Miracle-dz/UniFAR-FTUs huggingface dataset:
Short- and Long-Term High-Fat Diet Exposure Differentially Alters Phasic and Tonic GABAergic Signaling onto Lateral Orbitofrontal Pyramidal Neurons

The chronic consumption of caloric dense high-fat foods is a major contributor to increased body weight, obesity, and other chronic health conditions. The orbitofrontal cortex (OFC) is critical in guiding decisions about food intake and is altered with diet-induced obesity. Obese rodents have altered morphologic and synaptic electrophysiological properties in the lateral orbitofrontal cortex (lOFC). Yet the time course by which exposure to a high-fat diet (HFD) induces these changes is poorly understood. Here, male mice are exposed to either short-term (7 d) or long-term (90 d) HFD. Long-term HFD exposure increases body weight, and glucose signaling compared with short-term HFD or a standard control diet (SCD). Both short and long-term HFD exposure increased the excitability of lOFC pyramidal neurons. However, phasic and tonic GABAergic signaling was differentially altered depending on HFD exposure length, such that tonic GABAergic signaling was decreased with early exposure to the HFD and phasic signaling was changed with long-term diet exposure. Furthermore, alterations in the short-term diet exposure were transient, as removal of the diet restored electrophysiological characteristics similar to mice fed SCD, whereas long-term HFD electrophysiological changes were persistent and remained after HFD removal. Finally, we demonstrate that changes in reward devaluation occur early with diet exposure. Together, these results suggest that the duration of HFD exposure differentially alters lOFC function and provides mechanistic insights into the susceptibility of the OFC to impairments in outcome devaluation. SIGNIFICANCE STATEMENT This study provides mechanistic insight on the impact of short-term and long-term high-fat diet (HFD) exposure on GABAergic function in the lateral orbitofrontal cortex (lOFC), a region known to guide decision-making. We find short-term HFD exposure induces transient changes in firing and tonic GABA action on lOFC pyramidal neurons, whereas long-term HFD induces obesity and has lasting changes on firing, tonic GABA and inhibitory synaptic transmission onto lOFC neurons. Given that GABAergic signaling in the lOFC can influence decision-making around food, these results have important implications in present society as palatable energy dense foods are abundantly available.
On Frequency Combs in Monolithic Resonators

Abstract Optical frequency combs have become indispensable in astronomical measurements, biological fingerprinting, optical metrology, and radio frequency photonic signal generation. Recently demonstrated microring resonator-based Kerr frequency combs point the way towards chip scale optical frequency comb generator retaining major properties of the lab scale devices. This technique is promising for integrated miniature radiofrequency and microwave sources, atomic clocks, optical references and femtosecond pulse generators. Here we present Kerr frequency comb development in a historical perspective emphasizing its similarities and differences with other physical phenomena. We elucidate fundamental principles and describe practical implementations of Kerr comb oscillators, highlighting associated solved and unsolved problems.
Patterned Metal Grids for Flexible and Transparent Neural Microelectrode Arrays

Flexible and transparent microelectrodes can provide large-scale neural recordings with temporal and spatial resolution when used alongside functional calcium imaging. Patterned metal grids defined by direct laser writing (DWL) are a promising approach for these electrodes, as they resort to standard microfabrication processes and materials, allowing the possibility of mass production. For these reasons, a study exploring transparent grid-based electrodes using DWL for measuring electrocorticography signal was performed. Patterned metal grids with 1 μm of linewidth and 22 μm of spacing between lines showed a sheet resistance of 6 Ω/sq and a transmittance of 81% at 550 nm. The grids were transferred to a 5 μm Parylene-C membrane using an optimized procedure that involves an oxygen plasma pre-treatment. This procedure ensures mechanical robustness and stability of the grids. Finally, a flexible and transparent prototype was fabricated with a microelectrode array composed by 16 electrodes with 500 μm of diameter. These microelectrodes shown an impedance of 10 kΩ at 1 kHz in saline solution and they are highly conformal facilitating in vivo implantation and the recording of neural activity in the mouse cerebellum surface. To conclude, patterned metal grids based-electrodes exhibit a promising performance compared to transparent conductive oxides or graphene. Moreover, the introduction of DLW enables easy and fast manipulation of grid shape and dimensions without the need of physical masks, while keeping large scale compatibility, which is important for tools used in neuroscience community.
Progressive Senescence Programs Induce Intrinsic Vulnerability to Aging-related Breast Cancer

Cancer incidence escalates exponentially with advancing age; however, the underlying mechanism remains unclear. In this study, we built a chronological molecular clock at the single-cell transcription level with a mammary stem cell-enriched population to depict physiological aging dynamics in mice. We found that the mammary aging process was asynchronous and progressive, initiated by an early senescence program with elevated NF-kB and P53 signaling, succeeded by an entropic late senescence program with reduced NF-kB and P53 signaling and enhanced PI3K-Akt-mTOR, Wnt, Notch and pluripotent activity, vulnerable to cancer predisposition. The transition towards senescence program was governed by the master stem cell factor Bcl11b, loss of which accelerated mammary ageing with enhanced DMBA-induced tumor formation. We identified a drug TPCA-1 that can elevate Bcl11b, rejuvenate mammary cells and significantly reduce aging-related cancer incidence. Our findings established a molecular portrait of progressive mammary cell aging and elucidated the transcriptional regulatory network bridging mammary aging and cancer predisposition, which can be modulated to control cancer initiation; therefore, this study has potential implications for the management of cancer prevalence in the aged.
A Mechanism Underpinning the Bioenergetic Metabolism-Regulating Function of Gold Nanocatalysts

Bioenergetic deficits, such as mitochondrial impairments and dysfunction in glucose metabolism, have been identified as significant contributors to neurodegenerative diseases. Nevertheless, identifying safe and effective means to address intracellular bioenergetic deficits remains a significant challenge. This work provides mechanistic insights into the bioenergetic metabolism-regulating function of a suspension of gold (Au) nanocrystals, referred to as CNM-Au8®, that are synthesized electrochemically in the absence of any surface-capping organic ligands. When neurons are subjected to excitotoxic stressors or toxic peptides, treatment of neurons with CNM-Au8 results in dose-dependent neuronal survival and preservation of neurite networks across multiple neuronal subtypes. CNM-Au8 efficiently catalyzes the conversion of an energetic co-factor, nicotinamide adenine dinucleotide hydride (NADH), into its oxidized, dehydrogenated counterpart (NAD+), which triggers an increase in energy production in the form of adenosine triphosphate (ATP). Detailed kinetic measurements reveal that CNM-Au8-catalyzed NADH oxidation obeys Michaelis-Menten kinetics and exhibits pH-dependent kinetic profiles. CNM-Au8 functions as an NADH-dehydrogenase-mimicking nanozyme that effectively regulates intracellular bioenergetic metabolism. We further utilize photoexcited charge carriers and photothermal transduction, which can be generated through optical excitations of the plasmonic electron oscillations or the interband electronic transitions in CNM-Au8, as unique leverages to modulate reaction kinetics. Benefiting from their bio-compatibility, blood-brain barrier penetrance, tunable optical properties, and enzyme-mimicking functions, CNM-Au8 nanocrystals with deliberately tailored structures and surfactant-free clean surfaces hold great promise for developing next-generation therapeutic agents for neurodegenerative diseases.
Signal recovery in single cell batch integration

Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration usually involve a lot of guesswork: Cells across batches should be aligned to be “appropriately” mixed, while preserving “main cell type clusters”. We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a “pool-of-controls” design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are shown to be validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where the recovered biological signals are replicated in an independent study.
Research on Regulation Strategy of Energy-Saving Aggregation Control Device in Virtual Power Plant

With the large-scale access of massive distributed resources, the "double-high and double-peak" characteristics of the power grid are prominent, and the flexible adjustment capacity of traditional power grids is limited. It is urgent to tap new flexible resources with adjustable potential under the existing power grid form. The resource aggregation control device can realize the unified collection of distributed resources standardization, and optimize the energy consumption of the communication module by using the intelligent sleep mode, which gives the device a great space for energy saving. Based on this characteristic, firstly, based on the aggregated air-conditioning load model, the resource regulation strategy of the energy-saving aggregated control device is proposed. Secondly, based on the establishment of the mathematical model of the adjustable potential of the device, a control strategy for energy saving of the device considering the time characteristics is proposed. Finally, the analysis results of an example show that the resource regulation strategy can fine-tune and control flexible resources, realize the sinking of control functions, at the same time, the device can realize energy-saving control, which provides an idea for exploring the adjustable potential of various communication equipment in the future.
THE INFLUENCE OF RAINFALL ON THE YIELD OF WINTER WHEAT IN NORTH CHINA

The influence of rainfall on the yield of winter wheat in North China has been estimated by the application of R. A. Fisher's method of orthogonal polynomials. Data of winter wheat yield during 1955—57 of 11 localities in North China have been used.The results show:1) a high correlation between the total rainfall of the growing season and the yield,2) an increase in the yield caused by an additional rainfall at any time during the growing season,3) the highest effect of additional rainfall on the yield occurs during early wintering and around blooming stages. During these two stages every additional milimeter of rainfall may increase 0.4—0.45. catty per mou.
State Evaluation of 500 kV Transformer in DC Bias Based on Information Fusion

In order to accurately evaluate the direct current (DC) bias level of the transformer, an evaluation method based on information fusion is proposed in this paper. Firstly, the features of vibration signals in the time, frequency and time-frequency domains under different severity of DC bias are collected and analyzed. Then, a multi-level evaluation model combining radial basis function (RBF) artificial neural network and improved analytic hierarchy process (AHP) is established. The RBF neural network takes the features as input and outputs the probability of the DC bias level initially. Then, the outputs of RBF are assigned certain weights via improved AHP, and the evaluation of DC bias of transformers can be realized. The proposed method is verified based on field data from 500 kV transformers of China Southern Power Grid. The results show the accuracy and feasibility of the evaluation method.
Performance Evaluation of Two Particle Swarm Optimization Adaptations for Microwave Breast Hyperthermia Focusing

In microwave hyperthermia tumor therapy, optimization of specific absorption rate (SAR), is the conventional approach to focus the heat energy into the target region. Two versions of particle swarm optimization (PSO) were investigated, iw-PSO and CL-PSO, as the optimization techniques for the antenna array excitations in the quest. A two-layer 16-antenna fractal octagonal ring antenna (FORA) circular array was used as the MH applicator. The array lattice possesses symmetry infrastructure for which it is subdivided into iterative scaled-down sequences of the outer octagonal ring. The optimization was conducted on a 3D realistic breast model. The general parameters of the PSO algorithm were kept the same for the two versions and the results were compared in terms of target-to-breast temperature ratio and the average temperature at the healthy tissue. The results demonstrate that CL-PSO provided a remarkable performance over iw-PSO with the same number of iterations and population size.
Integral Equation for Analyzing Neuron Response to Non-invasive Electromagnetic Brain Stimulation

Non-invasive electromagnetic brain stimulation uses electrodes and/or coils to induce an E-field in the brain and modulate its activity. To better understand the neurological effects of non-invasive electromagnetic stimulation we introduce an integral equation formulation for modeling nonlinear response of neurons due to an incident E-field generated by electrode and coil sources. We approximate the new integral equation using boundary element method and compare its accuracy with an established finite element solver. Unlike previous approaches, this new formulation enables the modeling of action potential generation by an axon in a realistic head morphology. This method will aid the development of better neurological understanding, delineate the mechanisms by which electromagnetic stimulation engenders neuronal activity, and model local E-field coupling.
A Novel Mobility Induced Channel Prediction Mechanism for Vehicular Communications

The acquisition of channel state information (CSI) is a vital and challenging task in wireless communications, especially for high mobility scenarios with rapidly varying channels. Channel prediction, as an effective approach to acquire CSI, is considered a promising approach to improve the communication performance. However, most of the current prediction methods with the assumption of slowly varying channel components cannot cope with rapid variation. In this paper, we propose a novel channel prediction scheme to incorporate the mobility of both transceivers and scatterers. Based on the estimated mobility parameters, the component-wise channel extraction results are derived and then the channel frequency response is predicted. In addition, the prediction performance and the computational complexity of the proposed scheme are analyzed and evaluated, and the relationship between the mobility parameter estimation error and the channel component prediction accuracy is revealed as well. Through predicted results based on the simulated and measured data, the performance of the proposed scheme surpasses the conventional schemes with genie-aided information.
1612 MHz OH survey of IRAS point sources. I - Observations made at Dwingeloo, Effelsberg and Parkes

2703 IRAS point sources were observed at the 1612 MHz transition of OH and 738 OH/IR stars were detected, 597 of these are new discoveries. The IRAS sources were selected on the basis of their infrared colours using the 12, 25 and 60 μm fluxes. The survey is 70% complete for IRAS point sources in the southern hemisphere (δ −0.2 and log (f(60 μm)/f(12 μm)) < 0.6 and having a 12 μm flux exceeding 3 Jy. The majority of the OH profiles show twin peak maser features, characteristic of emission from an expanding circumstellar shell. The detection statistics of the survey are discussed.
Robust Day-ahead Economic Dispatch of Integrated Energy System Considering Load Uncertainties and Power Reserve Revenue

Power system integrated with large-scale renewable energy requires additional reserves to maintain power balance. However, from the perspective of regional integrated energy system (IES), it remains a robust optimization problem as to how to quantify reserves supplied to upper grid corresponding to different load uncertainties. This paper proposes a robust day-ahead economic dispatch model of integrated energy system considering heating/cooling load uncertainties and power reserve revenue. It aims at supplying reserves to upper grid while satisfying electric/heating/cooling load balance requirements considering load uncertainty in different scenarios. The proposed model is tested on a multi-energy complementary distributed system project in a province of China and the results turn out that the model is able to maintain the balance between energy supply and selling power reserve.
Harmonic Source Modeling Method Based on Random Forest Algorithm

In recent years, with the rapid development of power electronics technology, power electronic devices are widely used in life, making nonlinear loads a large class of harmonic sources injected into the power grid. Due to the small capacity and low power of loads for home users, the harmonic problem can be ignored, while industrial users, due to their large capacity and many nonlinear devices, cause serious harmonic problems that cannot be ignored. If the harmonics are not reasonably and effectively intervened, they will flow into a higher level power system and cause a series of related power quality problems, which makes the design of power quality management scheme difficult. In the process of harmonic analysis and management, it is necessary to establish an accurate model of harmonic sources. By applying the power quality monitoring data of industrial users, this paper proposes a harmonic source modeling method. Firstly, preprocess the power quality monitoring data, then the characteristic matrix of harmonic voltage and current is constructed, and the decision tree is built by training the harmonic data, and the unknown harmonic voltage and current values are regressed and analyzed, and finally a harmonic source model based on random forest algorithm is established, and the proposed method is verified by applying the power quality monitoring data of industrial users at 110 kV voltage level in a certain place. The results prove that the proposed method has higher accuracy for harmonic current prediction compared with the traditional harmonic source model, and can accurately evaluate and predict the impact of each harmonic voltage difference on harmonic current, which is beneficial to provide theoretical basis for preventing harmonic pollution and analyzing the influencing factors of power quality characteristics of power system.
Optimal Configuration of Electricity-Hydrogen Dominated Highway Service Station Integrated Energy System

In view of the tremendous changes on the way that humans use energy, the coupling of transportation network and energy network becomes much closer than ever before. Under this tendency, the highway service station will undertake a big part of the responsibilities of connecting the energy consumers and energy suppliers specifically on the transportation sector. Hence, this paper proposes a novel configuration approach for the highway service station integrated energy system (HSSIES). Dominated by electricity and hydrogen, the HSSIES can operate normally through the cooperations of the different devices. Rich renewable energy sources can be well utilized due to the multienergy conversion in HSSIES, which highly improves the energy utilization efficiency. By applying queueing theory to the traffic flow in the HSSIES, M/G/k model and M/M/k model are selected to model the electric vehicles (EVs) and the fuel cell vehicles (FCVs) respectively, where the static service intensity rate and the average waiting time are used to evaluate the configuration of charging piles. With the consideration of both the planning stage and operation stage, a stochastic optimization model is established to get comprehensive configuration results. A case study is carried out to prove the effectiveness of the proposed approach.
Multi-objective Optimization Method for Low-Carbon Development of Wind-Light-Biogas-Storage Integrated Energy Microgrids in Remote Areas

Remote areas are rich in renewable energy resources, but the comprehensive utilization rate is not high; and due to restrictions of bitty terrain, the reliability of power supply is relatively low. To realize safe, clean and reliable energy supply in remote areas, integrating clean energy such as wind-lightbiogas resources and constructing integrated energy microgrids (IEM) with renewable energy as the main body is one of the important ways to promote low-carbon, safe and efficient development of power systems in remote areas. Therefore, this paper presents a multi-objective programming method for IEM planning considering the coordinated operation of wind-light-biogas-storage. Firstly, based on the in-depth analysis of operation and correlation characteristics of various energy conversion equipment in IEM, equipment models in wind-light-biogas-storage IEMs are established. Then, with the minimum system cost and the maximum utilization of clean energy as the objectives, the multi-objective planning method of IEMs is proposed, while planning and operation constraints. Finally, NSGA-II algorithm is used to solve the programming problem, and the simulation results verify the effectiveness of the proposed model.
Cross-provincial Electricity Spot Trading Model for Optimizing Resource Allocation

China is characterized by uneven distribution of resources and regional economic development. It is necessary to strengthen inter-provincial electricity market construction, break inter-provincial barriers, and promote the optimization of the distribution of electricity resources in a larger area. The inter-provincial market is to promote the optimal allocation of electricity resources on a larger scale in each province and to promote the flow of electricity resources from electricity-rich areas to electricity-scarce areas. Therefore, this paper firstly analyzes the significance of the inter-provincial electricity spot convergence mechanism to play the role of the inter-provincial market in optimizing the allocation of resources on a large scale, and then constructs a model of inter-provincial and intra-provincial spot market transactions, analyzes the convergence mode of the two levels of market transactions under the convergence mechanism, and finally puts forward an outlook on the construction direction of China's future inter-provincial renewable energy market transaction mechanism.