T2Candida Assay in the Diagnosis of Intraabdominal Candidiasis: A Prospective Multicenter Study

The T2Candida magnetic resonance assay is a direct-from-blood pathogen detection assay that delivers a result within 3–5 h, targeting the most clinically relevant Candida species. Between February 2019 and March 2021, the study included consecutive patients aged >18 years admitted to an intensive care unit or surgical high-dependency unit due to gastrointestinal surgery or necrotizing pancreatitis and from whom diagnostic blood cultures were obtained. Blood samples were tested in parallel with T2Candida and 1,3-β-D-glucan. Of 134 evaluable patients, 13 (10%) were classified as having proven intraabdominal candidiasis (IAC) according to the EORTC/MSG criteria. Two of the thirteen patients (15%) had concurrent candidemia. The sensitivity, specificity, positive predictive value, and negative predictive value, respectively, were 46%, 97%, 61%, and 94% for T2Candida and 85%, 83%, 36%, and 98% for 1,3-β-D-glucan. All positive T2Candida results were consistent with the culture results at the species level, except for one case of dual infection. The performance of T2Candida was comparable with that of 1,3-β-D-glucan for candidemic IAC but had a lower sensitivity for non-candidemic IAC (36% vs. 82%). In conclusion, T2Candida may be a valuable complement to 1,3-β-D-glucan in the clinical management of high-risk surgical patients because of its rapid results and ease of use.

Developmental trajectories of school-beginner’s ability self-concept, intrinsic value, and performance in mathematics

This study investigated the developmental interdependence of Finnish school-beginners’ (N = 285) ability self-concept, intrinsic value, and performance in mathematics. More specifically, we examined: (i) whether and how children’s ability self-concept and intrinsic value in mathematics change over their first three years in school, (ii) how those changes are related to each other, (iii) how they predict later math performance, and (iv) whether there are gender differences in these trajectories. The results showed significant decrease over time in children’s ability self-concept and intrinsic value, but also significant individual differences in the trajectories. The high dependency between the levels and changes in children’s self-concept and intrinsic value led us to specify a factor-of-curves latent growth model, thus merging the trajectories of ability self-concept and intrinsic value into one common model (i.e., math motivation). The subsequent results showed prior math performance to predict change in children’s math motivation, meaning that higher initial competence was connected with less steep decrease in motivation. After controlling for the effects of first-grade math performance, both the level and change in math motivation predicted third-grade math performance and teacher-rated grades. That is, higher initial motivation and less steep decrease in it independently predicted better later math competence. Boys reported less steep decrease in math motivation than girls, despite no gender differences in initial math performance.

Adsorption Sites on Pd Nanoparticles Unraveled by Machine-Learning Potential with Adaptive Sampling

Catalytic properties of noble-metal nanoparticles (NPs) are largely determined by their surface morphology. The latter is probed by surface-sensitive spectroscopic techniques in different spectra regions. A fast and precise computational approach enabling the prediction of surface–adsorbate interaction would help the reliable description and interpretation of experimental data. In this work, we applied Machine Learning (ML) algorithms for the task of adsorption-energy approximation for CO on Pd nanoclusters. Due to a high dependency of binding energy from the nature of the adsorbing site and its local coordination, we tested several structural descriptors for the ML algorithm, including mean Pd–C distances, coordination numbers (CN) and generalized coordination numbers (GCN), radial distribution functions (RDF), and angular distribution functions (ADF). To avoid overtraining and to probe the most relevant positions above the metal surface, we utilized the adaptive sampling methodology for guiding the ab initio Density Functional Theory (DFT) calculations. The support vector machines (SVM) and Extra Trees algorithms provided the best approximation quality and mean absolute error in energy prediction up to 0.12 eV. Based on the developed potential, we constructed an energy-surface 3D map for the whole Pd55 nanocluster and extended it to new geometries, Pd79, and Pd85, not implemented in the training sample. The methodology can be easily extended to adsorption energies onto mono- and bimetallic NPs at an affordable computational cost and accuracy.

Acoustic streaming in Maxwell fluids generated by standing waves in two-dimensional microchannels

In this work, a semianalytic solution for the acoustic streaming phenomenon, generated by standing waves in Maxwell fluids through a two-dimensional microchannel (resonator), is derived. The mathematical model is non-dimensionalized and several dimensionless parameters that characterize the phenomenon arise: the ratio between the oscillation amplitude of the resonator and the half-wavelength ( $\eta =2A/\lambda _{a}$ ); the product of the fluid relaxation time times the angular frequency known as the Deborah number ( $De=\lambda _{1}\omega$ ); the aspect ratio between the microchannel height and the wavelength ( $\epsilon =2 H_{0}/\lambda _{a}$ ); and the ratio between half the height of the microchannel and the thickness of the viscous boundary layer ( $\alpha =H_{0}/\delta _{\nu }$ ). In the limit when $\eta \ll 1$ , we obtain the hydrodynamic behaviour of the system using a regular perturbation method. In the present work, we show that the acoustic streaming speed is proportional to $\alpha ^{2.65}De^{1.9}$ , and the acoustic pressure varies as $\alpha ^{6/5}De^{1/2}$ . Also, we have found that the growth of inner vortex is due to convective terms in the Maxwell rheological equation. Furthermore, the velocity antinodes show a high dependency on the Deborah number, highlighting the fluid's viscoelastic properties and the appearance of resonance points. Due to the limitations of perturbation methods, we will only analyse narrow microchannels.

OUTCOMES OF REFRACTORY STATUS EPILEPTICUS IN CHILDREN

Objective: To determine the outcome of Refractory Status Epilepticus (RSE) in children and the factors affecting the outcome. Study Design: Cross-sectional study. Place and Duration of Study: High Dependency Unit of Pediatrics Neurology Department at the Children’s Hospital and Institute of Child Health, from Aug 2019 to Mar 2020. Methodology: This study was conducted on the children presenting with refractory status epilepticus. Structured proforma was used for recording predictive factors. Modified Rankin scale prior to the presentation and Glasgow Coma Scale at presentation were documented and compared with the discharge scores. Results: Out of 75 children, 46 (61.4%) were males with mean age of 4.43 ± 3.47 years. Common etiologies were acute symptomatic in 37 (49.3%), progressive encephalopathy in 19 (25.3%), static encephalopathy in 9 (11.9%), remote symptomatic in 4 (5.3%), acute on remote symptomatic in 3 (4.0%), idiopathic and unclassified in remaining patients. Mean time between seizures onset and first benzodiazepine injection was 44 ± 36 minutes. Duration of RSE was <24 hours in 17 (22.7%), 24-48 hours in 15 (20.0%), 48-72 hours in 14 (18.6%), 72-96 hours in 12 (16%) and >96 hours in 17 (22.7%). At discharge 33 (44%) returned to baseline, 31 (41%) developed neurological disability while 11 (15%) expired during the stay. Etiology and duration of status epilepticus had significant impact on outcome with p-value of 0.021 and 0.041, respectively. Conclusion: Acute etiology was associated with higher mortality whereas return to baseline was also fair among survivors. This poses implications for emergency management to significantly............

CLIMATE CHANGE AND ITS IMPACT ON THE HUMAN RIGHTS: NORTH-EAST INDIA IN CONTEXT

The impact of climate change upon the basic human rights of the people is emerging as a frontline research area particularly in the North East region of India- the region which is more prone to the gushing impacts of global climate change due to its geo-ecological delicateness, shared international boundaries, rugged mountain terrains and the regions’ high dependency on climate sensitive livelihood such as agriculture, tea and forestry. Profound impacts of climate change in the region are being seen already in the form of rise in average temperature, slight changes in rainfall patterns, changes in the biodiversity, decrease of air quality which has ultimately threatened an array of human rights of the people in the region such as their Right to Life, Right to Food, Right to health and so on. Although Climate change has a cascading effect upon the people of the North East region of India, this area has been under-acknowledged and under-researched. Therefore, the paper purports to explore and evaluate the threats that anthropogenic climate change has imposed upon the human rights of the people of North-East India along albeit interrogating the measures that have been undertaken to address the issue and their efficacy.

Climate change adaptation governance in Nepal: a framework for sustainable generation of adaptation services

Poverty and high dependency of rural and mountainous households on the natural resources of Nepal have made the country more vulnerable to climate change. On the other hand, there is inadequacy of adaptation services provided to the vulnerable households and ecosystems. Responding to climate change necessitates amore consolidated effort and effective implementation interventions from both the government and non-government actors. To help achieve this very essence, this study has aimed to- (i) review the existing climate change adaptation (CCA) practices, processes and patterns of sustainable resource mobilization and benefit sharing, and (ii) develop a framework that ensures sustainability of resources and equitable sharing of services and benefits accrued from CCA. Consultations with the communities, key state and non-state stakeholders both at federal and provincial levels, reviews of national policies, strategies, periodic plans and programs and field visits were carried out to synthesize the information, document the knowledge, and highlight the gaps pertaining to CCA. Qualitative Content Analysis (QCA) was executed for analyzing qualitative information. Recently, the Government of Nepal has developed a priority framework on sustainable resource management and delivery of adaptation services. In line with the eight themes identified by the National Climate Change Policy (NCCP, 2019), the proposed framework has paid central attention on medium and long-term adaptation planning adhered with Ecosystem-based Adaptation (EbA) and Community-based Adaptation (CbA). Building resilience, reducing vulnerability, increasing capacities, enabling environment, and integrating CCA in development planning have been the focus of the framework. It is found that the delivery of adaptation services to the climate vulnerable groups and poor communities is well reached out through CbA and EbA approaches. It is therefore, crucial in strengthening community- and locally-based mechanisms (such as forest-user groups, farmers groups, agricultural and fisheries cooperatives, and community networks) for sustainable management and delivery of services to facilitate effective adaptation.

Sudden Obstacle Appearance Detection by Analyzing Flow Field Vector for Small-Sized UAV

Achieving a reliable obstacle detection and avoidance system that can provide an effective safe avoidance path for small unmanned aerial vehicle (UAV) is very challenging due to its physical size and weight constraints. Prior works tend to employ the vision based-sensor as the main detection sensor but resulting to high dependency on texture appearance while not having a distance sensing capabilities. The previous system only focused on the detection of the static frontal obstacle without observing the environment which may have moving obstacles. On the other hand, most of the wide spectrum range sensors are heavy and expensive hence not suitable for small UAV. In this work, integration of different based sensors was proposed for a small UAV in detecting unpredictable obstacle appearance situation. The detection of the obstacle is accomplished by analysing the flow field vectors in the image frames sequence. The proposed system was evaluated by conducting the experiments in a real environment which consisted of different configuration of the obstacles. The results from the experiment show that the success rate for detecting unpredictable obstacle appearance is high which is 70% and above. Even though some of the introduced obstacles are considered to have poor texture appearances on their surface, the proposed obstacle detection system was still able to detect the correct appearance movement of the obstacles by detecting the edges.