Miracle-dz/UniFAR-FTUs huggingface dataset:
DNA templated self-assembly of gold nanoparticle clusters in the colorimetric detection of plant viral DNA using a gold nanoparticle conjugated bifunctional oligonucleotide probe

The DNA templated self-assembly of gold nanoparticles clustered in different configurations (n = 2–∞) was investigated in the colorimetric detection of ToLCNDV DNA using a gold nanoparticle conjugated bifunctional oligonucleotide probe. The AuNP-bifunctional oligonucleotide probe conjugate was prepared using citrate capped AuNPs (∼19 nm) and virus specific ssoligo probes 1 and 2. Each conjugate consisted of ∼105 ssoligo probes 1 and 2, specific for the forward and reverse strands of the PCR amplified ToLCNDV dsDNA target. The intensity of the UV-visible absorbance spectra of AuNP-bifunctional oligonucleotide probes decreased gradually after hybridization with increasing ratios of dsDNA targets (0.0–1.0). Hybridized AuNP-bifunctional oligonucleotide probes showed gradual resistance against salt induced aggregation with increasing concentrations of dsDNA target, up to the ratio of 1.0. The color of solution remained red even after hybridization of the AuNP-bifunctional oligonucleotide probe with the dsDNA target. These hybridized AuNP-bifunctional oligonucleotide probes were found as clusters with different configurations (n = 2–11) and defined interparticle distances (1.3–2.1 nm). This target DNA guided the self-assembly of the AuNP-bifunctional oligonucleotide probes which is the reason for the different optical absorbance properties of hybridized solutions before and after the salt treatment. An exciting finding of this investigation is that the AuNP-bifunctional oligonucleotide probes were anchored on the center core particle, tending to form a AuNP cluster that incorporated from 1–6 Au-NP probes and extended to 8 and 10 with an increasing size of core particle diameter. The assembly of three dimensional DNA templated AuNP clusters in flower and pyramid shapes was possible with a dsDNA target and a AuNP-bifunctional oligonucleotide probe but not with a AuNP-monofunctional oligonucleotide probe. The limit of detection of dsDNA target in this bifunctional nanoprobe assay was ∼7.2 ng. Also, this AuNP-bifunctional oligonucleotide probe could reduce the concentration of target DNA required for colorimetric detection by half, as it could recognise both strands in the dsDNA target simultaneously. The proof of this concept will be used for further development of ultra sensitive nanoassay methods and will also be applicable for materials science applications.
Ab Initio Molecular Dynamics Study of the Dehydroxylation Reaction in a Smectite Model

We have studied the dehydroxylation reaction in a smectite model, with the purpose of determining the influence of the octahedral layer composition and the possible role of the interlayer cations. Employing ab initio molecular dynamics and with the help of metadynamics for accelerating the reaction, we study the two possible mechanisms for the formation of the first water molecule. In the first mechanism, the migration of the proton across the octahedral vacancy takes place by means of a stable intermediate in which the proton is coordinated with an apical oxygen. The formation of this intermediate is not possible without a local deformation of the tetrahedral sheet, in which a Si–O basal bond is broken so that the Si coordinates with the O that releases the H, stabilizing its residual charge. In turn, the basal oxygen that loses the Si is stabilized by the nearest interlayer Na atoms. This tetrahedral deformation is permanent at this step of the reaction. In the second mechanism the proton migrates to th.
An Assessment of Gold as a Hedge or Safe Haven: Evidence from Major Gold Producing Countries

JEL Classification: G01; G11; G15. In the era of globalization, co-movement among the various financial assets has increased drastically and diversification benefits have reduced. Further, increased uncertainties in the financial system posed a serious challenge to researchers and investors in the search for alternative avenues for safeguarding their wealth and risk management. Therefore, using daily time series data that spans from January 2000 to March 2020, the study assesses the role of gold as a hedge and safe haven against equity market indices of major gold producing countries. The GARCH model, which covers average gold returns, highlighted the hedge and safe haven ability of gold in different quantiles of the return distribution. The results revealed that the severity of shock rarely matters in capturing the demand for safe haven assets. Additionally, a comparative analysis during crises evidenced a more heterogeneous and larger effect during the dot-com bubble burst in comparison with other selected crises. The robustness analysis through wavelet coherence with the predominance of red bounds and westward-facing arrows indicates the strong safe haven effect of gold during most of the crisis periods in the cases of Australia and South Africa. Furthermore, an improved understanding of such hedge and safe haven behavior patterns may have substantial implications for hedgers, portfolio managers, investors, and policy makers.
A new generation of highly sensitive luminescent thermometers operating in the optical window of biological tissues

A new type of luminescent thermometer based on highly temperature dependent d–d Cr3+ transitions related to barely temperature dependent f–f Nd3+ transitions is reported for the first time, showing exceptionally high sensitivity and shifting current paradigms behind the physics and chemistry of luminescent nanothermometers. The highest sensitivity in the physiological temperature range was found for LiLaP4O12:1%Cr,10%Nd and reached 4.89%/°C – three times, up to over one order of magnitude higher than most luminescent thermometers reported to date. Moreover, the brightness of such probes based on Cr ions was around one order of magnitude higher than the Stokes Nd emission. Even higher sensitivities, up to 30%/°C, were found above 200 °C, indicating the importance of a rational approach to the design of chemical composition and smart involvement of photophysical processes, to significantly enhance the properties of luminescent thermometers. The thermal dependence of the luminescence intensity ratio from the two ions was investigated for different Cr3+ and Nd3+ ion concentrations, which actually had no severe impact, either on the LIR or on the sensitivity of such luminescent thermometers.
NUMERICAL EVALUATION OF UNDRAINED SEISMIC LIMITING PRESSURE BEHIND SOIL GAPS IN CONTIGUOUS PILE WALLS

2D plane strain Finite Element (FE) analyses are performed for rigid circular piles to evaluate undrained seismic limiting pressure behind soil gaps in contiguous pile walls (CPW). Taking advantage of symmetrical conditions, only one quarter of the rigid circular pile and one half of the soil gap have been modelled. The pile has been modelled as a rigid material, whereas soil (clay, with isotropic and constant undrained shear strength property) has been considered as a perfectly plastic Tresca material following an associated flow rule. Boundary conditions were considered as per the field conditions and seismic loads were considered as pseudo-static force applied in terms horizontal seismic coefficient, kh. Adaptive meshing technique was adopted to obtain results reasonably closer to exact solutions. The effect of soil gap ratios, adhesion factors and intensity of seismic loading on limiting pressure behind the piles are studied in detail using OptumG2 software based on finite element limit analysis approach. For deeper insights and influence of various affecting parameters non-dimensional charts are provided for seismic limiting pressure. Further, it has been observed that the considered parameters have a significant effect on the seismic limiting pressure behind the soil gap in CPW.
Assessment of the Finite VolumE Sea Ice Ocean Model (FESOM2.0), Part II: Partial bottom cells, embedded sea ice and vertical mixing library CVMIX

Abstract. The second part of the assessment and evaluation of the unstructured-mesh Finite-volumE Sea ice-Ocean Model version 2.0 (FESOM2.0) is presented. It focuses on the performance of partial cells, embedded sea ice and on the effect of mixing parameterisations available through the CVMIX package. It is shown that partial cells and embedded sea ice lead to significant improvements in the representation of the Gulf Stream and North Atlantic Current as well as the circulation of the Arctic Ocean. In addition to the already existing Pacanowski and Phillander (fesom_PP) and K-profile (fesom_KPP) parameterisations for vertical mixing in FESOM2.0, we document the impact of several mixing parameterisations from the Community Vertical Mixing (CVMIX) project library. Among them are the CVMIX versions of Pacanowski and Phillander (cvmix_PP) and K-profile (cvmix_KPP) parameterisations, the tidal mixing parameterisation (cvmix_TIDAL), a vertical mixing parameterisation based on turbulent kinetic energy (cvmix_TKE) as well as a combination of cvmix_TKE and the recent scheme for the computation of the Internal Wave Dissipation, Energy and Mixing (IDEMIX). The IDEMIX parameterises the redistribution of internal wave energy through wave propagation, nonlinear interactions and the associated imprint on the vertical background diffusivity. Further, the benefit from using a parameterisation of sea ice melt season mixing in the surface layer (MOMIX) for reducing Southern Ocean hydrographic biases in FESOM2.0 is presented. We document the implementation of different model components and illustrate their behaviour. This paper serves primarily as a reference for FESOM users but is also useful to the broader modelling community.
Nucleation of Graphene Precursors on Transition Metal Surfaces: Insights from Theoretical Simulations

We present quantum chemical simulations demonstrating graphene precursor formation on bcc (111) transition metal surfaces during the chemical vapor deposition process. We observe that the experimentally reported positive curvature of graphene precursors is a consequence of the natural tendency toward pentagon formation during the precursor self-assembly process. Density functional theory calculations reveal that the stability of these precursors is driven by the dominance of metal–carbon σ bonding over metal–carbon π bonding at the precursor edge. These simulations show that Fe(111) catalysts facilitate precursor formation at lower carbon densities and increase precursor stabilities. However, the stronger catalyst–carbon interaction strength in the case of Fe(111) significantly promotes catalyst surface degradation. The use of more weakly interacting catalysts, such as Ni(111) and Cu(111), circumvents this issue. However, QM/MD simulations of Ni(111)-catalyzed chemical-vapor deposition (CVD) show that gra.
Hate in Politics: Can Transfer Learning on Political Tweets Help Detect Hate Speech?

The Internet has revolutionized the way Americans process information. Social media platforms such as Twitter and Facebook have enabled discourse among citizens in a way never before possible. Often, these discussions are largely driven by partisan divide, where individuals sway towards predetermined schools of thought. Normally, it is healthy to discuss current events and analyze perspectives from all sides. However, social media has also allowed people to engage negatively with others and has provided a platform for hate speech. As the country has become increasingly polarized over certain issues, hate speech as become all the more common. Given the relationship between politicallycharged rhetoric and hate speech, we seek to determine whether this relationship can be observed through transfer learning. In this paper, we study the effect of pre-training on partisan/non-partisan political tweets for a hate speech classification model. We hypothesize that pre-trained parameters from a partisan classification problem will perform better in hate speech classification than randomly initialized parameters.
RESPONSE OF FIXED OFFSHORE OIL AND GAS PLATFORMS EQUIPPED WITH SHAPE MEMORY ALLOY ELEMENTS UNDER EXTREME WAVE LOADING

The main objective of present study is to prepare a different perspective toward fixed steel oil and gas offshore platforms to develop the modern systems for these structures. Response of these platforms equipped with shape memory alloy (SMA) bracing system under extreme wave loading is studied. In order to thoroughly investigate possible effective parameters on the behaviour of the platforms, the different seawater depth and bracing configuration of jacket have been considered. For this purpose, depths of 70, 110 and 150m and bracing types of A (K-shape), B (Λ-shape) and C (zigzag-shape) have been used, respectively. The results of investigation into depth parameter indicate that the platform which is located in the depth of 70m has a linear behaviour and using SMA cannot considerably affect its behaviour. However, at the depths of 110 and 150m, the residual strains in the structure can be removed by using the SMA bracing system. Examination of the parameters related to bracing configuration of the platform shows that the deck displacement for different bracing configurations is independent of the stiffness and ductility of structures, which should be taken as a separate parameter for the platform equipped with SMA bracing. At the end, upper plateau stress level and plateau length, among other parameters of SMA mechanical properties, identified as the most important ones. Increasing the upper plateau stress level improves stiffness of structure just a little and reduces natural period of structure to some extent. However, as long as the distance between the upper and lower plateau stress increases, the structure absorbs greater energy and the system’s ability to stand against the dynamic responses raises.
Chromatographic generator systems for the actinides and natural decay series elements

Abstract This work describes chromatographic radionuclide generator systems for the production of actinides and natural decay series elements. The generator systems begin with alpha emitting parent radioisotopes with half-lives (T1/2) of greater than one year and produce alpha or beta emitting radioisotopes with half-lives of hours to days. Chromatographic systems were chosen to minimize radiolytic damage to chromatographic supports, preserve the parent activity for repeated use, provide high purity daughter radionuclide tracers, and to minimize or eliminate the need for evaporation of solutions of the parent or daughter nuclides. Useful secondary separations involving the daughters of the initial parent radionuclide are also described. Separation systems for 210Bi, 210Po, 211Pb, 212Pb, 223Ra, 224Ra, 225Ra, 225Ac, 227Th, 228Th, 231Th, 234Th, and 239Np are outlined in detail.
Intelligent Personal Assistant Dialog Generation using Paraphrasing

Inspired to generate personal assistant dialogues using paraphrasing, we explore several deep learning architectures for the generic paraphrasing task. We build off on the original seq2seq + LSTM model with bag of words (BOW) sampling proposed in Fu et. al., 2019[1], and experiment with variations of this model using transformer architecture. We have found that the original paper’s innovative idea of using BOW sampling from source sentences to generate word embeddings to augment decoding process may not transfer well to transformer architecture
Reducing sampling bias of landslides by identifying unrecorded events from satellite images

Global records of landslides are subjected to incompleteness and sampling bias because of spatially different levels of monitoring and reporting. In this work, we use a convolutional neural network to detect landslides from globally available satellite images and compare the detection with existing records of landslides. We train the model us-ing an open high-resolution landslide dataset containing labeled landslide and non-landslide satellite images from the Bijie region in China. We test the trained model on low-resolution satellite images of Oregon, USA and Rwenzori Mountains, Uganda taken by Sentinel-2, which provides global coverage. To achieve better generalization of our model, we conduct multiple operations including downsam-pling the original training set to match the lower resolution of the test images, data augmentation, and distribution matching for the input test images. We visualize the model’s decision making process using Grad-CAM. By comparing the detected landslides with the existing record, we ﬁnd that the landslides in Rwenzori Mountains, Uganda are much more under-reported than in Oregon, USA. This work can be used to detect potentially unrecorded landslides and understand the landslide sampling bias globally.
Thermal Effects Study on Stimulated Brillouin Light Scattering in Photonic Crystal Fiber

we investigate the temperaturedependences of the Brillouin frequency shift in three different kind of single-mode fibers using a heterodyne method for sensing temperature. Positive dependences coefficients of 0.77, 0.56 and 1.45MHz/C are demonstrated for 25 km long single-mode fiber, 10 km long non-zero dispersion shifted fiber and 100 m photonic crystal fiber, respectively. The results indicate that microstructure fibers with a partially Gedoped small core have great potential for fiber Brillouin distributed sensing.
Mozart 2.0: Milestone 2 Report (Music Generation)

We are interested in using deep learning models to generate new classical style music, especially Mozart style music. We build a LSTM-based architecture trained with tokenized MIDI ﬁles with Maestro Dataset. The performance of our model is measured by comparing the generated notes with the ground truth. While music generation is a relatively well-explored ﬁeld of deep learning, we can still do new things like music style transfer.
CS230 - Spring 2020 Final Report

There has been numerous effort in recent technology to connect the analog and digital experience in reading and writing. For example, the pen scanner is one of the pieces of modern technology that is able to assist text recognition over printed text with Optical Character Recognition(OCR) technology and convert to encoded text. Such technology allows for easier text editing, text-to-speech applications, translation, preservation of data, and so on (5). However, most of the products in the market fall short of scanning mathematical equations but resort to saving them as an image (1).
AN EMPIRICAL COMPETENCIES STUDY ON THAILAND'S HOSPITALITY SECTOR: COMMUNITY-BASED CLUSTER IN PHATTHALUNG

Organizational competencies are the essential elements for the success of the organization that get the focus of the regulatory authorities and recent studies. Thus, this research examines the role of managerial, technology and workforce competencies on the hospitality firms’ performance in Thailand. This article also examines the mediating role of organizational strength among the links of managerial, technology, workforce competencies and hospitality firms’ performance in Thailand. This study has selected the questionnaires for the data collection and also executed the smart-PLS to examine the relationships among the variables. The results have exposed that managerial competencies, technology competencies and workforce competencies have positive association with hospitality firms’ performance in Thailand. The findings also revealed that organizational strength positively mediates among the nexus of managerial competencies, technology competencies, workforce competencies and hospitality firms’ performance in Thailand. This research guided to the regulation making authorities that they should increase their focus towards organizational competencies that could enhance the firm performance.
SEISMIC RISK IN CENTRAL ASIA, OUTCOMES OF THE EMCA PROJECT

Central Asia is a region historically prone to seismic activity, and whose economies are rapidly evolving. A better understanding of the mediumand long-term impact of earthquakes is therefore paramount for local decision-makers and civil protection authorities in order to devise the most appropriate risk prevention and mitigation measures. Within the framework of the Earthquake Model for Central Asia (EMCA) project, a new probabilistic seismic hazard model (PSHA) has been developed and complemented with a set of relevant earthquake scenarios. The hazard model has been combined with a multi-resolution exposure model focusing on the residential building stock, for which a set of fragility and vulnerability models has been selected.The expected amount and spatial distribution of loss have been then computed, using the GEM’s OpenQuake-engine, in terms of both probabilistic estimates over different return periods (e.g. 475 years, corresponding to an exceedance probability of 10% over 50 years) and event-based scenarios. The losses have been evaluated considering both direct economical damage and human losses, two of the most relevant risk metrics for decision makers. The results of the assessment show that the impact of earthquakes may, for countries such as Kyrgyzstan, Tajikistan and Uzbekistan, easily compare with the Gross Domestic Product (GDP), and is therefore necessary to take a proactive stance in order to undertake efficient Disaster Risk Reduction (DRR) measures in the region.
Blockade of the CD93 pathway normalizes tumor vasculature to facilitate drug delivery and immunotherapy

Disruption of the molecular interaction between CD93 and IGFBP7 improves tumor vascular functions and thereby facilitates therapeutic interventions. Fixing faulty tumor vessels Solid tumors have structurally abnormal blood vessels that lead to decreased drug accessibility and effector T cell infiltration into the tumor. Targeting this abnormal vasculature could potentially improve response to therapy. Here, Sun and colleagues identified the interaction between CD93 and insulin-like growth factor binding protein 7 as important for the formation of abnormal tumor vasculature. Treatment with monoclonal antibodies to inhibit this interaction led to improved tumor perfusion in mice, resulting in increased drug delivery and an increase in effector T cells within the tumor, sensitizing the tumors to immune checkpoint inhibition. Further study is required to assess whether targeting the CD93 pathway improves treatment response in humans. The immature and dysfunctional vascular network within solid tumors poses a substantial obstacle to immunotherapy because it creates a hypoxic tumor microenvironment that actively limits immune cell infiltration. The molecular basis underpinning this vascular dysfunction is not fully understood. Using genome-scale receptor array technology, we showed here that insulin-like growth factor binding protein 7 (IGFBP7) interacts with its receptor CD93, and we subsequently demonstrated that this interaction contributes to abnormal tumor vasculature. Both CD93 and IGFBP7 were up-regulated in tumor-associated endothelial cells. IGFBP7 interacted with CD93 via a domain different from multimerin-2, the known ligand for CD93. In two mouse tumor models, blockade of the CD93/IGFBP7 interaction by monoclonal antibodies promoted vascular maturation to reduce leakage, leading to reduced tumor hypoxia and increased tumor perfusion. CD93 blockade in mice increased drug delivery, resulting in an improved antitumor response to gemcitabine or fluorouracil. Blockade of the CD93 pathway triggered a substantial increase in intratumoral effector T cells, thereby sensitizing mouse tumors to immune checkpoint therapy. Last, analysis of samples from patients with cancer under anti–programmed death 1/programmed death-ligand 1 treatment revealed that overexpression of the IGFBP7/CD93 pathway was associated with poor response to therapy. Thus, our study identified a molecular interaction involved in tumor vascular dysfunction and revealed an approach to promote a favorable tumor microenvironment for therapeutic intervention.