Etiology and drug resistance analysis on fungal keratitis

AIM: To investigate and analyze corneal edge ulcer training results, pathogenic bacteria distribution and drug resistance status of fungal keratitis.<p>METHODS:Corneal edge ulcer of 68 fungal keratitis patients who were treated in our hospital from January 1, 2011 to December 31, 2012 was collected. They were sent to isolate culture, identification and drug sensitive test. The culture and drug sensitive test results, and pathological changes of corneal tissue were summarized.<p>RESULTS:Fifty strains of fungi strains were checked out from 68 corneal edge ulcer, most of them were sickle bacteria genus. The resistant rate of natamycin, fluconazole, amphotericin and itraconazole was 26%, 46%, 54% and 60%, respectively.<p>CONCLUSION: The most pathogenic bacteria were sickle bacteria genera, the resistance of amphotericin and itraconazole is higher, while that of itraconazole is lower

The Abel-Zeilberger Algorithm

We use both Abel's lemma on summation by parts and Zeilberger's algorithm to find recurrence relations for definite summations. The role of Abel's lemma can be extended to the case of linear difference operators with polynomial coefficients. This approach can be used to verify and discover identities involving harmonic numbers and derangement numbers. As examples, we use the Abel-Zeilberger algorithm to prove the Paule-Schneider identities, the Apery-Schmidt-Strehl identity, Calkin's identity and some identities involving Fibonacci numbers.Comment: 18 page

Adenoid cystic carcinoma of the esophagus: report of two cases and review of the Chinese literature

Squamous cell carcinoma is the major pathology type of esophageal cancer in China, where adenocarcinoma is rare and adenoid cystic carcinoma (ACC) is more rare comparing to the western countries. We report the surgical and pathologic findings of two cases of primary ACC of the esophagus, and review of the Chinese literature of this tumor. VIRTUAL SLIDES: The virtual slide(s) for this article can be found here: http://www.diagnosticpathology.diagnomx.eu/vs/150758223884324

Task Residual for Tuning Vision-Language Models

Large-scale vision-language models (VLMs) pre-trained on billion-level data have learned general visual representations and broad visual concepts. In principle, the well-learned knowledge structure of the VLMs should be inherited appropriately when being transferred to downstream tasks with limited data. However, most existing efficient transfer learning (ETL) approaches for VLMs either damage or are excessively biased towards the prior knowledge, e.g., prompt tuning (PT) discards the pre-trained text-based classifier and builds a new one while adapter-style tuning (AT) fully relies on the pre-trained features. To address this, we propose a new efficient tuning approach for VLMs named Task Residual Tuning (TaskRes), which performs directly on the text-based classifier and explicitly decouples the prior knowledge of the pre-trained models and new knowledge regarding a target task. Specifically, TaskRes keeps the original classifier weights from the VLMs frozen and obtains a new classifier for the target task by tuning a set of prior-independent parameters as a residual to the original one, which enables reliable prior knowledge preservation and flexible task-specific knowledge exploration. The proposed TaskRes is simple yet effective, which significantly outperforms previous ETL methods (e.g., PT and AT) on 11 benchmark datasets while requiring minimal effort for the implementation. Our code is available at https://github.com/geekyutao/TaskRes.Comment: Accepted to CVPR 202