Miracle-dz/UniFAR-FTUs huggingface dataset:
Targeting fat mass and obesity-associated protein mitigates human colorectal cancer growth in vitro and in a murine model

Introduction Colorectal cancer (CRC) remains a significant cause of cancer related mortality. Fat mass and obesity-associated protein (FTO) is a m6A mRNA demethylase that plays an oncogenic role in various malignancies. In this study we evaluated the role of FTO in CRC tumorigenesis. Methods Cell proliferation assays were conducted in 6 CRC cell lines with the FTO inhibitor CS1 (50-3200 nM) (± 5-FU 5-80 mM) and after lentivirus mediated FTO knockdown. Cell cycle and apoptosis assays were conducted in HCT116 cells (24 h and 48 h, 290 nM CS1). Western blot and m6A dot plot assays were performed to assess CS1 inhibition of cell cycle proteins and FTO demethylase activity. Migration and invasion assays of shFTO cells and CS1 treated cells were performed. An in vivo heterotopic model of HCT116 cells treated with CS1 or with FTO knockdown cells was performed. RNA-seq was performed on shFTO cells to assess which molecular and metabolic pathways were impacted. RT-PCR was conducted on select genes down-regulated by FTO knockdown. Results We found that the FTO inhibitor, CS1 suppressed CRC cell proliferation in 6 colorectal cancer cell lines and in the 5-Fluorouracil resistant cell line (HCT116-5FUR). CS1 induced cell cycle arrest in the G2/M phase by down regulation of CDC25C and promoted apoptosis of HCT116 cells. CS1 suppressed in vivo tumor growth in the HCT116 heterotopic model (p< 0.05). Lentivirus knockdown of FTO in HCT116 cells (shFTO) mitigated in vivo tumor proliferation and in vitro demethylase activity, cell growth, migration and invasion compared to shScr controls (p< 0.01). RNA-seq of shFTO cells compared to shScr demonstrated down-regulation of pathways related to oxidative phosphorylation, MYC and Akt/ mTOR signaling pathways. Discussion Further work exploring the targeted pathways will elucidate precise downstream mechanisms that can potentially translate these findings to clinical trials.
Large Neighborhood Search beyond MIP

Large neighborhood search (LNS) heuristics are an important component of modern branch-and-cut algorithms for solving mixed-integer linear programs (MIPs). Most of these LNS heuristics use the LP relaxation as the basis for their search, which is a reasonable choice in case of MIPs. However, for more general problem classes, the LP relaxation alone may not contain enough information about the original problem to find feasible solutions with these heuristics, e.g., if the problem is nonlinear or not all constraints are present in the current relaxation. In this paper, we discuss a generic way to extend LNS heuristics that have been developed for MIP to constraint integer programming (CIP), which is a generalization of MIP in the direction of constraint programming (CP). We present computational results of LNS heuristics for three problem classes: mixed-integer quadratically constrained programs, nonlinear pseudo-Boolean optimization instances, and resource-constrained project scheduling problems. Therefore, we have implemented extended versions of the following LNS heuristics in the constraint integer programming framework SCIP: Local Branching, RINS, RENS, Crossover, and DINS. Our results indicate that a generic generalization of LNS heuristics to CIP considerably improves the success rate of these heuristics.
Ingestion of Soybean Sprouts Containing a HASPIN Inhibitor Improves Condition in a Mouse Model of Alzheimer’s Disease

Simple Summary HASPIN plays important roles in chromosome segregation during cell division by phosphorylating histone H3 at threonine. We showed that HASPIN was expressed in the hippocampus and phosphorylated the tau protein, acting as a phosphorylation substrate. Oral administration of soybean sprouts grown for increased coumestrol (a HASPIN inhibitor) decreased the phosphorylated tau protein level in the hippocampus and suppressed short-term memory loss in the Alzheimer’s disease model mice (5xFAD). Functional analysis of HASPIN may help treatment of Alzheimer’s disease. Abstract The MATP/tau protein is hyperphosphorylated in Alzheimer’s patients. Therefore, research into the regulation of tau protein phosphorylation is important for understanding Alzheimer’s disease. HASPIN is a serine/threonine kinase that is expressed in various cells. To examine whether HASPIN is involved in the onset of Alzheimer’s disease through tau protein phosphorylation, we investigated the effects of a diet including soybean sprouts rich in the HASPIN inhibitor coumestrol in a mouse model of Alzheimer’s disease (5xFAD). The results showed that HASPIN was expressed in the hippocampus and phosphorylated tau protein, while the ingestion of soybean sprouts containing coumestrol suppressed the development of spatial cognitive dysfunction in 5xFAD. These results indicate that HASPIN may be one of the target molecules for the repression of tau phosphorylation in the treatment of Alzheimer’s disease.
Observations of a Convectively Coupled Kelvin Wave in the Eastern Pacific ITCZ

Abstract A case study of a convectively coupled Kelvin wave in the eastern Pacific intertropical convergence zone (ITCZ) is presented, as observed during the 1997 Pan American Climate Studies (PACS) Tropical Eastern Pacific Process Study (TEPPS). The large-scale convective envelope associated with this disturbance, with a zonal scale of approximately 1000–2000 km, propagates eastward at 15 m s−1 along the mean convective axis of the ITCZ. This envelope consists of many smaller-scale, westward-moving convective elements, with zonal scales on the order of 100–500 km. As the convectively coupled Kelvin wave disturbance propagates eastward, it exerts a strong control on local convection. Radar and vertical profiler data collected aboard the NOAA R/V Ronald H. Brown during the wave passage show that convection deepens rapidly as the Kelvin wave approaches from the west, progressing from isolated, shallow cumuli to organized deep convective features within just 12 h. Initially, rainfall in the vicinity of the s.
Shunt-analysis of epitaxial silicon thin-film solar cells by lock-in thermography

Lock-in thermography has been applied for shunt-analysis on epitaxial silicon thin-film solar cells. The solar cell material was made by epitaxial deposition of the base layer on highly doped monocrystalline (Czochralski) and multicrystalline silicon substrates in an APCVD-system. Solar cells were prepared in a laboratory-type and an industrial-type process. Characterization of the solar cells by infrared lock-in thermography and microscopy revealed a clear correlation between shunts and epitaxial defects in case of the lab-type solar cells. Furthermore an increased concentration of shunts located under the emitter grid lines of the industrial-type solar cells compared to the lab-type solar cells was observed. The analysis by thermography thus gave insight into the quality of the epitaxial layers and into problems concerning a transfer of the solar cell process from laboratory to industrial scale manufacturing.
Co-optimization of the mask, process, and lithography-tool parameters to extend the process window

Abstract. Optimization technologies have been widely applied to improve lithography performance, such as optical proximity correction and source mask optimization (SMO). However, most published optimization technologies were performed under fixed process conditions, and only a few parameters were optimized. A method for mask, process, and lithography-tool parameter co-optimization (MPLCO) is developed to extend the process window. A normalized conjugate gradient algorithm is proposed to improve the convergence efficiency of the MPLCO when optimizing different scale parameters. In addition, a parametric mask and source are used in the MPLCO that could obtain exceedingly low mask and source complexity compared with a traditional SMO.
Simultaneous Perturbation Stochastic Approximation with Norm‐Limited Update Vector

This paper addresses the convergence of simultaneous perturbation stochastic approximation (SPSA) with a norm‐limited update vector. We first illustrate an unstable solution of the standard SPSA algorithm which motivates the consideration of a modified version, where the norm of the update vector is limited to a certain value. Next, a result on the almost‐sure convergence is presented by reducing the modified algorithm into the standard SPSA algorithm and restricting the probability distribution for the perturbation to a Bernoulli distribution. Finally, we apply the modified algorithm to a system identification problem to demonstrate its performance.
Oxidative stress in cerebrovascular disease and associated diseases

Cellular aging is the most severe risk factor for neurodegenerative disease. Simultaneously, oxidative stress (OS) is a critical factor in the aging process, resulting from an imbalance between reactive oxygen and nitrogen species and the antioxidant defense system. Emerging evidence indicates that OS is a common cause of several age-related brain pathologies, including cerebrovascular diseases. Elevated OS disrupts endothelial functional ability by diminishing the bioavailability of nitric oxide (a vascular dilator), induces atherosclerosis, and impairs vasculature, which are all common characteristics of cerebrovascular disease. In this review, we summarize evidence supporting an active role of OS in cerebrovascular disease progression, focusing primarily on stroke pathogenesis. We briefly discuss hypertension, diabetes, heart disease, and genetic factors that are often linked to OS and are considered associated factors influencing stroke pathology. Finally, we discuss the current pharmaceutics/therapeutics available for treating several cerebrovascular diseases.
“Fine synergies” describe motor adaptation in people with drop foot in a way that supplements traditional “coarse synergies”

Synergy analysis via dimensionality reduction is a standard approach in biomechanics to capture the dominant features of limb kinematics or muscle activation signals, which can be called “coarse synergies. ” Here we demonstrate that the less dominant features of these signals, which are often explicitly disregarded or considered noise, can nevertheless exhibit “fine synergies” that reveal subtle, yet functionally important, adaptations. To find the coarse synergies, we applied non-negative matrix factorization (NMF) to unilateral EMG data from eight muscles of the involved leg in ten people with drop-foot (DF), and of the right leg of 16 unimpaired (control) participants. We then extracted the fine synergies for each group by removing the coarse synergies (i.e., first two factors explaining ≥85% of variance) from the data and applying Principal Component Analysis (PCA) to those residuals. Surprisingly, the time histories and structure of the coarse EMG synergies showed few differences between DF and controls—even though the kinematics of drop-foot gait is evidently different from unimpaired gait. In contrast, the structure of the fine EMG synergies (as per their PCA loadings) showed significant differences between groups. In particular, loadings for Tibialis Anterior, Peroneus Longus, Gastrocnemius Lateralis, Biceps and Rectus Femoris, Vastus Medialis and Lateralis muscles differed between groups (p<0.05). We conclude that the multiple differences found in the structure of the fine synergies extracted from EMG in people with drop-foot vs. unimpaired controls—not visible in the coarse synergies—likely reflect differences in their motor strategies. Coarse synergies, in contrast, seem to mostly reflect the gross features of EMG in bipedal gait that must be met by all participants—and thus show few differences between groups. However, drawing insights into the clinical origin of these differences requires well-controlled clinical trials. We propose that fine synergies should not be disregarded in biomechanical analysis, as they may be more informative of the disruption and adaptation of muscle coordination strategies in participants due to drop-foot, age and/or other gait impairments.
Energy-Efﬁcient Resource Allocation for Backscatter-Assisted Wireless Powered MEC

—Inthispaper,westudytheenergy-efﬁcientresourceallocation for a backscatter-assisted wireless powered mobile edge computing (MEC) network. Considering the binary ofﬂoading mode and the limited computation capacity of the MEC server, we propose to minimize the MEC server’s energy consumption by jointly optimizing the binary ofﬂoading decision of each IoT node, the computing frequencies and time at the MEC server and IoT nodes, the backscatter time and reﬂection coefﬁcients of IoT nodes, as well as the MEC server’s transmit power and time. It is formulated into a non-convex mixed integer programming problem. To solve this problem, we ﬁrst obtain the optimal computing time and frequency for the MEC server and the optimal computing time of each IoT node based on the proof by contradiction and the monotonicity of the objective function, then we decouple the resulting simpliﬁed problem into two subproblems and transform each subproblem into a convex one. On this basis, we propose a low-complexity iterative algorithm to solve the simpliﬁed problem by solving the two convex subproblems alternately. Simulation results verify the fast convergence of the proposed iterative algorithm and its superiority overthebaselineschemesintermsoftheMECserver’senergyconsumption.
Two new evolved bipolar planetary nebulae in the solar neighbourhood

We present AAO/UKST Hα+[N II] narrow-band imagery and low- and medium-resolution optical spectroscopy of RCW 24 and RCW 69. These nebulae were previously classified as H II regions, but we now show them to be two of the largest and nearest bipolar Type I PNe yet discovered. Distances were estimated using extinction-distance and kinematic methods, and via a new Ha surface brightness-radius relation. The adopted distances are 1.0 ± 0.3 kpc for RCW 24 and 1.3 ± 0.2 kpc for RCW 69. Both objects have enhanced nitrogen abundances, with log N/O ≃ +0.44 for RCW 24, and log N/O = +0.33 for RCW 69. Systemic velocities and |z| distances are V LSR = +5 km s -1 and |z| ∼ 23 pc for RCW 24, and V LSR = -33 km s -1 and only |z| ∼ 7 pc for RCW 69. Both PNe originated from massive progenitors (>2.0-2.5 M ⊙ ), as deduced from their chemical abundances, large ionized masses, small |z| distances, low peculiar velocities and relatively hot central stars. These two objects form an important addition to the small sample of evolved bipolar PNe in the solar neighbourhood.
On the evolution of material lines and vorticity in homogeneous turbulence

The evolution of material lines, $l$, and vorticity, $\omega$, is investigated experimentally through three-dimensional particle-tracking velocimetry (3D-PTV) in quasi-homogeneous isotropic turbulence at $Re_{\lambda }\,{=}\,50$. Through 3D-PTV data the full set of velocity derivatives, $\partial u_{i}/\partial x_{j}$, is accessible. This allows us to monitor the evolution of various turbulent quantities along fluid particle trajectories. The main emphasis of the present work is on the physical mechanisms that govern the Lagrangian evolution of $l$ and $\omega$ and the essential differences inherent in these two processes. For example, we show that vortex stretching is smaller than material lines stretching, i.e. $\langle\omega_{i}\omega_{j}s_{ij}/\omega^{2}\rangle \,{<}\,\langle l_{i}l_{j}s_{ij}/l^{2}\rangle$, and expand on how this issue is closely related to the predominant alignment of $\omega$ and the intermediate principal strain eigenvector $\lambda_{2}$ of the rate of strain tensor, $s_{ij}$. By focusing on Lagrangian quantities we discern whether these alignments are driven and maintained mainly by vorticity or by strain. In this context, the tilting of $\omega$ and the rotation of the eigenframe $\lambda_{i}$ of the rate of strain tensor $s_{ij}$ are investigated systematically conditioned on different magnitudes of strain, $s^{2}$, and enstrophy, $\omega^{2}$. Further, we infer that viscosity contributes through the term $\nu\omega_{i}\nabla^2\omega_{i}$ to ${\rm D}\omega^{2}/{\rm D}t$, whereas ${\rm D}l^{2}/{\rm D}t$ has no diffusive term. This difference plays a key role in defining the mutual orientation between $\omega$ and $\lambda_{i}$. Viscosity thus contributes significantly to the difference in growth rates of $\langle\omega_{i}\omega_{j}s_{ij}\rangle$ and $\langle l_{i}l_{j}s_{ij}\rangle$.
WHITE NOISE LÉVY–MEIXNER PROCESSES THROUGH A TRANSFER PRINCIPAL FROM ONE-MODE TO ONE-MODE TYPE INTERACTING FOCK SPACES

Consider the Levy–Meixner one-mode interacting Fock space {ΓLM, 〈 ⋅, ⋅ 〉LM}. Inspired by a derivative formula appearing in 〈 ⋅, ⋅ 〉LM, we define scalar products 〈 ⋅, ⋅ 〉LM, n in symmetric n-particle spaces. Then, we introduce a class of one-mode type interacting Fock spaces $\Gamma_{{{\rm LM}}}(\mathcal{H})$ naturally associated to the one-dimensional infinitely divisible distributions with Levy–Meixner type {μr; r > 0}. The Fourier transform in generalized joint eigenvectors of a family $\{{\bf J}_{\phi}; \phi\in \mathcal{E}\}$ of Levy–Meixner Jacobi fields provides a way to explicit a unitary isomorphism 𝔘LM between $\Gamma_{{{\rm LM}}}(\mathcal{H})$ and the so-called Levy–Meixner white noise space $L^{2}(\mathcal{E}', \mathcal{B}(\mathcal{E}'), \Lambda_{{{\rm LM}}})$. We derive a chaotic decomposition property of the quadratic integrable functionals of the Levy–Meixner white noise processes in terms of an appropriate Wick tensor product. For their stochastic regularity, we give explicit form and sharp .
Performance evaluation and modeling of active tile in raised-floor data centers: An empirical study on the single tile case

Raised-floor data centers usually suffer from the local hotspots resulted from uneven cool air delivery. These hotspots not only degrade server performance, but also threat equipment reliability. The commonly used industrial practice of increasing the Computer Room Air Conditioner (CRAC) blower speed for removing hotspots is energy inefficient and may lead to overcooling of some servers. In this paper, we explore the potential of active tiles in data center cooling management. In particular, we deploy a prototype of active tile in a production data center and conduct extensive experiments to investigate the cooling performance. It is shown that deploying the active tiles with even 10% fan speed increases the tile flow by 49%, and sealing the under-rack gap reduces the rack bottom temperature by up to 6°C. Moreover, three machine learning techniques, i.e., Gaussian Process Regression (GPR), Artificial Neural Network (ANN), and Multivariate Linear Regression (MLR) are employed to construct end-to-end data-driven thermal models for the active tile. Using field measured data as training and testing data sets, it is concluded that GPR and ANN are competent for accurate thermal modeling of active tiles. Specifically, GPR achieves the smallest prediction error which is around 0.3°C.
The Recent Advances in the Utility of Microbial Lipases: A Review

Lipases are versatile biocatalysts and are used in different bioconversion reactions. Microbial lipases are currently attracting a great amount of attention due to the rapid advancement of enzyme technology and its practical application in a variety of industrial processes. The current review provides updated information on the different sources of microbial lipases, such as fungi, bacteria, and yeast, their classical and modern purification techniques, including precipitation and chromatographic separation, the immunopurification technique, the reversed micellar system, aqueous two-phase system (ATPS), aqueous two-phase flotation (ATPF), and the use of microbial lipases in different industries, e.g., the food, textile, leather, cosmetics, paper, and detergent industries. Furthermore, the article provides a critical analysis of lipase-producing microbes, distinguished from the previously published reviews, and illustrates the use of lipases in biosensors, biodiesel production, and tea processing, and their role in bioremediation and racemization.
Dirac and Majorana edge states in graphene and topological superconductors

This dissertation is about transport and electronic properties of two types of electronic states occuring at the edges, which are protected by symmetry between positive and negative energies. One type of these states is shown to occur universally in graphene. It is also described how another type of edge states, Majorana fermions, can be used for topological quantum computation.
Application of flexural mechanical resonators to high throughput liquid characterization

Application of flexural mechanical resonators such as tuning forks, benders, etc. to liquid characterization is discussed. Additional complex impedance produced by a liquid environment to such resonators is considered. It was shown experimentally, that for the disk bender resonator this additional impedance can be represented by the sum of two terms: one that is proportional to liquid density and a second one that is proportional to the square root the of viscosity density product. Same model was earlier shown applicable to the tuning fork resonator. Interaction of a resonator of arbitrary shape oscillating in any mode with surrounding fluid is considered theoretically. The conditions on which this impedance model is applicable to any type of a resonator that directly displaces liquid are discussed. These conditions are satisfied for most of flexural resonators available, detailed consideration of tuning fork and disk bender is provided. Sensitivity of flexural resonator response to liquid electrical properties is discussed. Application of disk bender to density and viscosity measurements in strong electrolytes is considered.
Predicting Hepatotoxicity Associated with Low-Dose Methotrexate Using Machine Learning

An accurate prediction of the hepatotoxicity associated with low-dose methotrexate can provide evidence for a reasonable treatment choice. This study aimed to develop a machine learning-based prediction model to predict hepatotoxicity associated with low-dose methotrexate and explore the associated risk factors. Eligible patients with immune system disorders, who received low-dose methotrexate at West China Hospital between 1 January 2018, and 31 December 2019, were enrolled. A retrospective review of the included patients was conducted. Risk factors were selected from multiple patient characteristics, including demographics, admissions, and treatments. Eight algorithms, including eXtreme Gradient Boosting (XGBoost), AdaBoost, CatBoost, Gradient Boosting Decision Tree (GBDT), Light Gradient Boosting Machine (LightGBM), Tree-based Pipeline Optimization Tool (TPOT), Random Forest (RF), and Artificial Neural Network (ANN), were used to establish the prediction model. A total of 782 patients were included, and hepatotoxicity was detected in 35.68% (279/782) of the patients. The Random Forest model with the best predictive capacity was chosen to establish the prediction model (receiver operating characteristic curve 0.97, accuracy 64.33%, precision 50.00%, recall 32.14%, and F1 39.13%). Among the 15 risk factors, the highest score was a body mass index of 0.237, followed by age (0.198), the number of drugs (0.151), and the number of comorbidities (0.144). These factors demonstrated their importance in predicting hepatotoxicity associated with low-dose methotrexate. Using machine learning, this novel study established a predictive model for low-dose methotrexate-related hepatotoxicity. The model can improve medication safety in patients taking methotrexate in clinical practice.