Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI

The neuroimage analysis community has neglected the automated segmentation of the olfactory bulb (OB) despite its crucial role in olfactory function. The lack of an automatic processing method for the OB can be explained by its challenging properties. Nonetheless, recent advances in MRI acquisition techniques and resolution have allowed raters to generate more reliable manual annotations. Furthermore, the high accuracy of deep learning methods for solving semantic segmentation problems provides us with an option to reliably assess even small structures. In this work, we introduce a novel, fast, and fully automated deep learning pipeline to accurately segment OB tissue on sub-millimeter T2-weighted (T2w) whole-brain MR images. To this end, we designed a three-stage pipeline: (1) Localization of a region containing both OBs using FastSurferCNN, (2) Segmentation of OB tissue within the localized region through four independent AttFastSurferCNN - a novel deep learning architecture with a self-attention mechanism to improve modeling of contextual information, and (3) Ensemble of the predicted label maps. The OB pipeline exhibits high performance in terms of boundary delineation, OB localization, and volume estimation across a wide range of ages in 203 participants of the Rhineland Study. Moreover, it also generalizes to scans of an independent dataset never encountered during training, the Human Connectome Project (HCP), with different acquisition parameters and demographics, evaluated in 30 cases at the native 0.7mm HCP resolution, and the default 0.8mm pipeline resolution. We extensively validated our pipeline not only with respect to segmentation accuracy but also to known OB volume effects, where it can sensitively replicate age effects

Risk factors for symptomatic rotator cuff tears: a retrospective case–control study

BackgroundThe incidence and diagnostic rate of rotator cuff tears (RCTs) have increased significantly. The purpose of this study was to investigate and analyze the risk factors for symptomatic RCTs to provide a basis for their prevention and treatment.MethodsWe retrospectively analyzed the relevant clinical indicators of 193 patients with RCTs and 161 patients without RCTs hospitalized with shoulder pain as the main complaint from January 1, 2017, to August 31, 2021. Univariate analysis and multivariate logistic regression analysis were used to analyze the differences in potential risk factors between the two groups.ResultsUnivariate analysis revealed that age (p < 0.001), body mass index (BMI) (p = 0.036), hypertension (p < 0.001), coronary heart disease (p = 0.028), history of shoulder trauma (p < 0.001), hyperlipidemia (p = 0.025), type III acromion (p = 0.012) and critical shoulder angle (CSA) (p < 0.001) increased the risk of RCTs. Multivariate logistic regression analysis revealed that age ≥ 60 years (OR = 2.61, 95% CI = 1.23 to 5.12), CSA ≥ 35° (OR = 4.24, 95% CI = 1.60 to 11.22), hypertension (OR = 2.34, 95% CI = 1.33 to 4.11) and history of shoulder trauma (OR = 5.20, 95% CI = 2.87 to 9.45) were independent risk factors for symptomatic RCTs.ConclusionThe results of this study showed that age ≥ 60 years, CSA ≥35°, hypertension and history of shoulder trauma are independent risk factors for symptomatic RCTs and can provide directions for further development of prevention and treatment strategies. Future studies need to clarify the mechanism underlying the association between these risk factors and symptomatic RCTs

A survey of core and support activities of communicable disease surveillance systems at operating-level CDCs in China

<p>Abstract</p> <p>Background</p> <p>In recent years, problems like insufficient coordination, low efficiency, and heavy working load in national communicable disease surveillance systems in China have been pointed out by many researchers. To strengthen the national communicable disease surveillance systems becomes an immediate concern. Since the World Health Organization has recommended that a structured approach to strengthen national communicable disease surveillance must include an evaluation to existing systems which usually begins with a systematic description, we conducted the first survey for communicable disease surveillance systems in China, in order to understand the situation of core and support surveillance activities at province-level and county-level centers for disease control and prevention (CDCs).</p> <p>Methods</p> <p>A nationwide survey was conducted by mail between May and October 2006 to investigate the implementation of core and support activities of the Notifiable Disease Reporting System (NDRS) and disease-specific surveillance systems in all of the 31 province-level and selected 14 county-level CDCs in Mainland China The comments on the performance of communicable disease surveillance systems were also collected from the directors of CDCs in this survey.</p> <p>Results</p> <p>The core activities of NDRS such as confirmation, reporting and analysis and some support activities such as supervision and staff training were found sufficient in both province-level and county-level surveyed CDCs, but other support activities including information feedback, equipment and financial support need to be strengthened in most of the investigated CDCs. A total of 47 communicable diseases or syndromes were under surveillance at province level, and 20 diseases or syndromes at county level. The activities among different disease-specific surveillance systems varied widely. Acute flaccid paralysis (AFP), measles and tuberculosis (TB) surveillance systems got relatively high recognition both at province level and county level.</p> <p>Conclusions</p> <p>China has already established a national communicable disease surveillance framework that combines NDRS and disease-specific surveillance systems. The core and support activities of NDRS were found sufficient, while the implementation of those activities varied among different disease-specific surveillance systems.</p

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 &lt;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

Methodology for carbon emission flow calculation of integrated energy systems

Aggravation of energy shortage and global warming has raised public low-carbon awareness and promoted the development of integrated energy system (IES). However, there has not been a general method for analysis of carbon emission distribution in the IES yet. A methodology combining energy flow (EF) with carbon emission flow (CEF) is proposed in this paper to calculate and analyze the carbon emission from the IES, which is composed of AC/DC power systems and heating/cooling systems. Decoupling methods are used to improve computing efficiency with good convergence in solving energy flow equations of subsystems. A typical case is modeled and simulated using CloudPSS-IESLab platform, a high-efficient power flow solver, with calculation results and Sankey diagram demonstrating the CEF distribution in the IES

DynaMaR: Dynamic Prompt with Mask Token Representation

Recent research has shown that large language models pretrained using unsupervised approaches can achieve significant performance improvement on many downstream tasks. Typically when adapting these language models to downstream tasks, like a classification or regression task, we employ a fine-tuning paradigm in which the sentence representation from the language model is input to a task-specific head; the model is then fine-tuned end-to-end. However, with the emergence of models like GPT-3, prompt-based fine-tuning has been proven to be a successful approach for few-shot tasks. Inspired by this work, we study discrete prompt technologies in practice. There are two issues that arise with the standard prompt approach. First, it can overfit on the prompt template. Second, it requires manual effort to formulate the downstream task as a language model problem. In this paper, we propose an improvement to prompt-based fine-tuning that addresses these two issues. We refer to our approach as DynaMaR -- Dynamic Prompt with Mask Token Representation. Results show that DynaMaR can achieve an average improvement of 10% in few-shot settings and improvement of 3.7% in data-rich settings over the standard fine-tuning approach on four e-commerce applications