Multi-objective route planning and hierarchical grid model modeling code

For multi-objective route planning (C++) and hierarchical grid model modeling(Python).</p

Table_1_A cluster of Psittacosis cases in Lishui, Zhejiang Province, China, in 2021.docx

IntroductionPsittacosis, caused by Chlamydia psittaci, is widespread throughout the world. In humans, C. psittaci infection may lead to severe conditions and complications, including sepsis and multiple organ failure. We report a cluster of cases caused by C. psittaci in Zhejiang Province, 2021, which led to one death and three cases of hospitalization.MethodsThe cases were confirmed by nest-PCR, RT-PCR, and mNGS.ResultsThe four cases were related and the sequences obtained from the samples were closely correlated with those from Taiwan.DiscussionThis study is the first to report on the case of death from psittacosis in Zhejiang Province, and our results help to assess the disease and recommend effective measures to prevent further spread of C. psittaci.</p

Image_3_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

Image_2_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

Image_1_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

Grb2 mediates synergy between wnt, integrin and growth factor signaling.

<p>Grb2 coordinates signaling downstream of integrin/FAK and growth factor receptors to activate rac and jnk. Grb2 also interacts directly with Dvl2 downstream of Wnt/Fz. Pathways converge at the level of transcriptional activation through LEF/TCF.</p

Grb2 is necessary for optimal wnt signaling.

<p>(A) siRNA to Grb2 reduces protein expression by 50%. HEK293 cells were transfected with control or Grb2-specific siRNA duplexes and harvested at 24 hours for Western blot analysis. GSK-3β served as a loading control. (B) Knockdown of Grb2 reduces Wnt3a signaling. HEK293 were transfected with siRNAs as indicated along with sTF, and rested for 24 hours. Control or Wnt3a-conditioned medium was then added and cells harvested 24 hrs later for luciferase assay. * – p<0.05, relative to control; ** – p<0.05 relative to Wnt3a/siRNA control. (C) Schematic representation of WT and mutant forms of Grb2. (D) A western blot indicates similar expression levels for WT and mutant proteins. HA-tagged proteins were expressed in HEK293 cells and detected using an anti-HA antibody. GSK-3β served as a loading control. (E) Single SH3 domain mutants do not block wnt signaling. HEK293 cells were transfected with sTF along with a Wnt3a plasmid and either WT or W36K Grb2. Cells were harvested at 24 hrs for analysis of luciferase activity. Similar data were obtained for the other single domain mutants (see text). * – p<0.05, relative to Wnt3a/GFP; ** – p<0.01, relative to Wnt3a/GFP. (F) Grb2 with both SH3 domains mutated blocks Wnt3a-mediated signaling. Cells were transfected and analyzed as in (E), inset: western blot demonstrating equal expression of WT and dSH3m Grb2. * – p<0.05, relative to GFP/Wnt3a; ** – p<0.01, relative to GFP/Wnt3a. (G) Grb2 with both SH3 domains mutated blocks Dvl2-mediated signaling. Cells were transfected and analyzed as in (E). * – p<0.05, relative to GFP/Dvl2; ** – p<0.01, relative to GFP/Dvl2. All data are normalized to total protein content and mean and standard error of the mean are shown. Significance assessed by Student's t-test.</p

Dvl2-Grb2 interaction involves the Dvl2 proline-rich region (PRR)-2.

<p>(A) Site-directed mutagenesis was used to construct ΔPRRs from wild type (WT) Dvl2-FLAG, deleting the indicated sequences. (B) Mutant forms of Dvl2 are expressed equivalently to WT. Whole cell lysates were harvested from transfected HEK293s and western blotted using an anti-FLAG antibody. Blots were stripped and re-probed using or anti-tubulin antibodies as a loading control. (C) Loss of PRR1 or PRR2 reduces Dvl2-Grb2 interaction. FLAG-tagged WT or Dvl2 PRR mutants were co-transfected along with HA-Grb2 and immunoprecipitation was performed using anti-FLAG antibody as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0007841#pone-0007841-g001" target="_blank">Fig. 1</a>. Strips were cut from the same blot and were probed with anti-FLAG or anti-HA antibodies. (D) Synergy between Dvl2 and Grb2 depends on PRR2 but not PRR1. HEK293 cells were transfected with plasmids encoding WT or mutant Dvl2 along with Grb2 or GFP and sTF. Cells were harvested for luciferase activity at 24 hours. * – p<0.01 compared to WT Dvl2. All luciferase data are normalized to total protein content. Mean and standard error of the mean are shown. Significance assessed by Student's t-test.</p

Dvl2 contains putative SH3-binding domains.

<p>(A) Schematic representation of Dvl1, 2 and 3 showing the proline –rich, putative SH3-binding domains (Proline Rich Regions, or PRR). PRR2 was identified previously in drosophila Dsh as a domain critical for its function. (B) Dvl2 and Grb2 co-immunoprecipitate. FLAG-tagged Dvl2 and HA-tagged Grb2 were expressed individually or together in HEK293 cells. 24 hrs later, the cells (∼5–6×10⁶) were harvested and lysed for 15 min in RIPA buffer containing protease and phosphatase inhibitors. Lysates were cleared, and Dvl2 immunoprecipitated overnight at 4°C with anti-FLAG antibodies. Immune complexes were captured with protein-G coated sepharose beads for 2 hrs at RT. An isotype control antibody was used in parallel. Strips were cut from the same blot and were probed with anti-FLAG or anti-HA antibodies. Whole cell lysate (WCL) was used as a control. (C) Dvl2 and Grb2 co-localize in cells. Confocal microscopy demonstrates punctate distribution of both proteins with considerable cytoplasmic co-localization. Dvl2 is also found in the nucleus. Isotype control staining was negative. (D) Dvl2 and Grb2 synergize to drive LEF/TCF-dependent transcription. HEK293 cells were co-transfected with sTOPflash or the negative control sFOPflash along with GFP, Dvl2, Grb2 or Dvl2 and Grb2 together. Cells were harvested 24 hrs later for luciferase analysis. * – effect of Dvl2 + Grb2 differs significantly from effect of either alone: p<0.01. (E) Dose dependent synergy of Grb2 with Dvl2. HEK293 cells were transfected with sTF as in (D) along with Dvl2 and increasing doses of Grb2. Cells were harvested 24 hrs later. p<0.01 at all time points. (F) The onset of synergy is rapid. HEK293 cells were transfected with GFP, Dvl2, Grb2, or Dvl2 and Grb2 together. Three hrs post recovery was set as t = 0 as this was the earliest GFP could be detected above background. Cells were then harvested at: t = 2,6,10,18 and 24 hrs. Synergy between Dvl2 and Grb2 is achieved as early as 6 hrs (see inset). For all time points 6 hrs and beyond, p<0.01. All luciferase data are normalized to total protein content. Mean and standard error of the mean are shown – where absent, the SEM falls within the symbol. Significance assessed by Student's t-test.</p

Grb2 works downstream of β-catenin and requires jnk and rac activity.

<p>(A) Grb2 does not stabilize β-catenin protein. HEK293 cells were transfected with GFP or Grb2 expression constructs, along with the indicated amounts of Grb2 plasmid, and harvested 24 hrs later for analysis by western blot. Blots were probed with an anti-β-catenin antibody and then re-probed with an anti-HA antibody to confirm expression levels of Grb2. (B) Dvl2-Grb2 synergy requires jnk activity. Cells were transfected with sTF, Dvl2 and Grb2 plasmids as indicated. Nine hrs post transfection, one set of transfectants were harvested (t = 0), and SP600125 (JNK inhibitor II, 10 µM) or DMSO control was added to the remainder. Cells were harvested 5 hrs later and analyzed for luciferase activity. * – p<0.01, relative to GFP control, or Dvl2 or Grb2 alone; ** – p<0.01, relative to Dvl2/Grb2 + DMSO. (C) Dominant negative c-jun blocks Dvl2-Grb2 synergy. HEK293 cells were transfected with sTF, and the indicated plasmids (Dvl2, Grb2, junAA, GFP as a balancer). * – p<0.01, relative to GFP control, or Dvl2 or Grb2 alone; ** – p<0.01, relative to Dvl2/Grb2 + GFP (no junAA). (D) Dominant negative rac blocks Dvl2-Grb2 synergy. Cells were transfected with sTF and the indicated plasmids (Dvl2, Grb2, DN-rac, GFP as a balancer) and harvested at 24 hours for luciferase assay. * – p<0.01, relative to Dvl2 + Grb2 in the absence of DN-rac. All data are normalized to total protein content and mean and standard error of the mean are shown. Significance assessed by Student's t-test.</p