Multi-tissue integrative analysis of personal epigenomes

Evaluating the impact of genetic variants on transcriptional regulation is a central goal in biological science that has been constrained by reliance on a single reference genome. To address this, we constructed phased, diploid genomes for four cadaveric donors (using long-read sequencing) and systematically charted noncoding regulatory elements and transcriptional activity across more than 25 tissues from these donors. Integrative analysis revealed over a million variants with allele-specific activity, coordinated, locus-scale allelic imbalances, and structural variants impacting proximal chromatin structure. We relate the personal genome analysis to the ENCODE encyclopedia, annotating allele- and tissue-specific elements that are strongly enriched for variants impacting expression and disease phenotypes. These experimental and statistical approaches, and the corresponding EN-TEx resource, provide a framework for personalized functional genomics

Mechanistic Study on the Corrosion of (La,Sr)(Co,Fe)O_3-δ Cathodes Induced by CO₂

Solid Oxide Fuel Cell (SOFC) cathodes operating in ambient atmospheric conditions inevitably encounter CO2 contamination, leading to sustained performance deterioration. In this investigation, we examined the impact of CO2 on three variants of (La,Sr)(Co,Fe)O3-δ cathodes and employed the distribution of relaxation times method to distinguish distinct electrochemical processes based on impedance spectra analysis. We meticulously analyzed and discussed the corrosion resistance of these (La,Sr)(Co,Fe)O3-δ cathodes under high CO2 concentrations, relying on the experimental data. Electrochemical impedance spectroscopy results revealed that La0.6Sr0.4Co0.2Fe0.8O3-δ (LSCF−6428), La0.4Sr0.6Co0.2Fe0.8O3-δ (LSCF−4628), and La0.4Sr0.6Co0.2Fe0.7Nb0.1O3-δ (LSCFN−46271) cathodes exhibited persistent degradation when exposed to CO2 at temperatures of 650 °C or 800 °C during the durability-testing period. An increase in electrode polarization resistance was observed upon CO2 introduction to the electrode, but electrode performance recovered upon returning to a pure air environment. Furthermore, X-ray diffraction and scanning electron microscopy analyses confirmed that CO2 did not cause permanent damage to the (La,Sr)(Co,Fe)O3-δ cathodes. These findings indicate that the (La,Sr)(Co,Fe)O3-δ cathodes exhibit excellent resistance to CO2-induced corrosion

Voltage and Reactive Power-Optimization Model for Active Distribution Networks Based on Second-Order Cone Algorithm

To address the challenges associated with wind power integration, this paper analyzes the impact of distributed renewable energy on the voltage of the distribution network. Taking into account the fast control of photovoltaic inverters and the unique characteristics of photovoltaic arrays, we establish an active distribution network voltage reactive power-optimization model for planning the active distribution network. The model involves solving the original non-convex and non-linear power-flow-optimization problem. By introducing the second-order cone relaxation algorithm, we transform the model into a second-order cone programming model, making it easier to solve and yielding good results. The optimized parameters are then applied to the IEEE 33-node distribution system, where the phase angle of the node voltage is adjusted to optimize the reactive power of the entire power system, thereby demonstrating the effectiveness of utilizing a second-order cone programming algorithm for reactive power optimization in a comprehensive manner. Subsequently, active distribution network power quality control is implemented, resulting in a reduction in network loss from 0.41 MW to 0.02 MW. This reduces power loss rates, increases utilization efficiency by approximately 94%, optimizes power quality management, and ensures that users receive high-quality electrical energy

A Novel Underwater Image Enhancement Algorithm and an Improved Underwater Biological Detection Pipeline

For aquaculture resource evaluation and ecological environment monitoring, the automatic detection and identification of marine organisms is critical; however, due to the low quality of underwater images and the characteristics of underwater biological detection, the lack of abundant features can impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environments. Therefore, the goal of this study was to perform object detection in underwater environments. This study developed a novel method for capturing feature information by adding the convolutional block attention module (CBAM) to the YOLOv5 backbone network. The interference of underwater organism characteristics in object characteristics decreased and the output object information of the backbone network was enhanced. In addition, a self-adaptive global histogram stretching algorithm (SAGHS) was designed to eliminate degradation problems, such as low contrast and color loss, that are caused by underwater environmental features in order to restore image quality. Extensive experiments and comprehensive evaluations using the URPC2021 benchmark dataset demonstrated the effectiveness and adaptivity of the proposed methods. Additionally, this study conducted an exhaustive analysis of the impacts of training data on performance

NO-Releasing Enmein-Type Diterpenoid Derivatives with Selective Antiproliferative Activity and Effects on Apoptosis-Related Proteins

A series of nine enmein-type ent-kaurane diterpenoid and furoxan-based nitric oxide (NO) donor hybrids (10a–i) were designed and synthesized from commercially available oridonin (1). These hybrids were evaluated for their antiproliferative activity against Bel-7402, K562, MGC-803, and CaEs-17 human cancer cell lines and L-02 normal liver cells. The antiproliferative activity against tumor cells was stronger than the lead compound 1 and parent molecule 9 in most cases. Especially, compound 10f showed the strongest activity against human hepatocarcinoma Bel-7402 cell line with an IC50 of 0.81 μM and could also release 33.7 μmol/L NO at the time point of 60 min. Compounds 10a–i also showed cytotoxic selectivity between tumor and normal liver cells with IC50 ranging from 22.1 to 33.9 μM. Furthermore, the apoptotic properties on Bel-7402 cells revealed that 10f could induce S phase cell cycle arrest and apoptosis at low micromolar concentrations. The effects of 10f on apoptosis-related proteins were also investigated. The potent antiproliferative activities and mechanistic studies warrant further preclinical investigations

Antitumor and Antibacterial Derivatives of Oridonin: A Main Composition of Dong-Ling-Cao

Isodon rubescens has been used as a traditional green tea for more than 1000 years and many medicinal functions of I. rubescens are also very useful, such as its well-known antitumor and antibacterial activities. Oridonin, a bioactive ent-kaurane diterpenoid, is the major ingredient of this medicinal tea. Herein, 22 novel oridonin derivatives were designed and synthesized. The antibacterial activity was evaluated for the first time. Compound 12 was the most promising one with MIC of 2.0 μg/mL against B. subtilis, which was nearly 3-fold stronger than positive control chloromycetin. The antiproliferative property was also assayed and compound 19 showed stronger activity than taxol. The apoptosis-inducing ability, cell cycle arrest effect at S phase and influence of mitochondrial membrane potential by 19 in CaEs-17 cancer cells were first disclosed. Based on the above results, the cell apoptosis induced by compound 19 in CaEs-17 cells was most probably involved in the intrinsic apoptotic pathway

G2-ResNeXt: A Novel Model for ECG Signal Classification

Electrocardiograms (ECG) are the primary basis for the diagnosis of cardiovascular diseases. However, due to the large volume of patients’ ECG data, manual diagnosis is time-consuming and laborious. Therefore, intelligent automatic ECG signal classification is an important technique for overcoming the shortage of medical resources. This paper proposes a novel model for inter-patient heartbeat classification, named G2-ResNeXt, which adds a two-fold grouping convolution (G2) to the original ResNeXt structure, as to achieve better automatic feature extraction and classification of ECG signals. Experiments, conducted on the MIT-BIH arrhythmia database, confirm that the proposed model outperforms all state-of-the-art models considered (except the GRNN model for one of the heartbeat classes), by achieving overall accuracy of 96.16%, and sensitivity and precision of 97.09% and 95.90%, respectively, for the ventricular ectopic heartbeats (VEB), and of 80.59% and 82.26%, respectively, for the supraventricular ectopic heartbeats (SVEB)

Economic Scheduling Model of an Active Distribution Network Based on Chaotic Particle Swarm Optimization

With the continuous increase in global energy demand and growing environmental awareness, the utilization of renewable energy has become a worldwide consensus. In order to address the challenges posed by the intermittent and unpredictable nature of renewable energy in distributed power distribution networks, as well as to improve the economic and operational stability of distribution systems, this paper proposes the establishment of an active distribution network capable of accommodating renewable energy. The objective is to enhance the efficiency of new energy utilization. This study investigates optimal scheduling models for energy storage technologies and economic-operation dispatching techniques in distributed power distribution networks. Additionally, it develops a comprehensive demand response model, with real-time pricing and incentive policies aiming to minimize load peak–valley differentials. The control mechanism incorporates time-of-use pricing and integrates a chaos particle swarm algorithm for a holistic approach to solution finding. By coordinating and optimizing the control of distributed power sources, energy storage systems, and flexible loads, the active distribution network achieves minimal operational costs while meeting demand-side power requirements, striving to smooth out load curves as much as possible. Case studies demonstrate significant enhancements during off-peak periods, with an approximately 60% increase in the load power overall elevation of load factors during regular periods, as well as a reduction in grid loads during evening peak hours, with a maximum decrease of nearly 65 kW. This approach mitigates grid operational pressures and user expense, effectively enhancing the stability and economic efficiency in distribution network operations

Bioactive Natural Spirolactone-Type 6,7-<i>seco</i>-<i>ent</i>-Kaurane Diterpenoids and Synthetic Derivatives

Diterpenoids are widely distributed natural products and have caused considerable interest because of their unique skeletons and antibacterial and antitumor activities and so on. In light of recent discoveries, ent-kaurane diterpenoids, which exhibit a wide variety of biological activities, such as anticancer and anti-inflammatory activities, pose enormous potential to serve as a promising candidate for drug development. Among them, spirolactone-type 6,7-seco-ent-kaurane diterpenoids, with interesting molecular skeleton, complex oxidation patterns, and bond formation, exhibit attractive activities. Furthermore, spirolactone-type diterpenoids have many modifiable sites, which allows for linking to various substituents, suitable for further medicinal study. Hence, some structurally modified derivatives with improved cytotoxicity activities are also achieved. In this review, natural bioactive spirolactone-type diterpenoids and their synthetic derivatives were summarized