A Case Study of Upper-Room UVGI in Densely-Occupied Elementary Classrooms by Real-Time Fluorescent Bioaerosol Measurements

Recently, the requirement to continuously collect bioaerosol samples using shorter response times has called for the use of real-time detection. The decreased cost of this technology makes it available for a wider application than military use, and makes it accessible to pharmaceutical and academic research. In this case study, real-time bioaerosol monitors (RBMs) were applied in elementary school classrooms—a densely occupied environment—along with upper-room ultraviolet germicidal irradiation (UVGI) devices. The classrooms were separated into a UVGI group and a non-UVGI control group. Fluorescent bioaerosol counts (FBCs) were monitored on 20 visiting days over a four-month period. The classroom with upper-room UVGI showed significantly lower concentrations of fine size (\u3c3 μm) and total FBCs than the control classroom during 13 of the 20 visiting days. The results of the study indicate that the upper-room UVGI could be effective in reducing FBCs in the school environment, and RBMs may be applicable in reflecting the transient conditions of the classrooms due to the dynamic activity levels of the students and teachers

A Case Study of Upper-Room UVGI in Densely-Occupied Elementary Classrooms by Real-Time Fluorescent Bioaerosol Measurements

Recently, the requirement to continuously collect bioaerosol samples using shorter response times has called for the use of real-time detection. The decreased cost of this technology makes it available for a wider application than military use, and makes it accessible to pharmaceutical and academic research. In this case study, real-time bioaerosol monitors (RBMs) were applied in elementary school classrooms—a densely occupied environment—along with upper-room ultraviolet germicidal irradiation (UVGI) devices. The classrooms were separated into a UVGI group and a non-UVGI control group. Fluorescent bioaerosol counts (FBCs) were monitored on 20 visiting days over a four-month period. The classroom with upper-room UVGI showed significantly lower concentrations of fine size (\u3c3 μm) and total FBCs than the control classroom during 13 of the 20 visiting days. The results of the study indicate that the upper-room UVGI could be effective in reducing FBCs in the school environment, and RBMs may be applicable in reflecting the transient conditions of the classrooms due to the dynamic activity levels of the students and teachers

Statistical aspects of omics data analysis using the random compound covariate

BACKGROUND: Dealing with high dimensional markers, such as gene expression data obtained using microarray chip technology or genomics studies, is a key challenge because the numbers of features greatly exceeds the number of biological samples. After selecting biologically relevant genes, how to summarize the expression of selected genes and then further build predicted model is an important issue in medical applications. One intuitive method of addressing this challenge assigns different weights to different features, subsequently combining this information into a single score, named the compound covariate. Investigators commonly employ this score to assess whether an association exists between the compound covariate and clinical outcomes adjusted for baseline covariates. However, we found that some clinical papers concerned with such analysis report bias p-values based on flawed compound covariate in their training data set. RESULTS: We correct this flaw in the analysis and we also propose treating the compound score as a random covariate, to achieve more appropriate results and significantly improve study power for survival outcomes. With this proposed method, we thoroughly assess the performance of two commonly used estimated gene weights through simulation studies. When the sample size is 100, and censoring rates are 50%, 30%, and 10%, power is increased by 10.6%, 3.5%, and 0.4%, respectively, by treating the compound score as a random covariate rather than a fixed covariate. Finally, we assess our proposed method using two publicly available microarray data sets. CONCLUSION: In this article, we correct this flaw in the analysis and the propose method, treating the compound score as a random covariate, can achieve more appropriate results and improve study power for survival outcomes

Learning many-body Hamiltonians with Heisenberg-limited scaling

Learning a many-body Hamiltonian from its dynamics is a fundamental problem in physics. In this work, we propose the first algorithm to achieve the Heisenberg limit for learning an interacting $N$-qubit local Hamiltonian. After a total evolution time of $\mathcal{O}(\epsilon^{-1})$, the proposed algorithm can efficiently estimate any parameter in the $N$-qubit Hamiltonian to $\epsilon$-error with high probability. The proposed algorithm is robust against state preparation and measurement error, does not require eigenstates or thermal states, and only uses $\mathrm{polylog}(\epsilon^{-1})$ experiments. In contrast, the best previous algorithms, such as recent works using gradient-based optimization or polynomial interpolation, require a total evolution time of $\mathcal{O}(\epsilon^{-2})$ and $\mathcal{O}(\epsilon^{-2})$ experiments. Our algorithm uses ideas from quantum simulation to decouple the unknown $N$-qubit Hamiltonian $H$ into noninteracting patches, and learns $H$ using a quantum-enhanced divide-and-conquer approach. We prove a matching lower bound to establish the asymptotic optimality of our algorithm.Comment: 11 pages, 1 figure + 27-page appendi

(1R,1′S)-1,1′-Dihydr­oxy-1,1′-biisobenzofuran-3,3′(1H,1′H)-dione

In the title compound, C16H10O6, the complete mol­ecule is generated by a crystallographic centre of symmetry. In the crystal, O—H⋯O hydrogen bonds link the mol­ecules into (100) sheets and C—H⋯O links also occur

LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error

Tools are essential for large language models (LLMs) to acquire up-to-date information and take consequential actions in external environments. Existing work on tool-augmented LLMs primarily focuses on the broad coverage of tools and the flexibility of adding new tools. However, a critical aspect that has surprisingly been understudied is simply how accurately an LLM uses tools for which it has been trained. We find that existing LLMs, including GPT-4 and open-source LLMs specifically fine-tuned for tool use, only reach a correctness rate in the range of 30% to 60%, far from reliable use in practice. We propose a biologically inspired method for tool-augmented LLMs, simulated trial and error (STE), that orchestrates three key mechanisms for successful tool use behaviors in the biological system: trial and error, imagination, and memory. Specifically, STE leverages an LLM's 'imagination' to simulate plausible scenarios for using a tool, after which the LLM interacts with the tool to learn from its execution feedback. Both short-term and long-term memory are employed to improve the depth and breadth of the exploration, respectively. Comprehensive experiments on ToolBench show that STE substantially improves tool learning for LLMs under both in-context learning and fine-tuning settings, bringing a boost of 46.7% to Mistral-Instruct-7B and enabling it to outperform GPT-4. We also show effective continual learning of tools via a simple experience replay strategy.Comment: Code and data available at https://github.com/microsoft/simulated-trial-and-erro