Factors Affecting the Growth and Production of Milk-Clotting Enzyme by Amylomyces rouxii in Rice Liquid Medium

Amylomyces rouxii is one of the main fungi usually coexisting with yeasts in Chinese yeast ball, the starter of chiu-niang, a traditional Chinese fermented product from rice. In the present study, growth and production of milk-clotting enzyme (MCE) in gelatinous rice liquid culture of A. rouxii as influenced by waxy (gelatinous) rice content in the medium (5–20 %), temperature (25–40 °C), cultivation time (1–6 days), shaking speeds (0–150 rpm) and metal ions (Na+, K+, Zn2+, Mg2+, Mn2+, Cu2+, Ca2+, Fe3+ and Al3+) were investigated. Results revealed that rice content in the medium, shaking speed, temperature and cultivation time all affected the mycelial propagation and the production of milk-clotting enzyme by A. rouxii in the rice liquid culture. The maximum milk-clotting enzyme activity of ca. 1.22 unit/mL of medium was observed in the 3-day static culture of test organism grown at 30 °C in the medium containing 20 % of gelatinous rice, while mycelial propagation increased with the increase of cultivation time and shaking speed. Furthermore, a significant increase (p<0.05) in the milk-clotting enzyme activity of ca. 1.90 unit/mL of medium, which was about 1.55-fold of the control, was observed when Al3+ was added to the rice liquid medium

Visual gene-network analysis reveals the cancer gene co-expression in human endometrial cancer

Abstract Background Endometrial cancers (ECs) are the most common form of gynecologic malignancy. Recent studies have reported that ECs reveal distinct markers for molecular pathogenesis, which in turn is linked to the various histological types of ECs. To understand further the molecular events contributing to ECs and endometrial tumorigenesis in general, a more precise identification of cancer-associated molecules and signaling networks would be useful for the detection and monitoring of malignancy, improving clinical cancer therapy, and personalization of treatments. Results ECs-specific gene co-expression networks were constructed by differential expression analysis and weighted gene co-expression network analysis (WGCNA). Important pathways and putative cancer hub genes contribution to tumorigenesis of ECs were identified. An elastic-net regularized classification model was built using the cancer hub gene signatures to predict the phenotypic characteristics of ECs. The 19 cancer hub gene signatures had high predictive power to distinguish among three key principal features of ECs: grade, type, and stage. Intriguingly, these hub gene networks seem to contribute to ECs progression and malignancy via cell-cycle regulation, antigen processing and the citric acid (TCA) cycle. Conclusions The results of this study provide a powerful biomarker discovery platform to better understand the progression of ECs and to uncover potential therapeutic targets in the treatment of ECs. This information might lead to improved monitoring of ECs and resulting improvement of treatment of ECs, the 4th most common of cancer in women.Peer Reviewe

Factors Predicting Emotional Cue-Responding Behaviors of Nurses in Taiwan: An Observational Study

Objective Responding to emotional cues is an essential element of therapeutic communication. The purpose of this study is to examine nurses' competence of responding to emotional cues (CRE) and related factors while interacting with standardized patients with cancer. Methods This is an exploratory and predictive correlational study. A convenience sample of registered nurses who have passed the probationary period in southern Taiwan was recruited to participate in 15-minute videotaped interviews with standardized patients. The Medical Interview Aural Rating Scale was used to describe standardized patients' emotional cues and to measure nurses' CRE. The State-Trait Anxiety Inventory was used to evaluate nurses' anxiety level before the conversation. We used descriptive statistics to describe the data and stepwise regression to examine the predictors of nurses' CRE. Results A total of 110 nurses participated in the study. Regardless of the emotional cue level, participants predominately responded to cues with inappropriate distancing strategies. Prior formal communication training, practice unit, length of nursing practice, and educational level together explain 36.3% variances of the nurses' CRE. Conclusions This study is the first to explore factors related to Taiwanese nurses' CRE. Compared to nurses in other countries, Taiwanese nurses tended to respond to patients' emotional cues with more inappropriate strategies. We also identified significant predictors of CRE that show the importance of communication training. Future research and education programs are needed to enhance nurses' CRE and to advocate for emotion-focused communication

An All Deep System for Badminton Game Analysis

The CoachAI Badminton 2023 Track1 initiative aim to automatically detect events within badminton match videos. Detecting small objects, especially the shuttlecock, is of quite importance and demands high precision within the challenge. Such detection is crucial for tasks like hit count, hitting time, and hitting location. However, even after revising the well-regarded shuttlecock detecting model, TrackNet, our object detection models still fall short of the desired accuracy. To address this issue, we've implemented various deep learning methods to tackle the problems arising from noisy detectied data, leveraging diverse data types to improve precision. In this report, we detail the detection model modifications we've made and our approach to the 11 tasks. Notably, our system garnered a score of 0.78 out of 1.0 in the challenge.Comment: Golden Award for IJCAI CoachAI Challenge 2023: Team NTNUEE AIoTLa

Factors Affecting the Growth and Production of Milk-Clotting Enzyme by Amylomyces rouxii in Rice Liquid Medium

Amylomyces rouxii is one of the main fungi usually coexisting with yeasts in Chinese yeast ball, the starter of chiu-niang, a traditional Chinese fermented product from rice. In the present study, growth and production of milk-clotting enzyme (MCE) in gelatinous rice liquid culture of A. rouxii as influenced by waxy (gelatinous) rice content in the medium (5–20 %), temperature (25–40 °C), cultivation time (1–6 days), shaking speeds (0–150 rpm) and metal ions (Na+, K+, Zn2+, Mg2+, Mn2+, Cu2+, Ca2+, Fe3+ and Al3+) were investigated. Results revealed that rice content in the medium, shaking speed, temperature and cultivation time all affected the mycelial propagation and the production of milk-clotting enzyme by A. rouxii in the rice liquid culture. The maximum milk-clotting enzyme activity of ca. 1.22 unit/mL of medium was observed in the 3-day static culture of test organism grown at 30 °C in the medium containing 20 % of gelatinous rice, while mycelial propagation increased with the increase of cultivation time and shaking speed. Furthermore, a significant increase (p<0.05) in the milk-clotting enzyme activity of ca. 1.90 unit/mL of medium, which was about 1.55-fold of the control, was observed when Al3+ was added to the rice liquid medium

Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data

Creating large-scale and well-annotated datasets to train AI algorithms is crucial for automated tumor detection and localization. However, with limited resources, it is challenging to determine the best type of annotations when annotating massive amounts of unlabeled data. To address this issue, we focus on polyps in colonoscopy videos and pancreatic tumors in abdominal CT scans; both applications require significant effort and time for pixel-wise annotation due to the high dimensional nature of the data, involving either temporary or spatial dimensions. In this paper, we develop a new annotation strategy, termed Drag&Drop, which simplifies the annotation process to drag and drop. This annotation strategy is more efficient, particularly for temporal and volumetric imaging, than other types of weak annotations, such as per-pixel, bounding boxes, scribbles, ellipses, and points. Furthermore, to exploit our Drag&Drop annotations, we develop a novel weakly supervised learning method based on the watershed algorithm. Experimental results show that our method achieves better detection and localization performance than alternative weak annotations and, more importantly, achieves similar performance to that trained on detailed per-pixel annotations. Interestingly, we find that, with limited resources, allocating weak annotations from a diverse patient population can foster models more robust to unseen images than allocating per-pixel annotations for a small set of images. In summary, this research proposes an efficient annotation strategy for tumor detection and localization that is less accurate than per-pixel annotations but useful for creating large-scale datasets for screening tumors in various medical modalities.Comment: Published in Machine Intelligence Researc