Miracle-dz/UniFAR-FTUs huggingface dataset:
Monoacylglycerol Lipase Inhibitor MJN110 Reduces Neuronal Hyperexcitability, Restores Dendritic Arborization Complexity, and Regulates Reward-Related Behavior in Presence of HIV-1 Tat

While current therapeutic strategies for people living with human immunodeficiency virus type 1 (HIV-1) suppress virus replication peripherally, viral proteins such as transactivator of transcription (Tat) enter the central nervous system early upon infection and contribute to chronic inflammatory conditions even alongside antiretroviral treatment. As demand grows for supplemental strategies to combat virus-associated pathology presenting frequently as HIV-associated neurocognitive disorders (HAND), the present study aimed to characterize the potential utility of inhibiting monoacylglycerol lipase (MAGL) activity to increase inhibitory activity at cannabinoid receptor-type 1 receptors through upregulation of 2-arachidonoylglycerol (2-AG) and downregulation of its degradation into proinflammatory metabolite arachidonic acid (AA). The MAGL inhibitor MJN110 significantly reduced intracellular calcium and increased dendritic branching complexity in Tat-treated primary frontal cortex neuron cultures. Chronic MJN110 administration in vivo increased 2-AG levels in the prefrontal cortex (PFC) and striatum across Tat(+) and Tat(–) groups and restored PFC N-arachidonoylethanolamine (AEA) levels in Tat(+) subjects. While Tat expression significantly increased rate of reward-related behavioral task acquisition in a novel discriminative stimulus learning and cognitive flexibility assay, MJN110 altered reversal acquisition specifically in Tat(+) mice to rates indistinguishable from Tat(–) controls. Collectively, our results suggest a neuroprotective role of MAGL inhibition in reducing neuronal hyperexcitability, restoring dendritic arborization complexity, and mitigating neurocognitive alterations driven by viral proteins associated with latent HIV-1 infection.
A Universal Approach for Maximizing Terahertz Wave Absorption in Graphene Cut-Wires

Graphene micro-/nanostructures and their arrays have attracted considerable attention in infrared (IR) and terahertz (THz) applications due to their strong plasmon responses. However, as too many parameters, including geometry, carrier concentration, frequency, and adjacent substrate, can affect the plasmonic behaviors of the micro-/nanostructures, the optimization of the THz-IR responses, such as absorption and reflection, of these structures and their arrays require tremendous computations on parameter scanning. Here, we propose a theoretical approach to design graphene cut-wires with maximized THz wave absorption. Analytical expression describing the THz absorption/reflection of graphene cut-wires is derived. Accordingly, a maximum THz wave absorption of the array, regardless of its operating frequencies and geometrical parameters, can be achieved by simply tuning the cut-wires duty ratio. The analytical results are further validated by numerical simulations. This intuitive design manner is of significance for the design of graphene arrays with high-efficiency THz responses as well as promoting their practical applications in THz functional devices.
Network Structure of the Master Clock Is Important for Its Primary Function

A master clock located in the suprachiasmatic nucleus (SCN) regulates the circadian rhythm of physiological and behavioral activities in mammals. The SCN has two main functions in the regulation: an endogenous clock produces the endogenous rhythmic signal in body rhythms, and a calibrator synchronizes the body rhythms to the external light-dark cycle. These two functions have been determined to depend on either the dynamic behaviors of individual neurons or the whole SCN neuronal network. In this review, we first introduce possible network structures for the SCN, as revealed by time series analysis from real experimental data. It was found that the SCN network is heterogeneous and sparse, that is, the average shortest path length is very short, some nodes are hubs with large node degrees but most nodes have small node degrees, and the average node degree of the network is small. Secondly, the effects of the SCN network structure on the SCN function are reviewed based on mathematical models of the SCN network. It was found that robust rhythms with large amplitudes, a high synchronization between SCN neurons and a large entrainment ability exists mainly in small-world and scale-free type networks, but not other types. We conclude that the SCN most probably is an efficient small-world type or scale-free type network, which drives SCN function.
The Stimulation and Coordination Mechanisms of the Carbon Emission Trading Market of Public Buildings in China

The most important issue related to the establishment of carbon emission trading in China is how to motivate the owners of public buildings to participate. However, Existing research few considered the characteristics of public building owners and the influence of various uncertain factors in carbon emission trading investments. To fill this gap, this study constructs a carbon emission trading investment decision model of public building owners to study the mechanism that encourages them to participate, incorporating these characteristics and uncertain factors. The findings are as follows. First, carbon price is important in adjusting the emission reductions of different owners to minimize the total social cost of emission-reduction measures. Second, the price of carbon-emission permits has a significant impact on the investment threshold and decision-making behavior of public building owners. Finally, reducing the cost of energy-conservation and emission-reduction technologies in public buildings and appropriately subsidizing owners for their emission-reduction investment were effective methods to motivate them to participate in carbon emission trading. The results were used to quantitatively analyze the impact of a carbon emission trading mechanism on the decision-making behavior of public building owners and to construct the carbon emission trading mechanism used in China’s public building industry.
Graphene-Induced Hyperthermia (GIHT) Combined With Radiotherapy Fosters Immunogenic Cell Death

Radiotherapy and chemotherapy are the standard interventions for cancer patients, although cancer cells often develop radio- and/or chemoresistance. Hyperthermia reduces tumor resistance and induces immune responses resulting in a better prognosis. We have previously described a method to induce tumor cell death by local hyperthermia employing pegylated reduced graphene oxide nanosheets and near infrared light (graphene-induced hyperthermia, GIHT). The spatiotemporal exposure/release of heat shock proteins (HSP), high group mobility box 1 protein (HMGB1), and adenosine triphosphate (ATP) are reported key inducers of immunogenic cell death (ICD). We hypothesize that GIHT decisively contributes to induce ICD in irradiated melanoma B16F10 cells, especially in combination with radiotherapy. Therefore, we investigated the immunogenicity of GIHT alone or in combination with radiotherapy in melanoma B16F10 cells. Tumor cell death in vitro revealed features of apoptosis that is progressing fast into secondary necrosis. Both HSP70 and HMGB1/DNA complexes were detected 18 hours post GIHT treatment, whereas the simultaneous release of ATP and HMGB1/DNA was observed only 24 hours post combined treatment. We further confirmed the adjuvant potential of these released DAMPs by immunization/challenge experiments. The inoculation of supernatants of cells exposed to sole GIHT resulted in tumor growth at the site of inoculation. The immunization with cells exposed to sole radiotherapy rather fostered the growth of secondary tumors in vivo. Contrarily, a discreet reduction of secondary tumor volumes was observed in mice immunized with a single dose of cells and supernatants treated with the combination of GIHT and irradiation. We propose the simultaneous release of several DAMPs as a potential mechanism fostering anti-tumor immunity against previously irradiated cancer cells.
pH Homeostasis and Sodium Ion Pumping by Multiple Resistance and pH Antiporters in Pyrococcus furiosus

Multiple Resistance and pH (Mrp) antiporters are seven-subunit complexes that couple transport of ions across the membrane in response to a proton motive force (PMF) and have various physiological roles, including sodium ion sensing and pH homeostasis. The hyperthermophilic archaeon Pyrococcus furiosus contains three copies of Mrp encoding genes in its genome. Two are found as integral components of two respiratory complexes, membrane bound hydrogenase (MBH) and the membrane bound sulfane sulfur reductase (MBS) that couple redox activity to sodium translocation, while the third copy is a stand-alone Mrp. Sequence alignments show that this Mrp does not contain an energy-input (PMF) module but contains all other predicted functional Mrp domains. The P. furiosus Mrp deletion strain exhibits no significant changes in optimal pH or sodium ion concentration for growth but is more sensitive to medium acidification during growth. Cell suspension hydrogen gas production assays using the deletion strain show that this Mrp uses sodium as the coupling ion. Mrp likely maintains cytoplasmic pH by exchanging protons inside the cell for extracellular sodium ions. Deletion of the MBH sodium-translocating module demonstrates that hydrogen gas production is uncoupled from ion pumping and provides insights into the evolution of this Mrp-containing respiratory complex.
Transcriptome Profiling of Atlantic Salmon Adherent Head Kidney Leukocytes Reveals That Macrophages Are Selectively Enriched During Culture

The Atlantic salmon (Salmo salar) is an economically important fish, both in aquaculture and in the wild. In vertebrates, macrophages are some of the first cell types to respond to pathogen infection and disease. While macrophage biology has been characterized in mammals, less is known in fish. Our previous work identified changes in the morphology, phagocytic ability, and miRNA profile of Atlantic salmon adherent head kidney leukocytes (HKLs) from predominantly “monocyte-like” at Day 1 of in vitro culture to predominantly “macrophage-like” at Day 5 of culture. Therefore, to further characterize these two cell populations, we examined the mRNA transcriptome profile in Day 1 and Day 5 HKLs using a 44K oligonucleotide microarray. Large changes in the transcriptome were revealed, including changes in the expression of macrophage and immune-related transcripts (e.g. csf1r, arg1, tnfa, mx2), lipid-related transcripts (e.g. fasn, dhcr7, fabp6), and transcription factors involved in macrophage differentiation and function (e.g. klf2, klf9, irf7, irf8, stat1). The in silico target prediction analysis of differentially expressed genes (DEGs) using miRNAs known to change expression in Day 5 HKLs, followed by gene pathway enrichment analysis, supported that these miRNAs may be involved in macrophage maturation by targeting specific DEGs. Elucidating how immune cells, such as macrophages, develop and function is a key step in understanding the Atlantic salmon immune system. Overall, the results indicate that, without the addition of exogenous factors, the adherent HKL cell population differentiates in vitro to become macrophage-like.
Effect of Dissolved Nitrogen Gas on the Density of Di-2-Ethylhexyl Sebacate: Working Fluid of the NIST Oil Ultrasonic Interferometer Manometer Pressure Standard

Abstract: The National Institute of Standards and Technology (NIST) Low Pressure Manometry Laboratory maintains national pressure standards ranging from 1 mPa to 360 kPa through the operation of four ultrasonic interferometer manometer (UIM) pressure standards. NIST's newest UIM standard operates with Di-2-ethylhexyl sebacate (DEHS) oil as the working fluid over the range of 1 mPa to 140 Pa in absolute mode. The relative change in density of DEHS oil as a function of nitrogen gas pressure was determined using a vibrating-tube densimeter with measurement repeatability of 1 × 10−6 g/cm3 and measurement accuracy of 5 × 10−6 g/cm3 given by the instrument manufacturer. The densimeter was modified to operate at pressures below one atmosphere. The density of DEHS oil was observed to follow a linear dependence with the pressure of nitrogen gas exposure. The difference or change in density of DEHS oil after vacuum exposure (≤ 8 × 10−4 Pa) and pressures ranging from 138 Pa to 101 kPa was evaluated. For the pressures up to 1.367 kPa, there was no statistically significant change in the oil density observed. However, exposure to pressures between 10 kPa and 101 kPa showed significant density changes. The density of DEHS oil decreased by 46 ppm ± 5 ppm after 2 h of nitrogen gas exposure at 101 kPa. The density of DEHS oil decreased by 42 ppm ± 3 ppm after more than 24 h air exposure at 101 kPa. The results of this study show that the effect of dissolved gas on the density of DEHS oil does not contribute significantly to the pressure dependent uncertainty of the NIST oil UIM when operated over its range of 1 mPa to 140 Pa in absolute mode. Additionally, the results demonstrate that the oil density change is small enough that an oil manometer with an extended range of 1.4 kPa (10 torr) would not be adversely impacted by this gas density effect.
Evolution or revolution? The impact of a digital euro on the financial system

Paper banknotes are a case in point. As they were easy to carry, they made commerce more straightforward. But their success did not come easily. Attempts by central banks to issue banknotes in the 17th century resulted in too many being issued and even defaults, raising questions about their effects on stability and, ultimately, on the credibility of the sovereign. Yet modern banknotes eventually enhanced the benefits of central banking for society at large. [ ]
A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference

Circuit representations are becoming the lingua franca to express and reason about tractable generative and discriminative models. In this paper, we show how complex inference scenarios for these models that commonly arise in machine learning—from computing the expectations of decision tree ensembles to information-theoretic divergences of sum-product networks—can be represented in terms of tractable modular operations over circuits. Speciﬁcally, we characterize the tractability of simple transformations—sums, products, quotients, powers, logarithms, and exponentials—in terms of sufﬁcient structural constraints of the circuits they operate on, and present novel hardness results for the cases in which these properties are not satisﬁed. Building on these operations, we derive a uniﬁed framework for reasoning about tractable models that generalizes several results in the literature and opens up novel tractable inference scenarios.
A faster algorithm for the constrained minimum covering circle problem to expedite solving p‐center problems in an irregularly shaped area with holes

This paper introduces several means to expedite a known Voronoi heuristic method for solving a continuous p‐center location problem, which is to cover a polygon with p circles such that no circle's center lies outside the polygon, no circle's center drops inside a polygonal hole, and the radius of the largest circle is as small as possible. A key step in the Voronoi heuristic is the repeated task of determining the constrained minimum covering circle (CMCC), but the best‐known method for this task is brute‐force and inefficient. This paper develops an improved method by exploiting the convexity of a related subproblem and employing an optimized search procedure. The algorithmic enhancements help to drastically reduce the effort required for finding the CMCC, and in turn, significantly expedite the solution of the p‐center problem. On a realistic‐scale test case, the proposed algorithm ran 400× faster than the literature benchmark.
Computer-Aided Detection of Tuberculosis using Convolutional Neural Networks

- Early TB screening and diagnosis are critical in the control and treatment of tuberculosis infections. For the identification of several types of TB lesions in chest radiographs, an integrated computer-aided approach based on deep learning is presented. Chest radio-graphs are evaluated in clinical practice by competent physicians for the identification of tuberculosis. This is, however, a time-consuming and subjective procedure. X-ray pictures of TB are frequently misclassified as other illnesses with similar radiologic pat terns, resulting in patients receiving incorrect treatment and deterio-rating their health. In the available literature, transfer learning utilis ing Alexnet, Google-Net, Resnet, and other methods has been used to diagnose TB. However, transfer learning using pretrained CNNs such as Nasnet Mobile, Nasnet Large, Darknet-19, and other pre-trained CNNs has yet to be thoroughly studied. The proposed research seeks to create a completely automated computer-assisted TB detection system.
Generating Agent Based Models From Scratch With Genetic Programming

Program synthesis ( PS ) and genetic programming ( GP ) allow non-trivial programs to be generated from example data. Agent-based models ( ABM s) are a promising ﬁeld of application as their complexity at a macro level arises from simple agent-level rules. Previous attempts at using evolutionary algorithms to learn the structure of ABM s have focused on modifying and recombining existing models targeted to the domain in question, which requires prior domain knowledge. We demonstrate a new domain-independent approach which is able to evolve interpretable agent logic of an ABM from scratch. We employ a ﬂexible domain-speciﬁc language ( DSL ) which consists of basic mathematical building blocks. The ﬂexibility of our method is demonstrated by learning symbolic models in two different domains: ﬂocking and opinion dynamics, targeting data produced from reference models. We show that the evolved solutions are behaviourally identical to the reference models and generalise extremely well.
Database Technology for the Masses: Sub-Operators as First-Class Entities

A wealth of technology has evolved around relational databases over decades that has been successfully tried and tested in many settings and use cases. Yet, the majority of it remains overlooked in the pursuit of performance (e.g., NoSQL) or new functionality (e.g., graph data or machine learning). In this paper, we argue that a wide range of techniques readily available in databases are crucial to tackling the challenges the IT industry faces in terms of hardware trends management, growing workloads, and the overall complexity of a rapidly changing application and platform landscape. However, to be truly useful, these techniques must be freed from the legacy component of database engines: relational operators. Therefore, we argue that to make databases more flexible as platforms and to extend their functionality to new data types and operations requires exposing a lower level of abstraction: instead of working with SQL it would be desirable for database engines to compile, optimize, and run a collection of sub-operators for manipulating and managing data, offering them as an external interface. In this paper, we discuss the advantages of this, provide an initial list of such sub-operators, and show how they can be used in practice.
Context-Aware Routing in Fog Computing Systems

Fog computing enables the execution of IoT applications on compute nodes which reside both in the cloud and at the edge of the network. To achieve this, most fog computing systems route the IoT data on a path which starts at the data source, and goes through various edge and cloud nodes. Each node on this path may accept the data if there are available resources to process this data locally. Otherwise, the data is forwarded to the next node on path. Notably, when the data is forwarded (rather than accepted), the communication latency increases by the delay to reach the next node. To avoid this, we propose a routing mechanism which maintains a history of all nodes that have accepted data of each context in the past. By processing this history, our mechanism sends the data directly to the closest node that tends to accept data of the same context. This lowers the forwarding by nodes on path, and can reduce the communication latency. We evaluate this approach using both prototype- and simulation-based experiments which show reduced communication latency (by up to 23 percent) and lower number of hops traveled (by up to 73 percent), compared to a state-of-the-art method.
Preventive potential of Andrographis paniculata-derived compounds in metabolic syndrome-associated prostate cancer: A narrative review on the mechanism of action

Metabolic syndrome (MetS) and prostate cancer (PCa) are among the diseases with a concerning prevalence. The reported cases for both medical dilemmas have been steady with no sign of abating at the moment. More worrying is that researchers have started to discover that some of the components of MetS associatively worsen the prognosis of PCa. Even though the relationship is not fully known, its manifestation could cause extra burden to the currently implemented treatment approaches. The use of plant bioactive compounds as an alternative treatment has gained recognition, provided that its mechanisms of action, tolerability, efficacy, safety, and cost-effectiveness are well understood. Therefore, the current review intended to highlight the potential of bioactive compounds derived from plants to simultaneously target MetS, PCa, and MetS–PCa codisease. Additionally, emphasis on the potential of Andrographis paniculata (AP) as a candidate for the treatment of MetS–PCa is also highlighted.
The Grey-side of Audio Archives

Archives often include documents that can hardly be considered publications or grey literature as such, yet they maintain their documentary value and play a role of primary sources for the specialists. These documents, indeed, can help archivists to reveal the sedimentation process of the archive itself and to preserve the authentic context of the documentary production. They also appear to be very useful for the community of researchers and scholars. This happens more frequently with oral archives which include ‘non-conventional sources’, thus bringing together audio documents, fieldworks notes, correspondence, slipcases, analogic compact cassettes or open reels. At the cross-road of two disciplines, Archival Science and Grey Literature, this paper aims to argue the applicability of the concept of grey literature to this wide range of documentary materials, by showing the experience of Archivio Vi.Vo, a regional project aiming at building a model for archiving, preserving, managing and disseminating audio documents.