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

Densely connected convolutional networks with attention LSTM for crowd flows prediction

With the rapid progress of urbanization, predicting citywide crowd flows has become increasingly significant in many fields, such as traffic management and public security. However, influenced by the complex spatiotemporal relations in raw data and other factors, such as events and weather, obtaining a precise prediction is challenging. Some previous works attempted to address this problem using various ways, such as autoregressive integrated moving average, vector auto-regression and some deep learning models. However, seldom can these methods comprehensively capture the spatiotemporal correlations. In this paper, we propose a novel spatio-temporal prediction model that is based on densely connected convolutional networks and attention long short-term memory (ST-DCCNAL), to simultaneously predict the inflow and outflow of the crowds in regions divided within a specific city. The ST-DCCNAL model consists of three parts: spatial part, external factors part and temporal part. In the spatial part, we employ densely connected convolutional networks to extract spatial characteristics at different levels. The external factors part utilizes a fully connected network to extract features from auxiliary information. In the last part, an attention-based long short-term memory module is leveraged to capture the temporal pattern. To demonstrate the practicality and effectiveness of the proposed model, we evaluate it using two separate real-world datasets of taxis in Beijing and bikes in New York. The experimental results confirm that the performance of our model is better than that of other baseline methods

Predicting the impact of android malicious samples via machine learning

Recently Android malicious samples threaten billions of the mobile end users’ security or privacy. The community researchers have designed many methods to automatically and accurately identify Android malware samples. However, the rapid increase of Android malicious samples outpowers the capabilities of traditional Android malware detectors and classifiers with respect to the cyber security risk management needs. It is important to identify the small proportion of Android malicious samples that may produce high cyber-security or privacy impact. In this paper, we propose a light-weight solution to automatically identify the Android malicious samples with high security and privacy impact. We manuallycheck a number of Android malware families and corresponding security incidents, and define two impact metrics for Android malicious samples. Our investigation results in a new Android malware dataset with impact ground truth (low impact or high impact). This new dataset is employed to empirically investigate the intrinsic characteristics of low impact as well as high impact malicious samples. To characterize and captureAndroid malicious samples’ pattern, the reverse engineering is performed to extract semantic features to represent malicious samples. The leveraged features are parsed from both the AndroidManifest.xml files aswell as the disassembled binary classes.dex codes. Then the extracted features are embedded into numerical vectors. Furthermore, we train highly accurate Support Vector Machine and Deep Neural Network classifiers to categorize the candidate Android malicious samples into low impact or high impact. The empirical results validate the effectiveness of our designed light-weight solution. This method can be further utilized foridentifying those high impact Android malicious samples in the wild

Clinical Features of Children with Retinoblastoma and Neuroblastoma

Purpose. Retinoblastoma and neuroblastoma are the most common malignant extracranial solid tumors in children. This study aimed to summarize the clinical features, especially the delayed diagnosis in children with retinoblastoma and neuroblastoma. Methods. In a single hospital-based case-control study, a retrospective cohort of 175 children with retinoblastoma and neuroblastoma diagnosed from January 2016 to January 2018 were reviewed. The state of enucleation in retinoblastomas and pathological prognosis in neuroblastomas were outcome indicators. Hereby, the patients were divided into two groups, and clinical features including age at presentation and delayed diagnosis were compared. Results. A total of 112 patients with retinoblastoma and 63 with neuroblastoma were included. In the retinoblastoma cohort, the median age at presentation was 17.2 months (0.3–110 months). The mean delay of diagnosis was 1.6 ± 2.3 months, and the rate of enucleation was 61.6%. Unilateral disease, the International Classification of Intraocular Retinoblastoma (IIRC) stage E, and delay of diagnosis over 2.5 months were independent risk factors of ocular outcomes. Notably, the risk of enucleation was increased by 474% when the delay was longer than 2.5 months. In the neuroblastoma cohort, the delay of diagnosis of the unfavorable histology (UH) group was longer than that of the favorable histology (FH) group (1.9 months vs. 1.4 months, P=.487). The levels of serum ferritin and neuron-specific enolase were higher in the UH group than in the FH group (P<.05). Conclusions. This study summarized the clinical features and diagnosis biomarkers of retinoblastoma and neuroblastoma patients in China. These results might help to focus on early detection and treatment in children with retinoblastoma and neuroblastoma