Miracle-dz/UniFAR-FTUs huggingface dataset:
Intraoperative intracardiac echocardiography (ICE) during CABG surgery – A case report

Introduction. Today, in case of severe perioperative hemodynamic instability a multiplane transeosophageal echocardiography (TOE) is recommended to determine underlying pathophysiological causes, e.g. hypovolaemia, reduced myocardial contractility [1, 2]. However, there exist some contraindications to TEE insertion, e.g. eosophageal pathology, and an intraoperative transthoracic approach (TTE) is mostly impossible. Therefore, we here present our first experience of intraoperative ICE usage. Methods & Results. A 72-year old man suffering a 2-vessel coronary artery disease was schedulded for elective CABG surgery. He presented with a reduced left ventricular ejection fraction (LVEF 45%) and had previous eosophageal surgery due to cancer. After uncomplicated induction of general anaesthesia a 9 French introducer was inserted in the right internal jugular vein. The ICE probe (AccuNavs, 8 French) was covered with a sterile plastic sheet, which is normally used for pulmonary artery catheter, and then inserted via the introducer. The plastic sheet was connected to the retainer of the introducer. It was thus possible to advance, withdraw or rotate the probe in an absolute sterile manner. Finally, the probe was connected to a standard echocardiographic hardware system. With this approach we were able to qualitatively estimate a) filling and global contractility of both ventricles, b) the mitral valve, c) the aortic valve [3], d) the tricuspid valve and e) the pulmonary valve. Self-limitating rhythm disturbances when advancing the probe into the right ventricle for evaluation of the left ventricle and the subvalvular apparatus of the mitral valve were the only recognised ICE related side effects. Conclusion. Intraoperative ICE was feasible during CABG surgery. Biventricular function and all valves could be qualitatively examined. ICE thus seems to be an useful alternative in situations where TOE and TTE are impractical.
DIFFERENTIAL INVOLVEMENT OF S AND L ISOFORMS OF THE CENTROSOMAL MARK4 KINASE IN GLIOMA INITIATION AND PROGRESSION

2 MARK4 (MAP/Microtubule Affinity-Regulating Kinase 4) belongs to a family of serinethreonine kinases that are able to phosphorylate the Microtubule Associated Proteins, causing their detachment from the microtubules and thus increasing microtubule dynamics. MARK4 gene is ubiquitously expressed and encodes at least two alternatively spliced isoforms, L and S, which differ in the C-terminal region and are differentially regulated in human tissues, especially in the Central Nervous System (CNS). MARK4S predominance in normal brain has been related to a putative role in neuronal differentiation; MARK4L has been found up-regulated in hepatocarcinoma cells and highly expressed in gliomas, suggesting an involvement of the kinase in cycling cells. Gliomas are the most common tumors of the CNS and are characterized by elevated cellular heterogeneity, which has been imputed to the presence of different cells (mature cells / progenitors / stem cells) from which each glioma originates. To further investigate MARK4 up-regulation in glial tumors, we analyzed a panel of 35 glioma tissue samples and 21 glioma cell lines (both low and high malignancy grade) and 6 glioblastoma-derived cancer stem cell populations (GBM CSC; glioblastoma is a IV grade glioma). Human neural progenitors, total human normal brain and mouse neural stem cells (NSC, isolated from the sub-ventricular zone) were also included in the study. A quantitative approach (Real-time PCR; q-PCR) was applied for mRNA analyses whereas MARK4 protein levels and localization were tested by Immunoblotting (IB), Immunohistochemistry (IHC) and Immunofluorescence (IF). We first carried out mutational analysis of the main MARK4 domains, but it didn’t reveal any genomic alteration, as did not the previously performed array-CGH analysis. Integrated approaches of q-PCR, IB and IHC studies show that, although MARK4S and L have a heterogeneous expression within and across different glioma subtypes (consistent with the intrinsic cell heterogeneity of these brain tumors), MARK4L is the prevalent isoform in near all the glioma samples. Conversely, MARK4S mRNA levels display a significant decrease inversely correlating with malignancy grade and are also hardly detectable in both neural and GBM-derived cancer stem cell populations. Therefore, a higher MARK4L prevalence in parallel to low levels of MARK4S characterizes highly undifferentiated cells, such as NSC, and highly malignant cells, such as GBM CSC and glioblastomas, favouring the hypothesis that the ratio between the two MARK4 isoforms is strictly regulated along neural differentiation and may be subverted in gliomagenesis. These findings, together with the observation that in normal brain only the L isoform localizes in the embryonic ventricular zone (VZ) and
3-[(E)-2-(2-Methoxyphenyl)vinyl]-5,5-dimethylcyclohex-2-enone

The title compound, C17H20O2, has an E conformation about the bridging C=C bond. The cyclohexene ring adopts an envelope conformation with the dimethyl-substituted C atom as the flap. Its mean plane makes a dihedral angle of 7.20 (12)° with the benzene ring. In the crystal, neighbouring molecules are connected via C—H⋯O hydrogen bonds, forming chains running along the a-axis direction.
An introduction to molecular medicine and gene therapy

Molecular Medicine and Gene Therapy: An Introduction (T. Kresina & A. Branch). Nuclear Transplantation and New Frontiers in Genetic Molecular Medicine (D. Jerry, et al.). Building a Better Mouse: Genetically Altered Mice as Models for Gene Therapy (E. Sandgren & W. Kisseberth). Vectors in Gene Therapy (K. Ponder). Gene Targeting (E. Kmiec). Gene Therapy of Hematological Disorders (C. Dunbar & T. Wu). Gene Therapy for Liver Disease (C. Schilling, et al.). Gene Therapy in Cardiovascular Disease (V. Dzau, et al.). Components of Cell and Gene Therapy for Neurological Disorders (L. Doering). Gene Therapy in the Treatment of Cancer (S. Hall, et al.). Gene Therapy for HIV Infection (B. Bunnell). Gene Therapy Approaches for Rheumatoid Arthritis (R. Gay, et al.). Federal Oversight of Gene Therapy Research (T. Kresina). Ethical Issues in Molecular Medicine and Gene Therapy (L. Fleck). Epilogue: Personal Genetic Medicine-The Future Is Now (T. Kresina). Appendix. Index.
(E)-4-tert-Butyl-2-(2,6-diisopropylphenyliminomethyl)-6-(morpholinomethyl)phenol

In the molecule of the title compound, C28H40N2O2, the tert-butyl group is disordered over two positions; site-occupation factors were kept fixed at 0.5. The morpholine ring has a chair conformation. Intramolecular O—H⋯N hydrogen bonding results in the formation of a planar six-membered ring, which is oriented at a dihedral angle of 0.70 (3)° with respect to the adjacent aromatic ring. The dihedral angle between the benzene rings is 67.66 (3)°.
Disfunción ventricular asociada a sepsis

Hospital Regional «1o de Octubre» ISSSTE. RESUMEN La sepsis es la causa principal de muerte en Unidades de Cuidados Intensivos, generalmente como consecuencia de falla organica multiple. Las manifestaciones cardiovasculares de esta falla incluyen alteraciones en el tono vascular y en la funcion ventricular. La ecocardiografia es un metodo diagnostico que proporciona informacion rapida y confiable de la funcion ventricular. Objetivo: Evaluar la funcion ventricular sistolica y diastolica por ecocardiografia Doppler en pacientes con sepsis. Material y metodos: A 20 pacientes con criterios para sepsis se les realizo ecocardiografia transtoracica en las primeras 24 horas de ingreso, evaluando funcion sistolica y diastolica. Resultados: Se incluyeron 20 pacientes, 11 hombres y 9 mujeres, con edad promedio de 60 ± 17.4 anos. Los diagnosticos de ingreso incluyeron: sepsis abdominal (4), neumonia (7), urosepsis (4), pie diabetico (2), aborto septico (1), absceso hepatico (1) y erisipela (1). En todos los pacientes se identifico algun grado de disfuncion ventricular por ecocardiografia. Setenta por ciento presento disfuncion diastolica grado I y 30% disfuncion diastolica grado III. Conclusiones: La ecocardiografia permite evaluar rapidamente la funcion miocardica en pacientes septicos. Nuestro estudio reporta que la disfuncion ventricular se presenta en todos los pacientes con sepsis dentro de las primeras 24 horas del diagnostico. Palabras clave: Sepsis, ecocardiografia, funcion miocardica. SUMMARY Sepsis is the main cause of death in Units of Intensive Cares, generally as a result of multiorganic fault. The cardiovascular manifestations of this fault include alterations in the vascular tone and ventricular function. The echocardiographic is a method diagnosis that’s provides fast and reliable information of ventricular function. Objective: To evaluate the systolic and diastolic ventricular function in patients with sepsis. Material and methods: To 20 patients with criteria for sepsis echocardiographic was realized to them in the first 24 hours of entrance evaluating systolic and diastolic function. Results: 20 patients, 11 men and 9 women included themselves, with age average of 60 ± 17.4 years. The entrance diagnoses included: abdominal sepsis (4), pneumonia (7), urosepsis (4), diabetic foot (2), septic abortion (1), hepatic abscess (1) and erysipela (1). In all the patients some degree of ventricular dysfunction by echocardiographic was identified. A 70% present diastolic dysfunction degree I and a 30% report diastolic dysfunction degree III. Conclusions: Echocardiographic allows to quickly evaluate the miocardic function in septic patients. Our study reports that ventricular dysfunction appears in all patients with sepsis within the first 24 hours of diagnosis.
Sparse and redundant signal representations for x-ray computed tomography

Image models are central to all image processing tasks. The great advancements in digital image processing would not have been made possible without powerful models which, themselves, have evolved over time. In the past decade, patch-based models have emerged as one of the most effective models for natural images. Patch-based methods have outperformed other competing methods in many image processing tasks. These developments have come at a time when greater availability of powerful computational resources and growing concerns over the health risks of the ionizing radiation encourage research on image processing algorithms for computed tomography (CT). The goal of this paper is to explain the principles of patch-based methods and to review some of their recent applications in CT. We review the central concepts in patch-based image processing and explain some of the state-of-the-art algorithms, with a focus on aspects that are more relevant to CT. Then, we review some of the recent application of patch-based methods in CT.
Environment reconstruction on depth images using Generative Adversarial Networks

Robust perception systems are essential for autonomous vehicle safety. To navigate in a complex urban environment, it is necessary precise sensors with reliable data. The task of understanding the surroundings is hard by itself; for intelligent vehicles, it is even more critical due to the high speed in which the vehicle navigates. To successfully navigate in an urban environment, the perception system must quickly receive, process, and execute an action to guarantee both passenger and pedestrian safety. Stereo cameras collect environment information at many levels, e.g., depth, color, texture, shape, which guarantee ample knowledge about the surroundings. Even so, when compared to human, computational methods lack the ability to deal with missing information, i.e., occlusions. For many perception tasks, this lack of data can be a hindrance due to the environment incomplete information. In this paper, we address this problem and discuss recent methods to deal with occluded areas inference. We then introduce a loss function focused on disparity and environment depth data reconstruction, and a Generative Adversarial Network (GAN) architecture able to deal with occluded information inference. Our results present a coherent reconstruction on depth maps, estimating regions occluded by different obstacles. Our final contribution is a loss function focused on disparity data and a GAN able to extract depth features and estimate depth data by inpainting disparity images.
Tumour necrosis factor antagonists

1. Summary Tumour necrosis factor (TNF) has been shown to play a pivotal role in immune and inflammatory responses. Inappropriate or over-expression of TNF is a hallmark of a number of diseases including rheumatoid arthritis (RA), Crohn’s disease and sepsis. Inhibition of TNF production has been shown to be beneficial in a wide range of preclinical models of inflammatory disease making inhibition of TNF production or signalling an appealing target for the development of novel anti-inflammatory drugs. Initial efforts in this area have focused on the use of TNF binding proteins (monoclonal antibodies to TNF and soluble derivatives of the two TNF receptors) as therapeutic agents. This review will outline the data supporting a role for TNF as a mediator of inflammation and will subsequently focus on the recent clinical experience with TNF inhibitors in Crohn’s disease and RA.
Explaining 
b→sℓ+ℓ−
 data by sneutrinos in the 
R
-parity violating MSSM

The recent measurements on b→sl+l- processes suggest the existence of lepton-flavor-universality breaking new physics. In this work, we have explored the possibility of explaining these data by sneutrinos in the R-parity violating minimal supersymmetric standard model. We study the light sneutrinos, of order 1 TeV, and suppose that the rest of the sfermions are much heavier than them. This setup can solve the b→sμ+μ- anomaly well, and it is almost unconstrained by other related processes, such as Bs-B¯s mixing, as well as Bs0→τ+τ-, B+→K+τ+τ-, Bs0→τ±μ∓, B+→K+τ±μ∓, and B→K(*)νν¯ decays.
Towards integrability of a quartic analogue of the Kontsevich model

We consider an analogue of Kontsevich's matrix Airy function where the cubic potential $\mathrm{Tr}(\Phi^3)$ is replaced by a quartic term $\mathrm{Tr}(\Phi^4)$. Cumulants of the resulting measure are known to decompose into cycle types for which a recursive system of equations can be established. We develop a new, purely algebraic geometrical solution strategy for these equations, based on properties of Cauchy matrices. We explicitly solve the two initial equations of the recursion and outline how the same techniques should also solve the other equations. Thereby we provide strong evidence that this quartic analogue of the Kontsevich model is integrable.
RAN Slicing in a UAV Network for eMBB Service Provision

This paper is concerned with a problem of radio access network (RAN) slicing in a unmanned aerial vehicle (UAV) network for enhanced mobile broadband (eMBB) service provision. Unlike the literature focusing on RAN resource sharing in a UAV network, this paper simultaneously considers the enforcement of network slicing and the share of RAN resources. Specifically, this paper formulates the problem of RAN slicing in a UAV network as a time sequence optimization problem with a goal of providing an energy-efficient and fair eMBB service under constraints of both eMBB user equipments' quality of service requirements and UAVs' energy consumption and trajectories. This problem is a mixed-integer-non-convex-programming problem that is highly challenging to mitigate. As such, this paper proposes a framework that needs to repeatedly and separately enforce network slicing and optimize RAN resource allocation for mitigating it by exploring the Lyapunov optimization. Besides, this paper leverages a successive convex approximate method to transform the non-convex RAN resource optimization problem into an approximate convex optimization one. Simulation results show that the proposed framework can achieve significant performance gains as compared with other benchmark algorithms.
Solving Forward and Inverse Problems Using Autoencoders

This work develops a model-aware autoencoder networks as a new method for solving scientific forward and inverse problems. Autoencoders are unsupervised neural networks that are able to learn new representations of data through appropriately selected architecture and regularization. The resulting mappings to and from the latent representation can be used to encode and decode the data. In our work, we set the data space to be the parameter space of a parameter of interest we wish to invert for. Further, as a way to encode the underlying physical model into the autoencoder, we enforce the latent space of an autoencoder to be the space of observations of physically-governed phenomena. In doing so, we leverage the well known capability of a deep neural network as a universal function operator to simultaneously obtain both the parameter-to-observation and observation-to-parameter map. The results suggest that this simultaneous learning interacts synergistically to improve the the inversion capability of the autoencoder.
ragamAI: A Network Based Recommender System to Arrange a Indian Classical Music Concert

South Indian classical music (Carnatic music) is best consumed through live concerts. A carnatic recital requires meticulous planning accounting for several parameters like the performers' repertoire, composition variety, musical versatility, thematic structure, the recital's arrangement, etc. to ensure that the audience have a comprehensive listening experience. In this work, we present ragamAI a novel machine learning framework that utilizes the tonic nuances and musical structures in the carnatic music to generate a concert recital that melodically captures the entire range in an octave. Utilizing the underlying idea of playlist and session-based recommender models, the proposed model studies the mathematical structure present in past concerts and recommends relevant items for the playlist/concert. ragamAI ensembles recommendations given by multiple models to learn user idea and past preference of sequences in concerts to extract recommendations. Our experiments on a vast collection of concert show that our model performs 25%-50% better than baseline models. ragamAI's applications are two-fold. 1) it will assist musicians to customize their performance with the necessary variety required to sustain the interest of the audience for the entirety of the concert 2) it will generate carefully curated lists of south Indian classical music so that the listener can discover the wide range of melody that the musical system can offer.
Essential Finite Generation of Valuation Rings in Characteristic Zero Algebraic Function Fields

Let $K$ be a characteristic zero algebraic function field with a valuation $\nu$. Let $L$ be a finite extension of $K$ and $\omega$ be an extension of $\nu$ to $L$. We establish that the valuation ring $V_{\omega}$ of $\omega$ is essentially finitely generated over the valuation ring $V_{\nu}$ of $\nu$ if and only if the initial index $\epsilon(\omega|\nu)$ is equal to the ramification index $e(\omega|\nu)$ of the extension. This gives a positive answer, for characteristic zero algebraic function fields, to a question posed by Hagen Knaf.
Zero-shot Recognition of Complex Action Sequences

Zero-shot video classification for fine-grained activity recognition has largely been explored using methods similar to its image-based counterpart, namely by defining image-derived attributes that serve to discriminate among classes. However, such methods do not capture the fundamental dynamics of activities and are thus limited to cases where static image content alone suffices to classify an activity. For example, reversible actions such as entering and exiting a car are often indistinguishable. In this work, we present a framework for straightforward modeling of activities as a state machine of dynamic attributes. We show that encoding the temporal structure of attributes greatly increases our modeling power, allowing us to capture action direction, for example. Further, we can extend this to activity detection using dynamic programming, providing, to our knowledge, the first example of zero-shot joint segmentation and classification of complex action sequences in a larger video. We evaluate our method on the Olympic Sports dataset where our model establishes a new state of the art for standard zero-shot-learning (ZSL) evaluation as well as outperforming all other models in the inductive category for general (GZSL) zero-shot evaluation. Additionally, we are the first to demonstrate zero-shot decoding of complex action sequences on a widely used surgical dataset. Lastly, we show that that we can even eliminate the need to train attribute detectors by using off-the-shelf object detectors to recognize activities in challenging surveillance videos.
A Bayesian Perspective of Convolutional Neural Networks through a Deconvolutional Generative Model

Inspired by the success of Convolutional Neural Networks (CNNs) for supervised prediction in images, we design the Deconvolutional Generative Model (DGM), a new probabilistic generative model whose inference calculations correspond to those in a given CNN architecture. The DGM uses a CNN to design the prior distribution in the probabilistic model. Furthermore, the DGM generates images from coarse to finer scales. It introduces a small set of latent variables at each scale, and enforces dependencies among all the latent variables via a conjugate prior distribution. This conjugate prior yields a new regularizer based on paths rendered in the generative model for training CNNs-the Rendering Path Normalization (RPN). We demonstrate that this regularizer improves generalization, both in theory and in practice. In addition, likelihood estimation in the DGM yields training losses for CNNs, and inspired by this, we design a new loss termed as the Max-Min cross entropy which outperforms the traditional cross-entropy loss for object classification. The Max-Min cross entropy suggests a new deep network architecture, namely the Max-Min network, which can learn from less labeled data while maintaining good prediction performance. Our experiments demonstrate that the DGM with the RPN and the Max-Min architecture exceeds or matches the-state-of-art on benchmarks including SVHN, CIFAR10, and CIFAR100 for semi-supervised and supervised learning tasks.
6-(2-Methoxybenzylamino)purine

The title compound, C13H13N5O, consists of discrete molecules connected by N—H⋯N hydrogen bonds to form infinite chains, with N⋯N separations of 3.0379 (15) and 2.8853 (15) Å. The benzene and purine ring systems make a dihedral angle of 77.58 (3)°. The crystal structure is further stabilized by intramolecular N⋯O interactions [2.9541 (12) Å] and intermolecular C—H⋯C and C⋯C contacts [3.304 (2), 3.368 (2), 3.667 (2), 3.618 (2) and 3.512 (2) Å] which arrange the molecules into graphite-like layers. The interlayer separations are 3.248 and 3.256 Å.