Miracle-dz/UniFAR-FTUs huggingface dataset:
The effect of neuroimmune modulation on subjective response to alcohol in the natural environment.

BACKGROUND Despite the promising implications for novel immune therapeutics, few clinical trials have tested these therapies to date. An understanding of how immune pharmacotherapies influence complex alcohol use disorder (AUD) profiles, including subjective response to alcohol is very limited. Initial findings show that ibudilast, a neuroimmune modulator, reduces rates of heavy drinking and measures of alcohol craving. METHODS This study serves as a secondary analysis of a two-week clinical trial of ibudilast that enrolled a non-treatment seeking sample with AUD. Eligible participants (N = 52) were randomized to ibudilast or matched placebo and completed daily diary assessments (DDAs) during the two-week period. Each morning, participants retrospectively reported on their mood and craving levels both before and during the previous day's drinking episode, as well as stimulation and sedation levels during the previous day's drinking episode. Multilevel models compared the effects of ibudilast and placebo on subjective alcohol response. Exploratory analyses tested whether ibudilast moderated the relationship between daily stimulation/ sedation and alcohol intake and whether withdrawal-related dysphoria moderated ibudilast's effects on subjective response. RESULTS Ibudilast did not significantly alter mean levels of stimulation or sedation (p's > .05). It did, however, moderate the effect of daily stimulation on drinking (p = .045). Ibudilast attenuated alcohol-induced increases in craving compared with placebo (p = .047), but not other subjective response measures. Only among individuals without withdrawal-related dysphoria did ibudilast significantly temper daily alcohol-induced changes in urge to drink and positive mood. CONCLUSIONS Ibudilast's effects on subjective alcohol responses appear to be nuanced and perhaps most salient for individuals drinking for positive reinforcement versus to feel normal. Consistent with previous findings, reductions in alcohol craving may represent a primary mechanism of ibudilast. The ecologically valid nature of DDAs provide a clinically useful window into how individuals experience alcohol's effects while taking ibudilast.
A novel NCI‐H69V small cell lung cancer functional mini‐tumor model for future treatment screening applications

Small cell lung cancer (SCLC) is aggressive and despite multiple clinical trials, its standard of care is unchanged for the past three decades. In vitro cancer models are crucial in chemotherapy development, and three‐dimensional (3D) models aim to bridge the gap between two‐dimensional (2D) flat cultures and in vivo testing. Functional 3D spheroids can better represent the in vivo situation and tumor characteristics than 2D models. An NCI‐H69V SCLC mini‐tumor model was developed in a clinostat‐based rotating bioreactor system. Spheroid growth and viability were characterized for 30 days, and the ideal experimental window with mature and metabolically stable spheroids was determined. Application of the model for anticancer treatment screening was validated with the standard chemotherapeutic drug irinotecan, for an exposure period of 72 h. The following parameters were measured: soluble protein content, planar surface area measurements, intracellular adenosine triphosphate and extracellular adenylate kinase levels, and glucose consumption. Histological morphology of the spheroids was observed. The established model proved viable and stable, while treatment with irinotecan caused a decrease in cell growth, viability, and glucose consumption demonstrating reactivity of the model to chemotherapy. Therefore, this NCI‐H69V SCLC functional spheroid model could be used for future anticancer compound screening.
A Model for Types and Levels of Automation in Visual Analytics: a Survey, a Taxonomy, and Examples

The continuous growth in availability and access to data presents a major challenge to the human analyst. As the manual analysis of large and complex datasets is nowadays practically impossible, the need for assisting tools that can automate the analysis process while keeping the human analyst in the loop is imperative. A large and growing body of literature recognizes the crucial role of automation in Visual Analytics and suggests that automation is among the most important constituents for effective Visual Analytics systems. Today, however, there is no appropriate taxonomy nor terminology for assessing the extent of automation in a Visual Analytics system. In this paper, we aim to address this gap by introducing a model of levels of automation tailored for the Visual Analytics domain. The consistent terminology of the proposed taxonomy could provide a ground for users/readers/reviewers to describe and compare automation in Visual Analytics systems. Our taxonomy is grounded on a combination of several existing and well-established taxonomies of levels of automation in the human-machine interaction domain and relevant models within the visual analytics field. To exemplify the proposed taxonomy, we selected a set of existing systems from the event-sequence analytics domain and mapped the automation of their visual analytics process stages against the automation levels in our taxonomy.
Sensitivity of VHL mutant kidney cancers to HIF2 inhibitors does not require an intact p53 pathway

Significance VHL tumor suppressor gene inactivation is a hallmark of clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer, and promotes tumor growth by stabilizing the hypoxia-inducible factor 2 (HIF2) transcription factor. HIF2 inhibitors appear to be helpful for some, but not all, ccRCC patients in clinical trials. Previous preclinical and clinical data suggested that only ccRCCs that can activate the p53 tumor suppressor in response to DNA damage would respond to HIF2 inhibitors. Here, we show that an intact p53 pathway is neither necessary nor sufficient for the sensitivity of ccRCCs to HIF2 inhibitors, suggesting that it would be premature to use p53 status to determine which ccRCC patients should be treated with a HIF2 inhibitor.
Research progress of anti-glioma chemotherapeutic drugs

Glioma is the most common primary intracranial malignancy in the central nervous system. At present, the most important treatment option is surgical resection of the tumor combined with radiotherapy and chemotherapy. The principle of operation is to remove the tumor to the maximal extent on the basis of preserving brain function. However, prominent invasive and infiltrative proliferation of glioma tumor cells into the surrounding normal tissues frequently reduces the efficacy of treatment. This in turn worsens the prognosis, because the tumor cannot be completely removed, which can readily relapse. Chemotherapeutic agents when applied individually have demonstrated limited efficacy for the treatment of glioma. However, multiple different chemotherapeutic agents can be used in combination with other treatment modalities to improve the efficacy while circumventing systemic toxicity and drug resistance. Therefore, it is pivotal to unravel the inhibitory mechanism mediated by the different chemotherapeutic drugs on glioma cells in preclinical studies. The aim of the present review is to provide a summary for understanding the effects of different chemotherapeutic drugs in glioma, in addition to providing a reference for the preclinical research into novel chemotherapeutic agents for future clinical application.
Discovery of a novel inhibitor against urokinase-type plasminogen activator, a potential enzyme with a role in atherosclerotic plaque instability

Abstract The buildup of lipids, cholesterol, and other substances in and on the walls of the arteries is known as atherosclerosis and deposition is known as atherosclerotic plaque. Urokinase-type plasminogen activator (uPA) has multiple roles in the atherosclerotic plaque formation and even work simultaneously in making the atherosclerotic plaque unstable. Extracellular matrix plays a major role in the plaque remodeling and rapture. In this study, we have accessed that a higher interaction was observed in the molecular interaction score for uPA with ZINC380065722 having a GOLD fitness score of about 67.60, which is much higher as compared to the known standard inhibitor UK 122 which has reported an interaction score of 59.14. Ser217 and Asp192 are found to be the key amino acid residues in almost all the interactions. Protein frustration analysis has shown that these amino acid residues play a crucial role in the retention of the active pocket conformation and any mutation of these two residues can causes serious decrease in the overall function of the protein. It was observed that the molecule ZINC380065722 remained bound to the protein till 100 ns of simulation time. The average SASA for the apo-uPA and uPA-ligand complex was found to be stable. The network of hydrogen bonds for the intramolecular protein secondary structure and with the solvent system for the apo-protein and the uPA-ligand complex was found to be consistent. Communicated by Ramaswamy H. Sarma
SDN and NFV for satellite infrastructures

The integration of SDN and NFV enablers into the satellite network could prove to be an essential means to save on physical sites, improve the time to bring new services to the market and open new ways to improve network resiliency, availability and efficiency. It can be considered that the above two enablers can play a central role in the integration of satellite to terrestrial technologies by using federated management of the network resources.
Efficacy and safety of rt-PA intravenous thrombolysis in patients with wake-up stroke

Abstract Background : Recombinant tissue plasminogen activator (rt-PA) is one of the most effective therapies for patients with acute ischemic stroke. However, wake-up stroke (WUS) is typically excluded from intravenous thrombolytic therapy because of the unclear time of symptom onset. Therefore, we aimed to assess the efficacy and safety of rt-PA intravenous thrombolysis in patients with WUS by meta-analysis. Methods : We completed a systematic literature search of PubMed, Embase, the Cochrane Library, and SinoMed and included relevant studies of WUS patients covering rt-PA thrombolysis and nonthrombolysis (published from January 1, 2000, to February 28, 2021, with no language restrictions). The primary outcomes included safety outcomes and functional outcomes. Safety outcomes were measured according to the incidence of symptomatic intracranial hemorrhage and mortality within 90 days. The efficacy outcomes were measured based on 90-day modified Rankin Scale scores. We assessed pooled data using either a random-effects model (when P < .10, I2 > 50%) or a fixed-effects model (when P > .10, I2 < 50%). Results : A total of 913 patients from 9 studies were included in the meta-analysis. All patients had ischemic stroke confirmed by computed tomography or magnetic resonance imaging. The incidence of modified Rankin Scale 0 to 2 was significantly higher in the rt-PA thrombolysis group compared with the nonthrombolysis group. And rt-PA thrombolytic WUS patients did not differ significantly from nonthrombolytic WUS patients in terms of 90-day mortality. However, the incidence of Symptomatic intracranial hemorrhage was also significantly higher in the rt-PA thrombolysis group than that in the nonthrombolysis group. Conclusions : Patients with WUS who received rt-PA thrombolysis had a significant positive effect within 90 days. In addition, although there was no significant increase in mortality, we need to be aware of the risk of intracranial hemorrhage transformation associated with rt-PA thrombolysis despite no obvious increase in mortality. The safety of rt-PA intravenous thrombolysis should be closely monitored in patients with WUS.
Prognostic value of CD20 antigen mediated immune checkpoint inhibition in patients with acute or chronic lymphocytic leukemia

Abstract Background: The addition of rituximab to standard chemotherapy has been shown to improve response rates in patients with acute or chronic lymphocytic leukemia. However, the prognostic factors associated with progression-free survival in rituximab treated patients with lymphocytic leukemias remains unclear. We will perform a comprehensive systematic review and meta-analysis on available data on prognostic factors associated with the clinical outcomes of patients with acute and chronic lymphocytic leukemia. Methods and analysis: This protocol for a systematic review and meta-analysis of prognostic factors has been prepared following the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) 2015 guidelines. Electronic databases will be searched using keywords related to the objectives of this review. This systematic review and meta-analysis will include published randomized clinical trials, observational, prospective, and retrospective comparative cohorts. Two reviewers (ZAM and SAM) will independently screen studies, with a third reviewer consulted in cases of disagreements using a defined inclusion and exclusion criteria. Data items will be extracted using a predefined data extraction sheet. Moreover, the risk of bias and the quality of evidence were independently assessed using the quality in prognostic studies tool (QUIPS). The I2 and chi squared statistical tests will be used to analyze statistical heterogeneity across studies. An I2 values of > 50% will be considered substantial. All data analysis will be performed using STATA 16.0 (StataCorp LP, TX, USA). The outcomes examined will be progression-free and overall survival. Ethics and dissemination: No ethical approval will be required and the findings of this meta-analysis will be published in a peer-reviewed journal. Systematic review registration: International prospective Register of Systematic Reviews (PROSERO) number: CRD42021218997.
A Semi-Autonomous Security System for Coal Mine using Wireless Control Based on Real Time Operating System [RTOS]

Novel coal mine management system to solve the real time monitoring and high efficient rescue problems of underground work platforms. Here a robot is designed to detect the obstacles and transfer the information to the base station in order to avoid humane causalities. The robot is equipped with special wheel which is controlled by H-bridge module, allowing it to move in all the possible directions. Also, the robot is enhanced with special range sensor which is used to detect and locate the position of obstacles. In addition to this, gas alert, accident help request, video streaming and voice announcements are provided to the system with a wireless ZigBee which gets the real time values from fire, temperature and tilt sensors. Video camera will transmit the image in Motion-JPEG format along with the speaker which will allows to send emergency commands with the voice message tool. The application firmware works on the top of RTOS [Real Time Operating System] to handle multitasking requirements of the system. This technique has a practical benefit reducing the number of causalities with least buffering time.
RANS Simulations of Ship Motions In Regular And Irregular Head Seas Using the SWENSE Method

This paper presents recent advances of the SWENSE (Spectral Wave Explicit Navier-Stokes Equations) approach, a method for simulating fully nonlinear wave-body interactions including viscous effects. Potential flow theory is used to compute the incident waves while viscous effects are taken into account by using a Reynolds Averaged NavierStokes Equations (RANSE) solver to obtain the diffracted field in the full domain. Arbitrary incident wave systems can be described, including regular, irregular waves, multidirectional waves and focused wave events. The model may be fixed or moving with arbitrary speed and 6 degrees of freedom motion. This paper is devoted to the validation of the SWENSE method in irregular head waves, in the case of an extensively long 2 DoF simulation. Results of the present approach compare favorably with experimental data.
MBBOS-GCN: minimum bounding box over-segmentation—graph convolution 3D point cloud deep learning model

Abstract. Point cloud data with high accuracy and high density is an important data source for the depiction of real ground objects, and there is a broad research prospect of using point cloud data directly for 3D object detection and recognition using deep learning methods. However, many deep learning models in previous research ignored the point cloud structure information and the sampling randomness. To overcome this limitation, we proposed an innovative 3D point cloud deep learning model, namely, the minimum bounding box over-segmentation–graph convolution 3D point cloud deep learning network model (MBBOS-GCN) for enhancing the structural information perception capability of the model and reduce the sampling randomness. In MBBOS-GCN, the number of points sampled is used as the scale, and a modified graph convolution model is used to collect point cloud structure information from different scales. The point cloud is divided into several small regions by the minimum bounding box algorithm, and the farthest point sampling (FPS) algorithm is used to sample within each small region to reduce sampling randomness. The experiments on object classification and semantic scene data segmentation show that: (1) the MBBOS-GCN model has high classification and segmentation accuracy, which is up to 91.87% and 89.5% on the ModelNet40 dataset and ScanNet dataset, respectively; (2) the MBBOS-GCN model is provided has good stability and robustness with a little change in accuracy under the altering density of input point cloud data, and slight classification loss value; (3) the MBBOS-GCN model can be adapted to real complex scenes when the classification accuracy reaches up to 97.53%. These superior performance of the MBBOS-GCN model can provide an effective support for the construction of digital twin city background data and the calibration of multimode satellite feature inversion algorithm validation.
Convolutional networks for speckle-based orbital angular momentum modes classification

Abstract. Machine learning has emerged as a powerful tool for physicists for building empirical models from the data. We exploit two convolutional networks, namely Alexnet and wavelet scattering network for the classification of orbital angular momentum (OAM) beams. We present a comparative study of these two methods for the classification of 16 OAM modes having radial and azimuthal phase profiles and eight OAM superposition modes with and without atmospheric turbulence effects. Instead of direct OAM intensity images, we have used the corresponding speckle intensities as an input to the model. Our study demonstrates a noise and alignment-free OAM mode classifier having maximum accuracy of >94 % and >99 % for with and without turbulence, respectively. The main advantage of this method is that the mode classification can be done by capturing a small region of the speckle intensity having a sufficient number of speckle grains. We also discuss this smallest region that needs to be captured and the optimal resolution of the detector required for mode classification.
In-fiber interferometric high-sensitivity gas pressure sensor with microcavity

Abstract. A gas pressure sensor based on an in-fiber interferometer with a closed microcavity is proposed. Its temperature and gas pressure characteristics are theoretically and experimentally studied by investigating the dip wavelength of its interference spectrum. Based on the theoretical analysis, Fabry–Perot (F-P) and Mach–Zehnder interferences occur in the sensing process of this sensor. From our experiments, the wavelength sensitivity of this gas sensor is 83.6 pm/KPa at gas pressures from 101 to 120 KPa. And the hybrid interferometer has higher sensitivity than that of the single interferometer. This in-fiber sensor with a closed cavity has advantages of high reusability and simple structure, and it can not only reduce the optical power loss in transmission but also eliminate the influence of the gas refractive index. It can be used stably in a low-pressure monitoring system for flammable and explosive gas.
Integration of Neuroscience and Entrepreneurship: A Systematic Review and Bibliometric Analysis

In this study, we investigated the integration between neuroscience and entrepreneurship. First, we explored the concept of neuroentrepreneurship and the investigation of neuroentrepreneurship using scientific research methods. Second, we constructed a road map for entrepreneurial researchers interested in conducting neuroentrepreneurship-related research. This is an emerging research area; therefore, to more clearly analyze the dynamics of the research trends, we used a bibliometric method to capture patterns in current publications on subjects related to neuroentrepreneurship, examining papers published between 1999 and 2021 using the keywords “neuroscience” and “entrepreneurship” or “neuroentrepreneurship. ” To identify the keywords, we used two academic databases—the Social Science Citation Index and Science Citation Index—accessed through the Web of Science website. The three keywords were identified from studies integrating neuroscience with entrepreneurship. After carefully reviewing the research papers, we identified neuroentrepreneurship as a novel research area. The outcomes of this study provide a guide for describing the theoretical connection between neuroscience and entrepreneurship. In the future, this field of study should be empirically investigated.
From a Demand-Based to a Supply-Limited Framework of Brain Metabolism

What defines the rate of energy use by the brain, as well as per neurons of different sizes in different structures and animals, is one fundamental aspect of neuroscience for which much has been theorized, but very little data are available. The prevalent theories and models consider that energy supply from the vascular system to different brain regions is adjusted both dynamically and in the course of development and evolution to meet the demands of neuronal activity. In this perspective, we offer an alternative view: that regional rates of energy use might be mostly constrained by supply, given the properties of the brain capillary network, the highly stable rate of oxygen delivery to the whole brain under physiological conditions, and homeostatic constraints. We present evidence that these constraints, based on capillary density and tissue oxygen homeostasis, are similar between brain regions and mammalian species, suggesting they derive from fundamental biophysical limitations. The same constraints also determine the relationship between regional rates of brain oxygen supply and usage over the full physiological range of brain activity, from deep sleep to intense sensory stimulation, during which the apparent uncoupling of blood flow and oxygen use is still a predicted consequence of supply limitation. By carefully separating “energy cost” into energy supply and energy use, and doing away with the problematic concept of energetic “demands,” our new framework should help shine a new light on the neurovascular bases of metabolic support of brain function and brain functional imaging. We speculate that the trade-offs between functional systems and even the limitation to a single attentional spot at a time might be consequences of a strongly supply-limited brain economy. We propose that a deeper understanding of brain energy supply constraints will provide a new evolutionary understanding of constraints on brain function due to energetics; offer new diagnostic insight to disturbances of brain metabolism; lead to clear, testable predictions on the scaling of brain metabolic cost and the evolution of brains of different sizes; and open new lines of investigation into the microvascular bases of progressive cognitive loss in normal aging as well as metabolic diseases.