Novel insights into necrozoospermia from a single-center study: reference ranges, possible etiology, and impact on male fertility.

ABSTARCT: Necrozoospermia is a poorly documented condition with a low incidence, and its definition and clinical significance are unclear. Herein, we provide a reference range for necrozoospermia and discuss its possible etiology and impact on male fertility and assisted reproductive outcomes. We extracted relevant information from 650 Chinese male partners of infertile couples and statistically analyzed sperm vitality. Necrozoospermia was present in 3.4% (22/650) of our study population, and the lower cut-off value for sperm vitality was 75.3%. We compared two methods for assessing sperm vitality (eosin-nigrosin head staining and hypo-osmotic swelling test [HOST]), for which the percentage in the eosin-nigrosin group (mean ± standard deviation [s.d.]: 77.5% ± 10.5%) was significantly higher than that in the HOST group (mean ± s.d.: 58.1% ± 6.7% [5-10 min after incubation] and 55.6% ± 8.2% [25-30 min after incubation]; both P < 0.001). The incidence of necrozoospermia increased with age (odds ratio [OR] = 1.116, 95% confidence interval [CI]: 1.048-1.189, P = 0.001), while the percentage of normal sperm morphology and DNA fragmentation index (DFI) were significantly associated with necrozoospermia, with ORs of 0.691 (95% CI: 0.511-0.935, P = 0.017) and 1.281 (95% CI: 1.180-1.390, P < 0.001), respectively. In the following 6 months, we recruited 166 patients in the nonnecrozoospermia group and 87 patients in the necrozoospermia group to compare intracytoplasmic sperm injection (ICSI) and pregnancy outcomes between the two groups. The necrozoospermia group had a significantly lower normal fertilization rate (74.7% vs 78.2%, P = 0.041; OR = 0.822; 95% CI: 0.682-0.992) than that in the nonnecrozoospermia group. This study presents substantial information on necrozoospermia to establish comprehensive and applicable reference values for sperm vitality for spontaneous conception and artificially assisted reproductive management.

The Efficient Synthesis and Anti-Fatigue Activity Evaluation of Macamides: The Unique Bioactive Compounds in Maca.

Macamides are a class of amide alkaloids that are only found in maca and are widely considered to be its bioactive marker compounds. More than thirty macamide monomers have been identified in recent years; however, it is difficult to obtain a single macamide monomer from the maca plant because of their similar structures and characteristics. We used the carbodiimide condensation method (CCM) to efficiently synthesize five typical macamides, including N-benzyl-hexadecanamide (NBH), N-benzyl-9Z,12Z,15Z-octadecenamide, N-(3-methoxybenzyl)-9Z,12Z-octadecenamide, N-benzyl-9Z,12Z-octadecenamide, and N-(3-methoxybenzyl)-9Z,12Z,15Z-octadecadienamide. All the synthesized macamides were purified by a one-step HPLC with a purity of more than 95%. NBH is the most abundant macamide monomer in natural maca, and it was selected to evaluate the anti-fatigue effects of macamides. The results indicated that NBH could enhance the endurance capacity of mice by increasing liver glycogen levels and decreasing blood urea nitrogen, lactate dehydrogenase, blood ammonia, and blood lactic acid levels. Macamides might be the active substances that give maca its anti-fatigue active function.

Nested relation extraction via self-contrastive learning guided by structure and semantic similarity.

The conventional Relation Extraction (RE) task involves identifying whether relations exist between two entities in a given sentence and determining their relation types. However, the complexity of practical application scenarios and the flexibility of natural language demand the ability to extract nested relations, i.e., the recognized relation triples may be components of the higher-level relations. Previous studies have highlighted several challenges that affect the nested RE task, including the lack of abundant labeled data, inappropriate neural networks, and underutilization of the nested relation structures. To address these issues, we formalize the nested RE task and propose a hierarchical neural network to iteratively identify the nested relations between entities and relation triples in a layer by layer manner. Moreover, a novel self-contrastive learning optimization strategy is presented to adapt our method to low-data settings by fully exploiting the constraints due to the nested structure and semantic similarity between paired input sentences. Our method outperformed the state-of-the-art baseline methods in extensive experiments, and ablation experiments verified the effectiveness of the proposed self-contrastive learning optimization strategy.

A dual-path dynamic directed graph convolutional network for air quality prediction.

Accurate air quality prediction can help cope with air pollution and improve the life quality. With the development of the deployments of low-cost air quality sensors, increasing data related to air quality has provided chances to find out more accurate prediction methods. Air quality is affected by many external factors such as the position, wind, meteorological information, and so on. Meanwhile, these factors are spatio-temporal dynamic and there are many dynamic contextual relationships between them. Many methods for air quality prediction do not consider these complex spatio-temporal correlations and dynamic contextual relationships. In this paper, we propose a dual-path dynamic directed graph convolutional network (DP-DDGCN) for air quality prediction. We first create a dual-path transposed dynamic directed graph according to static distance relationships of stations and the dynamic relationships generated by wind speed and directions. Then based on the dual-path dynamic directed graph, we can capture the dynamic spatial dependencies more comprehensively. After that we apply gated recurrent units (GRUs) and add the future meteorological features, to extract the complex temporal dependencies of historical air quality data. Using dual-path dynamic directed graph blocks and the GRUs, we finally construct a dynamic spatio-temporal gated recurrent block to capture the dynamic spatio-temporal contextual correlations. Based on real-world datasets, which record a large amount of PM2.5 concentration data, we compare the proposed model with the benchmark models. The experimental results show that our proposed model has the best performance in predicting the PM2.5 concentrations.