Delivering nucleic acid therapeutics across cell membranes is a significant challenge. Cell-penetrating peptides (CPPs) containing arginine (R), tryptophan (W), and histidine (H) show promise for siRNA delivery. To improve siRNA delivery and silencing a model STAT3 gene, we hypothesized that oleyl acylation to CPPs, specifically (WRH)n, would enhance STAT3 silencing efficiency in breast and ovarian cancer cells. Using Fmoc/tBu solid-phase peptide chemistry, we synthesized, purified, and characterized the oleyl-conjugated (WRH)n (n = 1-4) peptides. The peptide:siRNA complexes were non-cytotoxic at N/P 40 (~20 μM) against MDA-MB-231, MCF-7, SK-OV-3, and HEK-293 cells after 72 h incubation. All peptide:siRNA complexes showed serum stability at N/P ≥40. The synthesized conjugates, with a diameter of <100 nm, formed nano complexes with siRNA and exhibited stable range of zeta potential values (13-18 mV at N/P 40). Confocal microscopy and flow cytometry analysis provided qualitative and quantitative evidence of successful cellular internalization of siRNA. Peptides oleyl-(WRH)3 and oleyl-(WRH)4 showed ~60% and ~75% cellular uptake of siRNA, respectively, in both MDA-MB-231 and SK-OV-3 cells. Western blot analysis of oleyl-(WRH)4 demonstrated effective silencing of the STAT-3 gene, with ~75% silencing in MDA-MB-231 cells and ~45% in SK-OV-3 cells.

The European Green Deal poses a two-pronged challenge for the automotive industry: migrating to solutions based in light structures, requiring light weight concepts and light materials, while at the same time avoiding dependence towards the importation of these advanced materials. Aluminium alloys are lightweight and cost-effective materials that can successfully cover the requirements of many structural applications, however their production requires from bauxite and other Critical Raw Materials (CRM), such as Si and Mg. Aluminium alloys are fully recyclable, but scrap is usually contaminated and its use is related to an increment of impurities, tramp element and undesired inclusions. Traditionally, the use of secondary alloys has been restricted to low performance applications. The present work analyses the effect that the use of scrap has in the quantity of inclusions present in the alloy and in other properties relevant for the material processing. The study was carried out for common alloys belonging to 3 of the most common aluminium processes used in the car manufacturing industry: High Pressure Die Casting (HPDC) (EN AC-43500), extrusion (6063) and sheet metal forming (5754 and 6181). Reference alloy were mixed with different level of scrap (0, 20, 40, 60, 80 and 100%) trying to keep the chemical composition as unaffected as possible. The inclusions level of the alloy was characterized with the Prefil Foot-printer® test. In addition, the obtained materials, after being cast in an open mould, were subjected to metallographic characterization. Relevant properties to assess the formability quality of the alloys for the corresponding transforming process, were obtained: flowability test for HPDC alloy and high temperature compression for extrusion alloys. The results obtained suggest that the number of inclusions present in the melt highly increase with the amount of scrap used to produce the alloy. These inclusions are also related with an important lost of flowability, but do not have a noticeable impact on microstructure.

Through its suggestive name, non-targeted methods (NTMs) do not aim at a predefined "needle in the haystack". Instead, they exploit all the constituents of the haystack. This new form of analytical methods is increasingly finding applications in food and feed testing. However, the concepts, terms, and considerations related to this burgeoning field of analytical testing needs to be propagated for the benefit of ones associated in academic research, commercial development, and official control. This paper addresses the frequently asked questions around notations and terminologies surrounding NTMs. The widespread development and adoption of these methods also necessitates the need to develop approaches to NTM validation, i.e., evaluating the performance characteristics of a method to determine if it is fit-for-purpose. This work aims to provide a roadmap to approaching NTM validation. In doing so, the paper deliberates on the different considerations that influence the approach to validation and provides suggestions thereof.

Non-targeted effects (NTE) such as bystander effects or genomic instability have been known for many years but their significance for radiotherapy or medical diagnostic radiology are far from clear. Central to the issue are reported differences in response of normal and tumour tissues to signals from directly irradiated cells. This review will discuss possible mechanisms and implications of these different responses and will then discuss possible new therapeutic avenues suggested by the analysis. Finally, the importance of NTE for diagnostic radiology and nuclear medicine which stems from the dominance of NTE in the low dose region of the dose response curve will be presented. Areas such as second cancer induction and microenvironment plasticity will be discussed.

In the field of railroad safety, effective detection of surface cracks is critical, necessitating reliable, high-speed non-destructive testing (NDT) methods. This study introduces a hybrid Eddy Current Testing (ECT) probe, specifically engineered for railroad inspection, to address the common issue of ’lift-off noise’ due to varying distances between the probe and test material. Unlike traditional ECT methods, this probe integrates transmit and differential receiver coils, aiming to enhance detection sensitivity and minimize lift-off impact. The study optimizes ECT probes employing different driver coils, emphasizing three main objectives: a) quantitatively evaluating each probe using signal- to-noise ratio (SNR) and outlining a real-time data processing algorithm based on SNR methodology; b) exploring the frequency range proximal to the electrical resonance of the receiver coil; c) examining sensitivity variations across varying lift-off distances. The experimental outcomes indicate that the newly designed probe with figure 8-shape driver coil significantly improves sensitivity in detecting surface cracks on railroads. It achieves an impressive SNR exceeding 100 for defects with minimal dimensions of 1 mm in width and depth. Simulation results closely align with experimental findings, validating the investigation of optimal operational frequency and lift-off distance for selected probe performance, determined to be 0.3 MHz and 0.5 mm, respectively. The realization of this project would lead to notable advancements in enhancing railroad safety by improving crack detection efficiency.

Metabolomics analysis generates vast arrays of data, necessitating comprehensive workflows involving expertise in analytics, biochemistry and bioinformatics, in order to provide coherent and high-quality data that enables discovery of robust and biologically significant metabolic findings. In this protocol article, we introduce NoTaMe, an analytical workflow for non-targeted metabolic profiling approaches utilizing liquid chromatography–mass spectrometry analysis. We provide an overview of lab protocols and statistical methods that we commonly practice for the analysis of nutritional metabolomics data. The paper is divided into three main sections: the first and second sections introducing the background and the study designs available for metabolomics research, and the third section describing in detail the steps of the main methods and protocols used to produce, preprocess and statistically analyze metabolomics data, and finally to identify and interpret the compounds that have emerged as interesting.

Mechanically homogenized composite of expanded graphite and cobalt(II) rhenate(VII) has been described. Cobalt(II) rhenate(VII) was obtained in a reaction of perrhenic acid with cobalt(II) nitrate. A simple mortar homogenization method was used to enhance intercalation of cobalt species within carbon matrix. The specific capacity of composite was enhanced by 50% in comparison to bare expanded graphite. The electrochemical characteristic was significantly improved including better cyclability, lower resistance of electrode material, lower iR drop, with respect to expanded graphite without cobalt(II) rhenate(VII) active species. Expanded graphite with its unique specific surface area and pore size diameter was proved as potential and cheap carbon support.

Sialylated milk oligosaccharides (SMOs) is the most important oligosaccharides in human milk oligosaccharides (HMOs), and have a multifunctional health benefit, and the most abundant SMOs are sialyllactose (SLs). The previous studies indicate that the growth of tumor cells, and angiogenesis on tumor tissues with Lewis lung carcinoma, melanoma, and colon carcinoma cells could be prevented by SL. Thus, it has been proposed that SL might be an ideal candidate for the development of anti-angiogenic drugs without any side effects. It has been proposed that the Sialic acid (Sia) can be produced through SL’s hydrolysis, and the dietary Sia is further transported to the brain and other tissues though blood for formation of CMP-Sia, the activated precursor of Sia, and then is catalyzed by polysialyltransferases (polySTs) for synthesis of polysialic acid (polySia) or polysialylation of neural cell adhesion molecule (NCAM). Thus, it’s necessary to study and develop new inhibitors, which not only modulate a proper balance between the gut health and neuros system development, but also inhibit the overexpression of polySia, and thus block the tumor cell migration. In this study, we proposed that 3’-SL and 6’-SL may be more ideal inhibitors comparing with two previous inhibitors, the low molecular weight heparin (LMWH) and cytidine monophosphate (CMP) in the previous studies.

Cancer, characterized by abnormal cell proliferation driven by genetic mutations, remains a significant health challenge. Tailoring effective therapies depends on factors like tumor location, grade, and stage, alongside patient condition. Leveraging Nano-carrier encapsulated drugs offers a promising avenue, as they accumulate preferentially at tumor sites, enhancing therapeutic efficacy while mitigating side effects. Polymeric Nano-carriers, with their customizable properties, further optimize drug delivery efficiency and specificity. Incorporating active targeting ligands such as antibodies, carbohydrates, peptides, folates, and aptamers-onto polymeric nanoparticle surfaces through techniques like functionalization, adsorption, and conjugation plays a pivotal role in directing therapeutic agents to tumor cells. Consequently, polymeric nanoparticles emerge as the carrier of choice for delivering drugs to various tumor types, including breast, colon, lung, prostate, liver, and spleen tumors.

The need for cohesive detection and defence methods against cyberattacks is significant now more than ever before to enforce security and privacy of user data and information. The inevitable increase in demand for home and flexible working from employees quite recently has meant there is a lack of awareness and training for cyberattacks. Hence, they have become prominent as attackers are aware of this and are benefitting from individuals’ lack of knowledge in how to better protect themselves and their confidential information. Employees are becoming more susceptible to such attacks and falling victim to these, resulting in economic losses for companies, data losses and decreased faith.

The expression of polysialic acid (polySia) on the neuronal cell adhesion molecule (NCAM), is called NCAM-polysialylation, which is strongly related to the migration and invasion of tumor cells and with aggressive in the clinic. Thus, it’s important to select a proper drug to block tumor cell migration for clinic treatment. In this study, we proposed that lactoferrin (LFcinB11) may be a more ideal candidate for inhibition of NCAM polysialylation comparing with CMP and low molecular weight heparin (LMWH), which were determined based on our NMR studies. On the other hand, because neutrophil extracellular traps (NET) is the most dramatic stage in cell death process, and NETs’ release are related to pathogenesis of autoimmune and inflammatory disorders, with proposed involvement in glomerulonephritis, chronic lung disease, sepsis and vascular disorders. In this study, the molecular mechanism of inhibition of NETs’ release using LFcinB11 as inhibitor was also determined. Based on these results, LFcinB11 is proposed to be bifunctional inhibitor for inhibiting both NCAM polysialylation and NETs releases.

We propose novel spectrometer designs that aim to enhance the measured spectral range 1 of ions on a finite-sized detector. In contrast to the traditional devices that use uniform magnetic 2 field, in which the deflection of particles increases inversely proportional to their momentum, in a 3 gradient magnetic field, the deflection of particles will be decreased due to the reduction of the 4 magnetic field along their propagation. Low energy ions are particularly impacted, as they are 5 deflected less and are more likely to reach the detector compared to being driven out of it while 6 using a uniform magnetic field. By the means of a gradient magnetic field, the nonlinear dispersion 7 of ions in a homogeneous magnetic field can approach a nearly linear dispersion. Nonetheless, 8 the dispersion of low energy ions in a homogeneous magnetic field remains unnecessarily high. 9 In this article, we explore linear, quadratic, and higher-order field profiles in comparison to the 10 homogeneous field case. We discuss the employed methodology and present simulation results of 11 the spectrometer with an extended ion spectral range, focusing on the minimum detectable energy 12 (energy dynamic range) and energy resolution

Lung cancer is the leading cause of cancer death worldwide, with non-small cell lung cancer (NSCLC) making up 80% of cases. Some genetic factors leading to NSCLC development include genetic mutations and PD-L1 expression. PD-L1 proteins are targeted in an NSCLC treatment called targeted gene therapy. However, this treatment is effective in a low percentage of patients. This study aimed to create machine learning models to use features like the number of mutations and the level of PD-L1 proteins in cancer cells, along with others, to predict whether a patient will receive clinical benefit from gene therapy treatment. This was done by downloading and merging datasets from cbioportal.org to create a sample size for the model. Features with high correlations to clinical benefit were identified. Three machine-learning models were created using these features to predict clinical benefits in patients, and each model’s accuracy was evaluated. All three models were accurate between 55-85%, with two of the models averaging an accuracy around 75%. Doctors can use these models to more accurately predict whether gene therapy treatment is likely to work in a patient before prescribing it to them.

The combined use of lasmiditan and triptan is unexplored in medical literature. This study aimed to investigate whether the intake of lasmiditan following triptan improves migraine pain. Following triptan intake, if headache relief was less than 50% at 1 hour, patients took 50 mg lasmiditan within 2 hours of migraine onset. Patients recorded headache intensity and adverse events(AEs) caused by lasmiditan at 1, 2, and 4 hours after intake of additional 50 mg of lasmiditan. A significant reduction in pain scale was observed post 50 mg lasmiditan intake (p < 0.001, t-test). Pain relief was reported for 32 migraine attacks (80%) at 1 hour after additional lasmiditan intake. Although AEs were observed in 63% of the patients who took an additional lasmiditan, most were mild and resolved 1 hour after lasmiditan intake. Our study revealed the significant headache reliefs provided by an additional lasmiditan for patients who did not achieve satisfactory results following initial triptan intake for treating migraine. The AEs associated with this treatment strategy were mild and lasted for a short term. This study suggested that the combination of triptan and lasmiditan is promising for the treatment of migraine and should be studied in a randomized placebo-controlled trial.

New furan, thiophene and triazole oximes were synthesized through several-step reaction paths to investigate their potential for development of central nervous systems (CNS)-active and cholinesterase-targeted therapeutics in organophosphorus compound (OP) poisonings. Acute OP poisoning is still a challenge in treating patients despite the development of a large number of oxime compounds that should have the capacity to reactivate acetylcholinesterase (AChE) and butyrylcholinesterase (BChE). The activity of these two enzymes, crucial for neurotransmission, are blocked by the OP, which has the consequence of disturbing normal cholinergic nerve signal transduction in the peripheral and CNS, leading to cholinergic crisis. Oximes in use have one or two pyridinium rings and cross the brain–blood barrier poorly due to the quaternary nitrogen. Following our recent study on 2-thienostilbene oximes, in this paper we described the synthesis of 63 heterostilbene derivatives out of which 26 oximes were tested as inhibitors and reactivators of AChE and BChE inhibited by OP nerve agents – sarin and cyclosarin. While the majority of oximes were potent inhibitors of both enzymes in micromolar range, we identified several oximes as BChE or AChE selective inhibitors with a potential for drug development. Moreover, even oximes were poor reactivators of AChE, four heterocyclic derivatives reactivated the cyclosarin-inhibited BChE up to 70%, and cis,trans-5 [2-((Z)-2-(5-((E)-(hydroxyimino)methyl)thiophen-2-yl)vinyl)benzonitrile] had comparable reactivation efficacy to the standard oxime HI-6. In silico analysis and molecular docking studies connected the kinetic data to the structural features of these oximes, and confirmed their productive interactions with the active site of the cyclosarin-inhibited BChE. Based on inhibition and reactivation and their ADMET properties regarding lipophilicity, CNS activity, and hepatotoxicity, these compounds could be considered for further development of CNS-active reactivators in OP poisoning as well as cholinesterase-targeted therapeutics in neurodegenerative diseases like Alzheimer’s and Parkinson’s.

Botnet Attacks are one of the many types of ways to attack users in their day to day browsing, often of different trends and techniques to take your information without your control. Most firewalls can amply protect you against these trends and techniques, but scarily enough, recent articles and research have shown that they are starting to bypass said firewalls through layers of attacks, confusing your best line of defense. In this Report, we will be looking into exactly what are Botnets and the different subsets, what we can do to combat Botnet Attacks, where do they come from, their origin, the analysis of their kit and what we can put together from information collected. Through this we will be able to reach a conclusion of what we can use to possibly mitigate the effect they have on the internet and stop them from enacting their purpose.

In the evolving landscape of cybersecurity threats, the Permanent Denial of Service (PDoS) attacks have emerged as a particularly damaging form of cyber aggression. Unlike the more well-known Denial of Service (DoS) attacks, which disrupt services temporarily, PDoS attacks aim to inflict irreversible damage to systems, often resulting in significant system overhauls and requiring hardware replacement. To enable the development of effective security measures, but also to address the knowledge gaps, this paper offers an in-depth exploration of PDoS attacks, emphasizing their distinguishing characteristics, underlying mechanisms, and potential further development. Through a comprehensive case study, the research highlights diverse tactics and strategies employed by attackers, from targeting IoT devices to manipulating boot processes and exploiting firmware vulnerabilities. A novel classification of PDoS attack vectors is proposed, that also explains the ways in which the systems can be compromised. The findings confirm the pressing need for adaptive and robust defense mechanisms to mitigate the threats posed by PDoS attacks in our interconnected digital world.

In a hyperconnected society, computer networks play a pivotal role in all activities that permeate our daily lives. In the context of smart cities, theses networks play a pivotal role in enhancing urban efficiency and sustainability. The continuous flow of packets in these networks poses security challenges, from personal data leakage to service unavailability. Anomaly-based methods are commonly used to detect network attacks. In this context, Software-Defined Networking (SDN) emerges as a solution for anomaly detection in network traffic, facilitating comprehensive, real-time analyses and dynamic adaptation to changes. This study aims to present a systematic review of feature selection techniques and evaluate the effectiveness of attribute selection with a multi-class classifier in network anomaly detection. The objective of this paper is to inspire future research and identify trade-offs among these techniques for detecting Denial of Service (DoS) attacks. Based on the findings, the analysis of the entire dataset yields superior results in terms of precision (99%), but using the output of OneR, although resulting in a slight loss of precision compared to the complete dataset, presents the highest precision among other studied techniques, indicating a trade-off between precision and processing time efficiency.

As one of the typical deposits in Sichuan-Yunnan-Guizhou Pb-Zn metallogenic province, the Daliangzi Pb-Zn deposit has a close genetic relation with the structural system of “black/fracture zone” formed under the action of NWW-approx. EW strike-slip structures in the metallogenic province, but the mechanism of rock and ore-controlling action of the structural system remains unclear. Based on detailed analysis of tectonite-mineralized alteration lithofacies of R1 “black/fracture zone”, the tectonite-mineralized alteration lithofacies zones can be divided into 4 types in succession outward from the Pb-Zn mineralization center, i.e., ① brecciated and stockwork-like Pb-Zn mineralization-complex breccia facies zone, ② stockwork-like Pb-Zn mineralization-simple breccia and cataclasite facies zone, ③ veined pyrite-sulfide-dolomitic cataclasite facies zone, and ④ fine-veined calcite-black carbonized dolomite facies zone; Cl- and SO42--rich high-temperature oxidizing ore-bearing fluid mixes with reducing fluid in Zone ① while migration and precipitation occur in Zone ④. With the evolution of ore-forming fluid, the homogenization temperature decreases from Zone ① to Zone ④, the salinity increases from Zone ① to Zone ② and then decreases to Zones ③ and ④, and the fluid density has little change on the whole; the contents of Zn, Pb, Cu, Ga, Ge, Cd, Ag, and other metallogenic elements, the Zn/Pb ratio, and the CaO/MgO mole ratio decrease gradually from Zone ① to Zone ④; and REE fractionation, calcilization, silicification, and pyritization enhance gradually from Zone ① to Zone ④. This series of changes are the product of diapirism (cryptoexplosion) of strike-slip structures and “black/fracture zone”, among which the second-order structures (F5, F100, and other faults) derived from NWW-approx. EW striking dextral shear-tension faults F1 and F15 control the brecciated and stockwork-like Pb-Zn mineralized complex breccia facies zones and the stockwork-like Pb-Zn mineralized simple breccia and cataclasite facies zones. Therefore, this paper establishes the zoning mode of tectonite-mineralized alteration lithofacies of “black/fracture zone” and proposes that Zones ① and ② are important prospecting criteria. conclusions.

The flowering period of rice significantly impacts variety adaptability and yield formation. Properly shortening the reproductive period of rice varieties can expand their ecological range without significant yield reduction. Targeted genome editing, like CRISPR/Cas9, is an ideal tool to fine-tune rice growth stages and boost yield synergistically. In this study, we developed a CRISPR/Cas9-mediated multiplex genome editing vector containing five genes related to three traits: Hd2, Ghd7, and DTH8 (flowering stage genes), along with the recessive rice blast resistance gene Pi21 and the aromatic gene BADH2. This vector was introduced into the high-quality Geng rice variety in Zhejiang province, Jiahe212 (JH212), resulting in 34 T0 plants with various effective mutations. Among the 17 mutant T1 lines, several displayed diverse flowering dates, but most exhibited undesirable agronomic traits. Notably, three homozygous mutant lines (JH-C15, JH-C18, and JH-C31) showed slightly earlier flowering dates without significant differences in yield-related traits compared to JH212. Through special Hyg and Cas marker selection of T2 plants, we identified 7, 6, and 2 fragrant glutinous plants devoid of transgenic components, respectively. These single plants will serve as sib lines of JH212 and potential resources for breeding applications, including maintenance lines for Xian-Geng interspecific three-line hybrid rice. In summary, our research offers practical insights and potential resources for targeted improvement of Geng rice varieties, and would also promote the Geng rice varieties expending from the southward to the northward.

The retinoblastoma (RB) transcriptional corepressor 1 (RB1) is a critical tumor suppressor gene, governing diverse cellular processes implicated in cancer biology. Dysregulation or deletion in RB1 contributes to the development and progression of various cancers, making it a prime target for therapeutic intervention. RB1's canonical function in cell cycle control and DNA repair mechanisms underscores its significance in restraining aberrant cell growth and maintaining genomic stability. Understanding the complex interplay between RB1 and cellular pathways is beneficial to fully elucidate its tumor-suppressive role across different cancer types and for therapeutic development. As a result, investigating vulnerabilities arising from RB1 deletion-associated mechanisms offers promising avenues for targeted therapy. Recently, several findings highlighted multiple methods as a promising strategy for combating tumor growth driven by RB1 loss, offering potential clinical benefits in various cancer types. This review summarizes the multifaceted role of RB1 in cancer biology and its implications for targeted therapy.

The unique structure of G4.0 PAMAM dendrimers allows the drug to be enclosed in internal spaces or immobilized on the surface. In the conducted research, the conditions for the formation of the active G4.0 PAMAM complex with doxorubicin (DOX) were optimized. The physicochemical properties of the system were monitored using DLS, CD, and fluorescence spectroscopy. The QCM-D method was chosen to determine the preferential conditions for the complex formation. The highest binding efficiency of the drug to the cationic dendrimer was observed under basic conditions when the DOX molecule was deprotonated. The decrease in the zeta potential of the complex confirms that DOX immobilizes through electrostatic interaction with the carrier's surface amine groups. The binding constants were determined from the fluorescence quenching of the DOX molecule in the presence of G4.0 PAMAM. The two-fold way of binding doxorubicin in the structure of dendrimers was visible in the ITC isotherm. Fluorescence spectra and release curves identified reversible binding of DOX to the nanocarrier. Among the selected cancer cells, the most promising anticancer activity of G4.0-DOX was observed in A375 malignant melanoma cells. Moreover, the preferred intracellular location of the complexes concerning the free drug was found, which is important from a therapeutic point of view.

In this study the impact of terrorist attacks on exchange rate is estimated. Particularly, the study focuses on Turkish terrorist attacks and its implication on Turkish lira versus pound sterling exchange rate. In order, to find the causal effect the study employed Autoregressive distributive lag (ARDL) bound testing approach as an estimation technique. Accordingly, the analysis reveals that terrorist attack has a negative impact on the exchange rate in both short and long-run. However, the negative effect of terrorism tends to be small in both the short-run and long-run. More precisely, terrorist attack depreciates the exchange rate between Turkish lira and pound sterling by approximately 0.00072 in the next trading day. The long-term effect also shows that terrorist attack depreciates the exchange rate on average by 0.00212.

This study provides an in-depth exploration and analysis of the complex aspects of Public Key Cryptography, focusing on the vulnerabilities, potential attack vectors, and the consequential impacts of key compromises. The discussion extends beyond the conventional technical paradigms, offering a holistic perspective that intertwines theoretical insights with practical implications, thereby contributing to a more comprehensive understanding of cryptographic security. The paper delves into the intricacies of brute-force attacks, elucidating the significance of key sizes, encryption algorithms, and the inherent resilience of cryptographic systems against such attacks. It further examines the ramifications of key compromises in Public Key Cryptography, highlighting the repercussions on confidentiality, integrity, trust, and legal and reputational standing. The study underscores the importance of maintaining the security and integrity of cryptographic keys and provides practical recommendations to mitigate the risks associated with key compromises. These include enhanced awareness and education, stringent protection of sensitive information, proactive risk management, strict compliance with regulations and standards, and the promotion of a security-centric culture. The insights gleaned from this study are pivotal in informing future research, policy development, cryptographic implementations, and the cultivation of secure practices, fostering a more secure, resilient, and ethical digital environment in the face of an evolving threat landscape. The contributions of this study are instrumental in advancing the field of cryptography and enhancing the overall understanding of the challenges and considerations inherent in implementing robust cryptographic practices.

In recent years, with the rapid development of technology, artificial intelligence(AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models and then transfer to attack the target model, and most AI models in real-world scenarios belong to black boxes, thus transferability becomes a key factor to measure the quality of adversarial samples. The traditional method relies on the decision boundary of the classifier and takes the boundary crossing as the only judgment metric without considering the probability distribution of the sample itself, which results in an irregular way of adding perturbations to the adversarial sample, an unclear path of generation, and a lack of transferability and interpretability. In the probabilistic generative model, after learning the probability distribution of the samples, a random term can be added to the sampling to gradually transform the noise into a new independent and identically distributed sample. Inspired by this idea, we believe that by removing the random term, the adversarial sample generation process can be regarded as static sampling of the probabilistic generative model, which guides the adversarial samples out of the original probability distribution and into the target probability distribution, and helps to improve transferability and interpretability. Therefore, we propose a Score Matching-Based Attack(SMBA) method to perform the adversarial sample attacks by manipulating the probability distribution of the samples, which can show good transferability in the face of different datasets and models, and give reasonable explanations from the perspective of mathematical theory and feature space. In conclusion, our research establishes a bridge between probabilistic generative models and adversarial samples, provides a new entry angle for the study of adversarial samples, and brings new thinking to AI security.

The delivery of Cytochrome c (Cyt c) to the cytosol stimulates apoptosis in cells were its release from mitochondria and apoptosis induction is inhibited. We developed a drug delivery system consisting of Cyt c nanoparticles decorated with folate-poly(ethylene glycol)-poly(lactic-co-glycolic acid)-thiol (FA-PEG-PLGA-SH) to deliver Cyt c into cancer cells and test their targeting in the Lewis Lung Carcinoma (LLC) mouse model. Cyt c-PLGA-PEG-FA nanoparticles (NPs) of 253 ± 55 and 354 ± 11 nm were obtained by Cyt c nanoprecipitation, followed by surface decoration with the co-polymer SH-PLGA-PEG-FA, and compared to a nanoparticle-free formulation. Overexpression of FA in LLC cells and internalization of Cyt c-PLGA-PEG-FA nanoparticles (NPs) was confirmed by confocal microscopy. Caspase activation assays show NPs retain 88-96% Cyt c activity. The NP formulations were more efficient in decreasing LLC cell viability than the NP-free formulation, with IC50: 49.2 to 70.1 μg/ml versus 129.5 μg/ml, respectively. Our NP system is thrice as selective towards cancerous than normal cells. In-vivo studies using tagged nanoparticles show accumulation in mouse LLC tumor 5 min post-injection. In conclusion, our NP delivery system for Cyt c shows superiority over the NP-free formulation and reaches a folic acid-overexpressing tumor in an immune-competent animal model.

As critical biocontrol agents, Trichoderma and Metarhizium have demonstrated vast potential in disease prevention and pest control respectively. Current applications, however, are predominantly confined to singular use or simplistic combinations of the two fungi. This study innovatively utilizes Metarhizium and Trichoderma as experimental subjects, employing non-targeted metabolomics to compare the differences in metabolites between co-culture and separate fermentation. The metabolite distinctions in co-culture are meticulously analyzed using sample correlation, least squares discriminant analysis, cluster analysis, and KEGG enrichment analysis. Metabolome sequencing analysis is then applied to compare the co-culture fermentation filtrate with Trichoderma atroviride and Metarhizium anisopliae. Differential metabolite analysis found that co-culture had significantly up-regulated or down-regulated metabolites compared to singular cultures.However,significant metabolite discrepancies are discovered in singular cultures, notably the up-regulated metabolites such as osthole and spirocyclic amine, which have documented antibacterial functions that may prove effective against Fusarium graminearum. Ultimately, the findings from this research contribute to the exploration of synergistic applications of biocontrol fungi and serve as a valuable reference for future development of bio-pesticides for disease and pest control.

For the treatment of cancer and other diseases, therapeutic targeting of non-coding RNAs (ncRNAs) such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) is an attractive approach. RNA-based therapeutics have been extensively researched over the past decade, with antisense oligonucleotides and small interfering RNAs being the most commonly used. Some of these have even gained FDA approval. However, the results of trials have been mixed, with some studies reporting strong effects while others have shown limited effectiveness or toxicity. Alternative treatments, such as antimiRNAs, are currently in clinical trials. There is growing interest in the use of lncRNA-based therapies. This Perspective discusses the main challenges of ncRNA therapeutics, including specificity, delivery and tolerability. It also highlights emerging approaches that aim to improve their success.

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

Some bispecific radiotracers have been developed to overcome the limitations of monospecific tracers and improve detection sensitivity for heterogeneous tumor lesions. Here we aim to synthesize two bispecific tracers targeting prostate-specific membrane antigen (PSMA) and fibroblast activation protein (FAP), which are key markers expressed in prostate cancer. A pyridine-based FAP-targeted ligand was synthesized through multi-step organic synthesis, and then connected to the 2-Nal-containing PSMA-targeted motif. Ki(PSMA) values of Ga-complexed bispecific ligands, Ga-AV01084 and Ga-AV01088, were 11.6 ± 3.25 and 28.7 ± 6.05 nM, respectively, and the IC50(FAP) values of Ga-AV01084 and Ga-AV01088 were 10.9 ± 0.67 and 16.7 ± 1.53 nM, respectively. The uptake values for PSMA-expressing LNCaP tumor xenografts were 9.05 ± 1.54 and 8.85 ± 1.25 %ID/g for [68Ga]Ga-AV010184 and [68Ga]Ga-AV01088, respectively, which were lower than the monospecific PSMA-targeted tracer [68Ga]Ga-PSMA-617 (16.7 ± 2.30 %ID/g). The uptake values for FAP-expressing HEK293T:hFAP tumor xenografts were 1.90 ± 0.41 and 1.20 ± 0.25 %ID/g for [68Ga]Ga-AV01084 and [68Ga]Ga-AV01088, respectively, which were also lower than the monospecific FAP-targeted tracer, [68Ga]Ga-AV02070 (7.93 ± 1.88 %ID/g). Further investigations are needed to optimize the selection of linkers and targeted pharmacophores to improve the tumor uptake of PSMA/FAP bispecific tracers for prostate cancer imaging.

The incidence of diabetes mellitus (DM) continues to rise worldwide and one of the most serious microvascular complications is diabetic kidney disease (DKD), which is the leading cause of end-stage renal disease. Current bi-omarkers such as urinary albumin excretion rate have limitation for early detection of DKD. In our study we used ultra-high-performance liquid chromatography coupled with electrospray ionization-quadrupole-time of flight-mass spectrometry (UHPLC-QTOF-ESI+-MS) techniques to quantify previously analyzed metabolites, such as tryptophan, kynurenic acid, taurine, l-acetylcarnitine, glycine, and tiglylglycine. We performed the targeted analy-sis of metabolites from urine and serum samples, collected from 110 subjects. Of these, 90 patients with type 2 DM (T2DM) were divided according to the urinary albumin/creatinine ratio (UACR) into normoalbuminuria 300 mg/g groups, respectively, while 20 subjects were rep-resented by healthy controls. Through various validation methods, we identified several metabolites as potential biomarkers, such as kynurenic acid, glycine, and l-tryptophan in serum and l-acetylcarnitine, tiglylglycine, and tau-rine in urine.

In the world's first pig-to-human cardiac xenotransplant, the elevated levels of porcine cytomeg-alovirus (PCMV) are considered key factors contributing to patient mortality. This has renewed attention to PCMV, a virus widely prevalent in pigs. Currently, there are no effective drugs or vaccines targeting PCMV, and its high detection difficulty poses challenges for prevention and control research. In this study, antiviral small hairpin RNA (shRNA) was selected and inserted into the Rosa26 and miR-17-92 loci of pigs via a CRISPR/Cas9-mediated knock-in strategy. Further in vitro viral challenge experiments demonstrated that these genetically edited pig cells could effec-tively limit PCMV replication. Through this process, we constructed a PCMV-infected cell model, validated partial viral interference sites, enhanced gene knock-in efficiency, performed gene editing at two different gene loci, and ultimately demonstrated that RNA interference (RNAi) technology combined with CRISPR/Cas9 has the potential to generate pig cells with enhanced antiviral infec-tion capabilities. This opens up possibilities for the future production of pig populations with anti-viral functionalities.

This study presents an in-depth analysis and evaluation of self-healing networks as an innovative solution to combat the escalating threat of ransomware attacks. Recognizing the limitations of traditional network security methods in the face of advanced cyber threats, the research focuses on the development and implementation of self-healing networks, characterized by their dynamic, intelligent response systems. Utilizing a combination of artificial intelligence and machine learning algorithms, these networks demonstrate remarkable adaptability and resilience. Through rigorous simulations, the study examines the efficacy of self-healing networks in detecting, isolating, and recovering from ransomware attacks, with an emphasis on their ability to learn and evolve from each incident. The research highlights the strengths and limitations of these networks and discusses their potential applications in various sectors. The findings suggest that self-healing networks, with their proactive and adaptive defense mechanisms, represent a significant advancement in cybersecurity strategies, offering a robust solution against a range of cyber threats. Future work is recommended to further enhance the capabilities of self-healing networks, particularly in the areas of speed, detection accuracy, and quantum-resistant security measures.

The number of connected IoT devices is increasing significantly due to their many benefits, including automation, improved efficiency and quality of life, and waste reducing. However, these devices have several vulnerabilities that have led to the rapid growth in the number of attacks. Therefore, several machine learning-based Intrusion Detection Systems (IDS) tools have been developed to detect intrusions and suspicious activity to and from a host (HIDS - Host IDS) or, in general, within the traffic of a network (NIDS - Network IDS). The proposed work performs a comparative analysis and an ablative study among recent machine learning-based NIDS to develop a benchmark of the different proposed strategies. The proposed work compares both shallow learning algorithms, such as Decision Trees, Random Forest, Naïve Bayes, Logistic Regression, XGBoost, and Support Vector Machine, and deep learning algorithms, such as DNN, CNN, and LSTM, whose approach is relatively new in the literature. The algorithms are evaluated on the KDD-99, NSL-KDD, UNSW-NB15, and IoT-23 datasets. The results show that the NIDS tools based on deep learning approaches achieve better performance in detecting network anomalies than shallow learning approaches.

: Digital communication is becoming an integral component of contemporary society and the use of mobile devices is increasing faster than ever. The function of message communication is becoming more popular as for mobile phone users, a short messaging service (SMS) requires an extremely high level of security. Any SMS that enters a person's smartphone must be considered a private affair, with privacy and security safeguarded. This research study found a novel Deoxyribonucleic acid (DNA) Cryptography method that uses a dynamic DNA sequence table to increase security. Cryptography is a popular approach for ensuring the confidentiality of a message. This paper proposed an Efficient Cryptographic Scheme for SMS (ECSS) algorithm to protect texts received from an Android smartphone. ECSS-based encryption has been identified as a novel method of securing information in the form of DNA molecules that employ DNA strands to conceal the information. This Paper provides the DNA cryptography approach for encrypting and decrypting plain texts in this work. The primary goal of ECSS cryptography is to guarantee secrecy when people transfer data across a network. This paper examines DNA Crypto, the distinction between classical cryptography and DNA Data encryption. An Android-based app is created to perform the proposed technique and the results show that the algorithms produce a consistent encryption result in which the length of the message before encryption is always the same as the length of the message after decryption. The suggested security solution has the potential to greatly improve the security of mobile communication. This Paper also provides experimental results for our chat application performance, such as received text message correctness, cipher text, average encryption time and average decryption time. According to the findings/results of this study, the speed of average encryption and decryption of the description is roughly 0.011 and 0.037 sec and prevents plaintext and dictionary attacks.

In today’s Industrial IoT (IIoT) environment, where different systems interact with the physical world, the state proposed by the Industry 4.0 standards can lead to escalating vulnerabilities, especially when these systems receive data streams from multiple intermediaries, requiring multilevel security approaches, in addition to link encryption. At the same time taking into account the heterogeneity of the systems included in the IIoT ecosystem and the non-institutionalized interoperability in terms of hardware and software, serious issues arise as to how to secure these systems. In this framework, given that the protection of industrial equipment is a requirement inextricably linked to technological developments and the use of the IoT, it is important to identify the major vulnerabilities, the associated risks and threats and to suggest the most appropriate countermeasures. In this context, this study provides a description of the attacks against IIoT systems, as well as a thorough analysis of the solutions against these attacks, as they have been proposed in the most recent literature.

Lung cancer (LC) continues to be an important public health problem being the most common form of cancer and a major cause of cancer deaths worldwide. Despite the great bulk of research to identify genetic susceptibility genes by genome-wide association studies, only few loci associated to nicotine dependence have been consistently replicated. Our previously published study in few phenotypically discordant sib-pairs identified a combination of germline truncating mutations in known cancer susceptibility genes in never smoker early-onset LC patient does not present in their healthy sib. These results firstly demonstrated the presence of an oligogenic combination of disrupted cancer predisposing genes in non-smokers patients, giving experimental support to a model of a “private genetic epidemiology”. Here, we used a combination of whole-exome and RNA sequencing coupled with a discordant sib’s model in a novel cohort of pairs of never-smokers early-onset LC patients and in their healthy sibs used as controls. We selected rare germline variants predicted as deleterious by CADD and SVM bio-informatics tools and absent in the healthy sib. Overall, we identified an average of 200 variants per patient, about 10 of which in cancer predisposing genes. In most of them, RNA sequencing data reinforced the pathogenic role of the identified variants showing: i) downregulation in LC tissue (indicating “second hit” in tumor suppressor genes); ii) upregulation in cancer tissue (likely oncogene); iii) downregulation in both normal and cancer tissue (indicating transcript instability). The combination of the two techniques demonstrates that each patient has an average of six (with a range from four to eight) private mutations with functional effect in tumor predisposing genes. The presence of a unique combination of disrupting events in the affected subjects may explain the absence of familial clustering of non-small cell lung cancer. In conclusion, these findings indicate that each patient has his/her own “predisposing signature” to cancer development and suggest the use of personalized therapeutic strategies in lung cancer.

The focus of this work lies in the construction of monitoring system designed in the form of a patch using a long-lasting potentiometric electrolyte concentration sensor from metal electrodes, in a galvanic cell configuration Mg(s)|NaCl(aq)|Cu(s). Initially, a mathematical analysis was conducted on the expected sensor output through the Nernst equation. The experimental phase involved conducting controlled tests in the laboratory using various molar concentrations of sodium chloride (NaCl) in aqueous solution. Results showed that the electrolyte sensor allows for monitoring electrolyte levels in NaCl in the range of 0.02 to 1 M/L, with electrode lifespan exceeding 10 days. On the other hand, when patch is using human sweat during controlled exercise routines, it showed the sensor's capability for monitoring changes in the subjects' hydration levels in real time. The sensor output potential is greater than 1.7 volts when the sensor electrodes are in contact with human sweat, which is equivalent to measuring molar concentrations at normal hydration and moderately high hydration levels. Sensor results indicate that the system can function in a self-powered manner without external batteries. Additionally, the extended lifespan of its electrodes allows for continuous monitoring of imbalances in the human body, showcasing the system capability for long-term health assessment.

Once discovered, cancer stem cells have become a hot topic in the research of cancer therapy. These cells possess stem cell-like self-renewal and differentiation capacities and are important factors that dominate cancer metastasis, therapy- resistance and recurrence. What's worse, their own characteristics make them difficult to be eliminated. Colorectal cancer is the third most common cancer and the second leading cause of cancer death worldwide. Targeting colorectal cancer stem cells (CRCSCs) can inhibit colorectal cancer metastasis, enhance therapeutic efficacy, and reduce recurrence. Here, we introduced the origin, marker proteins, identification, cultivation and research techniques of CRCSCs, summarized the signaling pathways that regulate the stemness of CRCSCs, such as Wnt, JAK/STAT3, Notch, and Hh signaling pathway. In addition to these, we also reviewed anti-CRCSC drugs targeting signaling pathways，markers，and other regulators in recent years. These will help researchers gain insight into the current agents targeting to CRCSCs, explore new cancer drugs, and propose potential therapy.

Epithelial ovarian cancer (EOC) has not significantly benefited from advances in immunotherapy, mainly because of the lack of well-defined actionable antigen targets. Using proteogenomic analyses of primary EOC tumors, we previously identified 91 aberrantly expressed tumor-specific antigens (TSAs) originating from unmutated genomic sequences. Most of these TSAs derive from non-exonic regions, and their expression results from cancer-specific epigenetic changes. The present study aimed to evaluate the immunogenicity of 48 TSAs selected according to two criteria: presentation by highly prevalent HLA allotypes and expression in a significant fraction of EOC tumors. Using targeted mass spectrometry analyses, we found that pulsing with synthetic TSA peptides leads to a high-level presentation on dendritic cells. TSA abundance correlated with the predicted binding affinity to the HLA allotype. We stimulated naïve CD8 T cells from healthy blood donors with TSA-pulsed dendritic cells and assessed their expansion with two assays: MHC-peptide tetramer staining and TCR Vβ CDR3 sequencing. We report that these TSAs can expand sizeable populations of CD8 T cells and, therefore, represent attractive targets for EOC immunotherapy.

In this COVID-19 pandemic, the use and dependency on Internet has grown exponentially. The number of people doing online activities such as e-learning, remote working, online shopping and others have increased. This has also led to increased vulnerability to cyber crimes. Cyber security attacks have become a serious problem. The common types of cyber security attacks are phishing, malware, ransomware, social engineering, identity theft and denial-of-service. The attackers target the victims in order to get their credential information or financial benefits. Those people who are doing online activities are vulnerable to cyber threats. This is because the network is not safe. The attackers are able to code according to the weaknesses of the Internet. Once the attackers hack into the devices, they have the root access and can do whatever they want to do with the device. In this research paper, the concept of cyber security attack and detailed research about real attacks are discussed. This is followed by detailed review about the recent cyber security attacks with a critical analysis. Moreover, the research paper will be proposing the latest research contribution of cyber security during COVID-19 and the implementation scenario which will give the examples about how the companies maintain privacy as well as the limitations. Then, the paper will be discussing the reasons that people are vulnerable to cyber security and the unique solution to the problems stated. Finally, this paper will conclude with an in-depth analysis and future direction for cyber security research.

Optical networks are prone to power jamming attacks intending service disruption. This paper presents a Machine Learning (ML) framework for detection and prevention of jamming attacks in optical networks. We evaluate various ML classifiers for detecting out-of-band jamming attacks with varying intensities. Numerical results show that artificial neural network is the fastest ($10^6$ detection per second) for inference and most accurate ($\approx 100 \%$) in detecting power jamming attacks as well as identifying the optical channels attacked. We also discuss and study a novel prevention mechanism when the system is under active jamming attacks. For this scenario, we propose a novel resource reallocation scheme that utilizes the statistical information of attack detection accuracy to lower the probability of successful jamming of lightpaths while minimizing lightpaths' reallocations. Simulation results show that the likelihood of jamming a lightpath reduces with increasing detection accuracy, and localization reduces the number of reallocations required.

The rise in dependency upon information present on web and social networks has increased the importance of content credibility systems. These sys- tems can help the users to make a right decision related to buying a product, utiliz- ing a service and etc. Application areas including social network blogs of different subjects such as health, food, education, politics and product review/ratings some- times suffer incredible and wrong information flow. The content credibility sys- tems can help users to identify the credible information on these various forums. Recently, researchers have proposed certain mechanisms for these systems, out of which reputation based systems have gained most attention. However, reputa- tion systems are vulnerable to reputation based attacks, like Sybil, Slandering, and Whitewash promoting spread of wrong information. The authors have proposed a defense mechanism against these attacks that is based upon Bayesian Model. The proposed defense mechanism provides protection against these attacks so that fake or wrong information could be prevented. The authors evaluated the proposed mechanism in three different scenarios and presented the results in terms of pre- cision, recall and rate of change in rank. The results reveal almost 88%prevention against these attacks in comparison to the baseline systems.

Studies have shown the unconditional danger of biological attacks on underground hydroelectric pumped-storage power stations. A hypothetical list of biological damage to water conduits and dams is determined. Relevant predictions are given, as well as accident events of hydraulic structures are described. A universal scheme for the application of hazard protection measures, which can be comprehensive, has been developed. Praemonitus praemunitus – precautions should be taken.

Wilms tumor (WT), the most prevalent type of renal cancer in children, exhibits overall survival rates exceeding 90%. However, chemotherapy resistance, which occurs in approximately 10% of WT cases, is a major challenge for the treatment of WT, particularly for advanced-stage patients. In this study, we aimed to discover potential mutation markers and drug targets associated with chemotherapy resistance in advanced stage WT. We performed exome sequencing to detect somatic mutations and molecular targets in 43 WT samples, comprising 26 advanced stage WTs, of which 7 cases are chemotherapy-resistant. Our analysis revealed four genes (ALPK2, C16orf96, PRKDC, and SVIL) that correlated with chemotherapy resistance and reduced disease-free survival in ad-vanced-stage WT. Additionally, we identified driver mutations in 55 genes within the chemo-therapy-resistant group, including 14 druggable cancer driver genes. Based on the mutation profile of the resistant WT samples, we propose potential therapeutic strategies involving platinum-based agents, PARP inhibitors, and antibiotic/antineoplastic agents. Our findings provide insights into the genetic landscape of WT and offer potential avenues for targeted treatment, particularly for patients with chemotherapy resistance.

Targeted radionuclide therapy is become increasingly prominent as a nuclear medicine subspe-cialty. For many decades, treatment with radionuclides has been mainly restricted to the use of iodine-131 in thyroid disorders. Currently, radiopharmaceuticals, consisting of a radionuclide coupled to a vector that binds to a desired biological target with high specificity, are being de-veloped. The objective is to be as selective as possible at the tumor level, while limiting the dose received at the healthy tissue level. In recent years, a better understanding of molecular mecha-nisms of cancer, as well as the appearance of innovative targeting agents (antibodies, peptides, small molecules) and the availability of new radioisotopes, have enabled considerable advances in the field of vectorized internal radiotherapy with a better therapeutic efficacy, radiation safety and personalized treatments. For instance, targeting the tumor microenvironment, instead of the cancer cells, now appears as particularly attractive. Several radiopharmaceuticals for therapeutic targeting have shown clinical value in severals types of tumors and have been or will soon be approved and authorized for clinical use.

Targeted Protein Silencing (TPS) is an elegant approach to investigate protein function and its role in the cellular landscape, overcoming limitations of genetic perturbation strategies. In contrast to CRISPR/Cas9 and RNA interference, these systems act in a reversible manner and reduce off-target effects. Several TPS have been developed and wisely improved, including compartment delocalization tools and protein degradation systems. In this review, we focus on Anchor-Away, deGradFP, auxin inducible degron (AID) and dTAG technologies, and discuss their recent applications and advances. Finally, we propose Nano-Grad, a novel nanobody-based protein degradation tool to specifically proteolyze endogenous tag-free target protein.

Microplastics (plastic particles < 5 mm) are ubiquitous pollutants that pose the ability to carry microbiota, including pathogens. Microbial adhesion is usually a sign of pathogenicity, thus we investigated the adherent strains found on 4 mm nylon strips, which were ingested and excreted by wild fish specimens. Polymer analysis of the excreted samples shown no signs of degradation, nor have their controls, represented by the nylon strips submerged in the same water tanks. Both the ingested samples and controls presented pathogens in large numbers, revealing the fact that nylon microplastics can be physical carriers for pathogens in the aquatic environment.

In recent years, drones have become increasingly prevalent in a wide range of applications, performing complex and critical tasks. To accomplish these tasks, drones cooperate by forming a Fly Ad-hoc Network (FANET) using specific routing protocols for communication. However, most of the routing protocols used in this type of network lack appropriate built-in security mechanisms, which creates numerous security challenges and concerns. To reduce the resulting security vulnerabilities and mitigate the impact of potential attacks, it is crucial to address these challenges before any deployment of FANETs. In this paper, we address two of the most harmful routing attacks - Sybil and Gray Hole and propose mitigating techniques to alleviate these attacks on FANET routing protocols. The proposed solution is secGPSR a secure version of the Greedy Perimeter Stateless Routing Protocol (GPSR). We validate the robustness of secGPSR through simulation using Omnet++ and present the results which show its effectiveness against Sybil and Gray Hole attacks.

As the application of cyber-physical systems (CPSs) becomes more and more widespread, its security is becoming a focus of attention. Currently, there has been much research on the security defense of the physical layer of the CPS. However, most of the research only focuses on one of the aspects, for example, attack detection, security state estimation or recovery control. Obviously, the effectiveness of security defense targeting only one aspect is limited. Therefore, in this paper, a set of security defense processes is proposed for the case that a CPS containing multiple sensors is subject to three kinds of stealthy attacks (i.e., zero-dynamics attack, covert attack, and replay attack). Firstly, the existing attack detection method based on improved residuals is used to detect stealthy attacks. Secondly, based on the detection results, an optimal state estimation method based on improved Kalman filtering is proposed to estimate the actual state of the system. Then, based on the optimal state, internal model control (IMC) is introduced to complete the recovery control of the system. Finally, the proposed methods are integrated to give a complete security defense process, and the simulation is verified for three kinds of stealthy attacks. The simulation results show that the proposed methods are effective.

In recent experimental studies, we reported the aggravating effects of Asian sand dust (ASD) on male reproductive function in mice. However, the effects of fetal ASD exposure on male reproductive function have not been investigated. The present study investigated the effects of fetal ASD exposure on reproductive function in male offspring. Using pregnant CD-1 mice, ASD was administered intratracheally on days 7 and 14 of gestation, and the reproductive function of male offspring was determined at 5, 10, and 15 weeks after birth. The secondary sex ratio was significantly lower in the fetal ASD-exposed mice than in the controls. Histologic examination showed partial vacuolation of seminiferous tubules in immature mice. Moreover, daily sperm production (DSP) was significantly less in the fetal ASD-exposed mice than in the controls. DSP in the fetal ASD-exposed mice was approximately 10% less than the controls at both 5 and 10 weeks. However, both the histologic changes and the DSP decrease were reversed as the mice matured. These findings suggest that ASD exposure affects both the fetal development and the reproductive function of male offspring. In the future, it will be necessary to clarify the onset mechanisms of ASD-induced male fetus death and male reproductive disorders.

Abstract Background: Periodontitis is associated with systemic chronic inflammatory diseases. There is limited evidence on the influence of anti-cytokine therapies on the periodontal condition and microbiome in the tooth pocket of such patients, so the aim of this study was to elucidate this issue. Methods: In this observational trial, the periodontal status and the gingival crevicular fluid of 13 patients with different chronic inflammatory diseases were obtained before initiation of anti-cytokine treatment and 14 weeks after. Gingival crevicular fluid was collected for 16S rRNA gene sequencing from a clinically healthy tooth and the deepest measured pocket. Results: In participants with advanced attachment loss at baseline, the clinical attachment level significantly decreased from 2.9 (2.3-3.4) to 2.4 (1.3-3.0) mm (p = 0.04), and the bleeding- on-probing index dropped from 19.4 (10.0-21.0) to 7.3 (6.9-9.9) mm (p = 0.02) without any further periodontal treatment. Shannon’s diversity index significantly increased in the deepest pockets of patients (p = 0.039). Conclusion: The data showed alterations in diversity of the subgingival microbiome and improvement of periodontal parameters over the course of the study, implying a shift towards a healthier condition after starting anti-cytokine therapy. Additional investigations are needed to analyze whether the administration of selective biologicals can improve periodontal conditions in patients with or without chronic inflammatory diseases.

Cytomegalovirus (CMV) is the leading infectious cause of brain defects and neurological dysfunctions, including sensorineural hearing loss (SNHL). A targeted screening in neonates failing the hearing screen is currently recommended in Italy according to national guidelines. However, SNHL may not be present at birth; also, congenital CMV (cCMV) may manifest with subtle signs other than SNHL. Therefore, inclusion of additional criteria for cCMV screening appears clinically valuable. Starting January 2021, we have implemented an expanded targeted cCMV screening at our center, with testing in case of maternal CMV infection during pregnancy, inadequate antenatal care, maternal HIV infection or immunosuppression, birthweight and/or head circumference.

With the continuous development of network technology, complex network systems generate massive unbalanced attack traffic. Due to the severe imbalance in the quantities of normal samples and attack samples, as well as among different types of attack samples, intrusion detection systems suffer from low detection rates for rare class attack data. In this paper, we propose a geometric synthetic minority oversampling technique based on optimized kernel density estimation algorithm. This method can generate diverse rare class attack data by learning the distribution of rare class attack data while maintaining similarity with the original sample features. Meanwhile, the balanced data is input to a feature extraction module built upon multiple denoising autoencoders, reducing information redundancy in high-dimensional data and improving the detection performance for unknown attacks. Subsequently, a soft voting ensemble learning technique is utilized for multi-class anomaly detection on the balanced and dimensionally reduced data. Finally, an intrusion detection system is constructed based on data preprocessing, imbalance handling, feature extraction, and anomaly detection modules, and validated on the NSL-KDD and N-BaIoT datasets. Comparative experiments with baseline models and other state-of-the-art methods demonstrate that the proposed system improves the detection rate of rare class attack data. Furthermore, it achieves a good overall detection rate on the Internet of Things dataset (N-BaIoT), indicating its strong applicability.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed. A real-time case study of stealthy local covert attacks with false data injection attacks is implemented on the DC-DC converter. A deep learning model is designed to mitigate cyber attacks, and its performance is evaluated.

Importance and need for cyber security have increased in folds since a decade. Indirectly, the country’s security depends on the country’s cyber-physical systems. Attackers are becoming more innovative, and attacks are becoming undetectable, causing huge risks to the systems. In this scenario, intelligent and evolving detection methods should be introduced to replace the basic and outworn ones. This article discusses about new-age intelligence and smart techniques dealing with artificial intelligence (AI) models. Artificial intelligence for cyber security is reviewed, and the performance of machine learning models (ML) and deep learning (DL) models are analysed.

There are introduced two new steganalytic methods not depending on the statistics of the cover objects, namely side attacks stegosystems. The first one assumes that the plaintext, encrypted before embedding, is known by the attacker. In this case, the stegosystem’s detection is based on the calculation of mutual information between message and extracted encrypted data. For this calculation, a notion of the k-nearest neighbor distance is applied. The second method is applied to HUGO, one of the most efficient steganographic algorithms. In this case the stegosystem’s detection is based on a verification of the NIST tests to the extracted encrypted messages. Moreover, we show that the problem to find a submatrix of­ the embedding matrix determining a trellis code structure in the HUGO algorithm provides a search of the stegokey of the proposed stegosystem.

The aryl hydrocarbon receptor (AhR) is a ligand-activated transcription factor that has been shown to be an essential regulator of a broad spectrum of biological activities required for maintaining the body's vital functions. AhR also plays a critical role in tumorigenesis. Its role in cancer is complex, encompassing both pro- and anti-tumorigenic activities. Its level of expression and activity are specific to each tumor and patient, increasing the difficulty of understanding the activating or inhibiting roles of AhR ligands. We explored the role of AhR in tumor cell lines and patients using genomic data sets and discuss the extent to which AhR can be considered as a therapeutic target.

The similarities between exosomes and liposomes, together with the high organotropism of several types of exosomes, have recently prompted the development of engineered-exosomes or exosome-mimetics, which may be artificial (liposomal) or cell-derived vesicles, as advanced platforms for targeted drug delivery. Here we provide the current state-of-the-art of using exosome or exosome-inspired systems for drug delivery. We review the various approaches investigated and the shortcomings of each approach. Finally the challenges identified up-to-date in this field are summarized.

Therapeutically effective treatments of cancer are limited. To calibrate the efficiency of the novel technique we recently discovered to modulate cancer cell viability using tuned electromagnetic fields; H1299 human lung cancer cells were irradiated in a sweeping regime of W-band (75-105 GHz) millimeter waves (MMW) at 0.2 mW/cm² (2 W/m²). Effects on cell morphology, cell death and senescence were examined and compared to that of non-tumorigenic MCF-10A human epithelial cells. MMW irradiation led to alterations of cell and nucleus morphology of H1299 cells, significantly increasing mortality and senescence over 14 days of observation. Extended irradiation of 10 minutes duration resulted in complete death of exposed H1299 cell population within two days, while healthy MCF-10A cells remained unaffected even after 16 minutes of irradiation under the same conditions. Irradiation effects were observed to be specific to MMW treated H1299 cells and absent in the control group of non-irradiated cells. MMW irradiation affected nuclear morphology of H1299 cells only and not of the immortalized MCF-10A cells. Irradiation with low intensity MMW shows an antitumor effect on H1299 lung cancer cells. This method provides a novel treatment modality enabling targeted specificity for various types of cancers.

Human epidermal growth factor receptor 2 (HER2) is a major prognostic and predictive marker overexpressed in 15- 20 % of breast cancers. The diagnostic reference standard for selecting patients for HER2-targeted therapy is by analysis of tumor biopsies for HER2 expression and amplification. Previously patients were either defined as HER2-positive or negative, however, with approval of novel treatment options, specifically the antibody-drug conjugate trastuzumab deruxtecan, many breast cancer patients with tumors expressing low levels of HER2 and lacking HER2 amplification have become eligible for HER2-targeted therapy. Such patients will need to be reliably identified by suitable diagnostic methods. Biopsy-based diagnostics are invasive, and repeat biopsies are not always feasible. They cannot visualize heterogeneity of HER2 expression, leading to a substantial number of misdiagnosed patients. An alternative and highly accurate diagnostic method is mo-lecular imaging with radiotracers. In case of HER2, various studies demonstrate the clinical utility and feasibility of such approaches. Affibody-based radiotracers are clinically validated molecules with favorable characteristics for imaging. In this article we summarize the HER2-targeted therapeutic landscape, describe the experience with imaging diagnostics for HER2, and review the currently available clinical data on HER2-Affibody-based molecular imaging as novel diagnostic tool in breast cancer and beyond.

Heart failure is a prevalent cardiovascular condition with significant health implications, necessitating effective diagnostic strategies for timely intervention. This study explores the potential of continuous monitoring of non-invasive signals, specifically integrating Photoplethysmogram (PPG) and Electrocardiogram (ECG), for enhancing early detection and diagnosis of heart failure. Leveraging a dataset from the MIMIC-III database, encompassing 682 heart failure patients and 954 controls. Feature selection techniques were used to systematically select key features which were identified for their clinical relevance and significance in capturing cardiovascular dynamics and to reduce computational complexity and to decrease the chance of overfitting the ML algorithms. These features are then utilized to train and evaluate machine learning algorithms, resulting in a model with an impressive accuracy of 98%, sensitivity of 97.60%, specificity of 96.90%, and precision of 97.20%. The integrated approach outperforms single-signal strategies, showcas-ng its potential for early, precise, and non-invasive heart failure diagnosis. Furthermore, the study underscores the significance of continuous monitoring through wearables, emphasizing the benefits of integrating multiple signals for a comprehensive evaluation of cardiovascular health. The proposed approach holds promise for implementation in hardware systems to enable continuous monitoring, aiding in early detection and prevention of critical health conditions.

Nano- and microparticles are increasingly widely used in biomedical research and applications, particularly as specific labels and targeted delivery vehicles. Silica has long been considered the best material for such vehicles, but it has some disadvantages limiting its potential, such as the proneness of silica-based carriers to spontaneous drug release. Calcium carbonate (CaCO3) is an emerging alternative, being easily available, cost-effective, biocompatible material with high porosity and surface reactivity, which makes it an attractive choice for targeted drug delivery. CaCO3 particles are used in this field in the form of either bare CaCO3 microbeads or core/shell microparticles representing polymer-coated CaCO3 cores. In addition, they serve as removable templates for obtaining hollow polymer microcapsules. Each of these types of particles has its specific advantages in terms of biomedical applications. CaCO3 microbeads are primarily used due to their capacity for carrying pharmaceutics, whereas core/shell systems ensure better protection of the drug-loaded core from the environment. Hollow polymer capsules are particularly attractive because they can encapsulate large amounts of pharmaceutical agents and can be so designed as to release their contents in the target site in response to specific stimuli. This review focuses first on the chemistry of the CaCO3 cores, core/shell microbeads, and polymer microcapsules. Then, systems using these structures for the delivery of therapeutic agents, including drugs, proteins, and DNA, are outlined. The results of systematic analysis of available data are presented. They show that the encapsulation of various therapeutic agents in CaCO3-based microbeads or polymer microcapsules is a promising technique of drug delivery, especially in cancer therapy, enhancing drug bioavailability and specific targeting of cancer cells while reducing side effects. To date, research in CaCO3-based microparticles and polymer microcapsules assembled on CaCO3 templates has mainly dealt with their properties in vitro, whereas their in vivo behavior still remains poorly studied. However, an enormous potential of these highly biocompatible carriers for in vivo applications is undoubted. This last issue is addressed in depth in the Conclusion and Outlook sections of the review.

The rapid growth of the smart Internet of Things (IoT) and massive propagation of wireless technologies revealed the recent opportunities for development in various domains of real life such as smart cities and E-Health applications. A slight defense against different forms of attacks is offered for the current secure and lightweight Routing Protocol for Low Power and Lossy Networks (RPL) of IoT resource-constrained devices. Data packets are highly likely to be exposed while transmitting them during data packets routing. The RPL rank and version number attacks, which are two forms of RPL attacks, can have critical consequences for RPL networks. The studies conducted on these attacks have several security defects and performance shortcomings. The research proposes a Secure RPL Routing Protocol (SRPL-RP) for rank and version number attacks. It mainly detects, mitigates and isolates attacks in the RPL networks. The detection is based on a comparison of ranks strategy. The mitigation uses threshold and attacks status tables, and the isolation adds them to a blacklist table and alerts relevant nodes to skip them. SRPL-RP supports diverse types of network topologies and is comprehensively analyzed with multiple studies such as Standard RPL with Attacks, SBIDS and RPL+ Shield. The analysis results showed that the SRPL-RP achieves great improvements with Packet Delivery Ratio (PDR) of 98.48%, control message value of 991 packets/second, and average energy consumption of 1231.75 joules. It provides a better accuracy rate with 98.17% under the attacks.

Non-isocyanate polyurethane synthesis by non-Sn catalysis is an essential challenge toward green polyurethane synthesis. Bismuth compounds are attractive candidates due to their low cost, low toxicity, and availability to urethane chemistry. We applied various Bi catalysts to the self-polycondensation of a bishydroxyurethane monomer and found BiCl3 to be an excellent catalyst through optimization. The catalytic activity and price of BiCl3 are comparable to those of Bu2SnO, while its toxicity is significantly low. BiCl3 is, therefore, a promising alternative to Sn-based catalysts in non-isocyanate polyurethane synthesis.

Nowadays, the Internet of Things (IoT) concept plays a pivotal role in society and brings new capabilities to different industries. The number IoT solutions in areas such as transportation and healthcare is increasing and new services are under development. In the last decade, society has experienced a drastic increase in IoT connections. In fact, IoT connections will increase in the next few years across different areas. Conversely, despite these benefits, several challenges still need to be faced to enable efficient and secure operations (e.g., interoperability, security, standards, and server technologies). Furthermore, although efforts have been made to produce datasets composed of attacks against IoT devices, several possible attacks are not considered. Most existing efforts do not consider an extensive network topology with real IoT devices. The main goal of this research is to propose a novel and extensive IoT attack dataset to foster the development of security analytics applications in real IoT operations. To accomplish this, 33 attacks are executed in an IoT topology composed of 105 devices. These attacks are classified into seven categories, namely DDoS, DoS, Recon, Web-based, Brute Force, Spoofing, and Mirai. Finally, all attacks are executed by malicious IoT devices targeting other IoT devices.

The aim of this randomized controlled clinical trial was to compare the 2-year clinical performance of resin composite restorations placed at non-caries cervical lesions (NCCL) with one-step self-etch, total-etch, and selective enamel etch & self-etch adhesive techniques. Thirty-two patients received three resin composite restorations each at NCCLs (Tetric EvoCeram/Ivoclar/Vivadent), bonded with a total-etch adhesive agent (ExciTE F/Ivoclar/Vivadent) and a self-etch (AdheSE One F/Ivoclar/Vivadent) without and with selective enamel etching. All restorations were evaluated by two examiners at baseline, 6-, 12-, 18- and 24-months post-operation regarding retention, caries occurrence, marginal adaptation, and marginal staining. A logistic regression analysis with generalized estimating equations was used. After 2 years, the retention rate was 86,8% for total-etch, 92.26% for self-etch, and 93.63% for selective enamel etching & self-etch. No caries were detected during all the restorations. Concerning marginal adaptation, the clinically perfect restorations were 26.9% for the total-etch technique, 16% for self-etch, and 25,9% for selective enamel etch & self-etch. All three adhesive strategies provided restorations with no significant differences in the retention rate and the marginal adaptation, whereas the total etch yielded better performance for marginal staining. All the restorations were assessed as clinically acceptable after 2 years.

Post-operative chemotherapy is still required for the treatment of glioblastoma (GBM), and nanocarrier-based drug delivery has been identified as one of the most effective methods. However, the blood-brain barrier (BBB) and non-specific delivery to non-tumor tissues can significantly limit drug accumulation in tumor tissues and cause damage to nearby normal tissues. This study describes a targeted cancer therapy approach that uses AS1411 aptamer-conjugated nanospheres, 100-300 nm in size, loaded with doxorubicin (Dox) to selectively identify tumor cells overexpressing nucleolin (NCL) proteins. The study demonstrates that the active target model, which employs aptamer-mediated drug delivery, is more effective than nonspecific enhanced permeability and maintenance (EPR)-mediated delivery and passive drug delivery in improving drug penetration and maintenance in tumor cells. Additionally, the study shows the potential for anticancer effects through 3D spheroidal and in vivo GBM xenograft models. The DNA-protein hybrid nanospheres utilized in this study offer numerous benefits, such as efficient synthesis, structural stability, high drug loading, dye labelling, biocompatibility, and biodegradability. When combined with nanospheres, the 1411 aptamer has been shown to be an effective drug delivery carrier for precisely targeting tumors. This combination has the potential to produce anti-tumor effects in active targeted therapy of GBM.

The escalating reliance of modern society on information and communication technology has rendered it vulnerable to an array of cyber-attacks, with distributed denial-of-service (DDoS) at-tacks emerging as one of the most prevalent threats. This paper delves into the intricacies of DDoS attacks, which exploit compromised machines numbering in the thousands or more to disrupt data services and online commercial platforms, resulting in significant downtime and financial losses. Recognizing the gravity of this issue, various detection techniques have been explored, yet the quantitative and prior detection of DDoS attacks has seen a decline in recent methods. This research introduces an innovative approach by integrating evolutionary optimization algorithms and machine learning techniques. Specifically, the study proposes XGB-GA Optimization, RF-GA Optimization, and SVM-GA Optimization methods, employing Evolutionary Algorithms Opti-mization with TOPOT-Genetic Programming. Datasets pertaining to DDoS attacks were utilized to train machine learning models based on XGB, RF, and SVM algorithms, and 10-fold cross-validation was employed. The models were further optimized using evolutionary algo-rithms, achieving remarkable accuracy scores: 99.99% with the XGB-GA method, 99.50% with RF-GA, and 99.99% with SVM-GA. Furthermore, the study employed TPOT to identify the optimal algorithm for constructing a machine learning model, with the genetic algorithm pinpointing XGB-GA as the most effective choice. This research significantly advances the field of DDoS attack detection by presenting a robust and accurate methodology, thereby enhancing the cybersecurity landscape and fortifying digital infrastructures against these pervasive threats.

Smart grid capabilities have grown significantly in recent years. The smart grid provides advanced real-time handling of faults, advanced automatic control for efficient electricity transmission, monitoring and collection of the electrical system's capacity, and communication for information sharing. Unfortunately, its exposure to public networks makes it increasingly vulnerable to privacy breaches, vulnerabilities, and cyber-attacks. Cyber security threats and vulnerabilities in smart grid networks have become a primary concern that needs to be addressed before deploying a smart grid. Furthermore, the wide range of protocols increases the attack surface of a smart grid. This study focuses on the vulnerability of Modbus, which is regarded as one of the most prevalent protocols in smart grid communication networks. This paper presents preliminary findings of analyzing cyber-attacks against the Modbus protocol using a virtual testbed to investigate its effects on the smart grid network protocol. The concept incorporates an emulated Modbus/TCP network environment built from open-source software components that imitate fundamental industrial control features of the smart grid. Finally, we analyze the cycle of a cyber-attack leading through Reconnaissance to a DoS attack on the Modbus/TCP protocol and propose improvements to the test bed for protocol attack detection and mitigation.

Internet of Things (IoT) is a massive network of tiny devices connected internally and to the in-ternet. It is uniquely identified in the network (i.e. dedicated IP) and can share the information with other devices. However, the low power and low resources that distinguish IoT devices render them unsecure and targeted by different kinds of attacks since IoT devices cannot toler-ate heavy security models. Also, due to the heavy nature of famous protocols such as HyperText Transport Protocol (HTTP), it is costly to be used with IoT devices, and alternatively, different lightweight protocols are implemented to fit IoT devices. One of the prevailing protocols used over IoT networks is the Constrained Application Protocol (CoAP). Therefore, CoAP is popular, and that makes it targeted by different types of attacks. One of the major attacks that target CoAP is distributed denial of service (DDoS) attacks. DDoS aims to overwhelm the resources of the target and make them unavailable to legitimate users. As a result, different kinds of meth-ods were used to secure CoAP against DDoS attacks such as Datagram Transport Layer Secu-rity (DTLS) and Lightweight and Secure Protocol for Wireless Sensor Networks (LSPWSN). However, the existing models suffer from two issues: DTLS is not designed for constrained devices and is considered a heavy protocol. Besides, LSPWSN is working over the network layer, not in the application layer that CoAP works on. In this paper, we build a machine learn-ing model that can detect the DDoS attacks against CoAP with an accuracy of 98%. The CIDAD dataset is extended from ~11000 to 100,000 samples using GANs because it has fewer samples of malware (less than 0.2% of the total dataset). Our model outperforms the existing models that target securing CoAP in the application layer and obtains 93% of accuracy.

Today's major challenge for smart Microgrids is to ensure the security of communications in a large number of changing data sets that are vulnerable to attacks by denial of services in constant evolution. The Internet Protocol Traceback defines a set of methods that help identify the source of an attack with minimal requirements for memory and processing. However, the concept of Traceback is not yet being used in smart Microgrids. As a result, the main challenge of this article is to incorporate a new Traceback approach into the cybernetic system of a smart mesh Microgrid, which can be tested using a network simulator (NS-3) based on delay, debit, and packet loss rate parameters. In fact, the simulation results show the efficacy of this approach compared to others existing in the literature. Furthermore, using the proposed Traceback technique and the mesh nodes, we were able to create a smart meshed Microgrid. Moreover, using the Traceback approach given for merging Intel Galileo Gen.1 nodes with the Compex WLE200NX.11a/b/g/n to establish a secure test bench, which is deployed as a prototype at the Sfax Digital Research Center in Tunisia, we were able to create an intelligent Microgrid. In fact, by identifying all attack vectors and revealing their origins, we could boost the efficiency of our operation by 100%.

Deep neural networks (DNN) have successfully delivered a cutting-edge performance in several fields. With the broader deployment of DNN models on critical applications, the security of DNNs becomes an active and yet nascent area. Attacks against DNNs can have catastrophic results, according to recent studies. Poisoning attacks, including backdoor and Trojan attacks, are one of the growing threats against DNNs. Having a wide-angle view of these evolving threats is essential to better understand the security issues. In this regard, creating a semantic model and a knowledge graph for poisoning attacks can reveal the relationships between attacks across intricate data to enhance the security knowledge landscape. In this paper, we propose a DNN Poisoning Attacks Ontology (DNNPAO) that would enhance knowledge sharing and enable further advancements in the field. To do so, we have performed a systematic review of the relevant literature to identify the current state. We collected 28,469 papers from IEEE, ScienceDirect, Web of Science, and Scopus databases, and from these papers, 712 research papers were screened in a rigorous process, and 55 poisoning attacks in DNNs were identified and classified. We extracted a taxonomy of the poisoning attacks as a scheme to develop DNNPAO. Subsequently, we used DNNPAO as a framework to create a knowledge base. Our findings open new lines of research within the field of AI security.

Objective: To study the changes in urine metabolism in female water polo players before and after high-intensity training by using ultra-high performance liquid chromatography-mass spectrometry, and to explore the biometabolic characteristics of urine after training and competition. Methods: Twelve young female water polo players (except goalkeepers) from Shanxi Province were selected. A 4-week formal training was started after one week of acclimatization according to experimental requirements. Urine samples (5 ml) were collected before formal training, early morning after 4 weeks of training, and immediately after 4 weeks of training matches, and labeled as T1, T2, and T3, respectively. The samples were tested by LC-MS after pre-treatment. XCMS, SIMCA-P 14.1, and SPSS16.0 were used to process the data and identify differential metabolites. Results: On comparing the immediate post-competition period with the pre-training period (T3 vs T1), 24 differential metabolites involved in 16 metabolic pathways were identified, among which niacin and niacinamide metabolism and purine metabolism were potential post-competition urinary metabolic pathways in the untrained state of the athletes. On comparing the immediate post-competition period with the post-training period (T3 vs T2), 10 metabolites involved in 3 metabolic pathways were identified, among which niacin and niacinamide metabolism was a potential target urinary metabolic pathway for the athletes after training. Niacinamide, 1-methylnicotinamide, 2-pyridone, L-Gln, AMP, and Hx were involved in two metabolic pathways before and after the training. Conclusion: Differential changes in urine after water polo games are due changes in the metabolic pathways of niacin and niacinamide.

Acute myeloid leukemia (AML) is a hematological malignancy that is the culmination of genetic and epigenetic alterations in the hematopoietic progenitor cells, leading to uncontrolled proliferation at the expense of normal hematopoiesis and bone marrow exhaustion. Although the outcomes for pediatric AML have improved in recent decades, at least one third of children still have relapses. Recent studies have notably highlighted the important role of dysregulated epi-genetic mechanisms in myeloid leukemogenesis. Epigenetic modifications are frequently reversible compared to genetic alterations, thus providing opportunities for targeted epigenetic therapy. In this review, we summarize the landscape of epigenetic alterations and the progress to date in epigenetic targeted therapy, and focus on the future role of epigenetic abnormalities in predicting relapse and the precision therapy in pediatric AML.

We performed a comprehensive analysis of angiosarcoma (AS) genomic biomarkers and their associations with the site of origin. We aimed to describe the genomic landscape of AS in a cohort of 143 cases of AS profiled by Caris Life Sciences. Data of Next Generation Sequencing (NGS) with a 592 gene panel was available for the entire cohort. Fifty-three cases had data of Whole Exome Sequencing (WES) which we used to study the microenvironment phenotype. Immuno-therapy (IO) response biomarkers: Tumor Mutation Burden (TMB), Microsatellite Instability (MSI) and PD-L1 status were included. IO-response markers were present in 36.4% of the cohort and in 65% of head and neck AS (H/N-AS) (p<0.0001). H/N-AS cases had predominantly muta-tions in TP53 (50.0%, p=0.0004), POT1 (40.5%, p<0.0001) and ARID1A (33.3%, p=0.5875). In breast AS, leading alterations were MYC amplification (63.3%, p<0.0001), HRAS (16.1%, p=0.0377), and PI3KCA (16.1%, p=0.2352). A microenvironment with a high immune signature, associated with better response to IO, was present in 13% of the cases. This signature was evenly distributed among different primary sites. We found that the molecular biology for AS varies significantly according to the primary site. Our findings can facilitate the design and optimiza-tion of therapeutic strategies for AS to overcome resistance to IO and targeted therapies.

Background: Convectional methods for drug delivery often faces setbacks due to systemic distribution, short half-life and degradation of therapeutics and therefore reduce concentrations of drug available to target tissue. Nanotheranostic provide a novel method for treating and diagnosing diseases Methodology: collection and review of relevant literatureResult: while nanotheranostic offer advantage of personalized medicine and often combines diagnosis and therapy using single molecular approach, nuclear medicine relies on radioactive isotopes to diagnosed and destroys cancer cells. In both cases, nanocarriers such as lipid-based, polymer-based, drug-conjugate, inorganic nanoparticles are used to deliver drugs/probes/isotopes to target site, generating images and thereafter chemotherapy/radiotherapy begins.Conclusion: Nanotheranostic plays important role in diseases diagnostic, therapy, imaging, monitoring of disease progression / response through the use of nanocarriers. This is made possible through nanoparticles/nanocarriers that delivers drug to the target tissues/cells.

A COVID-19 disease threatens the population and the economies of the countries significantly. Till now, this pandemic has affected 215 counties or territories. Unavailability of vaccine is the primary concern for the society. To avoid the spread of this disease, social isolation must be preserved and the inter and intra-population movement must be minimized. To reduce the possibility of transmission, the categorization of regions based on susceptibility to COVID-19 infection is a must.Due to the unavailability of a large amount of paper collection for this novel COVID-19 diseases, we used current literature available on a COVID-19 susceptibility of the diabetic patient, human reproductivity, hemodialysis patient’s, pregnant women and meteorological factors and geographical location. Countries in the cold region are more susceptible to the risk of COVID-19 transmission. There was no evidence of the spread of this disease from non-respiratory bodies. Diabetic patients and pregnant women were found to be more susceptible to COVID-19 infection. Anosmia was observed in the majority of the COVID-19 infected cases in European countries. No evidence indicates COVID-19's impact on the human reproductive system explicitly. No cases of vertical transmission of this disease have been observed until now. All the studies available till now is the small scale study. Correlation with something always does not mean causation. There are certain factor like pollution level, temperature Diurnal temperature range, geographical factor, humidity, pollution level, wind speed, population density, medical healthcare facilities social and political factor plays a critical role in transmitting the SARS-COV-2 virus. Besides the adverse effects, it has taught us to shed our selfish goals and to promote the welfare of all.

Osteoimmunology was coined about twenty years ago to identify a strict cross talk between bone niche and immune system both in physiological and pathological activities, including cancer. Several molecules are involved in the complex interaction between bone niche, immune and cancer cells. The Receptor Activator ok NF-kB (RANK)/RANK Ligand (RANKL/Osteoprotegerin (OPG) pathway plays a crucial role in bone cells/cancer interactions with subsequently immune system control failure, bone destruction, inhibition of effect and metastasis outcome. The bidirectional cross talk between bone and immune system could became a potential target for anticancer drugs. Several studies evidenced a direct anticancer role with improved survival of bone-targeted therapies such as bisphosphonates and RANKL antagonist Denosumab. Conversely, initial data evidenced a possible anti-bone resorption effect of systemic anticancer drugs through and immunomodulation activity, i.e. new generation antiandrogens (Abiraterone) in prostate cancer. All data could open a future rationale of combined bone, immunologic and targeted therapies in cancer treatment.

MicroRNAs (miRNA) exert regulatory actions via base-pairing with their binding sites on target mRNAs. Cooperative-binding, i.e., synergism, among binding sites on a mRNA is biochemically well-characterized. We studied whether this synergism is reflected in global relationship between miRNA-mediated regulatory activity and miRNA-binding-site count on the target mRNAs, i.e., leading to a non-linear relationship between the two. Recently, using our own and public datasets, we have enquired into miRNA regulatory actions: first, we analyzed the power-law distribution pattern of miRNA binding sites; second, strikingly, mRNAs for core miRNA regulatory apparatus proteins have extra-ordinarily high binding site counts, forming self-feedback-control loops; third, we revealed that tumor suppressor mRNAs generally have more sites than oncogene mRNAs; and fourth, we characterized enrichment of miRNA target mRNAs in translationally less-active polysomes relative to more-active polysomes. In these four studies, obvious correlation was observed qualitatively between the extent to which a mRNA is miRNA-regulated and its binding sites count. This paper summarizes the used datasets. We also re-analyzed the correlation by comparative linear and non-linear regression analyses. The results support a non-linear, instead of linear, relationship between the two parameters, conceivably a transcriptome-level reflection of cooperative-binding/synergism among miRNA binding sites on a target mRNA.

Meat serves as a vital component of the human diet, offering a rich source of protein and essential trace elements. China stands as both a significant producer and consumer of meat on a global scale, leading the world in terms of total meat production and consumption. As the standard of living within the country continues to rise, there is a growing emphasis on the nutritional value and safety of meat consumption. The tenderness of meat is a critical factor influencing its overall quality. Traditionally, tenderness has been assessed through shear force testing, a method that is inherently destructive, inefficient, and results in considerable sample wastage. In light of these drawbacks, non-destructive testing techniques have emerged as a preferred alternative, promising greater accuracy, efficiency, and convenience without compromising the integrity of the samples. This paper delves into the application of five advanced non-destructive testing technologies in the realm of meat tenderness assessment. These include near-infrared spectroscopy, hyperspectral imaging, Raman spectroscopy, airflow optical fusion detection, and nuclear magnetic resonance detection. Each technology is scrutinized for its respective strengths and limitations, providing a comprehensive overview of their current utility and potential for future development. Moreover, the integration of these techniques with the latest advancements in artificial intelligence (AI) technology is explored. The fusion of AI with non-destructive testing offers a promising avenue for the development of more sophisticated, rapid, and intelligent systems for meat tenderness evaluation. This integration is anticipated to significantly enhance the efficiency and accuracy of quality assessment in the meat industry, ensuring a higher standard of safety and nutritional value for consumers. The paper concludes with a set of technical recommendations aimed at guiding the future direction of non-destructive, AI-enhanced meat tenderness detection, thereby contributing to the ongoing improvement of the meat industry in China and beyond.

The state-of-the-art smart city has been calling for an economic but efficient energy management over large-scale network, especially for the electric power system. It is a critical issue to monitor, analyze and control electric loads of all users in system. In this paper, we employ the popular computer vision techniques of AI to design a non-intrusive load monitoring method for smart electric energy management. First of all, we utilize both signal transforms (including wavelet transform and discrete Fourier transform) and Gramian Angular Field (GAF) methods to map one-dimensional current signals onto two-dimensional color feature images. Second, we propose to recognize all electric loads from color feature images using a deep neural network with multi-scale feature extraction and attention mechanism. Third, we design our method as a cloud-based, non-intrusive monitoring of all users, thereby saving energy cost during electric power system control. Experimental results on both public and our private datasets have demonstrated our method achieves superior performances than its peers, and thus supports efficient energy management over large-scale Internet of Things.

This paper presents a theory on graph topological structure and graph coloring, proving that for any N-order graph structure (with a topological structure similar to ), the maximum number of colors required for coloring is less than or equal to . The Four Color Theorem is just one special case of this theory, with the maximum structure size for a four-color graph being a 4-order structure graph, hence requiring only a maximum of 4 colors for coloring.

Cancer is a highly heterogeneous disease and remains a global health challenge affecting millions of human lives worldwide. Despite advancements in conventional treatments like surgery, chemotherapy, and immunotherapy, the rise of multidrug resistance, tumor recurrence, and severe side effects necessitates innovative therapeutic approaches. The complex nature of the tumor microenvironment (TME) further compromises the efficacy of traditional chemotherapy drugs. Recently, stimulus-responsive nanomedicines designed to target TME characteristics (e.g., pH, redox, enzymes) have gained attention for their potential to enhance anticancer efficacy while minimizing adverse effects. Among various nanocarriers, hydrogels are an interesting class of nanocarriers used in cancer therapy due to their high water content, adjustable mechanical characteristics, and ability to respond to external/internal stimuli. These properties make hydrogels an ideal nanocarrier for controlled drug release within the TME. This review comprehensively surveys the latest advancements in stimulus-responsive hydrogels for cancer therapy, exploring various stimuli-responsive mechanisms, including biological (e.g., pH, redox), chemical (e.g., enzymes, glucose), and physical (e.g., temperature, light), as well as dual- or multi-stimuli responsiveness. This review will offer novel perspectives on the development of stimulus-responsive hydrogels for cancer therapy.

Colorectal cancer (CRC) is the third most common type of cancer and the second leading cause of cancer deaths worldwide. Surgery or surgery plus radiotherapy and/or chemotherapy for patients with metastatic CRC (mCRC) were accepted as the main therapeutic strategies until the early 2000s, when targeted drugs, like cetuximab and bevacizumab were developed. The use of targeted drugs in clinical practice has significantly increased patients’ overall survival. To date, the emergence of several types of targeted drugs has opened new possibilities and revealed new prospects for mCRC treatment. Therapeutic strategies are continually being updated to select the most suitable targeted drugs based on the results of clinical trials that are currently underway. This review discusses the up-to date molecular evidence of targeted therapy for mCRC and summarizes the Food and Drug Administration-approved targeted drugs including the results of clinical trials. We also explain their mechanisms of action and how these affect the choice of a suitable targeted therapy.

Nanotechnology is making significant transformation to our world, especially in healthcare and the treatment of diseases. It is widely used in different medical applications, such as in treatment and detection. Targeting diseased cell with nanomedicines is one of the numerous applications of nanotechnology. Targeted drug delivery systems for delivering various types of drugs to specific sites are such a dynamic area in pharmaceutical biotechnology and nanotechnology. Compared to conventional drugs, nanomedicines have a higher absorption and bioavailability rate, improving efficacy and minimizing side effects. There are several drug delivery systems including metallic nanoparticles, polymers, liposomes, and microspheres, but one of the most important is the niosomes, which are produced by nonionic surfactants. Because of the amphiphilic nature and structure, hydrophilic or hydrophobic drugs can be loaded into niosome structures. Other compounds, including cholesterol, can also be applied to the niosomes' backbone to rigidize the structure. Several variables such as the type of surfactant in niosome production, the preparation method, and the hydration temperature can affect the structure of the niosomes. Nevertheless, in-silico design of drug delivery formulations requires molecular dynamic simulation tools, molecular docking, and ADME (absorption; distribution; excretion; metabolism) properties, which evaluate physicochemical features of formulation and ADME attitudes before synthesis, investigating the interaction between nano-carriers and specific targets. Hence, experimenting in-vitro and in-vivo is essential. In this review, the basic aspects of niosomes are described including their structure, characterization, preparation methods, optimization with in-silico tools, factors affecting their formation, and limitations.

An existing power grid is going through a massive transformation. Smart grid technology is a radical approach for improvisation in prevailing power grid. Integration of electrical and communication infrastructure is inevitable for the deployment of Smart grid network. Smart grid technology is characterized by full duplex communication, automatic metering infrastructure, renewable energy integration, distribution automation and complete monitoring and control of entire power grid. Wireless sensor networks (WSNs) are small micro electrical mechanical systems which are accomplished to collect and communicate the data from surroundings. WSNs can be used for monitoring and control of smart grid assets. Security of wireless sensor based communication network is a major concern for researchers and developers. The limited processing capabilities of wireless sensor networks make them more vulnerable to cyber-attacks. The countermeasures against cyber-attacks must be less complex with an ability to offer confidentiality, data readiness and integrity. The address oriented design and development approach for usual communication network requires a paradigm shift to design data oriented WSN architecture. WSN security is an inevitable part of smart grid cyber security. This paper is expected to serve as a comprehensive assessment and analysis of communication standards, cyber security issues and solutions for WSN based smart grid infrastructure.

Glucose is a simple sugar molecule. The sugar molecule is chemically symbolised as C6H12O6. This means that the glucose molecule contains 6 carbon atoms (C), 12 hydrogen atoms (H) and 6 oxygen atoms (O). In human blood, the molecule glucose circulates as blood sugar. Normally after eating or drinking, our body breaks down the sugars in food and uses them to obtain energy for our cells. To do this, our pancreas produces insulin. Insulin "pulls" sugar from the blood and puts it into the cells for use. If someone has diabetes, their pancreas can't produce enough insulin. As a result, the level of glucose in the blood rises. This can lead one to many potential complications, including blindness, disease, nerve damage, amputation, stroke, heart attack and damage to blood vessels etc. In this study, a non-invasive method for monitoring blood glucose has been developed and is therefore easily usable. The device developed can measure the glucose level continuously and eliminates the disadvantages of the invasive system. Near-IR sensors (optical sensors) were used to estimate the concentration of glucose in the blood; these sensors have a wavelength of 940 nm. The sensor is placed on the tip of the finger and the output of the optical sensor is then connected to the microcontroller at the analogue input. Another sensor used, but only to provide more medical information was the heartbeat sensor. After processing and regression analysis, the glucose level is predicted, and data is sent via the Bluetooth network to a developed APP. The results of the implemented device were compared with available invasive methods. The study consists of a hardware platform inserted into an armband and then a small black parallelepiped-shaped box in which the optical sensors are inserted. The hardware consists of a microcontroller, a Near-IR optical sensor, a heartbeat sensor and a Bluetooth module.

A great deal of studies have assessed the expression of noncoding RNAs (ncRNAs) as well as their relevant molecular functions and biological mechanisms in the individuals of alcohol dependence. Alcohol dependence is one of the most prevalent neuropsychological disorders worldwide, and its pathogenesis is intricate and inadequately understood. There is considerable evidence demonstrating substantial links between multiple genetic factors and the development of alcohol dependence. Critical roles of ncRNAs have been emphasized in the pathophysiology of mental diseases including alcohol dependence. In the comprehension of ncRNAs action and their mechanisms of modification, they have emerged as therapeutic targets for a variety of psychiatric illness including alcohol dependence. Worth mentioning, dysregulated expression of ncRNAs has been regularly detected in individuals of alcohol dependence. An in-depth knowledge of roles of ncRNAs and m6A modification may be valuable for the development of a novel treatment against alcohol dependence. In general, a deeper comprehension of functional roles ncRNAs may make significant contributions to the precise diagnosis and/or actual treatment of alcohol dependence. Here in this review, we mostly focused on the up-to-date knowledge regarding alterations and/or modifications in the expression of ncRNAs in alcohol dependence, and then, presented their prospects for future research and therapeutic application with a novel concept of engram-system.

Sepsis is a life-threatening syndrome triggered by infection and accompanied by high mortality, with antimicrobial resistances (AMRs) further escalating clinical challenges. Rapid and reliable detection of causative pathogens and AMRs are key factors for fast and appropriate treatment, in order to improve outcome in septic patients. However, current sepsis diagnostics based on blood culture is limited by low sensitivity and specificity while current molecular approaches fail to enter clinical routine. Therefore, we developed a Suppression PCR-based selective enrichment sequencing approach (SUPSETS), providing a molecular method combining multiplex suppression PCR with Nanopore sequencing to identify most common sepsis-causative pathogens and AMRs using plasma cell-free DNA. Applying only 1 mL of plasma, we target eight pathogens across three kingdoms and ten AMRs in a proof-of-concept study. SUPSETS was successfully tested on first ten clinical samples and revealed comparable results to clinical metagenomics while clearly outperforming blood culture. Several clinically relevant AMRs could be detected additionally. Furthermore, SUPSETS provided first pathogen and AMR-specific sequencing reads within minutes of starting sequencing, thereby potentially decreasing time to results to 11 - 13 hours and suggesting diagnostic potential in sepsis.

Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by cognitive decline and neuronal loss, representing a most challenging health issue. We present a computational analysis of transcriptomic data of AD tissues vs. healthy controls, focused on elucidation of functional roles played by long non-coding RNAs (lncRNAs) throughout the AD progression. We first assembled our own lncRNA transcripts from the raw RNA-Seq data generated from 527 samples of dorsolateral prefrontal cortex, resulting in the identification of 31,574 novel lncRNA genes. Based on co-expression analyses between mRNAs and lncRNAs, a co-expression network is constructed. Maximal subnetworks with dense connections are identified as functional clusters. Pathway enrichment analyses are conducted over mRNAs and lncRNAs in each cluster, which serve as the basis for the inference of functional roles played by lncRNAs involved in each of the key steps in an AD development model that we have previously build based on transcriptomic data of protein-encoding genes. Detailed information is presented about the functional roles by lncRNAs in activities related to stress response, reprogrammed metabolism, cell-polarity, and development. Our analyses have also revealed that lncRNAs have discerning power in distinguishing between AD samples of each stage and healthy controls. This study represents the first of its kind.

This comprehensive analysis thoroughly examines the topic of adversarial attacks in artificial intelligence (AI), providing a detailed overview of the various methods used to compromise machine learning models. It explores different attack techniques, ranging from the simple Fast Gradient Sign Method (FGSM) to the intricate Carlini and Wagner Attack (C&W), emphasising the wide range of adversarial approaches and their intended goals. The discussion also distinguishes between targeted and non-targeted attacks, highlighting the adaptability and versatility of these malicious efforts. Additionally, the study delves into the realm of black-box attacks, revealing the capability of adversarial strategies to compromise models even with limited knowledge. Real-life examples illustrate the tangible consequences and potential dangers of adversarial attacks in various fields such as self-driving cars, multimedia, and voice assistants. These cases highlight the difficulties in ensuring the legitimacy and dependability of AI-powered technologies and programs. The article stresses the importance of ongoing research and innovation to address the growing difficulties posed by advanced methods like deepfakes and disguised voice commands in preserving the security of AI systems. This study provides valuable insights on how different adversarial strategies and defence mechanisms interact within AI. The results emphasise the urgent need for stronger and more secure AI models to combat the increasing number of adversarial threats in today's AI landscape. These findings can guide future research and innovations in developing more resilient AI technologies that can better withstand various adversarial vulnerabilities and challenges.

Modern cryptographic protocols such as zero-knowledge proofs and secure multi-party computation have increased the demand for a novel category of symmetric primitives. These primitives are not optimized for traditional platforms such as servers, microcontrollers, and desktop computers but rather for their ability to be implemented in arithmetic circuits. To enable efficient arithmetic operations, they define operations over larger finite fields and use low-degree invertible functions to construct their non-linear layers. Grendel is an arithmetization-oriented permutation that leverages the Legendre Symbol to enhance the growth of algebraic degrees in its non-linear layer. In this paper, we present a preimage attack on the sponge hash function instantiated with the full rounds of the Grendel permutation using algebraic methods. We introduce a technique that allows us to eliminate two full rounds of substitution permutation networks (SPN) in the sponge hash function with minimal or no additional cost. This method can be combined with univariate root-finding techniques and Gröbner basis attacks to break the number of rounds claimed by the designers. By utilizing this strategy, our attack achieves an improvement of two additional rounds compared to the previous state-of-the-art attack. While not breaking its security margin, it allows us to further understand the design and analysis of such cryptographic primitives.

Online Social Networks (OSNs), such as Facebook and Twitter, have become a very important part of many people’s daily lives. Unfortunately, the high popularity of these platforms makes them very attractive to spammers. Machine-learning (ML) techniques have been widely used as a tool to address many cybersecurity application problems (such as spam and malware detection). However, most of the proposed approaches do not consider the presence of adversaries that target the defense mechanism itself. Adversaries can launch sophisticated attacks to undermine deployed spam detectors either during training or the prediction (test) phase. Not considering these adversarial activities at the design stage makes OSNs’ spam detectors prone to a range of adversarial attacks. This paper thus surveys the attacks against Twitter spam detectors in an adversarial environment. In addition, a general taxonomy of potential adversarial attacks is proposed by applying common frameworks from the literature. Examples of adversarial activities on Twitter were provided after observing Arabic trending hashtags. A new type of spam tweet (Adversarial spam tweet), which can be used to undermine deployed classifier, were found. In addition, possible countermeasures that could increase the robustness of Twitter spam detectors against such attacks are investigated.

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

Background Sarcopenia is a state with progressive and generalized loss of skeletal muscle mass and strength. It is a serious complication occurring in chronic kidney disease (CKD) patients which increases morbidity and mortality rate. Sarcopenia is diagnosed when loss of both compartments - muscle strength and skeletal muscle quality or quantity are present. However, there are still numerous patients, also with CKD, diagnosed as pre-sarcopenic individuals with low muscle strength and normal skeletal muscle quality or quantity and conversely, with correct muscle strength and decreased skeletal muscle mass, who are prone to develop sarcopenia in future life. It is crucial to examine numerous variables possibly associated with changes of skeletal muscle mass and muscle strength in terms of preventing the development of sarcopenia. The aim of the study was to investigate anthropometric and clinical correlates and sources of variation in both skeletal muscle mass and muscle strength in advanced stages of CKD. Methods The study sample consisted of 84 non-dialysis dependent patients of both sexes (including 45.2% of women) with eGFR < 45 ml/min/1.73m²: 26.2% with eGFR 30-44 mL/min/1.73 m², 65.5% with eGFR 15-29 mL/min/1.73 m² and 8.3% with eGFR < 15 mL/min/1.73 m². Muscle strength was estimated by measuring hand grip strength (HGS) with the use of handheld hydraulic dynamometer. Muscle quantity was measured by bioimpedance spectroscopy using Body Composition Monitor. Appendicular skeletal muscle mass (ASM) was calculated according to Lin’s formula. Serum creatinine, urea, uric acid and albumin were measured. Results In bivariate analysis we found that ASM significantly and positively correlated with anthropometric variables such as body mass (r=0.903, p<0.001), NH weight (r=0.882, p<0.001), height (r=0.694, p<0.001), body mass index (BMI) (r=0.701, p<0.001), lean tissue mass (LTM) (r=0.664, p<0.001), lean tissue index (LTI) (r=0.433, p<0.001), fat mass (r=0.614, p<0.001) and fat tissue index (FTI) (r=0.449, p<0.001), and was negatively associated with clinical variable such as hydration status (p<0.001). HGS significantly and positively correlated with anthropometric parameters such as body mass (r=0.319, p=0.003), NH weight (r=0.315, p=0.004), height (r=0.666, p<0.001), LTM (r=0.490, p<0.001), LTI (r=0.238, p=0.029), and ASM (r=0.501, p<0.001), and was negatively associated with clinical variables like uric acid (p=0.039) and urea (p<0.001). After adjustment for age, sex and height, HGS remained significantly and negatively related with uric acid (β=-1.078, p=0.041) and hydration status (β=-0.805, p=0.042). Conclusions In CKD patients, ASM is determined mainly by anthropometric parameters but HGS - both by anthropometric and clinical variables specific for CKD.

This paper addresses the evolving landscape of electricity markets in Europe, with a focus on the integration of Renewable Energy Communities as introduced by the Renewable Energy Directive 2018/2001. Residents within a postal code area are highly incentivized to join a community, which enables them to exchange energy among themselves at lower procurement costs. Thereby, energy management systems optimize the operation of respective energy systems, with electrical load forecasting playing a key role. Given that prosumers may switch between communities on a daily basis, electricity demands within these groups will vary, leading to data that is non-stationary, discontinuous as well as non-identical and independently distributed. To encounter this issue, we propose a sophisticated forecasting model that applies federated learning, using informations from various distributed communities to learn domain-invariant features. To achieve this, we initially utilize synthetic electrical load time series at district level and aggregate them to profiles of Renewable Energy Communities with dynamic portfolios. Subsequently, we develop a forecasting model that accounts for the composition of residents of a Renewable Energy Community, adapt data pre-processing in accordance with the time series process, and detail a federated learning algorithm that incorporates weight averaging and data sharing. Following the training of various experimental setups, we ultimately evaluate their effectiveness. The findings suggest that our proposed framework is capable of effectively forecast non-stationary and discontinuous time series and that it can be applied to new, unseen data through the integration of knowledge from multiple sources.

The concept of targeted drug delivery can be described in terms of the drug’s ability to mimic the other biological objects’ property to localize to target cells or tissues. For example, drug delivery systems based on red blood cells or mimicking some of their useful features, such as long circulation in stealth mode, have been known for decades. On the contrary, therapeutic strategies based on macrophages have gained very limited attention until recently. Here we review two biomimetic strategies associated with macrophages, that can be used to develop new therapeutic modalities: First, the mimicry of certain types of macrophages (i.e., the use of macrophages, including tumor-associated, or macrophage-like particles as a carrier for targeted delivery of therapeutic agents); and Second, the mimicry of ligands, naturally absorbed by macrophages (i.e., the use of therapeutic agents specifically targeted at macrophages). We discuss potential applications of biomimetic systems involving macrophages for the new advancements in treatment of infections, inflammatory diseases, and cancer.

Up to 25% of early human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC) patients will relapse despite the improvement achieved by the introduction of HER2-targeted therapy. Beyond trastuzumab, other agents approved for early HER2+ BC include the monoclonal antibody pertuzumab, the antibody-drug conjugate (ADC) trastuzumab-emtansine (T-DM1) and the reversible HER2 inhibitor lapatinib. New agents, such as trastuzumab-deruxtecan or tucatinib in combination with capecitabine and trastuzumab, have also shown an improvement in the metastatic setting. Other therapeutic strategies to overcome treatment resistance have been explored in HER2+ BC, mainly in HER2+ that also overexpress estrogen receptors (ER+). One strategy has been to target the cyclin-dependent kinases 4/6 (CDK4/6) as they are downstream of HER2 and many of the cellular pathways associated with resistance to HER2-targeted therapies play a key role in cell cycle and proliferation. Different trials have explored these strategies with encouraging results, but definitive results are needed. In addition, HER2+ BC is known to be more immunogenic than other BC subgroups, with high variability between tumors. Different immunotherapeutic agents have been investigated in this setting, with promising but controversial results obtained to date.

With the rapid expansion of intelligent resource-constrained devices and high-speed communication technologies, Internet of Things (IoT) has earned a wide recognition as the primary standard for low-power lossy networks (LLNs). Nevertheless, IoT infrastructures are vulnerable to cyber-attacks due to the constraints in computation, storage, and communication capacity of the endpoint devices. From one side, the majority of newly developed cyber-attacks are formed by slightly mutating formerly established cyber-attacks to produce a new attack tending to be treated as a normal traffic through the IoT network. From the other side, the influence of coupling the deep learning techniques with cybersecurity field has become a recent inclination of many security applications due to their impressive performance. In this paper, we provide a comprehensive development of a new intelligent and autonomous deep learning-based detection and classification system for cyber-attacks in IoT communication networks leveraging the power of convolutional neural networks, abbreviated as (IoT-IDCS-CNN). The proposed IoT-IDCS-CNN makes use of the high-performance computing employing the robust CUDA based Nvidia GPUs and the parallel processing employing the high-speed I9-Cores based Intel CPUs. In particular, the proposed system is composed of three subsystems: Feature Engineering subsystem, Feature Learning subsystem and Traffic classification subsystem. All subsystems are developed, verified, integrated, and validated in this research. To evaluate the developed system, we employed the NSL-KDD dataset which includes all the key attacks in the IoT computing. The simulation results demonstrated more than 99.3% and 98.2% of cyber-attacks’ classification accuracy for the binary-class classifier (normal vs anomaly) and the multi-class classifier (five categories) respectively. The proposed system was validated using k-fold cross validation method and was evaluated using the confusion matrix parameters (i.e., TN, TP, FN, FP) along with other classification performance metrics including precision, recall, F1-score, and false alarm rate. The test and evaluation results of the IoT-IDCS-CNN system outperformed many recent machine-learning based IDCS systems in the same area of study.