Miracle-dz/UniFAR-FTUs huggingface dataset:
SEEKING MEDICAL ATTENTION AMONG JORDANIAN PATIENTS WITH ACUTE MYOCARDIAL INFARCTION

Background: Significant reduction of mortality rate among acute myocardial infarction (AMI) patients could be achieved by early reperfusion. Successful reperfusion inversely related to the time from onset of AMI symptoms to treatment. Unfortunately, only about 25% of patients with AMI receive reperfusion treatment and the primary reasons for withholding this treatment was the time delay before admitting to hospital. In Jordan, as many other countries there is incomplete understanding for the dimensions of the delay problem in Jordanian community. Purposes: To understanding factors leading to delay seeking treatment among Jordanian AMI patients. Specifically to explore the effects of sociodemographic, clinical, contextual and cognitive factors on delay; as well as identifying factors that predict delay among AMI Jordanian patients. Methods: Comparative descriptive design was used; where convenient sampling technique used to recruit AMI patients within 24-72 hours of admission to coronary care unit. Eligible patients were invited to complete the modified Response to Symptoms Questionnaire. Results: 150 patients were enrolled in the study. The mean delay time for Jordanian AMI patients was nine hours. Only 28% of patients were presented to the hospital within the first hour of symptoms experience. Six factors predicting patient’s delay; those are: living in impoverished area, having low income, being alone at the time of symptoms onset, experienced intermittent symptoms, took medicine to relive symptoms, and worried about troubling others. Conclusion: Jordanian AMI patients have a long delay time before they seek medical treatment. Sociodemographic, clinical, contextual, and cognitive factors were all found to be associated with delay seeking medical treatment. Findings highlighted the need to reduce the time to presentation among Jordanian AMI patients. Such reduction could be achieved by designing and implementing proper interventional programs, as well as improving public awareness of the appropriate responses to
CASE A Report of Two Cases

THE FIRST THREF CASES of so-called pseudohypoparathy- roidism to be describe(d were reported by Albright and his colleagues in 1942.2-4 Since then 22 additional cases have been reported. One of the authors (H.M.), unacquainted with the possibility that the condition might be familial (Elrick9), found five additional probable cases in three generations of the family from which the sister and brother reported upon herein derived. It seems highly probable that many subclinical cases-with subnormal mentality as a manifestation might be recognized if the relatives of each patient with florid expression of the disease were studied. Since Albright's published description of a pseudo form of this clinical entity, relatively few instances of "idiopathic hypoparathyroidism," a previously widely recognized clinical entity, have been recorded. Does this suggest that pseu- dohypoparathyroidism, especially in the incomplete form, may be more common than true instances of hypoparathy- roidism?
Molecular Imaging of Prostate Cancer 1

Prostate cancer is the most common noncutaneous malignancy among men in the Western world. The natural history and clinical course of prostate cancer are markedly diverse, ranging from small indolent intraprostatic lesions to highly aggressive disseminated disease. An understanding of this biologic heterogeneity is considered a necessary requisite in the quest for the adoption of precise and personalized management strategies. Molecular imaging offers the potential for noninvasive assessment of the biologic interactions underpinning prostate carcinogenesis. Currently, numerous molecular imaging probes are in clinical use or undergoing preclinical or clinical evaluation. These probes can be divided into those that image increased cell metabolism, those that target prostate cancer–specific membrane proteins and receptor molecules, and those that bind to the bone matrix adjacent to metastases to bone. The increased metabolism and vascular changes in prostate cancer cells can be evaluated with radiolabeled analogs of choline, acetate, glucose, amino acids, and nucleotides. The androgen receptor, prostate-specific membrane antigen, and gastrin-releasing peptide receptor (ie, bombesin) are overexpressed in prostate cancer and can be targeted by specific radiolabeled imaging probes. Because metastatic prostate cancer cells induce osteoblastic signaling pathways of adjacent bone tissue, bone-seeking radiotracers are sensitive tools for the detection of metastases to bone. Knowledge about the underlying biologic processes responsible for the phenotypes associated with the different stages of prostate cancer allows an appropriate choice of methods and helps avoid pitfalls.
Insight into the Gating Mechanism of Mechanosensitive Ion Channels using a simple structure: A step in the analysis of commotio cordis

Mechanosensation in cells is a well known phenomenon that is associated with cellular responses to force. Our knowledge of the trigger mechanism of this phenomenon is, however, limited. Earlier studies in this field have used atomic simulations, which although being accurate, are limited in their feasibility in multi-length scenarios like a mechanosensitive channel that undergoes micro-level changes in the composition of the protein to cause a macro-level change in the state of a biological structure such as the muscle. Finite Element Analysis has been used in various engineering fields to study the mechanical response of complex structures. The current study is a step in utilizing the phenomenal capabilities of Finite Element Analysis in developing and studying a 3D model (Membrane-Channel) of a mechanosensitive channel of large conductance (MscL). A simplified CAD structure of Mycobacterium tuberculosis (TbMscL) was developed in the first stage of this study. The authenticity of this model was tested by applying two types of loading conditions, namely (i) In-plane stretch and (ii) Out-of-plane bending. The results obtained from the first step of analysis are in accordance with previous experimental data, which elucidates the fact that tension within the membrane guides the gating mechanism of the channel and not the curvature of the membrane. The second stage of the analysis involved the use of the same model to study the disease commotio cordis. This was achieved by calculating the loading conditions during the onset of the condition in the human heart and then transferring those conditions to the Membrane-Channel model developed in the first stage. The result showed that although the channel did not fully open but there was a significant change in the channel‟s radius that might cause the flow of ions, thereby changing the state of the channel. It is anticipated that this model will help future research in areas that conventionally have been difficult to model.
Usefulness in Vascular Surgery

To the Editor: We read with interest the paper by Kokotsakis and colleagues in which the authors describe the use of adenosine in 3 patients who were operated upon due to heart trauma and wounds, in order to arrest cardiac contractions to expedite wound closure. We wish to clarify for your readers some misconceptions about the dosage of adenosine, the length of asystole, and the side-effect profile. First of all, adenosine is a natural purine nucleoside, used primarily by cardiologists at low doses (6–12 mg) as an antiarrhythmic agent, to slow down supraventricular tachycardia. It is rapidly inactivated by erythrocytes and vascular endothelial cells. The half-life of adenosine in human plasma is less than 10 seconds at therapeutic doses. Therefore, cardiac standstill can be reached only when large doses are given as bolus injections. Life-threatening events after adenosine would appear to be rare, although minor side effects are common, in awake patients. Most of these signs and symptoms are masked during general anes thesia. The authors stated that they administered a dose of 3 mg intravenously, which lasted long enough to place sutures (10–25 seconds) but took 20 to 40 seconds to take effect. They repeated this dose 2 to 4 times. Having performed several recent prospective research projects using adenosine in endovascular stent deployment in thoracic aortic aneurysms, we ascertained that adenosine’s onset is usually within 7 to 8 seconds at high doses when administered through a peripheral vessel— and quicker if given through a central line, because it reaches the myocardium immediately. Because adenosine is broken down so rapidly by red blood cells and vascular endothelium, its offset is not only rapid, but dose-dependent. See Table I for the results of our studies. The authors never state the temporal proximity in which their additional doses were administered. If time elapsed between their administration, a 3-mg dose (which is actually 1 mL of the drug) cannot last 10 to 20 seconds and would never take 20 to 40 seconds for onset of asystole, because of its rapid breakdown in the blood. We also suspect that the 3 patients being operated upon were under a general anesthetic (no mention of this by the authors). What were the side effects of adenosine in their patients? If their patients were asleep, how could the authors evaluate their patients’ side effects? The authors quote a side-effect profile that was inconsistent with their reference. Our studies were performed on awake patients under local anesthesia plus MAC (monitored anesthesia care: sedatives and analgesics). Therefore, our side-effect profile is more within reason (Table II).
Model Transferability with Responsive Decision Subjects

Given an algorithmic predictor that is accurate on some source population consisting of strategic human decision subjects, will it remain accurate if the population respond to it? In our setting, an agent or a user corresponds to a sample $(X,Y)$ drawn from a distribution $\cal{D}$ and will face a model $h$ and its classification result $h(X)$. Agents can modify $X$ to adapt to $h$, which will incur a distribution shift on $(X,Y)$. Our formulation is motivated by applications where the deployed machine learning models are subjected to human agents, and will ultimately face responsive and interactive data distributions. We formalize the discussions of the transferability of a model by studying how the performance of the model trained on the available source distribution (data) would translate to the performance on its induced domain. We provide both upper bounds for the performance gap due to the induced domain shift, as well as lower bounds for the trade-offs that a classifier has to suffer on either the source training distribution or the induced target distribution. We provide further instantiated analysis for two popular domain adaptation settings, including covariate shift and target shift.
Understanding developing turbulence by a study of the nonlinear energy transfer in the Navier–Stokes equation

In the present work, we investigate a numerical one-dimensional solver to the Navier–Stokes equation that retains all terms, including both pressure and dissipation. Solutions to simple examples that illustrate the actions of the nonlinear term are presented and discussed. The calculations take the full 4D flow as its starting point and continuously projects the forces acting on the fluid at a fixed Eulerian point in a stationary coordinate system onto the direction of the instantaneous velocity. Pressure is included through modeling. Adhering to the requirement that time must in general be considered an independent variable, the time development of the time records and power spectra of the velocity fluctuations are studied. It is found that the actions of the nonlinear term in the Navier–Stokes equation manifests itself by generating sharp pulses in the time traces, where the sharpness is bounded by the finite viscosity. In the spectral domain, the sharp gradients in the pulses generate energy contributions at high frequencies that yields a −2 slope across the inertial range. The −2 (or −6/3) slope is explained through a simple example and the classically expected −5/3 slope in the inertial range can be recovered from the pressure fluctuations from the full flow field that can be considered a noise contribution at the point considered. We also observe that the spectrum can in principle keep spreading to higher frequencies or wavenumbers without upper bound, as the viscosity is approaching the zero limit.
Large-scale earthquake sequence simulations on 3D nonplanar faults using the boundary element method accelerated by lattice H-matrices

Large-scale earthquake sequence simulations using the boundary element method (BEM) incur extreme computational costs through multiplying a dense matrix with a slip rate vector. Hierarchical matrices (H-matrices) have often been used to accelerate this multiplication. However, the complexity of the structures of the H-matrices and the communication costs between processors limit their scalability, and they therefore cannot be used efficiently in distributed memory computer systems. Lattice H-matrices have recently been proposed as a tool to improve the parallel scalability of H-matrices. In this study, we developed a method for earthquake sequence simulations applicable to 3D nonplanar faults with lattice H-matrices. We present a simulation example and verify the mesh convergence of our method for a 3D nonplanar thrust fault using rectangular and triangular discretizations. We also performed performance and scalability analyses of our code. Our simulations, using over ${10^5}$ degrees of freedom, demonstrated a parallel acceleration beyond ${10^4}$ MPI processors and a > 10-fold acceleration over the best performance when the normal H-matrices are used. Using this code, we can perform unprecedented large-scale earthquake sequence simulations on geometrically complex faults with supercomputers. The software is made an open-source and freely available.
Similarity of Sentence Representations in Multilingual LMs: Resolving Conflicting Literature and a Case Study of Baltic Languages

Low-resource languages, such as Baltic languages, beneﬁt from Large Multilingual Models (LMs) that possess remarkable cross-lingual transfer performance capabilities. This work is an interpretation and analysis study into cross-lingual representations of Multilingual LMs. Previous works hypothesized that these LMs internally project representations of different languages into a shared cross-lingual space. However, the literature produced contradictory results. In this paper, we revisit the prior work claiming that "BERT is not an Interlingua" and show that different languages do converge to a shared space in such language models with another choice of pooling strategy or similarity index. Then, we perform cross-lingual representational analysis for the two most popular multilingual LMs employing 378 pairwise language comparisons. We discover that while most languages share joint cross-lingual space, some do not. However, we observe that Baltic languages do belong to that shared space. 1
Bidirectional compression in heterogeneous settings for distributed or federated learning with partial participation: tight convergence guarantees

We introduce a framework – Artemis – to tackle the problem of learning in a distributed or federated setting with communication constraints and device partial participation. Several workers (randomly sampled) perform the optimization process using a central server to aggregate their computations. To alleviate the communication cost, Artemis allows to compress the information sent in both directions (from the workers to the server and conversely) combined with a memory mechanism. It improves on existing algorithms that only consider unidirectional compression (to the server), or use very strong assumptions on the compression operator, and often do not take into account devices partial participation. We provide fast rates of convergence (linear up to a threshold) under weak assumptions on the stochastic gradients (noise’s variance bounded only at optimal point) in noni.i. d. setting, highlight the impact of memory for unidirectional and bidirectional compression, analyze Polyak-Ruppert averaging. We use convergence in distribution to obtain a lower bound of the asymptotic variance that highlights practical limits of compression. We propose two approaches to tackle the challenging case of devices partial participation and provide experimental results to demonstrate the validity of our analysis.
Arithmetic {B}reuil-{K}isin-{F}argues modules and comparison of integral {p}-adic Hodge theories

Let K be a discrete valuation ﬁeld with perfect residue ﬁeld, we study the functor from weakly admissible ﬁltered ( ϕ,N,G K )-modules over K to the isogeny category of Breuil-Kisin-Fargues G K -modules. This functor is the composition of a functor deﬁned by Fargues-Fontaine from weakly admissible ﬁltered ( ϕ,N,G K )-modules to G K -equivariant modiﬁcations of vector bundles over the Fargues-Fontaine curve X FF , with the functor of Fargues-Scholze that between the category of admissible modiﬁcations of vector bundles over X FF and the isogeny category of Breuil-Kisin-Fargues modules. We study those objects that appear in the essential image of the above functor and call them arithmetic BKF modules. We show certain rigidity result of arithmetic BKF modules and use it to compare existing p -adic Hodge theories at A inf level.
Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies

Estimating the effect of intervention from observational data while accounting for confounding variables is a key task in causal inference. Oftentimes, the confounders are unobserved, but we have access to large amounts of additional unstructured data (images, text) that contain valuable proxy signal about the missing confounders. This paper argues that leveraging this unstructured data can greatly improve the accuracy of causal effect estimation. Specifically, we introduce deep multi-modal structural equations, a generative model for causal effect estimation in which confounders are latent variables and unstructured data are proxy variables. This model supports multiple multi-modal proxies (images, text) as well as missing data. We empirically demonstrate that our approach outperforms existing methods based on propensity scores and corrects for confounding using unstructured inputs on tasks in genomics and healthcare. Our methods can potentially support the use of large amounts of data that were previously not used in causal inference
Diagonalization of Polynomial-Time Deterministic Turing Machines via Nondeterministic Turing Machines

The diagonalization technique was invented by Georg Cantor to show that there are more real numbers than algebraic numbers and is very important in theoretical computer science. In this work, we enumerate all of the polynomial-time deterministic Turing machines and diagonalize against all of them by a universal nondeterministic Turing machine. As a result, we obtain that there is a language $L_d$ not accepted by any polynomial-time deterministic Turing machines but accepted by a nondeterministic Turing machine running within time $O(n^k)$ for any $k\in\mathbb{N}_1$. Based on these, we further show that $L_d\in\mathcal{NP}$ . That is, we present a proof that $\mathcal{P}$ and $\mathcal{NP}$ differ. Meanwhile, we show that there exists a language $L_s$ in $\mathcal{P}$ but the machine accepting it also runs within time $O(n^k)$ for all $k\in\mathbb{N}_1$. Lastly, we show that if $\mathcal{P}^O=\mathcal{NP}^O$ and on some rational base assumptions then the set $P^O$ of all polynomial-time deterministic oracle Turing machines with oracle $O$ is not enumerable, thus demonstrating that diagonalization technique (via a universal nondeterministic oracle Turing machine) will generally not apply to the relativized versions of the $\mathcal{P}$ versus $\mathcal{NP}$ problem.
A Scalable Substructuring Method By Lagrange Multipliers For Plate Bending Problems

We present a new Lagrange multiplier-based domain decomposition method for solving iteratively systems of equations arising from the finite element discretization of plate bending problems. The proposed method is essentially an extension of the finite element tearing and interconnecting substructuring algorithm to the biharmonic equation. The main idea is to enforce continuity of the transversal displacement field at the subdomain crosspoints throughout the preconditioned conjugate gradient iterations. The resulting method is proved to have a condition number that does not grow with the number of subdomains but rather grows at most polylogarithmically with the number of elements per subdomain. These optimal properties hold for numerous plate bending elements that are used in practice including the Hsieh--Clough--Tocher element, the discrete Kirchhoff triangle, and a class of nonlocking elements for the Reissner--Mindlin plate models. Computational experiments are reported and shown to confirm the theoretical optimal convergence properties of the new domain decomposition method. Computational efficiency is also demonstrated with the numerical solution in 45 iterations and 105 seconds on a 64-processor IBM SP2 of a plate bending problem with almost one million degrees of freedom.
r a c t i c e

359 ISSN 2045-0907 10.2217/CNS.13. 24 © 2013 Future Medicine Ltd CNS Oncol. (2013) 2(4), 359–376 1Clinical Cooperation Unit Pediatric Oncology (G340), German Cancer Research Center (DKFZ), Heidelberg, Germany 2Section of Pediatric Brain Tumors, Department of Pediatric Oncology, Hematology & Immunology, University Hospital Heidelberg, Heidelberg, Germany *Author for correspondence: t.milde@dkfz.de  Expression of histone deacetylases (HDACs) is frequently dysregulated in brain tumors compared with normal brain tissue.  Expression patterns differ depending on histology and tumor grade.  Mutation rate of HDAC genes in brain tumors is low to nonexistent.  HDACs have been validated as potential targets for the treatment of brain tumors in preclinical models.  Small-molecule HDAC inhibitors can induce apoptosis, differentiation and cell-cycle arrest in brain tumor cells.  Biomarkers for response prediction to HDAC inhibitor treatment are missing.  HDAC inhibitors might reveal their full anticancer potential in combination therapy approaches.  HDAC inhibitors currently used in ongoing clinical trials are vorinostat, panobinostat, entinostat and valproic acid.  Initial results from completed trials do not show efficacy of HDAC inhibitors as monotherapy.  Individual patients do show responses to HDAC inhibitor treatment.  Development of biomarkers for response to HDAC inhibition is crucial for the successful translation of the promising preclinical findings into the clinic.  Currently, HR23B is the most prominent predictive biomarker, and histone acetylation of peripheral blood mononuclear cells the most widely used pharmacodynamic biomarker for HDAC inhibition.  Critical points for the successful progression of research exploring the targeting of HDACs in brain tumors are elucidation of the function of HDAC isoenzymes, development of inhibitors with a classor isoenzyme-specific inhibitory profile, development of biomarkers for response prediction and investigation of rational combination therapies. Pr ac ti ce P oi nt s Targeting of histone deacetylases in brain tumors REVIEW