Moisture buffering and mould growth characteristics of naturally ventilated lime plastered houses.

Lime plaster is a sustainable building material that can be an effective passive cooling strategy. The moisture buffering quality of lime causes adsorption and desorption of moisture which moderates the indoor relative humidity. Its vapour permeability is also influential in moisture transfer across the building envelope. Lime plaster also has a self-healing quality which prevents the formation of inner cracks. Moreover, its strength increases with time leading to a longer life span. In old structures, an important function is the breathability of ceilings and walls. Hence, it is often used in conservation projects where it improves the appearance and durability of old buildings. Often organic additives employed to impart certain qualities to the lime mortar/plaster led to mould growth. Mould growth degrades indoor air quality, and the occupant health is compromised. To avoid mould-related problems, it is necessary to understand the behaviour of lime plaster with respect to the indoor relative humidity and surface moisture content. This paper focuses on the hygrothermal performance of lime plaster in naturally ventilated residential spaces. Surveys were carried out in 45 traditional buildings in Ahmedabad in India with measurements of ambient variables, such as temperature, relative humidity, wall moisture content, etc. The mould growth patterns of these spaces are related to the measured variables and wall characteristics. Hygrothermal simulations of some spaces were also carried out to observe the moisture buffering of lime plaster. Experimental observations were then compared to simulation results to see if the predictions of the hygrothermal models were realistic.

The causes of air movement in hidden indoor micro-environments: measurements in historic bookshelves.

The use of ventilation holes in small micro-environments has been proposed by the National Trust as a mechanism to improve the environmental conditions of moisture and temperature within bookshelves. At one National Trust historic property, this mechanism has been used to encourage air movement behind books as a possible strategy to reduce the risk of mould growth. It is believed that including ventilation holes as a passive design solution to promote airflow within micro-environments could prevent decay from occurring in the archives of historic buildings. This paper investigates the mechanisms that cause airflow behind bookshelves using field measurements in three National Trust historic libraries. The measurements indicate that small but measurable velocities, up to 4 cm/s, can be passively generated behind bookshelves. Air movement in such confined micro-environments is probably caused by a combination of natural convection, caused by temperature differences between the walls and the interior and the exterior of the bookshelf, and forced convection due to drafts in the surrounding environment. While in some cases one mechanism prevailed, both mechanisms may be present simultaneously in most cases. Further research is needed to clarify how surface temperature drives air motion behind shelves.

Structural behaviour and construction of a monumental ice structure.

This paper investigates the use of ice-based materials combined with Fiber-Reinforced Polymer (FRP) to enhance the bending capacity of ice sculptures. Through three-point bending tests and ABAQUS simulations, the study analyses the mechanical behaviour of GFRP and cellulose ice, both separately and together. The results show that FRP improves tensile resistance while cellulose ice adds mass and ductility. An ABAQUS simulation with a layered shell partially captures the mechanical behaviour but has limitations in modelling beyond the elastic phase due to assumptions of perfect connection and material failure. Applied to a monumental sculpture by artist KAWS, incorporating a steel framework, GFRP mould, and ice-based material, the study confirms the potential of combining FRP and cellulose ice to achieve improved structural performance in ice-based sculptures.

Interaction of Capnodium alfenasii with extrafloral nectaries of Azadirachta indica.

Sooty moulds are saprophytic epiphytic fungi that grow mostly on insect secretions, but they can also be associated with plant secretions. In this study, we aimed to describe de interaction of Capnodium alfenasii sooty mould with the extrafloral shoot nectaries of Azadirachta indica. Anatomical and histochemical studies were carried out on serial sections of extrafloral shoot nectaries of A. indica without and with C. alfenasii infestation. The total soluble sugar content of the secreted nectar was determined, and the conidial germination of the fungus in distilled water and in dextrose and nectar solutions was evaluated. The shoot nectaries of A. indica are elongated structures that occur in pairs near the base of the petiole. The exuded nectar contains an average of 534.8 µg of total soluble sugars per µL of nectar and provides ideal conditions for conidial germination and fungal growth. C. alfenasii hyphae grow on the nectary, penetrate through breaks in the cuticle, travel under the cuticle and penetrate the secretory tissue by inter- and intracellular routes. The present report is the first to describe the interaction of C. alfenasii with the A. indica nectary, including the penetration of hyphae into nectariferous tissues and the plant defence mechanisms.

Study on the Effect of Sooty Mould Disease in Tea Plants.

Sooty mould (SM) disease affects the growth, development and metabolism of plants and reduces the commodity and economic value of crops. SM disease is one of the important leaf diseases in tea plants. Nonetheless, studies on the effect of SM disease in tea plants are rare. Herein, we found that SM disease disrupted the cell morphology and structure and reduced the contents of caffeine, theanine, and catechins in the mature leaves of tea plants. Transcriptome analysis revealed that SM disease inhibited the biosynthesis of lignin, chlorophyll, catechin, caffeine, and theanine and affected the plant-pathogen interactions in the mature leaves of tea plants by downregulating gene expression. In addition, two fungal isolates, MTzyqA and MTzyqB, were obtained from the mature leaves of diseased tea plants. These strains were identified as Cladosporium pseudocladosporioides by mulitgene phylogenetic analysis, and they grew epiphytically on the leaves of tea plants. The biocontrol bacteria JT68, ZGT5, and BX1 had obvious inhibitory effect on MTzyqA and MTzyqB. These results provide a basis for understanding the effect of SM disease in tea plants.

Enhanced Multi-Strategy Slime Mould Algorithm for Global Optimization Problems.

In order to further improve performance of the Slime Mould Algorithm, the Enhanced Multi-Strategy Slime Mould Algorithm (EMSMA) is proposed in this paper. There are three main modifications to SMA. Firstly, a leader covariance learning strategy is proposed to replace the anisotropic search operator in SMA to ensure that the agents can evolve in a better direction during the optimization process. Secondly, the best agent is further modified with an improved non-monopoly search mechanism to boost the algorithm's exploitation and exploration capabilities. Finally, a random differential restart mechanism is developed to assist SMA in escaping from local optimality and increasing population diversity when it is stalled. The impacts of three strategies are discussed, and the performance of EMSMA is evaluated on the CEC2017 suite and CEC2022 test suite. The numerical and statistical results show that EMSMA has excellent performance on both test suites and is superior to the SMA variants such as DTSMA, ISMA, AOSMA, LSMA, ESMA, and MSMA in terms of convergence accuracy, convergence speed, and stability.

Twenty Years in EUCAST Anti-Fungal Susceptibility Testing: Progress & Remaining Challenges.

Since its inception in 2002, the EUCAST Antifungal Susceptibility Testing Subcommittee (AFST) has developed and refined susceptibility testing methods for yeast, moulds and dermatophytes, and established epidemiological cut-off values and breakpoints for antifungals. For yeast, three challenges have been addressed. Interpretation of trailing growth in fluconazole susceptibility testing, which has been proven without impact on efficacy if below the 50% endpoint. Variability in rezafungin MIC testing due to laboratory conditions, which has been solved by the addition of Tween 20 to the growth medium in E.Def 7.4. And third, interpretation of MICs for rare yeast with no breakpoints, where recommendations have been established for MIC-based clinical advice. For moulds, refinements include the validation of spectrophotometer reading for A. fumigatus to facilitate objective MIC determination, and for dermatophytes the establishment of a microdilution method with automated reading and a selective medium to minimise the risk of contaminations. Recent initiatives involve development and validation of agar-based screening assays for detection of potential azole and echinocandin resistance in A. fumigatus and Aspergillus species, respectively, and of terbinafine resistance in Trichophyton species. Moreover, the development of a EUCAST guidance document for molecular resistance testing represents an advancement, particularly for identifying target gene alterations associated with resistance. In summary, EUCAST AFST continues to play a pivotal role in standardizing AFST and facilitating accurate interpretation of susceptibility data for clinical decision-making. Adoption of EUCAST breakpoints for commercial test methods, however, requires thorough validation to ensure concordance with EUCAST reference testing species-specific MIC distributions.

Implementing high-dose rate surface mould brachytherapy for carcinoma of eyelid: a practical approach and weekly review.

Surface mould brachytherapy is a conformal radiotherapy technique that can deliver high dose to the target while sparing nearby normal structures, Here, we aim to describe the procedurals details for high-dose rate (HDR) surface mould brachytherapy in sebaceous carcinoma of eyelid in a 54-year old lady. She was hesitant for surgery and any form of invasive intervention like interstitial brachytherapy. So, she was treated with surface mould HDR brachytherapy to a total dose of 52 Gy in 13 fractions at a dose of 4 Gy per fraction delivered twice daily using Iridium-192 isotope with no acute side effects. She was evaluated on a weekly basis for any radiation side effects and now she is disease-free for 6 months post-treatment with only mild dry eye. A detailed step-by-step procedure of surface mould technique, simulation procedure, dose prescription, planning, plan evaluation and treatment has been described in this paper. Surface mould HDR brachytherapy can be safely used as organ preserving modality of treatment for eyelid carcinoma.

A novel contaminant in museums? A cross-sectional study on xerophilic Aspergillus growth in climate-controlled repositories.

In the last decade, extensive fungal growth has developed in Danish museums parallel to climate change, challenging occupational health and heritage preservation. The growth was unexpected as the museums strived to control relative humidity below 60 %, and it should exceed 75 % to risk growth. A Danish case study found xerophilic Aspergillus species able to grow at low relative humidity in a museum repository. This cross-sectional study aimed to examine whether xerophilic growth from Aspergillus section Restricti has become a novel contaminant nationally distributed in Danish museum repositories striving to control relative humidity according to international environmental recommendations for heritage collections. The study examined The National Museum of Denmark and eight large State Recognized museums distributed throughout Denmark. It was based on 600 swab and tape-lift samples and 60 MAS100-Eco and filter air samples analyzed for fungi with cultivation and morphological identification, Big-Dye-Sanger sequencing, CaM-Nanopore and ITS-Illumina amplicon sequencing. The study showed growth from seven xerophilic Aspergillus species: A. conicus, A. domesticus, A. glabripes, A. halophilicus, A. magnivesiculatus, A. penicilloides, A. vitricola, of which three are new to Denmark, and 13 xerotolerant Aspergillus species. There was no growth from other fungal species. The multiple detection approach provided a broad characterization; however, there was variance in the detected species depending on the analysis approach. Cultivation and Big-Dye Sanger sequencing showed the highest Aspergillus diversity, detecting 17 species; CaM-Nanopore amplicon sequencing detected 12 species; and ITS-illumina amplicon sequencing detected two species but the highest overall diversity. Cultivation, followed by Big-Dye Sanger and CaM-amplicon sequencing, proved the highest compliance. The study concluded that xerophilic Aspergillus growth is nationally distributed and suggests species from Aspergillus section Restricti as a novel contaminant in climate-controlled museum repositories. To safeguard occupational health and heritage preservation research in sustainable solutions, avoiding xerophilic growth in museum collections is most important.

Boosting prairie dog optimizer for optimal planning of multiple wind turbine and photovoltaic distributed generators in distribution networks considering different dynamic load models.

Deploying distributed generators (DGs) supplied by renewable energy resources poses a significant challenge for efficient power grid operation. The proper sizing and placement of DGs, specifically photovoltaics (PVs) and wind turbines (WTs), remain crucial due to the uncertain characteristics of renewable energy. To overcome these challenges, this study explores an enhanced version of a meta-heuristic technique called the prairie dog optimizer (PDO). The modified prairie dogs optimizer (mPDO) incorporates a novel exploration phase inspired by the slime mold algorithm (SMA) food approach. The mPDO algorithm is proposed to analyze the substantial effects of different dynamic load characteristics on the performance of the distribution networks and the designing of the PV-based and WT-based DGs. The optimization problem incorporates various operational constraints to mitigate energy loss in the distribution networks. Further, the study addresses uncertainties related to the random characteristics of PV and WT power outputs by employing appropriate probability distributions. The mPDO algorithm is evaluated using cec2020 benchmark suit test functions and rigorous statistical analysis to mathematically measure its success rate and efficacy while considering different type of optimization problems. The developed mPDO algorithm is applied to incorporate both PV and WT units, individually and simultaneously, into the IEEE 69-bus distribution network. This is achieved considering residential, commercial, industrial, and mixed time-varying voltage-dependent load demands. The efficacy of the modified algorithm is demonstrated using the standard benchmark functions, and a comparative analysis is conducted with the original PDO and other well-known algorithms, utilizing various statistical metrics. The numerical findings emphasize the significant influence of load type and time-varying generation in DG planning. Moreover, the mPDO algorithm beats the alternatives and improves distributed generators' technical advantages across all examined scenarios.

Sequential breakdown of the Cf-9 leaf mould resistance locus in tomato by Fulvia fulva.

Leaf mould, caused by Fulvia fulva, is a devastating disease of tomato plants. In many commercial tomato cultivars, resistance to this disease is governed by the Cf-9 locus, which encodes five paralogous receptor-like proteins. Two of these proteins confer resistance: Cf-9C recognises the previously identified F. fulva effector Avr9 and provides resistance during all plant growth stages, while Cf-9B recognises the yet-unidentified F. fulva effector Avr9B and provides mature plant resistance only. In recent years, F. fulva strains have emerged that can overcome the Cf-9 locus, with Cf-9C circumvented through Avr9 deletion. To understand how Cf-9B is circumvented, we set out to identify Avr9B. Comparative genomics, transient expression assays and gene complementation experiments were used to identify Avr9B, while gene sequencing was used to assess Avr9B allelic variation across a world-wide strain collection. A strict correlation between Avr9 deletion and resistance-breaking mutations in Avr9B was observed in strains recently collected from Cf-9 cultivars, whereas Avr9 deletion but no mutations in Avr9B were observed in older strains. This research showcases how F. fulva has evolved to sequentially break down the Cf-9 locus and stresses the urgent need for commercial tomato cultivars that carry novel, stacked resistance genes active against this pathogen.

The cytosolic form of dual localized BolA family protein Bol3 is important for adaptation to iron starvation in Aspergillus fumigatus.

Aspergillus fumigatus is the predominant mould pathogen for humans. Adaption to host-imposed iron limitation has previously been demonstrated to be essential for its virulence. [2Fe-2S] clusters are crucial as cofactors of several metabolic pathways and mediate cytosolic/nuclear iron sensing in fungi including A. fumigatus. [2Fe-2S] cluster trafficking has been shown to involve BolA family proteins in both mitochondria and the cytosol/nucleus. Interestingly, both A. fumigatus homologues, termed Bol1 and Bol3, possess mitochondrial targeting sequences, suggesting the lack of cytosolic/nuclear versions. Here, we show by the combination of mutational, proteomic and fluorescence microscopic analyses that expression of the Bol3 encoding gene leads to dual localization of gene products to mitochondria and the cytosol/nucleus via alternative translation initiation downstream of the mitochondrial targeting sequence, which appears to be highly conserved in various Aspergillus species. Lack of either mitochondrial Bol1 or Bol3 was phenotypically inconspicuous while lack of cytosolic/nuclear Bol3 impaired growth during iron limitation but not iron sensing which indicates a particular importance of [2Fe-2S] cluster trafficking during iron limitation. Remarkably, cytosolic/nuclear Bol3 differs from the mitochondrial version only by N-terminal acetylation, a finding that was only possible by mutational hypothesis testing.

Design and optimization of a castability test for copper alloys applied to plaster moulds.

The paper deals with the design and execution of the test of the castability into preheated plaster moulds. Castability is an important parameter for the evaluation of foundry alloys. When the metal does not run, it usually causes an irreparable defect in the casting. Therefore, it is necessary to verify this technological property correctly for different types of moulds. The castability test for plaster moulds and bronze is not defined, therefore The Vertical Bar Test was adopted, subsequently modified, and applied to plaster moulds. The models are made of wax and the plaster moulds are made according to the manufacturer's prescription and heated to a predetermined temperature. Copper alloy specifically CuSn10 was selected as the alloy to be investigated. The aim of the experiment is to correctly set up and observe the parameters and the method of performing the castability test using gravity casting technology in plaster moulds. It is assumed, as the casting temperature increases, the run-in length of the metal alloy will increase. An important goal is to obtain relevant results, as many variables affect the progress of the test and so there is a risk of error. Due to the time-consuming nature of producing a real disposable model and mould, any failure to succeed would have an economic impact on the foundry, affecting the price of castings. The new method for evaluating castability in plaster moulds can be applied to other types of alloys when temperature changes occur.

Advancing intrauterine adhesion severity prediction: Integrative machine learning approach with hysteroscopic cold knife system, clinical characteristics and hematological parameters.

Intrauterine Adhesion (IUA) constitute a significant determinant impacting female fertility, potentially leading to infertility, miscarriage, menstrual irregularities, and placental complications. The precise assessment of the severity of IUA is pivotal for the customization of personalized treatment plans, aimed at enhancing the success rate of treatments and mitigating reproductive health risks. This study proposes bTLSMA-SVM-FS, a novel feature selection machine learning model that integrates an enhanced slime mould algorithm (SMA), termed TLSMA, with support vector machines (SVM), aiming to develop a predictive model for assessing the severity of IUA. Initially, a series of optimization comparative experiments were conducted on the TLSMA using the CEC 2017 benchmark functions. By comparing it with eleven meta-heuristic algorithms as well as eleven SOTA algorithms, the experimental outcomes corroborated the superior performance of the TLSMA. Subsequently, the developed bTLSMA-SVM-FS model was employed to conduct a thorough analysis of the clinical features of 107 IUA patients from Wenzhou People's Hospital, comprising 61 cases of moderate IUA and 46 cases of severe IUA. The evaluation results of the model demonstrated exceptional performance in predicting the severity of IUA, achieving an accuracy of 86.700 % and a specificity of 87.609 %. Moreover, the model successfully identified critical factors influencing the prediction of IUA severity, including the preoperative Chinese IUA score, production times, thrombin time, preoperative endometrial thickness, and menstruation. The identification of these key factors not only further validated the efficacy of the proposed model but also provided vital scientific evidence for a deeper understanding of the pathogenesis of IUA and the enhancement of targeted treatment strategies.

Prevalence of co-existent COVID-19-associated pulmonary aspergillosis (CAPA) and its impact on early mortality in patients with COVID-19-associated pulmonary mucormycosis (CAPM).

BACKGROUND: Data on mixed mould infection with COVID-19-associated pulmonary aspergillosis (CAPA) and COVID-19-associated pulmonary mucormycosis (CAPM) are sparse. OBJECTIVES: To ascertain the prevalence of co-existent CAPA in CAPM (mixed mould infection) and whether mixed mould infection is associated with early mortality (≤7 days of diagnosis). METHODS: We retrospectively analysed the data collected from 25 centres across India on COVID-19-associated mucormycosis. We included only CAPM and excluded subjects with disseminated or rhino-orbital mucormycosis. We defined co-existent CAPA if a respiratory specimen showed septate hyphae on smear, histopathology or culture grew Aspergillus spp. We also compare the demography, predisposing factors, severity of COVID-19, and management of CAPM patients with and without CAPA. Using a case-control design, we assess whether mixed mould infection (primary exposure) were associated with early mortality in CAPM. RESULTS: We included 105 patients with CAPM. The prevalence of mixed mould infection was 20% (21/105). Patients with mixed mould infection experienced early mortality (9/21 [42.9%] vs. 15/84 [17.9%]; p = 0.02) and poorer survival at 6 weeks (7/21 [33.3] vs. 46/77 [59.7%]; p = 0.03) than CAPM alone. On imaging, consolidation was more commonly encountered with mixed mould infections than CAPM. Co-existent CAPA (odds ratio [95% confidence interval], 19.1 [2.62-139.1]) was independently associated with early mortality in CAPM after adjusting for hypoxemia during COVID-19 and other factors. CONCLUSION: Coinfection of CAPA and CAPM was not uncommon in our CAPM patients and portends a worse prognosis. Prospective studies from different countries are required to know the impact of mixed mould infection.

Advancing forensic-based investigation incorporating slime mould search for gene selection of high-dimensional genetic data.

Modern medicine has produced large genetic datasets of high dimensions through advanced gene sequencing technology, and processing these data is of great significance for clinical decision-making. Gene selection (GS) is an important data preprocessing technique that aims to select a subset of feature information to improve performance and reduce data dimensionality. This study proposes an improved wrapper GS method based on forensic-based investigation (FBI). The method introduces the search mechanism of the slime mould algorithm in the FBI to improve the original FBI; the newly proposed algorithm is named SMA_FBI; then GS is performed by converting the continuous optimizer to a binary version of the optimizer through a transfer function. In order to verify the superiority of SMA_FBI, experiments are first executed on the 30-function test set of CEC2017 and compared with 10 original algorithms and 10 state-of-the-art algorithms. The experimental results show that SMA_FBI is better than other algorithms in terms of finding the optimal solution, convergence speed, and robustness. In addition, BSMA_FBI (binary version of SMA_FBI) is compared with 8 binary algorithms on 18 high-dimensional genetic data from the UCI repository. The results indicate that BSMA_FBI is able to obtain high classification accuracy with fewer features selected in GS applications. Therefore, SMA_FBI is considered an optimization tool with great potential for dealing with global optimization problems, and its binary version, BSMA_FBI, can be used for GS tasks.

FUZ-SMO: A fuzzy slime mould optimizer for mitigating false alarm rates in the classification of underwater datasets using deep convolutional neural networks.

Sonar sound datasets are of significant importance in the domains of underwater surveillance and marine research as they enable experts to discern intricate patterns within the depths of the water. Nevertheless, the task of classifying sonar sound datasets continues to pose significant challenges. In this study, we present a novel approach aimed at enhancing the precision and efficacy of sonar sound dataset classification. The integration of deep long-short-term memory (DLSTM) and convolutional neural networks (CNNs) models is employed in order to capitalize on their respective advantages while also utilizing distinctive feature engineering techniques to achieve the most favorable outcomes. Although DLSTM networks have demonstrated effectiveness in tasks involving sequence classification, attaining their optimal performance necessitates careful adjustment of hyperparameters. While traditional methods such as grid and random search are effective, they frequently encounter challenges related to computational inefficiencies. This study aims to investigate the unexplored capabilities of the fuzzy slime mould optimizer (FUZ-SMO) in the context of LSTM hyperparameter tuning, with the objective of addressing the existing research gap in this area. Drawing inspiration from the adaptive behavior exhibited by slime moulds, the FUZ-SMO proposes a novel approach to optimization. The amalgamated model, which combines CNN, LSTM, fuzzy, and SMO, exhibits a notable improvement in classification accuracy, outperforming conventional LSTM architectures by a margin of 2.142%. This model not only demonstrates accelerated convergence milestones but also displays significant resilience against overfitting tendencies.

Correction of congenital auricle deformities with ear mould: A systematic review and meta-analysis.

OBJECTIVE: To assess the effectiveness and safety of ear moulds for congenital auricle deformities. METHODS: Databases including Medline, EMBASE, Cochrane Library, Chinese BioMedical Literature Database (CBM) and Web of Science were systematically reviewed. Randomised controlled trials (RCT), non-randomised control trials (non-RCT), quasi-randomised control trials (quasi-RCT) and self-controlled before-after trials were also included. Data extraction was independently conducted by two authors. The Risk Of Bias In Non-randomised Studies of Interventions (ROBIN-I) was used to evaluate the risk bias. Heterogeneity was assessed using I2 and chi-square tests. Effective rate, adverse reaction rate and their 95%CI were calculated. Funnel plots, Begg's test as well as sensitivity and subgroup analyses were performed. RESULTS: The analysis encompassed ten studies, comprising one RCT and nine self-controlled before-after trials, involving 1860 ears (1248 children). The pooled effective rate and adverse reaction rate of ear mould were 91% (95% CI: 0.87-0.94) and 9% (95%CI: 0.02-0.17), respectively. No serious adverse reactions were reported. The effective rate of ear mould intervention showed no significant difference between age at correction≤42 days group (90%, 95%CI: 0.85-0.94) and that >42 days group (93%, 95%CI: 0.83-0.99). Similarly, there was no statistical difference in the correction efficiency between duration of wearing ear mould≤30 days group (90%, 95%CI: 0.85-0.94) and that >30 days group (92%, 95%CI: 0.86-0.96). The effective rate for correcting cryptotia ear (98%, P < 0.001), cup ear (93%, P = 0.004) and prominent ear (90%, P = 0.014) was higher than that of helical rim deformity (66%). CONCLUSIONS: In the short term, the use of ear moulds is effective and safe in correcting congenital auricle deformities. Notably, the correction efficacy for cryptotia, cup ear and prominent ear deformities surpasses that of helical rim deformities. However, further research is required to ascertain the impact of the duration of wearing on the correction of congenital auricle deformities.

Utility of an in-house real-time PCR in whole blood samples as a minimally invasive method for early and accurate diagnosis of invasive mould infections.

INTRODUCTION: Invasive mould infections (IMIs) are a leading cause of death in patients with compromised immune systems. Proven invasive mould infection requires detection of a fungus by histopathological analysis of a biopsied specimen, sterile culture, or fungal DNA amplification by PCR in tissue. However, the clinical performance of a PCR assay on blood samples taken from patients suspected of invasive mould disease has not been fully evaluated, particularly for the differential diagnosis of invasive aspergillosis (IA) and invasive Mucormycosis (IM). OBJECTIVES: To assess the diagnostic utility of our previously validated in-house real-time PCR in blood samples for diagnosis of invasive aspergillosis and mucormycosis in patients with suspected invasive mould infection. METHODS: All patients with suspected invasive mould infection were prospectively enrolled from May 2021 to July 2021. Conventional fungal diagnosis was performed using tissue and respiratory samples. In-house PCR was performed on blood samples and its diagnostic performance evaluated. RESULTS: A total of 158 cases of suspected invasive mould infection were enrolled in the study. The sensitivity and specificity of in-house PCR performed on blood samples was found to be 92.5% and 81.4% respectively for diagnosis of probable IA, and 65% and 84.62% respectively for diagnosis of proven and probable IM. It was also able to detect 3 out of 5 cases of possible IM where no other microbiological evidence of IM was obtained. CONCLUSIONS: This assay could be helpful in minimally invasive diagnosis of IMIs for patients in whom invasive sampling is not feasible, especially as a preliminary or screening test. It can help in early diagnosis, anticipating conventional laboratory confirmation by days or weeks. Possible correlation between fungal load and mortality can help in initiating aggressive treatment for patients with high initial fungal load.

Research on Microgrid Optimal Dispatching Based on a Multi-Strategy Optimization of Slime Mould Algorithm.

In order to cope with the problems of energy shortage and environmental pollution, carbon emissions need to be reduced and so the structure of the power grid is constantly being optimized. Traditional centralized power networks are not as capable of controlling and distributing non-renewable energy as distributed power grids. Therefore, the optimal dispatch of microgrids faces increasing challenges. This paper proposes a multi-strategy fusion slime mould algorithm (MFSMA) to tackle the microgrid optimal dispatching problem. Traditional swarm intelligence algorithms suffer from slow convergence, low efficiency, and the risk of falling into local optima. The MFSMA employs reverse learning to enlarge the search space and avoid local optima to overcome these challenges. Furthermore, adaptive parameters ensure a thorough search during the algorithm iterations. The focus is on exploring the solution space in the early stages of the algorithm, while convergence is accelerated during the later stages to ensure efficiency and accuracy. The salp swarm algorithm's search mode is also incorporated to expedite convergence. MFSMA and other algorithms are compared on the benchmark functions, and the test showed that the effect of MFSMA is better. Simulation results demonstrate the superior performance of the MFSMA for function optimization, particularly in solving the 24 h microgrid optimal scheduling problem. This problem considers multiple energy sources such as wind turbines, photovoltaics, and energy storage. A microgrid model based on the MFSMA is established in this paper. Simulation of the proposed algorithm reveals its ability to enhance energy utilization efficiency, reduce total network costs, and minimize environmental pollution. The contributions of this paper are as follows: (1) A comprehensive microgrid dispatch model is proposed. (2) Environmental costs, operation and maintenance costs are taken into consideration. (3) Two modes of grid-tied operation and island operation are considered. (4) This paper uses a multi-strategy optimized slime mould algorithm to optimize scheduling, and the algorithm has excellent results.