicating commonality of emission sources. The calculated average ketene OH reactivity for the emission plumes over Daesan ranged from 3.33–7.75 s−1, indicating the importance of the quantification of ketene to address missing OH reactivity in the polluted environment. The calculated average O3 production potential for ketene ranged from 2.98–6.91 ppb h−1. Our study suggests that ketene, or any possible VOC species detected at m/z 43.018, has the potential to significantly influence local photochemistry, and therefore, further studies focusing on the photooxidation and atmospheric fate of ketene through chamber studies are required to improve our current understanding of VOC OH reactivity and, hence, tropospheric O3 production.

Abstract Background Following each cycle of peptide receptor radionuclide therapy (PRRT), absorbed doses by tumors and normal organs are typically calculated from three quantitative single-photon emission computed tomography (SPECT)/computed tomography (CT) studies acquired at t 1 = 24 h, t 2 = 96 h, t 3 = 168 h after the first cycle of treatment and from a single study at t 1 after the subsequent cycles. In the present study, we have assessed the feasibility of a single SPECT/CT study after each PRRT cycle using a trained multiple linear regression (MLR) model for absorbed dose calculation and have evaluated its impact on patient management. Quantitative [177Lu]-DOTA-TATE SPECT/CT data after PRRT of seventy-two consecutive metastatic neuroendocrine tumors patients were retrospectively evaluated. A set of 40 consecutive studies was used to train the MLR model. The two independent variables of the model included the time of imaging after administration of the treatment and the radiopharmaceutical activity concentration in a given organ/tumor. The dependent variable was the dose absorbed by the organ/tumor obtained with the standard protocol. For bone marrow dosimetry, the independent variables included the time of imaging, and the blood and remainder of the body activity concentration. The model was evaluated in 32 consecutive patients. Absorbed doses were assessed for kidneys, bone marrow, liver, spleen and tumor sites. Results There was no difference in management decisions, whether PRRT can be safely continued or not because unsafe absorbed dose to risk organs between the standard and the MLR model-based protocol using a single SPECT/CT study performed at t 3 = 168 h after the first cycle and at t 1 = 24 h after the subsequent cycles. Cumulative absorbed doses were obtained with mean relative differences of − 0.5% ± 5.4%, 1.6% ± 15.1%, − 6.2% ± 7.3%, − 5.5% ± 5.8% and 2.9% ± 12.7% for kidneys, bone marrow, liver, spleen and tumors, respectively (Pearson’s r correlation coefficient 0.99, 0.91, 0.99, 0.99 and 0.97, respectively). Conclusion Dosimetry calculations using a MLR model with a single SPECT/CT study are in good agreement with the standard protocol, while avoiding the use of dosimetry software and enabling improved patient comfort and reduced scanner and staff time.

In this study, we present column-averaged dry-air mole fractions of CO2 (XCO2), CH4 (XCH4) and CO (XCO) from a recently established measurement site in Gobabeb, Namibia. Gobabeb is a hyperarid desert site at the sharp transition zone between the sand desert and the gravel plains, offering unique characteristics with respect to surface albedo properties. Measurements started in January 2015 and are performed utilizing a ground-based Fourier transform infrared (FTIR) EM27/SUN spectrometer of the COllaborative Carbon Column Observing Network (COCCON). Gobabeb is the first measurement site observing XCO2 and XCH4 on the African mainland and improves the global coverage of ground-based remote-sensing sites. In order to achieve the high level of precision and accuracy necessary for meaningful greenhouse gas observations, we performed calibration measurements for 8 d between November 2015 and March 2016 with the COCCON reference EM27/SUN spectrometer operated at the Karlsruhe Institute of Technology. We derived scaling factors for XCO2, XCH4 and XCO with respect to the reference instrument that are close to 1.0. We compare the results obtained in Gobabeb to measurements from the Total Carbon Column Observing Network (TCCON) sites at Réunion Island and Lauder. We choose these TCCON sites because, while 4000 km apart, the instruments at Gobabeb and Réunion Island operate at roughly the same latitude. The Lauder station is the southernmost TCCON station and functions as a background site without a pronounced XCO2 seasonal cycle. We find a good agreement for the absolute Xgas values, apart from an expected XCH4 offset between Gobabeb and Lauder due to significantly different tropopause height, as well as representative intraday variability between TCCON and COCCON. Together with the absence of long-term drifts, this highlights the quality of the COCCON measurements. In the southern hemispheric summer, we observe lower XCO2 values at Gobabeb compared to the TCCON stations, likely due to the influence of the African biosphere. We performed coincident measurements with the Greenhouse Gases Observing Satellite (GOSAT), where GOSAT observed three nearby specific observation points, over the sand desert south of the station, directly over Gobabeb and over the gravel plains to the north. GOSAT H-gain XCO2 and XCH4 agree with the EM27/SUN measurements within the 1σ uncertainty limit. The number of coincident soundings is limited, but we confirm a bias of 1.2–2.6 ppm between GOSAT M-gain and H-gain XCO2 retrievals depending on the target point. This is in agreement with results reported by a previous study and the GOSAT validation team. We also report a bias of 5.9–9.8 ppb between GOSAT M-gain and H-gain XCH4 measurements which is within the range given by the GOSAT validation team. Finally, we use the COCCON measurements to evaluate inversion-optimized CAMS model data. For XCO2, we find high biases of 0.9 ± 0.5 ppm for the Orbiting Carbon Observatory-2 (OCO-2) assimilated product and 1.1 ± 0.6 ppm for the in situ-driven product with R2 > 0.9 in both cases. These biases are comparable to reported offsets between the model and TCCON data. The OCO-2 assimilated model product is able to reproduce the drawdown of XCO2 observed by the COCCON instrument at the beginning of 2017, as opposed to the in situ-optimized product. Also, for XCH4, the observed biases are in line with prior model comparisons with TCCON.

Abstract Predictive brain markers promise a number of important scientific, clinical, and societal applications. Over 600 predictive brain markers have been described in published reports, but very few have been tested in independent replication attempts. Here, we conducted an independent replication of a previously published marker predicting treatment response to cognitive-behavioral therapy for social anxiety disorder from patterns of resting-state fMRI amygdala connectivity1. The replication attempt was conducted in an existing dataset similar to the dataset used in the original report, by a team of independent investigators in consultation with the original authors. The precise model described in the original report positively predicted treatment outcomes in the replication dataset, but with marginal statistical significance, permutation test p = 0.1. The effect size was substantially smaller in the replication dataset, with the model explaining 2% of the variance in treatment outcomes, as compared to 21% in the original report. Several lines of evidence, including the current replication attempt, suggest that features of amygdala function or structure may be able to predict treatment response in anxiety disorders. However, predictive models that explain a substantial amount of variance in independent datasets will be needed for scientific and clinical applications.

Abstract Motivated by the desire to understand chaos in the S-matrix of string theory, we study tree level scattering amplitudes involving highly excited strings. While the amplitudes for scattering of light strings have been a hallmark of string theory since its early days, scattering of excited strings has been far less studied. Recent results on black hole chaos, combined with the correspondence principle between black holes and strings, suggest that the amplitudes have a rich structure. We review the procedure by which an excited string is formed by repeatedly scattering photons off of an initial tachyon (the DDF formalism). We compute the scattering amplitude of one arbitrary excited string and any number of tachyons in bosonic string theory. At high energies and high mass excited state these amplitudes are determined by a saddle-point in the integration over the positions of the string vertex operators on the sphere (or the upper half plane), thus yielding a generalization of the “scattering equations”. We find a compact expression for the amplitude of an excited string decaying into two tachyons, and study its properties for a generic excited string. We find the amplitude is highly erratic as a function of both the precise excited string state and of the tachyon scattering angle relative to its polarization, a sign of chaos.

Abstract Background Image quality and quantitative accuracy of positron emission tomography (PET) depend on several factors such as uptake time, scanner characteristics and image reconstruction methods. Ordered subset expectation maximization (OSEM) is considered the gold standard for image reconstruction. Penalized-likelihood estimation (PL) algorithms have been recently developed for PET reconstruction to improve quantitation accuracy while maintaining or even improving image quality. In PL algorithms, a regularization parameter β controls the penalization of relative differences between neighboring pixels and determines image characteristics. In the present study, we aim to compare the performance of Q.Clear (PL algorithm, GE Healthcare) and OSEM (3 iterations, 8 subsets, 6-mm post-processing filter) for 68Ga-DOTATATE (68Ga-DOTA) PET studies, both visually and quantitatively. Thirty consecutive whole-body 68Ga-DOTA studies were included. The data were acquired in list mode and were reconstructed using 3D OSEM and Q.Clear with various values of β and various acquisition times per bed position (bp), thus generating images with reduced injected dose (1.5 min/bp: β = 300–1100; 1.0 min/bp: β = 600–1400 and 0.5 min/bp: β = 800–2200). An additional analysis adding β values up to 1500, 1700 and 3000 for 1.5, 1.0 and 0.5 min/bp, respectively, was performed for a random sample of 8 studies. Evaluation was performed using a phantom and clinical data. Two experienced nuclear medicine physicians blinded to the variables assessed the image quality visually. Results Clinical images reconstructed with Q.Clear, set at 1.5, 1.0 and 0.5 min/bp using β = 1100, 1300 and 3000, respectively, resulted in images with noise equivalence to 3D OSEM (1.5 min/bp) with a mean increase in SUVmax of 14%, 13% and 4%, an increase in SNR of 30%, 24% and 10%, and an increase in SBR of 13%, 13% and 2%. Visual assessment yielded similar results for β values of 1100–1400 and 1300–1600 for 1.5 and 1.0 min/bp, respectively, although for 0.5 min/bp there was no significant improvement compared to OSEM. Conclusion 68Ga-DOTA reconstructions with Q.Clear, 1.5 and 1.0 min/bp, resulted in increased tumor SUVmax and in improved SNR and SBR at a similar level of noise compared to 3D OSEM. Q.Clear with β = 1300–1600 enables one-third reduction of acquisition time or injected dose, with similar image quality compared to 3D OSEM.

Energy consumption and the latency of convolutional neural networks (CNNs) are two important factors that limit their applications specifically for embedded devices. Fourier-based frequency domain (FD) convolution is a promising low-cost alternative to conventional implementations in the spatial domain (SD) for CNNs. FD convolution performs its operation with point-wise multiplications. However, in CNNs, the overhead for the Fourier-based FD-convolution surpasses its computational saving for small filter sizes. In this work, we propose to implement convolutional layers in the FD using the Hartley transform (HT) instead of the Fourier transformation. We show that the HT can reduce the convolution delay and energy consumption even for small filters. With the HT of parameters, we replace convolution with point-wise multiplications. HT lets us compress input feature maps, in convolutional layers, before convolving them with filters. In this regard, we introduce two compression techniques: fixed-rate and adaptive-rate. In the fixed-rate compression, we select frequency domain input feature map (IFMap) coefficients with a constant pattern over all convolutional layers. However, for the adaptive-rate IFMap compression, the network, itself, learns to keep or discard coefficients, during training. Also, to optimize the hardware implementation of our methods (fixed- and adaptive-rate compressions), we utilize stochastic computing (SC) to perform the point-wise multiplications in the FD. In this regard, we re-formalize the HT to better match with SC. We show that, compared to conventional Fourier-based convolution, Hartley SC-based convolution can achieve $1.33\times$ speedup, and energy is reduced by 23% on a Virtex 7 FPGA when we implement AlexNet over CIFAR-10 based on the fixed-rate compression. Also, we show that if we utilize the adaptive-rate compression, we receive 16% and 15% latency improvement and energy consumption reduction, respectively, compared to the fixed-rate method.

Prolonged cow-calf contact (CCC) could potentially improve dairy calf welfare. However, it is currently unknown how different types of CCC affect animals' biological functions. We evaluated health and performance parameters of dairy calves and their dams, where calves: (i) had no contact with their dam (NC), in which the calf was removed from the dam directly after birth (n = 10); (ii) were allowed to have partial contact (PC) with their dam, in which the calf was housed in a calf pen adjacent to the cow area allowing physical contact on the initiative of the dam but no suckling (n = 18); (iii) were allowed to have full contact (FC) with their dam, including suckling, in which calves were housed together with their dams in a free-stall barn (n = 20). Throughout the first 7 weeks postpartum, data were collected on the health status, fecal microbiota, hematological profile, immune and hormonal parameters, and growth rates of calves, and on the health status, metabolic responses, and performance of dams. Overall, FC calves had more health issues (P = 0.02) and a tendency for higher antibiotic usage (P = 0.07) than NC calves. Additionally, FC calves showed elevated levels of erythrocytes, hematocrit, hemoglobin, and leukocytes on day 49 compared to NC calves (P < 0.001). Calf fecal microbiota changed over time, and we found preliminary evidence that fecal microbiota is affected by the type of CCC, as reflected by differences in relative abundances of taxa including Lactobacillus in FC calves compared to NC and PC calves except on days 7 and 66. The FC calves had a greater average daily gain in body weight than NC and PC calves (P = 0.002). Cow health was not affected by the type of CCC, although in the first 7 weeks of lactation FC cows had a lower machine-gained milk yield accompanied by a lower fat percentage than NC and PC cows (P < 0.001). These results indicate that full contact posed a challenge for calf health, presumably because the housing conditions of FC calves in this experimental context were suboptimal. Secondly, ad libitum suckling leads to higher weight gains and negatively affected milk fat content besides machine-gained yields. More research into strategies to improve cow-calf housing and management in CCC systems is warranted.