Comparing Day 5 versus Day 6 euploid blastocyst in frozen embryo transfer and developing a predictive model for optimizing outcomes: a retrospective cohort study

BackgroundOptimal protocols for frozen-thawed embryo transfer (FET) after preimplantation genetic testing (PGT) remain unclear. This study compared Day 5 (D5) and Day 6 (D6) blastocysts and evaluated predictors of FET success.MethodsA total of 870 patients with genetic diseases or chromosomal translocations who received PGT at the First Affiliated Hospital of Zhengzhou University from January 2015 to December 2019 were recruited. All patients underwent at least one year of follow-up. Patients were divided into groups according to the blastocyst development days and quality. Univariate and multivariate logistic regression were applied to identify risk factors that affect clinical outcomes and to construct a predictive nomogram model. Area under the curve (AUC) of the subject’s operating characteristic curve and GiViTI calibration belt were conducted to determine the discrimination and fit of the model.ResultsD5 blastocysts, especially high-quality D5, resulted in significantly higher clinical pregnancy (58.4% vs 49.2%) and live birth rates (52.5% vs 45%) compared to D6. Multivariate regression demonstrated the number of blastocysts, endometrial preparation protocol, days of embryonic development and the quality of blastocysts independently affected live birth rates (P<0.05). A nomogram integrating these factors indicated favorable predictive accuracy (AUC=0.598) and fit (GiViTI, P=0.192).ConclusionsTransferring high-quality D5 euploid blastocysts after PGT maximizes pregnancy outcomes. Blastocyst quality, blastocyst development days, endometrial preparation protocols, and number of blastocysts, independently predicted outcomes. An individualized predictive model integrating these factors displayed favorable accuracy for counseling patients and optimizing clinical management

Super-resolution imaging and tracking of protein–protein interactions in sub-diffraction cellular space

Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein–protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB–EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB–EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB–EF-Tu interactions

Cross-Sectional Associations between Dietary Fat-Related Behaviors and Continuous Metabolic Syndrome Score among Young Australian Adults

Dietary guidelines recommend removing visible fat from meat, choosing low-fat options and cooking with oil instead of butter. This study examined cross-sectional associations between fat-related eating behaviors and a continuous metabolic syndrome (cMetSyn) score among young adults. During 2004-2006, 2071 participants aged 26-36 years reported how often they trimmed fat from meat, consumed low-fat dairy products and used different types of fat for cooking. A fasting blood sample was collected. Blood pressure, weight and height were measured. To create the cMetSyn score, sex-specific principal component analysis was applied to normalized risk factors of the harmonized definition of metabolic syndrome. Higher score indicates higher risk. For each behavior, differences in mean cMetSyn score were calculated using linear regression adjusted for confounders. Analyses were stratified by weight status (Body mass index (BMI) < 25 kg/m(2) or 25 kg/m(2)). Mean cMetSyn score was positively associated with consumption of low-fat oily dressing (P-Trend = 0.013) among participants who were healthy weight and frequency of using canola/sunflower oil for cooking (P-Trend = 0.008) among participants who were overweight/obese. Trimming fat from meat, cooking with olive oil, cooking with butter, and consuming low-fat dairy products were not associated with cMetSyn score. Among young adults, following fat-related dietary recommendations tended to not be associated with metabolic risk

Cross-Sectional Associations between Dietary Fat-Related Behaviors and Continuous Metabolic Syndrome Score among Young Australian Adults

Dietary guidelines recommend removing visible fat from meat, choosing low-fat options and cooking with oil instead of butter. This study examined cross-sectional associations between fat-related eating behaviors and a continuous metabolic syndrome (cMetSyn) score among young adults. During 2004&ndash;2006, 2071 participants aged 26&ndash;36 years reported how often they trimmed fat from meat, consumed low-fat dairy products and used different types of fat for cooking. A fasting blood sample was collected. Blood pressure, weight and height were measured. To create the cMetSyn score, sex-specific principal component analysis was applied to normalized risk factors of the harmonized definition of metabolic syndrome. Higher score indicates higher risk. For each behavior, differences in mean cMetSyn score were calculated using linear regression adjusted for confounders. Analyses were stratified by weight status (Body mass index (BMI) &lt; 25 kg/m2 or &ge; 25 kg/m2). Mean cMetSyn score was positively associated with consumption of low-fat oily dressing (PTrend = 0.013) among participants who were healthy weight and frequency of using canola/sunflower oil for cooking (PTrend = 0.008) among participants who were overweight/obese. Trimming fat from meat, cooking with olive oil, cooking with butter, and consuming low-fat dairy products were not associated with cMetSyn score. Among young adults, following fat-related dietary recommendations tended to not be associated with metabolic risk

Absorption enhancement in double-sided nanocone hole arrays for solar cells

Enhancing light absorption in thin-film silicon solar cells is important for improving light harvesting efficiency and reducing manufacture cost. In this paper, we introduce a double-sided nanocone hole (NCH) array structure for solar cells by extending a model suggested by Wang et al (2012 Nano Lett. 12 1616). The top-sided NCH structure is mainly used for increasing the antireflection of incident light, and the bottom-sided NCH structure for enhancing light trapping in the near-infrared spectrum. The theoretical analysis is performed on the proposed structure, from which the optimal geometric parameters of the structure are determined. The performance analysis shows that the proposed optimized double-sided NCH structure can yield a short-circuit current of 31.9 mA cm-2 with equivalent thickness of 1 μm, which is 12% and 190% higher than that of the top-sided NCH and planar film counterparts, respectively

Intent Disentanglement and Feature Self-supervision for Novel Recommendation

One key property in recommender systems is the long-tail distribution in user-item interactions where most items only have few user feedback. Improving the recommendation of tail items can promote novelty and bring positive effects to both users and providers, and thus is a desirable property of recommender systems. Current novel recommendation studies over-emphasize the importance of tail items without differentiating the degree of users' intent on popularity and often incur a sharp decline of accuracy. Moreover, none of existing methods has ever taken the extreme case of tail items, i.e., cold-start items without any interaction, into consideration. In this work, we first disclose the mechanism that drives a user's interaction towards popular or niche items by disentangling her intent into conformity influence (popularity) and personal interests (preference). We then present a unified end-to-end framework to simultaneously optimize accuracy and novelty targets based on the disentangled intent of popularity and that of preference. We further develop a new paradigm for novel recommendation of cold-start items which exploits the self-supervised learning technique to model the correlation between collaborative features and content features. We conduct extensive experimental results on three real-world datasets. The results demonstrate that our proposed model yields significant improvements over the state-of-the-art baselines in terms of accuracy, novelty, coverage, and trade-off

Needle profile grating structure for absorption enhancement in GaAs thin film solar cells

We conduct a systematic study of thin film solar cells consisting of a GaAs needle profile (NP) grating structure as a light-trapping layer. The influence of geometric parameters on the optical absorption of the NP grating is investigated using rigorous coupled wave analysis and the finite element method. This type of structure can lead to broadband optical absorption enhancement throughout the wavelength range that we studied. Our simulation results reveal that the absorption efficiency of NP grating can be improved significantly compared with its rectangular grating counterpart. The proposed structure is expected to illuminate the design and fabrication of high-efficiency solar cells

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

Point-of-Interest (POI) recommendation has been a trending research topic as it generates personalized suggestions on facilities for users from a large number of candidate venues. Since users' check-in records can be viewed as a long sequence, methods based on recurrent neural networks (RNNs) have recently shown promising applicability for this task. However, existing RNN-based methods either neglect users' long-term preferences or overlook the geographical relations among recently visited POIs when modeling users' short-term preferences, thus making the recommendation results unreliable. To address the above limitations, we propose a novel method named Long- and Short-Term Preference Modeling (LSTPM) for next-POI recommendation. In particular, the proposed model consists of a nonlocal network for long-term preference modeling and a geo-dilated RNN for short-term preference learning. Extensive experiments on two real-world datasets demonstrate that our model yields significant improvements over the state-of-the-art methods