Construction of Hierarchical Conductive Networks for LiNi0.8Mn0.1Co0.1O2 Cathode toward Stable Cycling at High Areal Mass Loadings.

Realizing high-performance thick electrodes is considered as a practical strategy to promote the energy density of lithium-ion batteries. However, establishing effective transport pathways for both lithium-ions and electrons in a thick electrode is very challenging. This study develops a hierarchical conductive network structure for constructing high-performance NMC811 (LiNi0.8Mn0.1Co0.1O2) cathode toward stable cycling at high areal mass loadings. The hierarchical conductive networks are composed of a Li+/e- mixed conducting interface (lithium polyacrylate/hydroxyl-functionalized multiwalled carbon nanotubes) on NMC811 particles, and a segregated network of single-walled carbon nanotubes in the electrode, without any additional binders or carbon black. Such strategy endows the NMC811 cathode (up to 250 µm and 50 mg cm-2) with low porosity/tortuosity, ultrahigh Li+/e- conductivities and excellent mechanical property at low carbon nanotube content (1.8 wt%). It significantly improves the electrochemical reaction homogeneity along the electrode depth, meanwhile effectively inhibits the side reactions at the electrode/electrolyte interface and cracks in the NMC particles during cycling. This work emphasizes the crucial role of the electronic/ionic cooperative transportation in the performance deterioration of thick cathodes, and provide guidance for architecture optimization and performance improvement of thick electrodes toward practical applications, not just for the NMC811 cathode.

Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.

BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encountering complications in early-stage cervical squamous cell carcinoma(ESCSCC). We aimed to investigate the feasibility of Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in ESCSCC and minimize the occurrence of adverse events associated with the treatment. METHODS: A dataset comprising MR images was obtained from 289 patients who underwent radical hysterectomy and pelvic lymph node dissection between January 2019 and April 2022. The dataset was randomly divided into two cohorts in a 4:1 ratio.The postoperative radiotherapy options were evaluated according to the Peter/Sedlis standard. We extracted clinical features, as well as intratumoral and peritumoral radiomic features, using the least absolute shrinkage and selection operator (LASSO) regression. We constructed the Clinical Signature (Clinic_Sig), Radiomics Signature (Rad_Sig) and the Deep Transformer Learning Signature (DTL_Sig). Additionally, we fused the Rad_Sig with the DTL_Sig to create the Deep Learning Radiomic Signature (DLR_Sig). We evaluated the prediction performance of the models using the Area Under the Curve (AUC), calibration curve, and Decision Curve Analysis (DCA). RESULTS: The DLR_Sig showed a high level of accuracy and predictive capability, as demonstrated by the area under the curve (AUC) of 0.98(95% CI: 0.97-0.99) for the training cohort and 0.79(95% CI: 0.67-0.90) for the test cohort. In addition, the Hosmer-Lemeshow test, which provided p-values of 0.87 for the training cohort and 0.15 for the test cohort, respectively, indicated a good fit. DeLong test showed that the predictive effectiveness of DLR_Sig was significantly better than that of the Clinic_Sig(P < 0.05 both the training and test cohorts). The calibration plot of DLR_Sig indicated excellent consistency between the actual and predicted probabilities, while the DCA curve demonstrating greater clinical utility for predicting the pathological features for adjuvant radiotherapy. CONCLUSION: DLR_Sig based on intratumoral and peritumoral MRI images has the potential to preoperatively predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma (ESCSCC).

Association of serum fatty acid pattern with depression in U.S. adults: analysis of NHANES 2011-2012.

BACKGROUND: Exposure to different concentration levels of fatty acids (FAs) may have an impact on depression. However, previous studies using individual FAs may not reflect the performance of mixtures of various FAs, and the associations of FA patterns with depression remain unclear. METHODS: We conducted the cross-sectional analysis in 792 adults aged 18 and older with available serum FAs and depression screening data in the National Health and Nutrition Examination Survey (NHANES) 2011-2012. The serum concentrations of thirty FAs were measured using gas chromatography-mass spectrometry and their percentage compositions were subsequently calculated. Depression was defined as the Patient Health Questionnaire-9 score ≥ 10. We employed principal component analysis to derive serum FA patterns. We examined the association between these patterns and depression in the overall population and various subgroups through survey-weighted logistic regression. RESULTS: Four distinct patterns of serum FAs were identified: 'high eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA); low docosatetraenoic acid (DTA) and docosapentaenoic acid (DPA) n-6', 'high long-chain saturated FA and long chain FA', 'low median-chain saturated FA and myristoleic acid' and 'low capric acid and lauric acid; high gamma-linolenic acid (GLA) and stearidonic acid (SDA)' pattern. Individuals in the high tertile of 'high EPA and DHA; low DTA and DPA n-6' pattern score had 0.46 (95% CI: 0.22, 0.93) lower odds of developing depression compared to individuals in the lowest tertile after adjusting for confounders such as age, sex, physical activity and total energy intake, etc. The odds ratio (OR) of depression was increased in the population with the highest tertile of 'low capric acid and lauric acid; high GLA and SDA' pattern (OR: 2.45, 95% CI: 1.24, 4.83). In subgroup analyses, we observed that the association between 'high EPA and DHA; low DTA and DPA n-6' and depression persisted among specific demographic and lifestyle subgroups, including females, non-Mexican Americans, non-obese, those aged over 60 years, smokers and drinkers. Similarly, 'low capric acid and lauric acid; high GLA and SDA' showed stable associations in female, non-Mexican Americans and smokers. CONCLUSIONS: Serum FA patterns are associated with depression, and their relationships vary across sex, race, BMI, age, smoking and drinking subgroups, highlighting the importance of considering specific FA patterns within these demographic and lifestyle categories. Utilization of combined FA administration may serve as a mitigation measure against depression in these specific populations.

Model-free robust motion control for biological optical microscopy using time-delay estimation with an adaptive RBFNN compensator.

The field of large numerical aperture microscopy has witnessed significant advancements in spatial and temporal resolution, as well as improvements in optical microscope imaging quality. However, these advancements have concurrently raised the demand for enhanced precision, extended range, and increased load-bearing capacity in objective motion carrier (OMC). To address this challenge, this study introduces an innovative OMC that employs a ball screw mechanism as its primary driving component. Furthermore, a robust nonlinear motion control strategy has been developed, which integrates fast nonsingular terminal sliding mode, experimental estimation techniques, and adaptive radial basis neural network, to mitigate the impact of nonlinear friction within the ball screw mechanism on motion precision. The stability of the closed-loop control system has been rigorously demonstrated through Lyapunov theory. Compared with other enhanced sliding mode control strategies, the maximum error and root mean square error of this controller are improved by 33% and 34% respectively. The implementation of the novel OMC has enabled the establishment of a high-resolution bio-optical microscope, which has proven its effectiveness in the microscopic imaging of retinal organoids.