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

31st Annual Meeting and Associated Programs of the Society for Immunotherapy of Cancer (SITC 2016) : part two

Background The immunological escape of tumors represents one of the main ob- stacles to the treatment of malignancies. The blockade of PD-1 or CTLA-4 receptors represented a milestone in the history of immunotherapy. However, immune checkpoint inhibitors seem to be effective in specific cohorts of patients. It has been proposed that their efficacy relies on the presence of an immunological response. Thus, we hypothesized that disruption of the PD-L1/PD-1 axis would synergize with our oncolytic vaccine platform PeptiCRAd. Methods We used murine B16OVA in vivo tumor models and flow cytometry analysis to investigate the immunological background. Results First, we found that high-burden B16OVA tumors were refractory to combination immunotherapy. However, with a more aggressive schedule, tumors with a lower burden were more susceptible to the combination of PeptiCRAd and PD-L1 blockade. The therapy signifi- cantly increased the median survival of mice (Fig. 7). Interestingly, the reduced growth of contralaterally injected B16F10 cells sug- gested the presence of a long lasting immunological memory also against non-targeted antigens. Concerning the functional state of tumor infiltrating lymphocytes (TILs), we found that all the immune therapies would enhance the percentage of activated (PD-1pos TIM- 3neg) T lymphocytes and reduce the amount of exhausted (PD-1pos TIM-3pos) cells compared to placebo. As expected, we found that PeptiCRAd monotherapy could increase the number of antigen spe- cific CD8+ T cells compared to other treatments. However, only the combination with PD-L1 blockade could significantly increase the ra- tio between activated and exhausted pentamer positive cells (p= 0.0058), suggesting that by disrupting the PD-1/PD-L1 axis we could decrease the amount of dysfunctional antigen specific T cells. We ob- served that the anatomical location deeply influenced the state of CD4+ and CD8+ T lymphocytes. In fact, TIM-3 expression was in- creased by 2 fold on TILs compared to splenic and lymphoid T cells. In the CD8+ compartment, the expression of PD-1 on the surface seemed to be restricted to the tumor micro-environment, while CD4 + T cells had a high expression of PD-1 also in lymphoid organs. Interestingly, we found that the levels of PD-1 were significantly higher on CD8+ T cells than on CD4+ T cells into the tumor micro- environment (p < 0.0001). Conclusions In conclusion, we demonstrated that the efficacy of immune check- point inhibitors might be strongly enhanced by their combination with cancer vaccines. PeptiCRAd was able to increase the number of antigen-specific T cells and PD-L1 blockade prevented their exhaus- tion, resulting in long-lasting immunological memory and increased median survival

Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research

Multi-dimensional quality-driven service recommendation with privacy-preservation in mobile edge environment

© 2020 Elsevier B.V. With the advance of mobile edge computing (MEC), the number of edge services running on mobile devices grows explosively. In this situation, it is becoming a necessity to recommend the most suitable edge services to a mobile user from massive candidates, based on the historical quality of service (QoS) data. However, historical QoS is a kind of private data for users, which needs to be protected from privacy disclosure. Currently, researchers often use the Locality-Sensitive Hashing (LSH) technique to achieve the goal of privacy-aware recommendations. However, existing LSH-based methods are only applied to the recommendation scenarios with a single QoS dimension (e.g., response time or throughput), without considering the multi-dimensional QoS (e.g., response time and throughput) ensemble, which narrow the application scope of LSH in privacy-preserving recommendations significantly. Considering this drawback, this paper proposes a multi-dimensional quality ensemble-driven recommendation approach named RecLSH-TOPSIS based on LSH and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) techniques. First, the traditional single-dimensional LSH recommendation approach is extended to be a multi-dimensional one, through which we can obtain a set of candidate services that a user may prefer. Second, we use TOPSIS technique to rank the derived multiple candidate services and return the user an optimal one. At last, a case study is presented to illustrate the feasibility of our proposal to make privacy-preserving edge service recommendations with multiple QoS dimensions

Deep Learning for Mobile Crowdsourcing Techniques, Methods, and Challenges: A Survey

With the ever-increasing popularity of mobile computing technology and the wide adoption of outsourcing strategy in labour-intensive industrial domains, mobile crowdsourcing has recently emerged as a promising resolution for solving complex computational tasks with quick response requirements. However, the complexity of a mobile crowdsourcing task makes it hard to pursue an optimal resolution with limited computing resources, as well as various task constraints. In this situation, deep learning has provided a promising way to pursue such an optimal resolution by training a set of optimal parameters. In the past decades, many researchers have devoted themselves to this hot topic and brought various cutting-edge resolutions. In view of this, we review the current research status of deep learning for mobile crowdsourcing from the perspectives of techniques, methods, and challenges. Finally, we list a group of remaining challenges that call for an intensive study in future research

The Successful Diagnosis and Typing of Systemic Amyloidosis Using A Microwave-Assisted Filter-Aided Fast Sample Preparation Method and LC/MS/MS Analysis.

Laser microdissection followed by mass spectrometry has been successfully used for amyloid typing. However, sample contamination can interfere with proteomic analysis, and overnight digestion limits the analytical throughput. Moreover, current quantitative analysis methods are based on the spectrum count, which ignores differences in protein length and may lead to misdiagnoses. Here, we developed a microwave-assisted filter-aided sample preparation (maFASP) method that can efficiently remove contaminants with a 10-kDa cutoff ultrafiltration unit and can accelerate the digestion process with the assistance of a microwave. Additionally, two parameters (P- and D-scores) based on the exponentially modified protein abundance index were developed to define the existence of amyloid deposits and those causative proteins with the greatest abundance. Using our protocol, twenty cases of systemic amyloidosis that were well-typed according to clinical diagnostic standards (training group) and another twenty-four cases without subtype diagnoses (validation group) were analyzed. Using this approach, sample preparation could be completed within four hours. We successfully subtyped 100% of the cases in the training group, and the diagnostic success rate in the validation group was 91.7%. This maFASP-aided proteomic protocol represents an efficient approach for amyloid diagnosis and subtyping, particularly for serum-contaminated samples

The application of fibular free flap with flexor hallucis longus in maxilla or mandible extensive defect: a comparison study with conventional flap

Abstract Background The repair and reconstruction of maxillary and mandibular extensive defects have put huge challenges to surgeons. The fibular free flap (FFF) is one of the standard treatment choices for reconstruction. The conventional FFF has deficiencies, such as forming poor oral mucosa, limited flap tissue, and perforator vessel variation. To improve the use of FFF, we add the flexor hallucis longus (FHL) in the flap (FHL-FFF). In this paper, we described the advantage and indication of FHL-FFF and conducted a retrospective study to compare FHL-FFF and FFF without FHL. Methods Fifty-four patients who underwent FFF were enrolled and divided into two groups: nFHL group (using FFF without FHL, 38 patients) and FHL group (using FHL-FFF, 16 patients). The perioperative clinical data of patients was collected and analyzed. Results The flaps all survived in two groups. We mainly used FHL to fill dead space, and the donor-site morbidity was slight. In FHL group, flap harvesting time was shorter (118.63 ± 11.76 vs 125.74 ± 11.33 min, P = 0.042), the size of flap’s skin paddle was smaller (16.5 (0–96) vs 21.0(10–104) cm2, P = 0.027) than nFHL group. There were no significant differences (P > 0.05) in hospital days, hospitalization expense, rate of perioperative complications, etc. between the two groups. Compared with FFF without FHL, FHL-FFF will neither affect the use of flap nor bring more problems. Conclusion The FHL-FFF simplifies the flap harvesting operation. The FHL can form good mucosa and make FFF rely less on skin paddle. It can be used for adding flap tissue and dealing with perforator vessel variation in reconstruction of maxillary and mandibular extensive defects