Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Visual Exploration of Cycling Semantics with GPS Trajectory Data

Cycling—as a sustainable and convenient exercise and travel mode—has become increasingly popular in modern cities. In recent years, with the proliferation of sport apps and GPS mobile devices in daily life, the accumulated cycling trajectories have opened up valuable opportunities to explore the underlying cycling semantics to enable a better cycling experience. In this paper, based on large-scale GPS trajectories and road network data, we mainly explore cycling semantics from two perspectives. On one hand, from the perspective of the cyclists, trajectories could tell their frequently visited sequences of streets, thus potentially revealing their hidden cycling themes, i.e., cyclist behavior semantics. On the other hand, from the perspective of the road segments, trajectories could show the cyclists’ fine-grained moving features along roads, thus probably uncovering the moving semantics on roads. However, the extraction and understanding of such cycling semantics are nontrivial, since most of the trajectories are raw data and it is also difficult to aggregate the dynamic moving features from trajectories into static road segments. To this end, we establish a new visual analytic system called VizCycSemantics for pervasive computing, in which a topic model (i.e., LDA) is used to extract the topics of cyclist behavior semantics and moving semantics on roads, and a clustering method (i.e., k-means ++) is used to further capture the groups of similar cyclists and road segments within the city; finally, multiple interactive visual interfaces are implemented to facilitate the interpretation for analysts. We conduct extensive case studies in the city of Beijing to demonstrate the effectiveness and practicability of our system and also obtain various insightful findings and pieces of advice

VampNet-based BTT vibration reconstruction

Blade tip-timing (BTT) vibration measurement is seriously under-sampled due to few BTT probes, so it often needs to reconstruct true blade vibration characteristics. In practice, the characteristic of sparsity makes compressed sensing be useful for under-sampled BTT vibration reconstruction. Most existing algorithms still depend on prior sparse information. In recent years, deep learning methods have been investigated to deal with compressed sensing (CS) without prior information. However, traditional deep neural network architectures need many layers for accuracy, leading to more complexity. In order to cope with it, iterative reconstruction algorithms have been recently studied to modify traditional neural-network architectures. Thus, this paper proposes a VampNet-based BTT vibration reconstruction method by using the approximate message passing algorithm. Firstly, the angular-domain CS model of BTT vibration measurement is built. Based on it, the VampNet model of BTT vibration reconstruction is derived. Next, Matlab/Simulink simulations are done to generate BTT samples under variable speeds and then validate the reconstruction accuracy. The simulation results demonstrate that the proposed method can carry out BTT vibration reconstruction faster than existing deep learning methods

MOOP: An Efficient Utility-Rich Route Planning Framework Over Two-Fold Time-Dependent Road Networks

International audienceUtility-rich (e.g., more attractive or safer) route planning on city-scale road networks is a common yet time-consuming task. Although both travel time and utility on edges are time-dependent concurrently in real cases, they are overlooked in most prior work. In this paper, we focus on the route planning over two-fold time-dependent road networks, i.e., both travel time and utility on edges are varying over time. We aim to find a route from an origin to a destination by maximizing the accumulated utility score within a time budget. Moreover, to satisfy users' real-time requests, the fast response is usually mandatory. Here, we propose a novel two-phase framework called , i.e., anaging ffline data for nline route lanning, to discover the near-optimal driving routes efficiently. Specifically, in the offline phase, we construct the auxiliary data structure, i.e., the edge table, to manage the time-dependent information about edges. In the following online phase, the route is generated sequentially by an iterative edge table visiting process. We evaluate the proposed thoroughly based on synthetic road networks and two real-world road networks in the city of Chengdu (4,819 nodes and 6,385 edges) and the city of Chongqing (5,056 nodes and 7,355 edges) in China. Results show that: (i) our framework can work adaptively for different time-varying utility patterns; (ii) the edge table is economic yet effective; (iii) our route planning algorithm outperforms other baselines in obtaining the highest utility value while costing the least running time

Targeting NRF2 uncovered an intrinsic susceptibility of acute myeloid leukemia cells to ferroptosis

Abstract Drug resistance and poor treatment response are major obstacles to the effective treatment of acute myeloid leukemia (AML). A deeper understanding of the mechanisms regulating drug resistance and response genes in AML is therefore urgently needed. Our previous research has highlighted the important role of nuclear factor E2-related factor 2 (NRF2) in AML, where it plays a critical role in detoxifying reactive oxygen species and influencing sensitivity to chemotherapy. In this study, we identify a core set of direct NRF2 targets that are involved in ferroptosis, a novel form of cell death. Of particular interest, we find that glutathione peroxidase 4 (GPX4) is a key ferroptosis gene that is consistently upregulated in AML, and high expression of GPX4 is associated with poor prognosis for AML patients. Importantly, simultaneous inhibition of NRF2 with ML385 and GPX4 with FIN56 or RSL3 synergistically targets AML cells, triggering ferroptosis. Treatment with ML385 + FIN56/RSL3 resulted in a marked reduction in NRF2 and GPX4 expression. Furthermore, NRF2 knockdown enhanced the sensitivity of AML cells to the ferroptosis inducers. Taken together, our results suggest that combination therapy targeting both NRF2 and GPX4 may represent a promising approach for the treatment of AML