Our ability to cure common diseases is still in danger due to the creation and spread of bacteria and fungi that are resistant to drugs and has developed new resistance mechanisms. The increasing global expansion of multi- and pan-resistant bacteria commonly referred to as "superbugs," which cause diseases that cannot be treated with current antimicrobial medications like antibiotics, is particularly concerning. Indirect and direct risks to public health are posed by microorganisms that are resistant to antibiotics in food. Food can get contaminated via cross-contamination, intentionally introducing resistance genes during food preparation, and antibiotic-resistant bacteria in food. Consuming unprocessed or unpreserved food products increases the danger of transferring antimicrobial resistance to people and other animals. Stressed cells in food can have an impact on food quality, safety, and security. They can also contribute to the development of resistance and afterwards spread it to unintended organisms in the food chain. These reviews look at a variety of topics to address the question of how antimicrobial resistance mechanisms work. The various types of bacteria that make up infections are examined, as well as the usage of antimicrobial agents and the requirement for alternative antimicrobial agents that will discourage the use of synthetic antimicrobial agents. Additionally, specific details are provided regarding the uniqueness, interdependence, and phylogenicity of organisms as well as their interactions with various ecological media. Thus, it is clear why it is important to work with food producers and handlers to stop the spread of pathogenic organisms.

New antimicrobial approaches are essential to counter antimicrobial resistance. The drug development pipeline is exhausted with emergence of resistance resulting in unsuccessful trials. The lack of an effective drug developed from the conventional drug portfolio has mandated the introspection into the list of potentially effective unconventional alternate antimicrobial molecules. Alternate therapies, that are clinically explicable forms include monoclonal antibodies, antimicrobial peptides, aptamers and phages. Clinical diagnostics optimizes the drug delivery. In the era of diagnostic-based applications, it is logical to draw diagnostic based treatment for infectious diseases. Selection criteria of alternate therapeutics in infectious disease include detection, monitoring of response and resistance mechanism identification. Integrating these diagnostic applications is disruptive to the traditional therapeutic development. The challenges and mitigation methods need to be noted. Applying the goals of clinical pharmacokinetics that include enhancing efficacy and decreasing toxicity of drug therapy this review analyses the strong correlation of alternate antimicrobial therapeutics in infectious diseases. The relationship between drug concentration and the resulting effect defined by the pharmacodynamic parameters are also analyzed. This review analyzes the perspectives of aligning diagnostic initiatives into the use of alternate therapeutics with a particular focus on companion diagnostic applications in infectious diseases.

Bacterial biofilms can cause widespread infection. In addition to causing urinary tract infections and pulmonary infections in patients with cystic fibrosis, biofilms can help microorganisms adhere to the surfaces of various medical devices, causing biofilm-associated infections on the surfaces of biomaterials such as venous ducts, joint prostheses, mechanical heart valves, and catheters. Biofilms provide a protective barrier for bacteria and provide resistance to antimicrobial agents, which increases the morbidity and mortality of patients. This review introduces the formation process and drug resistance mechanism of biofilms in detail, and further summarizes the main characteristics of clinical persistent infection caused by biofilms and the many methods of treating biomaterial-related biofilm. This provides ideas and directions for the development of new biofilm infection strategies related to therapeutic materials.

Weeds that infest crops are a primary factor limiting agricultural productivity world-wide. Weedy rice, also called red rice, has experienced independent evolutionary events through gene flow from wild rice relatives and de-domestication from cultivated rice. Each evolutionary event supplied/equipped weedy rice with competitive abilities that allowed it to thrive with cultivated rice and severely reduce yields in rice fields. Under-standing how competitiveness evolves is important not only for noxious agricultural weed management but also for the transfer of weedy rice traits to cultivated rice. Molec-ular studies of weedy rice using simple sequence repeat (SSR), restriction fragment length polymorphism (RFLP) and whole genome sequence have shown great genetic variations in weedy rice population globally. These variations are evident both at whole genome and at single allele level, including Sh4 (shattering), Hd1 (heading and flower-ing), and Rc (pericarp pigmentation). The goal of this review is to describe the genetic diversity of current weedy rice germplasm and the significance of weedy rice germplasm as a novel source of disease resistance. Understanding these variations, es-pecially at an allelic level, is also crucial as individual locus that control important traits can be of great target to rice breeders.

Objectives: Carbapenem-resistant Acinetobacter baumannii (CRAB) isolates are one of the most difficult pathogens to treat. Cefiderocol, a chlorocatechol-substituted siderophore antibiotic, was approved by the U.S. Food and Drug Administration (FDA) in 2019 indicated for the treatment of infections due to CRAB infections. Despite the initial positive treatment outcomes with this antimicrobial, recent studies reported higher than all-cause mortality rate in patients treated with cefiderocol. The cause(s) behind these outcomes remains unconfirmed. A plausible hypothesis is heteroresistance, a phenotype characterized by the survival of a small proportion of cells in a population seemingly isogenic. Recent results have shown that the addition of human fluids to CRAB cultures leads to cefiderocol heteroresistance. Here we describe molecular and phenotypic analyses of CRAB heteroresistant bacterial subpopulations to better understand the nature of the less-than-expected successful outcomes after cefiderocol treatment. Methods: Isolation of heteroresistant variants of the CRAB strain AMA40 was carried out in cultures supplemented with cefiderocol and human pleural fluid (HPF). Two AMA40 variants, AMA40 IHC1 and IHC2, were subjected to whole genome sequencing and transcriptional analysis to identify mutations and expression changes associated with cefiderocol heteroresistance. The impact of these mutations on the pharmacodynamic activity of cefiderocol was assessed via susceptibility testing, EDTA and boronic acid inhibition analysis, biofilm formation, and static time-kill assays. Results: Heteroresistant variants AMA40 IHC1 and AMA40 IHC2 have 53 chromosomal mutations, of which 40 are common to both strains. None of the mutations occurred in genes associated with high affinity iron-uptake systems or β-lactam resistance. However, transcriptional analyses showed significant modifications in levels of expression of genes associated with these functions. The blaNDM-1 and blaADC-2, as well as various iron-uptake system genes, were expressed at higher levels than the parental strain. On the other hand, the carO and ompA genes’ expression was reduced. One of the mutations common to both heteroresistant strains mapped within pipA, a gene associated with iron homeostasis in other species. Static time-kill assays showed that supplementing cation-adjusted Mueller-Hinton broth with human serum albumin, the main protein component of HPF, considerably reduced cefiderocol killing activity for all three strains tested. Notably, collateral resistance to amikacin was observed in both variants. Conclusions: We conclude that exposing CRAB to fluids containing high HSA facilitates the rise of heteroresistance associated with point mutations and upregulation of genes coding for β-lactamases and biofilm formation.

In this work, the resistance/susceptibility (R/S) profile of Staphylococcus equorum strains (n=30) from cheese to 16 antibiotics was determined by broth microdilution. The minimum inhibitory concentration (MIC) for all antibiotics was low in most strains, although higher MICs compatible with acquired genes were also noted. Genome analysis of 13 strains showed the S. equorum resistome to be composed of intrinsic mechanisms, acquired mutations, and acquired genes. However, the genetic data did not always correlate with the phenotype. As such, a cat gene providing resistance to chloramphenicol was found on a plasmid in one strain; this was able to provide resistance to Staphylococcus aureus after electroporation. An msr(A) polymorphic gene was identified in five strains. The Mrs(A) variants were associated with variable resistance to erythromycin. All strains harboured a polymorphic fosB/fosD gene, although only one acquired copy was associated with strong resistance to fosfomycin. Similarly, a plasmid-associated blaR1-blaZI operon encoding a penicillinase was identified in five ampicillin- and penicillin G-susceptible strains. Identified genes not associated with resistance further included mph(C) in two strains and norA in all strains. The antibiotic R/S status and gene content of S. equorum strains intended to be employed in food systems should be carefully determined.

The aim of this study was to verify the effects of blood flow restriction on movement velocity and muscle activity during the back-squat exercise. Methods: Twenty-four university students participated in this study. In two randomized 72-hours apart sessions, participants performed a 4-set protocol consisting of 30-15-15-15 repetitions performed at 30% of their one-repetition maximum in the back-squat exercise. In both sessions, neuromuscular function was monitored by surface electromyography (EMG) and movement velocity (mean propulsive velocity [MPV], peak concentric velocity [Vmax] and the effort index [EI]). Blood flow restriction (BFR) was applied during exercise in one of the experimental sessions with 80% of full arterial occlusion pressure over lower limbs. Results: BFR condition showed higher (p<0.05) EI, peak and rooted mean square normalized EMG in set 1 compared to set 2. Similar MPV and Vmax were observed at each set for both BFR and control conditions. No significant differences were observed between conditions at any set. Conclusions: BFR did not imply changes in neuromuscular performance during low-intensity resistance training, but it might induce greater intra-series velocity loss and less activation of the muscles involved.

Coronavirus disease 2019 (COVID-19) is a potentially serious acute respiratory infection caused by the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). Since the World Health Organization (WHO) declared COVID-19 a global pandemic, the virus has spread to more than 200 countries with more than 500 million cases and more than 6 million deaths reported globally. It has long been known that viral respiratory tract infections predispose patients to bacterial infections and that these co-infections often have an unfavourable clinical outcome. Moreover, nosocomial infections, also known as health care-associated infections (HAIs), are those infections that are absent at the time of admission and acquired after hospitalization. However, the impact of coinfections or secondary infections on the progression of COVID-19 disease and its lethal outcome is still debated. The aim of this review was to assess the literature on the incidence of bacterial co-infections and superinfections in patients with COVID-19. The review also highlights the importance of the rational use of antibiotics in patients with COVID-19 and the need to implement antimicrobial stewardship principles to prevent the transmission of drug-resistant organisms in healthcare settings. Finally, alternative antimicrobial agents to counter the emergence of multidrug-resistant bacteria causing healthcare-associated infections in COVID-19 patients will also be discussed.

This work highlights that monitoring the microbiological quality of animal products, such as raw sheep’s milk and cheese, is necessary for food safety. Sheep’s milk and its derivatives are not legislated in Brazil. Thus, present study aimed to evaluate: (i) the hygienic-sanitary quality of raw sheep’s milk and cheese produced in southern Brazil; (ii) the presence of enterotoxins and Staphylococcus spp. in these products; and (iii) the antimicrobial susceptibility profile and resistance genes of the isolated Staphylococcus spp. Thirty-five raw sheep’s milk and cheese samples were evaluated. Microbiological quality and the presence of enterotoxins were determined by Petrifilm and VIDAS SET2 methods, respectively. Antimicrobial susceptibility tests were conducted using VITEK 2 equipment and the disc diffusion method. The presence of resistance genes tet(L), sul1, sul2, ermB, tetM, AAC(6)', tetW, and strA were evaluated by PCR. In total, 39 Staphylococcus spp. were isolated. The tetM, ermB, strA, tetL, sul1, AAC(6)’, and sul2 resistance genes were detected in 82%, 59%, 36%, 28%, 23%, 3%, and 3% of isolates, respectively. The results showed that both products contained Staphylococcus spp. and these strains were resistant to antimicrobials as well carriers of resistance genes. These results highlight Brazil's need for specific legislation regarding the pro-duction and marketing of these products.

Long non-coding RNAs (LncRNAs) are mRNA-like molecules that do not encode for proteins and that are longer than 200 nucleotides. LncRNAs play important biological roles in normal cell physiology and organism development. Therefore, deregulation of their activities is involved in disease processes such as cancer. Lung cancer is the leading cause of cancer-related deaths due to late stage at diagnosis, distant metastasis, and high rates of therapeutic failure. LncRNAs are emerging as important molecules in lung cancer for their oncogenic or tumor suppressive functions. LncRNAs are highly stable in circulation, presenting an opportunity for use as non-invasive and early-stage cancer diagnostic tools. Here, we summarize latest works providing in vivo evidence available for LncRNAs role in cancer development, therapy-induced resistance, and their potential as biomarkers for diagnosis and prognosis, with a focus on lung cancer. Additionally, we discuss current therapeutic approaches to target LncRNAs. The evidence discussed here strongly suggests that investigation of LncRNAs in lung cancer in addition to protein-coding genes will provide a holistic view of molecular mechanisms of cancer initiation, development, and progression, and could open a new avenue for cancer treatment.

Ca2+ is a crucial second messenger in plant cells, playing a vital role in various physiological and biochemical processes, including plant growth and development, photosynthesis, and enzyme regulation. Exogenous calcium concentration can have different effects on plant growth. This study aimed to investigate the optimal calcium concentration for the growth of four tree species, namely Pinus tabuliformis, Pinus sylvestris var. mongolica, Populus, and Morus alba seedlings, and whether the optimal calcium concentration varies among different tree species. The study utilized five calcium concentration gradients (0, 100, 200, 400, 800 mg·kg-1) for each species with three repeated treatments. Various growth indexes, photosynthetic parameters, chlorophyll fluorescence parameters, water use efficiency, and antioxidant enzyme activities were assessed to determine each species' calcium concentration requirements. The results showed that exogenous calcium significantly affected the growth and development of each seedling. The growth, biomass, photosynthetic parameters, photosynthetic products, photosynthetic pigments, water use efficiency, and antioxidant enzyme activity all increased initially and then decreased with increasing calcium concentration. Leaf calcium concentration also increased with the exogenous calcium content. Thus, there is an optimal calcium concentration for plant growth, and high or low calcium concentration is not conducive to plant growth. Furthermore, the study found that different tree species have varying optimal calcium concentrations. The optimal calcium concentration for Pinus tabuliformis, Pinus sylvestris var. mongolica, Populus, and Morus alba seedlings was 100, 100, 200, and 400 mg·kg-1, respectively. Finally, the study revealed that broadleaf species require higher calcium concentrations than coniferous species.

Background: The AWaRe tool was set up by the World Health Organization (WHO) to promote the rational use of antimicrobials. Indeed, this tool classifies antibiotics into four groups, Access, Watch, Reserve and not-recommended antibiotics. In Republic Democratic of Congo, data on antibiotic dispensing (prescribing) by health professionals according to the AWaRe classification are scarce. In this research work, we aimed to explore antibiotic dispensing pattern from health professionals according to the WHO AWaRe classification to strengthen the national antimicrobial resistance plan. Methods: For this purpose, a survey was conducted from July to December 2022 in the district of Tshangu in Kinshasa. From randomly selected drugstores, drug-sellers were interviewed and randomly selected customers attending those drugstores were included in the study for medical prescriptions collection. The prescribed antibiotics were classified into the Access, Watch, Reserve and not recommended antibiotics group and by antibiotics number by prescription among pharmacies surveyed. Results: Of 400 medical prescriptions collected, 301 (75.25%) contained antibiotics. Of 301 prescriptions containing antibiotics, 164 (164/301; 54.5%) contained one antibiotic, 117 (117/301; 38.9%) two antibiotics, 15 (15/301; 5%) three antibiotics and 5 (5/301; 1.6%) 4 antibiotics. Out of the total of 463 antibiotics prescribed, 169 (169/463; 36.5%) were from the Access group, 200 (200/463; 43.2%) from the Watch group and 94 (94/463; 20.3%) from not recommended antibiotics group, respectively. Based on the anatomical, therapeutic and chemical (ATC) classification, it can be seen that third generation cephalosporins contained 34.33% of the prescribed antibiotics, followed by penicillins 17.17%, macrolides 7.63%, aminoglycosides 7. 36% and Imidazoles 7.36%, thus accounting approximately for 74% of the classes of antibiotics prescribed. Of the 463 antibiotics prescribed, the most frequently prescribed antibiotics were Ceftriaxone (21.38%), Amoxicillin (11.01%), Gentamycin (5.61%), Amoxicillin-clavulanic acid (5 .61%), Azithromycin (4.97%) and Metronidazole (4.75%), thus accounting for approximately 54% of all antibiotics prescribed. Conclusion: These results highlight the importance of strict implementation of the national plan to combat antimicrobial resistance and the need to train health workers in the correct application of the WHO AWaRe classification.

The antibody Trastuzumab (Tz) targeting ERBB2 has improved the prognosis of patients with breast cancer (BCa) that overexpress this receptor. Resistance to Tz negatively impacts prognosis. Several mechanisms have been reported to cause Tz resistance. The objective of this study was to identify common mechanisms in in vitro models of acquired BCa Tz resistance. In particular, we used three widely available ERBB2+ BCa cell lines adapted to grow in Tz. We performed several analyses to address possible changes in phenotype, proliferation, and ERBB2 membrane expres-sion common to the three Tz-R cell lines compared to wt, but none was found. High-resolution mass spectrometry analysis identified, instead, a common set of differentially expressed proteins (DEPs) in Tz-R vs. wt cells. Furthermore, bioinformatic tools revealed that all three Tz-R cell models shared a modulation of proteins involved in the metabolism of lipids, organophosphate biosynthetic process, and macromolecule methylation. Ultrastructural analysis confirmed altera-tion of lipid droplets in resistant cells. Our data strongly support the hypothesis that complex metabolic adaptation, including lipid metabolism, protein phosphorylation, and possibly chro-matin remodeling, may fuel Tz resistance. The identification of a common set of 10 DEPs in all three TZ-resistant cell lines may provide novel targets for therapeutic intervention.

conclusions. The blends of ultrahigh molecular weight polyethylene/polypropylene (UHMWPE/PP) possess excellent processability, but limited by its reduced mechnical properties. Herein, the high mechanical properties and wear—resistance UHMWPE/PP composites were fabricated by the addition of silicon carbide (SiC) nanoparticles under a consecutive elongational flow without any other additives or solvents. The morphology and thermal properties, mechanical properties, wear resistance as well as scratch resistance of UHMWPE/PP/SiC composites were investigated . The mechanical properties and friction resistance of composites increase initially then decrease with the increase content of SiC. The optimum mechanical properties of UHMWPE/PP/SiC composites are achieved when the content of SiC is 3 phr. In addition, both the hardness and scratch resistance of composites improved upon increasing the content of SiC. These improved mechanical properties and wear resistance originate from the well disperison of SiC nanoparticles along with favourable interfacial adhesive under a consecutive elongational flow.

The article presents the results of a morpho-physiological assessment of rice samples by the strength of growth, as well as an assessment of the degree of development of the conducting system of the flag leaves of rice plants. The resistance of varieties to flooding is very relevant in the fight against weeds due to the deep layer of water that weeds cannot overcome. Currently, there are no zoned varieties in Russia that would meet these requirements. Therefore, the problem of creating such rice varieties is urgent, as it will reduce production costs, reduce grain losses during harvesting, improve the quality of the products obtained, and also reduce the pesticide load on the ecosystem. As a result of research, a number of studied samples revealed the potential to quickly lengthen the first leaves, overcome a large layer of water and accumulate vegetative mass. As a result of the evaluation of the conducting system of the flag leaf of rice plants, it was found that in plants with the flood resistance gene Sub1A, which stops growth under water, the conductive beams were smaller in comparison with other fast-growing samples with the AG, Sk genes. Consequently, fast-growing samples have bigger sizes of the conducting system as a resistance mechanism.

Antibiotic resistance (ABR) is concerning issue due to its direct and indirect repercusions on public health, since decreased therapeutic effect of certain antibiotic to treatment complications that can cause death. There are several mechanism as to how ABR can be transferred from one microoorganisms to another, and many of them are dependant many environmental factors. The food supply chain is a environment in which ABR gene transfer can occur is multiple pathways, which generate concerns regarding food safety. Here, we summarize relevant mechanisms which are implied in ABR in food supply chain but also we are addressing routes of transmission and prevalence of ABR, implications on public health, and the application of new alternatives to antibiotics such as antimicrobial peptides, mainly bacteriocins, in order to countermeasure ABR.

Assessing the genetic diversity of metabolic resistance genes such as cytochrome P450s helps to understand the dynamics and evolution of resistance in the field. Here, we analysed the polymor-phisms of CYP6M2 and CYP6P4 associated with pyrethroid resistance in Anopheles coluzzii and An. gambiae, to detect potential resistance markers. Field-caught resistant mosquitoes and susceptible lab-strains were crossed and F4 was exposed to permethrin for 15min and 90min to discriminate highly susceptible (HS) and highly resistant (HR) mosquitoes respectively. Significant permethrin mortality reduction was observed after pre-exposure to PBO, suggesting P450s genes involvement. QPCR analysis revealed significant over-expression of CYP6M2 (FC=19.57 [95%CI 13.96-25.18] for An. coluzzii; 10.16 [7.86-12.46] for An. gambiae) and CYP6P4 (FC=6.73 [6.15-7.30] An. coluzzii; 23.62 [26.48-20.76] An. gambiae). Full-gene and ≈1kb upstream were sequenced. For CYP6M2, upstream region shows low diversity in HR and HS (overall Hd=0.49, π=0.018), whereas the full-gene shows allelic-variation but without evidence of ongoing selection. CYP6P4 upstream region showed a lower diversity in HR (Hd=0.48) than HS (Hd=0.86) of An. gambiae. These results highlighted that CYP6P4-associated resistance is potentially driven by modification in upstream region. However further work is needed to determine the real causative variants which will help design rapid detec-tion tools.

As a result of the COVID-19 pandemic, as well as other outbreaks such as SARS and Ebola, bats are recognized as a critical species for mediating zoonotic infectious disease spillover events. While there is a growing concern of increased antimicrobial resistance (AMR) globally during this pandemic, knowledge of AMR circulating between bats and humans is limited. In this paper, we have reviewed the evidence of AMR in bats and discussed the planetary health aspect of AMR to elucidate how this is associated with the emergence, spread and persistence of antibiotic resistance at the human-animal interface. The presence of clinically significant resistant bacteria in bats and wild life has reflective and broad impact on zoonotic pandemic surveillance, disease transmission and treatment modalities. We searched MEDLINE through PubMed and Google Scholar to retrieve relevant studies (n=38) that provided data on resistant bacteria in bats till September 30, 2022. There is a substantial variability in the results from studies measuring the prevalence of AMR based on geographic location, bat types and time. We found all major groups of gram positive and gram negative bacteria in bats which are resistant to commonly used antibiotics. The most alarming issue is- recent studies have increasingly identified Methicillin Resistant Staphylococcus aureus (MRSA), ESBL producing and Colistin resistant Enterobacteriaceae in samples from bats. This evidence of superbugs abundance in both humans and wild mammals like bats, could facilitate a greater understanding of which specific pathways of exposure should be targeted. We believe that these data will also facilitate future pandemic prepareness as well as global AMR containment during the pandemic events and beyond.

The discovery of first ATP-binding cassette (ABC) transporter, whose overexpression in cancer cells is responsible for exporting anticancer drugs out of tumor cells, initiated enormous efforts to overcome tumor cell multidrug resistance (MDR) by inhibition of ABC-transporter. Because of its many physiological functions, diverse studies have been conducted on the mechanism, function and regulation of this important group of transmembrane transport proteins. In this review, we will focus on the structural aspects of this transporter superfamily. Since the resolution revolution of electron microscope, experimentally solved structures increased rapidly. A summary of the structures available and an overview of recent structure-based studies are provided. More specifically, the artificial intelligence (AI)-based predictions from AlphaFold 2 will be discussed.

The synergistic effect of silicon and cerium addition on the oxidation resistance of pack cementation aluminide coating applied on Ni-based superalloy substrate was investigated. The effect of cerium and silicon on the scale morphology, oxidation behavior, and scale spallation tendency are discussed based on the experimental results, using X-ray diffraction (XRD), scanning electron microscopy (SEM), and energy-dispersive X-ray analyses (EDX). In addition, the oxidation resistance was evaluated by measuring the weight of samples after each 16-hour cycle at 1100˚C for 50 cycles. The experiments indicated that silicon addition to aluminide coating improves the oxidation resistance through the formation of β-NiAl1-nSin and δ-Ni2Al3-nSin and δ-Ni2Si phases. Furthermore, the addition of 1% cerium to modified aluminide enhances the formation of the fine-grained microstructure of the β-NiAl and δ-Ni2Al3 and reduces the outward/inward diffusion of elements (so-called blocking effect) which significantly modifies the hot oxidation resistance. The addition of 2% cerium, owing to the distortion of the β-NiAl and δ-Ni2Al3 phases, strictly decreases the hot oxidation resistance and the coating is exfoliated after 450 h at 1100 °C. The cyclic oxidation tests showed that the samples containing 1% cerium and 6% silicon possess the highest hot oxidation resistance in which 2 mg/cm2 weight loss of Ce-Si-aluminide was reported after 800 h. This is attributed to the simultaneous effect of cerium and silicon during the deposition process which reduces the oxygen diffusion and reduces the growth rate of α-Al2O3 during oxidation tests. The α-Al2O3 oxidation layer morphology showed that finer grains are formed in samples containing optimum ratios of cerium and silicon which indicates that these elements improve the coating adhesion and oxidation resistance simultaneously.

Antimicrobial resistance (AMR) due to community carriage of antibiotic-resistant bacteria is highly prevalent in the WHO South-East Asia region. One of the major reasons is the misuse of antibiotics in animal farming practices and at community level, which threatens both human and animal health. However, this multifaceted One Health (OH) problem of antibiotic use (ABU) in poultry farms and respective farmers is not well studied in countries like Pakistan. Therefore, we conducted n OH cross-sectional study in rural Punjab to explore the current practices of ABU in poultry and poultry farmers, associated factors, their healthcare-seeking behaviour and biosecurity practices. We found all the participating farmers using antibiotics for poultry, 60% of which were Colistin sulphate and Amoxicillin trihydrate. The significant consumption of antibiotics in poultry farms (60%) and poultry farmers (50%) was without prescription. Most of the farms (85%) had no wastewater drainage system, causing direct shedding of poultry waste and antibiotic residue in the surrounding environment. Lack of farmers’ education, professional farm training and duration of farming experience were the significantly associated factors with ABU and knowledge of AMR. Our study implies the necessity of an integrated OH-AMR policy with the inclusion of farmers’ education, mass awareness, and strict antibiotic usage guidelines.

Often yam varieties grown in different agro-ecologies show differential responses across production environments, a term known as genotype-by-environment interaction. Genotype-by-environment interaction makes selecting the best genotypes under varied production environments more complex. This study tested twenty yam genotypes evaluated in six test environments to assess genotype, environment, and the interaction between genotypes and environmental effect for tuber yield, yam mosaic virus, and dry matter content. The experiments were conducted in two seasons across three locations in Uganda using a randomized complete block design with three replications. The results showed a significant effect (p ≤ 0.001) for genotype (G), environment (E), and genotype by environment interaction for all the traits. Serere 2021 and Namulonge 2021 were identified as the most discriminating and representative environments for testing the yam mosaic virus, respectively. Serere 2021 was recognized as the most discriminating environment, whereas Arua 2021 was identified as the closest to an ideal environment for assessing yam tuber yields. The tested genotypes also exhibited high resistance to yam mosaic virus disease, high tuber yields, and high dry matter content. Genotypes UGY16020, UGY16034, UGY16042, and UGY16080 demonstrated great resistance to yam mosaic virus disease, high yielding, and considerable dry matter content and are thus potential parents for yam improvement. Further evaluation of the four genotypes should be done under farmers' production systems for selection, improvement, and release as new yam varieties for Uganda

Background: Nutrient supplements are widely used for type 2 diabetes (T2D) yet evidence-based guidance for clinicians is lacking. Methods: We searched the four electronic databases from November 2015–December 2021. The most recent, most comprehensive, high-ranked systematic reviews, meta-analyses and/or umbrella reviews of randomised controlled trials in adults with T2D were included. Data were extracted on study characteristics, aggregate outcome measures per group (glycemic control, measures of insulin sensitivity and secretion), adverse events, and GRADE assessments. Quality was assessed using AMSTAR-2. Results: Twelve meta-analyses and one umbrella review were included. There was very low certainty evidence that chromium, Vitamin C and omega-3 polyunsaturated fatty acids (-3 PUFAs) were superior to placebo for the primary outcome of HbA1c (MD -0.54%, -0.54% and ES -0.27 respectively). Probiotics were superior to placebo for HbA1c (WMD -0.43%). There was very low certainty evidence that Vitamin D was superior to placebo for lowering HbA1c in trials of <6 months (MD -0.17%). Magnesium, zinc, Vitamin C, probiotics and polyphenols were superior to placebo for FBG. Vitamin D was superior to placebo for insulin resistance. Data on safety was limited. Conclusions: Future research should identify who may benefit from nutrient supplementation, safety, and optimal regimens and formulations.

Multicomponent reactions (MCR) have been used to synthesis a wide range of analogs from several classes of heterocyclic compounds, with multifaceted medicinal uses. The synthesis of highly functionalized molecules in a single pot is a unique property of MCR, allowing researchers to quickly assemble libraries of compounds of biological interest and uncover novel leads as possible therapeutic agents. Isocyanide-based multicomponent reactions have proven to be extremely effective at swiftly specifying members of compound libraries, particularly in discovery of drug . The understanding of structure-activity correlations that drive the development of new goods and technology, requires structural variety in these libraries. In current world, antibiotic resistance is a major ongoing problem which is developing a problematic scenario in public health. The implementation of isocyanide based multicomponent reactions uphold a significant potential in this regard. By utilizing such reactions, new antimicrobial compounds can be discovered and fight against such concerns. This study discusses recent developments in antimicrobial medication discovery using isocyanide-based multicomponent reactions (IMCRs). Furthermore, the article emphasizes the potential of IMCRs in the near future.

Fungal infections impact the lives of at least 12 million people every year, killing over 1.5 million. Wide-spread use of fungicides and prophylactic antifungal therapy have driven resistance in many serious fungal pathogens, and there is an urgent need to expand the current antifungal ar-senal. Recent research has focused on improving azoles, our most successful class of antifungals, by looking for synergistic interactions with secondary compounds. Synergists can co-operate with azoles by targeting steps in related pathways, or they may act on mechanisms related to re-sistance like active efflux or on totally disparate pathways or processes. A variety of sources of potential synergists have been explored, including pre-existing antimicrobials, pharmaceuticals approved for other uses, bioactive natural compounds and phytochemicals, and novel synthetic compounds. Synergy can successfully widen the antifungal spectrum, decrease inhibitory dosag-es, reduce toxicity, and prevent the development of resistance. This review highlights the diversity of mechanisms that have been exploited for the purposes of azole synergy and demonstrates that synergy remains a promising approach for meeting the urgent need for novel antifungal strate-gies.

Resistance to Pyrimethamine, an antimalarial medicine has been reported to be due to mutations in Dihydrofolate reductase-thymidylate synthase (DHFR-TS). Phytochemicals, particularly from plants that been used in ethnomedicine, have been reported to have privileged structures that might bind strongly to the mutants of DHFR-TS. The aim of this study is to identify phytochemicals of Acalypha wilkesiana, Cymbopogon citratus, Azadirachta indica, and Morinda lucida with high binding affinities for the Plasmodium falciparum DHFR-TS. The three-dimensional structures of the phytochemicals, wide type and mutant forms of DHTR-TS were obtained from PubChem and Protein Databank (PDB) respectively. They were appropriately prepared and molecular docking simulations was implemented to predict binding affinities of the phytochemicals to the wildtype and mutant forms of DHTR-TS. Druglikeness assessment was implemented to triage the top binding phytochemicals and molecular dynamics simulations was done to establish the stability of the interaction of the top-ranked phytochemical with one of the mutants of DHFR-TS. Nineteen phytochemicals showed higher binding affinities to both the wild type and mutant forms DHFR-TS than Pyrimethamine. Molecular dynamics revealed that the receptor-ligand binding of luteolin, the top-ranked, drug like phytochemicals, to the quadruple mutant was stable.

(1) Background: Health Care-Associated Infections (HAIs)are a significant public health problem worldwide favoring multidrug-resistant (MDR) microorganisms. The SARS-CoV-2 infection had a negative association with the increase in antimicrobial resistance, and the ESKAPE group had the most significant impact on HAIs. The study aimed to evaluate the bactericidal effect of high concentration O3 gas on some reference and ESKAPE bacteria. (2) Material and Methods: Four standard strains and four clinical or environmental MDR strains were exposed to high ozone doses at different concentrations and times. Bacterial inactivation (growth and cultivability) was investigated using colony counts and resazurin as a metabolic indicator. Scanning electron microscopy (SEM) was performed. (3) Results: The culture exposure to a high level of O3 inhibited the growth of all bacterial strains tested with a statistically significant reduction in colony count compared to the control group. The cell viability of S. aureus (MRSA) (99,6%) and P. aeruginosa (XDR) (29,2%) was reduced considerably, and SEM showed damage on bacteria after O3 treatment (4) Conclusion: High doses of ozone were able to interfere in the growth of all strains studied, evidencing that ozone-based decontamination approaches may represent the future of hospital cleaning methods.

Resistance spot welding is an established joining process in the production of safety-relevant components in the automotive industry. Therefore, a consecutive process monitoring is essential to meet the high-quality requirements. Artificial neural networks can be used to evaluate the process parameters and signals to ensure the individual spot weld quality. The predictive accuracy of such algorithms depends on the provided training data set and the prediction of untrained data is challenging. The aim of this paper is to investigate the extrapolation capability of the multi-layer perceptron model. That means, that the predictive performance of the model will be tested with data that clearly differs from the training data in terms of material and coating composition. Therefore, three multi-layer perceptron regression models were implemented to predict the nugget diameter from process data. The three models were able to predict the trained datasets very well. The models, which were provided with features from the dynamic resistance curve predicted the new dataset better than the model with only process parameters. This study shows the beneficial influence of the process signals on the predictive accuracy and robustness of artificial neural network algorithms. Especially, when predicting a data set from outside of the training space.

Staphylococcus aureus can develop resistance by mutation, tranfection or biofilm formation. Resistance was induced in S. aureus by growth in sub-inhibitory concentrations of ciprofloxacin for 30 days. The ability of the antimicrobials to disrupt biofilms was determined using crystal violet and live/dead staining. Effects on the cell membranes of biofilm cells was evaluated by measuring release of dyes and ATP and nucleic acids. S. aureus did not develop resistance to the AMPs but resistance increased to ciprofloxacin by 128 times after 30 passages. Only peptides reduced biofilms of ciprofloxacin resistant cells. The antibiofilm effect of melimine with ciprofloxacin was more (27%) than with melimine alone at 1X MIC (p < 0.001). Similarly, at 1X MIC the combination of Mel4 and ciprofloxacin produced more (48%) biofilm disruption than Mel4 alone (p < 0.001). Combinations of either of the peptides with ciprofloxacin at 2X MIC released  66 nM ATP, more than either peptide alone (p  0.005). At 2X MIC, only melimine in combination with ciprofloxacin released DNA/RNA which was 3 times more than released by melimine alone (p = 0.043). These results suggest the potential use of melimine and Mel4 with conventional antibiotics for the treatments of S. aureus biofilms.

The emergence of multi-drug resistant E. coli is an important matter of increasing considerable concern to global public health. The aim of this study was to investigate the incidence, antibiotic resistance pattern, phylogroups and genetic variation of E. coli isolates from raw milk, vegetable salad and ground meat samples. Methods: Culture-based techniques, Kirby-Bauer disk diffusion susceptibility testing, PCR and RAPD assays were used to determine the incidence rate, antimicrobial resistance pattern, phylogenetic groups and genetic diversity of the E. coli isolates. Results: E. coli isolates were highly resistant to amoxicillin (79.16%), trime-thoprim-sulfamethoxazole (70.83%), amoxicillin-clavulanic acid (62.50%), tetracycline (54.16%), chloramphenicol (54.16%), nitrofurantoin (54.16%), ampicillin (45.83%), streptomycin (45.83%), and kanamycin (33.33%); and completely susceptible to norfloxacin and azithromycin. 70.83% of the isolates were multi-drug resistant. Most E. coli isolates (46%) belonged to phylogroup A. RAPD with UBC245 primer categorized the isolates into 11 clusters. A high level of genetic di-versity was found among the isolates; however, 33.3% of the isolates were grouped in a major cluster (R5). Conclusions: Antibiotic resistance patterns are randomly distributed among the ge-netic clusters. Novel, practical, efficient food safety control and surveillance systems of multi-drug resistant foodborne pathogens are required to control the foodborne pathogen contamina-tion.

The aminoglycoside 6′-N-acetyltransferase type Ib [AAC(6′)-Ib] is a common cause of resistance to amikacin and other aminoglycosides in Gram-negatives. Utilization of mixture-based combinatorial libraries and application of the positional scanning strategy identified an inhibitor of AAC(6′)-Ib. This inhibitor’s chemical structure consists of a pyrrolidine pentamine scaffold substituted at four locations (R1, R3, R4, and R5). The substituents are two S-phenyl (R1 and R4), an S-hydroxymethyl (R3), and a 3-phenylbutyl (R5) groups. Another location, R2, does not have a substitution, but it is named because its stereochemistry was modified in some compounds utilized in this study. Structure-activity relationship (SAR) analysis using derivatives with different functionalities, modified stereochemistry, and truncations were carried out by assessing the effect of the addition of each compound at 8 µM to 16 µg/ml amikacin-containing media and performing checkerboard assays varying the concentrations of the inhibitor analogs and the antibiotic. The results showed that: 1) the aromatic functionalities at R1 and R4 are essential, but the stereochemistry is essential only at R4, 2) the stereochemical conformation at R2 is critical, 3) the hydroxyl moiety at R3 as well as stereoconformation are required for full inhibitory activity, 4) the phenyl functionality at R5 is not essential and can be replaced by aliphatic groups, 5) the location of the phenyl group on the butyl carbon chain at R5 is not essential, 6) the length of the aliphatic chain at R5 is not critical, 7) all truncations of the scaffold resulted in inactive compounds. Molecular docking revealed that all compounds preferentially bind to the kanamycin C binding cavity, and binding affinity correlates with the experimental data for most of the compounds evaluated. The SAR results in this study will serve as the basis for the design of new analogs in an effort to improve their ability to induce phenotypic conversion to susceptibility in amikacin-resistant pathogens.

Cancer is one of the leading causes of death and morbidity with a complex pathophysiology. Traditional cancer therapies include chemotherapy, radiation therapy, targeted therapy, and immunotherapy. However, limitations such as lack of specificity, cytotoxicity, and multi-drug resistance pose a substantial challenge for favorable cancer treatment. The advent of nanotechnology has revolutionized the arena of cancer diagnosis and treatment. Nanoparticles (1-100nm) can be used in the treatment of cancer owing to their specific advantages such as biocompatibility, reduced toxicity, more excellent stability, enhanced permeability and retention effect, and precise targeting. Nanoparticles are classified into several main categories. The nanoparticle drug delivery system is particular and utilizes tumor and tumor environment characteristics. Nanoparticles not only solve the limitations of conventional cancer treatment but also overcome multidrug resistance. Additionally, as new multidrug resistance mechanisms are unraveled and studied, nanoparticles are being investigated more vigorously. Various therapeutic implications of nano-formulations have created brand new perspectives for cancer treatment. However, a majority of the research is limited to in vivo and in vitro studies, and the number of nano-drugs that are approved has not much amplified over the years. In this review, we discuss numerous types of nanoparticles, targeting mechanisms along with approved nanotherapeutics for oncological implications in cancer treatment. Further, we also summarize the current perspective, advantages, and challenges in clinical translation.

Gastric cancer, also known as stomach cancer, is a cancer which develops from the lining of the stomach. Accumulated evidences and epidemiological studies have been indicated that natural products play an important role in gastric cancer prevention and treatment, although its mechanism of action did not elucidate yet. Particularly, experimental studies have been showed that natural products displayed a protective effect against gastric cancer via numerous molecular mechanisms such as suppression of cell metastasis, anti-angiogenesis, inhibition of cell proliferation, induction of apoptosis, and modulation of autophagy. Although chemotherapy remains the standard treatment for advanced gastric cancer along with surgery, radiation therapy, hormone therapy and immunotherapy, but its adverse side effects including neutropenia, stomatitis, mucositis, diarrhea, nausea, and emesis are well documented. Additionally, intake of naturally occurring phytochemicals could increase the efficacy of gastric chemotherapy and chemotherapeutics resistance. However, natural product structural stability and powerful bioactivity are important to develop novel treatments for gastric cancer that may minimize such adverse effects. Therefore, the purpose of this review is to summarize the potential therapeutic effects of natural products on prevention and treatment of gastric cancer with intensive molecular mechanisms of action, bioavailability, and safety efficacy.

The control of Porcine Reproductive and Respiratory Syndrome (PRRS) is still a major issue worldwide in the pig farming sector. Despite extensive research efforts and the practical experience gained so far, the syndrome still heavily affects farmed pigs worldwide and challenges established beliefs in veterinary virology and immunology. The clinical and economic repercussions of PRRS are based on concomitant, additive features of virus pathogenicity, host susceptibility and influence of environmental, microbial and non-microbial stressors. This makes a case for integrated, multi-disciplinary research efforts in which the three types of contributing factors are critically evaluated toward the development of successful disease control strategies. These could be definitely eased by the definition of reliable markers of disease risk and virus pathogenicity. As for the host’s susceptibility to PRRSV infection and disease onset, the roles of both innate and adaptive immune responses are still ill-defined. In particular, the overt discrepancy between passive and active immunity and the uncertain role of adaptive immunity vis-à-vis an established PRRSV infection should prompt the scientific community to the development of novel research schemes, in which apparently diverging and contradictory findings could be reconciled, and eventually brought to a satisfactory conceptual framework.

The SiBOC precursor ceramics were fabricated by polymer-derived method SiBOC precursors with different B/Si molar ratios were synthesized by controlling monomer content. The effects of different process conditions on SiBOC ceramics and the oxidation resistance of SiBOC ceramics at a high temperature (1400 ℃) were studied. The SiBOC precursor’s bonds of Si-O-C, Si-O-Si, and Si-O-B were detected by FTIR. The precursor is converted to a rigid ceramic material that has an 85.3 wt % ceramic yield of SiBOC ceramics with B/Si=0.4 by TG-DSC. The results of SEM analysis show that the SiBOC ceramics surface bulges and folds increase with the increase of boric acid content. With the increase of pyrolysis temperature and pyrolysis time, the size and number of pores on the surface of SiBOC ceramics also increase. Static oxidation tests show that SiBOC ceramics with B/Si=0.4 have better oxidation resistance under other conditions being the same.

Lineage plasticity, the switching of cells from one lineage to another has been recognized to be a cardinal property essential for embryonic development, tissue repair and homeostasis. However, such a highly regulated process goes awry when cancer cells exploit this inherent ability to their advantage, resulting in tumorigenesis, relapse, metastasis and therapy resistance. In this review, we summarize our current understanding on the role of lineage plasticity in tumor progression and therapeutic resistance in multiple cancers. Lineage plasticity can be triggered by treatment itself and is reported across various solid as well as liquid tumors. Here we focus on the importance of lineage switching in tumor progression and therapeutic resistance of solid tumors such as the prostate, lung, hepatocellular and colorectal carcinoma and the myeloid and lymphoid lineage switch observed in leukemias. Besides this, we also discuss the role of Epithelial-Mesenchymal Transition (EMT) in facilitating the lineage switch in biphasic cancers such as aggressive carcinosarcomas. We also discuss the mechanisms involved, current therapeutic approaches and challenges that lie ahead in taming the scourge of lineage plasticity in cancer.

Treating infections caused by Pseudomonas aeruginosa is increasingly difficult due to high antimicrobial resistance, materialized through the presence of multiple resistance strains, as well as due to rapid development of resistance throughout treatment. The present survey was conducted to investigate the antibiotic susceptibility profile of Pseudomonas aeruginosa pathogens, in two University Veterinary hospitals from different geographical regions of Romania (i.e., south-west - Timisoara county and north-east – Iasi county) involved in canine superficial infections. A total of 142 swab specimens were collected from dogs with superficial infections (superficial skin infections, otitis externa, perianal abscess), with the aim of assessing the presence of Pseudomonas aeruginosa, based on phenotypic and molecular characterization. Fifty-eight samples (40.84%; 58/142) were positive for Pseudomonas aeruginosa (according to their confirmed morphological and molecular features). Susceptibility to usual antibiotics used in the treatment of canine skin conditions was tested for all Pseudomonas strains that were isolated from canine superficial infections, using the Kirby Bauer disc diffusion method. Drug resistance was observed in the case of all tested antibiotics. The susceptibility rate of P. aeruginosa strains that were tested in this study was in the following order: ampicillin sulbactam (55.17%; 32/58), followed by ceftazidime (53.44%; 31/58), aztreonam (51.72%; 30/58), amikacin (44.82%; 26/58), azithromycin (41.37%; 24/58), gentamycin (37.93%; 22/58), cefepime (36.20%; 21/58) meropenem (25.86%; 13/58), piperacillin-tazobactam (25.86%; 13/58) imipenem (22.41%; 13/158), ciprofloxacin (17.24%; 10/58) tobramycin (8.62; 5/58), and polymyxin B (1.72; 1/58) respectively. The results highlight the importance of antibiotic susceptibility testing in Pseudomonas aeruginosa isolates from dogs with superficial infections, in order to use an adequate treatment plan for the management of the skin condition, and other pathology (otitis externa and perianal abscesses).

Climate change is impacting the spread/intensity of vector-borne diseases, including malaria, and accelerating evolutionary/adaptive changes in vector species. These changes including chromoso-mal inversions and overexpression and/or changes in allele frequencies of thermotoler-ance-associated genes, may facilitate insecticide resistance through pleiotropy. This study investi-gated the impact of thermotolerance on pyrethroid resistance in four populations of malaria vector An. gambiae, from savanna/sub-Sahel of northern Nigeria. Anopheles coluzzii and An. gambiae were the only malaria vectors found, sympatric in all the sites, with the former species predominant. High thermotolerance was observed, with no mortality at 38°C, and LT50 of ~44°C. Significantly high permethrin resistance was observed (mortality <50%) in heat-hardened (44°C) larvae from two sites, BUK and Pantami, compared with control, and heat-hardened adult females from Auyo (mortality = 3.00%±1.20, χ2 = 5.83, p<0.01) compared with control (12.00%±4.65). The 2La chromosomal inver-sion was detected at ~50% in larvae and 58% in adult females. Significant association was observed (OR = 7.2, p<0.03) between permethrin resistance and 2La/+a rearrangement compared with 2L+a/+a, in BUK larvae. For all sites permethrin resistance correlated with 2La/a homozygosity in adult fe-males [OR = 5.02, p=0.01). qRT-PCR identified 6 genes commonly induced/overexpressed, including heat shock protein 70 (AGAP004581) which was 2468x and 5x overexpressed in heat-hardened and permethrin-resistant females, respectively, trehalose-6-phosphate synthase (AGAP008227), and ionotropic glutamate receptor genes, IR25a (AGAP010272) and IR21a (AGAP008511). This study highlights challenges associated with insecticide-based malaria vector control, and the epidemiological significance of taking climate variables into account for design/choice of control measures.

Aquaculture is one of the fastest-growing animal food-producing agricultural industries in the world and proper performance of fish in morphological, physiological and immunological aspects is important for fish production and sustainable expansion of aquaculture. But several inhibitors like disease, pathogen, and adverse environment can overpower these performances. At present, antibiotics in preventing these inhibitors have been seen as becoming favorable to those inhibitors. So, Bacillus, an important group of probiotic bacteria can be an alternative to these antibiotics in aquaculture. Bacillus has been seen used in different experiments, mainly as a supplement in feed at various concentrations. Bacillus showed effective results like improved growth with minimum cost, improvement in reproduction, hematology, improved immune response and disease, and stress resistance as well as better proximate composition in different fish species. Application of Bacillus strains has proven efficient in improving water quality by reducing ammonia and nitrite toxicity, harmful algal blooms and utilization of H⁺ ion. Larger application of probiotic Bacillus instead of the hazardous synthetic chemicals would promote eco-friendly low-input sustainable aquaculture for food and nutritional security of the increasing world population. So many more experiments should be conducted in commercially important fishes for better growth and health of fishes which will certainly increase fish

(1) Background: The aim of the present study was to evaluate the effect of different types of warm-ups on the strength and skin temperature of Paralympic powerlifting athletes; (2) Methods: The participants were 15 male Paralympic powerlifting athletes. It was analyzed the effects of three different types of warm-up (without warm-up (WW), traditional warm-up (TW), or stretching warm-up (SW)) on static and dynamic strength tests as well as in the skin temperature, which was monitored by thermal imaging; (3) Results: show no differences in the dynamic and static indicators of the force in relation to the different types of warm-up. No significant differences were found in relation to the Peak Torque (p = 0.055, F=4.560, η2p= 0.246 medium effect), and 1-Repetition Maximum (p = 0.139, F=3.191, η2p = 0.186, medium effect) between the different types of warm-up. In the thermographic analysis, there was a significant difference only in the Pectoral muscle clavicular portion between the TW (33.04 ± 0.71ºC) and the WW (32.51 ± 0.74ºC) (p = 0.038). The TW method also presented slightly higher values than the SW and WW in the Pectoral Muscles Sternal portion and in the Deltoid anterior portion, but with p-value &gt; 0.05; (4) Conclusions: that the types of warm-up studied do not seem to interfere with the performance of Paralympic Powerlifting athletes. However, the thermal images showed that traditional warm-up best meets the objectives expected for this preparation phase.

This work investigated the effects of manufacturing variations including coil resistance, initial pod mass, and e-liquid color on coil lifetime and aerosol generation of Vuse ALTO pods. Random samples of pods were used until failure (where e-liquid was consumed, and coil resistance increased to high value indicating a coil break). Initial coil resistance, initial pod mass, and e-liquid net mass ranged between 0.89 to 1.14 [], 6.48 to 6.61 [g], and 1.88 to 2.00 [g] respectively. Coil lifetime with light color e-liquid was (mean) = 149, (standard deviation) = 10.7 puffs while pods with dark color e-liquid was = 185, = 22.7 puffs with a difference of ~36 puffs (p &lt;0.001). Total mass of e-liquid consumed until coil failure was = 1.93, = 0.035 [g]. TPM yield per puff of all test pods for the first session (brand new pods) was = 0.0123, = 0.0003 [g]. During usage, TPM yield per puff of pods with light color e-liquid was relatively steady while it was continuously decreasing for pods with dark e-liquid. Coil lifetime and TPM yield per puff were not correlated with either variation in initial coil resistance or variation in initial pod mass. The absence of e-liquid in the pod is an important factor in causing coil failure. Small bits of the degraded coil could be potentially introduced to the aerosol. There is a potential correlation of e-liquid color with both coil lifetime and TPM yield per puff. Change of e-liquid color might have been a result of oxidation which changed some nicotine into nicotyrine.

The paper presents a three-dimensional slope stability limit equilibrium solution for translational, planar failure modes. The proposed solution uses Bishop’s average skeleton stress combined with the Mohr – Coulomb failure criterion to describe soil strength evolution under unsaturated conditions while its formulation ensures a natural and smooth transition from the unsaturated to the saturated regime and vice versa. The proposed analytical solution is evaluated by comparing its predictions with the results of the Ruedlingen slope failure experiment [1]. The comparison suggests that despite its relative simplicity the analytical solution can capture well the experimentally observed behaviour and highlights the importance of lateral resistance consideration together with a realistic interplay between mechanical parameters (cohesion) and hydraulic (pore water pressure) conditions.

Root rot diseases remain a major global threat to the productivity of agricultural crops. They are usually caused by more than one type of pathogen and are thus often referred to as a root rot complex. Fungal and oomycete species are the predominant participants in the complex, while bacteria and viruses are also known to cause root rot. Incorporating genetic resistance in cultivated crops is considered as the most efficient and sustainable solution to counter root rot; however, resistance is often quantitative in nature. Several genetics studies in various crops have identified quantitative trait loci associated with resistance. With access to whole genome sequences, the identity of the genes within the reported loci is becoming available. Several of the identified genes have been implicated in pathogen response. However, it is becoming apparent that at the molecular level, each pathogen engages a unique set of proteins to either infest the host successfully or be defeated or contained in doing so. In this review, a comprehensive summary of the genes and potential mechanisms underlying resistance or susceptibility against the most investigated root rots of important agricultural crops is presented.

Signaling from estrogen receptor alpha (ER) and its ligand estradiol (E2) is critical for growth of ~70% of breast cancers. Therefore, several drugs that inhibit ER functions are in clinical use for decades and new classes of anti-estrogens are continuously being developed. Although a significant number of ER+ breast cancers respond to anti-estrogen therapy, ~30% of these breast cancers recur, sometimes even after 20 years of initial diagnosis. Mechanism of resistance to anti-estrogens is one of the intensely studied disciplines in breast cancer. Several mechanisms have been proposed including mutations in ESR1, crosstalk between growth factor and ER signaling, and interplay between cell cycle machinery and ER signaling. ESR1 mutations as well as crosstalk with other signaling networks lead to ligand independent activation of ER thus rendering anti-estrogens ineffective, particularly when treatment involved anti-estrogens that do not degrade ERa. As a result of these studies, several therapies that combine anti-estrogens that degrade ER with PI3K/AKT/mTOR inhibitors targeting growth factor signaling or CDK4/6 inhibitors targeting cell cycle machinery are used clinically to treat recurrent ER+ breast cancers. In this review, we discuss nexus between ER-PI3K/AKT/mTOR pathways and how understanding of this nexus has helped to develop combination therapies.

Crystallization is a significant procedure in the manufacturing of many pharmaceutical and solid food products. In-situ Electrical Resistance Tomography (ERT) is a novel Process Analytical Tool (PAT) to provide a cheap and quick way to test, visualize, and evaluate the progress of crystallization processes. In this work, the spatial accuracy of the non-conductive phantoms in low conductivity solutions was evaluated. Gauss-Newton, Linear Back Projection, and iterative Total Variation reconstruction algorithms were used to compare the phantom reconstructions for tap water, industrial-grade saturated sucrose solution, and demineralized water. Cylindrical phantom measuring 10 mm in diameter and a cross-section area of 1.5 % of the total beaker area was detected at the center of the beaker. Two phantoms with a 10 mm diameter were visualized separately in non-central locations. The quantitative evaluations were done for the phantoms with radii ranging from 10 mm to 50 mm in demineralized water. Multiple factors such as ERT device and sensor development, FEM mesh density and simulations, image reconstruction algorithms, number of iterations, segmentation methods, and morphological image processing methods were discussed and analyzed to achieve spatial accuracy. The development of ERT imaging modality for the purpose of monitoring crystallization in low conductivity solutions was performed satisfactorily.

The coffee leaf miner (CLM) Leucoptera coffeella moth is a major threat to coffee production. Insect damage is related to the feeding behavior of the larvae on the leaf. During the immature life stages, the insect feeds in the mesophyll, triggering necrosis and causing loss of photosynthetic capacity, defoliation, and significant yield loss to coffee crops. Chemical control is mandatory to sustain the coffee production chain, though market requirements move towards conscious consumption, claiming for more sustainable methods. In this overview, we discuss aspects about the CLM concerning biology, history, geographical distribution, economic impacts, and the most relevant control strategies in progress. Insights to develop an integrate approach for a safer and eco-friendly control of the CLM are discussed, including bio-extracts, nanotechnology, pheromones, and tolerant cultivars.

Globally, rice is utilized as staple food, belongs to the family Poaceae. From the past few decades under variable climate conditions, it greatly affected by the bacterial leaf blight (BLB) disease caused by a bacterium Xanthomonas oryzae pv. oryzae (Xoo). Scientists studied causes of the disease and found more than 61 isolates of Xoo. About 39 Xa genes were noted that cause race specifically resistance either individually or in pairs against Xoo. The canvas on the characterization of these genes is still unrevealed. In this study, amino acid sequences of Xa15, Xa19, Xa20 and Xa21 were mined and used for motif-domain identification, characterization, and comparative analysis. It includes screening of physical and chemical characteristics, sequence comparison to find sequence similarity between them and their phylogenetic comparison with other Xa genes and other species based upon LRR and S_TKc domains to find evolutionary relationship among them. The comparison-based modeling was performed and assessed by different tools to gain better understanding and structural evaluation. The results showed identified domains are specific in function, each domain involved in resistance against biotic and abiotic stresses through regulating different cellular processes. This study also revealed high similarity (>98% sequence identity) between these genes and encode a similar leucine-rich repeat receptor kinase like protein. It will optimize the breeding programs as it will be useful for the selection of effective genes to produce resistance in rice varieties against the specific strains of Xoo that will be more effective against BLB than the other Xa genes.

Molluscan aquaculture is a major contributor to global seafood production, but is hampered by infectious disease outbreaks which can cause serious economic losses. Selective breeding has been widely used to improve disease resistance in major agricultural and aquaculture species, and has clear potential in molluscs, albeit its commercial application remains at a formative stage. Advances in genomic technologies, especially development of cost-efficient genomic selection, have potential to accelerate genetic improvement. However, tailored approaches are required due to the distinctive reproductive and lifecycle characteristics of molluscan species. Transgenesis and genome editing, in particular CRISPR/Cas systems, have been successfully trialled in molluscs, and may further understanding and improvement of genetic resistance to disease through targeted changes to the host genome. Whole organism genome editing is achievable on a much greater scale compared to other farmed species, making genome-wide CRISPR screening approaches plausible. This review discusses the current state and future potential of selective breeding, genomic tools, and genome editing approaches to understand and improve host resistance to infectious disease in molluscs.

Tomato (Solanum lycopersicum L.) is a valuable horticultural crop grown and consumed worldwide. Optimum production is hindered by several factors of which Verticillium dahliae, the cause of Verticillium wilt, is one of the major biological constraints in temperate production regions. V. dahliae is difficult to manage because it is a vascular pathogen, has a broad host range and worldwide distribution, and can persist in soil for years. Understanding the pathogen virulence and genetic diversity, host resistance, and plant-pathogen interactions can ultimately inform the development of integrated strategies to manage the disease. In recent years, considerable research has focused on providing new insight into these processes as well as the development and integration of environment-friendly management approaches. In this review, we discuss and summarize the recent findings on the race and population structure of V. dahliae; pathogenicity factors; host genes, proteins, and enzymes involved in defense; the emergent management strategies, and recent approaches to managing Verticillium wilt in tomatoes.

In Electronic Nicotine Delivery System (ENDS), coil resistance is an important factor in the generation of heat energy used to change e-liquid into vapor. An accurate and unbiased method for testing coil resistance is vital for understanding its effect on emissions and reporting results that are comparable across different types and brands of ENDS and measured in different laboratories. This study proposes a robust, accurate and unbiased method for measuring coil resistance. An apparatus is used which mimics the geometric configuration and assembly of ENDS pods and power control units. The method is demonstrated on two commonly used ENDS devices, the ALTO by Vuse and JUUL. Analysis shows that the proposed method is stable and reliable. The two-wire configuration introduced a positive measurement bias of 0.086 [ohm], which is a significant error for sub-ohm coil designs. The four-wire configuration is far less prone to bias error. We observed a significant difference in coil resistance of 0.593 [ohm] (p<0.001) between the two products tested. The mean resistance and standard deviation of the pod coil assemblies was shown to be 1.031 (0.067) [ohm] for ALTO and 1.624 (0.033) [ohm] for JUUL. The variation in coil resistance between products and within products can have significant impacts on aerosol emissions.

Different water treatment regiments are revealed to have potential in enriching antibiotic resistant bacteria (ARB). Advanced oxidation processes (AOPs) based disinfection techniques have been studied widely in the recent times due to their advantages over conventional treatment methods. However, bacterial response and adaptations against the hostile environments of AOPs is not clearly understood yet. Based on the existing knowledge on the ways in which bacteria surpass the antibiotic treatment, here we propose few important aspects of bacterial adaptation which could be true for AOPs as well since both antibiotics and AOPs generate reactive oxygen species (ROS) during their modes of action. We discuss the plausible role of ROS in the selection of ARB and bacterial heterogeneity as a strategy to bypass the lethal action of AOPs. Understanding bacterial adaptation during disinfection plays a vital role in devising strategies to outclass the bacterial survival. Hence, more importance should be given to such studies in the near future for the successful implementation of AOPs.

Insecticidal toxins from Bacillus thuringiensis (Bt) are valuable tools for pest management worldwide, contributing to the management of human disease insect vectors and phytophagous insect pests of agriculture and forestry. Here, we report the effects of dual and triple Bt toxins expressed in transgenic cotton cultivars on the fitness and demographic performance of Helicoverpa zea (Boddie), a noctuid pest known as cotton bollworm and corn earworm. Life-history traits were determined for individuals of three field populations from a region where H. zea overwintering is likely. Triple-gene Bt cotton cultivars expressing Cry and Vip3Aa toxins killed 100% of the larvae in all populations tested. In contrast, dual-gene Bt cotton expressing Cry1Ac+Cry1F and Cry1Ac+Cry2Ab2 allowed population growth with the intrinsic rate of population growth (rm) 38% lower than on non-Bt cotton. The insects feeding on Bt cotton plants expressing Cry1Ac+Cry2Ab2, Cry1Ac+Cry1F, or Cry1Ab+Cry2Ae exhibited reduced larval weight, survival rate, and increased development time. Additionally, fitness parameters varied significantly among the insect populations, even on non-Bt cotton plants, likely because of their different genetic background and/or previous Bt toxin exposure. This is the first report of the comparative fitness of H. zea field populations on dual-gene Bt cotton after the recent reports of field resistance to certain Bt toxins. These results document the population growth rates of H. zea from an agricultural landscape with 100% Bt cotton cultivars. Our results will help to refine models designed to predict resistance evolution and improve insect resistance management for Bt crops.

Escherichia coli is known as one of the most important foodborne pathogens in humans, and contaminated chicken meat is an important source of foodborne infection with this bacterium. The occurrence of extended-spectrum β-lactamase (ESBL)-producing E. coli (ESBL-Ec), in particular, in chicken meat is considered a global health problem. This study aimed to determine the magnitude of E. coli, with special emphasis on ESBL-Ec, along with their phenotypic antimicrobial resistance pattern in frozen chicken meat. The study also focused on the determination of ESBL-encoding genes in E. coli. A total of 113 frozen chicken meat samples were purchased from 40 outlets of nine branded supershops in five megacities in Bangladesh. Isolation and identification of E. coli were done based on cultural and biochemical properties, as well as PCR assay. The resistance pattern was determined by the disc diffusion method. ESBL-encoding genes were determined by multiplex PCR. The results showed that 76.1% of samples were positive for E. coli, of which 86% were ESBL producers. All the isolates were multidrug-resistant (MDR). Resistance to 9–11 and 12–13 antimicrobial classes was observed in 38.4% and 17.4% isolates, respectively, while only 11.6% were resistant to 3–5 classes. Possible extensive drug resistance (pXDR) was found in 2.3% of isolates. High single resistance was observed for oxytetracycline (93%) and amoxicillin (91.9%), followed by ampicillin (89.5%), trimethoprim–sulfamethoxazole, and pefloxacin (88.4%), and tetracycline (84.9%). Most importantly, 89.6% of isolates were resistant to carbapenems. All the isolates were positive for the blaTEM gene. However, the blaSHV and blaCTX-M-2 genes were identified in two ESBL-non producer isolates. None of the isolates carried the blaCTX-M-1 gene. This study provided evidence of the existence of MDR and pXDR ESBL-Ec in frozen chicken meat in Bangladesh, which may pose a risk to human health if the meat is not properly cooked or pickled raw only. This emphasizes the importance of the implementation of good slaughtering and processing practices by the processors.

Background and Aims: We aimed to investigate cross-sectional relationships of relative handgrip strength (RHGS) with presence of diabetes and hypertension in a community setting. Methods and Results: Between 2016 and 2018, we enrolled 601 consecutive women with an average age of 70.7 ± 6.9 years (mean ± SD). Nutritional status was evaluated by the Mini Nutritional Assessment (MNA) score. Muscular strength and level of fitness were assessed by handgrip strength (HGS) and other standardized physical functional tests. The majority of participants were overweight or obese (80% with BMI > 25). Prevalence of diabetes and hypertension was 13 and 60%, respectively. Participants in the lowest quartile of HGS adjusted for BMI (RHGS) had significantly higher prevalence of diabetes and hypertension compared with those in the lower quartile (20.7 vs. 5.3% and 49.3 vs. 39.3%, respectively, p < 0.01 for both), whereas differences in nutritional status were not observed. Likelihood of having diabetes was significantly reduced in women with higher RHGS values (OR 0.77; 0.59–0.86 CI95%; p=0.002), independently of age, abdominal adiposity and presence of hypertension. RHGS was positively correlated with most of the physical functional tests performed. Conclusion: RHGS is an easy-to-obtain and inexpensive measure of muscular strength, independently associated with presence of diabetes in overweight elderly women. Prospective studies are required to assess its predictive value in individuals at risk of new onset or progression of diabetes.

Cell death resistance is a key feature of tumor cells. One of the main anti-cancer therapies is increasing the susceptibility of cells to death. Cancer cells have developed a capability of tumor immune escape. Hence, restoring the immunogenicity of cancer cells can be suggested as an effective approach against cancer. Accumulating evidence proposes that several anticancer agents provoke the release of danger-associated molecular patterns (DAMPs) that are determinants of immunogenicity and stimulate immunogenic cell death (ICD). It has been suggested that ICD inducers are two different types according to their various activities. Here, we review the well-characterized DAMPs and focus on the different types of ICD inducers and recent combination therapies that can augment the immunogenicity of cancer cells.

Salmonella, especially invasive non-typhoidal Salmonella (iNTS) are responsible for developing various invasive diseases, and possess higher mortality rate, due to their higher antibiotic resistance profile than the other bacteria. Therefore, the present study was concerned to develop epitope based peptide vaccine against iNTS species as a successive and substitute protective measures. The study considered comprehensive Immunoinformatic approaches, followed by molecular docking and molecular dynamics simulation to predict the efficient vaccine candidate T cell and B cell epitopes, based on the outer membrane proteins. The study identified two best epitopes YGIFAITAL and KVLYGIFAI from total iNTS outer membrane proteins, which showed higher immunity, non-allergenicity, non-toxicity and also showed higher conservancy and population coverage values. Both epitopes showed higher binding affinity and stability towards HLA-C* 03:03. The MM-PBSA binding free energy showed the YGIFAITAL epitope binds more tightly with both MHC-I and MHC-II molecules. The total contact, H-bond analysis and RMSF results also validate the efficiency of these epitopes as vaccine candidate. The projected B cell epitopes AAPVQVGEAAGS, TGGGDGSNT and TGGGDGSNTGTTTT showed higher antigenicity. Overall, the study concluded that these epitopes can be considered as the potential vaccine candidate to make a successive vaccine against iNTS species. However, this result further needs to be validate by wet lab research to make successive vaccine with these projected epitopes.

Antimicrobial resistance (AMR) threatens to reverse the essential benefits of antibiotics not only in humans, where decades of advancements in healthcare outcomes are endangered but also in the food production industry. The emergence of AMR in the pre- and post-harvest systems presents a serious risk of contamination or infection directly by antibiotic-resistant bacteria (ARB) and genes (ARGs) for farmers, agricultural practitioners, abattoir workers, food handlers and their associated contacts as well as consumers at the end of the food chain. Any breach in the food safety barrier leading to the emergence and spread of ARB and ARGs has severe multi-sectorial implications and threatens to reverse decades of human and animal health improvements globally. As the world moves towards Sustainable Development Goals (SDGs), food safety is a critical element to improve and strengthen global health, security and ensure sustainable development. This paper presents the challenge of AMR through the lens of food safety, by highlighting its multi-sectoral and multi-dimensional implications not only the SDG on food safety but also on food security, public health, animal health and welfare, the environment and climate and socio-economic development.

Although Cucurbita pepo is one of the most variable species of the plant kingdom, Zucchini morphotype has undergone intensive breeding that has led to a narrow genetic base making the crop vulnerable to pest and diseases. This vulnerability makes the knowledge of resistance genes of utmost importance. In this study, a data mining search of Zucchini summer squash genome database was conducted to identify and annotate members of the NBS-encoding gene family. In order to characterize the retrieved genes in detail, they have been studied in the bases of phylogenetic relationships, structural diversity, conserved protein motifs, gene duplications and promoter region analysis. Our study shows that the NBS-encoding gene family is relatively small in Zucchini (34 members, which are separated into non-TIR- and TIR-NBS-LRR subfamilies) with a significantly lower number of R-genes than in other species. Duplications have not played a major role in the expansion of this type of genes in C. pepo. Among the cis-regulatory elements presented in these sequences, six motifs are over-represented. These elements were reported to be involved in pathogens or plant stress induced responses. These results will contribute to the identification, isolation and characterization of candidate R-genes, thereby providing insight into NBS gene family evolution in the species.

The mechanical hill wet-seeded rice machine is benefits to establish uniform seedling, and ditches were established by using this machine. However, little knowledge is known on the effect of the establishment of ditches on growth, lodging and yield, and their relationship with root traits. In this study, two field experiments were conducted during 2012 and 2013 with using two super rice varieties (i.e. hybrid rice ‘Peizataifeng’ and inbred rice ‘Yuxiangyouzhan’) grown under three ditches establishment treatments (i.e. T1: both water ditches and seed ditches were established by the machine, T2: seed ditches were established by the machine, T3: neither water nor seed ditches were established by the machine). The lodging index and lodging resistance traits, the grain yield and above-ground dry weight and the root traits were measured. The results showed that the lodging index was significantly affected by the treatments with ditches. The strongest lodging resistance was detected in mechanical hill wet-seeded rice with ditches treatment in both 2012 and 2013. The lodging resistance was strongly related to the breaking resistance, the root volume and root superficial area at the heading stage and maturity stage and the total root length at the heading stage. No significant difference was investigated in grain yield or dry weight of mechanical hill wet-seeded rice. Yuxiangyouzhan showed higher grain yield, dry weight and better lodging resistance but unfavorable root growth attributes than Peizataifeng. Therefore, the mechanical hill wet-seeded rice with ditches treatment increased rice lodging resistance is related to root traits.

Growing resistance is reported to carbamate insecticides in malaria vectors in Cameroon. However, the contribution of acetylcholinesterase (Ace-1) to this resistance remains uncharacterised. Here, we established that the G119S mutation is driving resistance to carbamates in Anopheles gambiae populations from Cameroon. Insecticide bioassay on field collected mosquitoes from Bankeng, a locality in southern Cameroon, showed high resistance to the carbamates bendiocarb (64.8 ± 3.5 % mortality) and propoxur (55.71 ± 2.9 %) but a full susceptibility to the organophosphate fenithrothion. The TaqMan genotyping of the G119S mutation in field-collected adults revealed the presence of this resistance allele (39%). A significant correlation was observed between the Ace-1^R and carbamate resistance at allelic [(bendiocarb; OR = 75.9; P<0.0001) and (propoxur; OR= 1514; P<0.0001)] and genotypic [RR vs SS (bendiocarb; OR = 120.8; P<0.0001) and (propoxur; OR= 3277; P<0.0001) levels. Furthermore, the presence of the mutation was confirmed by sequencing an Ace-1 portion flanking codon 119. The cloning of this fragment revealed a likely duplication of Ace-1 in Cameroon as mosquitoes exhibited at least three distinct haplotypes. Phylogenetic analyses showed that the predominant Ace-1^R allele is identical to that from West Africa suggesting a recent introduction of this allele in Central Africa from the West. The spread of this Ace-1^R represents a serious challenge to future implementation of IRS-based interventions using carbamates or organophosphates in Cameroon

Although the regulatory function of miRNAs and their targets have been characterized in model plants, a possible underlying role in the cotton response to herbivore infestation has not been determined. To investigate this, we performed small RNA and degradome sequencing between resistant and susceptible cotton cultivar following infestation with the generalist herbivore whitefly. In total, 260 miRNA families and 241 targets were identified. Quantitative-PCR analysis revealed that several miRNAs and their corresponding targets exhibited dynamic spatio-temporal expression patterns. Moreover, 17 miRNA precursors were generated from 29 long intergenic non-coding RNA (lincRNA) transcripts. Genome-wide analysis also led to the identification of 85 phased small interfering RNA (phasiRNA) loci. Among these, nine PHAS genes were triggered by miR167, miR390, miR482a, and two novel miRNAs, including those encoding a leucine-rich repeat (LRR) disease resistance protein, an auxin response factor (ARF) and MYB transcription factors. Through combined modeling and experimental data, we explored and expanded the miR390-tasiARF cascade during the cotton response to whitefly. Virus-induced gene silencing (VIGS) of ARF8 in whitefly-resistant cotton plants increased auxin and jasmonic acid (JA) accumulation, resulting in an increased tolerance to whitefly infestation. These results highlight the provides a useful transcriptomic resource for plant-herbivore interaction.

Heat shock protein 27 (HSP27), induced by heat shock, environmental, and pathophysiological stressors, is a multi-dimensional protein that acts as a protein chaperone and an antioxidant. HSP27 plays a major role in the inhibition of apoptosis and actin cytoskeletal remodeling. HSP27 is upregulated in many cancers and is associated with poor prognosis, as well as treatment resistance whereby cells are protected from therapeutic agents that normally induce apoptosis. This review highlights the most recent findings and role of HSP27 in cancer, as well as strategies for using HSP27 inhibitors for therapeutic purposes.

Selenium (Se) is an essential element of the human diet. Therefore, it is necessary to implement Se in agricultural fertilization, although it is not considered as an essential element for plants, Se provides benefits at the level of redox metabolism, increasing the resistance of plants to various stress factors. The increase of the availability of selenium with the use of biopolymer complexes was sought in Great Lakes lettuce grown in substrate pots treated with SeO₂ (5 mg L Se), Cs-PAA + Se (5 mg L Se), and Cs-PAA. The redox metabolism was modified by increasing the enzymatic activity of glutathione peroxidase. The use of Cs-PAA + Se biopolymer complexes increase selenium up to 24 mg/Kg dry weight (DW) in plant tissues.

The aim of this paper is to at first evaluate the influence of three key parameters including weld current, weld time and electrode force on nugget diameter and tensile strength in resistance projection welding. Then, a 2-D axis-symmetric finite element model is developed to simulate the projection welding and predict the nugget diameter. Finally, the FEM results are compared to experimental data to verify the simulation model and simulated results. In the finite element model, the temperature-dependent material properties were taken into account.

Bacteriophages, viruses that are widespread throughout the world, are highly specific for bacteria, usually of a single species and often of a particular strain. After being discovered and isolated 100 years ago, their use, called phage therapy, was instituted in medicine two years later and quickly used around the world to treat various bacterial infections. In the West, phage therapy was overshadowed in the second half of the 20th century by antibiotic therapy, which was then thought to be the definitive solution. But because of the increase in bacterial resistance to antibiotics, the idea of using bacteriophages in medicine has been reawakened. The innumerable observations reported over the years in the literature constitute an invaluable experience. We and some of our colleagues have, in the last decade treated some patients compassionately. With the available documentation and our own experience we discuss the potential indications and limitations of phage therapy. The observation of the increasing number of therapeutic failures in the announced perspective of a post-antibiotic era, we believe, that the introduction of bacteriophages into the therapeutic arsenal seems conceivable today to two preconditions: that their production as biologic drug meets current regulatory standards and that the benefit-risk assessment was conducted in a modern setting. Phage therapy could be applied as a substitution or supplement to antibiotic therapy under multiple circumstances in different modes, precise indications and limits.

We report on the fabrication and electrical characteristics of zinc-oxide (ZnO) based metal-insulator-semiconductor (MIS) type Schottky barrier diodes (SBHs). ZnO thin layer on the p-type silicon substrate was fabricated by atomic layer deposition (ALD). The structure and surface properties of the thin film were characterized by X-ray diffraction (XRD), atomic force microscope (AFM) and secondary ion mass spectrometer (SIMS). The current-voltage (I-V) characteristics of Al/ALD-grown ZnO/p-Si diodes were measured under dark at room temperature. The electrical parameters such as ideality factor (n), series resistance (R_s) and barrier height (ϕ_b) of the diodes were analyzed using standard thermionic emission (TE) theory, Norde and Cheung method. The barrier height value obtained from I-V and Cheung method was found to be 0.73 eV and 0.76 eV, respectively. The interface state density (Dit) of the diodes was determined from the I-V characteristics. The nonideal behavior of measured parameters suggested the presence of interface states. The obtained results showed that the prepared diode can be used for NIR Schottky photodetector applications.

The main objective of the present study was to investigate arsenate [As (V)] resistance genes in rice cultivars grown in arsenic contaminated Egyptian soil in order to genetically induce resistance against arsenic in the local rice varieties as well as defining contaminated rice grains and/or soil. Three local rice cultivars; Sakha 102-104 were cultivated on modified Murashige and Skoog Basal Medium (MS medium) containing elevated concentrations of arsenate (0.1, 1 and 10 mg/l). The three varieties showed different resistant attitudes against arsenate with Sakha 104 being the most resistant. Extracted messenger RNA (mRNA) from treated and untreated Sakha 104 plantlets was scanned using differential display to demonstrate the arsenate resistant genes using three different arbitrary primers. About 100 different RNAs with (1500 bp - 50 bp) were obtained from which seven were up-regulated genes, subjected to DNA cloning using TOPO TA system and the selected clones were sequenced. The sequence analysis described four genes out of the seven namely disease resistance protein RPM1, Epstein-Barr virus EBNA-1-like, CwfJ family protein and outer membrane lipoprotein OmlA while the other three genes were hypothetical proteins. It is concluded the four induced genes in the resistant rice cultivar considered as a direct response to arsenic soil pollution. Genes detected in the present study can be used as geno-sensors for rice grains and soil contamination with As (V). Moreover, local rice cultivars may be genetically modified with such genes to induce high resistance and to overcome arsenic soil pollution.

The object of this study was to discover an alternative therapeutic agent with fewer side effects against acne vulgaris, which is one of the most common skin diseases. Acne vulgaris often associates with acne-related bacteria such as <i>Propionibacterium acnes</i>, <i>Staphylococcus epidermidis</i>, <i>Staphylococcus aureus</i> and <i>Pseudomonas aeruginosa</i>, some of which exhibit a resistant against commercial antibiotics used in the treatment of acne vulgaris (tetracycline, erythromycin, and lincomycin). In the current study, we evaluated <i>in vitro</i> antibacterial activity of chitosan-phytochemical conjugates against acne-related bacteria. Three of chitosan-phytochemical conjugates used in this study showed stronger antibacterial activity than that of chitosan (unmodified control). Chitosan-caffeic acid conjugate (CCA) exhibited the highest antibacterial activity against acne-related bacteria with minimum inhibitory concentration values of 8 μg/mL to 256 μg/mL. In addition, the MICs of antibiotics against antibiotic resistant <i>P. acnes</i> and <i>P. aeruginosa</i> strains were dramatically reduced in the combination with CCA, suggesting that CCA would restore the antibacterial activity of the antibiotics. The analysis of fractional inhibitory concentration indices clearly revealed a synergistic antibacterial effect between CCA and the antibiotics. Thus, the median ∑FIC values against the antibiotic resistant bacterial strains were ranged from 0.375 to 0.533 in the combination mode of CCA and antibiotics.

Iron powders having average particle sizes of ~40µm are mechanically mixed thoroughly with aluminum powders ranging from 1 to 10 in wt.% with an average particle size of ~10µm. Two different powder metallurgical techniques cold and hot pressing are used to study the effect of the additive element powder on the mechanical properties, wear properties and the microstructure of the iron based alloys. The hot pressing technique was performed at a temperature up to 500°C at 445.6 MPa. The cold pressing technique was performed at 909 MPa in room temperature. By increasing the Al content to 10 wt. % in the base Fe-based matrix, the hardness was decreased from 780 to 690 MPa and the radial strength was decreased from 380 to 202 MPa with reductions of 11.5% and 40%, respectively. Improvement of the wear resistance with the increase addition of the Al powder to the Fe matrix up to 5 times was achieved compared to the alloy without Al addition for different wear parameters namely; wear time and sliding speed.

Type 2 diabetes mellitus (T2DM) and late-onset Alzheimer’s disease-dementia (LOAD) are increasing in global prevalence and current predictions indicate they will only increase over the coming decades. These increases may be a result of the concurrent increases of obesity and aging. T2DM is associated with cognitive impairments associated with metabolic factors and increases the cellular vulnerability to develop the age-related increased risk of LOAD. This review addresses possible mechanisms due to obesity, aging, multiple intersections between T2DM and LOAD and mechanisms for the continuum of progression. Multiple ultrastructural images in female diabetic db/db models are utilized to demonstrate marked cellular remodeling changes of mural and glia cells and provide for the discussion of functional changes in T2DM. Throughout this review multiple endeavors to demonstrate how T2DM increases the vulnerability of the brain’s neurovascular unit (NVU), neuroglia and neurons are presented. Five major intersecting links are considered: i. aging (chronic age-related diseases); ii. metabolic (hyperglycemia - advanced glycation end-products and its receptor (AGE/RAGE) interactions and hyperinsulinemia – insulin resistance (a linking linchpin); iii. oxidative stress (reactive oxygen-nitrogen species); iv. inflammation (peripheral macrophage and central brain microglia); v. vascular (macrovascular accelerated atherosclerosis - vascular stiffening and microvascular NVU/neuroglial remodeling) with resulting impaired cerebral blood flow.

This study presents a comprehensive systematic review that addresses the critical transition from ROS 1 to ROS 2, spotlighting the significant enhancements and the pressing need for a detailed exploration of ROS 2 within the robotics community. Despite the extensive deployment and adaptations of ROS in varied robotics applications, literature lacks a cohesive synthesis that delineates the advancements, limitations, and broader impacts of ROS 2 compared to its predecessor, ROS 1. Our contribution bridges this gap by assembling the largest database of ROS-related research, encompassing 7,498 articles, with a focused analysis in this survey on 431 ROS2-specific publications. We categorize these into i.) articles that discuss and analyze core ROS 2 concepts, ii.) articles that propose frameworks or tools for ROS 2, and iii.) articles utilizing ROS 2. Furthermore, we summarize literature findings of ROS 2 challenges, advancements, and future direction in the fields of a.) security, b.) real-time, c.) middleware, d.) embedded and distributed systems, e.) communication reliability and QoS, and f.) multi-robot systems. The methodology involved meticulous data collection and categorization from multiple databases, facilitating an in-depth online accessible resource. Results underscore ROS2’s enhancements in modularity, real-time capabilities, and security, extending its applicability across various robotic platforms and industries. However, challenges in scalability and reliability persist, signaling avenues for future enhancements. This review not only deepens the understanding of ROS2’s contributions but also charts a path for ongoing improvements in robotic systems design. The original data presented in the study are openly available in https://www.ros.riotu-lab.org/.

Speech emotion recognition (SER) is important in facilitating natural human-computer interactions. In speech sequence modelling, a vital challenge is to learn context-aware sentence expression and temporal dynamics of para-linguistic features to achieve unambiguous emotional semantic understand-ing. In previous studies, the SER method based on the single-scale cascade feature extraction module could not effectively preserve the temporal structure of speech signals in the deep layer, downgrading the sequence modeling performance. In this paper, we propose a novel multi-scale feature pyramid network to mitigate the above limitations. With the aid of the bi-directional feature fusion of the pyramid network, the emotional representation with adequate temporal semantics is obtained. Experiments on the IEMOCAP corpus demonstrate the effectiveness of the proposed methods and achieve competitive results under speaker-independent validation.

The maritime environment is the setting for a variety of economic activities, such as offshore wind energy, aquaculture, salt extraction, and oil and gas platforms. While some of these activities have a long-term presence, others require decarbonisation as they head towards their demise. In this context, the aim of this study is to develop a methodology to replace the electrical energy from high-emission industrial processes offshore with clean electricity generated by offshore wind energy. The proposal is structured in three phases: Initiation, which involves the collection of quantitative, technical and geospatial information of the study area; Indicators, where the main indicators are calculated and the best alternative is selected using multi-criteria evaluation methods; and finally, short, medium and long-term scenarios are proposed. The methodology is evaluated in Spain, the best alternative with a nominal power of 225 MW, is capable of avoiding up to 1.44 MtCO₂ by 2050.

The adversarial vulnerability of deep learning models is a critical issue that must be addressed to ensure the safe commercialization of AI technologies. Although numerous studies on adversarial defense methods are actively being conducted from various perspectives, most of them still provide limited robustness, and even the relatively trusted adversarial training is no exception. To develop more reliable defense methods, ongoing research exploring the properties and causes of adversarial vulnerabilities is essential. In this study, we focus on a hypothesis regarding the existence of adversarial examples: The adversarial examples represent low-probability “pockets” in the manifold. Assuming that the hypothesis holds true, we propose a method for generating perturbation: “mixed perturbation (MP)”, which aims at discovering diverse pocket samples in a defensive perspective. The proposed method generates perturbations by leveraging information from both the main task and auxiliary tasks in multi-task learning scenarios, combining them through random weighted summation. The generated mixed perturbation intends to maintain the primary directionality of the main task perturbation to improve the model’s main task recognition performance while introducing variability in the perturbation directions. We then utilize them for adversarial training to form more robust decision boundary. Through experiments and analyses conducted on five benchmark datasets, we validated the effectiveness of our proposed method.

For advancing manufacturing, arising AM, with an inverse philosophical approach compared to conventional procedures, has benefits that include intricate fabrication, reduced material waste, flexible design, and more. Regardless of its potential, AM must overcome several challenges due to multi-physical processes with miscellaneous physical stimuli in diverse materials systems and situations, such as anisotropic microstructure and mechanical properties, a restricted choice of materials, defects, and high cost. Unlike conventional experimental work that requires extensive trial and error resources and FEM, which generally consumes substantial computational power, the analytical approach based on physics is an exceptional choice. Understanding the relationship between the microstructure and material properties of the fabricated parts is a crucial focus in AM research. Texture is a vital factor in almost every modern industry. This study first proposed a physics-based model to foreshadow the multi-phase crystallographic orientation distribution in Ti-6Al-4V LPBF while considering the part boundary conditions due to the importance of part geometry in real industry. The thermal distribution obtained from this function operates as the information for the single-phase crystallographic texture model. In this model, we forerun and validate the orientations of single-phase materials utilizing three Euler Angles with the principles of CET and thermodynamics, as well as the intensity of the texture by approximating them with published results. Then, we transform the single-phase texture into a dual-phase texture in Bunge calculation, illustrating visualized by pole figures of both BCC and HCP phases. This texture evolution model provides a new paradigm for future researchers to model the texture or microstructure evolution semi-analytically and save many computational resources in a real-world perspective. Others have not yet done this work about simulating the multi-phase texture in an analytical approach, so this work bridges the gap in this field. Furthermore, this paper establishes the foundation for future research on materials properties affected by microstructure or texture in academic and industrial environments. The precision and dependability of the results obtained through this method make it a valuable tool for ongoing research and advancement.

Unmanned Aerial Vehicles (UAVs) have become increasingly important in various applications, including environmental monitoring, disaster response, and surveillance, due to their flexibility, efficiency, and ability to access hard-to-reach areas. Effective path planning for multiple UAVs exploring a target area is crucial for maximizing coverage and operational efficiency. This study presents a novel approach to optimizing collaborative navigation for UAVs using Multi-Agent Reinforcement Learning (MARL). To enhance the efficiency of this process, we introduce the Adaptive Dimensionality Reduction (ADR) framework, which includes Autoencoders (AEs) and Principal Component Analysis (PCA) for dimensionality reduction and feature extraction. The ADR framework significantly reduces computational complexity by simplifying high-dimensional state spaces while preserving crucial information. Additionally, we incorporate communication modules to facilitate inter-UAV coordination, further improving path planning efficiency. Our experimental results demonstrate that the proposed approach significantly enhances exploration performance and reduces computational complexity, showcasing the potential of combining MARL with ADR techniques for advanced UAV navigation in complex environments.

Tunnel fires are generally detected using various sensors, including measuring temperature, CO concentration, and smoke concentration. To address the ambiguity and inconsistency in multi-sensor data, this paper proposes a tunnel fire detection method based on an improved Dempster-Shafer (DS) evidence theory for multi-sensor data fusion. To solve the problem of evidence conflict in DS theory, a two-level multi-sensor data fusion framework is adopted. The first level of fusion involves feature fusion of the same type of sensor data, removing ambiguous data to obtain characteristic data, and calculating the basic probability assignment (BPA) function through the feature interval. The second level fusion derives basic probability numbers from the BPA, calculates the degree of evidence conflict, normalizes to obtain the relative conflict degree, and optimizes the BPA using the trust coefficient. The classical DS evidence theory is then used to integrate and obtain the probability of tunnel fire occurrence. Different heat release rates, tunnel wind speeds, and fire locations are set, forming four fire scenarios. Sensor monitoring data under each simulation condition are extracted and fused using the improved DS evidence theory. The results show that the proposed improved DS evidence theory method detects the probability of fire occurrence in the four scenarios as 67.5%, 71.0%, 82.8%, and 83.5%, respectively, and identifies fire occurrence in approximately 2.4 seconds, an improvement of 64.7% to 70% over traditional methods. This demonstrates the feasibility and superiority of the proposed method, highlighting its significant importance in ensuring personnel safety.

Target detection technology plays a crucial role in fishery ecological monitoring, fishery diversity research, and intelligent aquaculture. Deep learning, with its distinct advantages, provides significant convenience to the fishery industry. However, it still faces various challenges in practical applications, such as significant differences in image species and image blurring. To address these issues, this study proposes a multi-scale, multi-level, and multi-stage cross-domain feature fusion model. In order to train the model more effectively, a new dataset called Fish52 (Multi-scene Fish dataset) was constructed, on which the model achieved an mAP of 82.57%. Furthermore, we compared prevalent one-stage and two-stage detection methods on the Lahatan (single-scene fish dataset) and Fish30 dataset and tested them on the F4k and FishNet dataset. The mAP of our proposed model on the Fish30, Lahatan, F4k, and FishNet datasets reaches 91.72%, 98.7%, 88.6%,and 81.5% respectively, outperforming existing mainstream models. Comprehensive empirical analysis indicates that our model possesses high generalization ability and has reached advanced performance levels. In this study, the depth of the model backbone is deepened, a novel neck structure is proposed, and anew module is embedded therein. To enhance the fusion ability of the model, a new attention mechanism module is introduced. In addition, in the adaptive decoupling detection header module, introducing classes with independent parameter sand regression adapters reduces interaction between different tasks. The proposed model can better monitor fishery resources and enhance aquaculture efficiency. It not only provides an effective approach for fish detection but also has certain reference significance for the identification of similar target sin other environments, and offers assistance for the construction of smart fisheries and digital fisheries.

This paper presents a novel approach to designing beam codebooks for downlink multiuser hybrid multiple input multiple output (MIMO) wireless communication systems, leveraging multi-agent reinforcement learning (MARL). The primary objective is to develop an environment-specific beam codebook composed of non-interfering beams, learned by cooperative agents within the MARL framework. Machine learning (ML) based beam codebook design for downlink communications have been based on channel state information (CSI) feedback or only reference signal received power (RSRP) consisting of an offline training and user clustering phase. In massive MIMO, the full CSI feedback data is of large size and is resource-intensive to process, making it challenging to implement efficiently. RSRP alone for a stand-alone base station is not a good marker of the position of a receiver. Hence, in this work, uplink CSI estimated at the base station along with feedback of RSRP and binary acknowledgment of the accuracy of received data is utilized to design the beamforming codebook at the base station. Simulations using sub-array antenna and ray-tracing channel demonstrate the proposed system’s ability to learn topography-aware beam codebook for arbitrary beams serving multiple user groups simultaneously. The proposed method extends beyond mono-lobe and fixed beam architectures by dynamically adapting arbitrary shaped beams to avoid inter-beam interference, enhancing overall system performance. This work leverages MARL’s potential in creating efficient beam codebooks for hybrid MIMO systems, paving the way for enhanced multiuser communication in future wireless networks.

Investigation of functional magnetic resonance imaging (fMRI) data with machine learning (ML) techniques, including also deep learning (DL) methods, have been widely used to study Autism Spectrum Disorder (ASD). This disorder is characterized by symptoms that affect the individual’s behavioral aspects and social relationships. Early diagnosis is crucial for intervention, but the complexity of ASD poses challenges for treatment development. This study compares traditional ML techniques with deep learning (DL) methods in the analysis of functional connectivity measures obtained from the time series of multicentric ABIDE dataset. Specifically, Support Vector Machines (SVM) classifiers, with both linear and Radial Basis Function (RBF) kernels, as well as eXtreme Gradient Boosting (XGBoost) classifiers, are compared against the TabNet classifier, which is a DL architecture customized for tabular data analysis and a Multi Layer Perceptron (MLP). The findings suggest that DL classifiers may not be optimal for the type of data analyzed, as their performance trails behind that of standard classifiers. SVMs achieve performances, in terms of AUC, around 75%, compared to the best TabNet and MLP results, which are 65% and 71%, respectively. Additionally, this work investigates the brain regions that contribute most to the classification task, which are found to be those primarily responsible for sensory and spatial perception, as well as attention modulation, known to be altered in ASD.

This study involves the development of a hybrid forecasting framework that integrates two different models in a framework to improve prediction capability. Although the concept of hybridization is not a new issue in forecasting, our approach presents a new structure that combines two standard simple forecasting models uniquely for superior performance. Hybridization is significant for complex data sets with multiple patterns. Such data sets do not respond well to simple models, and hybrid models based on the integration of various forecasting tools often lead to better forecasting performance. The proposed architecture includes serially connected ARIMA and ANN models. The original data set is first processed by ARIMA. The error (i.e., residuals) of the ARIMA is sent to the ANN for secondary processing. Between these two models, there is a classification mechanism where the raw output of the ARIMA is categorized into three groups before they are sent to the secondary model. The algorithm is tested on well-known forecasting cases from the literature. The proposed model performs better than existing methods in most cases.

Compared to traditional multi-robot path planning problems, multi-chain robot path planning (MCRPP) is more challenging because it must account for collisions between robot units and between the bodies of a chain and the leading unit during towing. To address MCRPP more efficiently, we propose a novel algorithm called Multi-Train Safe Interval Path Planning (MT-SIPP). Based on safe interval path planning principles, we categorize conflicts in the multi-train planning process into three types: travel conflicts, waiting conflicts, and station conflicts. To handle travel conflicts, we use an improved k-robust method to ensure trains avoid collisions with other trains during movement. To resolve waiting conflicts, we apply a time correction method to ensure the safety of positions occupied by trains during waiting periods. To address station conflicts, we introduce node constraints to prevent other trains from occupying the station positions of trains that have reached their target stations and are stopped. Experimental results on three benchmark maps show that the MT-SIPP algorithm achieves about a 30% improvement in solution success rate and nearly a 50% increase in the maximum number of solvable instances compared to existing methods. These results confirm the effectiveness of MT-SIPP in addressing the challenges of MCRPP.

The radiative heat transfer in arc plasma models is considered from the point of view of its description in terms of a net emission coefficient, the method of spherical harmonics in its lowest order, and the discrete ordinate method. Net emission coefficients are computed applying approximate and numerical approaches, the multi-band representation of the spectral absorption coefficient with three kinds of its averaging and two datasets. The self-consistent access to the radiative heat transfer is applied to a two-dimensional axisymmetric model of free-burning arc in argon at atmospheric pressure. The results obtained from the models employing the net emission coefficient, the method of spherical harmonics, and the discrete ordinate method are compared.

Mitral regurgitation (MR) is a broadly diffuse valvular heart disease (VHD) with a significant impact on healthcare system and patient’s prognosis. Transcatheter mitral valve interventions (TMVI) are now well-established techniques included in the therapeutic armamentarium for managing patients with mitral regurgitation, either primary or functional MR. Even if guidelines give indications regarding the correct management of this VHD, the wide heterogeneity of patient’s clinical background, valvular and heart’s anatomy make each patient a unique case, in which the appropriate device’s selection requires a multimodality imaging evaluation and a multidisciplinary discussion. Proper pre-procedural evaluation plays a pivotal role to judge the feasibility of TMVI, as well as a cooperative works between imagers and interventionalist is crucial for procedural success. This manuscript aims to provide an exhaustive overview of the main parameters that need to be evaluated for appropriate device selection, pre-procedural planning, intraprocedural guidance and post-operative assessment in the setting of TMVI. In addition, it tries to give some insights about next perspective of structural cardiovascular imaging.

Electric vehicles (EVs) play a significant role in reducing carbon emissions. In the US, EVs are mostly owned by multi-vehicle households, and their usage is primarily studied in the context of vehicle miles traveled. This study takes a unique approach by analyzing EV usage through the lens of vehicle choice (between EVs and internal combustion engine vehicles) within multi-vehicle households. A two-step machine-learning framework (clustering and decision trees) is proposed. The framework determines the preferred trip category for EV use and captures the effects of household attributes, driver attributes, built-environment factors, and gas prices on EV use in multi-vehicle households. Results indicate that discretionary trips (accumulated local effect = 0.037) are mostly preferred for EV use. EV preference is more pronounced among households with fewer workers (<2) and lower income levels. These findings are valuable for policymakers and auto manufacturers in targeting specific market segments and promoting EV adoption.

CuAl8Fe5Ni4Zn4Sn1 (OF 2238) is a new lead-free copper alloy for plain bearing applications that was first officially presented in a scientific journal in 2020. Soon after its invention, the use of the alloy for connecting rod bushings in heavy duty internal combustion engines was promoted and validated with customers. The aim of this article is to describe the material properties of the new alloy in more detail than previously and explain how the advantageous properties of CuAl8Fe5Ni4Zn4Sn1 are generated. At the beginning of the article, the general development trends in the field of copper alloys for sliding applications are presented. This is followed by a presentation of the latest research results about CuAl8Fe5Ni4Zn4Sn, before concluding with a brief summary and future outlook, emphasizing on the fact that recent trends in copper-based sliding applications show great additional potential for further material developments.

Hyperuricemia is mainly caused by the overproduction of uric acid (UA) catalyzed from xanthine by xanthine oxidase (XO) in abnormal purine metabolism in human body. There is an increasing demand for developing novel XO inhibitors of plant origins. The inhibition kinetics and type as well as actions of hesperetin against XO were investigated in vitro using multi-spectroscopic methods including UV-Vis absorption, Fourier transform infrared spectroscopy (FT-IR), fluorescence spectroscopy and molecular docking simulation. Results indicated that hesperetin reversibly inhibited XO in a competitive manner with an inhibition constant (Ki) value of (2.15 ± 0.05) × 10-6 mol/L. Hesperetin had a higher scavenging rate against O2- radical generated by the XO reaction system. The binding of hesperetin to the active site of XO altered the protein’s secondary structure as demonstrated by FT-IR and fluorescence spectra. Molecular docking study revealed that hesperetin had highly affinity to XO and the bonding of hesperetin to the molybdopterin (Mo-pt) active center inhibited not only substrate entry, but also prevented oxygen binding to flavin adenine dinucleotide (another active center in XO). Additionally, hydrophobic interactions and hydrogen bonds contributed to the stabilization of the hesperetin-XO complex. The results suggested that hesperetin be a promising natural XO inhibitor to alleviate hyperuricemia and gout.

Recent studies, typically using patient cerebrospinal fluid (CSF), have suggested that different autoantibodies (Aabs) acting on their respective receptors, may underlie neuropsychiatric disorders. The NR1 subunit of the N-methyl-D-aspartate receptor (NMDAR) has been identified as a target of anti-NMDAR Aabs in a number of central nervous system (CNS) diseases including encephalitis and autoimmune epilepsy. However, the role or the nature of Aabs responsible for effects on neuronal excitability and synaptic plasticity is yet to be established fully. Patient-derived anti-NMDAR Aabs have been shown to bind to specific regions within the NR1 subunit; here, peptide immunisation was used to generate Aabs against selected specific NR1 extracellular sequences. ‘Protein A’ purification was used to obtain total IgG and further peptide purification used to obtain a greater percentage of NMDAR-target specific IgG Aabs. Binding and specificity of these anti-NMDAR Aabs was determined using a range of methodologies including enzyme-linked immunosorbent assay, immunocytochemistry and immunoblotting. Functional effects were determined using different in vitro electrophysiology techniques: two-electrode voltage-clamp in Xenopus oocytes and measures of long-term potentiation (LTP) in ex vivo hippocampal brain slices using multi-electrode arrays. We show that anti-NMDAR Aabs generated from peptide immunisation had specificity for NR1 immunisation peptides as well as target-specific binding to the native protein. Anti-NMDAR Aabs had no clear effect on isolated NMDARs in an oocyte expression system. However, we show that peptide-purified anti-NMDAR Aabs prevented the induction of LTP at Schaffer collateral-CA1 synapses in ex vivo brain slices. Our data demonstrate that anti-NMDAR Aabs cause synaptic NMDAR hypofunction at a network level. This work provides a solid basis to address outstanding questions regarding anti-NMDAR Aabs mechanisms of action and, potentially, development of therapies against CNS diseases.

The necessity for precise and current data concerning the dynamics of land cover change in Indonesia is crucial for efforts to reduce natural vegetation cover due to agricultural expansion. The functionality of monitoring systems that incorporate Terra-MODIS is currently compromised by the limited availability of data for the immediate future. This study seeks to assess the potential of VIIRS satellite imagery in developing an early warning system for monitoring vegetation cover change in Indonesia. The Normalized Differential Open Area Index (NDOAI) computed from the 8-day’ VIIRS data was employed to detect changes in vegetation cover based on pixel-by-pixel subtraction in the NDOAI data time series. Evaluating the pixel-level accuracy of change detection is complicated due to the fact that we evaluate a change map at a coarser resolution than the Landsat-based reference map. The results revealed that increasing the threshold percentage is associated with improved accuracy. In change detection, there is often a trade-off between accuracy and sensitivity. A threshold that is too low may result in false positives, while a threshold that is too high may lead to missed changes. This study demonstrates that when a threshold value of less than 20% is applied, Landsat can identify vegetation cover changes at an earlier stage. Conversely, when a threshold value greater than 20% is employed, VIIRS will detect the change 4.5 days earlier than Landsat. Additionally, VIIRS is capable of detecting changes 25.4 days and 54.8 days faster than Landsat, respectively, when using thresholds of 40% and 75%.

Typhoons bring great damages to transmission line systems located in coastal areas. Strong wind and extreme precipitation are the main sources of damaging effects. Transmission lines suffered from wind-driven rain exhibit more susceptibility to damage due to the coupled effect of wind and rain water. This paper presents an integrated numerical simulation framework based on mesoscale WRF model, multiphase CFD model and FEM model to analyze the motions of a transmission line subjected to coupled wind and rain loads during typhoon events. A full-scale transmission line in Zhoushan island is employed to demonstrate the effectiveness of the proposed framework by simulating typhoon evolution in terms of wind fields and rainfall, solving the coupled wind and rain fields around the conductor and predicting the dynamic responses of the transmission line during Super Typhoon Lekima (2019). The results show that the horizontal displacements of the transmission line under the joint actions of wind and rain increases approximately 17%~18% compared to those of wind loads only. It is important to consider the coupled effects of wind-driven rain on conductors in the design of transmission lines under typhoon conditions.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Ensuring optimal performance and reliability in rotor-bearing systems is crucial for industrial applications. Imbalance and shaft bow in these systems can lead to decreased efficiency and increased vibrations. Early detection and mitigation of a rotor’s faults are essential, and model-based fault identification has gained much attention in the manufacturing industry for years. Over the past two decades, however, the development of fault diagnosis rules with data-driven and artificial intelligence (AI) has become a trend, and in the foreseeable future, AI combined with big data will become mainstream. Nevertheless, the critical role of rotating machinery in manufacturing introduces a challenge, as the availability of fault data is often insufficient. This limitation renders the establishment of diagnostic rules using data-driven methods and AI technologies impractical. In light of these challenges, this study proposes a novel hybrid approach, that combines a physical model with machine learning (ML) techniques for the diagnosis of multi-faults (imbalance and shaft-bow demonstrated) in a Jeffcott rotor. To overcome the lack of real-world faulted, labeled datasets, a physics-based Jeffcott rotor model is first derived and then used to generate abundant fault datasets for ML. Subsequently, simulated data are employed for the training of an artificial neural network (ANN), enabling the network to learn from and analyze the vast array of generated data. The results prove that a well-trained feed-forward neural network (FNN) can accurately isolate and diagnose the imbalance and shaft-bow faults using the simulated data and the real data from the Jeffcott rotor experiment. These physics-based and ML approaches prove effective particularly for multi-fault, offering new possibilities for advanced rotor system monitoring and maintenance strategies in industrial applications.

As Artificial Intelligence (AI) technology, particularly Machine Learning algorithms, becomes increasingly ubiquitous, our abilities to understand and interpret AI and machine learning algorithms are becoming increasingly desirable. Visualization has been used as a common tool to view and understand complex machine learning processes. In this paper, we will focus on developing a general framework for the visualization of machine learning models as a scalar valued multi-dimensional function to help users understand how the models behave over different viewing spaces. We first give a formal definition of the visualization problem. Interpolation-based morphing and subspace sampling techniques are applied to generate various renderings through projections and cross-sections of the model space as 3D surfaces or heatmap images. This method will be applied to two real-world datasets and applications: the diagnosis of Alzheimer's Disease (AD) using a human brain networks dataset and a real-world benchmark dataset for predicting home credit default risks. The visualizations show that different machine learning algorithms can behave quite differently under different conditions.

Chipman contends, in stark contrast to the conventional view, that utility is not a real number but a vector and is inherently lexicographic in nature. According to these views, it will be proved that, for every preordered topological space (X,≾,t), the assumption t to be finer than the order topology t≾ on (X,t), i.e. t⊇t≾, and the assumption the quotient topology t∣∼≾ that is defined on the preordered set (X∣∼,≾∣∼) of indifference classes of ≾ to be Hausdorff imply that a cardinal number κ and a (complete) preorder ≲ on {0,1}κ that is coarser than the lexicographical ordering ≤lex on {0,1}κ, i.e. ≤lex⊂≲, can be chosen in such a way that there exists a continuous order-embedding ϑ:(X,≾,t)⟶({0,1}κ,≲,t≤lex). This theorem will be compared with a theorem that, in particular, describes necessary and sufficient conditions for ≾ to have a continuous multi-utility representation.

With the recent advances in Machine Learning (ML), several models have been successfully applied to financial and accounting data to predict the likelihood of companies’ bankruptcy. However, time series have received little attention in literature with a lack of studies on the application of Deep Learning sequence models as Recurrent Neural Networks (RNN) and the recent Attention-based models in general. In this research work, we investigated the application of Long Short Term Memory (LSTM) networks to exploit time series of accounting data for bankruptcy prediction. The main contributions of our work are the following: a) we have proposed a Multi-head LSTM that models each financial variable in a time window independently and compared it with a single-input LSTM and other traditional ML models. The Multi-head LSTM outperforms all the other models; b) We identify the optimal time series length for bankruptcy prediction to be equal to 4 years of accounting data; c) We made public the dataset we used for the experiments that include data from 8262 different public companies in the American stock market generated in the period between 1999-2018. Furthermore, we prove the efficacy of the Multi-head LSTM models in terms of lower false positives and better division of the two classes.

Abstract Background: Numerous studies have demonstrated that the amalgamation of populations belonging to the same breed or closely related breeds leads to enhanced accuracies in genomic predictions (GP). Extensive experimentation with diverse Bayesian and Genome-enabled best linear unbiased prediction (GBLUP) models has been conducted to explore multi-breed genomic selection (GS) in livestock, ultimately establishing they as successful approaches for predicting genomic estimated breeding value (GEBV). This study aimed to examine the efficacy of BayesR and GBLUP model with different weighted genomic relationship matrices (GRM) in making genomic predictions for three distinct beef cattle breeds. Subsequently, we conducted a comparative analysis of the predictive accuracy pertaining to various marker densities and genetic correlations across three distinct beef cattle breeds. This investigation aimed to identify the optimal approach for enhancing the predictive accuracy of multi-breed genomic selection in beef cattle. Results：Genetic relationship matrices revealed moderate similarities between YL and the other breeds, with a striking genetic similarity of 0.87 between WG and HX. In HX cattle, BayesR demonstrated an enhancement in prediction accuracy, achieving 0.52 with HD and 0.46 with WGS, a marked improvement over 0.41 with HD and 0.42 with WGS in GBLUP. In WG and YL breeds, both methods showed comparable accuracies with HD, but BayesR slightly outperformed GBLUP with WGS. Further, multi-breed GS analysis indicated that BayesR consistently surpassed GBLUP in prediction accuracy, particularly with WGS data. For instance, in a combined HX and WG reference population, BayesR achieved a superior accuracy of 0.53 with WGS in HX cattle, a significant enhancement over GBLUP models. The study also underscores the advantage of incorporating multiple breeds in the reference population, which improved prediction accuracy, underscoring the value of broad-based genomic selection strategies. Conclusion: The results show that accuracy of multi-breed genomic predictions was higher with BayesR than with GBLUP, especially for the distantly genetic relationship between reference and validation breeds. Further improvements of multi-breed accuracy of genomic predictions could be achieved by increasing the density of the SNP marker. These findings underscore that BayesR providing a substantial improvement in genomic prediction and the importance of considering genetic relationships in the development of GS strategies for multi-breed cattle populations. Further research is warranted to optimize GRM construction and to explore alternative models for genomic prediction across breeds.

Photovoltaic systems and wind energy systems are generally used in distributed system. However, zero-sequence and harmonic currents occur due to photovoltaic systems, and positive-sequence harmonic currents occur due to wind turbines. Thus, the sheath current in high voltage cables increases due to these currents and poor power quality in distributed generation plants. Metal parts of the cable are grounded to prevent the negative effects of sheath currents. When long high voltage cable lines are grounded with the method in the literature, high sheath current and voltage occur on the cable sheath. Therefore, the SSBLR method is designed as a new method for the distributed generation system. The SSBLR method has been optimized with prediction methods and multi-objective optimization methods to prevent negative effects of the sheath current. Pareto optimal method is used as multi-objective optimization method, and artificial neural network, hybrid artificial neural network and regression methods are used as prediction method. The most dominant result is searched using different estimation and optimization methods in pareto optimal method. When the artificial neural network-genetic algorithm hybrid method is used as a prediction method, and genetic algorithm used as optimization method, significant decreases are observed in sheath current and voltage.

Liquid argon technology is widely used by many previous and current neutrino experiments, and it is also promising for future large-scale neutrino experiments. When detecting neutrinos using liquid argon, many hadrons are involved, which can also interact with argon nuclei. In order to gain a better understanding of detection processes, and to simulate neutrino events, knowledge of hadron-argon cross sections is needed. This paper describes a new procedure, which improves upon the previous work with a multi-dimensional unfolding, to measure hadron-argon cross sections in a liquid argon time projection chamber. Through a simplified version of simulation, we demonstrate the validity of this procedure.

In this paper, we investigate a financial market in which asset prices evolve based on a multi-dimensional Brownian motion process and a multi-dimensional Poisson process with different credit and deposit rates We proceed to evaluate European options by establishing upper and lower hedging prices through a transition to a suitable auxiliary market. Additionally, we address the minimization of shortfall risk and no-arbitrage price bounds in incomplete markets within this framework.

One of the fastest growing customer service delivery platforms is online banking. However, the increasing number of attacks on online banking platforms is driving the banks to constantly review their security. This study developed an enhanced Multi-factor authentication scheme with a track and trace capability to reduce these threats. Five modalities of authentication were incorporated in the proposed scheme: The traditional username, password, personal identification number (PIN) and one-time PIN (OTP), augmented with fingerprints or facial scans, registered smart devices, and a time locked user's location. One of the scheme's most impressive accomplishments is its capacity to seamlessly detect undesired activities and send alerts in the form of secretly obtained photographs and location triangulation.