Optimizing the Conditions of Pretreatment and Enzymatic Hydrolysis of Sugarcane Bagasse for Bioethanol Production.

The agricultural waste sugarcane bagasse (SCB) is a kind of plentiful biomass resource. In this study, different pretreatment methods (NaOH, H2SO4, and sodium percarbonate/glycerol) were utilized and compared. Among the three pretreatment methods, NaOH pretreatment was the most optimal method. Response surface methodology (RSM) was utilized to optimize NaOH pretreatment conditions. After optimization by RSM, the solid yield and lignin removal were 54.60 and 82.30% under the treatment of 1% NaOH, a time of 60 min, and a solid-to-liquid ratio of 1:15, respectively. Then, the enzymolysis conditions of cellulase for NaOH-treated SCB were optimized by RSM. Under the optimal enzymatic hydrolysis conditions (an enzyme dose of 18 FPU/g, a time of 64 h, and a solid-to-liquid ratio of 1:30), the actual yield of reducing sugar in the enzyme-treated hydrolysate was 443.52 mg/g SCB with a cellulose conversion rate of 85.33%. A bacterium, namely, Bacillus sp. EtOH, which produced ethanol and Baijiu aroma substances, was isolated from the high-temperature Daqu of Danquan Baijiu in our previous study. At last, when the strain EtOH was cultured for 36 h in a fermentation medium (reducing sugar from cellulase-treated SCB hydrolysate, yeast extract, and peptone), ethanol concentration reached 2.769 g/L (0.353%, v/v). The sugar-to-ethanol and SCB-to-ethanol yields were 13.85 and 11.81% in this study, respectively. In brief, after NaOH pretreatment, 1 g of original SCB produced 0.5460 g of NaOH-treated SCB. Then, after the enzymatic hydrolysis, reducing sugar yield (443.52 mg/g SCB) was obtained. Our study provided a suitable method for bioethanol production from SCB, which achieved efficient resource utilization of agricultural waste SCB.

Automatic detection of standing dead trees based on improved YOLOv7 from airborne remote sensing imagery.

Detecting and localizing standing dead trees (SDTs) is crucial for effective forest management and conservation. Due to challenges posed by mountainous terrain and road conditions, conducting a swift and comprehensive survey of SDTs through traditional manual inventory methods is considerably difficult. In recent years, advancements in deep learning and remote sensing technology have facilitated real-time and efficient detection of dead trees. Nevertheless, challenges persist in identifying individual dead trees in airborne remote sensing images, attributed to factors such as small target size, mutual occlusion and complex backgrounds. These aspects collectively contribute to the increased difficulty of detecting dead trees at a single-tree scale. To address this issue, the paper introduces an improved You Only Look Once version 7 (YOLOv7) model that incorporates the Simple Parameter-Free Attention Module (SimAM), an unparameterized attention mechanism. This improvement aims to enhance the network's feature extraction capabilities and increase the model's sensitivity to small target dead trees. To validate the superiority of SimAM_YOLOv7, we compared it with four widely adopted attention mechanisms. Additionally, a method to enhance model robustness is presented, involving the replacement of the Complete Intersection over Union (CIoU) loss in the original YOLOv7 model with the Wise-IoU (WIoU) loss function. Following these, we evaluated detection accuracy using a self-developed dataset of SDTs in forests. The results indicate that the improved YOLOv7 model can effectively identify dead trees in airborne remote sensing images, achieving precision, recall and mAP@0.5 values of 94.31%, 93.13% and 98.03%, respectively. These values are 3.67%, 2.28% and 1.56% higher than those of the original YOLOv7 model. This improvement model provides a convenient solution for forest management.

Automatic Kidney Stone Composition Analysis Method Based on Dual-energy CT.

BACKGROUND: The composition of kidney stones is related to the hardness of the stones. Knowing the composition of the stones before surgery can help plan the laser power and operation time of percutaneous nephroscopic surgery. Moreover, patients can be treated with medications if the kidney stone is compounded by uric acid before treatment, which can relieve the patients of the pain of surgery. However, although the literature generally reports the kidney stone composition analysis method base on dual-energy CT images, the accuracy of these methods is not enough; they need manual delineation of the kidney stone location, and these methods cannot analyze mixed composition kidney stones. OBJECTIVE: This study aimed to overcome the problem related to identifying kidney stone composition; we need an accurate method to analyze the composition of kidney stones. METHODS: In this paper, we proposed the automatic kidney stone composition analysis algorithm based on a dual-energy CT image. The algorithm first segmented the kidney stone mask by deep learning model, then analyzed the composition of each stone by machine learning model. RESULTS: The experimental results indicate that the proposed algorithm can segment kidney stones accurately (AUC=0.96) and predict kidney stone composition accurately (mean Acc=0.86, mean Se=0.75, mean Sp=0.9, mean F1=0.75, mean AUC=0.83, MR (Exact match ratio)=0.6). CONCLUSION: The proposed method can predict the composition and location of kidney stones, which can guide its treatment. Experimental results show that the weighting strategy can improve kidney stone segmentation performance. In addition, the multi-label classification model can predict kidney stone composition precisely, including the mixed composition kidney stones.

Combined use of a 980­nm diode laser and preoperative intravesical instillation of pirarubicin for the prevention of short­term recurrence of non­muscle invasive bladder cancer: A pilot study.

At present, transurethral resection of bladder tumors (TURBT) is the main surgical method for treating non-muscle invasive bladder cancer (NMIBC), but its postoperative recurrence needs to be prevented. The aim of the present study was to investigate the efficacy of a 980-nm diode laser combined with preoperative intravesical instillation of pirarubicin (THP) for the prevention of NMIBC recurrence. The data of 120 patients with NMIBC who underwent transurethral resection between May 2021 and July 2022 were retrospectively collected, and these patients were followed up. The patients were divided into four groups based on the surgical method used and preoperative intravesical instillation of THP as follows: i) 980-nm diode laser with THP (LaT); ii) 980-nm diode laser alone (La); iii) TURBT with THP (TUT); and iv) TURBT alone (TU). Clinicopathological variables, postoperative complications and short-term outcomes among the aforementioned groups were analyzed. The blood loss volume and the incidence of perforation and delayed bleeding were significantly lower in the LaT and La groups compared with those in the TUT and TU groups. The days of bladder irrigation, catheter extubation and postoperative hospitalization were significantly shorter in the LaT and La groups compared with the TUT and TU groups. The detection rate of suspicious lesions was significantly higher in the THP irrigation groups (LaT and TUT) compared with that in the saline irrigation groups (La and TU). Tumor diameter and number, 980-nm laser and THP irrigation were shown to be independent risk factors in the Cox regression analysis. In addition, the recurrence-free survival (RFS) rate of the LaT group was significantly higher than that of the other three groups. In conclusion, a 980-nm diode laser can effectively reduce intraoperative blood loss and the incidence of perforation, and accelerate postoperative recovery. Preoperative intravesical instillation of THP is conducive to identifying suspicious lesions. The combination of a 980-nm laser with preoperative THP intravesical instillation can significantly prolong RFS time.

[Correlation of high-risk HPV 16/18 infections with prostate cancer].

OBJECTIVE: To study the correlation of high-risk human papillomavirus 16 and 18 (HPV16/18) infections with the risk of prostate cancer (PCa) and their association with the clinicopathologic indexes of PCa. METHODS: We collected tissue samples from 75 cases of PCa and 73 cases of benign prostatic hyperplasia (BPH). We detected HPV16/18 infections in the samples by immunohistochemistry and PCR combined with reverse dot blot (RDB) assay. RESULTS: Immunohistochemistry revealed 16 cases of HPV16/18 positive in the PCa (21.3%) and 7 cases in the BPH samples (9.5%), with statistically significant difference between the two groups (P=0.049). PCR combined with RDB assay showed 17 cases of HPV16 infection (22.6%) and 13 cases of HPV18 infection (17.8%), including 4 cases of HPV16/18 positive, in the PCa group, remarkably higher than 6 cases of HPV16 infection (8.2%), 3 cases of HPV18 infection (4.1%) and no HPV16/18 positive in the BPH controls (P=0.001). No significant differences were observed between the result of immunohistochemistry and that of PCR combined with RDB assay (P=0.069). The risk of HPV16/18 infections was found to be correlated with the clinical T-stage and Gleason score of PCa (P<0.05 ) but not with the patient's age, PSA level or lymph node metastasis (P>0.05 ). CONCLUSIONS: High-risk HPV16/18 infections are correlated with the risk of prostate cancer.