Background: Equine obesity is a growing concern, much of the current management advice centres on dietary restrictions, including the removal or limitation of grazing. Little is known about the effectiveness and impact of this approach on the overall welfare of the horse. Objective: This study investigates the impact of two commonly used grazing systems advocated for the control of weight – the ‘strip-grazing’ system and the ‘track’ system - on the behaviour and welfare of outdoor living ponies. Study design: A within-subject cross-over experimental design was used with four groups of pasture kept ponies experiencing each system for 4 weeks in a random order. Methods: Time budgets and behavioural indicators of welfare were measured using 24-hour electronic surveillance, morphometric parameters including weight, body condition score and cresty neck score were measured weekly and activity levels were tracked using GPS tracking units. Results: Ponies moved more (median (IQR), track: 3.23% (2.08%), strip: 2.02% (0.90%); P = 0.001) and travelled a greater distance in 24-hour period (median (IQR), track: 7013.47m (1761.49m), strip: 5331.91m (494.16m); P < 0.001) and engaged in less overt agonistic behaviour on the track system compared with the strip system (median IQR; track: 0.14 (0.30), strip: 0.21 (0.37) P = 0.02). Main Limitations: A relatively short time period of exposure to each grazing system. Conclusions: Track systems resulted in a significant increase in ambulatory behaviour compared with the strip system. Increased levels of agonistic interactions on the strip system maybe the result of perceived reduction of space or a spatial concentration of resources compared to the track system, although the actual accessible area was the same. These results suggest that there may be physical health benefits to the track system as the ponies engaged in more voluntary low intensity exercise as well as improvements to overall welfare.

A document by rawan azzam. Click on the document to view its contents.

Fast diagnostic methods are crucial to reduce the burden on healthcare systems. Currently, detection of diabetes complications such as neuropathy requires time-consuming approaches to observe the correlated red blood cells (RBCs) morphological changes. To tackle this issue, an optical analysis of RBCs in air was conducted in the 250-2500 nm range. The distinct oscillations present in the scattered and direct transmittance spectra have been analyzed with both Mie theory and anomalous diffraction approximation. The results provide information about the swelling at the ends of RBCs and directly relate the optical data to RBCs morphology and deformability. Both models agree on a reduction in the size and deformability of RBCs in diabetic patients, thus opening the way to diabetes diagnosis and disease progression assessment.

Note:  LSPR energy is sensitive to the dielectric performance of the material and the surrounding environment of  nano supercapacitors  , shape and size of nanoparticles. That is, if a ligand such as a protein  is attached to the surface of metal nanoparticles, its LSPR energy changes. Similarly, LSPR effects are sensitive to other changes in  nanosupercapacitors,  such as the spacing between nanoparticles, which  can be altered by the presence of surfactants or ions.One of the consequences of the LSPR effect in metal nanoparticles of nano  supercapacitors is the ability to absorb visible waves due to the coherent oscillations of plasmons. In  nano supercapacitors,  colloids of metal nanoparticles such as silver or gold can produce colors such as red, purple or orange. show that  cannot be seen in normal dimensions. This color change depends on the shape, size and surrounding environment of silver nanoparticles in  nano supercapacitors . In the structure of  nano supercapacitors,  one of the nano properties that distinguish metal nanoparticles from these large-scale materials is their optical properties. This is due to the  localized surface plasmon resonance. In simpler terms, when light hits metal surfaces of any size, some light waves travel along the metal surface. By creating surface plasmon, these waves actually give part of their energy to surface electrons and cause them to vibrate (scatter)   . When plasmons are generated in bulk metals, electrons can move freely through the material without recording any traces. In  nanoparticles, the surface plasmon is placed in a limited space, so that the electrons  oscillate back and forth in this small space and in the same direction. This effect is called Localized Surface Plasmon Resonance (LSPR), when the frequency of these oscillations is the same as the frequency of the light causing  the plasmon, it is said that the plasmon is in resonance with the light.

Note: Electrostatic nanocapacitors   also benefit from a very short distance between their electrodes, and  electrostatic nanocapacitors  are unique in this respect. If the electrodes are far apart, like charges on their surface strongly repel each other. When the electrodes are placed closer together, the negative and positive charges on either side balance these repulsive forces, and more total charge can be stored in a given area.The total thickness of each  electrostatic nanocapacitor  is only 25 nm, and many times they can be close together. So far, arrays of  electrostatic nanocapacitors  cannot store much total energy because they are too small. Electrostatic nanocapacitors  contain billions of nanocapacitors to store large amounts of energy.  Scaling up to a practical level is not trivial, but the pair work together to create larger arrays.

The kT/C cancellation technique can effectively reduce the DAC size for SAR ADCs and thus relax the burden for input drivers and ref-erence buffers. However, the prior kT/C cancellation technique suffers from a hard trade-off between the noise, amplifier bandwidth and linearity. In this work, an improved kT/C noise cancellation technique is proposed to break the trade-off. It uses presampling to hold the input signal unchanged during the noise cancelling phase, leading to sig-nificantly relaxed requirements on the bandwidth and linearity of the amplifier. The proposed technique is verified in a 14-bit 200MS/s SAR ADC with the DAC size of 128 fF. Simulation results show that this work achieves 75.6 dB SNDR and consumes 1.56 mW power.

The Acoustic Complexity Index (ACI) is one of the most used metrics in ecoacoustics, demonstrating reliability across a broad range of environments and ecological conditions. However, this index requires specific procedures to be applied in the correct way. Based on the “Canberra metric,” the ACI is an unsupervised metric formulated to extract information from fast Fourier transform (FFT) sonic matrices. The ACI measures contiguous differences in acoustic energy of each frequency bin i along temporal steps (ACItf) and of a temporal interval j along the frequency bins (ACIft). The aggregation of data after an FFT using a clumping procedure allows for better scaling of the sonic signals before the computation of the ACI. The application of a filter to reduce the effects of non-environmental signals produced by microphone electrical noise is mandatory to avoid masking effects. Due to a singularity of the index for values equal to 0, ACIs require ad hoc procedures to exclude from the comparisons pairs of elements of which one is equal to 0. The spectral sonic signature and temporal sonic signature are vectors obtained from the sequence of ACItf and ACIft values, respectively. The comparison between sonic signatures using the chord distance index returns spectral and temporal sonic dissimilarities that allow the evaluation of sonic patterns emerging at different temporal and spatial resolutions. The number of frequency bins, sonic variability, and sonic evenness are further derivative metrics that help to interpret sonic heterogeneity by distinguishing the temporal and spatial heterogeneity of sonoscapes. A change of the name of the “Acoustic Complexity Index” to the “Sonic Heterogeneity Index” is recommended.

Aim: The British Pharmacological Society and UK Medical Schools Council Prescription Safety Assessment (BPS/MSC PSA) is an electronic platform developed for assessing the prescription skills of medical students. Our aim was to investigate the feasibility of the PSA in addressing prescribing competencies among junior doctors in a hospital setting. Methods: The Department of Clinical Pharmacology at Odense University Hospital (OUH) established a Danish translated program using the BPS/MSC PSA platform. We launched a formal three year program in 2021, potentially assessing all first-year doctors at OUH and Esbjerg Regional Hospital. Participation was mandatory, followed by a survey (not mandatory). Results: During the period of 2021 to 2023 n= 364 doctors were invited, from which n=246 participated. The compliance rate increased from 38% in 2021 to 88% in 2023. The mean assessment score (points normalized to percentage) across n=246 participants was 71%, and 94% achieved a score of at least 50%. The survey was distributed to n=402 with 90 responses (22%) from which n=71 completed the questionnaire. Of these, 64% stated that the purpose with the assessment was clear. The two questions about difficulty level and the number of questions had similar evaluations. The majority of respondents found that the questions were of clinical relevance. Conclusion: It is feasible to translate and implement the PSA in a Danish hospital setting. The program provides insight into the prescribing competencies of junior doctors and the participants are generally positive.

With increasingly large genomic datasets, even routine bioinformatic tasks can be arduous, computationally demanding, and time-consuming. Additionally, most bioinformatic programs do not have a graphical user interface (GUI) and thus, require users to be minimally competent in command-line. These impediments present significant economic and technological barriers for practitioners who do not have access to advanced computational resources and support. In this issue of Molecular Ecology Resources, Handika and Esselstyn (2024) developed an ultrafast and memory-efficient bioinformatic tool, SEGUL, that performs common manipulations and calculations of summary statistics on phylogenomic datasets. SEGUL has two main features that distinguish it from other bioinformatic programs: (1) it is based on the recently emergent, high-performance programming language Rust, and (2) it has a GUI written using Flutter, a cross-platform programming framework that also supports mobile operating systems (mobile iOS, iPadOS, Android). By leveraging and combining the power of Rust and Flutter, SEGUL achieves significantly faster computation times and lower memory usage across different platforms and CPU architectures compared to similar programs. The unique and innovative features of SEGUL pave the way for a new era of bioinformatics that can be more energy-efficient, inclusive, and available to a broader swathe of users.

Ransomware has become one of the most persistent and damaging threats in the digital landscape, causing significant disruptions to organizations and individuals worldwide. The introduction of a novel ransomware detection methodology, which leverages cosine similarity of bytecode combined with advanced machine learning techniques, represents a significant advancement in the fight against this evolving menace. Unlike traditional detection methods that rely on static signatures or behavioral analysis, the proposed approach focuses on the structural characteristics of ransomware, enabling the detection of even the most obfuscated or previously unseen variants. Through rigorous experimentation and performance evaluation, the model demonstrated high accuracy, precision, recall, and F1scores across a diverse set of ransomware variants, validating its effectiveness and robustness. The study highlights the potential of integrating sophisticated similarity metrics with machine learning to create more adaptive and reliable ransomware detection systems, capable of addressing the dynamic and complex nature of modern cyber threats.

Cancer is a fatal genetic disease involving unregulated cell growth and proliferation with varying underlying complexities that requires carefully optimized treatment for a full cure. Cellular behaviors such as growth, proliferation, survival, or death rely upon the ability of cells to communicate with the surrounding system; therefore, effective targeting of dysregulated signaling pathways involving growth factors, regulatory proteins, cell adhesion molecules, and immune system molecules is critical to cancer therapy. They are mainly driven by alterations in tumor suppressor genes and oncogenes that may vary among different cancer types. Importantly, patients with the same cancer type respond differently to available cancer treatments, likely due to tumor-specific DNA, RNA, and proteins, indicating the need for patient-specific treatment options. Precision oncology has evolved as a form of cancer therapy focused on genetic and molecular profiling of tumors to identify specific molecular alterations involved in carcinogenesis for tailored individualized cancer treatment. This article aims to briefly explain the foundations and frontiers of precision oncology, highlighting the cutting-edge innovations in tools and techniques associated with it to assess its scope and importance in achieving efficacious cancer treatment over time.  

No abstract provided as per author guidelines for Clinical Experience submissions.

Note: Since the operation of  nMOS transistors is based on an electric field resulting from the input gate voltage (the name field effect is for this reason), it makes the field effect transistor a voltage-based component. nMOS transistors  are a single-pole semiconductor device whose characteristics are very similar to the same bipolar transistor.Some of the features of this part are high efficiency, instantaneous operation, resistance and cheapness, which can be replaced in most electronic circuits with bipolar junction transistors and structurally similar (such as BJT).

Note: Since the FinFEET nanotransistors in  to achieve, a larger discharge current in the nanotransistor, the width of the channel according to the height as well as the thickness, which the layers of nano CNTs It keeps it in a smaller range.FinFEET nanotransistors are a field-effect nanotransistor metal-oxide-semiconductor ) that is on a substrate < a i=3> is made. that the gate is located on two, three or four sides of the channel or is wrapped. The channel forms a double gate structure. To these devices, the general name "finfets" given because the source/drain region forms fins on the silicon surface. FinFET devices compared to flat technology and using nanowires in the structure and (complementary metal oxide and semiconductor) < a i=8>have significantly faster switching and higher current density.

Note: In  nMOS transistors,   current amplification varies depending on the direction of the electric field and responds to electric fields of different sizes.  This results in useful electronic behavior depending on how the voltage  (or electronic field  ) is applied, which in an nMOS transistor is called (bias).nMOS transistors use a voltage to apply to the input terminal, which is called the gate, and the current passing through it is proportional to this voltage.Since the operation of  nMOS transistors   is based on an electric field resulting from the input gate voltage (the name field effect is for this reason), it makes the field effect transistor a voltage-based component. nMOS transistors  are a single-pole semiconductor device whose characteristics are very similar to the same bipolar transistor. Some of the features of this part are high efficiency, instantaneous operation, resistance and cheapness, which can be replaced in most electronic circuits with bipolar junction transistors and structurally similar (such as BJT).Understanding how an  nMOS transistor works  is difficult because it involves  fairly advanced quantum mechanics. However, at the simplest level, the operation of an  nMOS transistor can be understood  by looking at the flow of positive charges (or "holes  ") and negative charges (electrons).  pn junctions  are also important in the operation of a transistor.  Proper operation of the  nMOS transistor  requires a process   known  as biasing .  Semiconductors can be left with materials so that they are more than electrons  that are easily displaced  - which is generally used in  the negative  or  n-type region  .  In general, as a hint - also, they can be made with elements that create an over-hole that easily absorbs these doped electrons   or   p   -type   region.

This article delves into the development and refinement of a K-Nearest Neighbor (K-NN) model for discriminating different grades of Aquilaria oil. The background highlights the significance of Aquilaria oil, a prized essential oil derived from Aquilaria trees, known for its diverse applications in perfumery and traditional medicine. With varying grades of Aquilaria oil available, the need for an effective model to discern these grades becomes imperative for quality control and market competitiveness. Traditional classification methods may fall short in capturing the intricacies of the oil’s composition, prompting the exploration of advanced machine learning techniques. The methodology section outlines the approach taken to formulate the K-NN model, with a specific focus on the variation of distance metrics—Mahalanobis, Correlation, and Cosine. The utilization of these metrics aims to enhance the model’s ability to discriminate between oil grades based on their distinct chemical profiles. The article discusses the data collection process, feature selection, and the parameters considered in implementing the K-NN algorithm. The experimentation with diverse distance metrics is anticipated to reveal nuances in classification that were previously unexplored. Insights gained from this study could potentially revolutionize the classification of essential oils, providing a robust framework for quality assessment.

                  KEYBYTE SYSTEMS, Melbourne, Australia pronab@keybytesystems.com

The rapid proliferation of AI-driven technologies has demonstrated the need for models that not only perform well but also align with ethical standards and user expectations. The concept of progressively selective weight enhancement represents a novel approach to addressing alignment challenges, offering a targeted method for refining model parameters to achieve more reliable and contextually appropriate outputs. Through the implementation of this technique in the Llama 2 model, significant improvements in alignment were observed, evidenced by enhanced accuracy, reduced bias, and increased robustness to adversarial inputs. The study's findings highlight the effectiveness of this approach in balancing the dual demands of alignment precision and computational efficiency, suggesting that such methodologies could play a crucial role in the future development of AI systems. The research further demonstrates the potential of the weight enhancement algorithm to advance the field of AI by providing a more adaptable and ethically sound framework for model training and deployment.

The incidence of melanoma among young adults has risen, yet mortality has declined annually since the introduction of immune checkpoint inhibitors (ICI). The utilization of peri-operative ICI has significantly altered the treatment landscape in melanoma, with PD-1 inhibitors showing promising efficacy in improving relapse-free survival rates in high-risk stage II-III disease. With the increasing use of ICI, secondary concerns have emerged regarding the impact of cancer drugs on fertility and reproductive health among child-bearing women, especially in early-stage cancer settings. The exclusion of pregnant women from trials contributes to limited human data and clinical uncertainties, such as maternal and fetal toxicities related to ICI exposure during pregnancy, as well as the value of fertility preservation prior to ICI therapy. Additionally, uncertainty persists regarding pregnancy planning after immunotherapy in an adjuvant setting, given the potential detrimental effects of hormonal and immunological changes during pregnancy on melanoma relapse. These considerations raise questions about whether pregnancy-associated melanoma (PAM) represents a distinct disease entity that warrants tailored management compared to non-pregnant cases.

Invasive species with native ranges spanning strong environmental gradients that establish in new habitats are particularly well-suited for examining the roles of selection and population history in rapid environmental adaptation, providing insight into potential evolutionary responses to climate change. The Atlantic oyster drill (Urosalpinx cinerea) is a marine snail with a native range spanning the strongest marine thermal gradient in the world that has established invasive populations on the U.S. Pacific coast. Here, we leverage this system using genome-wide SNPs and environmental data to examine invasion history and identify genotype-environment associations indicative of local adaptation across its native range, and then assess evidence for predicted allelic frequency shifts signaling rapid adaptation in invasive populations. We demonstrate strong genetic structuring among native regions aligned with life history expectations and identify southern New England as the source of invasive populations. We also identify putatively thermally adaptive loci across the native range, for which two invasive populations show significant divergence from source populations. However, we find no evidence of directional shifts in allele frequencies as would be predicted by environmental selection, suggesting that divergence is likely due to genetic drift rather than rapid adaptation. Alternatively, the success of new populations in environments differing from their origin may be due to relaxed selection pressures associated with more benign conditions, and/or standing capacity for phenotypic plasticity. This demonstrates the utility of invasive species for understanding evolutionary responses to changing environments, and the importance of considering population history and environmental selection pressures when evaluating adaptative capacity.

Single Nucleotide Polymorphism (SNP) panels are powerful tools for assessing genetic population structure and dispersal of fish that can enhance management practices for commercial, recreational, and subsistence mixed-stock fisheries. Arctic Char(Salvelinus alpinus), Brook Trout (Salvelinus fontinalis), and Lake Whitefish(Coregonus clupeaformis) are amongst the top harvested and consumed fish species in northern Indigenous communities in Canada, contributing significantly to food security, culture, and economy. Genomic resources like SNP panels, however, have not been widely accessible to Indigenous fisheries managers. We developed Genotyping-in-Thousands by sequencing (GT-seq) panels for population assignment and mixed-stock analyses for three salmonids to support fisheries co-management in northern Canada. Using low-coverage Whole-Genome Sequencing data from 418 individuals across source populations in Cambridge Bay (Nunavut), Great Slave Lake (Northwest Territories), James Bay (Québec), and Mistassini Lake (Québec), we developed a bioinformatic SNP filtering workflow to select informative SNP markers from genotype likelihoods. These markers were used to design GT-seq panels enabling high-throughput genotyping. Three GT-seq panels yielded an average of 413 autosomal loci and were validated with an average assignment accuracy of 98.5%. These GT-seq panels emerge as powerful tools for assessing population structure and quantifying the relative contributions of populations/stocks in mixed stock fisheries across multiple regions. Interweaving these novel genomic-derived tools with Traditional Ecological Knowledge will ensure the sustainable harvest of three culturally important salmonids in northern Indigenous communities, contributing to food security programs and the economy in northern Canada.

Bayesian optimization is a stochastic, global black-box optimization algorithm. By combining Machine Learning with decision-making, the algorithm can optimally utilize information gained during experimentation to plan further experiments-while balancing exploration and exploitation. Although Design of Experiments has traditionally been the preferred method for optimizing bioprocesses, AI-driven tools have recently drawn increasing attention to Bayesian optimization within bioprocess engineering. This review presents the principles and methodologies of Bayesian optimization and focuses on its application to various stages of bioprocess engineering in upstream and downstream processing.

A document by Steffen Mickenautsch. Click on the document to view its contents.

In the 30 years of its availability, satellite altimetry has established itself as an important tool for understanding the Earth system. Originally developed for oceanography and geodesy, it has also proven valuable for monitoring water level variation of lakes and rivers. However, when using altimetry for inland waters, there is always a critical issue: retracking i.e. the procedure in which the range from the satellite to the water surface is (re)estimated. The current retracking methods heavily rely on single waveforms, which results in a high sensitivity to every individual peak in the waveform and in a strong dependency on the waveform's shape. Here, we propose the Bin-Space-Time (BiST) retracking method that moves beyond finding a single point in a 1D waveform and instead seeks a retracking line within a 2D radargram, for which the temporal information over different cycles is also considered. The retracking line divides the radargram into two segments: the left (Front) and right-hand side (Back) of the retracking line. Such a segmentation approach can be interpreted as a binary image segmentation problem, for which spatiotemporal information can be incorporated. We follow a Bayesian approach, exploiting a probabilistic graphical model known as a Markov Random Field (MRF). There, the problem is arranged as a Maximum A Posteriori estimation of an MRF (MAP-MRF), which means finding a retracking line that maximizes a posterior probability density or minimizes a posterior energy function. Our posterior energy function is obtained by a prior energy function and a likelihood energy function, both of them depending on signal intensity and bin: 1) The prior: the bin-space energy function defined between firstorder neighbouring pixels of a radargram modeling the spatial dependency between their labels for given intensities and bins and 2) The likelihood: the temporal energy function of a pixel for labeling Front or Back given its overall temporal evolution. The realization of the field with the minimum sum of the bin-space and the temporal energy functions is then found through the maxflow algorithm. Consequently, the retracking line, the boundary between the Back and Front region is obtained. We apply our method to both pulse-limited and SAR altimetry data over nine lakes and reservoirs in the USA with different sizes and different altimetry characteristics. The resulting water level time series are validated against in situ data. Across the selected case studies, on average, the BiST retracker improves the RMSE by approximately 0.5 m compared to the best existing retracker. The main benefit of the proposed retracker, which operates in bin, space, and time domains, is its robustness against unexpected waveform variations, making it suitable for diverse inland water surfaces.