A document by James Oliver. Click on the document to view its contents.

This lab report explores adaptive signal processing techniques for analyzing multivariate signals. The study evaluates empirical and waveletbased methods, investigating their efficiency in extracting meaningful information. The results are supported by MATLAB implementations, demonstrating the practical applications of these methods.

Populations distributed across anthropogenic disturbance gradients can express different phenotypes adapted to distinct habitats. Previous studies have focused on the role of gene expression linking phenotypic and genetic variation in wild populations. However, recent studies have revealed the critical role of post-transcriptional processes such as alternative splicing in generating phenotypic variation in response to modified environments. Knowledge of alternative splicing should provide more comprehensive insights into understanding population responses to environmental change. Here, we examined gene expression and alternative splicing patterns in the wood frog, an amphibian known for strong phenotypic divergence in response to road-adjacency and runoff pollution. Roadside populations show locally maladaptive traits as embryos and larvae but adaptive traits as adults, suggesting potential tradeoffs across life history in response to road effects. Here, we found strong differences in gene expression and alternative splicing patterns between hatchlings and adults, along with population specific patterns. However, only two differentially expressed genes (Hsp70 and GPSM2) showed repeated divergence between roadside populations and populations located away from roads. These results suggest that while local populations and life history stages can diverge substantially in both gene expression and alternative splicing, a low degree of transcriptomic parallelism underlies (mal)adaptation to roads and pollution.

Silicon (Si) is a candidate anode for high-energy-density lithium-ion batteries due to its higher theoretical capacity (4200 mAh g-1) than graphite (372 mAh g-1). However, Si suffers rapid capacity fading owing to large volume changes and excessive solid electrolyte interphase (SEI) layer during repeated lithiation/delithiation. The design of the binder plays an important role in preventing such volume changes over a long cycle life. This study uses a crosslinked polymer composed of poly(acrylic acid) (PAA) and boric acid (BA) with the electron-deficient boron with the remaining hydroxyl groups (PBH) as a Si-based anode binder. The crosslinked PBH binder provides a robust electrode with improved adhesive strength at an optimized ratio, which effectively fixes Si particles and forms a LiF-rich SEI that can promote rigidity and ion conduction while suppressing the volume expansion of Si. The Si anode with the PBH binder achieves a high capacity of 2275 mAh g-1 after 150 cycles, with an average Coulombic efficiency of 98.8% and high-capacity retention of 88.85%. A NCM622||Si cell is shown to withstand 100 cycles and exhibit improved retention. The crosslinked polymer derived from electron-deficient components is a promising Si binder for high-energy and high-stability lithium-ion batteries.

Background: Meconium aspiration syndrome (MAS) is a severe respiratory disease in neonates, and the timing of the application of pulmonary surfactant (PS) is still controversial. Lung ultrasound (LUS), as a non-invasive, highly sensitive and specific imaging technique, may provide a basis for the precise use of PS. Methods: This study was a prospective observational study, and 218 MAS infants from two tertiary neonatal rescue centers were included. The LUS score (total score of 18 points, divided into 6 zones) was compared with traditional indicators (oxygenation index, oxygen concentration, etc.), and the ROC curve was used to analyze the predictive efficiency. Results: When the LUS score threshold was 5.5 points (actually 6 points), the AUC for predicting the timing of PS use was 0.9886 (95% CI: 0.9967-1), with a sensitivity of 99% and a specificity of 98%, which was significantly better than the oxygenation index (AUC=0.9673), oxygen concentration (AUC=0.9527) and pH value (AUC=0.9062). LUS is non-invasive and repeatable, and can reduce complications (such as PPHN) and hospital stay. Conclusion: LUS score ≥ 6 is a reliable indicator for initiating PS treatment in children with MAS, which can optimize the timing of treatment and improve clinical prognosis.

Teresa Silverthorn1* (ORCID: 0000-0001-6152-6573), Joachim Audet2 (ORCID: 0000-0001-5839-8793), Chris D. Evans3 (ORCID: 0000-0002-7052-354X), Judith van der Knaap4, Sarian Kosten4, José Paranaíba4 (ORCID: 0000-0003-0081-1295), Quinten Struik4 (ORCID: 0000-0002-7404-6963), Jackie Webb5,6 (ORCID: 0000-0003-1274-363X), Wenxin Wu7 (ORCID: 0009-0004-5551-2801), Zhifeng Yan7, Mike Peacock1,8 (ORCID: 0000-0002-3086-2854)

Aiming at the contradiction between the narrow switching frequency operating range and the wide output voltage range of the traditional LLC resonant converter, a three-switch LLC resonant converter with a variable structure type and wide output voltage range and its control method are proposed. The converter adopts three switching tubes in the primary inverter circuit, which can realise the independent or joint operation of two transformers. Four different operating modes can be realised by controlling the ON and OFF of the primary-side switching tubes and the secondary-side auxiliary switches, which shortens the operating range of the switching frequency and widens the voltage gain range, and meets the requirements of wide output voltage range applications. Since the converter always works in the weak inductive region, it can achieve zero-voltage turn-on of the primary switch and zero-current turn-off of the secondary diode over the whole range, which has good soft-switching performance. This paper analyses the working principle, voltage characteristics and parameter design of the converter. Finally, a 1kW experimental prototype is fabricated based on the operating principle of the converter, and the experimental results show that the converter meets the requirements of wide output voltage range applications in all aspects.

In order to improve text processing, this study introduces a multilingual AI-powered writing assistant with four main features: text production with translation, text summarisation, text paraphrasing, and grammatical correction. The grammar correction tool improves grammatical accuracy while maintaining writing style and supporting many languages. The LLaMA-implemented text summarisation function allows for organised summaries in a variety of formats (paragraphs, bullet points) and lengths (short, medium, and long). The text paraphrase feature uses DeepSeek to provide rewordings that are both styleadaptive and contextually appropriate. Using Microsoft's API, the text generation with translation feature first converts content into English before applying DeepSeek to enhance its coherence and fluency. This hybrid technique is a useful tool for multilingual communication and content creation since it guarantees high-quality text processing across a variety of languages.

Businesses have invested billions into artificial intelligence (AI) applications, leading to a sharp rise in the number of AI applications being released to customers. Taking into account previous approaches to attacking machine learning models, this article aims ‘to devise a practical means of benchmarking performance in more realistic scenarios.’[9] We conduct a comparative analysis of adversarial attacks, contrasting large language models (LLMs) being deployed through application programming interfaces (APIs) with the same attacks against locally deployed models. The article puts forward adversarial attacks that are adapted for remote model endpoints in order to create a threat model that can be used by security organisations to prioritise controls when deploying AI systems through APIs. This paper contributes: 1) a public repository of adversarial attacks adapted to handle remote models on https://github.com/l3ra/adversarial-ai, 2) benchmarking results of remote attacks comparing effectiveness of attacks on remote models with those on local models, and 3) a framework for assessing future AI system deployment controls. By providing a practical framework for benchmarking the security of remote AI systems, this study contributes to the understanding of adversarial attacks in the context of natural language processing models deployed by production applications.

A document by Kapil Khanal. Click on the document to view its contents.

Rheumatoid arthritis (RA) is a chronic, symmetrical, aggressive autoimmune disease involving multiple body joints. This study calculated the proportions of cell clusters, and 657 DEGs in CD16+ monocyte clusters were identified according to proportion and immune score. The interaction intensity of CD8+T cell clusters in RA was elevated, with strong interaction. The output signals of platelets, monocytes, and NK cells in RA were higher, as were the incoming signals of CD8+T cells. According to the incoming and outgoing cells, 10 ligand-receptor pairs were highly expressed in RA. Then, the association analysis of DEGs and differentially methylated genes (DMGs) was conducted, and four key genes of IFI44L (cg05696877), ISG15 (cg20062691), TAP1 (cg16853860), and TNFSF10 (cg01059398) were found. The expression analysis of these four genes in the validation set, ROC curves of the training and validation sets, and gene correlation analysis revealed an essential role in RA pathogenesis. The immune gene TNFSF10 had an AUC value > 0.9 in both the training and validation sets, indicating significant diagnostic ability. Finally, the TNFSF10 analysis in pan-cancer indicated a close relationship with the expression of various tumor patients and the degree of immune cell invasion. Therefore, this gene may be a potential target for human diseases, such as RA and tumors.

The Chikungunya virus, a mosquito-borne alphavirus, continues to pose a significant public health challenge with its capacity to cause widespread outbreaks. In 2023, Senegal experienced a major outbreak, initially emerging in the Kedougou region and subsequently spreading to Tambacounda. This study aimed to monitor the molecular evolution of Chikungunya virus using real-time genomic surveillance during the epidemic progression. A total of fourteen human samples blood collected from Chikungunya suspected cases in Tambacounda were analyzed. Whole genome sequencing revealed that the outbreak strain belonged to the West African genotype, closely related to strains from the Kedougou 2023 outbreak. Phylogenetic analysis highlighted three distinct genetic groups within the 2023 strains, with notable genetic variations between the Kedougou and Tambacounda clusters. Specifically, two amino acid mutations, A13T in the non-structural protein 1 (nsP1) and K270R in the structural E2 glycoprotein, were identified in the Tambacounda strains, suggesting potential adaptive changes. Our findings demonstrate the rapid evolution of Chikungunya virus in eastern Senegal, emphasizing the necessity for continuous genomic surveillance to detect emerging variants and understand their implications for virus transmissibility and virulence. This study underscores the critical role of genomic analysis in managing viral outbreaks and informing public health strategies.

The emergence of unexpected incidents in electrical power systems (EPSs) has led to partial disruptions of the national electricity grid, necessitating the deployment of emergency diesel generators (EDGs) to restore power supply to affected areas. This study aims to evaluate the operational effectiveness of three distinct EDG power plants associated with an oil facility and a nearby gas platform during power outages, specifically focusing on scenarios that involve starting power transformers (PTs) and induction motors (IMs). Through the application of technical specifications provided by Stamford Company and the utilization of the EMTP-RV software tool, the EDGs, featuring identical excitation systems and governor parameters, were systematically modeled and analyzed for comparative purposes. The results of this comparative investigation are anticipated to yield significant cost reductions for power plant operations while enhancing overall system safety.

South American savannas are a disjunct biome with an unclear evolutionary history. We tested hypotheses about their Quaternary history and evolution of savanna cores through fragmentation or dispersal from the Cerrado. We used genomic data (genotyping-by-sequencing) and ecological niche models of the Burnished-buff Tanager (Stilpnia cayana Linnaeus 1766) to evaluate intraspecific differentiation, gene flow, past range shifts, and landscape-genomics association. We found clear genomic differences between populations on each side of the Amazon basin and high admixture in the Marajó Island and Bolivia. Landscape genomics analysis indicated that the Amazon River, isolation by distance and temperature predict genomic differentiation in this bird. Taken together, the results suggest that a combination of dispersal from the Cerrado, isolation due to geographic distance and the Amazon River basin, and local adaptation shaped the species diversification. We propose considering three subspecies of S. cayana as full species, with one possibly of hybrid origin (S. huberi).

Migrating pain of extremities as the first symptom of two children with acute leukemia

Title PageTitle: An unusual case of ileitis and partial small bowel obstruction secondary to mesh erosion after totally extraperitoneal inguinal hernia repairAuthors: Pranesh de Silva MChD1,2*, Joseph Do Woong Choi FRACS1,3*, Craig Lynch FRACS1,2, Stephen Pillinger FRACS1,2, Saurabh Gupta FRACP1,2, Praveen Ravindran FRACS1,2Affiliations: 1Sydney Adventist Hospital, Wahroonga, New South Wales, Australia; 2Australian National University College of Health and Medicine, Canberra, Australian Capital Territory, Australia; 3The University of Sydney Faculty of Medicine and Health, Sydney, New South Wales, Australia;* PD and JC are joint first authors with equal contribution.Corresponding Author: Dr Joseph Do Woong Choi; E: joseph.choi@sydney.edu.au; Ph: +61 9480 9111Author declarations: All authors made substantial contributions to the conception or design of the work; or the acquisition of data; or the creation of new software used in the work; All authors drafted the work or revised it critically for important intellectual content; All authors approved the version to be published; All authors agreed to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.Statements and Declarations: There are no conflict of interest, or declarations to report. The patient has provided consent to publish this case report.Funding declaration: The authors do not have any funding sources to declare for this manuscript.

The structural strength of the levitation frame is of critical importance for the safe operation of medium and low-speed maglev vehicles. However, the diverse load conditions in the engineering application environment and the current lack of relevant test and evaluation standards pose greater challenges to its reliability and safety. Accordingly, this paper combined classical numerical and dynamic modeling to obtain load boundaries. Subsequently, a full-factor finite element model was established and a full-scale test bench was built. The fourth strength theory and the modified Goodman-Smith fatigue limit diagram are used to systematically evaluate the structural strength and mark the weak points. Finally, a fatigue test with enhanced cyclic loading of 1×10 7 sinusoidal alternating loads is carried out, and the DPT-5 penetrant inspection agent is used for overall flaw detection. The research results show that: (1) the structural strength of the lightweight-designed levitation frame meets the evaluation requirements; (2) the weak points mostly appear at the welds, rib plates and corners of the longitudinal beams, arms and anti-rolling beams under multi-directional loads; (3) no fatigue cracks are found in the overall structure and each weak point after each stage of the tests by penetrant inspection.

Blood collection in mice is a common procedure in biomedical research. The choice of blood collection method and the need for analgesia and/or anaesthesia depend on multiple factors, including the experimental setup, animal welfare considerations, and the intended downstream analyses. This minireview describes key non-surgical and surgical blood collection techniques, the appropriate use of analgesia and anaesthesia, and best practices for documentation and adherence to reporting standards in animal studies. We here provide a table summarizing collection procedures, a table listing animal welfare guidelines from multiple countries, a table describing the most common analgesics and anaesthetics, doses, and route of administration, as well as a table outlining key points for reporting blood collection, anaesthesia, and analgesia protocols. A decision chart is also included to assist in selecting the most suitable method. Ultimately, with this minireview, we aim to promote standardized practices, improve data reproducibility, and support ethical animal research.

Background and Purpose: Osteosarcoma presents significant clinical challenges due to its aggressive nature and propensity for metastasis, prompting the investigation of alternative treatment strategies. CBD has demonstrated potential in cancer treatment. This study aims to elucidate the effect of CBD on the malignant phenotype of osteosarcoma and to investigate its molecular mechanisms. Experimental Approach: The CCK-8 assay was employed to identify the effective concentration of CBD, and its impact on the malignant phenotype of osteosarcoma cells was investigated using EdU proliferation assays, Transwell experiments, and scratch assays. A mouse xenograft tumor model was established for in vivo observation. Utilizing network pharmacology and RNA-seq data, key pathways and targets were predicted and subsequently validated through ELISA, RT-qPCR, and Western blot techniques. To ascertain the targets of CBD action, molecular docking, CETSA, and SPR experiments were subsequently conducted. Key Results: CBD significantly inhibits the activity and proliferation, migration, and invasion capabilities of osteosarcoma cells, and suppresses the growth of transplanted tumors in nude mice in vivo. CBD is involved in the regulation of inflammation and the NF-κB signaling pathway in osteosarcoma cells. p65 and CCL5 form a positive feedback loop, and CBD directly targets p65, effectively disrupting the transcriptional activation of the NF-κB signaling pathway mediated by TNF-α. Conclusion and Implications: Our findings for the first time indicate that CBD inhibits the malignant progression of osteosarcoma by targeting and regulating the TNF-α/NF-κB/CCL5 chemokine axis, disrupting its coordinated inflammatory cascade. This highlights CBD’s potential as a novel therapeutic agent for osteosarcoma.

Internet of things has marked its dominance in multiple domains. One cannot deny their heavy influence and reliance on our day-to-day lives. From smart home gadgets to big sensor devices capable of automating industrial processes, this technology is widely growing and revolutionizing all around the globe. However, their wide popularity has exposed them to potential cyber threats. The major attributes of these devices which make them highly vulnerable to attacks are their easy availability, elementary configuring steps, less secure frameworks etc. In this paper, an in-depth analysis of novel and comprehensive dataset i.e. Edge IIoT dataset has been done. Data generation methods, tools for executing attacks, devices used for creating IoT testbed are consciously observed. Stacked LSTM which is a prominent deep Learning algorithm have been employed for both binary and multiclass classification. It has been observed that DL algorithms have significantly performed better in field of IoTs attack detection and presented good results with an overall accuracy of 100% and 91.6% for binary and multiclassification respectively.

Picocyanobacteria of the genus Synechococcus are a widespread component of marine planktonic communities and are seasonally dominant in the Baltic Sea. It has been shown that light, salinity, temperature, nutrients and grazing influence the niche segregation of Synechococcus. It is known that many Synechococcus strains have allelopathic properties, but the role of allelopathy remains unknown. Recent studies in two-species systems have provided experimental evidence for increased diversity at intermediate levels of allelopathy, while extreme levels (low or high) led to reduced diversity. Here we studied the role of Synechococcus allelopathy in structuring coexisting phytoplankton communities. We brought natural phytoplankton communities from the Baltic Sea to equilibrium in semi-continuous cultures under nitrate limitation. These communities were inoculated with two Synechococcus strains from the Baltic Sea (BA-124; BA-132) known to have allelopathic properties, with different inoculum sizes: low, medium and high, as a proxy of the strength of allelopathy. According to classical resource competition theory, the dominant species at equilibrium (and therefore community diversity) should be the same irrespective of the initial inoculum of any species. Our equilibrium communities were dominated by 2-4 genera, with diatoms predominating at all Synechococcus inoculum levels. Community diversity was significantly higher only at the medium size of Synechococcus inoculum, with strain BA-124. This was the expected effect of allelopathy, according to previous works in two-species systems. There is no other known factor that could produce such an effect considering our experimental design. Therefore, we state that allelopathy is the best hypothesis to explain these results. In strain BA-132, the allelopathic effect appeared to be weaker and had no effect on community diversity. These results are the first to show the influence of allelopathy on the structure of relatively complex phytoplankton communities, suggesting a new driving factor of phytoplankton succession in the Baltic Sea.

Rapid warming has considerably impacted fish populations, with responses influenced by traits related to migration and metabolism. However, previous studies on the effects of warming often struggled to isolate warming-specific impacts due to challenges in directly comparing affected and unaffected areas. Investigating the effects of rapid warming caused by nuclear power plants provides a unique and valuable opportunity to address this limitation. In this study, we focused on fish populations at two coastal sites in Japan: one affected by thermal discharge from a nuclear power plant, and the other serving as a control site. We compared fish populations between these two sites. We analyzed changes in abundance and body size, identifying traits that influenced these responses. The results revealed that 18% of species showed significant changes in abundance, while 40% exhibited significant changes in body size. Using a generalized linear regression model, we found that traits such as migratory behavior, trophic level, latitudinal distribution, and average body size were associated with these changes. Migratory and tropical species, in particular, were more likely to increase in both abundance and body size under warming conditions. These results suggest that warming enabled migratory and tropical species to overwinter and establish populations at the warming site. To better understand changes in fish communities driven by global warming along the Japanese coast, monitoring a broad range of species, as demonstrated in this study, would be highly effective. Additionally, focusing on traits related to migration and metabolism can help identify species particularly vulnerable to warming.

The purchase and sale of agricultural tractors are strongly influenced by supply and demand dynamics, making accurate forecasting essential for optimising these transactions. This study presents a novel hybrid methodology that integrates Long Short-Term Memory (LSTM) neural networks with Teaching-Learning-Based Optimisation (TLBO) to enhance decision-making in tractor sales forecasting. The proposed approach leverages TLBO to dynamically optimise critical LSTM hyperparameters, such as the number of layers, neurons, and learning rate, thereby reducing manual intervention and improving model adaptability. Using time series data from January 1961 to December 2020, the hybrid model was trained and evaluated, achieving significant improvements in predictive accuracy compared to standalone LSTM models. The results highlight the potential of this methodology for complex forecasting tasks, providing a scalable framework for decision-making in agricultural markets. Additionally, the versatility of the proposed approach makes it applicable to various domains requiring precise time series predictions.

A document by Santosh Pokhrel. Click on the document to view its contents.