Miracle-dz/UniFAR-FTUs huggingface dataset:
The Enhanced Affinity of WRKY Reinforces Drought Tolerance in Solanum lycopersicum L.: An Innovative Bioinformatics Study

In the scenario of global climate change, understanding how plants respond to drought is critical for developing future crops that face restricted water resources. This present study focuses on the role of WRKY transcription factors on drought tolerance in tomato, Solanum lycopersicum L. , which is a significant vegetable crop. WRKY transcription factors are a group of proteins that regulate a wild range of growth and developmental processes in plants such as seed germination and dormancy and the stress response. These transcription factors are defined by the presence of a DNA-binding domain, namely, the WRKY domain. It is well-known that WRKY transcription factors can interact with a variety of proteins and therefore control downstream activities. It aims to simulate the effect of curcumin, a bioactive compound with regulatory capacity, on the protein–protein interaction events by WRKY transcription factors with an emphasis on drought stress. It was found that curcumin binds to WRKY with an energy of −11.43 kcal/mol with inhibitory concentration (Ki) 0.12 mM and has the potential to improve fruit quality and reinforce drought tolerance of S. lycopersicum, according to the results based on bioinformatics tools. The root means square deviation (RMSD) of the C-α, the backbone of 2AYD with ligand coupled complex, displayed a very stable structure with just a little variation of 1.89 Å. MD simulation trajectory of Cα atoms of 2AYD bound to Curcumin revealed more un-ordered orientation in PC1 and PC10 modes and more toward negative correlation from the initial 400 frames during PCA. Establishing the binding energies of the ligand–target interaction is essential in order to characterize the compound’s binding affinity to the drought transcription factor. We think we have identified a phyto-agent called curcumin that has the potential to enhance the drought tolerance. Compared to the part of the mismatch repair-base technique that can be used to fix drought related genes, curcumin performed better in a drop-in crop yield over time, and it was suggested that curcumin is a potential candidate factor for improving drought tolerance in tomatoes, and it needs future validation by experiments in laboratory and field.
Engaging Gen Z Students with Economic Lessons Featuring MrBeast

We summarize the application of economic concepts in viral YouTube videos created by Jimmy Donaldson, known more popularly online as MrBeast. His viral videos are known for expensive challenges and are incredibly popular among Gen Z students. We present three lesson plans based on three different MrBeast videos that include episode summaries, key economic concepts, and multiple assessment questions. The lesson plans focus on core economic concepts and include opportunities to teach concepts such as scarcity, opportunity cost, marginal analysis, business costs, and production. With more than 170 million subscribers as of this writing and over 29 billion views, MrBeast’s viral videos offer a unique opportunity for creatively teaching economics to a new generation of students.
Single Photon Counting Readout IC With 44 e− rms ENC and 5.5 e− rms Offset Spread With Charge Sensitive Amplifier Active Feedback Discharge

The paper presents the design and measurements of a low noise integrated circuit in a CMOS 130 nm for the readout of a hybrid pixel detector operating in a single photon counting mode. A core of IC contains a matrix of <inline-formula> <tex-math notation="LaTeX">$128\times256$ </tex-math></inline-formula> square shaped pixels of <inline-formula> <tex-math notation="LaTeX">$75~\mu \text{m}$ </tex-math></inline-formula> pitch. Each readout pixel consists of a charge sensitive amplifier (CSA), a shaper, two discriminators, and two 14-bit counters. We present the novel CSA feedback circuitry with an effective resistance of 0.6 <inline-formula> <tex-math notation="LaTeX">$\text{G}\Omega $ </tex-math></inline-formula> and sets of switches, which allows low noise operation and fast return to the CSA output baseline at the same time providing the possibility of operation with a higher flux of input pulses. The measured equivalent noise charge of IC (bump-bonded to silicon sensor) is only 44 <inline-formula> <tex-math notation="LaTeX">$\text{e}^{-}$ </tex-math></inline-formula> rms at 15°C, the offset spread is 5.5 <inline-formula> <tex-math notation="LaTeX">$\text{e}^{-}$ </tex-math></inline-formula> rms and the energy resolution is FWHM = 380 eV @ 8keV, which is currently the best result in single photon counting pixel detectors. The hybrid pixel detector can register up to 107 million photons/mm2 per second, while the power consumption per single pixel is about <inline-formula> <tex-math notation="LaTeX">$42~\mu \text{W}$ </tex-math></inline-formula>.
Combining Neuropsychological Assessment with Neuroimaging to Distinguish Early-Stage Alzheimer’s Disease from Frontotemporal Lobar Degeneration in Non-Western Tonal Native Language-Speaking Individuals Living in Taiwan: A Case Series

Neuropsychological tests (NPTs), which are routinely used in clinical practice for assessment of dementia, are also considered to be essential for differential diagnosis of Alzheimer’s disease (AD) and frontotemporal lobar degeneration (FTLD), especially the behavioral variants of frontotemporal dementia (bvFTD) and primary progressive aphasia (PPA) at their initial clinical presentations. However, the heterogeneous features of these diseases, which have many overlapping signs, make differentiation between AD and FTLD highly challenging. Moreover, NPTs were primarily developed in Western countries and for native speakers of non-tonal languages. Hence, there is an ongoing dispute over the validity and reliability of these tests in culturally different and typologically diverse language populations. The purpose of this case series was to examine which of the NPTs adjusted for Taiwanese society may be used to distinguish these two diseases. Since AD and FTLD have different effects on individuals’ brain, we combined NPTs with neuroimaging. We found that participants diagnosed with FTLD had lower scores in NPTs assessing language or social cognition than AD participants. PPA participants also had lower measures in the Free and Cued Selective Reminding Test than those diagnosed with bvFTD, while bvFTD participants showed poorer performances in the behavioral measures than PPA participants. In addition, the initial diagnosis was supported by the standard one-year clinical follow-up.
Expediting Distributed DNN Training With Device Topology-Aware Graph Deployment

This paper presents TAG, an automatic system to derive optimized DNN training graph and its deployment onto any device topology, for expedited training in device- and topology- heterogeneous ML clusters. We novelly combine both the DNN computation graph and the device topology graph as input to a graph neural network (GNN), and join the GNN with a search-based method to quickly identify optimized distributed training strategies. To reduce communication in a heterogeneous cluster, we further explore a lossless gradient compression technique and solve a combinatorial optimization problem to automatically apply the technique for training time minimization. We evaluate TAG with various representative DNN models and device topologies, showing that it can achieve up to 4.56x training speed-up as compared to existing schemes. TAG can produce efficient deployment strategies for both unseen DNN models and unseen device topologies, without heavy fine-tuning.
Technological Evolution in the Instrumentation of Ataxia Severity Measurement

Cerebellar ataxia is the poorly coordinated movement that results from injury or disease affecting the cerebellum. The diagnosis and assessment of ataxia are significantly challenging due to dependency on clinicians’ experience and the attendant subjectivity of such a process. In recent years, neuroimaging and sensor-based approaches, supported by effective machine learning techniques have made advances in the pursuit of addressing these clinical challenges. In this work, we present an outline of approaches to applying machine learning to this clinical challenge. We first provide a fundamental clinical overview with practical problems and then from a machine learning perspective, outline possible approaches with which to address these clinical challenges. Also discussed are the limitations in existing methods, the provision of cross disciplinary approaches and the current state-of-the-art as a potential basis for future research.
Comparison of 3 Enzyme-Linked Immunoassay Methods to Evaluate Serum Concentrations of Infliximab and Antibodies to Infliximab in 32 Patients with Moderate to Severe Inflammatory Bowel Disease

Background Monitoring of trough levels and anti-drug antibodies is important when patients with inflammatory bowel disease (IBD) are treated with anti-TNF biologics due to guided therapeutic decisions. The comparability of 3 ELISA tests for detection of the lowest serum concentration of infliximab (IFX) or antibodies to IFX (ATIs) was evaluated. Material/Methods Two commercial assays for measuring IFX levels were compared with the in-house (UHL) test. ATIs were measured with 1 commercial test and compared to the in-house test. According to the guidelines, IFX levels were within the range of 3 to 7 μg/mL. Results The decision to continue therapy would be the same for 11 out of 16 patients when comparing the apDia Infliximab ELISA and UHL test, and for 12 out of 18 patients when comparing the Lisa-Tracker and in-house UHL test. Linear correlations between the tests were R=0.92 (UHL and apDia), R=0.91 (apDia and Lisa-Tracker), and R=0.89 (UHL and Lisa-Tracker) with P<0.001, respectively. Conclusions As the IFX levels are important for decisions on further therapy, detectable IFX levels realistically reflect the presence of the drug in the patients’ blood and thus control inflammatory activity. The tests were found as comparable and performed well in this aspect and might be used in everyday clinical practice.
Continuous-time methods in the study of discretely sampled functionals of Lévy processes. I. The positive process case

In this paper we develop a constructive approach to studying continuously and discretely sampled functionals of Lévy processes. Estimates for the rate of convergence of the discretely sampled functionals to the continuously sampled functionals are derived, reducing the study of the latter to that of the former. Laguerre reduction series for the discretely sampled functionals are developed, reducing their study to that of the moment generating function of the pertinent Lévy processes and to that of the moments of these processes in particular. The results are applied to questions of contingent claim valuation, such as the explicit valuation of Asian options, and illustrated in the case of generalized inverse Gaussian Lévy processes.
TRANSFER PROCESSES IN RHEOLOGICAL MEDIA MATHEMATICAL SIMULATION OF A SWIRLING VISCOPLASTIC FLUID FLOW IN A CYLINDRICAL CHANNEL

The paper describes the results of investigations of a swirling viscoplastic fl uid fl ow in a cylindrical channel. It has been established that on increase in the limit shear stress and plastic viscosity, the effect of swirling on the fl ow structure becomes less expressed: the depth of the axial velocity dip decreases to its complete disappearance, with the hydrodynamic fl ow stabilization being observed over a smaller distance from the channel inlet. It is shown that at the same Rossby number the recirculation intensity increase with decreasing limit shear stress and plastic viscosity.
Dynamic Fuzzy Rule Interpolation

Designers of effective and efficient fuzzy systems have long recognised the value of inferential hybridity in the implementation of sparse fuzzy rule based systems. Which is to say: such systems should have recourse to fuzzy rule interpolation (FRI) only when no rule matches a given observation; otherwise, when an observation partially or exactly matches at least one of the rules of the sparse rule base, a compositional rule of inference (CRI) should be used in order to avoid the computational overheads of interpolation. Sparse fuzzy rule bases are constructed by experts or derived from data and may support FRI reasoning in long run. However, two potential problems arise: (1) a system’s requirements may change over time leading to rule redundancy; and (2) the system may cease in the long run to provide precise and pertinent results. The need to maintain the concurrency and accuracy of a sparse fuzzy rule base, in order that it generates the most precise and relevant results possible, motivates consideration of a dynamic (real-time) fuzzy rule base. This thesis therefore presents a framework of dynamic fuzzy rule interpolation (D-FRI), integrated with general fuzzy inference (CRI), which uses the FRI result set itself for the selection, combination and promotion of informative, frequentlyused intermediate rules into the existing rule base. Here two versions of the D-FRI approach are presented:k-means-based and GA-aided. Integration uses the concept of α-cut overlapping between fuzzy sets to decide an exact or partial matching between rules and observation so that CRI can be utilised for reasoning. Otherwise, the best closest rules are selected for FRI by exploiting the centre of gravity (COG), Hausdorff distance (HD) and earth mover’s distance (EMD) metrics. Testing seeks to show that dynamically-promoted rules generate results of greater accuracy and robustness than would be achievable through conventional FRI tout court, and to support the claim that the D-FRI approach results in a more effective interpolative reasoning system. To this end, an implementation of D-FRI is applied to the problem domain of intrusion detection systems (IDS), by integrating it with Snort in order to improve port-scanning detection and increase the level of accuracy of alert predictions.
Growth mechanism of hydrogenated amorphous silicon studied by in situ scanning tunneling microscopy

In situ scanning tunneling microscopy has been used to study the evolution of the surface topography of the growing surface of hydrogenated amorphous silicon (a-Si:H) in order to understand its growth mechanism. The surface is found to possess an island-like structure and the island diameter is found to increase with increasing growth temperature. A Fourier analysis of the surface roughness has an exponent of i=1.17. A comparison of the roughness of films of different thickness gives a dynamic scaling exponent of β=0.28, but the films are not particularly self-affine in character. It is argued that the exponent i is not evidence of a viscous flow regime, but that nonstochastic growth of a random network occurs, caused by a preferential hydrogen abstraction at kink-like and step-like surface sites. A simple simulation of the topography is used to support this conclusion.
Non-Contact Thermometer for Improved Air Temperature Measurements

A compact thermometer for air temperature based on the measurement of the speed of sound was developed at INRIM. This paper focuses on the comparison of this instrument with platinum resistance thermometers in a climatic chamber over a temperature range (−30 ÷ +55) °C, relative humidity (10 ÷ 90)%rh, and irradiation (>1 kW/m2) values similar to those of surface atmospheric conditions. Overall uncertainty values of 0.2 °C over the range from −30 °C to +30 °C, and from 0.6 °C to +55 °C, were found. Moreover, the instrument proved to be immune to irradiation errors and free from the need for temperature calibration.
Simulation and implementation of a harmonics suppression technique for a single-phase full-bridge converters

ABSTRACT This paper presents a harmonic suppression technique for a single-phase full-bridge converter, and the mathematical principles underlying a modified PWM technique are discussed. To verify the effectiveness of the proposed technique, voltage and current sensor circuits in a single-phase full-bridge converter connected to a digital signal processor controller with an analog-to-digital converter were analyzed experimentally. The output voltage harmonics of the converter were examined, and the frequency spectra of the output voltage were analyzed. Furthermore, a digital control system with a TMS320F2808 digital signal microcontroller was realized. The experimental results verified the effectiveness of the proposed technique in suppressing low-frequency harmonic in a prototype of a converter.
Prioritizing TCR Repertoire Network
Properties - A Novel Approach to
Select Network Signatures

T-cells are one of the key components of the adaptive immune system. T-cell Receptors (TCR) are a group of protein complexes found on the surface of T-cells. TCRs are responsible for recognizing and binding to certain antigens found on abnormal cells or potentially harmful pathogens. Once the TCRs bind to the pathogens, the T-cells attack these cells and help the body fght infection, cancer, or other diseases. TCR repertoires, which are continually shaped throughout the lifetime of an individual in response to pathogenic exposure, can serve as a fngerprint of an individual’s current immunological profle. The similarity among TCRs sequence directly infuences the antigen recognition breadth. Network analysis, which allows interrogation of sequence similarity, thereby adds an important layer of information. Due to the heterogeneous nature of TCR network properties, it is extremely difcult to perform statistical inference or machine learning directly between subjects. In this work, a novel method is proposed to prioritize the network properties that are associated with the outcome of interest, based on features extracted from heterogeneous global/local network properties. Schemes to select the top features associated and to simulate the network properties using the real data are also presented. Extensive simulation studies and real data analysis were performed to demonstrate the proposed methods. Performance measures including F-1 score, false discovery rate, sensitivity, power, and stability were calculated for each model and are used for model comparison.
A Probabilistic Propagation Graph Modeling Method for Channel Characterization in Dense Urban Environments

With the rapid development of mobile broadband communications, such as the “fifth generation (5G)” systems, accurate and efficient simulation-based channel modeling becomes more essential for propagation channel characterization in the complicated environments. In the previous works, the so-called “propagation-graph-based channel modeling” method has been introduced and effectively applied to the simulation of channel characteristics. Compared with the conventional ray-tracing-based approaches, the graph-based modeling may significantly reduce the computational complexity. In this contribution, without increasing computational complexity, the graph-based channel modeling method is proposed for channel characterization in the dense urban environments with the following improvements, i.e., realistic geometry-assisted scattering point placement, scattering coefficient definition, and line-of-sight (LoS) path presence probability settings. By comparing the simulated channel characteristics with those obtained in the real measurements in two urban scenarios, the proposed improvements are demonstrated in predicting channel characteristics more accurately.
Cognitive Reorganization Due to Mental Workload: A Functional Connectivity Analysis Based on Working Memory Paradigms

Mental workload has a major effect on the individual’s performance in most real-world tasks, which can lead to significant errors in critical operations. On this premise, the analysis and assessment of mental workload attain high research interest in both the fields of Neuroergonomics and Neuroscience. In this work, we implemented an EEG experimental design consisting of two distinct mental tasks (mental arithmetic task, n-back task), each with two conditions of complexity (low and high) to investigate the task-related and task-unrelated workload effects. Since mental workload is an intricate phenomenon involving multiple brain areas, we performed a graph theoretical analysis estimating the Phase Locking Index (PLI) in four frequency bands (delta, theta, alpha, beta). The brainwave-dependent network results show statistically significant reductions in clustering coefficient, characteristic path length, and small-worldness metrics with higher workload in both tasks across several bands. Moreover, functional connectivity analysis indicates a task-independent fashion of the brain topological re-organization with increasing mental load. These results revealed how the brain network is re-organized with increasing mental workload in a task-independent way. Finally, the network metrics were used as classification features, leading to high performance in workload level discrimination.
A new method for determining the CO to H2 conversion factor for translucent clouds

In this paper we describe a new technique for obtaining the conversion factor between the molecular hydrogen column density and the CO(J = 1-0) integrated antenna temperature. This factor, typically known as X(sub CO) is often to be of order a few times 10(exp 20)/sq cm/K km/s) for the molecular clouds in the Galaxy and is one of the primary means of determining the molecular cloud mass from CO observations. However, for the low-extinction interstellar clouds known as the translucent molecular clouds, estimates of X(sub CO) vary by up to a factor of 60 depending on the object and techniques employed to calibrate X(sub CO). Since the cloud mass is directly proportional to X(sub CO) uncertainties in mass estimates of translucent clouds can be more than an order of magnitude. We calibrate the H2 content in translucent clouds by using the linear relationship between the CH and H2 column densities. The CH column density is readily determined from observations of the CH ground-state hyperfine main-line transition at 3335 MHz. Using CH as a surrogate tracer for H2 and CO(J = 1-0) observations of a sample of translucent and dark molecular clouds, we find a wide variation in values for X(sub CO). For translucent clouds, X(sub CO) ranges from 0.3 to 6.8 x 10(exp 20) and for dark clouds the values range from 0.8 to 8.6. Although the average values for both types of cloud are similar to the canonical value determined for the Galactic molecular cloud ensemble (2-4 x 10(exp 20)), the scatter in individual X(sub CO) values may indicate that X(sub CO) for a given translucent cloud cannot be determined a priori and must be obtained for each cloud so that a reliable mass determination may be made.