Miracle-dz/UniFAR-FTUs huggingface dataset:
Current advances in cancer vaccines targeting NY-ESO-1 for solid cancer treatment

New York-esophageal cancer 1 (NY-ESO-1) belongs to the cancer testis antigen (CTA) family, and has been identified as one of the most immunogenic tumor-associated antigens (TAAs) among the family members. Given its ability to trigger spontaneous humoral and cellular immune response and restricted expression, NY-ESO-1 has emerged as one of the most promising targets for cancer immunotherapy. Cancer vaccines, an important element of cancer immunotherapy, function by presenting an exogenous source of TAA proteins, peptides, and antigenic epitopes to CD4+ T cells via major histocompatibility complex class II (MHC-II) and to CD8+ T cells via major histocompatibility complex class I (MHC-I). These mechanisms further enhance the immune response against TAAs mediated by cytotoxic T lymphocytes (CTLs) and helper T cells. NY-ESO-1-based cancer vaccines have a history of nearly two decades, starting from the first clinical trial conducted in 2003. The current cancer vaccines targeting NY-ESO-1 have various types, including Dendritic cells (DC)-based vaccines, peptide vaccines, protein vaccines, viral vaccines, bacterial vaccines, therapeutic whole-tumor cell vaccines, DNA vaccines and mRNA vaccines, which exhibit their respective benefits and obstacles in the development and application. Here, we summarized the current advances in cancer vaccines targeting NY-ESO-1 for solid cancer treatment, aiming to provide perspectives for future research.
Unraveling the potentials of resveratrol: An important polyphenolic compound in health with clinical significance in chronic diseases

A stilbenoid , natural phenol, called resveratrol (3, 5, 4′ -trihydroxy-trans-stilbene), is a phytoalexin produced by a number of plants in reaction to injury by pathogens like bacteria or fungi. As a member of the viniferin family of polyphenols, resveratrol is one of the most well-known and extensively researched substances. Free radical-induced oxidative stress is a key component in the pathogenesis of atherosclerosis, neoplasia
Dynamic video service migration in flying edge computing networks

Recently, Mobile Edge Computing (MEC) and service migration policies have shown promising results to improve the user experience and optimize infrastructure resources. In addition, Unmanned Aerial Vehicles (UAVs) appear as a promising solution to provide cloud service in collaboration with traditional MEC scenarios. However, in conjunction with the resources available on users' devices, contextual information has been ignored by most policies. In this article, we propose a service migration strategy based on contextual information and evaluate the influence of user mobility on migration strategies, called DVSM. Simulation results highlight the superior performance of the DVSM compared to state-of-the-art algorithms and a performance equivalent to the optimal solution when the collection and analysis of context information are carried out correctly.
EFFECT OF FORMULATION VARIABLES ON SENSORY QUALITY OF THERMO-REVERSIBLE FRUIT FILLING FOR FROZEN SEMI-FINISHED BAKERY PRODUCTS: MODELLING AND OPTIMIZATION

Development of a food product requires integrated scientific and professional knowledge on the technological process, the final product and, the quality of the ingredients and their interaction. This research examined the effect of formulation variables on the sensory quality of a thermo-reversible sour cherry fruit filling, as well as the modelling and optimization of the variables for achieving the quality suitable for filling frozen semi-finished bakery products in industrial conditions. Each formulation for 15 model-samples was successively corrected, to achieve the appropriate sensory quality of the filling in finished products after baking. Their quality was evaluated by descriptive sensory and physico-chemical methods of analysis and compared. The basic formulation for the filling was defined after the successive production of 7 model-samples in laboratory conditions, their use in industrial conditions (puff pastry filling, shaping, quick freezing, storage for at least 24 hours at -18°C, baking and cooling to room temperature) and analysing their quality. Further formulation optimization included selecting: the type and amount of fruits (sour cherry, apple), food additives and cherry flavour for 8 new model-samples. The formulation and ingredients for the fruit filling production (30% sour cherry fruit, 30% apple puree, 58% sugar, 2% starch, 1% pectin, 0.03% firming agent E509, 0.03% acidity regulator E331, 0.025% cherry flavour) and quality parameters (dry matter 65%, acidity 0.85%, pH = 3.2-3.4) were defined. The fruit filling had appropriate sensory quality before and after baking of filled puff pastry: clear cherry-red colour, moderately firm consistency and stable volume, rich fruity aroma, and refreshing sweet-sour taste. The results also confirmed that the product was suitable for manual or machine filling of raw dough.
Exploiting Censored Information in Self-Training for Time-to-Event Prediction

A common problem in medical applications is predicting the time until an event of interest such as the onset of a disease, time to tumor recurrence, and time to mortality. Traditionally, classical survival analysis techniques have been used to address this problem. However, these techniques are of limited usage when considering nonlinear and interaction effects among biomarkers, and high profiling survival datasets. Although supervised machine learning techniques have shown some advantages over standard statistical methods in handling high-dimensional datasets, their application to survival analysis, particularly in the context of feature-based approaches, is at best limited. A major reason behind this is the difficulty in processing censored data, which is a common component of survival analysis. In this paper, we have transformed the time-to-event prediction problem into a semi-supervised regression problem. We utilize a self-training wrapper approach, where an outer layer guides the iterative refinement of predictions. This approach enhances the performance of our model by leveraging confident predictions from censored instances. The self-training wrapper is applied in conjunction with random survival forests as the base learner. In this approach, censored observations are introduced as partially labeled observations since their predicted time (target value) should exceed the censoring time. First, the algorithm builds a base model over the observed instances and then augments them iteratively with highly confident predictions over the censored set, using a smart stopping criterion based on the censoring time. The proposed approach has been evaluated and compared on fifteen real-world survival analysis datasets, including clinical and high-dimensional data. The ability of our proposed approach to integrate partial supervision information within a semi-supervised learning strategy has enabled it to achieve competitive performance compared to baseline models, particularly in the case of a high-dimensional regime.
Understanding neuropsychiatric symptoms in Alzheimer’s disease: challenges and advances in diagnosis and treatment

Neuropsychiatric symptoms (NPS) in Alzheimer’s disease (AD) affect up to 97% of AD patients, with an estimated 80% of current AD patients experiencing these symptoms. Common AD-associated NPS include depression, anxiety, agitation, aggression, and apathy. The severity of NPS in AD is typically linked to the disease’s progression and the extent of cognitive decline. Additionally, these symptoms are responsible for a significant increase in morbidity, mortality, caregiver burden, earlier nursing home placement, and greater healthcare expenditure. Despite their high prevalence and significant impact, there is a notable lack of clinical research on NPS in AD. In this article, we explore and analyze the prevalence, symptom manifestations, challenges in diagnosis, and treatment options of NPS associated with AD. Our literature review reveals that distinguishing and accurately diagnosing the NPS associated with AD remains a challenging task in clinical settings. It is often difficult to discern whether NPS are secondary to pathophysiological changes from AD or are comorbid psychiatric conditions. Furthermore, the availability of effective pharmaceutical interventions, as well as non-pharmacotherapies for NPS in AD, remains limited. By highlighting the advance and challenges in diagnosis and treatment of AD-associated NPS, we aspire to offer new insights into the complexity of identifying and treating these symptoms within the context of AD, and contribute to a deeper understanding of the multifaceted nature of NPS in AD.
Index-Modulation OAM Detectors Resistant to Beam Misalignment

Orbital angular momentum with index modulation (OAM-IM) has a great potential of providing high spectral efficiency and energy efficiency by utilizing the indices of the orthogonal OAM modes. However, the harsh requirement of perfect alignment of the transceiver beams introduces great challenges to OAM-IM wireless communications. Therefore, we first propose an angle of arrival (AoA)-based robust detector for the misaligned OAM-IM system, which explicitly estimates the AoA of the OAM beam and then utilizes the estimate to detect the transmitted symbols. To further reduce the system overhead and complexity, we propose another deep learning (DL)-based robust detector, which implicitly estimates the AoA and directly recovers the transmitted information bits. By using the dataset collected through simulation, the first step is to train the DL-based robust detector offline to minimize the mean-squared error, and the second step is to use the trained model for real-time OAM-IM signal detection online. Numerical simulations validate that the both proposed robust detectors can address the channel distortion in OAM channels with beam misalignment and achieve superior bit error rate (BER) performance at high spectral efficiency. Moreover, the proposed DL-based robust detector is less complicated on runtime than the traditional OAM-IM detector.
Effects of Different Proportions of Amaranthus hypochondriacus Stem and Leaf Powder Inclusions on Growth Performance, Carcass Traits, and Blood Biochemical Parameters of Broilers

Simple Summary Considering the shortage of conventional feeds, such as corn and soybeans, researchers have gradually started to develop and apply unconventional feed resources. As an unconventional feed resource, Amaranthus hypochondriacus is a potential high-quality feed resource. Hence, this study applied different proportions of Amaranthus hypochondriacus stem and leaf powder in experimental diets to assess its nutritional value for broilers. Abstract This experiment aimed to study the effects of different proportions of Amaranthus hypochondriacus stem and leaf powder (AHSL) on the growth performance, apparent nutrient digestibility, carcass traits, meat quality, and blood biochemical parameters of broilers from day 1 to day 42. The experiment utilized a single-factor experimental design, with a total of 216 one-day-old male broilers (Ross 308) randomly assigned to three dietary treatment groups (eight replicate cages of nine birds per cage). The dietary treatments included a control diet, a 3% AHSL diet and a 6% AHSL diet for days 0–21. Then, the 3% and 6% AHSL diets were changed to 5% and 10% AHSL for days 22–42. The results showed that the inclusion levels of AHSL did not affect growth performance, carcass traits, or meat quality on days 21 and 42 (p > 0.05). However, the inclusion levels of AHSL decreased the apparent nutrient digestibility (AND) of dry matter (DM) (p ˂ 0.001) and neutral detergent fiber (NDF) (p ˂ 0.001) and increased the serum concentration of phosphorus (p ˂ 0.001) on day 21. On day 42, the inclusion levels of AHSL decreased the AND of DM (p = 0.025) and NDF content (p ˂ 0.001), but increased the AND of crude protein (CP) (p = 0.004). In particular, the diet containing 10% AHSL significantly increased the serum enzyme activity of alkaline phosphatase (ALP) (p = 0.046) and the serum concentration of total protein (TP) (p ˂ 0.001) on day 42. Overall, AHSL can be used as a new and effective feed ingredient in broiler diets. It can replace part of the corn–soybean meal diet without any adverse effects, which is beneficial for conserving feed resources. Additionally, AHSL can be included at a level of up to 10% during the broiler growth period.
Analytical advantages of highly stable stationary phases for reversed-phase LC

can be exploited (see Figure 1). For example, chromatographers can use high pH to suppress amine protonation and low pH to suppress the ionization of acidic solutes. Extreme chemical stability also enables the cleaning of fouled columns under very acidic conditions and the sterilization or depyrogenation of columns with alcoholic basic solutions. By using low-pH conditions, analysts can increase the retention of anions by protonating them, thereby avoiding the need for quaternary amine ion-pairing agents. Similarly, one can, in principle, increase the retention of positively charged amines by raising the pH to higher than the pKa of the protonated Bronsted acid. Unfortunately, the pH required to fully protonate many carboxylic acids and completely deprotonate aliphatic amines lies outside the stable range (pH 2–8) of silica-based phases (3). Thermal column stability has its own distinct inherent advantages (see Figure 1). It can enable analyses at higher column temperatures, which lower mobile-phase viscosity and lessen the mechanical wear and tear on LC pumping systems. Higher run temperatures also lower the retention of solutes in reversedphase mode, and this process often results in During the past five years, many manufacturers of high performance liquid chromatography (HPLC) columns have focused on improving stationaryphase stability and reproducibility. Improved column stability — both chemical and thermal — offers new advantages, such as decreased analysis time and new methods of selectivity optimization. More stable HPLC packing materials have been achieved through advances in silane chemistry; however, the greatest improvements in stability have resulted from the use of alternative nonsilica supports such as synthetic organic polymers, alumina, and zirconia. In this article, the authors describe their use of various test solutes to compare the efficiency, selectivity, and hydrophobic retention mechanisms of five commercially available HPLC columns based on silica, alumina, zirconia, and polystyrene cross-linked with divinylbenzene (PS–DVB). Analytical Advantages of Highly Stable Stationary Phases for Reversed-Phase LC
Cold atmospheric plasma-induced acidification of tissue surface: visualization and quantification using agarose gel models

The biological effects induced by cold atmospheric plasmas (CAPs) on human tissues are mainly due to the production of reactive oxygen and nitrogen species (RONS). Some RONS are also responsible for pH lowering of the treated medium. The CAP-induced acidification has beneficial effect on biological tissues, contributing to the anti-bacterial effect and to the healing improvement observed in treated wound. In this work we investigated the local acidification induced by a helium CAP treatment using tissue models made of agarose gels with adjusted pH around 7.4 to mimic generic organs or around 5.5 to simulate skin surface pH. Using fluorescein as a pH-sensitive fluorescent marker, we developed a useful technique to visualize and quantify the local acidification induced by CAP exposure of tissue surface. The different capillaries used to produce the plasma jet, the treatment time, the initial pH of the surface and the buffer capacity of the tissue model were shown to modulate both the size of the impacted surface and the intensity of the pH decrease. The proposed technique can be advantageous to study the acidifying effect induced by plasma. This method can help to plan safe and controlled plasma treatments in order to avoid hyper-acidification of the tissue, especially when a localized treatment is administered.
Systematic Configuration for Hyperparameters Optimization in Transferring of CNN Model to Disaster Events Classification from UAV Images

Deep learning and computer vision-based approaches incorporated with the evolution of the relevant technologies of Unmanned Aerial Vehicles (UAVs) and drones have significantly motivated the advancements of disaster management applications. This research studied a classification method for disaster event identification from UAV images that is suitable for disaster monitoring. A Convolution Neural Network (CNN) of GoogleNet models that were pretrained from ImageNet and Place365 datasets was explored to find the appropriate one for fine-tuning to classify the disaster events. In order to get the optimal performance, a systematic configuration for searching the hyperparameters in fine-tuning the CNN model was proposed. The top three hyperparameters that affect the performance, which are the initial learning rate, the number of epochs, and the minibatch size, were systematically set and tuned for each configuration. The proposed approach consists of five stages, during which three types of trials were used to monitor different sets of the hyperparameters. The experimental result revealed that by applying the proposed approach the model performance can increase up to 5%. The optimal performance achieved was 98.77 percent accuracy. For UAV/drone applications, where a small onboard model is preferred, GoogleNet that is quite small in model size and has a good structure for further fine tuning is suitable to deploy.
Engagement in seniors from southwestern Ontario : Can it predict physical and cognitive function?

With the increasing number of individuals over the age of 65 years worldwide, it is critical for society to recognize the importance of helping seniors maintain their health, physical and cognitive functioning as well as their engagement with life. These three dimensions provide the foundation for successful aging (SA). The positive role of engagement with cognitive and physical functioning has been understated to date in the literature. The purpose of this study was to examine and compare how the frequency of engagement measured as the percentage of productive, social, passive and active leisure activities participated in, and the overall frequency of participation (high, moderate, low) can predict physical and cognitive functioning. A secondary data set (n = 287 participants, mean = 68.7± 8.09 years of age) provided the data, which was analyzed through a series of hierarchical regression analyses. Results suggest that activities participated in at a high frequency predict both physical and cognitive functioning. Even though on average active leisure activities were participated in the least frequently they predicted physical functioning. Findings from the present study augment the successful aging literature and theoretical, methodological and practical implications are discussed.
SC-CO2 extraction of lycopene from red papaya using rice bran oil as a co-solvent lessens its degradation during storage

ABSTRACT Lycopene was extracted from freeze-dried papaya using supercritical carbon dioxide and rice bran oil (RBO) as a co-solvent. Central composite design (Response surface methodology) with five levels of each process variable, i.e. pressure (25–50 MPa), temperature (55–85°C), and extraction time (60–210 min), was used for conducting the experiments. The single optimal point for various response variables was realized at 35.4 MPa, 67°C, and 130 min. Under these conditions, a lycopene extraction efficiency of 79.27 ± 1.69% and a γ-oryzanol content of 11,079 ± 146 mg/L were predicted. The combination of extraction temperature and time beyond 80°C and 180 minutes showed a detrimental effect on lycopene. Furthermore, the storage of extract under no-light conditions showed first-order degradation kinetics with half-life values of lycopene of 42, 24, and 9 days at 3, 10, and 25°C, respectively. Results show that the use of RBO as a co-solvent improved the extraction efficiency and prevented lycopene degradation during extraction and storage.
A Post-Quantum Compliant Authentication Scheme for IoT Healthcare Systems

In an Internet of Things (IoT)-based healthcare system, medical IoT devices gather and transmit critical patient data. Ensuring the security and privacy of medical data is paramount. One of the most critical challenges in this regard is the authentication of participating entities. The literature proposes specific authentication approaches for healthcare systems based on integer factorization and discrete logarithm problems. However, the advent of quantum computers would fundamentally break all of these protocols. In this study, we conducted an analysis of a recently proposed authentication and access control scheme for e-health systems, which is based on lattice-based cryptography and was developed by Gupta et al. Our analysis revealed that the scheme is vulnerable to several types of attacks, including impersonation, de-synchronization, and smart card stolen attacks, which could compromise the confidentiality and integrity of sensitive medical data. To address these security challenges, we propose an alternative authentication and access control scheme that uses Saber, a finalist lattice-based key encapsulation algorithm from round three of the NIST post-quantum cryptography standardization. One of the biggest advantages of Saber is its simplicity and efficiency. Our proposed scheme is designed specifically for e-health systems and provides robust protection against the vulnerabilities identified in Gupta et al.’s scheme. We believe that our proposed scheme represents a significant improvement over existing approaches and could help to enhance the security and privacy of e-health systems. Upon completion of our improved protocol, we proceeded to implement it within the Vivado 2018.3 environment for Zynq UltraScale FPGAs. To gather insight into its performance, we conducted a performance comparison study with various related protocols.
Effect of synthesis parameters on cobalt oxide nanostructures morphology

A facile approach was employed for the synthesis of cobalt oxide nanorods (NRs) using cobalt nitrate, sodium oxalate and ethylene glycol as precursors via a hydrothermal process. The hydrothermal conditions, such as temperature and time, were varied to optimize the morphological characteristics of the NRs. After undergoing filtration, washing, and drying, the resulting material was characterized using several techniques, including field emission scanning electron microscopy (FESEM), energy-dispersive X-ray spectroscopy (EDS), X-ray diffraction (XRD), and transmission electron microscopy (TEM). Our findings reveal that the NRs exhibit diverse morphologies, depending on the hydrothermal conditions, with the smallest aspect ratio observed when prepared at 200 °C for 24 hours. In addition, we investigated the gas sensing capabilities of the NRs to ammonia under these conditions.