Miracle-dz/UniFAR-FTUs huggingface dataset:
Endophytic Nanotechnology: An Approach to Study Scope and Potential Applications

Nanotechnology has become a very advanced and popular form of technology with huge potentials. Nanotechnology has been very well explored in the fields of electronics, automobiles, construction, medicine, and cosmetics, but the exploration of nanotecnology’s use in agriculture is still limited. Due to climate change, each year around 40% of crops face abiotic and biotic stress; with the global demand for food increasing, nanotechnology is seen as the best method to mitigate challenges in disease management in crops by reducing the use of chemical inputs such as herbicides, pesticides, and fungicides. The use of these toxic chemicals is potentially harmful to humans and the environment. Therefore, using NPs as fungicides/ bactericides or as nanofertilizers, due to their small size and high surface area with high reactivity, reduces the problems in plant disease management. There are several methods that have been used to synthesize NPs, such as physical and chemical methods. Specially, we need ecofriendly and nontoxic methods for the synthesis of NPs. Some biological organisms like plants, algae, yeast, bacteria, actinomycetes, and fungi have emerged as superlative candidates for the biological synthesis of NPs (also considered as green synthesis). Among these biological methods, endophytic microorganisms have been widely used to synthesize NPs with low metallic ions, which opens a new possibility on the edge of biological nanotechnology. In this review, we will have discussed the different methods of synthesis of NPs, such as top-down, bottom-up, and green synthesis (specially including endophytic microorganisms) methods, their mechanisms, different forms of NPs, such as magnesium oxide nanoparticles (MgO-NPs), copper nanoparticles (Cu-NPs), chitosan nanoparticles (CS-NPs), β-d-glucan nanoparticles (GNPs), and engineered nanoparticles (quantum dots, metalloids, nonmetals, carbon nanomaterials, dendrimers, and liposomes), and their molecular approaches in various aspects. At the molecular level, nanoparticles, such as mesoporous silica nanoparticles (MSN) and RNA-interference molecules, can also be used as molecular tools to carry genetic material during genetic engineering of plants. In plant disease management, NPs can be used as biosensors to diagnose the disease.
The Prognostic Value of the Acute Phase Systemic Immune–Inflammation Index in Patients With Intracerebral Hemorrhage

Background and Purpose: The systemic immune–inflammation index (SII) is a novel prognostic index in various diseases. We evaluated the predictive value of SII in patients with intracerebral hemorrhage (ICH). Methods: Patients with primary spontaneous ICH were enrolled. SII was constructed based on peripheral platelet (P), neutrophil (N), and lymphocyte (L) and defined as P*N/L. In addition to admission testing, acute phase SII was collected to analyze the potential dynamic change. Poor outcome was defined as modified Rankin Scale of more than 3 at 90 days. Results: We included 291 patients; 98 (34%) achieved favorable functional outcomes. Day-1 SII was higher and was more related to poor outcome than was admission SII. Median time of day-1 SII was 29 h from onset. Day-1 SII had an OR in outcome (mRS >3) 1.74 (95% CI = 1.03–3.00, p = 0.04). The binary cutoff point of SII calculated using the area under the curve (AUC) method was 1,700 × 109/L, AUC 0.699 (95% CI = 0.627–0.774) (sensitivity 53.3%, specificity 77.3%) (OR = 2.36, 95% CI = 1.09–5.26, p = 0.03). Conclusions: SII, especially day-1 SII, was highly associated with 90-day functional outcome in patients with ICH and could be used to predict outcomes.
Novel Long Non-coding RNA and LASSO Prediction Model to Better Identify Pulmonary Tuberculosis: A Case-Control Study in China

Introduction The insufficient understanding and misdiagnosis of clinically diagnosed pulmonary tuberculosis (PTB) without an aetiological evidence is a major problem in the diagnosis of tuberculosis (TB). This study aims to confirm the value of Long non-coding RNA (lncRNA) n344917 in the diagnosis of PTB and construct a rapid, accurate, and universal prediction model. Methods A total of 536 patients were prospectively and consecutively recruited, including clinically diagnosed PTB, PTB with an aetiological evidence and non-TB disease controls, who were admitted to West China hospital from Dec 2014 to Dec 2017. The expression levels of lncRNA n344917 of all patients were analyzed using reverse transcriptase quantitative real-time PCR. Then, the laboratory findings, electronic health record (EHR) information and expression levels of n344917 were used to construct a prediction model through the Least Absolute Shrinkage and Selection Operator algorithm and multivariate logistic regression. Results The factors of n344917, age, CT calcification, cough, TBIGRA, low-grade fever and weight loss were included in the prediction model. It had good discrimination (area under the curve = 0.88, cutoff = 0.657, sensitivity = 88.98%, specificity = 86.43%, positive predictive value = 85.61%, and negative predictive value = 89.63%), consistency and clinical availability. It also showed a good replicability in the validation cohort. Finally, it was encapsulated as an open-source and free web-based application for clinical use and is available online at https://ziruinptb.shinyapps. io/shiny/. Conclusion Combining the novel potential molecular biomarker n344917, laboratory and EHR variables, this web-based prediction model could serve as a user-friendly, accurate platform to improve the clinical diagnosis of PTB.
Article Title Variations in Growing Season NDVI and Its Sensitivity to Climate Change Responses to Green Development in Mountainous Areas

The Normalized Difference Vegetation Index (NDVI) is sensitive to changes in surface vegetation cover. Research into how climate change impacts surface vegetation cover is essential to manage ecological systems and promote green development. The Western Henan Mountains, located in the transitional zone between the northern subtropical and warm temperate zones of China, is an ideal location to study the impacts of climate change on surface vegetation cover. Combining a digital elevation model (DEM) with temperature and precipitation data; and MODIS-NDVI imagery (2000∼2017) for the Western Henan Mountains, this study explores variations in the growing season NDVI and its response to climate change. Results show that there are significant changes with fluctuations in NDVI values from 2000 to 2017. NDVI increased at a growth rate of 0.027 per decade (p < 0.05) overall, indicating vegetation conditions have gradually improved. Although the NDVI value showed an overall increasing trend, 19.12% of the areas showed a decreasing trend, interspersing and intersecting spatially, showing significant spatial differences. NDVI increased initially, but then decreased as a function of elevation, which was shown to be proportional to slope and independent of aspect. Variables including elevation and slope gradient are shown to provide high explanation of NDVI variability, whilst temperature is shown to have a more significant impact on NDVI than precipitation. However, vegetation responses to temperature and precipitation covaried with both slope and aspect. Positive NDVI trends were strongest at low elevations (i.e., <1,100 masl), which we attribute to vegetation restoration activities. Lower NDVI values characterized gentle slopes (<5°), whilst higher values were, in contrast, associated with steeper slopes (5∼10°). This study highlights the complex mechanisms and their relations governing vegetation response to climate change and should form an instructive basis for both future modeling studies investigating the response of vegetation to future global warming.
DeepFoG: An IMU-Based Detection of Freezing of Gait Episodes in Parkinson’s Disease Patients via Deep Learning

Freezing of Gait (FoG) is a movement disorder that mostly appears in the late stages of Parkinson’s Disease (PD). It causes incapability of walking, despite the PD patient’s intention, resulting in loss of coordination that increases the risk of falls and injuries and severely affects the PD patient’s quality of life. Stress, emotional stimulus, and multitasking have been encountered to be associated with the appearance of FoG episodes, while the patient’s functionality and self-confidence are constantly deteriorating. This study suggests a non-invasive method for detecting FoG episodes, by analyzing inertial measurement unit (IMU) data. Specifically, accelerometer and gyroscope data from 11 PD subjects, as captured from a single wrist-worn IMU sensor during continuous walking, are processed via Deep Learning for window-based detection of the FoG events. The proposed approach, namely DeepFoG, was evaluated in a Leave-One-Subject-Out (LOSO) cross-validation (CV) and 10-fold CV fashion schemes against its ability to correctly estimate the existence or not of a FoG episode at each data window. Experimental results have shown that DeepFoG performs satisfactorily, as it achieves 83%/88% and 86%/90% sensitivity/specificity, for LOSO CV and 10-fold CV schemes, respectively. The promising performance of the proposed DeepFoG reveals the potentiality of single-arm IMU-based real-time FoG detection that could guide effective interventions via stimuli, such as rhythmic auditory stimulation (RAS) and hand vibration. In this way, DeepFoG may scaffold the elimination of risk of falls in PD patients, sustaining their quality of life in everyday living activities.
Impact of Lymphadenectomy on Outcomes of Early-Stage Ovarian Cancer: A Systematic Review and Meta-Analysis

Objective: The study aimed to assess if additional lymphadenectomy with primary staging surgery improves overall survival (OS) and disease-free survival (DFS) of early-stage ovarian cancer (ESOC). Methods: PubMed and Embase databases were searched for any type of study comparing OS or DFS between lymphadenectomy and control groups for any type of ESOC. Adjusted hazard ratios (HR) were pooled in a random-effects model. Results: Twelve studies were included. Meta-analysis indicated that lymphadenectomy is associated with significantly improved OS only for epithelial tumors (HR 0.75 95% CI 0.68, 0.82 I2 = 0% p < 0.00001) but not for malignant germ cell tumors (HR 1.31 95% CI 0.88, 1.94 I2 = 0% p = 0.18). Single studies indicated a tendency of improved OS with lymphadenectomy which was significant for ovarian carcinosarcoma but not for sex cord-stromal tumors. On meta-regression of all histological types, the percentage of patients with lymph node metastasis in the lymphadenectomy group was not found to influence the effect size. Meta-analysis also indicated that lymphadenectomy is associated with significantly improved DFS for epithelial tumors (HR 0.59 95% CI 0.45, 0.77 I2 = 0% p < 0.0001). Single studies on malignant germ cell and sex cord-stromal tumors failed to demonstrate any significant beneficial effect of lymphadenectomy on DFS. Conclusions: Within the limitations of the review, lymphadenectomy may improve OS and DFS for epithelial ESOC. Scarce data suggest that lymphadenectomy is not associated with improved outcomes for malignant germ cell and sex cord-stromal tumors but may benefit ovarian carcinosarcoma. Large-scale RCTs and robust observational studies shall improve current evidence.
Effect of a “Dual Trigger” Using a GnRH Agonist and hCG on the Cumulative Live-Birth Rate for Normal Responders in GnRH-Antagonist Cycles

“Dual triggering” for final oocyte maturation using a combination of a gonadotropin-releasing hormone agonist (GnRHa) and human chorionic gonadotropin (hCG) can improve clinical outcomes in high responders during in vitro fertilization–intracytoplasmic sperm injection (IVF–ICSI) GnRH-antagonist cycles. However, whether this dual trigger is also beneficial to normal responders is not known. We retrospectively analyzed the data generated from 469 normal responders from 1 January to 31 December 2017. The final oocyte maturation was undertaken with a dual trigger with a GnRHa combined with hCG (n = 270) or hCG alone (n = 199). Patients were followed up for 3 years. The cumulative live-birth rate was calculated as the first live birth achieved after all cycles having an embryo transfer (cycles using fresh embryos and frozen–thawed embryos) among both groups. Women in the dual-trigger group achieved a slightly higher number of oocytes retrieved (11.24 vs. 10.24), higher number of two-pronuclear (2PN) embryos (8.37 vs. 7.67) and a higher number of embryos available (4.45 vs. 4.03). However, the cumulative live-birth rate and the all-inclusive success rate for assisted reproductive technology was similar between the two groups (54.07 vs. 59.30%). We showed that a dual trigger was not superior to a hCG-alone trigger for normal responders in GnRH-antagonist cycles in terms of the cumulative live-birth rate.
Changes of humic substances and organic nitrogen forms during municipal solid waste composting

Municipal solid wastes (MSW) composting with industrialized technology was studied in order to characterize the transformation of humic substances and nitrogen forms during the process. At different compost stages, the samples were taken for the determination of the humic substances and nitrogen forms. The results showed organic C, fulvic acid, total N, and acid hydrolyzing organic N were decreased by 53.00%, 64.73%, 21.78%, and 22.80% at final stage compared to those at initial stage of composting, respectively. Humus, humic acid, and amino acid N declined from 0 d to 35 d, then increased from 35 d to 63 d. Amide N and amino sugar N achieved to the maximum value at 21d during composting. The linear relation was found between humic substances and humic acid, as well as between acid hydrolyzing organic N and total N. The characteristics of fourier transform infrared spectroscopy (FTIR) indicated that aromatization of humic acid increased during composting.
Dynamics and Stability of Carbon Nanotubes

The low-frequency oscillations and energy localization of Single-Walled Carbon Nanotubes (SWNTs) are studied in the framework of the Sanders-Koiter shell theory. The circumferential flexure modes (CFMs) are analysed. Simply supported, clamped and free boundary conditions are considered. Two different approaches are proposed, based on numerical and analytical models. The numerical model [1] uses in the linear analysis a double mixed series expansion for the displacement fields based on Chebyshev polynomials and harmonic functions. The Rayleigh-Ritz method is applied to obtain approximate natural frequencies and mode shapes. In the nonlinear analysis, the three displacement fields are re-expanded by using approximate eigenfunctions. An energy approach based on Lagrange equations is considered in order to obtain a set of nonlinear ordinary differential equations, which is solved by the Runge-Kutta numerical method. The analytical model [2] considers a reduced version of the Sanders-Koiter shell theory obtained by assuming small circumferential and tangential shear deformations. These two assumptions allow to condense the longitudinal and circumferential displacement fields into the radial one. A nonlinear fourth-order partial differential equation for the radial displacement field is derived, which allows to calculate the natural frequencies and to estimate the nonlinearity effect. An analytical solution of this equation is obtained by the multiple scales method. The previous models are validated in linear field by means of comparisons with experiments, molecular dynamics simulations and finite element analyses retrieved from the literature [3]. The concept of energy localization in SWNTs is introduced, which is a strongly nonlinear phenomenon. The low-frequency nonlinear oscillations of the SWNTs become localized ones if the intensity of the initial excitation exceeds some threshold which depends on the SWNTs length. This localization results from the resonant interaction of the zone-boundary and nearest nonlinear normal modes leading to the confinement of the vibration energy in one part of the system [4]. The value of the initial excitation corresponding to this energy confinement is referred to as energy localization threshold. The effect of the aspect ratio on the analytical and numerical values of the energy localization threshold is investigated; different boundary conditions are considered.
Systemic gene delivery using lipid envelope systems and its potential in overcoming challenges

DOI:https://doi.org/10.47957/ijpda.v 9i1.458 Nonviral vectors which offer a safer and versatile alternative to viral vectors have been developed to overcome problems caused by viral carriers. However, their transfection efficacy or level of expression is substantially lower than viral vectors. Among various nonviral gene vectors, lipid envelops systems are an ideal platform for the incorporation of safety and efficacy into a single delivery system. Emerging strategies for gene delivery using lipid-based delivery systems mainly aim at improving the transfection efficiency and potency while reducing toxicity, achieving prolonged release, cell-specific targeting, co-delivery of drug and gene. Earlier efforts to improve the transfection efficiency while overcoming the toxicity led to the need for preparing conjugates of lipids with polyamines. In this review, we highlight current lipidic vectors that have been developed for gene therapy, challenges, and their solutions.
How may I persuade you to trust AI? Promote Customized Explainable AI through Information Vividness

Artificial intelligence (AI) has been criticized for its black-box nature that confuses how outputs are derived. Some have proposed that explainable artificial intelligence (XAI) can address the issue and enhance users’ trust in AI. Drawing on the lens of persuasion theory, we develop a research model that depicts how explanation with vividness and user characteristics independently and jointly shape trust in AI. To test the model and associated hypotheses, we conduct an online experiment. The results suggest that individual characteristics not only directly affect trust but also moderate the relationship between explanation vividness and trust.
AM-AN: Adversarial Network Based on Attention Mechanism for Medical Image Segmentation

Image segmentation is crucial to a series of medical applications. One of the current problems is that imbalanced pixel classes have a negative effect on the results of medical image segmentation. However, the method of employing weighted loss cannot address this problem well. Therefore, we are motivated to propose a novel method based on Generative Adversarial Networks (GANs) and attention mechanisms to train our segmentation model with effective loss functions. Firstly, the proposed model consists of the segmentor network and the critic network, which are trained by adversarial learning. Moreover, the attention module we used in each residual block takes not only channel attention but also spatial attention into consideration and makes the segmentor perform better. The overhead of computation and parameters costed by our attention module is negligible. In addition, our work extends other methods by means of using pixel-wise loss functions, which include multi-scale loss and binary cross entropy loss. Finally, our work presents the results of comparing three different methods on medical image segmentation and our method yields a higher performance than theirs on MICCAI PROMISE12, DSB2018 and the lung nodule datasets.
Contribution to infra-specific taxonomy of Primula vulgaris Huds. (Primulaceae) along an altitudinal gradient in Turkey

This study was performed to explore the relationship between morphological characters and nrDNA ITS data belonging to two subspecies of Primula vulgaris Huds. (Primulaceae) along the altitudinal gradient over NE Anatolia. There were no significant molecular differences between P. vulgaris subsp. vulgaris and P. vulgaris subsp. sibthorpii (Hoffmanns) W.W. Sm. & Forrest related to the flower colour and altitudinal gradient. However, Maximum parsimony (MP) analysis showed that all the examined populations fall in two distinct clusters confirming that the analyzed sample corresponds to two subspecies. It was also determined that the presence or absence of leaf pubescence does not vary along the altitudinal gradient within each subspecies.
Efficient Modelling of ICS Communication For Anomaly Detection Using Probabilistic Automata

Industrial Control System (ICS) communication transmits monitoring and control data between industrial processes and the control station. ICS systems cover various domains of critical infrastructure such as the power plants, water and gas distribution, or aerospace traffic control. Security of ICS systems is usually implemented on the perimeter of the network using ICS enabled firewalls or Intrusion Detection Systems (IDSs). These techniques are helpful against external attacks, however, they are not able to effectively detect internal threats originating from a compromised device with malicious software. In order to mitigate or eliminate internal threats against the ICS system, we need to monitor ICS traffic and detect suspicious data transmissions that differ from common operational communication. In our research, we obtain ICS monitoring data using standardized IPFIX flows extended with meta data extracted from ICS protocol headers. Unlike other anomaly detection approaches, we focus on modelling the semantics of ICS communication obtained from the IPFIX flows that describes typical conversational patterns. This paper presents a technique for modelling ICS conversations using frequency prefix trees and Deterministic Probabilistic Automata (DPA). As demonstrated on the attack scenarios, these models are efficient to detect common cyber attacks like the command injection, packet manipulation, network scanning, or lost connection. An important advantage of our approach is that the proposed technique can be easily integrated into common security information and event management (SIEM) systems with Netflow/IPFIX support. Our experiments are performed on IEC 60870-5-104 (aka IEC 104) control communication that is widely used for the substation control in smart grids.
ANALYSIS OF THE DEVELOPMENT AND CURRENT STATE OF THE USE OF POWER PLANTS BASED ON PNEUMATIC ENGINES

Abstract. The article presents the main stages of development and use of compressed air energy in transport plants for various purposes. The domestic and foreign literature on the development of the current state and prospects of the use of compressed air energy and pneumatic engines in the power plant of vehicles have been analyzed. The list of problems arising in the application of pneumatic engines is determined, as well as rational ways of further increasing the efficiency of power plants are established. The main advantages and disadvantages of pneumatic engine using in the composition of transport power plants are highlighted. So, the main advantages include high environmental friendliness, depreciation of vehicle production, lower mass and dimensions parameters. Disadvantages include low efficiency coefficient due to multiple energy conversion, limited power consumption of filling cylinders and engine cooling due to the expansion of air. The most common pneumatic engine designs (piston and rotary) and their performance properties are considered. It has been established that rotary pneumatic engines, in terms of energy, mass and dimensions, and performance parameters, exceed other engines. Some of their inherent operational disadvantages, which may limit their use in some spheres, can be eliminated or reduced to acceptable standards at the right design solution. The variants of the basic schemes of power transport plants with pneumatic engines, namely the use of a pneumatic engine as an auxiliary power unit (hybrid units) or, as the main one in aeromobiles, are considered.