Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Leukocyte Antigen-A Allele Distribution in Nasopharyngeal Carcinoma Patients Showing Anti-Melanoma-Associated Antigen A or Synovial Sarcoma X-2 T Cell Response in Blood

Background: Development of innovative immunotherapy is imperative to improve the poor survival of the nasopharyngeal carcinoma (NPC) patients. In this study, we evaluated the T cell response to melanoma-associated antigen (MAGE)-A1, MAGE-A3, or synovial sarcoma X-2 (SSX-2) in the peripheral blood of treatment-naive NPC patients. The relationship of responses among the three proteins and the human leukocyte antigen (HLA)-A types were analyzed to provide evidence of designing novel therapy. Methods: Sixty-one NPC patients admitted into the Tumor Hospital affiliated to the Xinjiang Medical University between March 2015 and July 2016 were enrolled. Mononuclear cells were isolated from the peripheral blood before any treatment. HLA-A alleles were typed with Sanger sequence-based typing technique. The T cell response to the MAGE-A1, MAGE-A3, or SSX-2 was evaluated with the Enzyme-Linked ImmunoSpot assay. Mann-Whitney U-test was used to compare the T cell responses from different groups. Spearman's rank correlation was used to analyze the relationship of T cell responses. Results: HLA-A*02:01, A*02:07, and A*24:02 were the three most frequent alleles (18.9%, 12.3%, and 11.5%, respectively) among the 22 detected alleles. 31.1%, 19.7%, and 16.4% of the patients displayed MAGE-A1, MAGE-A3, or SSX-2-specific T cell response, respectively. The magnitudes of response to the three proteins were 32.5, 38.0, and 28.7 SFC/106 peripheral blood mononuclear cells, respectively. The T cell response against the three proteins correlated with each other to different extent. The percentage of A*02:01 and A*24:02 carriers were significantly higher in patients responding to any of the three proteins compared to the nonresponders. Conclusion: MAGE-A1, MAGE-A3, or SSX-2-specific T cell responses were detectable in a subgroup of NPC patients, the frequency and magnitude of which were correlated.
Current Evidence on the Effects of Intensive Early Reading Interventions

Many students at risk for or identified with reading disabilities need intensive reading interventions. This meta-analysis provides an update to the Wanzek and Vaughn synthesis on intensive early reading interventions. Effects from 25 reading intervention studies are analyzed to examine the overall effect of intensive early reading interventions as well as relationships between intervention and student characteristics related to outcomes. The weighted mean effect size (ES) estimate (ES = 0.39), with a mean effect size adjusted for publication bias (ES = 0.28), both significantly different from zero, suggested intensive early reading interventions resulted in positive outcomes for early struggling readers in kindergarten through third grades. There was no statistically significant or meaningful heterogeneity in the study-wise effect sizes. Exploratory examination of time in intervention, instructional group size, initial reading achievement, and date of publication are provided.
Simultaneous Clustering and Ensemble

Ensemble Clustering (EC) has gained a great deal of attention throughout the fields of data mining and machine learning, since it emerged as an effective and robust clustering framework. Typically, EC methods try to fuse multiple basic partitions (BPs) into a consensus one, of which each BP is obtained by performing traditional clustering method on the same dataset. One promising direction for ensemble clustering is to derive pairwise similarity from BPs, and then transform it as a graph partition problem. However, these graph based methods may suffer from an information loss when computing the similarity between data points, because they only utilize the categorical data provided by multiple BPs, yet neglect rich information from raw features. This problem can badly undermine the underlying cluster structure in the original feature space, and thus degrade the clustering performance. In light of this, we propose a novel Simultaneous Clustering and Ensemble (SCE) framework to alleviate such detrimental effect, which employs the similarity matrix from raw features to enhance the co-association matrix summarized by multiple BPs. Two neat closed-form solutions given by eigenvalue decomposition are provided for SCE. Experiments conducted on 16 real-world datasets demonstrate the effectiveness of the proposed SCE over the traditional clustering and state-of-the-art ensemble clustering methods. Moreover, several impact factors that may affect our method are also explored extensively.
CAD Tool Flow for Variation-Tolerant Non-Volatile STT-MRAM LUT based FPGA

Field Programmable Gate Array (FPGA) is a reconfigurable circuit and it is used for various applications such as image processing, digital signal processing and neural network. FPGA adopts a logic circuit called Look-Up Table (LUT) as a basic circuit structure. Commonly used FPGAs have volatile characteristic because it consists of SRAM based LUT that adopts SRAM as a memory cell. Volatile FPGAs have a disadvantage in terms of power management efficiency. Variation-Tolerant Non-Volatile STT-MRAM (VTNV) LUT has been studied for a non-volatile FPGAs and it has unique characteristics that can only operate in the half clock period. Accordingly, VTNV LUT based FPGA cannot operate normally with conventional FPGA CAD tool flow. We propose FPGA CAD (Computer Aided Design) tool flow for VTNV LUT based FPGA with supporting unique characteristic of VTNV LUT, and implement a non-volatile FPGA. Through proposed FPGA CAD tool flow, non-volatile FPGA based on VTNV LUT could operate normally. Because of high parameters of VTNV LUT, experimental results show that power increases by 29% and critical path delay increases by 16%, but it'll be improved sufficiently by future VTNV LUT researches.
Portfolio Selection via Subset Resampling

As the cornerstone of the modern portfolio theory, Markowitz's mean-variance optimization is a major model adopted in portfolio management. However, the estimation errors in its input parameters substantially deteriorate its performance in practice. Specifically, loss could be huge when the number of assets for investment is not much smaller than the sample size of historical data. To hasten the applicability of Markowitz's portfolio optimization to large portfolios, in this paper, we propose a new portfolio strategy via subset resampling. Through resampling subsets of the original large universe of assets, we construct the associated subset portfolios with more accurately estimated parameters without requiring additional data. By aggregating a number of constructed subset portfolios, we attain a well-diversified portfolio of all assets. To investigate its performance, we first analyze its corresponding efficient frontiers by simulation, provide analysis on the hyperparameter selection, and then empirically compare its out-of-sample performance with those of various competing strategies on diversified datasets. Experimental results corroborate that the proposed portfolio strategy has marked superiority in extensive evaluation criteria.
Cognitive Performance and Locomotor Adaptation in Persons With Anterior Cruciate Ligament Reconstruction

Background. Persons with anterior cruciate ligament reconstruction (ACLR) show deficits in gait and neuromuscular control following rehabilitation. This altered behavior extends to locomotor adaptation and learning, however the contributing factors to this observed behavior have yet to be investigated. Objective. The purpose of this study was to assess differences in locomotor adaptation and learning between ACLR and controls, and identify underlying contributors to motor adaptation in these individuals. Methods. Twenty ACLR individuals and 20 healthy controls (CON) agreed to participate in this study. Participants performed four cognitive and dexterity tasks (local version of Trail Making Test, reaction time test, electronic pursuit rotor test, and the Purdue pegboard). Three-dimensional kinematics were also collected while participants walked on a split-belt treadmill. Results. ACLR individuals completed the local versions of Trails A and Trails B significantly faster than CON. During split-belt walking, ACLR individuals demonstrated smaller step length asymmetry during EARLY and LATE adaptation, smaller double support asymmetry during MID adaptation, and larger stance time asymmetry during DE-ADAPT compared with CON. Conclusions. ACLR individuals performed better during tasks that required visual attention and task switching and were less perturbed during split-belt walking compared to controls. Persons with ACLR may use different strategies than controls, cognitive or otherwise, to adapt locomotor patterns.
Communications in Mathematical Physics Resonance Tongues and Instability Pockets in the Quasi – Periodic Hill – Schrödinger Equation

This paper concerns Hill’s equation with a (parametric) forcing that is real analytic and quasi-periodic with frequency vectorω ∈ R and a ‘frequency’(or ‘energy’) parameter a and a small parameter b. The 1-dimensional Schrödinger equation with quasi-periodic potential occurs as a particular case. In the parameter plane R2 = {a, b}, for small values of b we show the following. The resonance “tongues” with rotation number 1 2 〈k, ω〉,k ∈ Z haveC∞-boundary curves. Our arguments are based on reducibility and certain properties of the Schrödinger operator with quasi-periodic potential. Analogous to the case of Hill’s equation with periodic forcing (i.e., d = 1), several further results are obtained with respect to the geometry of the tongues. One result regards transversality of the boundaries at b = 0. Another result concerns the generic occurrence of instability pockets in the tongues in a reversible near-Mathieu case, that may depend on several deformation parameters. These pockets describe the generic opening and closing behaviour of spectral gaps of the Schrödinger operator in dependence of the parameter b. This result uses a refined averaging technique. Also consequences are given for the behaviour of the Lyapunov exponent and rotation number in dependence of a for fixed b.
On Universally Good Flower Codes

For a Distributed Storage System (DSS), the \textit{Fractional Repetition} (FR) code is a class in which replicas of encoded data packets are stored on distributed chunk servers, where the encoding is done using the Maximum Distance Separable (MDS) code. The FR codes allow for exact uncoded repair with minimum repair bandwidth. In this paper, FR codes are constructed using finite binary sequences. The condition for universally good FR codes is calculated on such sequences. For some sequences, the universally good FR codes are explored.
A Lyapunov-based Approach to Safe Reinforcement Learning

In many real-world reinforcement learning (RL) problems, besides optimizing the main objective function, an agent must concurrently avoid violating a number of constraints. In particular, besides optimizing performance it is crucial to guarantee the safety of an agent during training as well as deployment (e.g. a robot should avoid taking actions - exploratory or not - which irrevocably harm its hardware). To incorporate safety in RL, we derive algorithms under the framework of constrained Markov decision problems (CMDPs), an extension of the standard Markov decision problems (MDPs) augmented with constraints on expected cumulative costs. Our approach hinges on a novel \emph{Lyapunov} method. We define and present a method for constructing Lyapunov functions, which provide an effective way to guarantee the global safety of a behavior policy during training via a set of local, linear constraints. Leveraging these theoretical underpinnings, we show how to use the Lyapunov approach to systematically transform dynamic programming (DP) and RL algorithms into their safe counterparts. To illustrate their effectiveness, we evaluate these algorithms in several CMDP planning and decision-making tasks on a safety benchmark domain. Our results show that our proposed method significantly outperforms existing baselines in balancing constraint satisfaction and performance.
Old and Young Actors Playing Novel Roles in the Drama of Multiple Myeloma Bone Marrow Microenvironment Dependent Drug Resistance

Multiple myeloma (MM) is the second most frequent hematologic cancer. In addition to the deleterious effects of neoplastic plasma cell growth and spreading during the disease evolution, this tumor is characterized by the serious pathological consequences due to the massive secretion of monoclonal immunoglobulins and by the derangement of bone physiology with progressive weakening of the skeleton. Despite significant progresses having been made in the last two decades in the therapeutic management of this plasma cell tumor, MM remains invariably lethal, due to its extremely complex genetic architecture and to the constant protection it receives from the tumor niche, which is represented by the bone marrow microenvironment. While it is predictable that the discovery of novel therapies against the first of these two pathobiological features will take a longer time, the identification of the cellular and molecular mechanisms underlying the pro-growth effects of the myeloma milieu is a task that could lead to the development of novel treatments in a shorter timeframe. In this regard, aside from known “old” determinants of the cross-talk between bone marrow and MM cells, “young” cellular and molecular factors are now emerging, taking the scene of this complex neoplastic setting. In this review we aimed at giving insights on the latest evidence of potentially-targetable modes that MM cells exploit to increase fitness and gain a survival advantage. The benefits coming from the derangements of stress-managing pathways, autophagy, transcriptional rewiring, and non-coding RNAs are examples of such methods that MM cells utilize to escape cell death, but that hopefully will offer novel targets for the ever-increasing anti-MM therapeutic armamentarium.
Social Relations and Technology: Continuity, Context, and Change

Abstract Social relations, although basic to human nature, health and well-being, have become increasingly complicated as a result of changing population demography and technology. In this essay, we provide a historical overview of social relations, especially as they affect older people. We briefly review the evolution of theory and measurement surrounding social relations as well as early empirical evidence. We consider how social relations have changed over time as well as continuity and change regarding basic characteristics of social relations. Of special interest is the emerging influence of technology on how people maintain contact, especially the changing ways people can use technology to increase, decrease, maintain, or avoid social relations. We consider both negative and positive aspects of these new technologies and their influence on health and well-being. Finally, we conclude that new and emerging technologies hold great promise for the future by overcoming traditional barriers to maintaining social contact, support exchange, and information acquisition. Nevertheless, we caution that these new technologies can have the dehumanizing effect of distance thus creating the potential for insensitivity and increased negativity. In sum, we are cautiously optimistic about the promise of technology to expand, but not replace, traditional forms of social contact.
Central Neural Correlates During Inhibitory Control in Lifelong Premature Ejaculation Patients

Lifelong premature ejaculation (LPE) is a common male sexual dysfunction. Lack of active control for rapid ejaculation brought great distress to sexual harmony and even fertility. Previous neurophysiology studies revealed an ejaculation-related control mechanism in the brain. However, it remains unclear whether this inhibitory network is altered in LPE patients. The present study investigated the central inhibitory network function of LPE patients by using stop signal task (SST)-related functional magnetic resonance imaging (fMRI) and resting-state functional connectivity (FC) analysis. The results showed no difference in task-related behavioral performance or neural activation during response inhibition between LPE patients and controls. However, LPE patients showed a significantly different correlation pattern between the stop signal reaction time (SSRT) and left inferior frontal gyrus (IFG) activation during successful inhibition, in which a typical negative correlation between SSRT and the activation was completely disappeared in patients. In addition, using the left IFG as a seed, patients showed weaker FC between the seed and two areas (left dentate nucleus (DN) and right frontal pole) compared with controls. These data suggest that LPE patients have an abnormal brain control network, which may contribute to the reduced central control of rapid ejaculation. This study provides new insights into the neural mechanism of LPE involving the central inhibitory network, which may offer an underlying intervention target for future treatment.
Ergonomic Evaluation of Castor Decortication by Women-Conventional Method versus Manual Decorticator

The manual hand operated multipurpose decorticator developed by ANGRAU was tested for castor decortications to study the physiological work load and muscular stresses. Results revealed significant reduction in physiological work load and muscular stresses when decorticator was used compared to hand beating the castor pods. A 33 percent reduction in time was also observed when decorticator was used over manual method which was significant.
Cytokines, Adhesion Molecules, and Matrix Metalloproteases as Predisposing, Diagnostic, and Prognostic Factors in Venous Thrombosis

The inflammatory response is a well-established part of, and a prerequisite for, venous thrombosis. To better understand the pathophysiology of venous thrombosis and to identify improved diagnostic biomarkers, further studies of the relationship between inflammation and coagulation are needed. We review previous studies concerning inflammatory biomarkers in venous thromboembolism, in particular cytokines, soluble adhesion molecules and matrix metalloproteases as predisposing, diagnostic and prognostic factors in venous thrombosis. Elevated cytokines and genetic alterations coding for cytokines are found in several patient cohorts which indicate that cytokines are involved as predisposing factors in venous thrombosis development. Increased levels of pro-inflammatory cytokines are detected both in animal models and in patients with acute venous thrombosis and clinical trials, although currently without evident diagnostic value. Adhesion molecules are crucial in the development of venous thrombosis, especially P-selectin seems important in initiating leukocyte accumulation and adhesion to endothelium for subsequent platelet accumulation. Several studies have demonstrated increased soluble P-selectin levels in patients with venous thrombosis, emphasizing its potential role as diagnostic marker and also as a therapeutic target. Matrix metalloproteases are essential effectors during venous thrombosis resolution and may impact vessel wall fibrosis, and together with their natural occurring inhibitors are crucial in acute and chronic thrombosis pathophysiology. Furthermore, studies in animal models of venous thrombosis have demonstrated anti-inflammatory treatment to be effective in terms of thrombus resolution and reduction of vessel wall damage, without increase in bleeding risk during the course of treatment. Thus, soluble mediators should be further investigated both as possible biomarkers and therapeutic targets in venous thromboembolic disease.
Microfluidic Cultivation and Laser Tweezers Raman Spectroscopy of E. coli under Antibiotic Stress

Analyzing the cells in various body fluids can greatly deepen the understanding of the mechanisms governing the cellular physiology. Due to the variability of physiological and metabolic states, it is important to be able to perform such studies on individual cells. Therefore, we developed an optofluidic system in which we precisely manipulated and monitored individual cells of Escherichia coli. We tested optical micromanipulation in a microfluidic chamber chip by transferring individual bacteria into the chambers. We then subjected the cells in the chambers to antibiotic cefotaxime and we observed the changes by using time-lapse microscopy. Separately, we used laser tweezers Raman spectroscopy (LTRS) in a different micro-chamber chip to manipulate and analyze individual cefotaxime-treated E. coli cells. Additionally, we performed conventional Raman micro-spectroscopic measurements of E. coli cells in a micro-chamber. We found observable changes in the cellular morphology (cell elongation) and in Raman spectra, which were consistent with other recently published observations. The principal component analysis (PCA) of Raman data distinguished between the cefotaxime treated cells and control. We tested the capabilities of the optofluidic system and found it to be a reliable and versatile solution for this class of microbiological experiments.
A theoretical prediction of friction drag reduction in turbulent flow by superhydrophobic surfaces

We present a theoretical prediction for the drag reduction rate achieved by superhydrophobic surfaces in a turbulent channel flow. The predicted drag reduction rate is in good agreement with results obtained from direct numerical simulations at Reτ≃180 and 400. The present theory suggests that large drag reduction is possible also at Reynolds numbers of practical interest (Reτ∼105–106) by employing a hydrophobic surface, which induces a slip length on the order of ten wall units or more.
Efficient and Robust Question Answering from Minimal Context over Documents

Neural models for question answering (QA) over documents have achieved significant performance improvements. Although effective, these models do not scale to large corpora due to their complex modeling of interactions between the document and the question. Moreover, recent work has shown that such models are sensitive to adversarial inputs. In this paper, we study the minimal context required to answer the question, and find that most questions in existing datasets can be answered with a small set of sentences. Inspired by this observation, we propose a simple sentence selector to select the minimal set of sentences to feed into the QA model. Our overall system achieves significant reductions in training (up to 15 times) and inference times (up to 13 times), with accuracy comparable to or better than the state-of-the-art on SQuAD, NewsQA, TriviaQA and SQuAD-Open. Furthermore, our experimental results and analyses show that our approach is more robust to adversarial inputs.
Analyzing Earnings Management for Cross-listed Firms and Interaction between Two Futures Exchanges

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