In vivo microelectrode arrays for detecting multi-region epileptic activities in the hippocampus in the latent period of rat model of temporal lobe epilepsy

Temporal lobe epilepsy (TLE) is a form of refractory focal epilepsy, which includes a latent period and a chronic period. Microelectrode arrays capable of multi-region detection of neural activities are important for accurately identifying the epileptic focus and pathogenesis mechanism in the latent period of TLE. Here, we fabricated multi-shank MEAs to detect neural activities in the DG, hilus, CA3, and CA1 in the TLE rat model. In the latent period in TLE rats, seizures were induced and changes in neural activities were detected. The results showed that induced seizures spread from the hilus and CA3 to other areas. Furthermore, interneurons in the hilus and CA3 were more excited than principal cells and exhibited rhythmic oscillations at approximately 15 Hz in grand mal seizures. In addition, the power spectral density (PSD) of neural spikes and local field potentials (LFPs) were synchronized in the frequency domain of the alpha band (9–15 Hz) after the induction of seizures. The results suggest that fabricated MEAs have the advantages of simultaneous and precise detection of neural activities in multiple subregions of the hippocampus. Our MEAs promote the study of cellular mechanisms of TLE during the latent period, which provides an important basis for the diagnosis of the lesion focus of TLE

Effects of Plot Size on Airborne LiDAR-Derived Metrics and Predicted Model Performances of Subtropical Planted Forest Attributes

Investigating the impact of field plot size on the performance of estimation models for forest inventory attributes could help optimize the technical schemes for an operational airborne LiDAR-assisted forest resource inventory. However, few studies on the topic have focused on subtropical forests. In this study, 104 rectangular plots of 900 m2 (subdivided into nine quadrats with an area of 10 × 10 m) in subtropical planted forests (Chinese fir, pine, eucalyptus, and broad-leaved forest, 2–56 years old) were used to establish four datasets with six different plot sizes (100, 200, 300, 400, 600, and 900 m2) by combining quadrats. The differences in the LiDAR-derived metrics and forest attributes between plots of different sizes were statistically analyzed. Based on the multivariate power models with stable structures, the differences in estimation accuracies of the stand volume (VOL) and basal area (BA) using plot data of different sizes were compared. The results indicated that: (1) the mean differences in LiDAR-derived metrics of the plots of different sizes in all forest types were small, and most of them had no statistically significant differences (α = 0.05) between the plots of different sizes and the 900 m2 plots; however, the standard deviation of the difference increased rapidly with decreasing plot size; (2) except for the maximal tree height of the plots, the other forest attributes, including the mean tree height, diameter at breast height, BA, and VOL of all forest types, showed no statistically significant differences between the plots of different sizes and the 900 m2 plots; and (3) with increasing plot size, the accuracies of VOL and BA estimations improved markedly, and the effects of plot size on the estimation accuracies of the different forest attributes and different forest types were essentially the same. Spatial averaging resulted in the variations in the independent variables (LiDAR variables) and dependent variables (forest attributes) decreasing gradually with the increasing plot size, which was the main reason for the model’s accuracy improving. In applying airborne LiDAR to a large-scale subtropical planted forest inventory, the plot size should be at least 600 m2 for all forest types

Effects of Plot Size on Airborne LiDAR-Derived Metrics and Predicted Model Performances of Subtropical Planted Forest Attributes

Investigating the impact of field plot size on the performance of estimation models for forest inventory attributes could help optimize the technical schemes for an operational airborne LiDAR-assisted forest resource inventory. However, few studies on the topic have focused on subtropical forests. In this study, 104 rectangular plots of 900 m2 (subdivided into nine quadrats with an area of 10 × 10 m) in subtropical planted forests (Chinese fir, pine, eucalyptus, and broad-leaved forest, 2–56 years old) were used to establish four datasets with six different plot sizes (100, 200, 300, 400, 600, and 900 m2) by combining quadrats. The differences in the LiDAR-derived metrics and forest attributes between plots of different sizes were statistically analyzed. Based on the multivariate power models with stable structures, the differences in estimation accuracies of the stand volume (VOL) and basal area (BA) using plot data of different sizes were compared. The results indicated that: (1) the mean differences in LiDAR-derived metrics of the plots of different sizes in all forest types were small, and most of them had no statistically significant differences (α = 0.05) between the plots of different sizes and the 900 m2 plots; however, the standard deviation of the difference increased rapidly with decreasing plot size; (2) except for the maximal tree height of the plots, the other forest attributes, including the mean tree height, diameter at breast height, BA, and VOL of all forest types, showed no statistically significant differences between the plots of different sizes and the 900 m2 plots; and (3) with increasing plot size, the accuracies of VOL and BA estimations improved markedly, and the effects of plot size on the estimation accuracies of the different forest attributes and different forest types were essentially the same. Spatial averaging resulted in the variations in the independent variables (LiDAR variables) and dependent variables (forest attributes) decreasing gradually with the increasing plot size, which was the main reason for the model’s accuracy improving. In applying airborne LiDAR to a large-scale subtropical planted forest inventory, the plot size should be at least 600 m2 for all forest types

Raw qRT-PCR data

The data file provides the raw Ct values for the qRT-PCR assay. The qRT-PCR assay was performed using total RNA isolated from midgut of 4th instar larvae of B. mori or S. c. ricini. Three biological replicates and three technical replicates were performed for each sample. The coding gene of Ribosomal protein 49 of B. mori and S. c. ricini was used as an internal control

Influence mechanism of cerium addition on precipitation behaviour of super austenitic stainless steel S32654

The influence mechanisms of Ce addition on the precipitation behaviour of σ phase and nitrides were systematically investigated. The results demonstrated that the addition of Ce significantly promoted the nucleation and slightly inhibited the growth of intergranular and intragranular σ phases. Meanwhile, Ce addition also accelerated the precipitations of π phase and Cr2N. Ce promoted the nucleation of intergranular σ phase mainly through: (i) leading to a linear increase in the driving force for σ phase and Cr activity; (ii) resulting in apparent grain refinement and providing more favorable nucleation sites. Besides increasing driving force and Cr activity, Ce addition also promoted the nucleation of intragranular σ phase by: (i) leading to lattice distortion, which not only caused local element concentration, but also reduced σ/γ interface energy, jointly increasing nucleation sites; (ii) providing other nucleation sites (Ce-bearing inclusions) for σ phase; (iii) enhancing Cr and Mo diffusion coefficients, which accelerated the supply of elements required for nucleation. Furthermore, Ce addition enhanced the driving force for Cr2N, Cr activity as well as Cr and N diffusions, and it also promoted the formation of more favorable nitride nucleation sites around σ phases. These combined actions accelerated the precipitations of π phase and Cr2N

Stress-Relief Engineering in a N‑Doped C‑Modified Hierarchical Nanoporous Si Anode with a Microcurved Pore Wall Structure for Enhanced Lithium Storage

The commercialization of alloy-type anodes has been hindered by rapid capacity degradation due to volume fluctuations. To address this issue, stress-relief engineering is proposed for Si anodes that combines hierarchical nanoporous structures and modified layers, inspired by the phenomenon in which structures with continuous changes in curvature can reduce stress concentration. The N-doped C-modified hierarchical nanoporous Si anode with a microcurved pore wall (N-C@m-HNP Si) is prepared from inexpensive Mg-55Si alloys using a simple chemical etching and heat treatment process. When used as the anode for lithium-ion batteries, the N-C@m-HNP Si anode exhibits initial charge/discharge specific capacities of 1092.93 and 2636.32 mAh g–1 at 0.1 C (1 C = 3579 mA g–1), respectively, and a stable reversible specific capacity of 1071.84 mAh g–1 after 200 cycles. The synergy of the hierarchical porous structure with a microcurved pore wall and the N-doped C-modified layer effectively improves the electrochemical performance of N-C@m-HNP Si, and the effectiveness of stress-relief engineering is quantitatively analyzed through the theory of elastic bending of thin plates. Moreover, the formation process of Li15Si4 crystals, which causes substantial mechanical stress, is investigated using first-principles molecular dynamic simulations to reveal their tendency to occur at different scales. The results demonstrate that the hierarchical nanoporous structure helps to inhibit the transformation of amorphous LixSi into metastable Li15Si4 crystals during lithiation

Ordonnance du roi, pour établir les appointemens dont jouiront, en tems de guerre, les capitaines-commandans des compagnies-colonelles & lieutenantes-colonelles des régimens d'infanterie . Du 1er janvier 1768

[Acte royal. 1768-01-01. Versailles]Avec mode text

Robust machine−learning based prognostic index using cytotoxic T lymphocyte evasion genes highlights potential therapeutic targets in colorectal cancer

Abstract Background A minute fraction of patients stands to derive substantial benefits from immunotherapy, primarily attributable to immune evasion. Our objective was to formulate a predictive signature rooted in genes associated with cytotoxic T lymphocyte evasion (CERGs), with the aim of predicting outcomes and discerning immunotherapeutic response in colorectal cancer (CRC). Methods 101 machine learning algorithm combinations were applied to calculate the CERGs prognostic index (CERPI) under the cross−validation framework, and patients with CRC were separated into high− and low−CERPI groups. Relationship between immune cell infiltration levels, immune−related scores, malignant phenotypes and CERPI were further analyzed. Various machine learning methods were used to identify key genes related to both patient survival and immunotherapy benefits. Expression of HOXC6, G0S2, and MX2 was evaluated and the effects of HOXC6 and G0S2 on the viability and migration of a CRC cell line were in−vitro verified. Results The CERPI demonstrated robust prognostic efficacy in predicting the overall survival of CRC patients, establishing itself as an independent predictor of patient outcomes. The low−CERPI group exhibited elevated levels of immune cell infiltration and lower scores for tumor immune dysfunction and exclusion, indicative of a greater potential benefit from immunotherapy. Moreover, there was a positive correlation between CERPI levels and malignant tumor phenotypes, suggesting that heightened CERPI expression contributes to both the occurrence and progression of tumors. Thirteen key genes were identified, and their expression patterns were scrutinized through the analysis of single−cell datasets. Notably, HOXC6, G0S2, and MX2 exhibited upregulation in both CRC cell lines and tissues. Subsequent knockdown experiments targeting G0S2 and HOXC6 resulted in a significant suppression of CRC cell viability and migration. Conclusion We developed the CERPI for effectively predicting survival and response to immunotherapy in patients, and these results may provide guidance for CRC diagnosis and precise treatment