An Integrated Analysis of miRNA and mRNA Expressions in Non-Small Cell Lung Cancers

Using DNA microarrays, we generated both mRNA and miRNA expression data from 6 non-small cell lung cancer (NSCLC) tissues and their matching normal control from adjacent tissues to identify potential miRNA markers for diagnostics. We demonstrated that hsa-miR-96 is significantly and consistently up-regulated in all 6 NSCLCs. We validated this result in an independent set of 35 paired tumors and their adjacent normal tissues, as well as their sera that are collected before surgical resection or chemotherapy, and the results suggested that hsa-miR-96 may play an important role in NSCLC development and has great potential to be used as a noninvasive marker for diagnosing NSCLC. We predicted potential miRNA target mRNAs based on different methods (TargetScan and miRanda). Further classification of miRNA regulated genes based on their relationship with miRNAs revealed that hsa-miR-96 and certain other miRNAs tend to down-regulate their target mRNAs in NSCLC development, which have expression levels permissive to direct interaction between miRNAs and their target mRNAs. In addition, we identified a significant correlation of miRNA regulation with genes coincide with high density of CpG islands, which suggests that miRNA may represent a primary regulatory mechanism governing basic cellular functions and cell differentiations, and such mechanism may be complementary to DNA methylation in repressing or activating gene expression

Guidance Compliance Behavior on VMS Based on SOAR Cognitive Architecture

SOAR is a cognitive architecture named from state, operator and result, which is adopted to portray the drivers' guidance compliance behavior on variable message sign (VMS) in this paper. VMS represents traffic conditions to drivers by three colors: red, yellow, and green. Based on the multiagent platform, SOAR is introduced to design the agent with the detailed description of the working memory, long-term memory, decision cycle, and learning mechanism. With the fixed decision cycle, agent transforms state through four kinds of operators, including choosing route directly, changing the driving goal, changing the temper of driver, and changing the road condition of prediction. The agent learns from the process of state transformation by chunking and reinforcement learning. Finally, computerized simulation program is used to study the guidance compliance behavior. Experiments are simulated many times under given simulation network and conditions. The result, including the comparison between guidance and no guidance, the state transition times, and average chunking times are analyzed to further study the laws of guidance compliance and learning mechanism

Research trend of epigenetics and depression: adolescents' research needs to strengthen

ObjectiveWith its high prevalence, depression's pathogenesis remains unclear. Recent attention has turned to the interplay between depression and epigenetic modifications. However, quantitative bibliometric analyses are lacking. This study aims to visually analyze depression epigenetics trends, utilizing bibliometric tools, while comprehensively reviewing its epigenetic mechanisms.MethodsUtilizing the Web of Science core dataset, we collected depression and epigenetics-related studies. Employing VOSViewer software, we visualized data on authors, countries, journals, and keywords. A ranking table highlighted field leaders.ResultsAnalysis encompassed 3,469 depression epigenetics studies published from January 2002 to June 2023. Key findings include: (1) Gradual publication growth, peaking in 2021; (2) The United States and its research institutions leading contributions; (3) Need for enhanced collaborations, spanning international and interdisciplinary efforts; (4) Keyword clustering revealed five main themes—early-life stress, microRNA, genetics, DNA methylation, and histone acetylation—highlighting research hotspots; (5) Limited focus on adolescent depression epigenetics, warranting increased attention.ConclusionTaken together, this study revealed trends and hotspots in depression epigenetics research, underscoring global collaboration, interdisciplinary fusion, and multi-omics data's importance. It discussed in detail the potential of epigenetic mechanisms in depression diagnosis and treatment, advocating increased focus on adolescent research in this field. Insights aid researchers in shaping their investigative paths toward understanding depression's epigenetic mechanisms and antidepressant interventions

Research on Mud Flow Rate Measurement Method Based on Continuous Doppler Ultrasonic Wave

In deep-water drilling processes, the flow rate of drilling mud inside an annular pipe is significant judgment data for early kick detection. On the basis of the continuous-wave Doppler ultrasound, this paper proposes a new detection method of nonoriented continuous-wave Doppler ultrasound. The method solves the problem of the ultrasound having great attenuation in mud and not receiving effective signals by using a continuous ultrasound. Moreover, this method analyzes the nonoriented characteristics of ultrasound reflection on principle and proposes the detection of ultrasound Doppler frequency shift by detecting Lamb wave, which releases the detection of oil-based mud flow rate in a nonintrusive annular pipe. The feasibility of the method is verified through theoretical analysis and numerous experiments on a gas kick simulation platform. The measurement result has reached a flow accuracy approximating to the intrusive flow meter

PIH1D3-knockout rats exhibit full ciliopathy features and dysfunctional pre-assembly and loading of dynein arms in motile cilia

Background: Recessive mutation of the X-linked gene, PIH1 domain-containing protein 3 (PIH1D3), causes familial ciliopathy. PIH1D3 deficiency is associated with the defects of dynein arms in cilia, but how PIH1D3 specifically affects the structure and function of dynein arms is not understood yet. To gain insights into the underlying mechanisms of the disease, it is crucial to create a reliable animal model. In humans, rats, and mice, one copy of the PIH1D3 gene is located on the X chromosome. Interestingly, mice have an additional, intronless copy of the Pih1d3 gene on chromosome 1. To develop an accurate disease model, it is best to manipulate the X-linked PIH1D3 gene, which contains essential regulatory sequences within the introns for precise gene expression. This study aimed to develop a tailored rat model for PIH1D3-associated ciliopathy with the ultimate goal of uncovering the intricate molecular mechanisms responsible for ciliary defects in the disease.Methods: Novel Pih1d3-knockout (KO) rats were created by using TALEN-mediated non-homologous DNA recombination within fertilized rat eggs and, subsequently, underwent a comprehensive characterization through a battery of behavioral and pathological assays. A series of biochemical and histological analyses were conducted to elucidate the identity of protein partners that interact with PIH1D3, thus shedding light on the intricate molecular mechanisms involved in this context.Results: PIH1D3-KO rats reproduced the cardinal features of ciliopathy including situs inversus, defects in spermatocyte survival and mucociliary clearance, and perinatal hydrocephalus. We revealed the novel function of PIH1D3 in cerebrospinal fluid circulation and elucidated the mechanism by which PIH1D3 deficiency caused communicating hydrocephalus. PIH1D3 interacted with the proteins required for the pre-assembly and uploading of outer (ODA) and inner dynein arms (IDA), regulating the integrity of dynein arm structure and function in cilia.Conclusion: PIH1D3-KO rats faithfully reproduced the cardinal features of ciliopathy associated with PIH1D3 deficiency. PIH1D3 interacted with the proteins responsible for the pre-assembly and uploading of dynein arms in cilia, and its deficiency led to dysfunctional cilia and, thus, to ciliopathy by affecting the pre-assembly and uploading of dynein arms. The resultant rat model is a valuable tool for the mechanistic study of PIH1D3-caused diseases

结合轻量级麦穗检测模型和离线Android软件开发的田间小麦测产

The number of spikes per unit area is a key yield component for cereal crops such as wheat, which is popularly used in wheat research for crop improvement. With the fast maturity of smartphone imaging hardware and recent advances in image processing and lightweight deep learning techniques, it is possible to acquire high-resolution images using a smartphone camera, followed by the analysis of wheat spikes per unit area through pre-trained artificial intelligence algorithms. Then, by combining detected spike number with variety-based spikelet number and grain weight, it is feasible to carry out a near real-time estimation of yield potential for a given wheat variety in the field. This AI-driven approach becomes more powerful when a range of varieties are included in the training datasets, enabling an effective and valuable approach for yield-related studies in breeding, cultivation, and agricultural production. In this study, we present a novel smartphone-based software application that combines smartphone imaging, lightweight and embedded deep learning, with yield prediction algorithms and applied the software to wheat cultivation experiments. This open-source Android application is called YieldQuant-Mobile (YQ-M), which was developed to measure a key yield trait (i.e. spikes per unit area) and then estimate yield based on the trait. Through YQ-M and smartphones, we standardized the in-field imaging of wheat plots, streamlined the detection of spikes per unit area and the prediction of yield, without a prerequisite of in-field WiFi or mobile network. In this article, we introduce the YQ-M in detail, including: 1) the data acquisition designed to standardize the collection of wheat images from an overhead perspective using Android smartphones; 2) the data pre-processing of the acquired image to reduce the computational time for image analysis; 3) the extraction of wheat spike features through deep learning (i.e. YOLOV4) and transfer learning; 4) the application of TensorFlow.lite to transform the trained model into a lightweight MobileNetV2-YOLOV4 model, so that wheat spike detection can be operated on an Android smartphone; 5) finally, the establishment of a mobile phone database to incorporate historic datasets of key yield components collected from different wheat varieties into YQ-M using Android SDK and SQLite. Additionally, to ensure that our work could reach the broader research community, we developed a Graphical User Interface (GUI) for YQ-M, which contains: 1) the spike detection module that identifies the number of wheat spikes from a smartphone image; 2) the yield prediction module that invokes near real-time yield prediction using detected spike numbers and related parameters such as wheat varieties, place of production, accumulated temperature, and unit area. During our research, we have tested YQ-M with 80 representative varieties (240 one-square-meter plots, three replicates) selected from the main wheat producing areas in China. The computed accuracy, recall, average accuracy, and F1-score for the learning model are 84.43%, 91.05%, 91.96%, and 0.88, respectively. The coefficient of determination between YQ-M predicted yield values and post-harvest manual yield measurement is 0.839 (n=80 varieties, P<0.05; Root Mean Square Error=17.641 g/m2). The results suggest that YQ-M presented here has a high accuracy in the detection of wheat spikes per unit area and can produce a consistent yield prediction for the selected wheat varieties under complex field conditions. Furthermore, YQ-M can be easily accessed and expanded to incorporate new varieties and crop species, indicating the usability and extendibility of the software application. Hence, we believe that YQ-M is likely to provide a step change in our abilities to analyze yield-related components for different wheat varieties, a low-cost, accessible, and reliable approach that can contribute to smart breeding, cultivation and, potentially, agricultural production

Detergent-insoluble PFN1 inoculation expedites disease onset and progression in PFN1 transgenic rats

Accumulating evidence suggests a gain of elusive toxicity in pathogenically mutated PFN1. The prominence of PFN1 aggregates as a pivotal pathological hallmark in PFN1 transgenic rats underscores the crucial involvement of protein aggregation in the initiation and progression of neurodegeneration. Detergent-insoluble materials were extracted from the spinal cords of paralyzed rats afflicted with ALS and were intramuscularly administered to asymptomatic recipient rats expressing mutant PFN1, resulting in an accelerated development of PFN1 inclusions and ALS-like phenotypes. This effect diminished when the extracts derived from wildtype PFN1 transgenic rats were employed, as detergent-insoluble PFN1 was detected exclusively in mutant PFN1 transgenic rats. Consequently, the factor influencing the progression of ALS pathology in recipient rats is likely associated with the presence of detergent-insoluble PFN1 within the extracted materials. Noteworthy is the absence of disease course modification upon administering detergent-insoluble extracts to rats that already displayed PFN1 inclusions, suggesting a seeding rather than augmenting role of such extracts in initiating neuropathological changes. Remarkably, pathogenic PFN1 exhibited an enhanced affinity for the molecular chaperone DNAJB6, leading to the sequestration of DNAJB6 within protein inclusions, thereby depleting its availability for cellular functions. These findings shed light on a novel mechanism that underscores the prion-like characteristics of pathogenic PFN1 in driving neurodegeneration in the context of PFN1-related ALS

Cobalt phosphosulfide nanoparticles encapsulated into heteroatom-doped carbon as bifunctional electrocatalyst for Zn−air battery

The design of non‐noble electrocatalysts with low overpotential, high effectiveness, and good cycling stability for oxygen evolution reaction (OER) and oxygen reduction reaction (ORR) is highly desirable for high-performance Zn–air batteries. Herein, CoPS nanoparticles encapsulated with S, P, N tri-doped carbon material (SPNC) is proposed as bifunctional electrocatalyst of ORR and OER derived from zeolitic imidazolate framework-67. Density functional theory calculations consistently reveal that P element in CoPS@SPNC improve the electrical conductivity and reduce OH* hydrogenation energy barrier on Co sites, thereby facilitating the overall ORR/OER activities. A flexible Zn–air battery with CoPS@SPNC delivers an overpotential of 0.49 V, an energy efficiency above 80%, and a discharge voltage of 1.29 V at 2 mA cm−2 for 80 h. Such strategy could provide numerous possibilities for the design of highly efficient electrocatalysts

Second Law Analysis of Spectral Radiative Transfer and Calculation in One-Dimensional Furnace Cases

This study combines the radiation transfer process with the thermodynamic second law to achieve more accurate results for the energy quality and its variability in the spectral radiation transfer process. First, the core ideas of the monochromatic photon exergy theory based on the equivalent temperature and the infinite-staged Carnot model are reviewed and discussed. Next, this theory is combined with the radiation transfer equation and thus the spectral radiative entropy and the radiative exergy transfer equations are established and verified based on the second law of thermodynamics. Finally, one-dimensional furnace case calculations are performed to determine the applicability to engineering applications. It is found that the distribution and variability of the spectral radiative exergy flux in the radiation transfer process can be obtained using numerical calculations and the scatter media could slightly improve the proportion of short-wavelength radiative exergy during the radiation transfer process. This has application value for research on flame energy spectrum-splitting conversion systems

SPARK-X: non-parametric modeling enables scalable and robust detection of spatial expression patterns for large spatial transcriptomic studies

Abstract Spatial transcriptomic studies are becoming increasingly common and large, posing important statistical and computational challenges for many analytic tasks. Here, we present SPARK-X, a non-parametric method for rapid and effective detection of spatially expressed genes in large spatial transcriptomic studies. SPARK-X not only produces effective type I error control and high power but also brings orders of magnitude computational savings. We apply SPARK-X to analyze three large datasets, one of which is only analyzable by SPARK-X. In these data, SPARK-X identifies many spatially expressed genes including those that are spatially expressed within the same cell type, revealing new biological insights.http://deepblue.lib.umich.edu/bitstream/2027.42/173866/1/13059_2021_Article_2404.pd