Multi-labelled proteins recognition for high-throughput microscopy images using deep convolutional neural networks

BACKGROUND

Proteins are of extremely vital importance in the human body, and no movement or activity can be performed without proteins. Currently, microscopy imaging technologies developed rapidly are employed to observe proteins in various cells and tissues. In addition, due to the complex and crowded cellular environments as well as various types and sizes of proteins, a considerable number of protein images are generated every day and cannot be classified manually. Therefore, an automatic and accurate method should be designed to properly solve and analyse protein images with mixed patterns.

RESULTS

In this paper, we first propose a novel customized architecture with adaptive concatenate pooling and "buffering" layers in the classifier part, which could make the networks more adaptive to training and testing datasets, and develop a novel hard sampler at the end of our network to effectively mine the samples from small classes. Furthermore, a new loss is presented to handle the label imbalance based on the effectiveness of samples. In addition, in our method, several novel and effective optimization strategies are adopted to solve the difficult training-time optimization problem and further increase the accuracy by post-processing.

CONCLUSION

Our methods outperformed the SOTA method of multi-labelled protein classification on the HPA dataset, GapNet-PL, by above 2% in the F1 score. Therefore, experimental results based on the test set split from the Human Protein Atlas dataset show that our methods have good performance in automatically classifying multi-class and multi-labelled high-throughput microscopy protein images.

Effectiveness and mechanisms of adipose-derived stem cell therapy in animal models of Parkinson's disease: a systematic review and meta-analysis

Animal models provide an opportunity to assess the optimal treatment way and the underlying mechanisms of direct clinical application of adipose-derived stem cells (ADSCs). Previous studies have evaluated the effects of primitive and induced ADSCs in animal models of Parkinson's disease (PD). Here, eight databases were systematically searched for studies on the effects and in vivo changes caused by ADSC intervention. Quality assessment was conducted using a 10-item risk of bias tool. For the subsequent meta-analysis, study characteristics were extracted and effect sizes were computed. Ten out of 2324 published articles (n = 169 animals) were selected for further meta-analysis. After ADSC therapy, the rotation behavior (10 experiments, n = 156 animals) and rotarod performance (3 experiments, n = 54 animals) were improved (P < 0.000 01 and P = 0.000 3, respectively). The rotation behavior test reflected functional recovery, which may be due to the neurogenesis from neuronally differentiated ADSCs, resulting in a higher pooled effect size of standard mean difference (SMD) (- 2.59; 95% CI, - 3.57 to - 1.61) when compared to that of primitive cells (- 2.18; 95% CI, - 3.29 to - 1.07). Stratified analyses by different time intervals indicated that ADSC intervention exhibited a long-term effect. Following the transplantation of ADSCs, tyrosine hydroxylase-positive neurons recovered in the lesion area with pooled SMD of 13.36 [6.85, 19.86]. Transplantation of ADSCs is a therapeutic option that shows long-lasting effects in animal models of PD. The potential mechanisms of ADSCs involve neurogenesis and neuroprotective effects. The standardized induction of neural form of transplanted ADSCs can lead to a future application in clinical practice.

Forecast analysis of the epidemics trend of COVID-19 in the USA by a generalized fractional-order SEIR model

In this paper, a generalized fractional-order SEIR model is proposed, denoted by SEIQRP model, which divided the population into susceptible, exposed, infectious, quarantined, recovered and insusceptible individuals and has a basic guiding significance for the prediction of the possible outbreak of infectious diseases like the coronavirus disease in 2019 (COVID-19) and other insect diseases in the future. Firstly, some qualitative properties of the model are analyzed. The basic reproduction number R 0 is derived. WhenR 0 < 1 , the disease-free equilibrium point is unique and locally asymptotically stable. WhenR 0 > 1 , the endemic equilibrium point is also unique. Furthermore, some conditions are established to ensure the local asymptotic stability of disease-free and endemic equilibrium points. The trend of COVID-19 spread in the USA is predicted. Considering the influence of the individual behavior and government mitigation measurement, a modified SEIQRP model is proposed, defined as SEIQRPD model, which is divided the population into susceptible, exposed, infectious, quarantined, recovered, insusceptible and dead individuals. According to the real data of the USA, it is found that our improved model has a better prediction ability for the epidemic trend in the next two weeks. Hence, the epidemic trend of the USA in the next two weeks is investigated, and the peak of isolated cases is predicted. The modified SEIQRP model successfully capture the development process of COVID-19, which provides an important reference for understanding the trend of the outbreak.

IGFBP2 promotes immunosuppression associated with its mesenchymal induction and FcγRIIB phosphorylation in glioblastoma

Immunotherapy shows a promise for treating glioblastoma (GBM), the most malignant and immunosuppressive glioma. The mesenchymal phenotype of cancer cells was frequently reported to be associated with their induction of immunosuppression within the cancer microenvironment. Overexpressed insulin-like growth factor binding protein 2 (IGFBP2) promotes GBM cell migration and invasion, and contributes to glioma progression and cancer recurrence and poor survival in GBM. However, whether IGFBP2 can induce immunosuppression in GBM was not reported yet. Thus, the study applied a syngeneic mouse GBM model, human GBM samples, and cancer-immune cell co-culture experiments to investigate the effect of IGFBP2 on GBM exposed immune cells and its association with the mesenchymal induction. We found that IGFBP2 promoted the mesenchymal feature of GBM cells. The inhibition of IGFBP2 relieved immunosuppression by increasing CD8⁺ T and CD19⁺ B cells and decreasing CD163⁺ M2 macrophages. Further, the IGFBP2-promoted immunosuppression was associated with its induction of the mesenchymal feature of GBM cells and the inhibitory phosphorylated FcγRIIB of GBM exposed immune cells. Blocking IGFBP2 suppressed tumor growth and improved survival of tumor bearing mice in the mouse GBM model. These findings support the notion that targeting the IGFBP2 may present an effective immunotherapeutic strategy for mesenchymal GBMs.

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data

BACKGROUND

Above-ground biomass (AGB) is a basic agronomic parameter for field investigation and is frequently used to indicate crop growth status, the effects of agricultural management practices, and the ability to sequester carbon above and below ground. The conventional way to obtain AGB is to use destructive sampling methods that require manual harvesting of crops, weighing, and recording, which makes large-area, long-term measurements challenging and time consuming. However, with the diversity of platforms and sensors and the improvements in spatial and spectral resolution, remote sensing is now regarded as the best technical means for monitoring and estimating AGB over large areas.

RESULTS

In this study, we used structural and spectral information provided by remote sensing from an unmanned aerial vehicle (UAV) in combination with machine learning to estimate maize biomass. Of the 14 predictor variables, six were selected to create a model by using a recursive feature elimination algorithm. Four machine-learning regression algorithms (multiple linear regression, support vector machine, artificial neural network, and random forest) were evaluated and compared to create a suitable model, following which we tested whether the two sampling methods influence the training model. To estimate the AGB of maize, we propose an improved method for extracting plant height from UAV images and a volumetric indicator (i.e., BIOVP). The results show that (1) the random forest model gave the most balanced results, with low error and a high ratio of the explained variance for both the training set and the test set. (2) BIOVP can retain the largest strength effect on the AGB estimate in four different machine learning models by using importance analysis of predictors. (3) Comparing the plant heights calculated by the three methods with manual ground-based measurements shows that the proposed method increased the ratio of the explained variance and reduced errors.

CONCLUSIONS

These results lead us to conclude that the combination of machine learning with UAV remote sensing is a promising alternative for estimating AGB. This work suggests that structural and spectral information can be considered simultaneously rather than separately when estimating biophysical crop parameters.

A geographical location prediction method based on continuous time series Markov model

Trajectory data uploaded by mobile devices is growing quickly. It represents the movement of an individual or a device based on the longitude and latitude coordinates collected by GPS. The location based service has a broad application prospect in the real world. As the traditional location prediction models which are based on the discrete state sequence cannot predict the locations in real time, we propose a Continuous Time Series Markov Model (CTS-MM) to solve this problem. The method takes the Gaussian Mixed Model (GMM) to simulate the posterior probability of a location in the continuous time series. The probability calculation method and state transition model of the Hidden Markov Model (HMM) are improved to get the precise location prediction. The experimental results on GeoLife data show that CTS-MM performs better for location prediction in exact minute than traditional location prediction models.

Preparation and applications of guard cell protoplasts from the leaf epidermis of Solanum lycopersicum

BACKGROUND

Guard cell protoplasts (GCPs) isolated from various plants have proven to be especially useful for studies of signal transduction pathways and plant development. But it is not easy to isolate highly purified preparations of large numbers of GCPs from plants. In this research, our focus is on a method to isolate large numbers of guard cells from tomato leaves. The protocols described yield millions of highly purified, viable GCPs, which are also suitable for studies on guard cell physiology.

RESULTS

We developed an efficient method for isolating GCPs from epidermal fragments of tomato leaves. The protocol requires a two-step digestion to isolate high-quality tomato GCPs. In this procedure, cellulysin (in method L) was replaced by cellulose "Onozuka" RS (in method S) in the first digestion step, which indicated that cellulase RS was more effective than cellulysin. Method S dramatically shortened the time required for obtaining high yields and high-quality GCPs. Moreover, according to the GCP yields, hydroponic plants were more effective than substrate-cultured plants.

CONCLUSIONS

In this paper, protocols for large-scale preparation of GCPs and mesophyll cell protoplasts were described, followed by some success examples of their use in biochemical and molecular approaches such as reverse-transcription polymerase chain reaction, real-time polymerase chain reaction and sodium dodecyl sulfate-polyacrylamide gel electrophoresis. The method was proved to be a more efficient GCP-isolating method, capable of providing high yields with better quality in less time.

A meta-analysis: Is there any association between MiR-608 rs4919510 polymorphism and breast cancer risks?

Object

To combine the data from previously conducted studies about the associations between miR-608 rs4919510 polymorphism (C>G) and breast cancer risks.

Methods

According to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, we conducted a systematic review of the related literatures searched from PubMed, Embase, Cochrane Library, Web of Science, and China National Knowledge Internet (CNKI) (time: ~ December 2016). Using DerSimonian-Laird random-effects models [Pooling Model: Mantel Haenszel (MH)], odd ratios (ORs) with 95% confidence intervals (95% CIs) were estimated in the allele model, homozygote model, heterozygote model, dominant model and recessive model. Heterogeneity was analyzed using Labbr plots and I² statistic. Publication bias was analyzed using contour-enhanced funnel plots.

Results

We included 5 eligible studies with 7948 patients. The ORs and their 95% CIs in the 5 genetic models mentioned above were 1.009 (95% CI: 0.922, 1.104; p = 0.847), 1.098 (95% CI: 0.954, 1.264; p = 0.194), 1.076 (95% CI: 0.956, 1.211; p = 0.227), 1.043 (95% CI: 0.880, 1.236; p = 0.628), 1.007 (95% CI: 0.906, 1.118; p = 0.899), respectively.

Conclusion

In the present meta-analysis, no relationships between miR-608 rs4919510 polymorphism (C>G) and the risk of breast cancer were found. More studies are warranted to further validate the conclusion.

Craniofacial similarity analysis through sparse principal component analysis

The computer-aided craniofacial reconstruction (CFR) technique has been widely used in the fields of criminal investigation, archaeology, anthropology and cosmetic surgery. The evaluation of craniofacial reconstruction results is important for improving the effect of craniofacial reconstruction. Here, we used the sparse principal component analysis (SPCA) method to evaluate the similarity between two sets of craniofacial data. Compared with principal component analysis (PCA), SPCA can effectively reduce the dimensionality and simultaneously produce sparse principal components with sparse loadings, thus making it easy to explain the results. The experimental results indicated that the evaluation results of PCA and SPCA are consistent to a large extent. To compare the inconsistent results, we performed a subjective test, which indicated that the result of SPCA is superior to that of PCA. Most importantly, SPCA can not only compare the similarity of two craniofacial datasets but also locate regions of high similarity, which is important for improving the craniofacial reconstruction effect. In addition, the areas or features that are important for craniofacial similarity measurements can be determined from a large amount of data. We conclude that the craniofacial contour is the most important factor in craniofacial similarity evaluation. This conclusion is consistent with the conclusions of psychological experiments on face recognition and our subjective test. The results may provide important guidance for three- or two-dimensional face similarity evaluation, analysis and face recognition.

National Prociency Testing Result of CYP2D6*10 Genotyping for Adjuvant Tamoxifen Therapy in China

Tamoxifen has been successfully used for treating breast cancer and preventing cancer recurrence. Cytochrome P450 2D6 (CYP2D6) plays a key role in the process of metabolizing tamoxifen to its active moiety, endoxifen. Patients with variants of the CYP2D6 gene may not receive the full benefit of tamoxifen treatment. The CYP2D6*10 variant (the most common variant in Asians) was analyzed to optimize the prescription of tamoxifen in China. To ensure referring clinicians have accurate information for genotype-guided tamoxifen treatment, the Chinese National Center for Clinical Laboratories (NCCL) organized a national proficiency testing (PT) to evaluate the performance of laboratories providing CYP2D6*10 genotyping. Ten genomic DNA samples with CYP2D6 wild-type or CYP2D6*10 variants were validated by PCR-sequencing and sent to 28 participant laboratories. The genotyping results and pharmacogenomic test reports were submitted and evaluated by NCCL experts. Additional information regarding the number of samples tested, the accreditation/certification status, and detecting technology was also requested. Thirty-one data sets were received, with a corresponding analytical sensitivity of 98.2% (548/558 challenges; 95% confidence interval: 96.7–99.1%) and an analytic specificity of 96.5% (675/682; 95% confidence interval: 97.9–99.5%). Overall, 25/28 participants correctly identified CYP2D6*10 status in 10 samples; however, two laboratories made serious genotyping errors. Most of the essential information was included in the 20 submitted CYP2D6*10 test reports. The majority of Chinese laboratories are reliable for detecting the CYP2D6*10 variant; however, several issues revealed in this study underline the importance of PT schemes in continued external assessment and provision of guidelines.

In Vitro Interactions between Non-Steroidal Anti-Inflammatory Drugs and Antifungal Agents against Planktonic and Biofilm Forms of Trichosporon asahii

Increasing drug resistance has brought enormous challenges to the management of Trichosporon spp. infections. The in vitro antifungal activities of non-steroidal anti-inflammatory drugs (NSAIDs) against Candida spp. and Cryptococcus spp. were recently discovered. In the present study, the in vitro interactions between three NSAIDs (aspirin, ibuprofen and diclofenac sodium) and commonly used antifungal agents (fluconazole, itraconazole, voriconazole, caspofungin and amphotericin B) against planktonic and biofilm cells of T. asahii were evaluated using the checkerboard microdilution method. The spectrophotometric method and the XTT reduction assay were used to generate data on biofilm cells. The fractional inhibitory concentration index (FICI) and the ΔE model were compared to interpret drug interactions. Using the FICI, the highest percentages of synergistic effects against planktonic cells (86.67%) and biofilm cells (73.33%) were found for amphotericin B/ibuprofen, and caspofungin/ibuprofen showed appreciable percentages (73.33% for planktonic form and 60.00% for biofilm) as well. We did not observe antagonism. The ΔE model gave consistent results with FICI (86.67%). Our findings suggest that amphotericin B/ibuprofen and caspofungin/ibuprofen combinations have potential effects against T. asahii. Further in vivo and animal studies to investigate associated mechanisms need to be conducted.

The PI3K/Akt, p38MAPK, and JAK2/STAT3 signaling pathways mediate the protection of SO2 against acute lung injury induced by limb ischemia/reperfusion in rats

Sulfur dioxide (SO2) is naturally synthesized by glutamate-oxaloacetate transaminase (GOT) from L-cysteine in mammalian cells. We found that SO2 may have a protective effect on acute lung injury (ALI) induced by limb ischemia/reperfusion (I/R) in rats. The PI3K/Akt, p38MAPK, and JAK2/STAT3 pathways are crucial in cell signaling transduction. The present study aims to verify the role of SO2 on limb I/R-induced ALI, and investigate whether PI3K/Akt, p38MAPK, and JAK2/STAT3 pathways were involved, as well as the relationship among the three pathways; we used specific inhibitors (LY294002, SB03580, and Stattic) to block them, respectively. The experimental methods of Western, ELISA, TUNEL, etc., were used to test the results. In the I/R group, the parameters of lung injury (MDA, MPO, TUNEL, cytokines) increased significantly, but the administration of Na2SO3/NaHSO3 attenuated the damage in the lung. The Western results showed that the rat's lung exist expression of P-STAT3, P-AKT, and P-p38 proteins. After I/R, P-STAT3, P-Akt, and P-p38 proteins expression all increased. After using Na2SO3/NaHSO3, P-Akt, and P-p38 proteins expression increased, but P-STAT3 protein expression decreased. We also found a strange phenomenon; compared to the I/R + SO2 group, the administration of stattic, P-p38 protein expression showed no change, but P-Akt protein expression increased (p < 0.05). In conclusion, SO2 has a protective effect on rats with limb I/R-induced ALI. The JAK2/STAT3, PI3K/Akt, and p38MAPK pathways are likely all involved in the process, and the JAK2/STAT3 pathway may have an impact on the P13K/Akt pathway.