Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive analysis and establishment of a prognostic model based on non-genetic predictors in multiple myeloma

BACKGROUND: Multiple myeloma (MM) is a systemic hematological malignancy usually incurable. The value of some important prognostic factors may gradually decrease. OBJECTIVE: We aimed to explore the non-genetic indexes, prognostic models, and significance of clinical staging systems of MM. METHODS: A retrospective analysis was conducted on clinical data from 110 patients with MM who first visit the First Affiliated Hospital of Guangzhou Medical University between September 2005 to December 2018. RESULTS: Bone marrow plasma cell percentage (BMPC%), cystatin C (CysC), and β2 microglobulin (β2-MG) were positively correlated with Durie-Salmon (D-S) and international staging system (ISS) stages, while red blood cell count (RBC) and hemoglobin volume (HGB) were negatively correlated (P< 0.05). Univariate analysis showed that ISS stage, treatment protocol, immunofixation electrophoresis (IFE), ratio of red cell distribution width to platelet count (RPR), monocyte count (MONO), lactate dehydrogenase, and immunoglobulin G were significantly associated with the three-year overall survival (OS). IFE, treatment protocol, and β2-MG significantly affected progression-free survival (P< 0.05). Multivariate analysis showed that the treatment protocol, ISS stage, RPR, MONO, and IFE were independent prognostic factors for three-year OS (P< 0.05). CONCLUSIONS: BMPC%, CysC, and β2-MG were positively correlated with both clinical staging systems and RBC and HGB were negatively correlated. RPR and MONO affect MM prognosis and the established prognostic model can guide patient prognosis.
Semi‐Global Robust Output Regulation for A Class of Singular Nonlinear Systems with Unknown Algebraic Equations

This paper considers semi‐global robust output regulation problem for a class of singular nonlinear systems whose algebraic equations are not precisely known. Since the algebraic equations are not known, the output regulation problem of singular nonlinear systems cannot be solved by directly reducing the singular nonlinear system into a normal nonlinear system. Based on internal model principle, we convert the robust output regulation problem of singular nonlinear systems into a robust stabilization problem of an augmented singular nonlinear system. The augmented singular nonlinear system is also with unknown algebraic equations. However, without transforming the singular nonlinear system into a normal nonlinear system, it is shown that the augmented singular nonlinear system can be semi‐globally stabilized by a high gain output feedback control law under some reasonable assumptions. Moreover, the semi‐global stabilization control law of the augmented singular nonlinear systems also solves the semi‐global robust output regulation problem of the original singular nonlinear system.
Beta‐cell function in type 2 diabetes (T2DM): Can it be preserved or enhanced?

Type 2 diabetes (T2DM) is a complex metabolic disorder manifested by hyperglycemia, insulin resistance, and deteriorating beta‐cell function. A way to prevent progression of the disease might be to enhance beta‐cell function and insulin secretion. However, most previous studies examined beta‐cell function while patients were using glycemia‐lowering agents without an adequate period off medications (washout). In the present review we focus on studies with a washout period. We performed a literature search (2010 to June 2021) using beta‐cell function and enhancement. The evidence shows that beta‐cell function can be enhanced. Bariatric surgery and very low calorie diets show improvement in beta‐cell function in many individuals. In addition, use of glucagon‐like peptide‐1 receptor agonists for prolonged periods (3 years or more) can also lead to improvement of beta‐cell function. Further research is needed to understand the mechanisms leading to improved beta‐cell function and identify agents that could enhance beta‐cell function in patients with T2DM.
Long-term bio-functional performance of a novel, self-positioning balloon expandable transcatheter biological aortic valve system in the ovine aortic banding model

Background The aim of the study was to evaluate bio-functionality of a novel, proprietary balloon-expandable biological transcatheter aortic valve implantation (TAVI) system (InFlow, CardValve Consortium, Poland) in an ovine model of aortic banding. Methods Surgical ascending aorta banding was created in 21 sheep. Two weeks later, 18 biological valves were implanted within the model using 15–16 F InFlow TAVI systems and carotid cut-down approach. Follow-up transthoracic echocardiography was performed at 30, 90, and 180-day. At designated time, animals were euthanized and valves harvested for analysis. Results All sheep survived the banding procedure. There were 4 (22%) procedure related deaths within a 7-day period. During the observation an additional 2 sheep died. In one, the valve dislocated after the procedure — the animal was excluded. Two animals completed 30-day follow up, five 90-day follow-up and four terminal follow-up of 180 days. Valves examined via transesophageal echocardiography showed proper hemodynamic parameters without evidence of structural valve deterioration. The maximum and average flow gradients at 180 days were 31.4 (23.3–37.7) and 17.5 (13.1–20.2) mmHg, respectively. There was one case of moderate insufficiency and no case of perivalvular leaks. By histopathology, there were no inflammation, thrombosis, nor calcifications in any tested valves at long-term follow-up. Neointimal coverage of stent struts increased with time from basal part in “early” groups to nearly 3/4 of stent length in the 180-day group. The pannus tissue showed maturation that increased with time with no stenotic “collar” visible in orthotopically implanted valves. Conclusions The study showed good hemodynamic performance, durability and biocompatibility of the novel biological transcatheter heart valve.
Products of Doubly Stochastic Matrices

Doubly stochastic matrices constitute an important class of stochastic matrices, playing a critical role in the study of discrete-time distributed averaging and distributed optimization algorithms. In this extended abstract, we report on several properties of such matrices. Furthermore, we utilize these properties to establish necessary and sufficient conditions for deciding whether a set of doubly stochastic matrices is a consensus set or not.
Putting into action the REGCM4.6 regional climate model for the study of climate change, variability and modeling over Central America and Mexico

What: International experts and attendees from several countries of Central America, Mexico, the Caribbean (CAM), and South America (SA) met to discuss regional issues on climate variability and climate change to learn the use of the non-hydrostatic version of the International Center for Theoretical Physics (ICTP) RegCM4.6 model, and to establish a regional modeling scientific community for understanding the physics of climate processes and the generation of regional climate change scenarios. When: 14-18 November 2016. Where: Center for Geophysical Research (CIGEFI in Spanish) and School of Physics, University of Costa Rica (UCR), San Jose, Costa Rica.
Age-related decline in bone mineral transport and bone matrix proteins in osteoblasts from stromal stem cells

We studied osteoblast bone mineral transport and matrix proteins as a function of age. In isolated bone marrow cells from long bones of young (3-4 month) and old (18-19 month) mice, age correlated with reduced mRNA of mineral transport proteins: alkaline phosphatase (ALP), ankylosis (ANK), the Cl-/H+ exchanger ClC3, and matrix proteins collagen 1 (Col1) and osteocalcin (BGLAP). Some proteins including the neutral phosphate transporter2 (NPT2) were not reduced. These are predominately osteoblast proteins, but in mixed cell populations. Remarkably, in osteoblasts differentiated from preparations of stromal stem cells (SSC) made from bone marrow cells in young and old mice, differentiated in vitro on perforated polyethylene terephthalate membranes, mRNA confirmed decreased expression with age of most transport-related and bone matrix proteins. Additional mRNAs in osteoblasts in vitro included ENPP1, unchanged, and ENPP2, reduced with age. Decrease with age in ALP activity and protein by Western Blot were also significant. Transport protein findings correlated with micro-computed tomography of lumbar vertebra showing that trabecular bone of old mice is osteopenic relative to young mice, consistent with other studies. Pathway analysis of osteoblasts differentiated in vitro showed that cells from old animals had reduced Erk1/2 phosphorylation and decreased Smad 2 mRNA, consistent with TGF-β pathway, and reduced β-catenin mRNA consistent with WNT pathway regulation. Our results show that decline in bone density with age reflects, selective changes resulting effectively in a phenotype modification. Reduction of matrix and mineral transport protein expression with age is regulated by multiple signaling pathways.
Stacking-Controlled Growth of rBN Crystalline Films with High Nonlinear Optical Conversion Efficiency up to 1.

Nonlinear optical crystals lie at the core of ultrafast laser science and quantum communication technology. The emergence of two-dimensional (2D) materials provides a revolutionary potential for nonlinear optical crystals due to their exceptionally high nonlinear coefficients. However, uncontrolled stacking orders generally induce the destructive nonlinear response due to the optical phase deviation in different 2D layers. Therefore, conversion efficiency of 2D nonlinear crystals is typically limited to less than 0.01% (far below the practical criterion of >1%). Here, we controllably synthesize crystalline films of rhombohedral boron nitride (rBN) with parallel stacked layers. This success is realized by the utilization of vicinal FeNi (111) single crystal, where both the unidirectional arrangement of BN grains into a single-crystal monolayer and the continuous precipitation of (B, N) source for thick layers are guaranteed. The preserved in-plane inversion asymmetry in rBN films keeps the in-phase second harmonic generation field in every layer and leads to a record-high conversion efficiency of 1% in the whole family of 2D materials within the coherence thickness of only 1.6 μm. Our work provides a route for designing ultrathin nonlinear optical crystals from 2D materials, and will promote the on-demand fabrication of integrated photonic and compact quantum optical devices. This article is protected by copyright. All rights reserved.
The relationship between pes planus and venous insufficiency

Introduction The presence of pes planus may be responsible for the disorder of venous return during ambulation. Our aim in this study is to reveal that the risk of venous insufficiency may be increased in patients with pes planus. Methods A total of 59 patients with 29 bilateral pes planus and 30 normal arches included in the study. Djian Annonier angle measurement, bilateral lower extremity venous Doppler ultrasonography (USG), foot function index (AFI), Comprehensive Classification System for Chronic Venous Disorders (CEAP) clinical scoring and Short Form-36 (SF-36) was evaluated. Results There was a statistically significant difference in AFI total, AFI pain, AFI disability and AFI activity limitation scores, bilateral lower extremity doppler USG and CEAP clinical scores in the patient group compared to the control group. Conclusion These findings suggest that there may be a relationship between pes planus and venous insufficiency. However, large-scale studies with more patient involvement are needed.
CRISPR/Cas9 screen for genome‐wide interrogation of essential MYC‐bound E‐boxes in cancer cells

The transcription factor MYC is a proto‐oncogene with a well‐documented essential role in the pathogenesis and maintenance of several types of cancer. MYC binds to specific E‐box sequences in the genome to regulate gene expression in a cell‐type‐ and developmental‐stage‐specific manner. To date, a combined analysis of essential MYC‐bound E‐boxes and their downstream target genes important for growth of different types of cancer is missing. In this study, we designed a CRISPR/Cas9 library to destroy E‐box sequences in a genome‐wide fashion. In parallel, we used the Brunello library to knock out protein‐coding genes. We performed high‐throughput screens with these libraries in four MYC‐dependent cancer cell lines—K562, ST486, HepG2, and MCF7—which revealed several essential E‐boxes and genes. Among them, we pinpointed crucial common and cell‐type‐specific MYC‐regulated genes involved in pathways associated with cancer development. Extensive validation of our approach confirmed that E‐box disruption affects MYC binding, target‐gene expression, and cell proliferation in vitro as well as tumor growth in vivo. Our unique, well‐validated tool opens new possibilities to gain novel insights into MYC‐dependent vulnerabilities in cancer cells.
Efficient Algorithms for Monroe and CC Rules in Multi-Winner Elections with (Nearly) Structured Preferences

We investigate winner determination for two popular proportional representation systems: the Monroe and Chamberlin-Courant (abbrv. CC) systems. Our study focuses on (nearly) single-peaked resp. single-crossing preferences. We show that for single-crossing approval preferences, winner determination of the Monroe rule is polynomial, and for both rules, winner determination mostly admits FPT algorithms with respect to the number of voters to delete to obtain single-peaked or single-crossing preferences. Our results answer some complexity questions from the literature [18, 28, 21].
Measuring the Temperature of Starspots from Multi-filter Photometry

Using simultaneous multi-filter observations during the transit of an exoplanet around a K dwarf star, we determine the temperature of a starspot through modeling the radius and position with wavelength-dependent spot contrasts. We model the spot using the starspot modeling program STarSPot (STSP), which uses the transiting companion as a knife-edge probe of the stellar surface. The contrast of the spot, i.e., the ratio of the integrated flux of a darker spot region to the star's photosphere, is calculated for a range of filters and spot temperatures. We demonstrate this technique using simulated data of HAT-P-11, a K dwarf (T eff = 4780 K) with well-modeled starspot properties for which we obtained simultaneous multi-filter transits using Las Cumbres Observatory's MuSCAT3 instrument on the 2m telescope at Haleakala Observatory, which allows for simultaneous, multi-filter, diffuser-assisted high-precision photometry. We determine the average (i.e., a combination of penumbra and umbra) spot temperature for HAT-P-11's spot complexes is 4500 K ± 100 K using this technique. We also find for our set of filters that comparing the SDSS g′ and i′ filters maximizes the signal difference caused by a large spot in the transit. Thus, this technique allows for the determination of the average spot temperature using only one spot occultation in transit and can provide simultaneous information on the spot temperature and spot properties.
Deep Reinforcement Learning of Dexterous Pre-Grasp Manipulation for Human-Like Functional Categorical Grasping

Many objects such as tools and household items can be used only if grasped in a very specific way-grasped functionally. Often, a direct functional grasp is not possible, though. We propose a method for learning a dexterous pre-grasp manipulation policy to achieve human-like functional grasps using deep reinforcement learning. We introduce a dense multi-component reward function that enables learning a single policy, capable of dexterous pre-grasp manipulation of novel instances of several known object categories with an anthropomorphic hand. The policy is learned purely by means of reinforcement learning from scratch, without any expert demonstrations, and implicitly learns to reposition and reorient objects of complex shapes to achieve given functional grasps. Learning is done on a single GPU in less than three hours.
An image authentication technology based on depth residual network

ABSTRACT The traditional image authentication technique generally determines the image attribution by extracting specific features and combining the similarity calculation algorithm. Because of the selected features dimensions, characterization and other factors, the accuracy and speed of image authentication have been restricted. In this paper, Recog-Net, an end-to-end image authentication model based on convolution neural network has been proposed. Deep residual network is chosen as the features extractor. Mahalanobis distance and threshold method are used to complete the image authentication. Experiments show that the performance of the extractor's features, compared with the traditional features and the features of other convolution neural network architectures, is more excellent, with a high degree of generality, recognition rate and robustness, still having these advantages even after a substantial compression. The Recog-Net for image authentication is able to accurately authenticate the images tampered with certain range.
Causal Inference for Banking Finance and Insurance A Survey

Causal Inference plays an significant role in explaining the decisions taken by statistical models and artificial intelligence models. Of late, this field started attracting the attention of researchers and practitioners alike. This paper presents a comprehensive survey of 37 papers published during 1992-2023 and concerning the application of causal inference to banking, finance, and insurance. The papers are categorized according to the following families of domains: (i) Banking, (ii) Finance and its subdomains such as corporate finance, governance finance including financial risk and financial policy, financial economics, and Behavioral finance, and (iii) Insurance. Further, the paper covers the primary ingredients of causal inference namely, statistical methods such as Bayesian Causal Network, Granger Causality and jargon used thereof such as counterfactuals. The review also recommends some important directions for future research. In conclusion, we observed that the application of causal inference in the banking and insurance sectors is still in its infancy, and thus more research is possible to turn it into a viable method.
Asymptotic behavior of the first Dirichlet eigenvalue of AHE manifolds

In this article, we investigate the rate at which the first Dirichlet eigenvalue of geodesic balls decreases as the radius approaches infinity. We prove that if the conformal infinity of an asymptotically hyperbolic Einstein manifold is of nonnegative Yamabe type, then the two-term asymptotic of the eigenvalues is the same as that in hyperbolic space.
Mapping tissue microstructure of brain white matter in vivo in health and disease using diffusion MRI

Abstract Diffusion magnetic resonance imaging offers unique in vivo sensitivity to tissue microstructure in brain white matter, which undergoes significant changes during development and is compromised in virtually every neurological disorder. Yet, the challenge is to develop biomarkers that are specific to micrometer-scale cellular features in a human MRI scan of a few minutes. Here, we quantify the sensitivity and specificity of a multicompartment diffusion modeling framework to the density, orientation, and integrity of axons. We demonstrate that using a machine learning-based estimator, our biophysical model captures the morphological changes of axons in early development, acute ischemia, and multiple sclerosis (total N = 821). The methodology of microstructure mapping is widely applicable in clinical settings and in large imaging consortium data to study development, aging, and pathology.
Multi Agent Navigation in Unconstrained Environments using a Centralized Attention based Graphical Neural Network Controller

In this work, we propose a learning based neural model that provides both the longitudinal and lateral control commands to simultaneously navigate multiple vehicles. The goal is to ensure that each vehicle reaches a desired target state without colliding with any other vehicle or obstacle in an unconstrained environment. The model utilizes an attention based Graphical Neural Network paradigm that takes into consideration the state of all the surrounding vehicles to make an informed decision. This allows each vehicle to smoothly reach its destination while also evading collision with the other agents. The data and corresponding labels for training such a network is obtained using an optimization based procedure. Experimental results demonstrate that our model is powerful enough to generalize even to situations with more vehicles than in the training data. Our method also outperforms comparable graphical neural network architectures. Project page which includes the code and supplementary information can be found here: https://yininghase.github. io/multi-agent-control/
Fluctuations and moderate deviations for the mean fields of Hawkes processes

The Hawkes process is a counting process that has self- and mutually-exciting features with many applications in various fields. In recent years, there have been many interests in the mean-field results of the Hawkes process and its extensions. It is known that the mean-field limit of a multivariate nonlinear Hawkes process is a time-inhomogeneous Poisson process. In this paper, we study the fluctuations for the mean fields and the large deviations associated with the fluctuations, i.e., the moderate deviations.
Opportunistic Air Quality Monitoring and Forecasting with Expandable Graph Neural Networks

Air Quality Monitoring and Forecasting has been a popular research topic in recent years. Recently, data-driven approaches for air quality forecasting have garnered significant attention, owing to the availability of well-established data collection facilities in urban areas. Fixed infrastructures, typically deployed by national institutes or tech giants, often fall short in meeting the requirements of diverse personalized scenarios, e.g., forecasting in areas without any existing infrastructure. Consequently, smaller institutes or companies with limited budgets are compelled to seek tailored solutions by introducing more flexible infrastructures for data collection. In this paper, we propose an expandable graph attention network (EGAT) model, which digests data collected from existing and newly-added infrastructures, with different spatial structures. Additionally, our proposal can be embedded into any air quality forecasting models, to apply to the scenarios with evolving spatial structures. The proposal is validated over real air quality data from PurpleAir.
Analyzing Cryptocurrency trends using Tweet Sentiment Data and User Meta-Data

Cryptocurrency is a form of digital currency using cryptographic techniques in a decentralized system for secure peer-to-peer transactions. It is gaining much popularity over traditional methods of payments because it facilitates a very fast, easy and secure way of transactions. However, it is very volatile and is influenced by a range of factors, with social media being a major one. Thus, with over four billion active users of social media, we need to understand its influence on the crypto market and how it can lead to fluctuations in the values of these cryptocurrencies. In our work, we analyze the influence of activities on Twitter, in particular the sentiments of the tweets posted regarding cryptocurrencies and how it influences their prices. In addition, we also collect metadata related to tweets and users. We use all these features to also predict the price of cryptocurrency for which we use some regression-based models and an LSTM-based model.