Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of levosimendan and role of cardiac magnetic resonance in cardiogenic shock due to COVID-19 related lymphocytic myocarditis in the course of viral sepsis

ABSTRACT COVID-19 and sepsis pose great challenges to clinicians and growing evidence is demonstrating links between the two conditions. Both can be complicated by acute heart failure. The use of levosimendan in patients with ventricular dysfunction during COVID-19 infection and sepsis has very little evidence. A 46-year-old, hypertensive and obese patient was admitted for severe left ventricular failure and shock during sepsis following a COVID-19 infection. The patient was treated first with norepinephrine, which was partially effective, then with the addition of levosimendan as a continuous 24 hours infusion. Vital signs and echocardiographic systolic performance indices, such as FE, SVi, CI, dP/dT, TAPSE, and tricuspid S-wave velocity, as well as diastolic function, were recorded at access, 12 and 24 hours. After initiation of levosimendan, a rapid improvement in vital signs and systolic and diastolic performance indices was observed, not depending on changes in preload, afterload, and inflammatory status. Blood cultures were negative for the presence of bacteria, thus defining the picture of likely viral sepsis. Cardiac magnetic resonance was determinant, showing a picture of myocarditis sustained by immune processes rather than direct viral injury, which was confirmed by endomyocardial biopsy. In conclusion, this case highlights the efficacy of levosimendan in acute heart failure complicated by shock due to COVID-19-related myocarditis and concomitant sepsis and confirms cardiac magnetic resonance as the gold standard for the diagnosis of myocardial inflammatory disease. To the best of our knowledge, this is the first documented case of effective use of levosimendan in this context.
Mixed neuroendocrine-non-neuroendocrine carcinoma (MiNEN) in gallbladder with liver metastasis of neuroendocrine carcinoma component: a case report

Abstract Mixed neuroendocrine-non-neuroendocrine neoplasms of the gallbladder are rare with a lack of established standardized therapeutic strategies. We report a case of gallbladder mixed neuroendocrine-non-neuroendocrine neoplasm with liver metastasis of neuroendocrine carcinoma. A patient who underwent radical cholecystectomy for gallbladder adenocarcinoma was detected with increasing liver mass, and hepatectomy was performed. Pathological report revealed neuroendocrine carcinoma. To find primary origin, pathological review of the old specimen from previous cholecystectomy with a slightly different perspective was conducted, where the neuroendocrine component was positively dyed. In conclusion, though it might be impossible to review every pathological result in cases with ambiguous findings, reviewing the previous specimen can be a useful option in diagnosis.
Laparoscopic appendectomy in an adult patient with situs inversus totalis

Abstract Left-sided appendicitis is usually caused by situs inversus totalis or midgut malrotation. Clinical and imaging diagnoses have been presented relatively fully in the literature. However, this is a rare condition, and each related case should be further reported to help the day-to-day clinician better investigate and understand. Therefore, in this paper, we present a case of left-sided acute appendicitis in an adult male patient with situs inversus totalis. In addition, we also discuss the laparoscopic technique of the left-sided appendectomy as it is technically more difficult because of the mirror nature of the anatomy.
On fixed points of multifunctions in ordered spaces

Let P be an ordered topological space and a multivalued mapping for which and X 1≤x 2 imply y 1≤y 2 for some y 2∊Fx 2 Several fixed point theorems are derived for F under the above condition and some extra conditions imposed on P and/or F. The use of a generalized iteration method allows us to drop all the continuity properties of F, and even the topology of P from these conditions. Some of the results obtained are new also for single-valued mappings. Applications are given to mapping families and operator equations.
Cerebral effects of different prime solutions used during cardiopulmonary bypass

Background This study aims to compare the cerebral, hemodynamic, and metabolic effects of different prime solutions used in patients undergoing coronary artery bypass grafting. Methods Between May 2013 and May 2014, a total of 30 patients (25 males, 5 females; mean age: 59.5±9 years; range, 42 to 78 years) who were schedule for elective isolated coronary artery bypass grafting were included in this prospective study. The patients were randomized into three groups: Group 1 (n=10) (ringer"s lactate [RL]), Group 2 (n=10) (6% hydroxyethyl starch [HES] 130/0.4), and Group 3 (n=10) (RL + 6% HES 130/0.4). Hemodynamic parameters, arterial blood gas analyses, hemoglobin, hematocrit, cerebral regional oxygen saturation, urine output and fluid balance were recorded preoperatively, before and after anesthesia, 10 min after the transition to extracorporeal circulation, while weaning from extracorporeal circulation, and at the end of surgery. Preoperatively and on postoperative Day 5, neuron-specific enolase enzyme and S-100 β protein were assessed. On Day 5 and Week 3 postoperatively, the Standardized Mini-Mental Test was administered to the patients. Results The serum neuron-specific enolase enzyme and S-100 β protein levels of the patients were within physiological limits, and there were no clinical findings suggestive of cerebral damage, or changes in the Standardized Mini-Mental Test scores in any of the patients. There was a decrease of more than 20% of the baseline value of cerebral regional oxygen saturation in a total of four patients, one in Group 1 and three in Group 3. No significant difference was observed among the groups in terms of the other parameters. Conclusion The prime solution content has no effect on the development of cerebral damage after cardiopulmonary bypass, and the main factor in preventing the development of cerebral damage was the preservation of cerebral perfusion, which can be achieved by monitoring cerebral perfusion in these patients.
Wavelet Method for a Class of Fractional Klein-Gordon Equations

Wavelet analysis is a recently developed mathematical tool in applied mathematics. A wavelet method for a class of space-time fractional Klein–Gordon equations with constant coefficients is proposed, by combining the Haar wavelet and operational matrix together and efficaciously dispersing the coefficients. The behavior of the solutions and the effects of different values of fractional order α are graphically shown. The fundamental idea of the Haar wavelet method is to convert the fractional Klein–Gordon equations into a group of algebraic equations, which involves a finite number of variables. The examples are given to demonstrate that the method is effective, fast, and flexible; in the meantime, it is found that the difficulties of using the Daubechies wavelets for solving the differential equation, which need to calculate the correlation coefficients, are avoided.
Efficacy of PP121 in primary and metastatic non‑small cell lung cancers

Tyrosine kinase inhibitors are a clinically standard treatment option for non-small cell lung cancers (NSCLCs), the leading cause of cancer-related deaths in the US. These targeted agents include first, second and third generation tyrosine kinase inhibitors; however, these lack clinical efficacy in the treatment of NSCLC due to intrinsic and acquired resistance. This resistance may be a result of genetic aberrations in oncogenic signaling mediators of divergent pathways. The present study aimed to investigate a novel dual tyrosine kinase and PI3K inhibitor, PP121, as a targeted agent in NSCLC cell lines. The present study co-cultured PP121 with healthy human astrocytes, a prevalent cell type located in the brain of NSCLC brain metastases. To date, few preclinical studies have examined the efficacy of PP121 as an anticancer agent, and to the best of my knowledge, no previous studies have previously evaluated its therapeutic potential in the treatment of NSCLC. To investigate the clinical heterogeneity of NSCLC, patient-derived adenocarcinoma (ADC) and squamous cell carcinoma (SCC) xenograft models were used, which exhibited epidermal growth factor receptor (EGFR) mutations and mesenchymal-epithelial transition (MET) factor amplifications. Notably, both EGFR and MET are known contributors to tyrosine kinase inhibitor resistance; thus, the aforementioned mutations and amplifications enabled the effects of PP121 to be evaluated in these solid tumors. In addition, a co-cultured model system using both NSCLC cells and astrocytes was employed to assess the effects of PP121 on the invasion of ADC and SCC cells in a multicellular environment. Results of the present study demonstrated that PP121 exerted an antitumorigenic effect in the aforementioned model systems via downregulation of pharmacodynamic targets.
Diurnal and Seasonal Mapping of Martian Ices With EMIRS

Condensation and sublimation of ices at the surface of the planet is a key part of both the Martian H2O and CO2 cycles, either from a seasonal or diurnal aspect. While most of the ice is located within the polar caps, surface frost is known to be formed during nighttime down to equatorial latitudes. Here, we use data from the Emirates Mars Infrared Spectrometer onboard the Emirates Mars Mission to monitor the diurnal and seasonal evolution of the ices at the surface of Mars over almost one Martian year. The unique local time coverage provided by the instrument allows us to observe the apparition of equatorial CO2 frost in the second half of the Martian night around the equinoxes, to its sublimation at sunrise.
SpiderMesh: Spatial-aware Demand-guided Recursive Meshing for RGB-T Semantic Segmentation

For semantic segmentation in urban scene understanding, RGB cameras alone often fail to capture a clear holistic topology in challenging lighting conditions. Thermal signal is an informative additional channel that can bring to light the contour and fine-grained texture of blurred regions in low-quality RGB image. Aiming at practical RGB-T (thermal) segmentation, we systematically propose a Spatial-aware Demand-guided Recursive Meshing (SpiderMesh) framework that: 1) proactively compensates inadequate contextual semantics in optically-impaired regions via a demand-guided target masking algorithm; 2) refines multimodal semantic features with recursive meshing to improve pixel-level semantic analysis performance. We further introduce an asymmetric data augmentation technique M-CutOut, and enable semi-supervised learning to fully utilize RGB-T labels only sparsely available in practical use. Extensive experiments on MFNet and PST900 datasets demonstrate that SpiderMesh achieves state-of-the-art performance on standard RGB-T segmentation benchmarks.
On the convergence analysis of the decentralized projected gradient descent

In this work, we are concerned with the decentralized optimization problem: \begin{equation*} \min_{x \in \Omega}~f(x) = \frac{1}{n} \sum_{i=1}^n f_i (x), \end{equation*} where $\Omega \subset \mathbb{R}^d$ is a convex domain and each $f_i : \Omega \rightarrow \mathbb{R}$ is a local cost only known to agent $i$. A fundamental algorithm is the decentralized projected gradient method (DPG) given by \begin{equation*} x_i(t+1)=\mathcal{P}_\Omega\Big[\sum^n_{j=1}w_{ij} x_j(t) -\alpha(t)\nabla f_i(x_i(t))\Big] \end{equation*} where $\mathcal{P}_{\Omega}$ is the projection operator to $\Omega$ and $ \{w_{ij}\}_{1\leq i,j \leq n}$ are communication weight among the agents. While this method has been widely used in the literatures, its sharp convergence property has not been established well so far, except for the special case $\Omega = \mathbb{R}^n$. This work establishes new convergence estimates of DPG when the aggregate cost $f$ is strongly convex and each function $f_i$ is smooth. If the stepsize is given by constant $\alpha (t) \equiv\alpha>0$ and suitably small, we prove that each $x_i (t)$ converges to an $O(\sqrt{\alpha})$-neighborhood of the optimal point. In addition, we take a one-dimensional example $f_i$ and prove that the point $x_i (t)$ converges to an $O(\alpha)$-neighborhood of the optimal point. Also, we obtain convergence estimates for decreasing stepsizes. Numerical experiments are also provided to support the convergence results.
Combined investigation of collective amplitude and phase modes in a quasi-one-dimensional charge density wave system over a wide spectral range

We investigate experimentally both the amplitude and phase channels of the collective modes in the quasi-1D charge-density-wave (CDW) system, K0.3MoO3, by combining (i) optical impulsive-Raman pump-probe and (ii) terahertz time-domain spectroscopy (THz-TDS), with high resolution and a detailed analysis of the full complex-valued spectra in both cases. This allows an unequivocal assignment of the observed bands to CDW modes across the THz range up to 9 THz. We revise and extend a time-dependent Ginzburg-Landau model to account for the observed temperature dependence of the modes, where the combination of both amplitude and phase modes allows one to robustly determine the bare-phonon and electron-phonon coupling parameters. While the coupling is indeed strongest for the lowest-energy phonon, dropping sharply for the immediately subsequent phonons, it grows back significantly for the higher-energy phonons, demonstrating their important role in driving the CDW formation. We also include a reassessment of our previous analysis of the lowest-lying phase modes, whereby assuming weaker electronic damping for the phase channel results in a qualitative picture more consistent with quantum-mechanical treatments of the collective modes, with a strongly coupled amplitudon and phason as the lowest modes.
Electric-field-induced topological changes in multilamellar and in confined lipid membranes

It is well known that lipid membranes respond to a threshold transmembrane electric field through a reversible mechanism called electroporation, where hydrophilic water pores form across the membrane, an effect widely used in biological systems. The effect of such fields on interfacially-confined (stacked or supported) lipid membranes, on the other hand, which may strongly modulate interfacial properties, has not to our knowledge been previously studied. Motivated by recent surface forces experiments showing a striking effect of electric fields on lubrication by confined lipid bilayers, we carried out all-atom molecular dynamics simulations of such membranes under transverse electric fields. We find that in addition to electroporation, a new feature emerges of locally merged bilayers which act to bridge the confining interfaces. These features shed light on the remodelling of confined lipid membrane stacks by electric fields, and provides insight into how such fields may modulate frictional and more generally surface interactions in the presence of lipid-based boundary layers.
UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation

Large Language Models (LLMs) are popular for their impressive abilities, but the need for model-specific fine-tuning or task-specific prompt engineering can hinder their generalization. We propose UPRISE (Universal Prompt Retrieval for Improving zero-Shot Evaluation), which tunes a lightweight and versatile retriever that automatically retrieves prompts for a given zero-shot task input. Specifically, we demonstrate universality in a cross-task and cross-model scenario: the retriever is tuned on a diverse set of tasks, but tested on unseen task types; we use a small frozen LLM, GPT-Neo-2.7B, for tuning the retriever, but test the retriever on different LLMs of much larger scales, such as BLOOM-7.1B, OPT-66B and GPT3-175B. Additionally, we show that UPRISE mitigates the hallucination problem in our experiments with ChatGPT, suggesting its potential to improve even the strongest LLMs. Our model and code are available at https://github.com/microsoft/LMOps.
Clinical Concept and Relation Extraction Using Prompt-based Machine Reading Comprehension

OBJECTIVE To develop a natural language processing system that solves both clinical concept extraction and relation extraction in a unified prompt-based machine reading comprehension (MRC) architecture with good generalizability for cross-institution applications. METHODS We formulate both clinical concept extraction and relation extraction using a unified prompt-based MRC architecture and explore state-of-the-art transformer models. We compare our MRC models with existing deep learning models for concept extraction and end-to-end relation extraction using 2 benchmark datasets developed by the 2018 National NLP Clinical Challenges (n2c2) challenge (medications and adverse drug events) and the 2022 n2c2 challenge (relations of social determinants of health [SDoH]). We also evaluate the transfer learning ability of the proposed MRC models in a cross-institution setting. We perform error analyses and examine how different prompting strategies affect the performance of MRC models. RESULTS AND CONCLUSION The proposed MRC models achieve state-of-the-art performance for clinical concept and relation extraction on the 2 benchmark datasets, outperforming previous non-MRC transformer models. GatorTron-MRC achieves the best strict and lenient F1-scores for concept extraction, outperforming previous deep learning models on the 2 datasets by 1%-3% and 0.7%-1.3%, respectively. For end-to-end relation extraction, GatorTron-MRC and BERT-MIMIC-MRC achieve the best F1-scores, outperforming previous deep learning models by 0.9%-2.4% and 10%-11%, respectively. For cross-institution evaluation, GatorTron-MRC outperforms traditional GatorTron by 6.4% and 16% for the 2 datasets, respectively. The proposed method is better at handling nested/overlapped concepts, extracting relations, and has good portability for cross-institute applications. Our clinical MRC package is publicly available at https://github.com/uf-hobi-informatics-lab/ClinicalTransformerMRC.
Single-out fake posts: participation game and its design

Crowd-sourcing models, which leverage the collective opinions/signals of users on online social networks (OSNs), are well-accepted for fake post detection; however, motivating the users to provide the crowd signals is challenging, even more so in the presence of adversarial users.We design a participation (mean-field) game where users of the OSN are lured by a reward-based scheme to provide the binary (real/fake) signals such that the OSN achieves (θ,δ)-level of actuality identification (AI) - not more than δ fraction of nonadversarial users incorrectly judge the real post, and at least θ fraction of non-adversarial users identify the fake post as fake. An appropriate warning mechanism is proposed to influence the decision-making of the users such that the resultant game has at least one Nash Equilibrium (NE) achieving AI. We also identify the conditions under which all NEs achieve AI. Further, we numerically illustrate that one can always design an AI game if the normalized difference in the innate identification capacities of the users is at least 1%, when desired θ = 75%.
Solving Rehabilitation Scheduling problems via a Two-Phase ASP approach

A core part of the rehabilitation scheduling process consists of planning rehabilitation physiotherapy sessions for patients, by assigning proper operators to them in a certain time slot of a given day, taking into account several legal, medical, and ethical requirements and optimizations, for example, patient’s preferences and operator’s work balancing. Being able to efficiently solve such problem is of upmost importance, in particular after the COVID-19 pandemic that significantly increased rehabilitation’s needs. In this paper, we present a two-phase solution to rehabilitation scheduling based on Answer Set Programming, which proved to be an effective tool for solving practical scheduling problems. We first present a general encoding and then add domain-specific optimizations. Results of experiments performed on both synthetic and real benchmarks, the latter provided by ICS Maugeri, show the effectiveness of our solution as well as the impact of our domain-specific optimizations.
Learning to Incentivize Information Acquisition: Proper Scoring Rules Meet Principal-Agent Model

We study the incentivized information acquisition problem, where a principal hires an agent to gather information on her behalf. Such a problem is modeled as a Stackelberg game between the principal and the agent, where the principal announces a scoring rule that specifies the payment, and then the agent then chooses an effort level that maximizes her own profit and reports the information. We study the online setting of such a problem from the principal's perspective, i.e., designing the optimal scoring rule by repeatedly interacting with the strategic agent. We design a provably sample efficient algorithm that tailors the UCB algorithm (Auer et al., 2002) to our model, which achieves a sublinear $T^{2/3}$-regret after $T$ iterations. Our algorithm features a delicate estimation procedure for the optimal profit of the principal, and a conservative correction scheme that ensures the desired agent's actions are incentivized. Furthermore, a key feature of our regret bound is that it is independent of the number of states of the environment.
A Chandra X-Ray Survey of Optically Selected Close Galaxy Pairs: Unexpectedly Low Occupation of Active Galactic Nuclei

High-resolution X-ray observations offer a unique tool for probing the still-elusive connection between galaxy mergers and active galactic nuclei (AGNs). We present an analysis of nuclear X-ray emission in an optically selected sample of 92 close galaxy pairs (with projected separations ≲20 kpc and line-of-sight velocity offsets <500 km s−1) at low redshift ( z¯∼0.07 ), based on archival Chandra observations. The parent sample of galaxy pairs is constructed without imposing an optical classification of nuclear activity, thus it is largely free of selection effect for or against the presence of an AGN. Nor is this sample biased for or against gas-rich mergers. An X-ray source is detected in 70 of the 184 nuclei, giving a detection rate of 38%−5%+5% , down to a 0.5–8 keV limiting luminosity of ≲1040 erg s−1. The detected and undetected nuclei show no systematic difference in their host galaxy properties such as galaxy morphology, stellar mass, and stellar velocity dispersion. When potential contamination from star formation is avoided (i.e., L 2−10 keV > 1041 erg s−1), the detection rate becomes 18%−3%+3% (32/184), which shows no excess compared to the X-ray detection rate of a comparison sample of optically classified single AGNs. The fraction of pairs containing dual AGN is only 2%−2%+2% . Moreover, most nuclei at the smallest projected separations probed by our sample (a few kiloparsecs) have an unexpectedly low apparent X-ray luminosity and Eddington ratio, which cannot be solely explained by circumnuclear obscuration. These findings suggest that close galaxy interaction is not a sufficient condition for triggering a high level of AGN activity.