Miracle-dz/UniFAR-FTUs huggingface dataset:
Elevated Expression of Notch 2 & Notch 3 is associated with Disease Progression in Colorectal Cancer

Background: The potential involvement of Notch signaling pathway in cell fate decision, tumor heterogeneity and angiogenesis in solid tumors can be explored in colorectal cancer (CRC). This might further help to improve outcomes in CRC. Here, the promoter methylation of Notch receptor gene ( Notch2 and Notch3 ) and their co-expression with its downstream transcription factor Hes1 has been analyzed. Methods: Seventy-two CRC patients were enrolled to study the role of Notch2 , Notch3 and Hes1 genes in colorectal cancer. Promoter methylation and mRNA expression in tumor and adjoining normal tissue were assessed by Methylation Specific PCR and quantitative Real time PCR respectively. Statistical correlation was done by using SPSS. Results: We found that Notch2 and Notch3 were hypomethylated in 52/72 (72.22%) and 54/72 (75%) cases respectively. Hypomethylation of Notch 2 and Notch 3 showed significant association with advanced stage ( p =0.001) and (p=0.003) and nodal metastasis ( p =0.036) and ( p =0.012) respectively. Both Notch 2 and Notch 3 showed increased mRNA expression in 49 (68.05%) and 51(70.84%) patients with a fold change of 3.37 and 5.43 respectively. Positive correlation between hypomethylation and expression was observed for both genes. High expression of Hes1 was found in 53(73.61%) of cases which was highly relatable with over expression of notch receptor genes. Upregulation of Notch 2, Notch 3 and Hes1 showed significant association with high grade tumors, advance stage and presence of LN metastasis, additionally Notch 3 and Hes1 showed significant association with distant metastasis. Conclusion: Hypomethylation of Notch 2 and 3 receptors is playing crucial in regulating the expression of these genes in CRC. Overexpression of Notch 2, Notch 3 and Hes1 are associated with disease progression in CRC.
Small-World Networks: Evidence for a Crossover Picture

Watts and Strogatz [Nature (London) 393, 440 (1998)] have recently introduced a model for disordered networks and reported that, even for very small values of the disorder $p$ in the links, the network behaves as a ``small world. '' Here, we test the hypothesis that the appearance of small-world behavior is not a phase transition but a crossover phenomenon which depends both on the network size $n$ and on the degree of disorder $p$. We propose that the average distance $\ensuremath{\ell}$ between any two vertices of the network is a scaling function of $n/{n}^{*}$. The crossover size ${n}^{*}$ above which the network behaves as a small world is shown to scale as ${n}^{*}(p\ensuremath{\ll}1)\ensuremath{\sim}{p}^{\ensuremath{-}\ensuremath{\tau}}$ with $\ensuremath{\tau}\ensuremath{\approx}2/3$.
Upstream influence in a two-fluid system

Benjamin (1970) has calculated the upstream influence in open-channel flow, and has argued that a similar effect occurs when a body is moved along the axis of a tube of uniformly rotating fluid. In the present paper Benjamin's work is extended to the case of interfacial waves in a two-fluid system. It is shown that there are certain special flows for which the upstream influence vanishes.
Exploration of Brain-Computer Interaction for Supporting Children’s Attention Training: A Multimodal Design Based on Attention Network and Gamification Design

Recent developments in brain–computer interface (BCI) technology have shown great potential in terms of estimating users’ mental state and supporting children’s attention training. However, existing training tasks are relatively simple and lack a reliable task-generation process. Moreover, the training experience has not been deeply studied, and the empirical validation of the training effect is still insufficient. This study thusly proposed a BCI training system for children’s attention improvement. In particular, to achieve a systematic training process, the attention network was referred to generate the training games for alerting, orienting and executive attentions, and to improve the training experience and adherence, the gamification design theory was introduced to derive attractive training tasks. A preliminary experiment was conducted to set and modify the training parameters. Subsequently, a series of contrasting user experiments were organized to examine the impact of BCI training. To test the training effect of the proposed system, a hypothesis-testing approach was adopted. The results revealed that the proposed BCI gamification attention training system can significantly improve the participants’ attention behaviors and concentration ability. Moreover, an immersive, inspiring and smooth training process can be created, and a pleasant user experience can be achieved. Generally, this work is promising in terms of providing a valuable reference for related practices, especially for how to generate BCI attention training tasks using attention networks and how to improve training adherence by integrating multimodal gamification elements.
Multi-Messenger Astrophysics: Harnessing the Data Revolution

The past year has witnessed discovery of the first identified counterparts to a gravitational wave transient (GW 170817A) and a very high-energy neutrino (IceCube-170922A). These source identifications, and ensuing detailed studies, have realized longstanding dreams of astronomers and physicists to routinely carry out observations of cosmic sources by other than electromagnetic means, and inaugurated the era of "multi-messenger" astronomy. While this new era promises extraordinary physical insights into the universe, it brings with it new challenges, including: highly heterogeneous, high-volume, high-velocity datasets; globe-spanning cross-disciplinary teams of researchers, regularly brought together into transient collaborations; an extraordinary breadth and depth of domain-specific knowledge and computing resources required to anticipate, model, and interpret observations; and the routine need for adaptive, distributed, rapid-response observing campaigns to fully exploit the scientific potential of each source. We argue, therefore, that the time is ripe for the community to conceive and propose an Institute for Multi-Messenger Astrophysics that would coordinate its resources in a sustained and strategic fashion to efficiently address these challenges, while simultaneously serving as a center for education and key supporting activities. In this fashion, we can prepare now to realize the bright future that we see, beyond, through these newly opened windows onto the universe.
Comments on “Calibrated Surface Temperature Forecasts from the Canadian Ensemble Prediction System Using Bayesian Model Averaging”

Wilson et al. (2007, hereafter W07) recently described the application of the Bayesian model averaging (BMA; Raftery et al. 2005, hereafter R05) calibration technique to surface temperature forecasts using the Canadian ensemble prediction system. The BMA technique as applied in W07 produced an adjusted probabilistic forecast from an ensemble through a twostep procedure. The first step was the correction of biases of individual members through regression analyses. The second step was the fitting of a Gaussian kernel around each bias-corrected member of the ensemble. The amount of weight applied to each member’s kernel and the width of the kernel(s) were set through an expectation maximization (EM) algorithm (Dempster et al. 1977). The final probability density function (pdf) was a sum of the weighted kernels. W07 reported (their Fig. 2) that at any given instant, a majority of the ensemble members were typically assigned zero weight, while a few select members received the majority of the weight. Which members received large weights varied from one day to the next. These results were counterintuitive. Why effectively discard the information from so many ensemble members? Why should one member have positive weight one day and none the next? This comment to W07 will show that BMA where the EM is permitted to adjust the weights individually for each member is not an appropriate application of the technique when the sample size is small; specifically, the radically unequal weights of W07 exemplify an “overfitting” (Wilks 2006a, p. 207) to the training data. A symptom of overfitting is an improved fitted relationship to the training data but a worsened relationship with independent data. This may happen when the statistician attempts to fit a large number of parameters using a relatively small training sample. In W07, the EM algorithm was required to set the weights of 16 individual ensemble members and a kernel standard deviation with between 25 and 80 days of data. To illustrate the problem of overfitting in W07’s methodology, a reforecast dataset was used. This was composed of more than two decades of daily ensemble forecasts with perturbed initial conditions, all from a single forecast model. This large dataset permitted a comparison of BMA properties based on small and large training samples. This reforecast dataset used a T62, circa 1998 version of the National Centers for Environmental Prediction (NCEP) Global Forecast System. A 15-member forecast, consisting of a control and seven bred pairs (Toth and Kalnay 1997) was integrated to 15 days lead for every day from 1979 to current. For more details on this reforecast dataset, please see Hamill et al. (2006). The verification data were from the NCEP–National Center for Atmospheric Research reanalysis (Kalnay et al. 1996).
Quantitative measurement of light emission during the early stages of electrical breakdown in epoxy and unsaturated polyester resins

Quantitative light emission studies of the initiation and early growth stages of electrical treeing in synthetic resins have been undertaken to gain insight into the underlying physical mechanisms responsible for these processes. Mains-synchronous photon detection techniques coupled with an ultra-sensitive photomultiplier and large-area light collection optics were used to measure the low-level light emission from pin-plane CT200 epoxy and polyester specimens subjected to 50 Hz AC step ramp electrical stress. Three types of light emission are observed consistently and correspond to (i) electroluminescence related to charge injection (Fowler-Nordheim or Schottky depending on the local electric field), (ii) microdischarge activity with formation of microchannels and (iii) conventional partial discharge activity during tree growth. No material-dependent threshold voltage/field was found for electroluminescence and hence charge injection in these materials.
A model function method in regularized total least squares

In this article, we investigate the dual regularized total least squares (dual RTLS) from a computational aspect. More precisely, we propose a strategy for finding two regularization parameters in the resulting equation of dual RTLS. This strategy is based on an extension of the idea of model function originally proposed by Kunisch, Ito and Zou for a realization of the discrepancy principle in the standard one-parameter Tikhonov regularization. For dual RTLS we derive a model function of two variables and show its reliability using standard numerical tests.
Error assessment and correction for extrusion-based bioprinting using computer vision method

299Bioprinting offers a new approach to addressing the organ shortage crisis. Despite recent technological advances, insufficient printing resolution continues to be one of the reasons that impede the development of bioprinting. Normally, machine axes movement cannot be reliably used to predict material placement, and the printing path tends to deviate from the predetermined designed reference trajectory in varying degrees. Therefore, a computer vision-based method was proposed in this study to correct trajectory deviation and improve printing accuracy. The image algorithm calculated the deviation between the printed trajectory and the reference trajectory to generate an error vector. Furthermore, the axes trajectory was modified according to the normal vector approach in the second printing to compensate for the deviation error. The highest correction efficiency that could be achieved was 91%. More significantly, we discovered that the correction results, for the first time, were in a normal distribution instead of a random distribution.
A stereo camera system for autonomous maritime navigation (AMN) vehicles

Spatial Integrated System (SIS), Rockville, Maryland, in collaboration with NSWC Combatant Craft Division (NSWCCD), is applying 3D imaging technology, artificial intelligence, sensor fusion, behaviors-based control, and system integration to a prototype 40 foot, high performance Research and Development Unmanned Surface Vehicle (USV). This paper focus on the developments of the stereo camera system in the USV navigation that currently consists of two high-resolution cameras and will incorporate an array of cameras in the near future. The objectives of the camera system are to re-construct 3D objects and detect them in the sea water surface. The paper reviews two critical technological components, namely camera calibration and stereo matching. In stereo matching, a comprehensive study is presented to compare the algorithmic performances resulted from the various information sources (intensity, RGB values, Gaussian gradients and Gaussian Laplacians), patching schemas (single windows, and multiple windows with same/different centers), and correlation metrics (convolution, absolute difference, and histogram). To enhance system performance, a sub-pixel edge detection technique has been introduced to address the precision requirement and a noise removal post-processing step added to eliminate noisy points from the reconstructed 3D point clouds. Finally, experimental results are reported to demonstrate the performance of the stereo camera system.
Structural Characterization and In Vivo Anti-Inflammatory Activity of Fucoidan from Cystoseira crinita (Desf.) Borry

The aim of this study was to evaluate the effects of fucoidan isolated from C. crinita on histamine-induced paw inflammation in rats, and on the serum levels of TNF-α, IL-1β, IL-6, and IL-10 in rats during systemic inflammation response. The levels of TNF-α in a model of acute peritonitis in rats were also investigated. The isolated crude fucoidan was identified as a sulfated xylogalactofucan with high, medium, and low molecular weight fractions and a content of fucose of 39.74%, xylose of 20.75%, and galactose of 15.51%. Fucoidan from C. crinita showed better anti-inflammatory effects in the rat paw edema model, and this effect was present during all stages of the experiment. When compared to controls, a commercial fucoidan from F. vesiculosus, the results also displayed anti-inflammatory activity on the 60th, 90th, and 120th minute of the experiment. A significant decrease in serum levels of IL-1β in rats treated with both doses of C. crinita fucoidan was observed in comparison to controls, whereas TNF-α concentrations were reduced only in the group treated with fucoidan from C. crinita at the dose of 25 mg/kg bw. In the model of carrageenan-induced peritonitis, we observed a tendency of decrease in the levels of the pro-inflammatory cytokine TNF-α in peritoneal fluid after a single dose of C. crinita fucoidan, but this did not reach the statistical significance margin. Single doses of C. crinita fucoidan did not alter serum levels of the anti-inflammatory cytokine IL-10 in animals with lipopolysaccharide-induced systemic inflammation.
Quantum transport in Weyl semimetal thin films in the presence of spin-orbit coupled impurities

Topological semimetals have been at the forefront of experimental and theoretical attention in condensed matter physics. Among these, recently discovered Weyl semimetals have a dispersion described by a three-dimensional Dirac cone, which is at the root of exotic physics such as the chiral anomaly in magnetotransport. In a time reversal symmetric (TRS) Weyl semimetal film, the confinement gap gives the quasiparticles a mass, while TRS is preserved by having an even number of valleys with opposite masses. The film can be tuned through a topological phase transition by a gate electric field. In this work, we present a theoretical study of the quantum corrections to the conductivity of a topological semimetal thin film, which is governed by the complex interplay of the chiral band structure, mass term, and scalar and spin-orbit scattering. We study scalar and spin-orbit scattering mechanisms on the same footing, demonstrating that they have a strong qualitative and quantitative impact on the conductivity correction. We show that, due to the spin structure of the matrix Green's functions, terms linear in the extrinsic spin-orbit scattering are present in the Bloch and momentum relaxation times, whereas previous works had identified corrections starting from the second order. In the limit of small quasiparticle mass, the terms linear in the impurity spin-orbit coupling lead to a potentially observable density dependence in the weak antilocalization correction. Moreover, when the mass term is around 30 percent of the linear Dirac terms, the system is in the unitary symmetry class with zero quantum correction and switching the extrinsic spin-orbit scattering drives the system to the weak antilocalization. We discuss the crossover between the weak localization and weak antilocalization regimes in terms of the singlet and triplet Cooperon channels, tuning the spin-orbit scattering strength.
Time-domain training sequences design for MIMO OFDM channel estimation

This paper describes a Least Squares (LS) channel estimation scheme for MIMO OFDM systems based on time-domain training sequence. We first compute the minimum mean square error (MSE) of the LS channel estimation, and then derive the optimal criteria of the training sequence with respect to the minimum MSE. It is shown that optimal time-domain training sequence should satisfy two criteria. First, the autocorrelation of the sequence transmitted from the same antenna is an impulse function in a region longer than the channel maximum delay. Second, the cross-correlation between sequences transmitted from different antennas is zero in this region. Simulation results show that the estimator using optimal time-domain training sequences has better performance than that using optimal frequency training sequence at low signal-to-noise ratio (SNR). To reduce the training overhead, a suboptimal training sequence is also proposed. Comparing with optimal training sequence, it has low computation complexity and high transmission efficiency at the expense of little performance degradation.
“It’s Really Hard to Have Your Own Place”: The At-Home Experiences of Latina/o/x Undergraduates During COVID-19

The purpose of this study was to understand the at-home lived experiences of Latina/o/x college students during the COVID-19 pandemic. Using Critical Race Theory as the guiding framework, the authors used storytelling to capture the at-home experiences of 19 Latina/o/x college students during the pandemic. Findings revealed how the pandemic shifted meaning of the home space, increased roles and responsibilities, and increased stress and mental health demands for students.
Large-scale dimension densities for heart rate variability analysis.

In this work, we reanalyze the heart rate variability (HRV) data from the 2002 Computers in Cardiology (CiC) Challenge using the concept of large-scale dimension densities and additionally apply this technique to data of healthy persons and of patients with cardiac diseases. The large-scale dimension density (LASDID) is estimated from the time series using a normalized Grassberger-Procaccia algorithm, which leads to a suitable correction of systematic errors produced by boundary effects in the rather large scales of a system. This way, it is possible to analyze rather short, nonstationary, and unfiltered data, such as HRV. Moreover, this method allows us to analyze short parts of the data and to look for differences between day and night. The circadian changes in the dimension density enable us to distinguish almost completely between real data and computer-generated data from the CiC 2002 challenge using only one parameter. In the second part we analyzed the data of 15 patients with atrial fibrillation (AF), 15 patients with congestive heart failure (CHF), 15 elderly healthy subjects (EH), as well as 18 young and healthy persons (YH). With our method we are able to separate completely the AF $({\ensuremath{\rho}}_{\mathrm{ls}}^{\ensuremath{\mu}}=0.97\ifmmode\pm\else\textpm\fi{}0.02)$ group from the others and, especially during daytime, the CHF patients show significant differences from the young and elderly healthy volunteers (CHF, $0.65\ifmmode\pm\else\textpm\fi{}0.13$; EH, $0.54\ifmmode\pm\else\textpm\fi{}0.05$; YH, $0.57\ifmmode\pm\else\textpm\fi{}0.05$; $pl0.05$ for both comparisons). Moreover, for the CHF patients we find no circadian changes in ${\ensuremath{\rho}}_{\mathrm{ls}}^{\ensuremath{\mu}}$ (day, $0.65\ifmmode\pm\else\textpm\fi{}0.13$; night, $0.66\ifmmode\pm\else\textpm\fi{}0.12$; n.s.) in contrast to healthy controls (day, $0.54\ifmmode\pm\else\textpm\fi{}0.05$; night, $0.61\ifmmode\pm\else\textpm\fi{}0.05$; $p=0.002$). Correlation analysis showed no statistical significant relation between standard HRV and circadian LASDID, demonstrating a possibly independent application of our method for clinical risk stratification.
A Comparison of Genetic Algorithms and Simulated Annealing in Maximizing the Thermal Conductivity of Discrete Massive Chains

Functions of chemical composition are complex and discrete in nature making it impossible to optimize them with gradient methods. Genetic algorithms, which do not use derivative information, are used to maximize the thermal conductivity of one-dimensional classical harmonic oscillators made from a fixed library of randomly generated molecular units. The ability for the genetic algorithm to build structures with components having no physical increment is important in optimizing molecules with a library of unrelated polymer units. The performance of genetic algorithms in this problem is compared with simulated annealing. Hyper-parameters for these routines are selected from a grid search in order to optimize their expected solution strength. The solutions found via the genetic algorithm consistently outperform those of simulated annealing at the cost of longer computer time. Together, these algorithms are able to find thermal conductor candidates that mirror solutions in continuous media.
Fast Fluid Antenna Multiple Access Enabling Massive Connectivity

Massive connectivity over wireless channels relies on aggressive spectrum sharing techniques. Conventionally, this may be achieved by sophisticated signal processing and optimization of applying multiple antennas and/or complex multiuser decoding at each user terminal (UT). Different from previous methods, this letter proposes a radical approach for massive connectivity, which employs fluid antenna at each UT to exploit the interference null, created naturally by multipath propagation and the randomness of UT’s data, on a symbol-by-symbol basis for multiple access. The proposed fast fluid antenna multiple access ( $f$ -FAMA) system adopts a large, distributed antenna array at the base station (BS) to transmit each UT’s signal from each of the BS antennas and lets each UT overcome the interference on its own using its fluid antenna. Our main contribution is a technique that estimates the best port of fluid antenna for reception at every symbol instance. The proposed approach needs only cross-correlation calculations and single-user decoding at each UT and requires no precoding at the BS. Simulation results demonstrate that for a BS with 16 antennas supporting 16 co-channel users, a multiplexing gain of 14.87 is achieved even when the channel has a strong line-of-sight (LoS) and multipath is few. The multiplexing gain can also rise to 24.36 if a 30-antenna BS is serving 30 co-channel users.