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Object recognition is an important ability that relies on distinguishing between similar objects (e.g., deciding which utensil(s) to use at different stages of meal preparation). Recent work describes the fine-grained organization of knowledge about manipulable objects via the study of the constituent dimensions that are most relevant to human behavior, for example, vision, manipulation, and function-based properties. A logical extension of this work concerns whether or not these dimensions are uniquely human, or can be approximated by deep learning. Here, we show that behavioral dimensions are generally well-predicted by CLIP-ViT - a multimodal network trained on a large and diverse set of image-text pairs. Moreover, this model outperforms comparison networks pre-trained on smaller, image-only datasets. These results demonstrate the impressive capacity of CLIP-ViT to approximate fine-grained object knowledge. We discuss the possible sources of this benefit relative to other models (e.g., multimodal vs. image-only pre-training, dataset size, architecture)., Competing Interests: The authors declare no competing interests., (© 2024 The Authors.)

A naphthalene diimide dye with two side amine arm was prepared. Uv-Vis and fluorescence spectroscopic techniques are used for their photophysical and solvatochromic characteristics in different solvents. The Lippert-Mataga plot for naphthalene diimide demonstrated a negative linear dependence by increasing polarity. Results showed naphthalene diimide is more polar in the ground than in the excited state. A quenching study was conducted for interacting the naphthalene diimide as a fluorophore and graphene oxide as a quencher. Fluorescence quenching-based platforms in nanoscale have been used in sensing systems. Raman, FTIR, Uv-Vis and fluorescence spectroscopic techniques were used to study the quenching mechanism. The results indicated that graphene plays an effective quencher against the naphthalene diimide, with a quenching efficiency 91%. The Stern-Volmer analysis results show a mix of static and dynamic quenching mechanisms. The binding constant of the quencher-fluorophore and the number of binding sites have been reported. Thermodynamic parameters of their interaction were evaluated. The negative values of the Gibbs free energy confirm that the complexation process is spontaneous. Meanwhile, the positive entropy value confirms that the favorable pathway process., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background and Aims: The opioid epidemic has extended to many countries. Data regarding the accuracy of conventional prediction models including the Simplified Acute Physiologic Score (SAPS) II and acute physiology and chronic health evaluation (APACHE) II are scarce in opioid overdose cases. We evaluate the efficacy of adding quantitative electroencephalogram (qEEG) data to clinical and paraclinical data in the prediction of opioid overdose mortality using machine learning., Methods: In a prospective study, we collected clinical/paraclinical, and qEEG data of 32 opioid-poisoned patients. After preprocessing and Fast Fourier Transform analysis, absolute power was computed. Also, SAPS II was calculated. Eventually, data analysis was performed using SAPS II as a benchmark at three levels to predict the patient's course in comparison with SAPS II. First, the qEEG data set was used alone, secondly, the combination of the clinical/paraclinical, SAPS II, qEEG datasets, and the SAPS II-based model was included in the pool of classifier models., Results: Seven out of 32 (22%) died. SAPS II (cut-off of 50.5) had a sensitivity/specificity/positive/negative predictive values of 85.7%, 84.0%, 60.0%, and 95.5% in predicting mortality, respectively. Adding majority voting on random forest with qEEG and clinical data, improved the model sensitivity, specificity, and positive and negative predictive values to 71.4%, 96%, 83.3%, and 92.3% (not significant). The model fusion level has 40% less prediction error., Conclusion: Considering the higher specificity and negative predictive value in our proposed model, it could predict survival much better than mortality. The model would constitute an indicator for better care of opioid poisoned patients in low resources settings, where intensive care unit beds are limited., Competing Interests: The authors declare no conflict of interest., (© 2022 The Authors. Health Science Reports published by Wiley Periodicals LLC.)

Emerging infectious diseases are a growing threat in sub-Saharan African countries, but the human and technical capacity to quickly respond to outbreaks remains limited. Here, we describe the experience and lessons learned from a joint project with the WHO Regional Office for Africa (WHO AFRO) to support the sub-Saharan African COVID-19 response.In June 2020, WHO AFRO contracted a number of consultants to reinforce the COVID-19 response in member states by providing actionable epidemiological analysis. Given the urgency of the situation and the magnitude of work required, we recruited a worldwide network of field experts, academics and students in the areas of public health, data science and social science to support the effort. Most analyses were performed on a merged line list of COVID-19 cases using a reverse engineering model (line listing built using data extracted from national situation reports shared by countries with the Regional Office for Africa as per the IHR (2005) obligations). The data analysis platform The Renku Project ( https://renkulab.io ) provided secure data storage and permitted collaborative coding.Over a period of 6 months, 63 contributors from 32 nations (including 17 African countries) participated in the project. A total of 45 in-depth country-specific epidemiological reports and data quality reports were prepared for 28 countries. Spatial transmission and mortality risk indices were developed for 23 countries. Text and video-based training modules were developed to integrate and mentor new members. The team also began to develop EpiGraph Hub, a web application that automates the generation of reports similar to those we created, and includes more advanced data analyses features (e.g. mathematical models, geospatial analyses) to deliver real-time, actionable results to decision-makers.Within a short period, we implemented a global collaborative approach to health data management and analyses to advance national responses to health emergencies and outbreaks. The interdisciplinary team, the hands-on training and mentoring, and the participation of local researchers were key to the success of this initiative., (© 2022. The Author(s).)

Breast cancer etiology is associated with both proliferation and DNA damage induced by estrogens. Breast cancer risk factors (BCRF) such as body mass index (BMI), smoking, and intake of estrogen-active drugs were recently shown to influence intratissue estrogen levels. Thus, the aim of the present study was to investigate the influence of BCRF on estrogen-induced proliferation and DNA damage in 41 well-characterized breast glandular tissues derived from women without breast cancer. Influence of intramammary estrogen levels and BCRF on estrogen receptor (ESR) activation, ESR-related proliferation (indicated by levels of marker transcripts), oxidative stress (indicated by levels of GCLC transcript and oxidative derivatives of cholesterol), and levels of transcripts encoding enzymes involved in estrogen biotransformation was identified by multiple linear regression models. Metabolic fluxes to adducts of estrogens with DNA (E-DNA) were assessed by a metabolic network model (MNM) which was validated by comparison of calculated fluxes with data on methoxylated and glucuronidated estrogens determined by GC- and UHPLC-MS/MS. Intratissue estrogen levels significantly influenced ESR activation and fluxes to E-DNA within the MNM. Likewise, all BCRF directly and/or indirectly influenced ESR activation, proliferation, and key flux constraints influencing E-DNA (i.e., levels of estrogens, CYP1B1, SULT1A1, SULT1A2, and GSTP1). However, no unambiguous total effect of BCRF on proliferation became apparent. Furthermore, BMI was the only BCRF to indeed influence fluxes to E-DNA (via congruent adverse influence on levels of estrogens, CYP1B1 and SULT1A2)., (© 2021. The Author(s).)

CdFe 12 O 19 nanostructures and nanocomposites were synthesized via sol-gel auto-combustion method. The CdFe 12 O 19 /CdTiO 3 nanocomposites were prepared at 850 °C after 1 h calcination, when molar ratio of Cd:Ti is 1:1. Effects of the molar ratio of Cd:Ti, type of precursor and calcination temperature were investigated. Both nanostructures and nanocomposites were ferromagnetic at room temperature, with saturation magnetizations (Ms) 2.561 and 11.269 emu g -1 , and coercivities (Hc) 696.517 and 969.068 Oe, respectively. SEM, TEM, FTIR, EDS and XRD showed morphology, composition and particle size of the products. VSM, UV-vis and CV were used to investigate the magnetic, optical and electrochemical properties. The band gap values of the nanocomposites were larger than that in the nanostructures. Also photocatalytic properties of the nanocomposites were investigated. This is the first attempt on the study of photocatalytic performance of the cadmium hexaferrite nanostructures and nanocomposites. The effects of various factors including composition and particle size of the nanocomposites, the Ti:Cd molar ratio and also kind of organic dye on the photocatalytic behavior of the products were evaluated. The photocatalytic activity achieved a maximum when ratio of Ti:Cd was 1:1. Methyl orange, methylene blue and methylene violet decompositions were about 71.35, 55.08 and 62.27% by nanocomposites, respectively., (Copyright © 2019 Elsevier B.V. All rights reserved.)

It is the first time that PbFe 12 O 19 nanostructures were successfully synthesized by sonochemical method. The instrumental and experimental parameters were optimized to achieve the appropriate product. The results showed that Pb +2 to Fe +3 molar ratio and the type of capping agent as experimental parameters and time and power of sonication as instrumental variables can influence on the purity and particle size of products, respectively. According to the results, the synthesis process could improve to sol-gel assisted sonochemical method in presence of PEG as capping agent. In this method, pure product obtained by using the high temperature and pressure in sonication treatment and hydrolysis and condensation processes in sol-gel method, simultaneously. Concurrent presence of sonication treatment and PEG were necessary for preparation of pure hexaferrite nanostructures. Because of metal oxides nanostructures as major product and hexaferrite as minor product were produced in the absence of them. So, sol-gel assisted sonochemical method can be introduced as an effective method for preparation of hexaferrite nanostructures. Furthermore, it was found that the instrumental parameters should be optimized, because of increasing the time and power of sonication is not always favorable for preparation of ultrafine particles and small structures., (Copyright © 2017 Elsevier B.V. All rights reserved.)