Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect and Optimal Dosage of Dexmedetomidine Plus Sufentanil for Postoperative Analgesia in Elderly Patients With Postoperative Delirium and Early Postoperative Cognitive Dysfunction: A Single-Center, Prospective, Randomized, Double-Blind, Controlled Trial

Background Postoperative delirium (POD) and postoperative cognitive dysfunction (POCD) are common complications after major surgery among elderly patients. Dexmedetomidine (DEX) is less frequently explored for its effects in patients with postoperative neurocognitive disorders. This study investigated the effect and optimal dosage of DEX for patient-controlled analgesia (PCA) on POD and early POCD after major surgery among elderly patients. Methods Patients in four groups received continuous infusion of DEX 0, 100, 200, and 400 μg with sufentanil 150 μg for PCA immediately after surgery. POD and POCD were assessed on postoperative days 1, 2, 3, and 7 by using the Confusion Assessment Method (CAM) and Mini-Mental State Examination (MMSE) scales. Furthermore, the incidence of POD and POCD of all the four groups in postoperative 7 days classified by high risk factors (age, education, surgical site, and surgical category), sedation level, postoperative pain intensity, and side effects were assessed. Results The overall incidence rates of POD and early POCD 7 days after surgery were lower in the DEX 200 μg 400 μg groups than in the DEX 0 μg and 100 μg groups (P < 0.05). Compared with DEX 200 μg, DEX 400 μg reduced early POCD in patients who underwent open surgery (P < 0.05). There were no intergroup differences in the postoperative sedation level, pain intensity, and side effects. Conclusion The continuous infusion of DEX 200 μg or DEX 400 μg in PCA significantly decreased the incidence of POD and early POCD after major surgery without increasing any side effects. Compared with DEX 200 μg, DEX 400 μg was preferred for reducing early POCD in patients who underwent open surgery.
Gut Microbiome Signatures Are Biomarkers for Cognitive Impairment in Patients With Ischemic Stroke

Post-stroke cognitive impairment (PSCI) is a common neuropsychiatric complication of stroke. Mounting evidence has demonstrated a connection between gut microbiota (GM) and neuropsychiatric disease. Our previous study revealed the changes in the GM in a mouse model of vascular dementia. However, the characteristic GM of PSCI remains unclear. This study aimed to characterize the GM of PSCI and explored the potential of GM as PSCI biomarkers. A total of 93 patients with ischemic stroke were enrolled in this study. The patients were divided into two groups according to their MoCA scores 3 months after stroke onset. Clinical data and biological variables were recorded. GM composition was analyzed using 16S ribosomal RNA sequencing, and the characteristic GM was identified by linear discriminant analysis Effect Size (Lefse). Our results showed that Proteobacteria was highly increased in the PSCI group compared with the post-stroke non-cognitive impairment (PSNCI) group, the similar alterations were also observed at the class, order, family, and genus levels of Proteobacteria. After age adjustments, the abundance of Firmicutes, and its members, including Clostridia, Clostridiales, Lachnospiraceae, and Lachnospiraceae_other, were significantly decreased in the age-matched PSCI group compared with the PSNCI group. Besides, the GM was closely associated with MoCA scores and the risk factors for PSCI, including higher baseline National Institute of Health Stroke Scale score, higher homocysteine (Hcy) level, higher prevalence of stroke recurrence, leukoaraiosis, and brain atrophy. The KEGG results showed the enriched module for folding, sorting and degradation (chaperones and folding catalysts) and the decreased modules related to metabolisms of cofactors and vitamins, amino acid, and lipid in PSCI patients. A significant correlation was observed between PSCI and the abundance of Enterobacteriaceae after adjustments (P = 0.035). Moreover, the receiver operating characteristic (ROC) models based on the characteristic GM and Enterobacteriaceae could distinguish PSCI patients from PSNCI patients [area under the curve (AUC) = 0.840, 0.629, respectively]. Our findings demonstrated that the characteristic GM, especially Enterobacteriaceae, might have the ability to predict PSCI in post-stroke patients, which are expected to be used as clinical biomarkers of PSCI.
Agreement Between the OptoGait and Instrumented Treadmill System for the Quantification of Spatiotemporal Treadmill Running Parameters

The measurement of spatiotemporal gait parameters is commonly utilized to assess gait in healthy and injured individuals. The OptoGait system is a portable system and can be mounted to a treadmill to collect data in a clinical, training, or research setting. The purpose of this method comparison study was to examine the agreement of spatiotemporal gait parameters calculated by the OptoGait compared to an instrumented treadmill system during running. Thirty healthy runners ran on an instrumented treadmill with the OptoGait 1-m system mounted along the treadmill platform. Spatiotemporal running variables of step rate, step length, and contact time were calculated during the final minute of treadmill running. The level of agreement between the OptoGait and treadmill was analyzed using intraclass correlation coefficients [ICC (2,3)] for step rate, step length, and contact time. Step rate and step length demonstrated excellent agreement. Contact time demonstrated good agreement. Intraclass correlation coefficients for spatiotemporal parameters ranged from 0.83 to 0.99. The OptoGait demonstrated good to excellent agreement in the evaluation of running step rate, step length, and contact time and should be considered for use in clinical, training, or research settings.
Natural Biomaterials for Cardiac Tissue Engineering: A Highly Biocompatible Solution

Cardiovascular diseases (CVD) constitute a major fraction of the current major global diseases and lead to about 30% of the deaths, i.e., 17.9 million deaths per year. CVD include coronary artery disease (CAD), myocardial infarction (MI), arrhythmias, heart failure, heart valve diseases, congenital heart disease, and cardiomyopathy. Cardiac Tissue Engineering (CTE) aims to address these conditions, the overall goal being the efficient regeneration of diseased cardiac tissue using an ideal combination of biomaterials and cells. Various cells have thus far been utilized in pre-clinical studies for CTE. These include adult stem cell populations (mesenchymal stem cells) and pluripotent stem cells (including autologous human induced pluripotent stem cells or allogenic human embryonic stem cells) with the latter undergoing differentiation to form functional cardiac cells. The ideal biomaterial for cardiac tissue engineering needs to have suitable material properties with the ability to support efficient attachment, growth, and differentiation of the cardiac cells, leading to the formation of functional cardiac tissue. In this review, we have focused on the use of biomaterials of natural origin for CTE. Natural biomaterials are generally known to be highly biocompatible and in addition are sustainable in nature. We have focused on those that have been widely explored in CTE and describe the original work and the current state of art. These include fibrinogen (in the context of Engineered Heart Tissue, EHT), collagen, alginate, silk, and Polyhydroxyalkanoates (PHAs). Amongst these, fibrinogen, collagen, alginate, and silk are isolated from natural sources whereas PHAs are produced via bacterial fermentation. Overall, these biomaterials have proven to be highly promising, displaying robust biocompatibility and, when combined with cells, an ability to enhance post-MI cardiac function in pre-clinical models. As such, CTE has great potential for future clinical solutions and hence can lead to a considerable reduction in mortality rates due to CVD.
Clustering-based Inference for Zero-Shot Biomedical Entity Linking

Due to large number of entities in biomedical knowledge bases, only a small fraction of entities have corresponding labelled training data. This necessitates a zero-shot entity linking model which is able to link mentions of unseen entities using learned representations of entities. Existing zero-shot entity linking models however link each mention independently, ignoring the inter/intra-document relationships between the entity mentions. These relations can be very useful for linking mentions in biomedical text where linking decisions are often difficult due mentions having a generic or a highly specialized form. In this paper, we introduce a model in which linking decisions can be made not merely by linking to a KB entity but also by grouping multiple mentions together via clustering and jointly making linking predictions. In experiments on the largest publicly available biomedical dataset, we improve the best independent prediction for zero-shot entity linking by 2.5 points of accuracy, and our joint inference model further improves entity linking by 1.8 points.
Maximum black hole mass across cosmic time

At the end of its life, a very massive star is expected to collapse into a black hole. The masses of these black holes are pivotal for our understanding of the evolution and fate of these stars, as well as for galaxy evolution and the build-up of black hole masses through Cosmic time. The recent detection of an 85 solar mass black hole from the gravitational wave event GW 190521 appears to present a fundamental problem as to how such heavy black holes exist above the approximately 50 solar mass pair-instability limit where stars are expected to be blown to pieces with no remnant left. Here we show that for stellar models with non extreme assumptions, 90.100 solar mass stars at reduced heavy element content can produce blue supergiant progenitors with core masses sufficiently small to remain below the fundamental pair-instability limit, yet at the same time lose an amount of mass small enough to end up in an 'impossible' 85 solar mass heavy black hole. The key point is a proper consideration of stellar wind physics and an improved scaling of mass loss with the heavy element fraction of the host galaxy, providing a novel and robust scenario that doubles the maximum black hole mass set by pair instability. Our study shows how our Universe is capable of producing heavy black holes, which are important seeds for the production of supermassive black holes that regulate the evolution of galaxies. Our evolutionary channel to the formation of an 85 solar mass black hole is of fundamental relevance for the manner in which metals are released in the outflows and explosions of the most massive stars, which is shown to be a strong function of Cosmic time.
Pushing The Limit of Type I Codebook For Fdd Massive Mimo Beamforming: A Channel Covariance Reconstruction Approach

There is a fundamental trade-off between the channel representation resolution of codebooks and the overheads of feedback communications in the fifth generation new radio (5G NR) frequency division duplex (FDD) massive multiple-input and multiple-output (MIMO) systems. In particular, two types of codebooks (namely Type I and Type II codebooks) are introduced with different resolution and overhead. Although the Type I codebook based scheme requires lower feedback overhead, its channel state information (CSI) reconstruction and beamforming performance are not as good as those from the Type II codebook based scheme. However, since the Type I codebook based scheme has been widely used in 4G systems for many years, replacing it by the Type II codebook based scheme overnight is too costly to be an option. Therefore, in this paper, using Type I codebook, we leverage advances in cutting plane method to optimize the CSI reconstruction at the base station (BS), in order to close the gap between these two codebook based beamforming schemes. Numerical results based on channel samples from QUAsi Deterministic RadIo channel GenerAtor (QuaDRiGa) are presented to show the excellent performance of the proposed algorithm in terms of beamforming vector acquisition.
Tidal Dissipation in Dual-body, Highly Eccentric, and Nonsynchronously Rotating Systems: Applications to Pluto–Charon and the Exoplanet TRAPPIST-1e

Using the Andrade-derived Sundberg–Cooper rheology, we apply several improvements to the secular tidal evolution of TRAPPIST-1e and the early history of Pluto–Charon under the simplifying assumption of homogeneous bodies. By including higher-order eccentricity terms (up to and including e20), we find divergences from the traditionally used e2 truncation starting around e = 0.1. Order-of-magnitude differences begin to occur for e > 0.6. Critically, higher-order eccentricity terms activate additional spin–orbit resonances. Worlds experiencing nonsynchronous rotation can fall into and out of these resonances, altering their long-term evolution. Nonzero obliquity generally does not generate significantly higher heating; however, it can considerably alter orbital and rotational evolution. Much like eccentricity, obliquity can activate new tidal modes and resonances. Tracking the dual-body dissipation within Pluto and Charon leads to faster evolution and dramatically different orbital outcomes. Based on our findings, we recommend future tidal studies on worlds with e ≥ 0.3 to take into account additional eccentricity terms beyond e2. This threshold should be lowered to e > 0.1 if nonsynchronous rotation or nonzero obliquity is under consideration. Due to the poor convergence of the eccentricity functions, studies on worlds that may experience very high eccentricity (e ≥ 0.6) should include terms with high powers of eccentricity. We provide these equations up to e10 for arbitrary obliquity and nonsynchronous rotation. Finally, the assumption that short-period, solid-body exoplanets with e ≳ 0.1 are tidally locked in their 1:1 spin–orbit resonance should be reconsidered. Higher-order spin–orbit resonances can exist even at these relatively modest eccentricities, while previous studies have found such resonances can significantly alter stellar-driven climate.
Unrolling of Deep Graph Total Variation for Image Denoising

While deep learning (DL) architectures like convolutional neural networks (CNNs) have enabled effective solutions in image denoising, in general their implementations overly rely on training data, lack interpretability, and require tuning of a large parameter set. In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design—one that utilizes interpretable analytical low-pass graph filters and employs 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN. Specifically, to construct a suitable similarity graph for graph spectral filtering, we first adopt a CNN to learn feature representations per pixel, and then compute feature distances to establish edge weights. Given a constructed graph, we next formulate a convex optimization problem for denoising using a graph total variation (GTV) prior. Via a l1 graph Laplacian reformulation, we interpret its solution in an iterative procedure as a graph low-pass filter and derive its frequency response. For fast filter implementation, we realize this response using a Lanczos approximation. Experimental results show that in the case of statistical mistmatch, our algorithm outperformed DnCNN by up to 3dB in PSNR.
Investigating the True Performance of Transformers in Low-Resource Languages: A Case Study in Automatic Corpus Creation

Transformers represent the state-of-the-art in Natural Language Processing (NLP) in recent years, proving effective even in tasks done in low-resource languages. While pretrained transformers for these languages can be made, it is challenging to measure their true performance and capacity due to the lack of hard benchmark datasets, as well as the difficulty and cost of producing them. In this paper, we present three contributions: First, we propose a methodology for automatically producing Natural Language Inference (NLI) benchmark datasets for low-resource languages using published news articles. Through this, we create and release NewsPH-NLI, the first sentence entailment benchmark dataset in the low-resource Filipino language. Second, we produce new pretrained transformers based on the ELECTRA technique to further alleviate the resource scarcity in Filipino, benchmarking them on our dataset against other commonly-used transfer learning techniques. Lastly, we perform analyses on transfer learning techniques to shed light on their true performance when operating in low-data domains through the use of degradation tests.
Screening and Functional Prediction of Key Candidate Genes in Hepatitis B Virus-Associated Hepatocellular Carcinoma

Background The molecular mechanism by which hepatitis B virus (HBV) induces hepatocellular carcinoma (HCC) is still unknown. The genomic expression profile and bioinformatics methods were used to investigate the potential pathogenesis and therapeutic targets for HBV-associated HCC (HBV-HCC). Methods The microarray dataset GSE55092 was downloaded from the Gene Expression Omnibus (GEO) database. The data was analyzed by the bioinformatics software to find differentially expressed genes (DEGs). Gene Ontology (GO) enrichment analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, ingenuity pathway analysis (IPA), and protein-protein interaction (PPI) network analysis were then performed on DEGs. The hub genes were identified using Centiscape2.2 and Molecular Complex Detection (MCODE) in the Cytoscape software (Cytoscape_v3.7.2). The survival data of these hub genes was downloaded from the Gene Expression Profiling Interactive Analysis (GEPIA). Results A total of 2264 mRNA transcripts were differentially expressed, including 764 upregulated and 1500 downregulated in tumor tissues. GO analysis revealed that these DEGs were related to the small-molecule metabolic process, xenobiotic metabolic process, and cellular nitrogen compound metabolic process. KEGG pathway analysis revealed that metabolic pathways, complement and coagulation cascades, and chemical carcinogenesis were involved. Diseases and biofunctions showed that DEGs were mainly associated with the following diseases or biological function abnormalities: cancer, organismal injury and abnormalities, gastrointestinal disease, and hepatic system disease. The top 10 upstream regulators were predicted to be activated or inhibited by Z-score and identified 25 networks. The 10 genes with the highest degree of connectivity were defined as the hub genes. Cox regression revealed that all the 10 genes (CDC20, BUB1B, KIF11, TTK, EZH2, ZWINT, NDC80, TPX2, MELK, and KIF20A) were related to the overall survival. Conclusion Our study provided a registry of genes that play important roles in regulating the development of HBV-HCC, assisting us in understanding the molecular mechanisms that underlie the carcinogenesis and progression of HCC.
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution

We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel’s measurements are given as convolution of a common source signal and sparse filter. Unlike prior works where the compression is achieved either through random projections or by applying a fixed structured compression matrix, this paper proposes to learn the compression matrix from data. Given the full measurements, the proposed network is trained in an unsupervised fashion to learn the source and estimate sparse filters. Then, given the estimated source, we learn a structured compression operator while optimizing for signal reconstruction and sparse filter recovery. The efficient structure of the compression allows its practical hardware implementation. The proposed neural network is an autoencoder constructed based on an unfolding approach: upon training, the encoder maps the compressed measurements into an estimate of sparse filters using the compression operator and the source, and the linear convolutional decoder reconstructs the full measurements. We demonstrate that our method is superior to classical structured compressive sparse multichannel blind-deconvolution methods in terms of accuracy and speed of sparse filter recovery.
No-Go Theorems for Quantum Resource Purification: New Approach and Channel Theory

It is recently shown that there exist fundamental limits to the accuracy and efficiency of purifying generic noisy resource states in any well-behaved quantum resource theory [Fang/Liu, Phys. Rev. Lett. 125, 060405 (2020)]. Here, we develop a novel and powerful method for analyzing the limitations of quantum resource purification, which not only leads to improved bounds that rule out exact purification for a broader range of noisy states and are tight in certain cases, but also allow us to establish a no-purification theory for quantum channel (dynamical) resources. More specifically, we employ the new method to derive universal bounds on the error and cost of transforming generic noisy channels (multiple instances can be used adaptively, in contrast to the state theory) to some unitary resource channel under any free channel-to-channel map. We address several cases of practical interest in more concrete terms, and discuss the connections and applications of our general results to quantum error correction, quantum Shannon theory, and quantum circuit synthesis.
Interspecies mating interactions between Duttaphrynus stomaticus (Marbled Toad) and Sphaerotheca breviceps (Indian burrowing frog) at the central Aravalli foothills, Rajasthan, India

Anurans show a wide variety of complex mating behaviours. Most of these comprise external fertilization, which takes place in water bodies. Male frogs initiate this process by producing species-specific mating calls (also known as advertisement calls) which attract conspecific females. Amplexus follows, during which the male grasps the female with its front legs, leading to fertilization of the eggs deposited by the female. Any deviation to this characteristic mating strategy is known as abnormal amplexus. This phenomenon can be categorized into specific types, including multiple amplexus (Ayres, 2008), amplexus from the abdominal side; amplexus between a living
Spatial Attention as an Interface for Image Captioning Models

The internal workings of modern deep learning models stay often unclear to an external observer, although spatial attention mechanisms are involved. The idea of this work is to translate these spatial attentions into natural language to provide a simpler access to the model's function. Thus, I took a neural image captioning model and measured the reactions to external modification in its spatial attention for three different interface methods: a fixation over the whole generation process, a fixation for the first time-steps and an addition to the generator's attention. The experimental results for bounding box based spatial attention vectors have shown that the captioning model reacts to method dependent changes in up to 52.65% and includes in 9.00% of the cases object categories, which were otherwise unmentioned. Afterwards, I established such a link to a hierarchical co-attention network for visual question answering by extraction of its word, phrase and question level spatial attentions. Here, generated captions for the word level included details of the question-answer pairs in up to 55.20% of the cases. This work indicates that spatial attention seen as an external interface for image caption generators is an useful method to access visual functions in natural language.