Miracle-dz/UniFAR-FTUs huggingface dataset:
Classifiers of Medical Eponymy in Scientific Texts

Many concepts in the medical literature are named after persons. Frequent ambiguities and spelling varieties, however, complicate the automatic recognition of such eponyms with natural language processing (NLP) tools. Recently developed methods include word vectors and transformer models that incorporate context information into the downstream layers of a neural network architecture. To evaluate these models for classifying medical eponymy, we label eponyms and counterexamples mentioned in a convenience sample of 1,079 Pubmed abstracts, and fit logistic regression models to the vectors from the first (vocabulary) and last (contextualized) layers of a SciBERT language model. According to the area under sensitivity-specificity curves, models based on contextualized vectors achieved a median performance of 98.0% in held-out phrases. This outperformed models based on vocabulary vectors (95.7%) by a median of 2.3 percentage points. When processing unlabeled inputs, such classifiers appeared to generalize to eponyms that did not appear among any annotations. These findings attest to the effectiveness of developing domain-specific NLP functions based on pre-trained language models, and underline the utility of context information for classifying potential eponyms.
A more accurate method to estimate muscle mass: A new estimation equation

Measurement of muscle mass is important in the diagnosis of sarcopenia. Current measurement equipment are neither cost‐effective nor standardized and cannot be used in a variety of medical settings. Some simple measurement tools have been proposed that are subjective and unvalidated. We aimed to develop and validate a new estimation equation in a more objective and standardized way, based on current proven variables that accurately reflect muscle mass.
A Modeling of 4H-SiC Super-Junction MOSFETs with Filtered High Energy Implantation

In this paper, the modeling of SJ-MOSFETs beyond the voltage class of 3.3 kV simulated with verified deep aluminum box-like shaped profiles by using TCAD simulation is described. The simulation results are used to investigate the influence of ion implantation parameters on electrical characteristics. For the formation of pillar regions, high energy implantation is performed through energy filter with a multi epitaxial growth method using a patterned mask. While high thickness of epitaxial layer is indispensable for obtaining a high blocking capability, it is revealed that the optimization of doping concentration of p-pillar and drift layer parameters yields similar on-state-resistance by charge compensations of SJ-structure.
CFD Performance Analysis of Journal Bearing Using Coconut Oil as Lubricant

Lubrication is one of the important factors for a journal bearing to function well. Therefore, the use of bio-lubricants such as coconut oil and vegetable oil are highly recommended for their high level of biodegradability to reduce the risk of environmental pollution. Vegetable oil like coconut oil has a great lubricating quality including low friction coefficient and improved oxidation stability which is very suitable to be used as a lubricant. The performance assessment of journal bearings utilizing coconut oil as a bio-lubricant and varying angular velocity and eccentricity ratio will be the main emphasis of this study. The behavior of the journal bearing with coconut oil as the lubricant will be examined using the Computational Fluid Dynamics (CFD) program, ANSYS Fluent. Using ANSYS Fluent, the pressure distribution of pure coconut oil is analytically studied. To evaluate the efficacy of each lubricant with a varied value of angular velocity utilized by the journal in journal bearings, average viscosity, viscosity index, and produced maximum pressure can be discovered under the lubrication of the journal bearing. Using the values of angular velocity with the same value of dynamic viscosity and density as properties for the coconut oil were used to run the simulation for the journal bearing. The result obtained from the simulation for eccentricity ratio of 0.2 with an angular velocity of 5000rpm is 205926.1 meanwhile the result for eccentricity ratio of 0.8 with same angular velocity value is 9661441. Each result signifies those different value of angular velocity with different value of eccentricity ratio that effects the bearing design itself occurs different result.
The odyssey to next-generation computers: cognitive computers (κC) inspired by the brain and powered by intelligent mathematics

Cognitive computers (κC) are intelligent processors advanced from data and information processing to autonomous knowledge learning and intelligence generation. This work presents a retrospective and prospective review of the odyssey toward κC empowered by transdisciplinary basic research and engineering advances. A wide range of fundamental theories and innovative technologies for κC is explored, and a set of underpinning intelligent mathematics (IM) is created. The architectures of κC for cognitive computing and Autonomous Intelligence Generation (AIG) are designed as a brain-inspired cognitive engine. Applications of κC in autonomous AI (AAI) are demonstrated by pilot projects. This work reveals that AIG will no longer be a privilege restricted only to humans via the odyssey to κC toward training-free and self-inferencing computers.
Feedback Control of a Class of Nonholonomic Hamiltonian Systems

Feedback control of nonholonomic systems has always been problematic due to the nonholonomic constraints that limit the space of possible system velocities. This property is very basic, and Brockett proved that a nonholonomic system cannot be asymptotically stabilized by a time-invariant smooth feedback. This thesis presents a novel way of controlling a special class of nonholonomic Hamiltonian systems. The basic idea is to split the configuration coordinates in two; a primary part that we wish to asymptotically stabilize, and a secondary part that not necessarily has to be stabilized, but is useful when controlling the primary part. The secondary part is introduces as the integral of so-called kinematic inputs. The kinematic inputs have the property that they cannot change the amount of energy in the system, i.e., the Hamiltonian function is invariant with respect to the kinematic inputs. The resulting nonholonomic Hamiltonian system with kinematic inputs shares many of the properties of the classical Hamiltonian system, and some of the methods involved in controlling classical systems are proved to also apply to the augmented system. The extra degree of freedom provided by the kinematic inputs turns out to be useful when stabilizing the nonholonomic system. If the system is properly actuated it is possible to asymptotically stabilize the primary part of the configuration coordinates via a passive energy shaping and damping injecting feedback. The feedback is smooth and time-invariant, but since it does not asymptotically stabilize the secondary part of the configuration coordinates, it does not violate Brockett’s obstruction. The results from the general class of nonholonomic Hamiltonian systems with kinematic inputs are applied to a real implementation of a four wheel steered, four wheel driven nonholonomic robotic vehicle, where the velocity of the steering motors are assumed to satisfy the conditions of proper kinematic inputs. The proposed controller is general enough to achieve both global asymptotic stabilization and path tracking for the robot. To improve the operation of the closed loop system some extensions are provided: integral action for asymptotic stabilization under the influence of disturbances, and an adaptive damping scheme ensuring that the robot travels at a predefined speed when tracking a path. Both of these extensions are defined in the framework of Hamiltonian systems.
Improving the efficacy of exposure therapy using projection-based augmented reality for the treatment of cockroach phobia: a randomised clinical trial protocol

Introduction In vivo exposure is the treatment of choice for specific phobia (SP), but this technique presents limitations related to access and acceptability. Augmented reality (AR) offers advantages like maximising strategies such as ‘variability’ (varying stimuli, durations, levels of intensity or the order of the items), control by the therapist, or ‘exposure to multiple contexts’, which can produce positive effects in terms of fear renewal and generalisation of the results. The aim of this study is to test the efficacy of varying the phobic stimuli during treatment with AR: using multiple stimuli (MS) versus a single stimulus (SS) in participants with SP. Methods and analysis Participants (N=80) with a diagnosis of an SP of cockroaches will be randomised into two conditions: (1) projection-based AR exposure therapy with MS (P-ARET MS); (2) P-ARET with an SS (P-ARET SS). The measures are related to the efficacy results (fear, avoidance and negative thoughts, performance on the behavioural avoidance test (BAT) and preferences). The primary outcome measure is the BAT, and the secondary outcome measures are the BAT through AR, Fear of Cockroaches Questionnaire, Cockroach Phobia Beliefs Questionnaire, Fear and Avoidance Scales Patient’s Improvement Scale, and Beck Depression Inventory Second Edition. Five evaluation moments will be included: preintervention, postintervention, and 1-month, 6-month, and 12-month follow-ups. The treatment will follow the guidelines of the ‘one-session treatment’. Student’s t-tests to compare the two groups on the post-test will be applied. In addition, two-way analysis of variances with repeated measures in one of the two factors (pretest, post-test and follow-ups) will be carried out to compare intragroup differences. Ethics and dissemination The Universitat Jaume I Ethics Committee (Castellón, Spain) granted approval for the study (CD/64/2019). Dissemination will include publications and presentations at national and international conferences. Trial registration number NCT04563403.
An Intervisibility Testing Algorithm Based on Geodetic Coordinates

The intervisibility testing algorithm between two points is an important algorithm in operation simulation system.The current intervisibility testing algorithms are mostly based on rectangular coordinates and need translating the given geodetic coordinates to rectangular coordinates.Through analyzing the basic intervisibility model and the current algorithms based on rectangular coordinates,an intervisibility testing algorithm between points based on geodetic coordinates is presented.The new algorithm tests intervisibility through computing the longitude and latitude coordinates of the intersection of the projection of the line of sight and the line connecting terrain sampling points on the surface of the earth and then calculating altitudes of the two lines at the longitude and latitude using line difference,which doesn't need translating geodetic coordinates to rectangular coordinates and solves the effect of earth curvature on intervisibility.The experiment analysis shows that the algorithm is usable for simulation system.
Controllable Generative Modeling via Causal Reasoning

Deep latent variable generative models excel at generating complex, high-dimensional data, often exhibiting impressive generalization beyond the training distribution. However, many such models in use today are black-boxes trained on large unlabelled datasets with statistical objectives and lack an interpretable understanding of the latent space required for controlling the generative process. We propose CAGE, a framework for controllable generation in latent variable models based on causal reasoning. Given a pair of attributes, CAGE infers the implicit cause-effect relationships between these attributes as induced by a deep generative model. This is achieved by defining and estimating a novel notion of unit-level causal effects in the latent space of the generative model. Thereafter, we use the inferred cause-effect relationships to design a novel strategy for controllable generation based on counterfactual sampling. Through a series of large-scale synthetic and human evaluations, we demonstrate that generating counterfactual samples which respect the underlying causal relationships inferred via CAGE leads to subjectively more realistic images.
A geometrical connection between sparse and low-rank matrices and its application to manifold learning

We consider when a sparse nonnegative matrix S can be recovered, via an elementwise non-linearity, from a real-valued matrix L of significantly lower rank. Of particular interest is the setting where the positive elements of S encode the similarities of nearby points on a low dimensional manifold. The recovery can then be posed as a problem in manifold learning— in this case, how to learn a norm-preserving and neighborhood-preserving mapping of high dimensional inputs into a lower dimensional space. We describe an algorithm for this problem based on a generalized low-rank decomposition of sparse matrices. This decomposition has the interesting property that it can be encoded by a neural network with one layer of rectified linear units; since the algorithm discovers this encoding, it can be viewed as a layerwise primitive for deep learning. The algorithm regards the inputs x i and x j as similar whenever the cosine of the angle between them exceeds some threshold τ ∈ (0 , 1). Given this threshold, the algorithm attempts to discover a mapping x i 7→ y i by matching the elements of two sparse matrices; in particular, it seeks a mapping for which S = max(0 , L ), where S ij = max(0 , x i · x j − τ ∥ x i ∥∥ x j ∥ ) and L ij = y i · y j − τ ∥ y i ∥∥ y j ∥ . We apply the algorithm to data sets where vector magnitudes and small cosine distances have interpretable meanings (e.g., the brightness of an image, the similarity to other words). On these data sets, the algorithm discovers much lower dimensional representations that preserve these meanings.
Xylazine co-self-administration suppresses fentanyl consumption during self-administration and induces a unique sex-specific withdrawal syndrome that is not altered by naloxone in rats

Prescription and illicit opioid use are a public health crisis, with the landscape shifting to fentanyl use. Since fentanyl is 100-fold more potent than morphine, its use is associated with a higher risk of fatal overdose that can be remediated through naloxone (Narcan) administration. However, recent reports indicate that xylazine, an anesthetic, is increasingly detected in accidental fentanyl overdose deaths. Anecdotal reports suggest that xylazine may prolong the fentanyl “high”, alter the onset of fentanyl withdrawal, and increase resistance to naloxone-induced reversal of overdose. To date no preclinical studies have evaluated the impacts of xylazine on fentanyl self-administration (SA; 2.5 μg/kg/infusion) or withdrawal to our knowledge. We established a rat model of xylazine/fentanyl co-SA and withdrawal and evaluated outcomes as a function of biological sex. When administered alone, chronic xylazine (2.5 mg/kg, IP) induced unique sex-specific withdrawal symptomatology whereby females showed delayed onset of signs and a possible enhancement of sensitivity to the motor-suppressing effects of xylazine. Xylazine reduced fentanyl consumption both male and female rats regardless of whether it was experimenter-administered or added to the intravenous fentanyl product (0.05. 0.10, and 0.5 mg/kg/infusion) when compared to fentanyl SA alone. Interestingly, this effect was dose-dependent when self-administered intravenously. Naloxone (0.1 mg/kg, SC) did not increase somatic signs of fentanyl withdrawal, regardless of the inclusion of xylazine in the fentanyl infusion in either sex; however, somatic signs of withdrawal were higher across timepoints in females after xylazine/fentanyl co-SA regardless of naloxone exposure as compared to females following fentanyl SA alone. Together, these results indicate that xylazine/fentanyl co-SA dose-dependently suppressed fentanyl intake in both sexes, and induced a unique withdrawal syndrome in females which was not altered by acute naloxone treatment.
Lower childhood subjective social status is associated with greater neural responses to ambient auditory deviance

Humans’ early life experience varies by socioeconomic status, raising the question of how this difference is reflected in the adult brain. An important aspect of brain function is the ability to detect salient ambient changes while focusing on a task. Here we ask whether perceived childhood social standing is reflected by the way young adults’ brain signals correlate with detecting changes in irrelevant information. In two studies (total N = 58), we examine electrical brain responses in the frontocentral region to a series of auditory tones, consisting of “standard” stimuli (80%) and “deviant” stimuli (20%) interspersed randomly, while participants were engaged in various visual tasks. Both studies showed stronger automatic change detection indexed by mismatch negativity (MMN) in lower-SES individuals, regardless of the sound’s features, emotional content, or study type. Moreover, we observed a larger MMN in lower-SES participants, even though they did not show differences in brain and behavior responses to the attended task and did not involuntarily orient more attention to deviance, as indexed by the P3a. The study indicates that individuals with lower subjective social status may have an increased ability to automatically detect changes in their environment, which may suggest their adaptation to their childhood environments.
MASIv2 enables standardization and integration of multi-modal single-cell and spatial omics data with one general framework

Data annotation and integration are two common tasks in large-scale and collaborative single-cell research. Rapid technological advancements have made diverse single-cell and spatial data modalities available. This data deluge brought up great challenges in data annotation and integration. Though different biological modalities preserve shared features to define the same cellular system, they often present unique angles to unravel a multi-level understanding about this system. Here, we present one general framework that uses modality-shared and -specific features for annotation and integration of single-cell and spatial omics data. We benchmark our framework with existing methods across different datasets and demonstrate its application in two real world tasks.
Optimization design of bent circular waveguide TM_(01)-TE_(11) mode converter

The bent TM_(01)-TE_(11) mode converter is analyzed by theoretical calculation and numerical simulation, keeping the axes of the output port and that of the input in parallel. The mode converter is minimized by decreasing the inner radius of waveguide. The optimized results are shown as follows: the inner diameter of waveguide is 7.0 cm, the distance between the axes of the two ports of the mode converter is 8.03 cm, and the axial length is 15.11 cm. The overall power conversion efficiency exceeds 90% in the frequency range from 3.41 to 3.74 GHz and the efficiency reaches up to 95.6% at 3.60 GHz.
Unravelling a novel role for Cannabidivarin in the modulation of subventricular zone postnatal neurogenesis

Postnatal neurogenesis has been shown to rely on the endocannabinoid system. Here we aimed at unravelling the role of Cannabidivarin (CBDV), a non-psychoactive cannabinoid, with high affinity for the non-classical cannabinoid receptor TRPV1, on subventricular zone (SVZ) postnatal neurogenesis. Using the neurosphere assay, SVZ-derived neural stem/progenitor cells (NSPCs) were incubated with CBDV and/or 5’-Iodoresinferotoxin (TRPV1 antagonist), and their role on cell viability, proliferation, and differentiation were dissected. CBDV was able to promote, through a TRPV1-dependent mechanism, cell survival, cell proliferation and neuronal differentiation. Furthermore, pulse-chase experiments revealed that CBDV-induced neuronal differentiation was a result of cell cycle exit of NSPCs. Regarding oligodendrocyte differentiation, CBDV inhibited oligodendrocyte differentiation and maturation. Since our data suggested that the CBDV-induced modulation of NSPCs acted via TRPV1, a sodium-calcium channel, and that intracellular calcium levels are known regulators of NSPCs fate and neuronal maturation, single cell calcium imaging was performed to evaluate the functional response of SVZ-derived cells. We observed that CBDV-responsive cells displayed a two-phase calcium influx profile, being the initial phase dependent on TRPV1 activation. Taken together, this work unveiled a novel and untapped neurogenic potential of CBDV via TRPV1 modulation. These findings pave the way to future neural stem cell biological studies and repair strategies by repurposing this non-psychoactive cannabinoid as a valuable therapeutic target.
“Ghost” fragment ions in structure and site-specific glycoproteomics analysis

Mass spectrometry (MS) can unlock crucial insights into the intricate world of glycosylation analysis. Despite its immense potential, the qualitative and quantitative analysis of isobaric glycopeptide structures remains one of the most daunting hurdles in the field of glycoproteomics. The ability to distinguish between these complex glycan structures poses a significant challenge, hindering our ability to accurately measure and understand the role of glycoproteins in biological systems. A few recent publications described the use of collision energy (CE) modulation to improve structural elucidation, especially for qualitative purposes. Different linkages of glycan units usually demonstrate different stabilities under CID/HCD fragmentation conditions. Fragmentation of the glycan moiety produces low molecular weight ions (oxonium ions) that can serve as a structure-specific signature for specific glycan moieties, however, specificity of these fragments has never been examined closely. Here, we investigated fragmentation specificity using synthetic stable isotope-labelled glycopeptide standards. These standards were isotopically labelled at the reducing terminal GlcNAc, which allowed us to resolve fragments produced by oligomannose core moiety and fragments generated from outer antennary structures. Our research identified the potential for false positive structure assignments due to the occurrence of “Ghost” fragments resulting from single glyco unit rearrangement or mannose core fragmentation within the collision cell. To mitigate this issue, we have established a minimal intensity threshold for these fragments to prevent the misidentification of structure-specific fragments in glycoproteomics analysis. Our findings provide a crucial step forward in the quest for more accurate and reliable glycoproteomics measurements. Graphical abstract