Counting Method Research Articles

Evaluation of the Antibacterial Activity of Antibiotics with different Antibacterial Mechanisms using Electrochemical Signals of Heat‐Treated Escherichia Coli

AbstractElectrochemistry with purines as indicators may be a promising new method for antibiotic activity testing. However, the traditional electrochemical detection method (S‐LSV) requires a long accumulation time for purines and has low signal intensity. High‐temperature treatment of bacterial samples may significantly enhance the signal intensity and speed of detection. However, this process may also impact the structure and metabolism of bacteria. Further research is necessary to determine how electrochemical signals respond to antibiotic antibacterial activity. In this paper, the electrochemical detection method based on heat‐treated Escherichia coli (E. coli, H‐LSV) was used to investigate the effects of 11 antibiotics with four different antibacterial mechanisms on the electrochemical behavior of E. coli. The results showed that compared to the S‐LSV, the electrochemical signal of E. coli detected by the H‐LSV increased by 362%, and showed a strong linear relationship with bacterial counts up to 1.88×108 CFU/mL, with a detection limit of 1.15×106 CFU/mL. The electrochemical signal consistently correlated with changes in viable E. coli counts following treatment with the four different antibacterial mechanisms. HPLC analysis further confirmed that the changes in purine contents in bacteria after antibiotic treatment were consistent with the trends in electrochemical signals. The evaluation of antibiotic antibacterial ability by H‐LSV showed good consistency with results from plate counting and turbidity methods, but was closer to the results of plate counting. The inhibition rates of antibiotics on E. coli detected by H‐LSV and S‐LSV also showed high consistency, indicating that short‐term high‐temperature treatment of E. coli did not affect the response of the bacterium to the inhibitory activity of antibiotics.

Revealing Spatial Molecular Heterogeneity of High-Density Biofunctionalized Surfaces Using DNA-PAINT.

The quantification and control of molecular densities and distributions on biofunctionalized surfaces are key for enabling reproducible functions in biosciences. Here, we describe an analysis methodology for quantifying the density and spatial distribution of high-density biofunctionalized surfaces, with densities in the order of 102-105 biomolecules per μm2 area, in a short measurement time. The methodology is based on single-molecule DNA-PAINT imaging combined with simulation models that compensate for lifetime and spatial undersampling effects, resulting in three distinct molecule counting methods and a statistical test for spatial distribution. The analysis methodology is exemplified for a surface with ssDNA affinity binder molecules coupled to a PLL-g-PEG antifouling coating. The results provide insights into the biofunctionalization efficiency, yield, and homogeneity. Furthermore, the data reveal that heterogeneity is inherent to the biofunctionalization process and shed light on the underlying molecular mechanisms. We envision that DNA-PAINT imaging with the developed analysis framework will become a versatile tool to study spatial heterogeneity of densely biofunctionalized surfaces for a wide range of applications.

A method of nucleus image segmentation and counting based on TC-UNet++ and distance watershed

Nucleus segmentation and counting play a crucial role in many cell analysis applications. However, the dense distribution and blurry boundaries of nucleus make nucleus segmentation tasks challenging. This paper proposes a novel segmentation and counting method. Firstly, TC-UNet++ is proposed to achieve a global segmentation. Then, the distance watershed method is used to finish local segmentation, which separate the adhesion and overlap part of the image. Finally, counting method is performed to obtain information on the counting number, area and center of mass of nucleus. TC-UNet++ achieved a Dice coefficient of 89.95% for cell instance segmentation on the Data Science Bowl dataset, surpassing the original U-Net++ by 0.23%. It also showed a 5.09% improvement in counting results compared to other methods. On the ALL-IDB dataset, TC-UNet++ reached a Dice coefficient of 83.97%, a 7.93% increase over the original U-Net++. Additionally, its counting results improved by 16.82% compared to other approaches. These results indicate that our method has a more complete and reasonable nucleus segmentation and counting scheme compared to other methods.

On-Tree Mango Fruit Count Using Live Video- Split Image Dataset to Predict Better Yield at Pre-Harvesting Stage

This study introduces a method for fruit counting in agricultural settings using video capture and the YOLOv7 object detection model. By splitting captured videos into frames and strategically selecting representative frames, the approach aims to accurately estimate fruit counts while minimizing the risk of double counting. YOLOv7, known for its efficiency and accuracy in object detection, is employed to analyze selected frames and detect fruits on trees. Demonstrated the method's effectiveness through its ability to provide farmers with precise yield estimations, optimize resource management, and facilitate early detection of orchard issues such as pest infestations or nutrient deficiencies. This technological integration reduces labor costs and supports sustainable agricultural practices by improving productivity and decision-making capabilities. The scalability of the approach makes it suitable for diverse orchard sizes and types, offering a promising tool for enhancing agricultural efficiency and profitability. The researcher compared YOLOv5n, YOLOv5s, YOLOv7, and YOLOv7-tiny with eight-sided imaging techniques around the tree. The experimental results of YOLOv7 with the eight-sided technique performed best and achieved a count accuracy of 97.7% on a single tree in just 17.112 ms of average inference time. On multiple trees, it is 95.48% in just 17 ms of average inference time, with the help of an eight- sided method on tree images.

Wildlife density estimation by distance sampling: A novel technique with movement compensation.

Estimates of population density are fundamental to wildlife conservation and management. Distance sampling from line transects is a widely used sample count method and is most often analysed using Distance software. However, this method has limited capabilities with mobile populations (e.g., birds), which tend to encounter an observer more often than immobile ones. This paper presents a novel distance sampling method based on a different set of models and assumptions, named WildlifeDensity after its associated software. It is based on mechanistic modelling of visual detections of individuals or groups according to radial distance from the observer or perpendicular distance from the transect line. It also compensates for population-observer relative movement to avoid the detection overestimates associated with highly mobile populations. The models are introduced in detail and then tested in three ways: 1) WildlifeDensity is applied to several 'benchmark' populations of known density and no-to-low mobility, 2) the movement compensation model is tested on two highly mobile songbird populations, and 3) a fairly difficult case is analysed: a low-density, highly mobile bird population in a forest habitat. The results show that 1) using either radial or perpendicular distance data from surveys of immobile populations, WildlifeDensity provides similar estimates (and errors) to Distance, with radial WildlifeDensity analysis appearing to be slightly better for surveys of low-mobility populations (kangaroos), 2) the movement compensation model effectively removes the correlation between observer speed and detection numbers, and 3) WildlifeDensity provides acceptable estimates where conventional Distance analysis overestimates density due to high movement. In summary, WildlifeDensity extends the capabilities of distance sampling by 1) compensating for movement, 2) not requiring complete detectability on the transect line and 3) supporting the use of radial distances, which simplifies fieldwork and increases measurement accuracy.

Multi-Stage Rolling Grid Expansion Planning for Distribution Networks Considering Conditional Value at Risk

Existing single-stage planning and multi-stage non-rolling planning methods for distribution networks have problems such as low equipment utilization efficiency and poor investment benefits. In order to solve the above problems, this paper firstly proposes a multi-stage rolling planning method for distribution networks based on analyzing the limitations of the existing planning methods, which divides the planning cycle of the distribution network into multiple planning stages, and makes rolling amendments to the planning scheme of each stage according to the latest information during the planning cycle. Then, a multi-stage rolling planning model of distribution network taking into account conditional value at risk is established with the objective of minimizing the total investment and operation cost of the distribution network. On the one hand, the users’ electricity bill is taken into account in the objective function, and the necessity of this part of the benefits is demonstrated. On the other hand, the conditional value at risk is used to quantify the uncertainty of the operation cost in the process of the expansion planning of the distribution network, which reduces the operation cost risk of the distribution network. Next, this paper uses the rainflow counting method to characterize the capacity decay characteristics of energy storage in the distribution network, and proposes an iterative solution framework that considers energy storage capacity decay to solve the proposed model. Finally, the proposed method is applied to an 18-node distribution network planning case. This confirms that the multi-stage rolling planning method could improve the investment benefits and reduce the investment cost by approximately 27.27%. Besides, it will increase the total cost by approximately 2750 USD in the case if the users’ electricity bill is not taken into account. And the maximum capacity of energy storage may decay to 87.6% of the initial capacity or even lower during operation, which may cause the line current to exceed the limit if it is not taken into account.

Riverine macroplastic survey along the segments of Tullahan River in Metro Manila, Philippines

The Philippines is one of the world’s main contributors to global marine plastic pollution. However, field data remains scarce, so model estimates of riverine plastic pollution may be inaccurate. This paper aims to characterize the macrolitter observed along the Tullahan River, with focus on plastics, passing through the barangays of Quezon City and Valenzuela City in Metro Manila. The impact of solid waste management and land use activities on plastic flux were also investigated. For the floating litter, visual counting and float method were used to determine the plastic flux and river velocity, respectively. Riverbank litter was collected manually to characterize it based on plastic product and polymer type. Results show that the macroplastic flux was lowest on the site where residential houses are farthest from the stream. Waste characterization revealed that riverbank litter was primarily plastic – 30%–41% were residuals, and 5%–21% were recyclables. Both methods revealed that wrappers and thin PE plastics are the top contributors to riverine macroplastic pollution. Therefore, the entry of macroplastics into the river may be due to land use activities and weak enforcement of existing solid waste management (SWM) policies. The points discussed in the study can help in improving SWM and land use planning. The results can also increase the accuracy of model estimates.

Diagnostic Utility of Modified Method of AgNOR Staining in the Evaluation of Benign and Malignant Effusions

An exact identification of malignant cells in fluid by cytological examination is a well-known diagnostic challenge. One of the common problems is to distinguish reactive mesothelial cells from malignant cells. Conventional smears reported as ‘suspicious for malignancy’ indicate that the suspicious cases could not be classified with certainty as to whether they were reactive mesothelial cells or malignant cells. It poses problem in clinical staging of tumor, treatment and prognosis of malignancy. The purpose of the study was to determine the role of modified method of AgNOR staining in the evaluation of benign and malignant effusions. This cross-sectional study was conducted in the Department of Pathology, BIRDEM General Hospital, Dhaka, from July 2019 - June 2021. A total of 115 cases of effusion were included. All the samples were centrifuged and then smears were prepared from the deposit followed by staining with Hematoxylin & Eosin stain, Papanicolaou stain and AgNOR stain. At first the diagnosis was made on conventional smear method. Then the findings were compared and analyzed by modified AgNOR staining method. In malignant cells, the mean AgNOR count was 5.59±1.05 (±SD) and the AgNORs were multiple and irregular in shape. On the other hand, in benign cells the AgNORs were comparatively larger, single dots with a mean count of 1.31±0.48.The AgNOR count method has definite role in differentiating benign from malignant effusion. This method has supportive value which can be utilized in differentiating malignant effusions from the benign ones, especially in suspicious cases.

Impacts of counting protocols for light microscopy on estimates of biodiversity and algal density of phytoplankton

AbstractKnowledge on the biodiversity and abundance of phytoplankton is key for many ecological and societal (e.g., blue growth) questions. Gathering temporal variation and spatial patterns on key indicators requires reliable and standardized protocols on sampling, species identification and counting. Numerous methods are used but consequences for comparing the biodiversity and abundance of phytoplankton of these different techniques are not well known. We evaluated the consequences of different counting protocols using light microscopy (i.e., subsampling transects or wedges within counting chambers) for these indices using samples collected weekly to bi‐weekly (n = 398, 2009–2018) from the Wadden Sea (southern North Sea). Phytoplankton cells were counted (by one person under similar conditions) in a fixed number of viewing fields (58, 70, and 29) at three respective magnifications (10 × 100, 10 × 40, and 10 × 10). Patterns in the spatial distribution of phytoplankton cells varied among species and clustering of cells occurred in more than one‐fifth of the samples. This will induce error in the conversion from counts (per viewing field) to abundance (cells mL−1). Our present effort resulted in a high accuracy (95%) in overall cell abundances. This was not the case for species richness, for example, capturing 90% of all species present in the sample would require an almost threefold increase in effort for the 10 × 40 and 10 × 10 magnifications. We recommend that counting methods be tailored to the main research objectives and that counting protocols should quantify uncertainty as well as potential bias to provide an estimation of the error in phytoplankton abundance and species composition.

Assessment of Avifaunal Status, Diversity, and Conservation Implications in and around the Wetlands of Dharwad, Karnataka, India

The study shows an overall avifaunal diversity and status of selected wetlands in Dharwad district and conveys the presence/absence of bird species on habitat changes caused due to varying water levels (exposure/submergence of mudflats) and destruction of riparian vegetation such as the reed species. These wetlands have attracted bird species from local to migratory to forage, roost and breed in the area. The avifaunal diversity was found to fairly vary across the different wetlands. The data portrayed here are from the surveys during wintering months of 2017-18 and 2021-22. Line transects, point count and total count methods were used for surveying. Diversity indices (Beta, Shannon, Simpson, Evenness) and similarity matrices (Bray-Curtis cluster) were calculated using Past 4 package. The Pearson correlation, beta diversity and Bray-Curtis’s clustering were used to compare the wetlands for the similarity/dissimilarities in species composition. Overall, Shannon and Simpson diversity of the wetlands were H’=4.67 & D= 0.98 with total of 177 species, which portrays high degree of diversity. There were around 46 species of migrant birds and 133 resident species. The work also reports a few rare and coastal birds. The study suggests the management plans for the conservation of avifauna in and around the wetlands to prevent human disturbances and also considering the importance of faunal habitats before adopting lake developmental plans.

Integrating Automated Labeling Framework for Enhancing Deep Learning Models to Count Corn Plants Using UAS Imagery.

Plant counting is a critical aspect of crop management, providing farmers with valuable insights into seed germination success and within-field variation in crop population density, both of which are key indicators of crop yield and quality. Recent advancements in Unmanned Aerial System (UAS) technology, coupled with deep learning techniques, have facilitated the development of automated plant counting methods. Various computer vision models based on UAS images are available for detecting and classifying crop plants. However, their accuracy relies largely on the availability of substantial manually labeled training datasets. The objective of this study was to develop a robust corn counting model by developing and integrating an automatic image annotation framework. This study used high-spatial-resolution images collected with a DJI Mavic Pro 2 at the V2-V4 growth stage of corn plants from a field in Wooster, Ohio. The automated image annotation process involved extracting corn rows and applying image enhancement techniques to automatically annotate images as either corn or non-corn, resulting in 80% accuracy in identifying corn plants. The accuracy of corn stand identification was further improved by training four deep learning (DL) models, including InceptionV3, VGG16, VGG19, and Vision Transformer (ViT), with annotated images across various datasets. Notably, VGG16 outperformed the other three models, achieving an F1 score of 0.955. When the corn counts were compared to ground truth data across five test regions, VGG achieved an R2 of 0.94 and an RMSE of 9.95. The integration of an automated image annotation process into the training of the DL models provided notable benefits in terms of model scaling and consistency. The developed framework can efficiently manage large-scale data generation, streamlining the process for the rapid development and deployment of corn counting DL models.

Discrepancy between Subjective and Objective Measurements for the Evaluation of Medication Adherence-A Cross-Sectional Study in Patients with Cardiovascular Diseases.

Medication adherence is important for the appropriate drug-based treatment in patients with chronic diseases, especially those with cardiovascular diseases (CVDs). The purpose of the present study was to evaluate medication adherence among patients with CVDs using subjective and objective measurements. We enrolled outpatients who visited Fukuoka University Chikushi Hospital from June to December 2022. As a subjective measurement, we used a self-reported questionnaire developed by Ueno et al., which consists of 12 questionnaire items grouped into the following four domains: medication compliance (subjective compliance), collaboration with health care providers (collaboration), willingness to access and use information about medication (willingness), and acceptance to take medication and how taking medication fits a patient's lifestyle (acceptance). The pill counting method was used as an objective measurement to calculate the medication adherence rate; Poor Adherence was defined as a medication adherence rate of <100%. Ninety-four patients were analyzed. No statistically significant differences were observed between the patients in the Good and Poor Adherence groups classified by pill counting, an objective indicator; in the subjective evaluation index Ueno scale scores of subjective compliance, collaboration, willingness, and acceptance domains; and in the total score. A multivariate analysis revealed that obesity (odds ratio, 3.527; 95% confidence interval, 1.387-9.423; p = 0.008) was an independent factor associated with Poor Adherence. In conclusion, we found a discrepancy between subjective and objective measurements for the evaluation of medication adherence. Furthermore, obesity was an independent factor associated with poor medication adherence assessed by the pill counting method; thus, patients with CVD and obesity require a careful follow-up.

Illustrated eating plan as a strategy for adherence to carbohydrate counting in patients with diabetes mellitus

The carbohydrate counting method is an effective meal planning tool used in diabetes mellitus (DM) patients that focuses on carbohydrates as the main nutrient that affects the glycemic response. Therefore, this study aims to develop a nutritional therapy with carbohydrate counting, providing the inclusion of patients with DM on insulin therapy. This is a clinical-longitudinal and descriptive study, carried out on patients diagnosed with insulin-dependent type 1 or 2 DM, treated at a reference university hospital, in Belém/Pará. It was found that there was a reduction in weight and BMI until the fourth consultation, with an increase in the fifth. Average fasting glucose and total cholesterol decreased significantly throughout the consultations. However, systolic blood pressure and TGO levels showed variations, highlighting the need for continuous monitoring. This study demonstrated the benefits of technology in helping glycemic control in insulin-dependent patients.

Optical interferometry implementation considerations for sinusoidal low frequency accelerometer measurements and calibrations

Vibration metrology utilizing sine excitation stimulus, based either on fringe counting or minimum point methods, employs Michelson interferometers for the calibration of accelerometers. The former method combines the optical reference and measurements arms from a static mirror and reflected by the accelerometer respectively which are then directed to a suitable photodiode for processing. The stroking motion due to the oscillation of the accelerometer will contribute to the misalignment of the two optical paths and there are three methods to compensate for this. The first requires the mounting of a mirror on the accelerometer itself and the effect of the added mass from the mirror can be accounted for and corrected. The second requires the mounting of three mirrors on the mounting surface of the accelerometer but this requires the alignment of the Michelson interferometer for each mirror. The third is to use two retro reflectors so that the returning measurement beams are always along the same path with the reference; however, this requires the alignment of the interferometer for each retro-reflector. This paper will discuss such considerations on the optical systems, their components and associated instrumentation and discuss the design of an alternative approach that can overcome certain limitations.

Distribution of Immunomodulation, Protection and Regeneration Factors in Cleft-Affected Bone and Cartilage.

Craniofacial clefts can form a significant defect within bone and cartilage, which can negatively affect tissue homeostasis and the remodeling process. Multiple proteins can affect supportive tissue growth, while also regulating local immune response and tissue protection. Some of these factors, like galectin-10 (Gal-10), nuclear factor kappa-light-chain-enhancer of activated B cells protein 65 (NF-κB p65), heat shock protein 60 (HSP60) and 70 (HSP70) and cathelicidin (LL-37), have not been well studied in cleft-affected supportive tissue, while more known tissue regeneration regulators like type I collagen (Col-I) and bone morphogenetic proteins 2 and 4 (BMP-2/4) have not been assessed jointly with immunomodulation and protective proteins. Information about the presence and interaction of these proteins in cleft-affected supportive tissue could be helpful in developing biomaterials and improving cleft treatment. Two control groups and two cleft patient groups for bone tissue and cartilage, respectively, were organized with five patients in each group. Immunohistochemistry with the semiquantitative counting method was implemented to determine Gal-10-, NF-κB p65-, HSP60-, HSP70-, LL-37-, Col-I- and BMP-2/4-positive cells within the tissue. Factor-positive cells were identified in each study group. Multiple statistically significant correlations were identified. A significant increase in HSP70-positive chondrocytes in cleft patients could indicate that HSP70 might be reacting to stressors caused by the local tissue defect. A significant increase in Col-I-positive osteocytes in cleft patients might indicate increased bone remodeling and osteocyte activity due to the presence of a cleft. Correlations between factors indicate notable differences in molecular interactions within each group.

Tendon fatigue analysis of tension leg platform using a new time-domain quasi-dynamic method

Due to the non-Gaussian characteristics of the tendon tension response of tension leg platform (TLP), the common statistical model is not adequate to estimate the fatigue damage accurately in the frequency domain, thus the time domain analysis is required. A new quasi-dynamic method is proposed to calculate the tendon tension response considering various factors. An equivalent analytical tendon model is employed and the post-treatment has been carried out for the reconstruction of the high-frequency stress response based on the vertical motion QTFs of each tendon by HydroSTAR. Forty sets of environmental cases are extracted from a scatter diagram. The rainflow counting method is applied to deal with the stress time series and the fatigue damage can be obtained combined with the relevant S-N curve. The number of cycles of tension for each tendon is discussed, considering the contribution of the first-order wave load, the second-order diff-frequency wave load, and the second-order sum-frequency wave load. The comparison of annual fatigue damage based on the quasi-dynamic method and coupled method by Orcaflex is carried out. The study proves that the quasi-dynamic method is an efficient way to estimate the fatigue life of each tendon in the time domain considering numerous cases of discrete sea states.

A-147 Comparative Precision of Platelet Counting by Optical, Fluorescent, and Impedance Techniques in Thrombocytopenia and Microcytosis Patients

Abstract Background In today's era of automation driven by Artificial Intelligence, even the most efficient automated hematology analyzer may struggle to accurately determine platelet count in patients with low platelet count and microcytosis. The Sysmex XN-series analyzer (Sysmex, Kobe, Japan) uses optical density, fluorescence, and electronic impedance methods for platelet counting. This study aimed to evaluate the accuracy of optical, fluorescence, and electrical impedance methods in estimating precise platelet counts in thrombocytopenic and microcytic patient samples. Methods A cross-sectional study was conducted in the Department of Laboratory Medicine at AIIMS, New Delhi, for 30 days consecutively. A total of 200 blood samples collected, of which 100 samples were of thrombocytopenia (&amp;lt;100x10^9/L) and 100 samples were of microcytosis (Mean corpuscular volume &amp;lt;76 femtoliters). MCV&amp;lt;76 fl was further categorized into 2 groups- MCV 60-70 fl and MCV 71-76 fl accordingly. Males and females were categorized and analyzed distinctly for thrombocytopenia and microcytosis. Statistical analysis was performed using SPSS 16.0 for Windows (SPSS Inc., Chicago, IL, USA) and the Bland Altman plot was analyzed. Results Of the 100 patients with thrombocytopenia (platelet&amp;lt;1 lakh/L) females were predominantly 54% while males were 46% and the median age was 29 years (0.02-70). Of the 100 patients with low MCV (&amp;lt;76 fl) females were 53%, while males were 47% and the median age was 29 years (0.3-69). The findings were compared against the standard manual platelet count method in peripheral smears verified by two expert pathologists. The Bland Altman plot for Platelet &amp;lt;1 lakh/L and MCV &amp;lt;76 fl were compared with standard manual platelet count method and the following findings were obtained i) In female with PLT- &amp;lt;1 lakh/L, PLT-O showed least bias (9.8) followed by PLT-F (10.4) and PLT-I (12.1) ii) In males with PLT-&amp;lt;1 lakh/L, PLT-O showed least bias(14.1) followed by PLT-F(16.5) and PLT-I(17.0) iii) For MCV 61-70fl, PLT-O showed the least bias (4.4) followed by PLT-F (6.7) and PLT-I (28.5) iv)For MCV 71-76fl, PLT-F showed least bias (-0.2) followed by PLT-O (1.5) and PLT-I(31.2). Conclusions Platelet count by optical method (PLT-O) detects platelet count more accurately with minimum bias when compared with PLT-F and PLT-I in patients with thrombocytopenia. In patients with microcytosis, PLT-I is less reliable in comparison to PLT-O and PLT-F. A limitation of the automated hematology analyzer is the inability to detect platelet clumps which is appreciated in peripheral smears.

Effect of pH on the microstructure and antibacterial properties of MgO nanoparticles by microwave-assisted solution combustion

The application of magnesium oxide (MgO) nanoparticles as a candidate material for antibacterial purposes has been limited by their relatively low antibacterial activity. In this study, antibacterial MgO nanoparticles were synthesised in one step using magnesium nitrate and citric acid as raw materials using an economical and environmentally friendly microwave-assisted solution combustion approach, while the effect of the pH of the reaction system (2, 4, and 7) on the structure and antimicrobial properties of the synthesised MgO nanoparticles was investigated. X-ray diffraction (XRD) analysis demonstrated the formation of cubic-phase MgO nanoparticles, and electron microscopy studies confirmed that MgO nanoparticles synthesised at a precursor solution pH of 4 had uniform size distribution with a small microcrystalline size (∼19 nm) and spherical granular morphology. In addition, the results of the plate colony counting method showed that the antibacterial rate of MgO-4 against Gram-negative Escherichia coli (106 CFU/mL) at a sample concentration of 100 μg/mL was as high as 99.86 %, which showed excellent antibacterial performance. These findings provide valuable insights into how to increase the degree of oxygen adsorption on the sample surface to participate in the reaction, as well as a potential method for constructing nanomaterials with high antimicrobial activity.

Developing microalgae harvesting: Enhanced efficiency with magnetite-urea nanocomposites for sustainable astaxanthin biorefinery

Harvesting costs, accounting for 15–30 % of total expenses, are a major bottleneck in microalgae biorefinery due to low cell density and energy-intensive processes. This study investigates Fe3O4-Urea (Fe@urea) nanocomposites (NCs) to replace conventional methods and improve the harvesting efficiency (HE) of Chlorella zofingiensis, known for its high lipid and astaxanthin content. Urea-coated magnetite nanoparticles (NPs) with additional functional groups significantly improved harvesting performance across all pH levels compared to conventional Fe3O4 NPs. HE was evaluated using both optical density and cell counting methods, with the latter proving superior results by eliminating interference from unreacted salts and debris. Optimization of nanocomposite concentrations and culture pH with Fe@urea achieves 95 % HE at 800 mg L−1 and pH 3–4. The Langmuir and Freundlich models confirmed the nanocomposite's multifaceted capabilities. Characterization techniques included SEM, FTIR, EDX, zeta potential analysis, and TEM. This innovative approach can revolutionize large-scale microalgae biorefineries, enhancing sustainable biofuel and bioproduct production and supporting the Sustainable Development Goal of Affordable and Clean Energy (SDGs 7).

Vegetation productivity determines the response of butterflies along elevation gradients in the trans‐Himalayas, Nepal

AbstractEnvironmental parameters along elevational gradients affect the number of butterflies and the variety of species. However, which variables play significant roles and how they operate can be difficult to untangle. Here, we examine the relationships between observed butterfly richness (overall and subgroups) at different elevation gradients and remotely sensed environmental variables (vegetation productivity, surface temperature, landscape heterogeneity, and moisture stress) using generalized linear models. We surveyed butterflies with a fixed‐point count method in 19 elevation bands within 1600–5200 m above sea level in Manang district, trans‐Himalayan region, north‐central Nepal. The number of butterflies in each elevation band was studied and estimated, then interpolated across the lowest and highest elevation to estimate butterfly species richness. Then, the selection of models was performed on butterfly richness and elevations to test the best model support based on the lowest value of the Akaike information criterion and a multimodel averaging for other environmental variables. Altogether, 94 butterfly species, representing 20 subfamilies and six families, were recorded throughout the study periods. We obtained cubic model support for overall species richness, Papilionidae, and Hesperiidae, quadratic to Nymphalidae and Pieridae, and the linear model to Lycaenidae. In our study, vegetation productivity was found to have a significant positive impact on butterfly communities. Our study further suggests species richness of Papilionidae and Hesperiidae has a strong positive correlation with surface temperature and landscape heterogeneity and negative associations with moisture stress but other subgroups of butterfly communities including overall species richness showed insignificant relationships with these variables. This study provides significant information related to the responses of montane butterflies to environmental variables along elevational gradients from the Himalayas Nepal. However, further detailed studies on the functional behaviors of butterflies potentially offer more insights into their distribution patterns and ecological relationship in the montane environment.