Miracle-dz/UniFAR-FTUs huggingface dataset:
Drought tolerance enhancment in cotton (Gossypium hirsutum L.) by mepiquate chloride seed priming

To study pre-sowing seed treatment (seed priming) of hormone Mepiquat chloride (MC) (PIX) on physiological properties and increase drought tolerance in cotton (Shayan cultivar), a factorial experiment was performed in a completely randomized design with 3 replications. In this research, the seeds were initially exposed to two doses of the growth-regulatory hormone mepiquat chloride, including 5 and 10 g/l and control before being planted in pots.After the emergence of the plant from the soil, dry treatments based on the soil field capacity (FC) at three levels of irrigation at 25, 50, and 100% of field capacity for six weeks are applied to them, and growth indices, the activity of antioxidant enzymes, amount of hydrogen peroxide and malondialdehyde as well as changes in the two hormones of abscisic acid and endogenous gibberellin was investigated. According to the results of this research, the effect of seed priming with PIX and the interaction of drought × pre-sowing seed treatment on all physiological traits were significant at the level of 1%. The results of experimental data showed that drought stress (especially severe drought) reduced most growth indicator and physiological traits (fresh and dry weight of root and shoot, shoot length, fresh and dry weight of leaf, leaf area, root peroxidation activity, leaf phenol content, amount of gibberellin ) and only increased abscisic acid content (74%), root diameter (38%), root length(14%), leaf thickness, (15%) compared to the control. pre-sowing seed treatment with PIX, while increasing the dry weight (28% roots and 34% leaves) and thickness roots (58%) and 96% leaves (96%), increased catalase (88%) and peroxidase (6%) activity and abscisic acid content (73%) of leaves, reduced hydrogen peroxide (17%), malondialdehyde (20%) of leaves, and gibberellin (40%) of roots. Under extreme drought stress, the application of PIX decreased the amount of phenolic compounds in leaves by 44% as compared to the control. The interplay of drought stress × PIX decreased root gibberellin by 25% and raised leaf abscisic acid by 183%. According to the study results, seed priming with PIX improved the physiological and biochemical responses of cotton under drought conditions
SPECULATIVE BUBBLE AND ITS EXISTENCE IN STOCK MARKET OF PAKISTAN

This empirical study is an attempt to detect speculative bubbles in different indexes of the Pakistan stock market (KSE-100, KSE-30 & KMI-30) using monthly time series data from (2005– 2020) to evaluate all four Rtadf (Recursive, Right tail Augmented Dickey-Fuller) tests. This includes Standard ADF, Rolling-window ADF, Supreme ADF (SADF) and Generalized SADF (GSADF) with Monte-Carlo simulation, under Gaussian assumptions. The findings of this study confirm the two prominent episodes of exuberance and collapse of multiple speculative bubbles in the KSE-100 index while only a single (multiple) bubbles in KSE-30 and KMI-30 indexes using RADF & GSADF tests. The results, in other words, suggest among all four tests, RADF and GSADF are better models to detect the past bubbles in PSX. These results contribute to scarce literature through testing and date stamping bubbles in different indexes of (PSX) with the use of (all) right-tailed tests. For future research, advanced techniques can be employed to predict future bubbles along with their direction.
Detection of Ultra-Rare ESR1 Mutations in Primary Breast Cancer Using LNA-Clamp ddPCR

Simple Summary ESR1 mutations in breast cancer are one of the mechanisms of resistance to aromatase inhibitors (AI). These mutations are common in metastatic breast cancer (MBC). In past reports, mutations in primary tumors were so rare that they were thought to occur de novo with AI therapy. Conversely, previous studies using droplet digital polymerase chain reaction (ddPCR) have suggested the existence of ESR1-mutant minor clones with low allele frequencies; however, no large-scale studies have been conducted. In this study, we attempted to detect ultra-rare ESR1 mutations in primary breast cancer tumors using locked nucleic acid (LNA)-clamp ddPCR, and 28 ESR1 mutations were found in 27 patients. Most of these mutations were minor clones with a variant allele frequency of <0.1% which may have been overlooked by conventional methods. LNA-clamp ddPCR can also be applied to detect other gene mutations, which would be very useful. Abstract ESR1 mutations in breast cancer are one of the mechanisms of resistance to aromatase inhibitors. These mutations are common in metastatic breast cancer; however, these are rare in primary breast cancer. However, these data have been analyzed mainly in formalin-fixed, paraffin-embedded tissue; thus, rare mutations that may be present in primary breast cancer may be overlooked. In this study, we developed a highly sensitive mutation detection method called locked nucleic acid (LNA)-clamp droplet digital PCR (ddPCR) and validated it. The mutation detection sensitivity was substantiated to 0.003%. Then, we used this method to analyze ESR1 mutations in fresh-frozen (FF) tissues of primary breast cancer. cDNA extracted from the FF tissues of 212 patients with primary breast cancers were measured. Twenty-eight ESR1 mutations were found in twenty-seven (12.7%) patients. Sixteen (7.5%) patients had Y537S mutations and twelve (5.7%) had D538G mutations. Two mutations with a variant allele frequency (VAF) of ≥0.1% and twenty-six mutations with a VAF of <0.1% were found. By using this LNA-clamp ddPCR, this study demonstrated the presence of minor clones with a VAF of <0.1% in primary breast cancer.
Additive discrete 1D linear canonical transform

The continuous linear canonical transforms (LCT) can describe a wide variety of wave field propagations through paraxial (first order) optical systems. Digital algorithms to numerically calculate the LCT are therefore important in modelling scalar wave field propagations and are also of interest for many digital signal processing applications. The continuous LCT is additive, but discretization can remove this property. In this paper we discuss three special cases of the LCT for which constraints can be identified to ensure the DLCT is additive.
MagNeed – Needle-Shaped Electromagnets for Localized Actuation Within Compact Workspaces

Electromagnetic actuation of micro-/milli-sized agents has traditionally relied on large electromagnets positioned at considerable distances from the agents. As a result, the electromagnets consume kilowatts of power to overcome the limited generation of magnetic field gradients. Miniaturized electromagnets offer an alternative approach for reducing power consumption via localized actuation of micro-/milli-sized agents. Typically, the generation of magnetic field gradients in the vicinity of a miniaturized electromagnet is comparable with traditional electromagnetic actuation systems. Miniaturized electromagnets can be positioned near target sites in microfluidic channels or ex vivo vasculatures. Thereby, localized trapping and actuation of magnetic micro-/milli-sized agents are carried out. This study introduces MagNeed – an electromagnetic actuation system composed of three needle-shaped electromagnets (NSEs). MagNeed can determine compact workspaces by positioning the NSEs at different spatial configurations. Each NSE generates magnetic field gradients (up to 3.5 T/m at 5 mm from the NSE tip axis) while keeping a maximum power consumption (0.5 W) and temperature ($< $42 $^{\circ}$C). MagNeed is complemented by a framework that reconstructs the pose of the NSEs. Experiments test MagNeed and framework on a transparent Teflon tube (5 mm inner diameter). MagNeed demonstrates localized trapping and actuation of a 1 mm NdFeB bead against a flow of water and silica gel particles (1–3 mm diameter).
Identification of Alzheimer’s disease through Feature Extraction using Latent Factor of Multi-Channel EEG

One of the most prevalent and rapidly spreading neurological disorders in the world is Alzheimer's disease. There are several biomarkers available for the evaluation &growth of Alzheimer's disease, and these tools might be seen as crucial for therapeutic applications. EEG signals produce a potent and reasonably priced tool for the diagnosis of many neurological diseases. In this instance, it is necessary to increase the EEG signals' diagnostic precision. A novel approach for utilising EEG signals to diagnose Alzheimer's disease is suggested in this study, which will improve the diagnostic efficacy of the EEG signals and diagnosis. EEG Spectro-temporal Modulation Energy Based aspects, Spectral Features, Coherence Features, and other aspects of EEG Signals are examined in detail.It may be inferred from the characteristics of the EEG signal that were previously mentioned that it can be useful in the diagnosis of dementia and Alzheimer's disease. The main goal of the suggested strategy is to increase the precision of the patient monitoring system, remove variations in the EEG signal, and increase the precision of the procedure.
Brain Trauma and the Secondary Cascade in Humans: Review of the Potential Role of Vitamins in Reparative Processes and Functional Outcome

An estimated sixty-nine million people sustain a traumatic brain injury each year. Trauma to the brain causes the primary insult and initiates a secondary biochemical cascade as part of the immune and reparative response to injury. The secondary cascade, although a normal physiological response, may also contribute to ongoing neuroinflammation, oxidative stress and axonal injury, continuing in some cases years after the initial insult. In this review, we explain some of the biochemical mechanisms of the secondary cascade and their potential deleterious effects on healthy neurons including secondary cell death. The second part of the review focuses on the role of micronutrients to neural mechanisms and their potential reparative effects with regards to the secondary cascade after brain injury. The biochemical response to injury, hypermetabolism and excessive renal clearance of nutrients after injury increases the demand for most vitamins. Currently, most research in the area has shown positive outcomes of vitamin supplementation after brain injury, although predominantly in animal (murine) models. There is a pressing need for more research in this area with human participants because vitamin supplementation post-trauma is a potential cost-effective adjunct to other clinical and therapeutic treatments. Importantly, traumatic brain injury should be considered a lifelong process and better evaluated across the lifespan of individuals who experience brain injury.
Distributed Multiple Line-Outages Detection in Power Grid With Finite Time Observer

Multiple line-outages detection is an important and meaningful research task for power grid security. In this paper, we model the power grid as a distributed complex network and obtain real time power data from phase measurenment units (PMUs). A novel distributed multiple line-outages detection algorithm is proposed, in which each bus sensor receives data from its neighbor buses. By constructing a virtual finite time observer network, the topology variation of power grid can be detected in finite time. Finally, simulation results based on IEEE9 bus system show that the proposed algorithm is effective to detect line-outages in real time.
Sediment-encased pressure–temperature maturation experiments elucidate the impact of diagenesis on melanin-based fossil color and its paleobiological implications

Abstract. Melanin pigments are central to colors and patterns in modern vertebrate integuments, which inform upon ecological and behavioral strategies like crypsis, aposematism, and sociosexual selection. Over the last decade, melanin has emerged as a valuable tool for predicting color in exceptionally preserved fossil feathers and subsequent testing of paleobiological hypotheses in long-extinct dinosaurs and birds. Yet much remains to be learned about melanin stability, diagenetic alterations to melanin chemistry, and their implications for “paleocolor reconstruction. ” Pressure–temperature maturation experiments with modern feathers offer a way to examine these topics but have mostly been conducted in closed-system capsules or open-system aluminum foil. Both methods have operational limitations and do not consider the filtering effect of porous sediment matrices on thermally labile chemical groups versus stable ones during natural fossilization. We use sediment-encased maturation to resolve this issue and demonstrate replication of organic preservation of melanin highly comparable to compression fossils. Our experiments, coupled with time-of-flight secondary ion mass spectrometry, show predictable volatilization of N/S-bearing molecules and increased melanin cross-linking with elevated temperatures. We also suggest that eumelanin is more stable compared with pheomelanin at higher temperatures, explaining why eumelanic colors (black, dark brown, iridescent) are better preserved in fossils than pheomelanic ones (reddish brown). Furthermore, we propose that proteins preferentially undergo hydrolysis more so than forming N-heterocycles in selectively open systems analogous to natural matrices. Thus, we conclude that melanin pigments and not diagenetically altered protein remnants are the key players in promoting fossilization of soft tissues like feathers.
P3 AD: Privacy-Preserved Payload Anomaly Detection for Industrial Internet of Things

Payload-based anomaly detection (PAD) model is commonly built upon a big data of normal payload samples, and hence is able to discover zero-day attacks and unknown faults without the need of any negative samples in training phase. But such detection model encounters new challenges to adapt well to the emerging Industrial Internet of Things (IIoT). That is, the modern industrial processes are usually running in a very high complexity, resulting the payloads much more complex and diverse. Further, the industrial data is likely too sensitive to be shared in public, and thus induces a new privacy concern. To tackle these challenges, we propose ${\mathrm{ P}}^{3}{\mathrm{ AD}}$ , a novel privacy-preserved payload-based anomaly detection model for IIoT. The basic idea is to train a two-dimensional convolutional neural network (2D-CNN) based auto-encoder using normal payloads over a federated GAN (Generative Adversarial Network) architecture, and then to detect anomalies by an unexpected dissimilarity between the original payloads and the payloads reconstructed by the auto-encoder. By the 2D-CNN, we can model the normal payloads from both the request and response directions simultaneously, and thus have more opportunities to capture the complex and dynamic industrial behaviors that are possibly reflected in the bi-directional network communications. By the GAN, we can train a more generalized auto-encoder that is able to reconstruct more general payload samples without the need to have them in advance for model training. By the federated architecture, we can remove the need of direct sharing of normal payloads, and learn them indirectly by aggregating local models across different industrial data owners, hence ensuring the payload privacy. We have evaluated ${\mathrm{ P}}^{3}{\mathrm{ AD}}$ using four public industrial payload datasets as well as considering four typical IIoT PAD scenarios. The detection results achieve more than 0.966 in terms of F1 score for global condition and at least 0.753 for all kinds of federated settings, proving the effectiveness of our ${\mathrm{ P}}^{3}{\mathrm{ AD}}$ with privacy preserved.
Measuring Carotid Intima-Media Thickness in Young Adults Born Preterm: Comparison of Manual Versus Semi-automated B-Mode Ultrasound

Introduction: Young adults born very preterm have increased cardiovascular risks. While intima-media thickness of the common carotid artery is an early marker of atherosclerosis in adults, the most reliable method of its measurement is not established in this population. The purpose of this study is to compare manual and semi-automated B-mode ultrasound intima-media thickness measurement techniques in adults born very preterm and full-term. Methods: Intima-media thickness was measured in adults 18 to 29 years born very preterm (<30 weeks’ gestation) or full-term (≥37 weeks’ gestation) using B-mode ultrasound. Analyses included intraclass correlation coefficient (ICC) and relationships with prematurity status, age, body mass index, and metabolic and respiratory comorbidities. Results: There were 86 preterm-born participants (58% women) and 85 full-term (56% women), mean age 23 ± 2 years. Intra-observer agreement was better using semi-automated (ICC = 0.89; 95% confidence interval (CI) = [0.86-0.91]) than manual measurement (ICC = 0.73; 95% CI = [0.65-0.79]). Measures obtained using both techniques did not detect any differences according to prematurity status, did not correlate with age, and moderately correlated with body mass index. The semi-automated technique showed higher intima-media thickness values in men vs women and overall gave higher values. Results obtained through both techniques moderately correlated with each other when looking at subgroups: preterm group ICC of 0.53, full-term group ICC of 0.62. Conclusion: Manual and semi-automated intima-media thickness measurements moderately correlate in young adults. The semi-automated technique is more reproducible. Both techniques are useful in discriminating intima-media thickness according to weight. This study identifies variance of results according to different techniques of measurements. Adherence to a single technique is recommended in young adults born prematurely. Clinical implication includes adherence to the same technique for risk stratification and for follow-up of these patients.
A Comprehensive Evaluation Method of Energy Storage Battery Health Status Based on Combined Weights of Lifetime Characteristics and Operating Characteristics

As an important link to promote renewable energy consumption and ensure the normal operation of power system, the comprehensive evaluation of the health status of battery energy storage system is of great significance to improve the safety and stability of energy storage power plant operation. In this context, this paper takes battery energy storage system as the research object, focuses on the health status of energy storage battery, conducts innovative research on the establishment of its evaluation index system, and aims to propose a method of evaluating the health status of energy storage battery based on the combination of subjective and objective assignment method. Firstly, the evaluation index system including life time characteristics and operational safety is constructed to reflect the performance of the actual operation of the energy storage power plant comprehensively. Then, the subjective and objective weights are calculated based on Analytic Hierarchy Process (AHP) and inverse entropy weighting method and the optimal combination weights are obtained. Finally, a comprehensive evaluation model of energy storage battery health state is established based on the combination weights. The evaluation model is applied to three energy storage battery packs of a battery storage system, and the evaluation results are consistent with the actual operation, achieving an objective and reasonable evaluation of the health status of the energy storage battery, which is important for improving the safe and efficient operation of the power grid.
A Data-driven Approach with Improved Generalization Performance to Modeling Transient Behaviors of DC-DC Converters

Nowadays, the data-driven approaches to modeling power electronics (PE) systems are mostly based on sequential neural networks (NNs). These approaches may require too much data since the NNs can not generalize across a wide range of inputs. To address this issue, this paper proposes a new data-driven approach to modeling the transient behaviors of DC-DC converters, which is based on fully-connected NNs. The proposed method introduced prior knowledge about linear systems and thus significantly improved the generalization performance. In this method, circuit parameters are first mapped into linear system characteristics by fully-connected NNs, and then the outputs are calculated by the inputs and the system characteristics. Experiment results show that the entire circuit topology with configurable parameter settings and initial conditions can be successfully modeled. Parameter change events are also supported by this approach.
Well-Being, Resilience and Social Support of Athletes with Disabilities: A Systematic Review

Sport for people with disabilities appears to play a positive role in the well-being, resilience and social support of athletes with disabilities. Thus, this systematic review aims to evaluate the effect of adapted sport on the well-being, resilience and social support in a population with disabilities. The Pubmed, Web of Science, Scopus and SportDiscus databases were used, with several descriptors and Boolean operators. A total of 287 studies were identified through searching the databases. After the data extraction process, twenty-seven studies were included for analysis. In general, these studies show that adapted sport has a positive impact on the levels of well-being, resilience and social support resources for people with disabilities, contributing to their personal development, quality of life and integration into society. Considering the impact on the variables studied, these results are important to support and encourage the development of adapted sport.
In Vitro Gametogenesis in Oncofertility: A Review of Its Potential Use and Present-Day Challenges in Moving toward Fertility Preservation and Restoration

Current fertility preservation options are limited for cancer survivor patients who wish to have their own biological children. Human in vitro gametogenesis (IVG) has the hypothetical ability to offer a unique solution to individuals receiving treatment for cancer which subsequently shortens their reproductive lifespan. Through a simple skin punch biopsy, a patient’s fertility could be restored via reprogramming of dermal fibroblast cells to induced pluripotent stem cells, then from primordial germ cell-like cells into viable oocytes and spermatocytes which could be used for embryogenesis. Induced pluripotent stem cells could also be used to form in vitro environments, similar to the ovary or testes, necessary for the maturation of oogonia. This would allow for the entire creation of embryos outside the body, ex vivo. While this area in stem cell biology research offers the potential to revolutionize reproduction as we know it, there are many critical barriers, both scientific and ethical, that need to be overcome to one day see this technology utilized clinically.
Signal Processing with Spiking Neuron Model

The article deals with signal processing models that use the representation of signals by spike trains. The “input-output” model of an integrate and fire spiking neuron is considered. The possibilities of using this model for spectral analysis and adaptive filtering are investigated. To solve the problem of adaptive processing of spike sequences, a generalized multi-input vector-matrix model of a spiking neuron is introduced. Based on this model, we derive supervised learning rules to adapt the dynamics of the spiking neuron. Examples of adaptive signal recovery from spike trains, additive noise suppression, and adaptive synthesis of band-pass filters are demonstrated.