Ascitic fluid analysis for the differentiation of malignancy related and nonmalignant ascites

The authors tried to differentiate malignancy-related from nonmalignant ascites with a sequence of sensitive followed by specific ascitic-fluid parameters. There were four results of this study. First, of nine parameters investigated in a first series of 48 patients, 28 with nonmalignant and 20 with malignancy-related ascites, ascitic-fluid cholesterol and fibronectin yielded the best negative predictive value of 92% each. Carcinoembryonic antigen (CEA) and cytologic examination both showed a positive predictive value of 100%. Second, combining cytologic examination (sensitivity, 70%) and CEA determination (sensitivity, 45%) increased the sensitivity to 80%. Third, cytologic findings were negative in all ascitic-fluid samples with a cholesterol concentration below the cutoff value of 45 mg/100 ml. Fourth, based on the results of the first series of 48 patients, the diagnostic sequence with cholesterol as a sensitive parameter, followed by the combination of cytologic examination and CEA determination as specific parameters, was tested in a second series of 71 patients, 37 with nonmalignant and 34 with malignancy-related ascites. Again cytologic examination was negative in all samples with cholesterol levels below 45 mg/100 ml. In the total of 119 patients, this diagnostic sequence did not identify 9% of patients with malignancy-related ascites, and 82% of samples classified as malignancy related by cholesterol levels above 45 mg/100 ml were confirmed by positive cytologic examination and/or CEA level above 2.5 ng/ml. Thus, a diagnostic sequence with ascitic-fluid cholesterol determination, followed by cytologic examination and CEA determination, in samples with cholesterol levels above 45 mg/100 ml should permit a cost-efficient routine differentiation of malignancy-related from nonmalignant ascites

Calculation method for holding prestress of corroded prestressed anchor cable in long-term operation slope

Due to the rich water in the weathered layer of the free section, the prestressed anchor cable of the long-term operating slope is severely corroded and its mechanical properties are deteriorated, affecting the stability of the slope. Based on a certain number of long-term operation highway anchor cable excavation tests, the author found that the free section of the anchor cable orifice was seriously corroded. Currently, there is very little research on the relationship between the holding capacity of anchor cables and the degree of corrosion of the free section of the cable, and the research is mainly focused on the life of the anchor section. Therefore, the constitutive relationship of the cable body is established on the basis of corrosion force coupled statistical damage mechanics, and the relationship between the degree of corrosion of the cable body and the holding prestress of the operating slope anchor cables is derived using the load transfer method. The rationality of prestressed anchor cables on highway slopes during the operation period was verified by actual measurement. This study has positive significance for long-term stability analysis of slopes

Rethinking Multi-Interest Learning for Candidate Matching in Recommender Systems

Existing research efforts for multi-interest candidate matching in recommender systems mainly focus on improving model architecture or incorporating additional information, neglecting the importance of training schemes. This work revisits the training framework and uncovers two major problems hindering the expressiveness of learned multi-interest representations. First, the current training objective (i.e., uniformly sampled softmax) fails to effectively train discriminative representations in a multi-interest learning scenario due to the severe increase in easy negative samples. Second, a routing collapse problem is observed where each learned interest may collapse to express information only from a single item, resulting in information loss. To address these issues, we propose the REMI framework, consisting of an Interest-aware Hard Negative mining strategy (IHN) and a Routing Regularization (RR) method. IHN emphasizes interest-aware hard negatives by proposing an ideal sampling distribution and developing a Monte-Carlo strategy for efficient approximation. RR prevents routing collapse by introducing a novel regularization term on the item-to-interest routing matrices. These two components enhance the learned multi-interest representations from both the optimization objective and the composition information. REMI is a general framework that can be readily applied to various existing multi-interest candidate matching methods. Experiments on three real-world datasets show our method can significantly improve state-of-the-art methods with easy implementation and negligible computational overhead. The source code will be released.Comment: RecSys 202