Miracle-dz/UniFAR-FTUs huggingface dataset:
Striatal Neurons Partially Expressing a Dopaminergic Phenotype: Functional Significance and Regulation

Since the discovery of striatal neurons expressing dopamine-synthesizing enzymes, researchers have attempted to identify their phenotype and functional significance. In this study, it was shown that in transgenic mice expressing green fluorescent protein (GFP) under the tyrosine hydroxylase (TH) gene promoter, (i) there are striatal neurons expressing only TH, only aromatic L-amino acid decarboxylase (AADC), or both enzymes of dopamine synthesis; (ii) striatal neurons expressing dopamine-synthesizing enzymes are not dopaminergic since they lack a dopamine transporter; (iii) monoenzymatic neurons expressing individual complementary dopamine-synthesizing enzymes produce this neurotransmitter in cooperation; (iv) striatal nerve fibers containing only TH, only AADC, or both enzymes project into the lateral ventricles, providing delivery pathways for L-3,4-dihydroxyphenylalanine and dopamine to the cerebrospinal fluid; and (v) striatal GFP neurons express receptor genes for various signaling molecules, i.e., classical neurotransmitters, neuropeptides, and steroids, indicating fine regulation of these neurons. Based on our data, it is assumed that the synthesis of dopamine by striatal neurons is a compensatory response to the death of nigral dopaminergic neurons in Parkinson’s disease, which opens broad prospects for the development of a fundamentally novel antiparkinsonian therapy.
Weld Classification With Feature Extraction by FRESH Algorithm Based on Surface Topographical Optical Coherence Tomography Data for Laser Welding of Copper

The topographical information of a weld seam bears information about quality relevant characteristics such as humping or spatter. Optical coherence tomography (OCT) can be used for inline scanning the weld topography coaxially mounted at a laser scanning optic. Feature extraction from this topographical information is challenging due to finding mathematical representations for the identification of relevant features. Feature extraction based on scalable hypothesis tests (FRESH) allows for feature extraction by a combination of various time series characterization methods. FRESHs feature selection is supported with an automatically configured hypothesis test and hence allows for quick extraction of significant features from sensing data in laser welding processes. In this work, a proof-of-concept is demonstrated for weld result categorization from OCT data by feature extraction using the FRESH algorithm. Changes in weld topography are characterized in a vast variety of process parameters for weld categories such as spatter, deep penetration welding, humping and heat conduction welding. As a result, a quantified separation of weld categories is possible and shows the feasibility of the FRESH algorithm for future quality assessments with different sensing technologies in laser welding.
A High-Gain and High-Efficiency Photovoltaic Grid-Connected Inverter with Magnetic Coupling

Conventional photovoltaic (PV) grid-connected systems consist of a boost converter cascaded with an inverter, resulting in poor efficiency due to performing energy processing twice. Many pseudo DC-link inverters with single energy processing have been proposed to improve system efficiency and simplify circuits. However, their output voltage gain is limited by the non-ideal characteristics of the power diode, making them difficult to apply in high-output voltage applications. This paper proposes combining a boost converter with magnetic coupling and a full-bridge unfolding circuit to develop an inverter featuring high voltage-gain and high efficiency. According to the desired instantaneous output voltage, the high-gain boost converter and the full-bridge unfolding circuit are sequentially and respectively controlled by SPWM. A sinusoidal output voltage can be generated by performing energy processing only once, effectively improving the conversion efficiency. Magnetic coupling is adopted to increase the voltage gain of step-up, and the step-down function is realized by the full-bridge unfolding circuit to reduce conduction loss. Finally, a 500 W prototype was fabricated for the proposed high-gain inverter. The experimental results were used to verify the correctness of the theoretical analysis and the feasibility of the circuit structure.
A variational method for finite element stress recovery: Applications in one-dimension

It is well-known that stresses (and strains) calculated by a displacement-based finite element analysis are generally not as accurate as the displacements. In addition, the calculated stress field is typically discontinuous at element interfaces. Because the stresses are typically of more interest than the displacements, several procedures have been proposed to obtain a smooth stress field, given the finite element stresses, and to improve the accuracy. Hinton and Irons introduced global least squares smoothing of discrete data defined on a plane using a finite element formulation. Tessler and co-workers recently developed a conceptually similar formulation for smoothing of two-dimensional data based on a discrete least square approximation with a penalty constraint. The penalty constraint results in a stress field which is C(exp 1)-continuous, a result not previously obtained. The approach requires additional, 'smoothing' finite element analysis and for their two-dimensional application, they used a conforming C(exp 0)-continuous triangular finite element based on a conforming plate element. This paper presents the results of a detailed investigation into the application of Tessler's smoothing procedure to the smoothing of finite element stresses from one-dimensional problems. Although the one-dimensional formulation has some practical applicability, such as in truss, beam, axisymmetric mechanics, and one-dimensional heat conduction, the primary motivation for developing the one-dimensional smoothing case is to explore the characteristics of the general smoothing strategy. In particular, it is used to describe the behavior of the method and to explore the suitability of criteria proposed for the smoothing analysis. Prior to presenting numerical results, the variational formulation of the smoothing strategy is presented and a criterion for the smoothing analysis is described.
Toward ultrahigh thermal conductivity graphene films

With increasing demands of high-performance and functionality, electronics devices generate a great amount of heat. Thus, efficient heat dissipation is crucially needed. Owing to its extremely good thermal conductivity, graphene is an interesting candidate for this purpose. In this paper, a two-step temperature-annealing process to fabricate ultrahigh thermal conductive graphene assembled films (GFs) is proposed. The thermal conductivity of the obtained GFs was as high as 3826 ± 47 W m−1 K−1. Extending the time of high-temperature annealing significantly improved the thermal performance of the GF. Structural analyses confirmed that the high thermal conductivity is caused by the large grain size, defect-free stacking, and high flatness, which are beneficial for phonon transmission in the carbon lattice. The turbostratic stacking degree decreased with increasing heat treatment time. However, the increase in the grain size after long heat treatment had a more pronounced effect on the phonon transfer of the GF than that of turbostratic stacking. The developed GFs show great potential for efficient thermal management in electronics devices.
Using Flickr Data to Understand Image of Urban Public Spaces with a Deep Learning Model: A Case Study of the Haihe River in Tianjin

Understanding public perceptions of images of urban public spaces can guide efforts to improve urban vitality and spatial diversity. The rise of social media data and breakthroughs in deep learning frameworks for computer vision provide new opportunities for studying public perceptions in public spaces. While social media research methods already exist for extracting geo-information on public preferences and emotion analysis findings from geodata, this paper aims at deep learning analysis by building a VGG-16 image classification method that enhanced the research content of images without geo-information. In this study, 1940 Flickr images of the Haihe River in Tianjin were identified in multiple scenes with deep learning. The regularized VGG-16 architecture showed high accuracies of 81.75% for the TOP-1 and 96.75% for the TOP-5 and Grad-CAM visualization modules for the interpretation of classification results. The result of the present work indicate that images of the Haihe River are dominated by skyscrapers, bridges, promenades, and urban canals. After using kernel density to visualize the spatial distribution of Flickr images with geodata, it was found that there are three vitality areas in Haihe River. However, the kernel density result also shows that judging spatial visualization based solely on geodata is incomplete. The spatial distribution can be used as an assistant function in the case of the under-representation of geodata. Collectively, the field of how to apply computer vision to urban design research was explored and extended in this trial study.
Proactive Eavesdropping via Jamming Over Short Packet Suspicious Communications With Finite Blocklength

Short packet communications play key roles in the Internet-of-Things. Conventional Shannon’s coding theorem is not applicable for short packet communications, and the achievable rate in the finite blocklength regime is related to the blocklength and the decoding error probability. Contrary to conventional physical layer security, wireless information surveillance assumes that the communication users are suspicious users and the eavesdroppers are legitimate monitors, and proactive eavesdropping via jamming has been proposed to improve the eavesdropping performance. Existing works on proactive eavesdropping ignored the scenario when the suspicious users adopt the short packet communications. Thus, this paper tries to develop effective proactive eavesdropping schemes suitable for the short packet suspicious communications. Under the truncated channel inversion power allocation and the modified constant power allocation policies at the suspicious source, the problems of jamming power allocation for maximizing the monitoring success probability subject to the average transmit power constraint at the monitor are investigated. Based on the Dinkelbach-type algorithm, the Lagrange duality method and a novel tractable approximate expression for the decoding error probability, we derive suboptimal solutions to the problems. Simulation results demonstrate the effectiveness of the proposed solutions compared to various benchmark algorithms in existing literature.
Vertical Variation in Leaf Traits and Crown Structure Promote the Coexistence of Forest Tree Species

Vertical stratification in trees may respond to selective pressures to enhance light interception and utilization; therefore, the vertical functional variation in leaf traits may indicate niche partitioning within forests. In this study, vertical variations in leaf and crown structure traits of seven common tree species were analysed with respect to differences between species in different height groups, within the same height range, in the same species across tree height, and different parts of the individual tree crown to reveal coexistence mechanisms in subtropical forest tree species. There were multiple levels of trait variation in the vertical dimension, validating the existence of vertical niche differentiation in subtropical forest species. The functional trait differences arose among different height groups, among species co-occurring within the same height range, in the same species across tree height, and among different parts of the individual tree crown. Variation in comparative advantages, which was characterised by those traits between species across different height ranges, was also one of the manifestations of niche differentiation in the vertical dimension. Moreover, contrasting results between lower height ranges and higher ranges in the relationship between species’ differences in functional traits and species’ difference of abundance were found, further confirming that there was obvious vertical niche separation in the community. This study emphasised the importance of vertical variation in species’ performances in elucidating the mechanisms of tree species coexistence in subtropical forests.
Metabolic Potential of the Gut Microbiome Is Significantly Impacted by Conditioning Regimen in Allogeneic Hematopoietic Stem Cell Transplantation Recipients

Allogeneic hematopoietic stem cell transplantation (aHSCT) is a putative curative treatment for malignant hematologic disorders. During transplantation, the immune system is suppressed/eradicated through a conditioning regimen (non-myeloablative or myeloablative) and replaced with a donor immune system. In our previous study, we showed changes in gut taxonomic profiles and a decrease in bacterial diversity post-transplant. In this study, we expand the cohort with 114 patients and focus on the impact of the conditioning regimens on taxonomic features and the metabolic functions of the gut bacteria. This is, to our knowledge, the first study to examine the metabolic potential of the gut microbiome in this patient group. Adult aHSCT recipients with shotgun sequenced stool samples collected day −30 to +28 relative to aHSCT were included. One sample was selected per patient per period: pre-aHSCT (day −30–0) and post-aHSCT (day 1–28). In total, 254 patients and 365 samples were included. Species richness, alpha diversity, gene richness and metabolic richness were all lower post-aHSCT than pre-aHSCT and the decline was more pronounced for the myeloablative group. The myeloablative group showed a decline in 36 genera and an increase in 15 genera. For the non-myeloablative group, 30 genera decreased and 16 increased with lower fold changes than observed in the myeloablative group. For the myeloablative group, 32 bacterial metabolic functions decreased, and one function increased. For the non-myeloablative group, three functions decreased, and two functions increased. Hence, the changes in taxonomy post-aHSCT caused a profound decline in bacterial metabolic functions especially in the myeloablative group, thus providing new evidence for associations of myeloablative conditioning and gut dysbiosis from a functional perspective.
Effectiveness and Safety of Molecular-Targeted Therapy after Nivolumab Plus Ipilimumab for Advanced or Metastatic Renal Cell Carcinoma: A Multicenter, Retrospective Cohort Study

Simple Summary We evaluated the efficacy and safety of molecular-targeted therapies (MTTs) in 29 patients who discontinued the combination therapy of nivolumab plus ipilimumab (NIVO+IPI) for advanced or metastatic renal cell carcinoma as real-world outcomes. Patients receiving MTTs had a median follow-up of 8 months. The objective response rate was 44.8%, and the disease control rate was 72.4%. After NIVO+IPI, the median overall survival was 18 months, and progression-free survival (PFS) was 8 months. Patients with bone metastases had a significantly shorter median PFS when treated with MTTs after NIVO+IPI than those without bone metastases (4 vs. 12 months, p = 0.012). MTTs may be a useful secondary treatment option after the discontinuation of NIVO+IPI. Abstract This study aimed to evaluate the effectiveness and safety of molecular-targeted therapies (MTTs) after the discontinuation of nivolumab and ipilimumab (NIVO+IPI) combination therapy in patients who had been diagnosed with advanced/metastatic renal cell carcinoma as real-world outcomes. We enrolled patients treated with MTTs following initial therapy with NIVO+IPI at nine institutions in Japan. We evaluated the objective response rate (ORR) as the primary endpoint and disease control rate (DCR), best overall response, and oncological outcomes (overall survival (OS) and progression-free survival (PFS)) as the secondary endpoints. We also evaluated factors predictive of disease progression after the administration of MTTs. Patients were followed up for a median of 8 months. The ORR was 44.8%, and the DCR was 72.4%. The median OS and PFS of MTTs after NIVO+IPI were 18 months and 8 months, respectively. A total of 31% of patients experienced grade 3/4 MTT-related adverse events. The median PFS in patients with bone metastases was significantly shorter than that in those without bone metastases (4 vs. 12 months, p = 0.012). MTTs may be a useful secondary treatment option after the discontinuation of NIVO+IPI.
Social Affect Regulation in University Students During the COVID-19 Pandemic

Given how much time humans spend in social contexts, interest has been growing in socially mediated forms of affect regulation. Historically, though, research on affect regulation has focused on individual forms of regulation, such as cognitive reappraisal. To address this gap, we investigated social affect regulation in university students through an online survey, with a particular focus on social reappraisal. Specifically, we tested whether the frequency with which students communicate with their social contacts is related to how much social reappraisal support they receive from those contacts, and whether social reappraisal support is associated with mental health. Our final sample consisted of 152 undergraduates from across North America who reported on a total of 1,124 social contacts. We consistently found that communication frequency was positively associated with perceived social reappraisal support across several modalities of communication (e.g., text-based, video-based, in-person). However, we observed no associations between levels of social reappraisal support and measures of mental health. This research was part of a preregistered project on social affect regulation in university students in the context of the COVID-19 pandemic (https://osf.io/q7bvw/). Thus, we present findings in relation to this context. These findings underscore that social forms of affect regulation play a significant role in university students’ lives, emphasizing the value of further research into their mechanisms and effects.
Adoption and Impact of Integrated Soil Fertility Management Technology on Food Production

Amid recent climate difficulties, integrated soil fertility management (ISFM) strategies are vital in restoring soil fertility, enhancing yield, and achieving the farmer community’s well-being. This study examines ISFM’s adoption and impact on wheat yields in Punjab, Pakistan, by employing an endogenous switching regression model (ESRM). The selection equation highlights the multiple factors such as age, gender, education, extension access, credit access, and social influence as essential predictors of ISFM adoption. Treatment effects showed that the average wheat yield is higher for adopters. The findings suggest refining the current institutional system will enhance adoption and food security by improving agricultural production.
Augmenting IoT Intrusion Detection System Performance Using Deep Neural燦etwork

: Due to their low power consumption and computing power, Internet of Things (IoT) devices are difficult to secure, and the rapid growth of IoT devices in the home increases the risk of cyber-attacks. One method of preventing cyberattacks is to employ an intrusion detection system (IDS), which detects incoming attacks and notifies the user, allowing for the implementation of appropriate countermeasures. Attempts have been made in the past to detect new attacks using machine learning and deep learning, but these efforts have been unsuccessful. In this paper, we classify network attacks using two Convolutional Neural Networks (CNN) models i.e., MyCNN and IoTCNN to automatically detect various kind malignant and benign intrusion in IoT network. The purpose of this research is to evaluate the use of deep learning in IoT IDS. The neural network was trained in this experiment using the NF-UNSW-NB15-v2 dataset, which contains nine distinct types of attacks. The data from the network stream was converted to Red Green and Blue (RGB) images, which were then used to train the neural network. To establish baseline models, we proposed two models with the name of MyCNN and IoTCNN. When compared the proposed MyCNN convolutional neural network for network attack classification, the IoTCNN was outperformed by the MyCNN model. Additionally, it demonstrates that both networks achieve higher accuracy in the majority of categories but the IoTCNN achieved lower than the proposed MyCNN model for network attack detection. We discovered that the MyCNN is generally more suitable to be deployed for intrusion detection in IoT devices.
Adaptive Robust Control for Nonlinear Mechanical Systems With Inequality Constraints and Uncertainties

The inequality constraints, system nonlinearities, parameter uncertainties, and external disturbances are always unavoidable in practical mechanical systems. This article proposes an adaptive robust control (ARC) algorithm from the view of servo constraint following to tackle the control problem of mechanical systems subject to the above factors. For the inequality constraints, a creative diffeomorphism which could convert the two-sided bounded state variables to the unbounded ones is explored, which could render the transformed nonlinear system free from inequality constraints. For the system uncertainties, a leakage-type ARC algorithm is developed, which could render the system the practical stability. The permanent magnet linear motor (PMLM) system is utilized as a typical application to verify the proposed state transformation and ARC approach. Numerical simulations show that the displacement of the PMLM system could well track the desired trajectory without violating the given bound line.
Multiple Periodic Solutions of a Nonlinear Suspension Bridge System of Partial Differential Equations

A nonlinear system of partial differential equations is presented to model the vertical motions of the roadbed and main cable of suspension bridge. The model is subjected to periodic forcing and numerical methods are presented for the computation of the periodic responses in the system. Newton’s method, continuation algorithms and Floquet theory are used to produce bifurcation diagrams which capture the multiplicity and stability of the periodic solutions. Separable solutions are investigated using methods for ordinary differential equations while general solutions are investigated using a finite difference scheme and an implicit-explicit initial value solver. The method of steepest descent is used to find entirely new branches of periodic solutions. Multiple periodic solutions are found, including subharmonic solutions whose period is a multiple of the period of the forcing. Multiple Periodic Solutions of a Nonlinear Suspension Bridge System of Partial Differential Equations