During the last years some new variants of Permutation entropy have been introduced and applied to EEG analysis, among them a conditional variant and variants using some additional metric information or being based on entropies different from the Shannon entropy. In some situations it is not completely clear what kind of information the new measures and their algorithmic implementations provide. We discuss the new developments and illustrate them for EEG data.

This study examined the association between pressure injuries and complexity of abdominal temperature measured in residents of a nursing facility. The temperature served as a proxy measure for skin thermoregulation. Refined multiscale sample entropy and bubble entropy were used to measure the complexity of the temperature time series measured over two days at 1-minute intervals. Robust summary measures were derived for the multiscale entropies and used in predictive models for pressure injuries that were built with adaptive lasso regression and neural networks. Both types of entropies were lower in the group of participants with pressure injuries (n=11) relative to the group of non-injured participants (n=15). This was generally true at the longer temporal scales, with the effect peaking at scale τ=23 minutes. Predictive models for pressure injury on the basis of refined multiscale sample entropy and bubble entropy yielded 92% accuracy, outperforming predictions based on any single measure of entropy. Combining entropy measures with a widely used risk assessment score led to the best prediction accuracy. Complexity of abdominal temperature series could therefore serve as an indicator of risk of pressure injury.

We define degree of dependence of two events A and B in a probability space by using Boltzmann-Shannon entropy function of an appropriate probability distribution produced by these events and depending on one parameter (the probability of intersection of A and B) varying within a closed interval I. The entropy function attains its global maximum when the events A and B are independent. The important particular case of discrete uniform probability space motivates this definition in the following way. The entropy function has a minimum at the left endpoint of I exactly when one of the events and the complement of the other are connected with the relation of inclusion (maximal negative dependence). It has a minimum at the right endpoint of I exactly when one of these events is included in the other (maximal positive dependence). Moreover, the deviation of the entropy from its maximum is equal to average information that carries one of the binary trials defined by A and B with respect to the other. As a consequence, the degree of dependence of A and B can be expressed in terms of information theory and is invariant with respect to the choice of unit of information. Using this formalism, we describe completely the screening tests and their reliability, measure efficacy of a vaccination, the impact of some events from the financial markets to other events, etc. A link is available for downloading an Excel program which calculates the degree of dependence of two events in a sample space with equally likely outcomes.

Approximate entropy (ApEn) and sample entropy (SampEn) are statistical indices designed to quantify the regularity or predictability of time series data. Although ApEn has been a prominent choice for use in the analysis of non-linear data, it is currently unclear as to which method and parameter selection combination is optimal for its application in biomechanics. The goal of this research was to examine the differences between ApEn and SampEn related to center of pressure (COP) data during tandem standing balance tasks, while also changing the tolerance window, r. Six participants completed five, 30-second trials, feet-together and tandem standing with eyes-open and eyes-closed. COP data (fs = 60 Hz, downsampled from 1200 Hz) from ground reaction force platforms were collected. ApEn and SampEn were calculated using a constant vector length, i.e., m = 2, but differing values of r (tolerance window). For each of the participants, four separate one-way analysis of variance analyses (ANOVA) were conducted for ApEn and SampEn in the anterior-posterior (AP) and medial-lateral (ML) directions. Dunnett's intervals were applied to the one-way ANOVA analyses to determine which tandem conditions differed significantly from the baseline condition. ApEn and SampEn provided comparable results in the predictability of patterns for different stability conditions, with increasing instability, i.e., tandem eyes closed postures, being associated with greater unpredictability. The selection of r had a relatively consistent effect on mean ApEn and SampEn values across r = 0.15 * SD to r = 0.25 * SD, where both entropy methods tended to decrease as r increased. Mean SampEn values were generally lower than ApEn values. The results suggest that both ApEn and SampEn indices demonstrated relative consistency and were equally effective in quantifying the level of the center of pressure signal regularity during quiet tandem standing postural balance tests.

The oncogenic potential of human papillomavirus (HPV) may be used to determine the tissue tropism of each HPV type. Cervical cancer develops in the squamo-columar junction of the cervices, and most such lesions are induced by high-risk (HR) HPV types. This suggests that HR types preferentially infect the cervix, whereas the preferential infection site for low-risk (LR) types is not well defined. Determination of HPV tropism when using cytology samples can be uncertain since it is difficult to avoid contamination of cell samples between the cervix and the vagina. Herein, cell samples were carefully collected by independently scraping the cervix and vagina, after which the HPV types were determined. HPV tissue tropism was determined by considering what HPV types were positive at only one of the sites (the cervix or the vagina) as the viruses that preferentially infected that site. This method revealed that all LR types were only identified in vaginal samples, whereas 87% of HR types were identified in cervical sites. Thus, LR types may preferentially infect the vagina, whereas HR types infect the cervix. These findings suggest that preferential tissue tropism of certain HPV types is a probable factor for malignant progression.

The aim of this study was to assess the ability of multiscale sample entropy (MSE), refined composite multiscale entropy (RCMSE), and complexity index (CI) to characterize gait complexity through trunk acceleration patterns in subjects with Parkinson's disease (swPD) and healthy subjects, regardless of age or gait speed. The trunk acceleration patterns of 51 swPD and 50 HS were acquired using a lumbar-mounted magneto-inertial measurement unit during their walking. MSE, RCMSE, and CI were calculated on 2000 data points, using scale factors (τ) 1-6. Differences between swPD and HS were calculated at each τ, and the area under the receiver operating characteristics, optimal cutoff points, post-test probabilities, and diagnostic odds ratios were calculated. MSE in the antero-posterior direction at τ 4 and τ 5, and MSE in the ML direction at τ 4 outperformed the other entropy measures in terms of discriminative ability and characterized the gait of swPD with 79%, 82%, and 78% probabilities, respectively, and correlated with the motor disability, pelvic kinematics, and stance phase. Using a time series of 2000 data points, a scale factor of 4 or 5 in the MSE procedure can be considered as a marker of gait variability and complexity in swPD.

In the paper we present a new test for comparison of the means of multivariate samples with unknown distributions. The test is based on the comparison of the distributions of the distances between the samples’ elements and their means using univariate two-sample Kolmogorov-Smirnov test. The activity of the suggested method is illustrated by numerical analysis of the real-world and simulated data.

The successful 2020 launch and 2021 landing of the National Aeronautics and Space Administration’s (NASA) Perseverance Mars rover initiated the first phase of the NASA and European Space Agency (ESA) Mars Sample Return (MSR) campaign. The goal of the MSR campaign is to collect scientifically interesting samples from the Martian surface and return them to Earth for further study in terrestrial laboratories. The MSR campaign consists of three major spacecraft components to accomplish this objective: the Perseverance Mars rover, the Sample Retrieval Lander (SRL) and the Earth Return Orbiter (ERO). Onboard the ERO spacecraft is the Capture, Containment and Return System (CCRS). CCRS will capture, process and return to Earth the samples that have been collected after they are launched into Mars orbit by the Mars Ascent Vehicle (MAV), which is delivered to Mars onboard the SRL. To facilitate the processing of the orbiting sample (OS) by CCRS we have designed and developed a vision system to determine the OS capture orientation. The vision system is comprised of two cameras sensitive to the visible portion of the electromagnetic spectrum and two illumination modules constructed from broadband light emitting diodes (LED). Vision system laboratory tests and physics-based optical simulations predict CCRS ground processing will be able to correctly identify the OS post-capture orientation using only a single vision system image that is transmitted to Earth from Mars orbit.

In recent years, with the rapid development of technology, artificial intelligence(AI) security issues represented by adversarial sample attack have aroused widespread concern in society. Adversarial samples are often generated by surrogate models and then transfer to attack the target model, and most AI models in real-world scenarios belong to black boxes, thus transferability becomes a key factor to measure the quality of adversarial samples. The traditional method relies on the decision boundary of the classifier and takes the boundary crossing as the only judgment metric without considering the probability distribution of the sample itself, which results in an irregular way of adding perturbations to the adversarial sample, an unclear path of generation, and a lack of transferability and interpretability. In the probabilistic generative model, after learning the probability distribution of the samples, a random term can be added to the sampling to gradually transform the noise into a new independent and identically distributed sample. Inspired by this idea, we believe that by removing the random term, the adversarial sample generation process can be regarded as static sampling of the probabilistic generative model, which guides the adversarial samples out of the original probability distribution and into the target probability distribution, and helps to improve transferability and interpretability. Therefore, we propose a Score Matching-Based Attack(SMBA) method to perform the adversarial sample attacks by manipulating the probability distribution of the samples, which can show good transferability in the face of different datasets and models, and give reasonable explanations from the perspective of mathematical theory and feature space. In conclusion, our research establishes a bridge between probabilistic generative models and adversarial samples, provides a new entry angle for the study of adversarial samples, and brings new thinking to AI security.

Many natural and artificial liquid environments such as rivers, oceans, lakes, water storage tanks, aquariums, and urban water distribution systems, are difficult to access. As a result, technology is needed to enable autonomous liquid sampling to monitor water quality and ecosystems. The existing in-situ sample acquisition and handling systems for liquid environments are currently limited to a single use and are semi-autonomous, relying on an operator. Liquid sampling systems should be robust and light and withstand long-term operation in remote locations. The system components involved in liquid sampling should be sterilisable to ensure reusability. Here, we introduce a prototype of a liquid sampler that can be used in various liquid environments and may be valuable for the scientific characterisation of different natural, remote, and planetary settings. The Autonomous Planetary Liquid Sampler (APLS) is equipped with pre-programmed fully autonomous extraction, cleaning, and sterilisation functionalities. It can operate in temperatures between -10 ℃ and 60 ℃ and pressure of up to 0.24 MPa (~24 m depth below mean sea level on Earth). As part of the control experiment, we demonstrate its safe and robust autonomous opera-tion in a laboratory environment using a liquid media with Bacillus subtilis. A typical sampling procedure required 28 s to extract 250 mL of liquid, 5 s to fill the MilliQ water, 25 s for circulation within the system for cleaning and disposal, and 200 s to raise the system temperature from ~30 °C ambient laboratory temperature to 150 °C. The temperature is then maintained for another 3.2 hours to sterilise the critical parts, allowing a setup reset for a new experiment. In the future, the liquid sampler will be combined with various existing analytical instruments to characterise the liquid solution and enable the autonomous, systematic monitoring of liquid environments on Earth.

Sample size using prevalence has been one of the most important medical research methodologies and has been used in original articles more often than any other statistical tool available. The simple formula consists of variables interdependent and it is as important to understand how they influence each other as it is to learn to use the formula to calculate the sample size. With the choice of different amounts of precision and levels of confidence available to a medical researcher, it should be of paramount importance to understand what a slight change in choosing the parameter would mean at the end of the research. Understanding of different Z values, Levels of precision, and ranging Prevalence is required to understand the effect on the sample size and subsequently on the research. Topological representation using parameter ranges can be used to demonstrate this relationship.

Microcrystal delivery instruments are pivotal to perform serial femtosecond crystallography experiments at the XFEL facilities. We present a novel sample delivery technique based on a micro-tubing reeling system (MRS). Even though the tiny size of the micro-tubing, the MRS device has an advantage of operation without real-time position adjustment of the tube to match with the XFEL pulses. Moreover, the applicable repetition rate is more flexible than the previously reported chip-based one-dimensional fixed target system.

Peptide mapping is an important tool to confirm the correct sequence expressed for a protein and to evaluate protein post-translational modifications (PTMs) that may arise during production, processing, or storage of protein drugs. Our new orally administered drug (Ab-1), a single-domain antibody, is highly stable and resistant to proteolysis. Analysis by the commonly used tryptic map method did not generate sufficient sequence coverage. Alternative methods were needed to study the Ab-1 drug substance (75 mg/mL) and drug product (3 mg/mL). To meet analytical needs, we developed two new peptide map methods with lysyl endopeptidase (Lys-C) digestion. These newly developed protein digestion protocols do not require desalting/buffer-exchange steps, thereby reducing sample preparation time and improving method robustness. Additionally, the protein digestion was performed under neutral pH with methionine acting as a scavenger to minimize artifacts, such as deamidation and oxidation, which are induced during sample preparation. Further, the method for low concentration samples performs comparably to the method for high concentration samples. Both methods provide 100% sequence coverage for Ab-1, and therefore, enable comprehensive characterization for its product quality attributes (PQA) assessment. Both methods can be used to study other antibody modalities.

Background. Inferior quality of biological material compromises data, slows discovery, and wastes research funds. The gut microbiome plays a critical role in human health and disease, yet little attention has been given to optimizing collection and processing methods of human stool. Methods. We collected the entire bowel movement from 2 healthy volunteers: one to examine stool sample heterogeneity and one to test stool sample handling parameters. Sequencing and bi-oinformatic analyses were used to examine the microbiome composition. Results. The microbiome profile varied depending on where the subsample was obtained from the stool. The exterior cortex of the stool was rich in specific phyla and deficient in others while the interior core of the stool revealed opposite microbiome profiles. Sample processing also re-sulted in varying microbiome profiles. Homogenization and stabilization at 4°C gave superior microbial diversity profiles compared to the fresh or frozen subsamples of the same stool sample. Bacterial proliferation continued in the fresh subsample when processed at ambient temperature. Bacteroidetes proliferated and Firmicutes diminished during the 30-minute processing of fresh sample. The frozen sample had good overall diversity but Proteobacteria diminished likely be-cause of the freeze/thaw. Conclusions. The microbiome profile is specific to the section of the stool being sampled. Stool sample collection, homogenization, and stabilization at 4°C for 24 hours provides a “neat”, high-quality sample of sufficient quantity that can be banked into aliquots with nearly identical microbial diversity profiles. This collection pipeline is essential to accelerate our understanding of the gut microbiome in health and disease.

Background: The integration of unmanned aerial vehicles (UAVs), commonly known as drones, into medical logistics offers transformative potential for the transpor-tation of sensitive medical materials, such as blood samples. Traditional car transportation is often hindered by traffic delays, road conditions, and geographic barriers, which can compromise timely delivery. Methods: This study provides a comprehensive analysis comparing high-speed drone transportation with traditional car transportation under various weather conditions. Blood samples, including EDTA whole blood, Serum, Li-Heparin Plasma, and citrate plasma tubes, were transported via both methods across temperatures ranging from 4 to 20 degrees Celsius. The integrity of the samples was assessed using a wide array of analytes and statistical analyses, including Passing-Bablok regression and Bland-Altman plots. Results: The results demonstrated that drone transportation maintains blood sample integrity comparably to traditional car transpor-tation. For serum samples, the correlation coefficients (r) ranged from 0.830 to 1.000, and the slopes varied from 0.913 to 1.111, with minor discrepancies in five analytes (total bilirubin, calcium, ferritin, potassium, and sodium). Similar patterns were observed for EDTA, Lithium-Heparin, and Citrate samples, indicating no significant differences between transportation methods. Conclusions: These findings highlight the potential of drones to enhance the efficiency and reliability of medical sample transport, particularly in scenarios requiring rapid and reliable delivery. Drones could significantly improve logistical operations in healthcare by overcoming traditional transportation challenges.

This paper examines the impact of random sample sizes on the extreme value theory of competing risks, a significant area in finance and environmental science. We capture limit distributions of two extreme types under random sampling sizes, known as accelerated mixed l-max and p-max stable type distributions. The study presents results for both maxima and minima in competing risks scenarios, addressing cases of independent and non-independent random sample sizes. Numerical examples validate our theoretical findings, demonstrating the applicability of our approach to various random sample size distributions, including time-shifted Poisson or binomial, geometric, and negative binomial distributions.

Most existing data synthesis methods are designed to tackle problems such as dataset imbalance, data anonymization and insufficient sample size. There is a lack of effective synthesis methods for the limited number of datasets which contain a large of features and unknown noise to expand the size of the dataset. We propose a data synthesis method, named Adaptive Subspace Interpolation for Sample Optimization (ASISO). The idea is to divide the original feature space into several subspaces with an equal number of samples, and then perform interpolation for the samples in the adjacent subspaces. This method can adaptively adjust the size of the dataset containing unknown noise, and the expanded data typically contain minimal error with actual. Moreover, it adjusts the structure of the samples, which can significantly reduce the proportion of samples with large errors. In addition, the hyperparameters of this method have an intuitive explanation and usually require little calibration. Experimental results on artificial data and benchmark data sets demonstrate that ASISO is a robust and stable method to optimize samples.

We summarize how to obtain protein crystals from which better diffraction images can be obtained. In particular, we describe in detail the quality evaluation of the protein sample, the crystallization methods and crystallization conditions, the flash-cooling protection of the crystal, and the crystallization under a microgravity environment.

Community mobilisation is a vital process for raising awareness and increasing participation in healthcare interventions, research and programs that require human sample collection and mass management. In this report, we present the community mobilisation approach undertaken for the implementation of granular schistosomiasis and soil-transmitted helminths operational mapping and assessment in Ekiti State Nigeria. The mobilisation was conducted in 177 communities/wards of the 16 Local Government Areas. A total of 15,340 urine and stool samples were collected in 34 days. The efficacy and success of the strategy was evaluated through three performance metrics: Community compliance rate, the participant response rate at community level and overall compliance response rate of the 4 most sensitive LGAs. Community compliance was 93.7% as sample collection was denied in 9 communities. Two other communities demanded return of collected samples. Participant response rate at the community level was 86.7%. Three of the 4 sensitive LGAs (based on previous assessment programs) demonstrated satisfactory compliance rates of 100%, while a response rate of 64.0% was computed for one of the LGAs. We believe our approach contributed to effective community mobilisation & awareness and the developed model has potential for improving participation rates in large healthcare assessments and intervention programs.

Dairy products are an important source of protein and other nutrients in the Mediterranean diet. In these countries the most common source of milk for producing dairies are cow, goat, sheep and buffalo. Andalusia is traditionally the largest producer of goat milk in Spain. Kefir is a fermented product made from bacteria and yeasts which has health benefits beyond its nutritional properties. There is a lack of knowledge about the molecular mechanisms and metabolites that bring about these benefits. In this work, the combination of analytical techniques (GC-FID, UHPLC-MS-QToF, GC-QqQ-MS, and GC-ToF-MS) resulted in the detection of 105 metabolites in kefir produced with goat milk from two different thermal treatments (raw and pasteurized) fermented at four times (12, 24, 36, and 48 h, using 0 h as control). Of these, 27 metabolites differed between kefir produced with raw and pasteurized milk. These changes could be possibly caused by the effect of pasteurization on the microbial population in the starting milk. Some interesting molecules were identified, as shikimic acid, dehydroabietic acid, GABA and tyramine that could be related to antibacterial properties, enhance of immune system and arterial pressure. Moreover, a viability assay of the NIRS technique was performed to evaluate its use on monitoring the fermentation and classification of samples, which had resulted in a 90 % of correct classified samples according to their fermentation time. This study represents the most comprehensive metabolomic analysis of goat milk kefir so far, revealing the intricate changes in metabolites during fermentation and the impact of milk treatment.

Terpenes and terpenoids content in cannabis plant was already studied in the past with three used methods. Since these works did not compare the content of these substances under the same conditions, we tried to make this comparison exactly. Three different gas chromatography/mass spectrometry (GS/MS) methods - hexane based liquid extraction (Liq), static head-space extraction (HS), and head-space solid phase microextraction (SPME) – were compared to identify volatile compounds in four different cannabis chemotypes - Green fields, Titan chemotype, Black Domina chemotype, and Neptune chemotype. The main compounds focused on were monoterpenes/monoterpenoids and sesquiterpenes/sesquiterpenoids. Extraction with hexane gave comparable results, however the other two methods allowed for the identification of more substances. For the final evaluation of the comparison of the three methods of analysis, extraction with hexane gives comparable results (which is advantageous for quantitative analysis), although the other two methods allowed for the identification of more substances. This means that the same method should be used everywhere for the quantitative evaluation of constituents in cannabis.

In the current scenario, liver abnormalities are one of the most serious public health concerns. Cirrhosis of the liver is one of the foremost causes of demise from liver diseases. To accurately predict the status of liver cirrhosis, physicians frequently use automated computer-aided approaches. In this paper the clustering techniques like Fuzzy C Means (FCM), Possibilistic Fuzzy C Means (PFCM), Possibilistic C Means (PCM), Sample Entropy features are extracted from normal and cirrhotic liver ultrasonic Images. The extracted features are classified as normal and Cirrhosis one through Gaussian Mixture Model(GMM), Softmax Dis(SDC), Harmonic Search Algorithm (HSA), SVM(Linear), SVM(RBF), SVM(Polynomial), Artificial Algae Optimization (AAO) and hybrid classifier Artificial Algae Optimization (AAO) with Gaussian Mixture Mode (GMM). The classifiers performances are compared based on Accuracy, F1 Score, MCC, F Measure, Error rate and Jaccard metric (JM).The hybrid classifier AAO GMM with PFCM feature outperforms than other classifiers and attained an accuracy of 99.03 % with MCC of 0.90

Exploring the effect of the sample size on the estimation accuracy of airborne LiDAR forest attributes in a large-scale area can help in optimizing the technical application scheme of operational ALS-based large-scale forest stand inventories. In our study, sample datasets composed of different sample plots were constructed by repeated sampling from 1003 sample plots in a subtropical study area covering 2376 × 103 km2. Sixteen multiplicative power models were built in each forest type consisting of four forest attributes. Through these models, the variations of standard deviation (SD) and coefficient of variation (CV) of R2 and rRMSE of forest attribute estimation models for different quantity levels of sample plots were also analyzed. The results showed that, first, when the sample size increased from 30 to the top limit, the SD of the forest attributes and LiDAR variables showed a decreasing trend. Second, as the sample size increased, the rRMSE of the 16 forest attribute estimation models gradually decreased, while the R2 gradually increased. Third, when the sample size was small, both the SD of R2 and rRMSE of the models were large, and the SD of R2 and rRMSE gradually decreased as the sample size increased. In 50 models conducted for each attribute at the same sample size, for the mean standard deviations of forest attributes, the ten best performing models were lower than those of the total 50 models, and the worst ten models were the opposite. When the sample size increased, the accuracy of each forest attribute estimation model for each forest type gradually improved. The variation of forest attributes and the LiDAR variable of the construction model are critical factors that affect the model’s accuracy. To efficiently apply airborne LiDAR in order to survey large-scale subtropical forest resources, the sample size of the Chinese fir forest, pine forest, eucalyptus forest, and broad-leaved forest should be 110, 80, 85, and 70, respectively.

Scanning magnetic microscopy is a new tool that has recently been used to map magnetic fields with good spatial resolution and field sensitivity. This technology has great advantages over other instruments; for example, its operation does not require cryogenic technology, which reduces its operational cost and complexity. Here, we describe the construction of a customizing scanning magnetic microscope based on commercial Hall-effect sensors at room temperature that achieves a spatial resolution of 200 µm. Two scanning stages on the x- and y-axes of precision, consisting of two coupled actuators, control the position of the sample, and this microscope can operate inside or outside a magnetic shield. We obtained magnetic field sensitivities better than 521 nT_rms/√Hz between 1 and 10 Hz, which correspond to a magnetic momentum sensitivity of 9.20 × 10^–10 Am². In order to demonstrate the capability of the microscopy, polished thin sections of geological samples, samples containing microparticles and magnetic nanoparticles were measured. For the geological samples, a theoretical model was adapted from the magnetic maps obtained by the equipment. Vector field maps are valuable tools for the magnetic interpretation of samples with a high spatial variability of magnetization. These maps can provide comprehensive information regarding the spatial distribution of magnetic carriers. In addition, this model may be useful for characterizing isolated areas over samples or investigating the spatial magnetization distribution of bulk samples at the micro and millimeter scales. As an auxiliary technique, a magnetic sweep map was created using Raman spectroscopy; this map allowed the verification of different minerals in the samples. This equipment can be useful for many applications that require samples that need to be mapped without a magnetic field at room temperature, including rock magnetism, the nondestructive testing of steel materials and the testing of biological samples. The equipment can not only be used in cutting-edge research but also serve as a teaching tool to introduce undergraduate, master's and Ph.D. students to the measurement methods and processing techniques used in scanning magnetic microscopy.

We improved a magnetic scanning microscope for measuring the magnetic properties of minerals in thin sections of geological samples at submillimeter scales. The microscope is comprised of a 200 µm diameter Hall sensor that is 142 µm from the sample; an electromagnet capable of applying to the sample up to 500 mT dc magnetic fields over a 40 mm diameter region; a second Hall sensor arranged in a gradiometric configuration to cancel the background signal applied by the electromagnet and reduce overall noise in the system; a custom-designed electronics to bias the sensors and provide adjustment for background signal cancelation; and a scanning XY stage with micrometer resolution. Our system achieves a spatial resolution of 220 µm with noise at 6.0 Hz of »300 nT_rms/(Hz)^1/2 in an unshielded environment. The magnetic moment sensitivity is 1.3 × 10⁻¹¹ Am².^1/2 We successfully measured the representative magnetization of a geological sample using an alternative model that takes into account the sample geometry and identified different micrometric characteristics in the sample slice.

In this note, we suggest a one-sample version of the recently proposed two-sample test for comparison of the means of multivariate samples. The test checks a hypothesis that the mean of multivariate sample is equal to a certain value with the alternative hypothesis that the mean does not equal to this value. The suggested test is illustrated by analysis of real-world and simulated data.

Background：Increasingly, epidemiological surveys and experimental research findings indicate the involvement of immune cells in the onset and progression of prostate cancer. However, it remains unclear whether the relationship between specific immune cell types and prostate cancer is causal or influenced by bias. Through a two-sample Mendelian randomization design, our study aims to uncover the association between different immune cells and the risk of prostate cancer.Methods：A comprehensive study was conducted to investigate the association between immunophenotypes and prostate cancer risk. The study included a total of 731 immunophenotypes as potential exposure factors and analyzed data from 182,625 individuals of European ancestry from the UK Biobank. A screening of 12,097,504 single nucleotide polymorphisms (SNPs) was performed. The main analysis utilized the two-sample Mendelian randomization method with inverse variance weighting (IVW). Additionally, multiple analysis methods were combined, and various tests were conducted to assess heterogeneity and pleiotropy.Results：The resulting study showed a causal relationship between a total of 34 immune phenotypes and prostate cancer. Among them, the four immune phenotypes of CD62L- DC AC, CD19 on sw mem, FSC-A on plasmacytoid DC and HLA DR on monocyte simultaneously meet three or more statistical methods, suggesting a significant causal relationship with prostate cancer. Additionally, among the 34 immunophenotypes, CD28-CD8dim AC was shown to be positive in reverse Mendelian randomization. Most of these cell phenotypes have been reported to be potentially associated with prostate cancer.Conclusions：Our study demonstrates a causal link between immune cells and prostate cancer risk. Filled in the possible roles of all current immune cell pathways in the development and progression of prostate cancer. The next step is to verify the content and influencing mechanism of these immune phenotypes in cancer cells and adjacent cells through experiments, so as to provide new ideas and methods for the prevention and treatment of prostate cancer.

Organizational climate is the ascribed psychological meanings and significance associated with the procedures, policies and practices that are recognized and rewarded in the workplace, and hence mediates the effects of environmental stimuli on individuals’ response. Safety climate is a specific organizational climate, i.e., organizational climate for safety. Previous research claimed that organizational climate provides foundation for safety climate, but without elaboration on the foundational mechanisms. This paper attempts to fill up this knowledge gap. As organizational climate is a multi-dimensional phenomenon, this paper chooses two dimensions, i.e., perceived organizational support (POS) and participative decision-making (PaDM), for illustrative purposes. Drawing on an interactive approach to forming climate perceptions, this paper introduces two interactive constructs, i.e., leader-member exchange (LMX) and team member exchange (TMX), and establishes a multiple mediation model depicting the foundational effect of organizational climate on safety climate. A random sample of Hong Kong based construction personnel is used to validate the model. The results show that both POS and PaDM are positively associated with perceived safety climate, both LMX and TMX fully mediate the effect of PaDM on safety climate, and only LMX partially mediates the effect of POS on safety climate. This study sheds light on the foundational effects of organizational climate on safety climate. POS can improve the quality of reciprocal exchange about safety matters between construction personnel and their supervisors, and hence raise construction personnel’s awareness of the priority of safety. PaDM can improve the quality of reciprocal exchange about safety matters vertically and horizontally, and hence have construction personnel aware the importance of safety. In practice, this paper suggests that project managers timely recognize and reward construction personnel’s contribution, genuinely cares about their well-being, and take their suggestions seriously in making decisions. In this way, the quality of both vertical and horizontal exchange about safety matters improves, and a sound and positive safety climate ensues.

We introduce a magnetic bead-based sample preparation scheme for enabling a Raman spectroscopic differentiation of severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) positive and negative samples. The beads were functionalized with the angiotensin-converting enzyme 2 (ACE2) receptor protein, which is used as recognition element to selectively enrich SARS-CoV-2 on the surface of the magnetic beads. Subsequent Raman measurements directly enable discriminating SARS-CoV-2 positive and negative samples. The proposed approach is applicable for other virus species, too, when the specific recognition element is exchanged. Series of Raman spectra were measured on three types of samples, namely SARS-CoV-2, Influenza A H1N1 virus and a negative control. For each sample type, eight independent replicates were considered. All spectra are dominated by the magnetic bead substrate and no obvious differences between sample types are apparent. In order to address the subtle differences in the spectra, we calculated different correlation coefficients, namely the Pearson coefficient and the Normalized Cross Correlation coefficient. By comparing the correlation with the negative control differentiating between SARS-CoV-2 and Influenza A virus is possible. This study provides a first step towards the detection and potential classification of different viruses with the use of conventional Raman spectroscopy.

Soybean, maize and bean are crops of great economic importance, but in the last years suffered with infestations of the caterpillar Helicoverpa armigera, being the main problem the resistance of this pest to most pesticides. Avermectin emamectin benzoate was recently released to control this pest. Other avermectins, like abamectin, doramectin, eprinomectin and ivermectin are used in large scale because they potent acaricidal, anthelmintic, and insecticidal activities. Thus, a simple and fast method for the determination of avermectins in these crops based on a quick, easy, cheap, effective, rugged and safe (QuEChERS) extraction procedure and ultra-high performance liquid chromatography with tandem mass spectrometry (UHPLC-MS/MS) analysis was developed and validated. For extraction, water followed by acetonitrile:isopropanol and a partition step with salts was stablished. With the clean-up step using activated EMR-Lipid, limits of detection of 1.2 μg kg-1 for abamectin, doramectin, emamectin benzoate and ivermectin, and of 2.4 μg kg-1 for eprinomectin were achieved. Accuracy and precision evaluated at low levels presented satisfactory results. The method was successfully applied in commercial samples and is a good alternative for routine analysis.

This study focuses on the decisions of picking, inventory, ripening, delivering, and selling mangoes in a harvesting season. Demand, supply, and prices are uncertain, and their probability density functions are fitted based on actual trading data collected from the largest spot market in Taiwan. A stochastic programming model is formulated to minimize the expected cost under the considerations of labor, storage space, shelf life, and transportation restrictions. We implement the sample-average approximation to obtain a high-quality solution of the stochastic program. The analysis compares deterministic and stochastic solutions to assess the uncertain effect on the harvest decisions. Finally, the optimal harvest schedule of each mango type is suggested based on the stochastic program solution.

Distinguishable annual growth rings produce in Pinus roxburghii are an asset to find out the age of individual tree. This paper aimed to determine the age of P. roxburghii through coring samples and test the relationship with forest production. The biomass estimated, girths measured at two different sections and heights measured which allowed to determine the rate of tapering of the stand. The regression analysis was performed to test the relationship between various variables. The mean age of the P. roxburghii stand was found to be 23.97 (~24 years). The result showed the significant (p<0.05) positive correlation coefficient has been seen between age with girth at breast height, biomass, volume and carbon stock. However, no significant (p>0.05) correlation (r = 0.08) was found between age and height of the stand. In contrast, a correlation between diameter at breast height (DBH) was significant (p<0.05) and positive with volume, biomass, but no significant (p>0.05) correlation (r = 0.14) found between DBH and height of the stand. However, height has a significant (p<0.05) positive correlation with biomass. The mean biomass was 375 kg and mean annual increment (MAI) was 15 kg per tree. Rate of tapering of the studied stand predicted to be 3 cm diameter decreased with trunk height running at 100 cm from base to upward of P. roxburghii stand and vice versa. Result suggests that height-age relation is very weak whereas age, DBH, biomass and carbon has a significant correlation signifies that time-based forests' production and potential production estimation can be obtained in a relatively accurate way by utilizing the age of stand. The time-based forest production analysis is pioneer work in Nepal. The study affirms the tree ring count in P. roxburghii would be a credible and accurate method to determine the age of standing trees.

In this work, the structural, magnetic, and mechanical properties of Nd16Fe76-xCoxB8 alloys varying the Co content at x = 0, 10, 20 and 25, were experimentally investigated by X-Ray Diffraction (XRD), Mössbauer Spectrometry (MS), Vibrating Sample Magnetometry (VSM) at room temperature (RT), and microhardness test were performed too. The system presents the hard Nd2Fe14B and the Nd1.1Fe4B4 phases for samples with x = 0 and 10. When concentration increases to x= 20 and 25, the CoO phase appears. All MS show the ferromagnetic behavior (eight sextets: sites 16k1, 16k2, 8j1, 8j2, 4c, 4e, sb) associated to the hard and soft magnetic phases, and one paramagnetic component (doublet: site d) associated to the minority Nd1.1Fe4B4 phase, which was not identified by XRD. All samples are magnetically hard present a hard magnetic behavior. The increase of Co content in these samples did not improve the hard magnetic behavior, but increased the critical temperature of the system and decrease the crystallite size of the hard phase. The hysteresis loop showed that predominates the Nd2Fe14B hard magnetic phase. There is a general tendency to increase microhardness with cobalt content, attributable to cobalt doping reduces the lattice parameters and porosities in the sample improving it hardness.

This study have uniquely mad use of Box-Jenkins ARIMA models to address the core of the threes objectives set out in view of the focus to add meaningful value to knowledge exploration. The outcome of the research have testify the achievements of this through successful nine months out-of-sample forecasts produced from the program codes, with indicating best model choices from the empirical estimation. In addition, the results also provide description of risks produced from the uncertainty Fan Chart, which clearly outlined possible downside and upside risks to tourist visitations in the country. In the conclusion, it was suggested that downside risks to the low level tourist arrival can be managed through collaboration between authorities concerned with the management of tourist arrivals in the country.

Data generated by metagenomic and metatranscriptomic experiments is both enormous and inherently noisy [1]. When using taxonomy-dependent alignment-based methods to classify and label reads, such as MEGAN [2], the first step consists in performing homology searches against sequence databases. To obtain the most information from the samples, nucleotide sequences are usually compared to various databases (i.e., nucleotide and protein) using local sequence aligners such as BLASTN and BLASTX [3]. Nevertheless, the analysis and integration of these results can be problematic because the outputs from these searches usually show differences, which can be notorious when working with RNA-seq (Personal observation; Graphical abstract). These inconsistencies led us to develop the HoSeIn workflow to determine the unequivocal taxonomic and functional profile of environmental samples, based on the assumption that the sequences that correspond to a certain taxon are composed of (Graphical abstract): 1) sequences that were assigned to the same taxon by both homology searches, plus 2) sequences that were assigned to that taxon by one of the homology searches but returned no hits in the other one, and vice versa.

Live fuel moisture content (LFMC) influences fire activity at landscape scale and fire behavior in laboratory experiments. However, field evidences linking LFMC to fire behavior are very limited despite numerous field experiments. In the present study, we reanalyze a shrubland fire dataset with a special focus on LFMC to explain this counterintuitive outcome. We found that this controversy might result from three reasons. First, the range of experimental LFMC data was too moist to reveal significant effect with the widespread exponential or power functions. Indeed, LFMC exhibited a strong effect below 100%, but marginal above this threshold, contrary to these functions. Second, we found that the LFMC significance was unlikely when the size of the dataset was smaller than 40. Finally, a complementary analysis suggested that 10 to 15% of random measurement error in variables could lead to an underestimation by 30 % of the LFMC effect. The effect of LFMC in field experiments is thus stronger than previously reported in the range prevailing during the actual French fire season and in accordance with observations at different scales. This highlights the need to improve our understanding of the relationship between LFMC and fire behavior to refine fire danger predictions.

Electrochromic measurements have been performed to optimize the composition of reactive magnetron-sputtered mixed layers of Zinc oxide and Tin oxide (ZnO-SnO2). A combinatorial material synthesis approach was applied throughout the deposition experiments, and the samples represented the full composition range of the binary SnO2-ZnO system. The Coloration Efficiency (CE) of the mixed oxide films were determined with simultaneous measurement of layer transmittance and applied electric current through the using organic propylene carbonate electrolyte cells in a conventional three-electrode configuration. Optical parameters and composition have been determined and mapped by using Spectroscopic Ellipsometry (SE). Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray Spectroscopy (EDS) has been used to check the SE results as it was used in our earlier paper [N. T. Ismaeel et al, Materials 2023, 16(12), 4204] for (TiO2-SnO2). Zn and Sn targets were put separately from each other, and the Indium-Tin-Oxide (ITO) covered glass and Si-probes on a glass substrate (30 cm × 30 cm) were moved under the two separated targets (Zn and Sn) in a reactive Argon-Oxygen (Ar-O2) gas mixture. By using this combinatorial process, all the compositions (from 0 to 100%) were achieved in the same sputtering chamber after one sputtering process. CE data evaluated from the primary data plotted against the composition displayed a characteristic maximum at around 29 % ZnO. Our combinatorial approach allows the localization of the maximum at 5% accuracy.

Background: Coronavirus disease 2019 (COVID-19) caused several cardiovascular complications, including acute myocardial infarction(AMI) in infected patients. Our study aims to understand the overall trends of AMI among COVID-19 patients during the first two years of the pandemic and the disparities and outcomes between the first and second years. Methods: Our retrospective analysis via the 2020 and 2021 National Inpatient Sample(NIS) for hospitalizations between April 2020 and December 2021 analyzed adults with a primary diagnosis of COVID-19 who experienced events of AMI. We compared month-to-month events of AMI and mortality of AMI patients with COVID-19, patient characteristics, and outcomes between the 2020 and 2021 AMI samples. Results: There were 2,541,992 COVID-19 patients, with 3.55% experiencing AMI. The highest rate of AMI was in December 2021 (4.35%). No statistical differences in trends of AMI mortality were noted over the 21 months. AMI cases in 2021 had higher odds of undergoing PCI (aOR 1.627, p

Some tumors occur in anatomical regions that are hard to biopsy with a needle. Such regions include the brain, spinal cord, liver, and lungs. For the latter two, magnetic nanoparticle-loaded macrophages could be intravenously infused and driven via an MRI machine into the tumor or tumors. Once there, they can be induced to phagocytose whole tumor cells. They would keep their target in a non-digested form by inhibiting phagosome maturation - and be directed via magnetotaxis or chemotaxis to an extraction point in the body where they can be more easily collected via needle.

Highly reactive decomposition products of (deuterated) chloroform can deteriorate samples dissolved in this commonly used NMR solvent. For sensitive samples (such as peptides, unsaturated fatty acids, vitamins), this can lead to abnormal NMR spectra (e.g. signal shifts depending on pH, attenuation of signals over time due to chemical changes of analytes, new signals from reaction products). Such irreproducibly influenced spectra are especially problematic for non-targeted analysis methods. To prevent these artefacts, chlorine, phosgene and hydrochloric acid need to be eliminated from deuterated chloroform prior to its use. Since the common stabilization methods have proven insufficient for sensitive NMR samples, another purging method has been tested: Mitigation is easily and reliably achieved by washing the deuterated chloroform with concentrated Na2CO3-solution and subsequent desiccation with oven-dried Na2CO3.

Microfluidic devices which integrate both rapid mixing and liquid jetting for sample delivery are an emerging solution for studying molecular dynamics via X-ray diffraction. Here we use finite element modelling to investigate the efficiency and time-resolution achievable using microfluidic mixers within the parameter range required for producing stable liquid jets. Three-dimensional simulations, validated by experimental data, are used to determine the velocity and concentration distribution within these devices. The results show that by adopting a serpentine geometry, it is possible to induce chaotic mixing, which effectively reduces the time required to achieve a homogeneous mixture for sample delivery. Further, we investigate the effect of flow rate and the mixer microchannel size on the mixing efficiency and minimum time required for complete mixing of the two solutions whilst maintaining a stable jet. In general, we find that the smaller the cross-sectional area of the mixer microchannel, the shorter the time needed to achieve homogeneous mixing for a given flow rate. The results of these simulations will form the basis for optimised designs enabling the study of molecular dynamics occurring on millisecond timescales using integrated mix-and-inject microfluidic devices.

Steroid analysis is important in the clinical assessment of endocrine function in health and disease. Although tandem mass spectrometry methods coupled with chromatographic separation are considered the gold standard analytical technique in this setting, enabling profiling of multiple steroids in a single sample, sample processing can be labour-intensive. Here we present a simple, efficient automated 96-well Supported Liquid Extraction method with dichloromethane/isopropanol as organic solvent, carried out on a Extrahera automated sample handler (Biotage), which completes sample preparation of 80 plasma samples (200µL) in 90 minutes. Compounds were separated on a Kinetex C18 column (150x3mm;2.6um) using a mobile phase of methanol and water (0.1% formic acid). The run time was 16 minutes on a Nexera uHPLC system (Shimadzu) with a QTrap 6500+ linear ion trap mass spectrometer (AB Sciex). Precisions ranged 8.1 to 18.1% RSD, bias -10.1-5.8%, and extraction recoveries 73.5-111.9%. LOQs ranged between 0.025–0.500 ng/mL.

This paper models the factors explaining households members economic well-being in Burkina Faso, with a focus on the relative influence of gender inequality in literacy status. It does so, using data from the 2014 survey on household living conditions and a semi-parametric bi-variate sample selection modeling approach. This approach compared to the classic Heckman two-step estimator is methodologically innovative because it deals simultaneously with non-random sample selection using conventional systems of two equations, non-linear covariates' effects using spline approach, and the non-normal bivariate distribution using copula functions. The graphical results from the Lorenz curves combined with the numerical Atkinson and Gini coefficients suggest that inequality in overall per-capita consumption spending among households headed by literates is higher than that of their illiterate counterparts in 2009 and 2014. However, independently of the head of household’s literacy status, the level of inequality in total economic well-being decreased from 2009 to 2014. Using the poverty indices of Watts, Sen, Foster ( alpha= 1)) we found that poverty among households headed by literates is lower than that of their illiterates counterparts for both years, although overall poverty decreased nationally between 2009 and 2014. The results also show that although the gender inequality in literacy status does not translate into inequalities in non-food wellness, it does however for food-wellness as female headed households have 38.9% less per-capita food consumption spending than their men counterparts. Combining both food and non-food consumption spending, total economic well-being also seems to exhibit gender inequality as female headed households now have relatively 26.7% less combined per-capita consumption spending.

Artificial intelligence (A.I.) is the key to process big amounts of data to useful information for operators. In the present study the suitability of a 24/7 A.I. sound based coughing monitoring system for early detection of respiratory distress caused by porcine respiratory disease complex (PRDC) associated pathogens was assessed in the nursery of a conventional pig farm. Screening for PRDC associated pathogens was conducted by PCR examination of oral fluids (OFs) and bioaerosol samples (AS). A significant correlation between A.I. and human gained health data was observed in both batches. An increase in coughing episodes, either measured by A.I. or human, was significantly correlated with decreasing Ct-values of swIAV. The Odds to detect nucleic acids of PRRSV or Actinobacillus (A.) pleuropneumonia was significantly higher for OFs compared to AS. Moreover, PCR examinations of OFs revealed significantly lower Ct-values for swIAV and A. pleuropneumonia compared to AS. The quantity of swIAV RNA in OFs and AS was significantly associated with the A.I. calculated respiratory health score and thus, with clinical disease. Due to its reliable data A.I. based health monitoring is beneficial for early recognition of respiratory distress and thus can assist farmer’s daily work.

Electrochromic measurements have been performed throughout the full composition range of reactive magnetron-sputtered mixed Titanium oxide and Molybdenum oxide (TiO2-MoO3) to search for enhanced Coloration Efficiency (CE). Optical parameters and composition have been determined and mapped by using Spectroscopic Ellipsometry (SE). Scanning Electron Microscopy (SEM) with Energy-Dispersive X-ray Spectroscopy (EDS). EDS has been used to check the SE results as it was used in our earlier paper [N. T. Ismaeel et al, Materials 2023, 16(12), 4204] for (TiO2-SnO2). Ti and Mo targets were put separately from each other, and the Indium-Tin-Oxide (ITO) covered glass and Si-probes on a glass substrate (30 cm × 30 cm) were moved under the two separated targets (Ti and Mo) in a reactive Argon-Oxygen (Ar-O2) gas mixture. By using this combinatorial process, all the compositions (from 0 to 100%) were achieved in the same sputtering chamber after one sputtering process. The CE (the change of light transmission for the unit electric charge) of mixed metal oxides (TiO2-MoO3) that are deposited by reactive sputtering has been studied in an electrochemical cell. This paper aims to assess the results of investigations of such materials showing the enhanced electrochromic behavior compared to the pure materials and to enhance the Coloration Efficiency.

Pragmatic trials aim to assess intervention efficacy in usual patient care settings, contrasting with explanatory trials conducted under controlled conditions. In aging research, pragmatic trials are important designs for obtaining real-world evidence in elderly populations, which are often underrepresented in trials. In this review, we discuss statistical considerations from a frequentist approach for the design and analysis of pragmatic trials. When choosing the dependent variable, it is essential to use an outcome that is highly relevant to usual medical care while also providing sufficient statistical power. Besides traditionally used binary outcomes, ordinal outcomes can provide pragmatic answers with gains in statistical power. Cluster randomization requires careful consideration of sample size calculation and analysis methods, especially regarding missing data and outcome variables. Mixed effects models and generalized estimating equations (GEEs) are recommended for analysis to account for center effects, with tools available for sample size estimation. Multi-arm studies pose challenges in sample size calculation, requiring adjustment for design effects and consideration of multiple comparison correction methods. Secondary analyses are common but require caution due to the risk of reduced statistical power and false-discovery rates. Safety data collection methods should balance pragmatism and data quality. Overall, understanding statistical considerations is crucial for designing rigorous pragmatic trials that evaluate interventions in elderly populations under real-world conditions. In conclusion, this review focuses on various statistical topics of interest to those designing a pragmatic clinical trial, with consideration of aspects of relevance in the aging research field.

This paper elucidates the impact of uncertainty on the Markowitz asset allocation and how it performs. The findings imply that when evaluated out-of-sample, estimation errors in parameters might significantly affect how well an allocation performs. Numerous publications that address this ambiguity have been highlighted to emphasize our findings further. In our work, we compare these approaches to alternative allocation strategies and explain their performance in both expected and real out-of-sample events. We find that the Markowitz framework can be improved by using tactics that take uncertainty into account. Longer sample numbers, however, may not always translate into better outcomes. Applying a short-sale constraint can also enhance the initial portfolio. Finally, we find that even more basic approaches to asset allocation, such as equally weighted allocation, perform reasonably well.

In this paper we study a special kind of finite mixture model. The sample drawn from the model consisits of three parts. The first two parts are drawn from specified distributions f1 and f2, while the third one is drawn from the mixture. A problem of interest is whether the two distributions f1 and f2 are the same. To test this hypothesis, we first define the regular location and scale family of distributions and assume that f1 and f2 are regular denisty functions. Then the hypothesis transforms to the equalities of the location and scale parameters, respectively. To utilize the information in the sample, we use Bayes’ theorem to obtain the posterior distribution and give the sampling method. Then we propose the posterior p-value to test the hypothesis. The simulation studies show that our posterior p-value largely improves the power in both normal and logistic cases while nicely controls the Type-I error. A real halibut dataset is used to illustrate the validity of our method.

In Los Angeles, at one point, the Covid-19 testing positivity rate was 6.25%, or one in sixteen. This translates to, on average, one in sixteen specimens testing positive and the vast majority testing negative. Usually, we run sixteen tests on sixteen specimens to identify the positive one(s). This process can be time consuming and expensive. Since a group of negative specimens pooled together for testing will produce a negative result, one single test could potentially eliminate many specimens. Only when the pooled specimen tests positive do we need further testing to identify the positive one(s). Based on this concept, we designed a strategy that will identify the positive specimen(s) efficiently. Assuming one in sixteen specimens is positive, we find that only four tests are needed. Furthermore, we can run them simultaneously, saving both resources and time. Although, in the real world, we cannot make the assumption of only one positive specimen, the same strategy works with slight modification and proves to be much more efficient than the conventional testing. Our strategy returns an answer 48% of the time in four tests and one time cycle. Overall, the average number of tests is seven or eight depending on the follow-up testing, and the average time cycle is about one and a half.

The Brahmaputra is one of the largest rivers in the world, ranking fifth in average discharge. As a result, it is heavily braided with various intricate paths in order to dissipate its huge energy. Although this river is normally classed as a braided river, it has recently been classified as an anastomosing river due to its multi-channel features over alluvial plains. Additionally, the Brahmaputra river’s morphology is random in nature as a result of its high flow variability and bank erodibility. Its anastomosing planform changes in response to seasonal water and sediment waves, resulting in a morphology that is extremely complex. The purpose of this study is to examine the Brahmaputra river’s anastomosing planform entropy as a measure of complexity, power spectral density as a measure of fluctuation and their relationship to the energy expenditure as an imprint of flflow rate of river systems on alluvial landscapes.

Fabric phase sorptive extraction, an innovative integration of solid phase extraction and solid phase microextraction principles, has been combined with gas chromatography-mass spectrometry for the rapid extraction and determination of nineteen organochlorine pesticides in various fruit juices and water samples. FPSE consolidates the advanced features of sol-gel derived extraction sorbents with the rich surface chemistry of cellulose fabric substrate, which could directly extract target analytes from complex sample matrices and substantially simplifies the sample preparation operation. Important FPSE parameters including sorbent chemistry, extraction time, stirring speed, type and volume of back-extraction solvent and back-extraction time have been optimized. Calibration curves were obtained in the concentration range 0.1-500 ng/mL. Under the optimum conditions, the limits of detection were obtained in the range 0.007-0.032 ng/mL with satisfactory precision (RSD<6%). The relative recoveries obtained by spiking organochlorine pesticides in water and selected juice samples were in the range of 91.56–99.83%. The sorbent sol-gel poly(ethylene glycol)-poly(propylene glycol)-poly(ethylene glycol) was applied for the extraction and preconcentration of organochlorine pesticides in aqueous and fruit juice samples prior to analysis with gas chromatography-mass spectrometry. The results demonstrated that the present method is simple, rapid, and precise for the determination of organochlorine pesticides in aqueous samples.

The main objective of this paper is to find a close link between the adaptive level of significance, presented here, and the sample size. We, statisticians, know of the inconsistency, or paradox, in the current classical tests of significance that are based on p-value statistics that is compared to the canonical significance levels (10%, 5% and 1%): "Raise the sample to reject the null hypothesis" is the recommendation of some ill-advised scientists! This paper will show that it is possible to eliminate this problem of significance tests. The Bayesian Lindley's paradox – "increase the sample to accept the hypothesis" – also disappears. Obviously, we present here only the beginning of a possible prominent research. The intention is to extend its use to more complex applications such as survival analysis, reliability tests and other areas. The main tools used here are the Bayes Factor and the extended Neyman-Pearson Lemma.

This work aims to verify whether the continuum mechanics assumption holds for the numerical simulation of a typical sample delivery system in Serial Femtosecond Crystallography (SFX). Knudsen numbers were calculated based on the numerical simulation results of helium flow through the gas-focused liquid sheet nozzle into the vacuum chamber, representing the upper limit of Knudsen number for such systems. The analysed flow is considered steady, compressible and laminar. The numerical results are mesh-independent, with a Grid Convergence Index significantly lower than 1 % for global and local analysis. The study is based on an improved definition of the numerical Knudsen number, a combination of cell Knudsen number and physical Knudsen number. In the analysis, no-slip boundary and low-pressure boundary slip conditions are compared. No significant differences are observed. The study justifies using computational fluid dynamics (CFD) analysis for SFX sample delivery systems based on the assumption of continuum mechanics.

Clinical microbiology studies and systematic reviews have largely concluded that SARS-CoV-2 RNA is rarely present in the urine of COVID-19 patients. Thus, wastewater surveillance has largely focused on human stool and defecation as the relevant matrix and behavior. But, the over-performance of wastewater testing during near-source surveillance, especially in settings where on-site defecation is presumed to be de minimis such as aircraft and non-residential schools, suggests the possibility of SARS-CoV-2 RNA input from a non-fecal source. To revisit the possibility of urine as an explanation, data was compiled from 45 studies reporting on SARS-CoV-2 RNA in the urine of 1,924 COVID-19 patients. In general, the reporting quality was low with most studies (86.7%) failing to report even basic details such as the volume of urine collected. Only 11 reported quantitative data and of these only a single study reported its experiments to the standards established for publication of quantitative PCR data. Taking the data at its face value, the pooled prevalence of urinary SARS-CoV-2 RNA shedding was 11.3% (95%CI: 8.4 - 14.3), which roughly agrees with previous analyses. However, the percentage of variability in the estimate attributable to heterogeneity between studies (I² = 93.66%) warrants careful consideration. Notably, for the 8 studies reporting sufficient workflow information, the observed urinary RNA shedding prevalence was normally distributed (r² = 1.00) as a function of the volume of urine being assayed in a single PCR reaction (i.e., the equivalent sample volume, ESV). The study achieving the highest ESV (83.3 µL of urine) detected SARS-CoV-2 RNA in urine sediments of 95.5% of COVID-19 patients. Conversely, during two studies achieving ESVs of only 2 µL and 11.7 µL of urine, SARS-CoV-2 RNA was not detected in the urine of 72 and 132 patients, respectively. For wastewater surveillance of COVID-19, these observations suggest the rarity of SARS-CoV-2 RNA in urine may be a methodological artifact. Further, the data on which rarity is presumed are of low quality. Beyond COVID-19, the findings demonstrate that clinical studies, by virtue of their design to answer clinically relevant questions, could produce datasets subject to large biases that make them less useful when assessing the feasibility of wastewater surveillance of infectious agents. Wastewater surveillance practitioners must carefully consider the appropriateness and limitations of clinical data considering the important differences between the methods and aims of the two disciplines. Given the great potential of wastewater testing, clinical and environmental microbiologist should collaborate to produce robust quantitative datasets for reliable feasibility assessments for both community and near-source public health surveillance.

In the field of recommender systems, difficult negative samples that are easily misclassified by classifiers have higher learning value. In order to solve the problem that the existing graph convolutional network lacks the exploration of negative sampling strategy, a multivariate sampling based on graph convolution collaborative filtering recommendation model was proposed. Firstly, part of the information in the positive sample was injected into the negative sample, and the high quality difficult negative sample was selected by the inner product method. Then, contrastive learning is used to maximize the similarity between the difficult negative samples and the positive samples, so that the difficult negative samples are as close to the positive samples as possible in the feature space. Ultimately, a multi-task approach was employed to concurrently enhance both the recommendation supervision task and the contrastive learning task. To validate the effectiveness of our approach, we conducted comparative evaluations utilizing three publicly accessible datasets. The experimental outcomes indicate that our proposed model surpasses the baseline model in terms of performance.

In the dynamic landscape of online shopping, predicting user behaviors such as clicks, cart additions, and orders is crucial for optimizing sales strategies. Traditional recommendation systems often focus on a single objective, limiting their effectiveness in a multifaceted e-commerce environment. This article proposes a multi-objective recommendation system that leverages previous events in user sessions to forecast these key metrics, addressing challenges such as imbalanced positive and negative samples, varying session lengths, and the need for effective sampling techniques. Our approach integrates LightGBM, XgBoost, and Recbole GRU4Rec through ensemble learning, combining the strengths of these models to enhance prediction performance. Extensive evaluations demonstrate that our model outperforms existing methods, offering significant improvements in accuracy and robustness. This work provides a comprehensive solution for online retailers to better predict user behaviors and optimize their sales strategies, ultimately enhancing customer satisfaction and business outcomes.

Named Entity Recognition (NER) is a fundamental and pivotal stage in the development of various knowledge-based support systems, including knowledge retrieval and question-answering systems. In the domain of pig diseases, Chinese NER models encounter several challenges such as the scarcity of annotated data, domain-specific vocabulary, diverse entity categories, and ambiguous entity boundaries. To address these challenges, we propose PDCNER, a Pig Disease Chinese Named Entity Recognition method leveraging lexicon-enhanced BERT and contrastive learning. Firstly, we construct a domain-specific lexicon and pre-train word embeddings in the pig disease domain. Secondly, we integrate lexicon information of pig diseases into the lower layers of BERT using a Lexicon Adapter layer, which employs char-word pair sequences. Thirdly, to enhance feature representation, we propose a lexicon-enhanced contrastive loss layer on top of BERT. Finally, a Conditional Random Field (CRF) layer is employed as the model's decoder. Experimental results show that our proposed model demonstrates superior performance over several mainstream models, achieving a precision of 87.76%, a recall of 86.97%, and an F1-score of 87.36%. The proposed model outperforms BERT-BiLSTM-CRF and LEBERT by 14.05% and 6.8%, respectively, with only 10% of the samples available, showcasing its robustness in data scarcity scenarios. Furthermore, the model exhibits generalizability across publicly available datasets. Our work provides reliable technical support for the information extraction of pig diseases in Chinese and can be easily extended to other domains, thereby facilitating seamless adaptation for named entity identification across diverse contexts. The codes have been open-sourced at https://github.com/tufeifei923/pdcner.

Estimating parameters for multiple datasets can be time consuming, especially when the number of datasets is large. One solution is to sample from multiple datasets simultaneously using Bayesian methods such as adaptive multiple importance sampling (AMIS). Here we use the AMIS approach to fit a von Misses distribution to multiple datasets for wind trajectories derived from a Lagrangian Particle Dispersion Model driven from a 3D meteorological data. The primary objective is to characterise the uncertainties in wind trajectories in a form that can be used as inputs for predictive models of wind-dispersed insect pests and pathogens of agricultural crops for use in evaluating risk and in planning mitigation actions. Our results show that AMIS can significantly improve the efficiency of parameter inference for multiple datasets.

Foodborne illnesses can be infectious and dangerous and most of them are caused by bacteria. Some types of common food-related bacteria exist widely in nature and pose a serious threat to both humans and animals, and can cause poisoning, diseases, disabilities and even death. Rapid, reliable and cost-effective methods for bacteria detection are of paramount importance in food-safety and environmental monitoring. Polymerase chain reaction (PCR), lateral flow immunochromatographic assay (LFIA) and electrochemical method have been widely used in food-safety and environmental monitoring. In this paper, the recent developments (2013-2023) covering PCR, LFIA and electrochemical method for various bacteria detection (Salmonella, Listeria, Campylobacter, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli)) considering different foods types, analytical performances and the reported limit of detection (LOD) are discussed. It is found that the bacteria type and food sample type contribute significantly to the analytical performance and LOD. Detection by LFIA has higher average LOD (24 CFU/ml) than detection by electrochemical method (12 CFU/ml) and PCR (6 CFU/ml). Salmonella and E. coli in the Pseudomonadota domain usually have low LODs. LODs are usually lower for detection in fish and eggs. LFIA with gold and iron nanoparticles are prominent in the majority of articles of 26 CFU/ml and 12 CFU/ml respectively. Electrochemical method reveals that the average LOD is highest for cyclic voltammetry (CV) of 18 CFU/ml), followed by electrochemical impedance spectroscopy (EIS) of 12 CFU/ml and differential pulse voltammetry (DPV) of 8 CFU/ml. LOD usually decreases when replicate number increases until it remain unchanged. Exponential relations with (R²>0.95) between LOD of Listeria in milk by LFIA and electrochemical method with replicate number have been obtained. Finally, the review discusses the challenges and future perspectives (including the role of nanomaterials/advanced materials) to improve the analytical performance for bacterial detection.

The issue of optimising the initial stand density (ISD) of tree plantations has high practical importance. The objective of this study was to nondestructively evaluate the influence of the initial stand density of Scots pine plantations located in the European part of the Russian Federation on wood basic density (BD), moisture content (MC), ultrasound velocity (UV), latewood content, and drilling resistance (DR). 45-year-old trees with stand densities of 500, 1000, 3000, 5000, and 10000 trees/ha were tested by a 5-cm-long core sample for gravimetric MCGM and BD, by PULSAR-2.2 for UV along the height (UVH) and through the tree trunk diameter (UVD), by the IML-RESI PD-400 tool for DR, and by GANN HT 85T for MC based on the electrical resistance method (MCERM). A significant influence of ISD was found on DBH, UVD, MCGM, and MCERM. ISD had no significant impact on BD, UVH, and DR. The wood BD ranged from 356 to 578 kg/m-3, with a mean value of 434±3.3 kg/m-3, and was restricted by the soil and environmental factors. DBH and 70% MCERM were good indicators of tree vitality. Linear correlations between DBH and MCERM (R2=0.67), DBH and MCGM (R2=0.74), DR and BD (R2=0.71), and the two-factor model MCGM=f(DBH, BD), (R2=0.76) were found.

The introduction of exoskeletons in the industry has focused on improving worker safety. Exoskeletons have the objective of decreasing the risk of injury or fatigue when performing physically demanding tasks. The exoskeleton’s effects on the muscles is one of the most common focus in the assessments. The present study aims to analyse the muscle interactions generated by using a passive lumbar exoskeleton during load-handling tasks in laboratory conditions with and without an exoskeleton. Electromyographic data of the muscles involved in the task were recorded from twelve participants performing load-handling tasks. Correlation Coefficient, Coherence Coefficient, Mutual Information, and Multivariate Sample Entropy were calculated to determine if there were significant differences in muscle interactions between the two test conditions. Results showed statistically significant differences for all pairs of muscles and indicated that the use of the exoskeleton implied more constant values throughout the exercise. The Directed Conditional Granger Causality was obtained to study the directionality of the interactions, with significant differences in two muscle pairs Gluteus-Quadriceps and Gluteus-Lumbar in the gravity-positive direction in both cases. In conclusion, EMG parameters chosen appear to be appropriate measurements for studying the exoskeleton effects over muscle couplings.

Urinary tract infections are among the most frequent infectious diseases and require screening of a great amount of urine samples from patients. However, a high percentage of samples results as negative after urine culture plate test (CPT), demanding a simple and fast preliminary technique to screen out the negative samples. We propose a digital holographic microscopy (DHM) method to inspect fresh urine samples flowing in a glass capillary for 3 minutes, recording holograms at 2 frames per second. After digital reconstruction, bacteria, white and red blood cells, epithelial cells and crystals were identified and counted and the samples were classified as negative or positive according to clinical cutoff values. Taking CPT as reference, we processed 180 urine samples and compared the results with those of urine flow cytometry (UFC). Using standard evaluation metrics for our screening test, we found similar performance for DHM and UFC, indicating DHM as a suitable and fast screening technique retaining several advantages. As a benefit of DHM, the technique is label-free and does not require sample preparation. Moreover, the phase and amplitude images of the cells and other particles present in urine are digitally recorded and can serve for further investigation afterwards.

Ayurveda as a healthcare system has survived for thousands of years but continues to be dogged by reported lack of efficacy of the treatments in clinical trials. The reported lack of efficacy could be due to a real lack of efficacy (which then contradicts the survival of Ayurveda as a functional medical system enjoying considerable public patronage) or could be attributed to inadequacies in the efforts towards evidence generation or in a larger context the overall scientific conduct of research in Ayurveda. In an effort towards better evidence generation, there is an immediate need for standardizing the design, conduct and reporting of clinical trials of Ayurveda but it is a daunting task. For this effort to benefit the scientific endeavors of Ayurveda researchers, it should allow the researchers to be able to apply Ayurveda’s multi-component, individualized and inherently holistic approach. Statistical principles can benefit this effort. Statistical hypothesis testing (SHT) is central to these statistical principles and also aligns well with conventional scientific principles of evidence generation. Although there are challenges with SHT, good practitioners engaged in it do much more than just apply the mathematical theory behind it. As a particular example, lot of time in clinical trial designing is spent in addressing biases and designing trials prudently by minimizing the effect of such biases. SHT can benefit such an effort objectively. There is a need for Ayurveda researchers to engage deeply and mindfully about biases in study design in order to gain scientific validity and acceptability. The article highlights issues that arise in Ayurveda research, and discusses few ways of dealing with these issues using statistical principles.

The separation of rare earth metals (REM) from a neodymium magnet has been widely studied in the last year. We have shown that the waste of computer hard disk contains 25.41 % neodymium, 64.09 % iron, and &lt;&lt;1 % boron. To further isolate rare-earth metals, the magnet was acidically dissolved in open and closed systems. In both methods of dissolution was used concentrated nitric acid. The difference between these methods are the conditions of dissolution of magnet. The magnet was dissolved in a microwave sample preparation system at different temperatures and pressures in a closed system. In the open system, the acid dissolution of the magnet conducted at room temperature. 0.2 g of the neodymium magnet sample was taken under two conditions, and the dissolution process in the closed system lasted 1 hour, and in the open system-30-40 minutes. The open system is a non-laborious, simple and cheap method of dissolving the magnet by comparing both systems. Therefore, an open sample preparation system is used for further work. To remove the iron in the magnet, oxalic acid was used and precipitated as oxalates under both conditions. According to the result of the ICP-MS method, it is shown that the neodymium and iron contents in the precipitate are 24.66 % and 0.06 %, respectively. This shows that the iron has almost completely passed to the filtrate. Thus, it is possible to remove the iron from the sample.

For the electromagnetic eddy current testing, various methods have been proposed for reducing the lift-off error on the measurement of samples. In this paper, instead of eliminating the measurement error caused by the lift-off effect, an algorithm has been proposed to directly measure the lift-off distance between the sensor and non-magnetic conductive plates. The algorithm is based on a sample-independent inductance (SII) feature. That is, under high working frequencies, the inductance is found sensitive to the lift-off distance and independent of the test piece under an optimal single high working frequency (43.87 kHz). Furthermore, the predicted lift-off distance is used for the thickness prediction of the non-magnetic conductive samples using an iterative method. Considering the eddy current skin depth, the thickness prediction is operated under a single lower frequency (0.20 kHz). As the inductance has different sensitivities to the lift-off and thickness, the prediction error of the sample thickness is different from that of the lift-off distance. From the experiments on three different nonmagnetic samples – aluminium, copper, and brass, the maximum prediction error of the lift-off distance and sample thickness is 1.1 mm and 5.42 % respectively at the lift-off of 12.0 mm.

The theory and working principle of fabric phase sorptive extraction (FPSE) is presented that eloquently explains the mystery behind this new and powerful sample preparation technique. FPSE innovatively integrates the benefits of sol-gel coating technology and the rich surface chemistry of cellulose/polyester/fiberglass fabric, resulting in a microextraction device with very high sorbent loading in the form of an ultra-thin coating. This porous sorbent coating and the permeable substrate synergistically facilitate very fast extraction equilibrium. The flexibility of the FPSE device allows for direct insertion into original, unmodified samples of different origin. Strong chemical bonding between the sol-gel sorbent and the fabric substrate permits the exposure of FPSE devices to any organic solvent for analyte back-extraction/elution and to highly acidic or basic environments (pH 1-12) if required. A sol-gel derived sorbent, highly polar sol-gel poly(ethylene glycol) coating, was generated on cellulose substrates. Five cm2 segments of these coated fabrics were used as the FPSE devices for sample preparation using direct immersion. An important class of environmental pollutants, substituted phenols, was used as model compounds to evaluate the extraction performance of FPSE. The high primary contact surface area (PCSA) of the FPSE device and porous structure of the sol-gel coatings resulted in very high sample capacities and incredible extraction sensitivities for both the compound classes in a relatively short period of time. Different extraction parameters were evaluated and optimized. The new extraction devices demonstrated part per trillion level detection limits for substituted phenols, a wide range of detection linearity, and good performance reproducibility.

Background: Amidst escalating climate change concerns, sustainable practices in healthcare logistics are critical. This study evaluates the ecological impact and efficiency of medical sample transportation using drones, combustion cars, and electric cars across diverse terrains and weather in Liechtenstein and Switzerland. Methods: We conducted a comparative analysis of CO2 emissions, transport distances, and delivery times for each transportation modality. Data collection involved multiple runs under various conditions, with CO2 emissions measured using onboard systems, and transport efficiency assessed via GPS tracking and time monitoring. Results: Combustion cars exhibited the highest average CO2 emissions at 159.5 grams per kilometer (g/km). Electric cars emitted significantly less, averaging 3.43 g/km, which is 2.15% of combustion car emissions. Drones were the most environmentally friendly, with an average emission of 0.09 g/km, representing 0.07% of combustion car emissions and 2.6% of electric car emissions. Drones also showed substantial transport efficiency, covering 17% shorter distances on flat terrain and 24% shorter in mountainous areas, with time savings ranging from 13% to 80%. Conclusion: This study highlights the potential of drone technology to revolutionize healthcare logistics by reducing environmental impact and optimizing operational efficiency. Integrating drones can significantly lower the carbon footprint, improve sample integrity, and enhance patient care, promoting a greener, more resilient healthcare system.

This short report extends our previous paper “Multiplicity of human scent signature” and transfers canine identification to digital processing of data. The question is whether current GCxGC-MS technology and computer-based identification can perform identification as well as canines. This would objectify the identification of persons. Working with digital scent samples enables the use of multiple scent signatures for more exact identification. More differently constructed signatures during the identification process may represent the key point in identification of persons in olfactronics. Several types of scent signatures were considered in this article. The digital scent signatures were constructed based on the relative contents of the chemical compounds in the samples and relative ratios of compounds in the samples. Further on the signatures were made from the parts of the original samples fractioned by the volatility of compounds and by the relative abundance of the compounds in the samples. These results confirm the scent multiplicity phenomenon in digital scent samples. Using differently constructed signatures may be the key point in identification of persons using digital scent samples. This approach enables a transition from subjectivity to objectivity and a change from physical olfactory to olfactronic processing of digital scent samples.

Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

Parkinson’s disease (PD) is the second-most common neurodegenerative disorder worldwide and is diagnosed based upon motor impairments. Non-motor symptoms are well-recognized in PD, and peripheral neuropathy is a frequent but poorly appreciated non-motor sign. Study of how the central and peripheral sensory systems are affected can contribute to the development of targeted therapies and deepen the understanding of the pathophysiology of PD. Although the cause of sporadic PD is unknown, chronic exposure to the pesticide rotenone in humans increases the risk of development of PD. Here, we aimed to investigate whether peripheral neuropathy is present in a traditional model of PD. Mice receiving intrastriatal rotenone showed greatly reduced dopamine terminals in the striatum and reduction in tyrosine hydroxylase-positive neurons in the Substantia nigra pars compacta and developed progressive motor impairments in hindlimb stepping and rotarod but no change in spontaneous activity. Interestingly, repeated testing using gold-standard protocols showed no change in gut motility, a well-known non-motor symptom of PD. Importantly, we did not observe any change in heat, cold, or touch sensitivity, again based upon repeated testing with well-validated protocols that were statistically well-powered. Therefore, this traditional model fails to replicate PD, and our data again reiterate the importance of the periphery to the disorder that is PD.

The impact of COVID-19 pandemic on the cardiovascular healthcare services in Croatia has not been systematically examined. In this study, we compared utilization and provision of cardiovascular services in Croatia during the pre-pandemic period (2017 to end of 2019) and pandemic period (2020 to end of 2021) by using large nationwide data from the Croatian Health Insurance Fund, an institution covering 95% of all insurance claims in Croatia. We report that the use of advanced heart failure treatment modalities increased while the number of coronary angiographies decreased while the number of performed percutaneous coronary interventions remained similar, even increasing in the setting of acute myocardial infarction, comparing pandemic to pre-pandemic period. Similarly, the number of transcatheter aortic valve implantations increased significantly during the pandemic era. There was a significant increase in laboratory diagnostic testing with troponin and natriuretic peptide assays during the pandemic period while the use of most cardiovascular services remained stable or only slightly decreased, compared to pre-pandemic period. These data suggest that Croatian cardiovascular healthcare services during COVID-19 pandemic showed resilience and managed to maintain or even increase the number of various diagnostic and interventional procedures despite the widespread societal and logistical challenges encountered during pandemic.

Single sample face recognition (SSFR) is a computer vision challenge. In this scenario, there is only one example from each individual on which to train the system, making it difficult to identify persons in unconstrained environments, particularly when dealing with changes in facial expression, posture, lighting, and occlusion. This paper suggests a different method based on a variant of the Binarized Statistical Image Features (BSIF) descriptor called Multi-Block Color-Binarized Statistical Image Features (MB-C-BSIF) to resolve the SSFR Problem. First, the MB-C-BSIF method decomposes a facial image into three channels (e.g., red, green, and blue), then it divides each channel into equal non-overlapping blocks to select the local facial characteristics that are consequently employed in the classification phase. Finally, the identity is determined by calculating the similarities among the characteristic vectors adopting a distance measurement of the k-nearest neighbors (K-NN) classifier. Extensive experiments on several subsets of the unconstrained Alex &amp; Robert (AR) and Labeled Faces in the Wild (LFW) databases show that the MB-C-BSIF achieves superior results in unconstrained situations when compared to current state-of-the-art methods, especially when dealing with changes in facial expression, lighting, and occlusion. Furthermore, the suggested method employs algorithms with lower computational cost, making it ideal for real-time applications.

The advancement of the pediatric traumatic brain injury (TBI) knowledge base requires biospecimens and data from large samples. This study seeks to describe a novel clinical research modality to establish best practices for enrolling a diverse pediatric TBI population and quantifying key information on enrollment into biobanks. Screening form responses were standardized and cleaned through Google Sheets. Data was used to analyze total individuals at each enrollment stage. R was utilized for final analysis, including chi-square goodness of fit and proportion statistical tests, to determine further significance and relationships. Issues throughout data cleaning shed light on limitations of the consent modality. Results suggest that through a diverse research team, the recruited sample exceeds traditional measures of representation (e.g. sex, race, ethnicity). Sex demographics of the study are representative of the local population. Screening for candidates is critical to the success of the consent modality. The consent modality may be modified to increase diversity of study population and accept bilingual candidates. Researchers must implement best practices, including increasing inclusivity of bilingual populations, utilizing technology, and improving participant follow-up, to improve health disparities for understudied clinical populations.

Methods for high-quality DNA extraction and knowledge of sex expression and flowering time are essential for applying genomic-assisted breeding and improve the success with hybridization in Guinea yam. A dioecious or monoecious pattern of flowering and sometimes non-flowering is a common phenomenon within and between the Dioscorea species. The flowering in yam plants raised from botanical seeds often takes an extended period, mostly till the first clonal generation after propagation from the tubers. The prolonged process of testing required to identify plant sex and flowering intensity in yam breeding often poses a challenge to realize reduced breeding cycle and apply genomic selection. This study assessed sample preservation methods for DNA quality during extraction and potential of DNA marker to diagnose plant sex at the early seedling stage in white Guinea yam. The predicted sex at the seedling stage was further validated with the visual score for the sex phenotype at the flowering stage. DNA extracted from leaf samples preserved in liquid nitrogen, silica gel, dry ice, and oven drying methods was similar in quality with a high molecular weight than samples stored in ethanol solution. Yam plant sex diagnosis with the DNA marker (sp16) identified a higher proportion of ZW genotypes (female or monoecious phenotypes) than the ZZ genotypes (male phenotype) in the studied materials with 74% prediction accuracy. The results from this study provided valuable insights on suitable sample preservation methods for quality DNA extraction and the potential of DNA marker sp16 to predict sex in white Guinea yam.

High-resolution laboratory-based thermal infrared spectroscopy is an up-and-coming tool in the field of geological remote sensing. Its spatial resolution allows for detailed analyses at centimeter to sub-millimeter scale. However, this increase in resolution creates challenges with sample characteristics such as grain size, surface roughness and porosity that can influence the spectral signature. This research explores the effect of rock sample surface preparation on the TIR spectral signatures. We applied three surface preparation methods (split, saw and polish) to determine how the resulting differences in surface roughness affects both the spectral shape as well as the spectral contrast. The selected samples are a pure quartz sandstone, a quartz sandstone containing a small percentage of kaolinite, and an intermediate-grained gabbro. To avoid instrument or measurement type biases we conducted measurements on three TIR instruments, resulting in directional hemispherical reflectance spectra, emissivity spectra and bi-directional reflectance images. Surface imaging and analyses were performed with scanning electron microscopy and profilometer measurements. We demonstrate that surface preparation affects the TIR spectral signatures influencing both the spectral contrast as well as the spectral shape. The results show that polished surfaces predominantly display a high spectral contrast while the sawed and split surfaces display up to 25% lower reflectance values. Furthermore, the sawed and split surfaces display spectral signature shape differences at specific wavelengths, which we link to mineral transmission features, surface orientation effects and multiple reflections in fine-grained minerals. Hence, the influence of rock surface preparation should be taken in consideration to avoid an inaccurate geological interpretation.

The aim of this document is to provide practical guidance on the choice of target difference used in the sample size calculation of a randomised controlled trial (RCT). Guidance is provided with a definitive trial, one that seeks to provide a useful answer, in mind and not those of a more exploratory nature. The term “target difference” is taken throughout to refer to the difference that is used in the sample size calculation (the one that the study formally “targets”). Please see the glossary for definitions and clarification with regards other relevant concepts. In order to address the specification of the target difference, it is appropriate, and to some degree necessary, to touch on related statistical aspects of conducting a sample size calculation. Generally the discussion of other aspects and more technical details is kept to a minimum, with more technical aspects covered in the appendices and referencing of relevant sources provided for further reading.The main body of this guidance assumes a standard RCT design is used; formally, this can be described as a two-arm parallel-group superiority trial. Most RCTs test for superiority of the interventions, that is, whether or not one of the interventions is superior to the other (See Box 1 for a formal definition of superiority, and of the two most common alternative approaches). Some common alternative trial designs are considered in Appendix 3. Additionally, it is assumed in the main body of the text that the conventional (Neyman-Pearson) approach to the sample size calculation of an RCT is being used. Other approaches (Bayesian, precision and value of information) are briefly considered in Appendix 2 with reference to the specification of the target difference.

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). This paper proposes a refined equivalent modeling method for mixed WFs based on artificial intelligence technology. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the main factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on sample data, to explore the nonlinear relationship between the dependent variables and the independent variables. Finally, the electromechanical transient simulations of the power systems with a mixed WF is carried out by using the MATLAB platform. Simulation results show that the proposed model can reflect the external characteristics of the mixed WF in different wind scenarios and voltage dips.

I want to meaningfully average a pathalogical function (i.e., an everywhere surjective function whose graph has zero Hausdorff measure in its dimension). In case this impossible, we wish to average a nowhere continuous function defined on the rationals. We do this by taking the most generalized, satisfying extension of the expected value, w.r.t the Hausdorff measure in its dimension, on bounded functions with domains of finite measure which takes finite values only. As of now, I’m unable to solve this due to limited knowledge of advanced math and most people are too busy to help. Therefore, I’m wondering if anyone knows a research paper which solves my doubts. Unlike the previous paper, “Finding a Research Paper Which Meaningfully Averages Pathalogical Functions”) we add motivations, examples, and change parts of the original paper.

Serial femtosecond crystallography (SFX) using an X-ray free-electron laser (XFEL) enables determination of room-temperature structures without causing radiation damage. Using an optical pump-probe or mix-and-injection, SFX enables the intermediate state visualization of a molecular reaction. In SFX experiments, serial and stable microcrystal delivery to the X-ray interaction point is vital for reasonable data collection and efficient beam time. The Pohang Accelerator Laboratory X-ray Free Electron Laser (PAL-XFEL) facility established SFX instruments at a nanocrystallography and coherent imaging (NCI) experimental station. Various sample delivery methods, including injection, fixed-target scanning, and hybrid methods, have been developed and applied to collect XFEL diffraction data. Herein, we review the currently available sample delivery methods for SFX at the NCI experimental station at the PAL-XFEL. This article will help PAL-XFEL users access the SFX system for their experiments.

Glycosphingolipids (GSL) represent a highly heterogeneous class of lipids with many cellular functions, implicated in a wide spectrum of human diseases. Their isolation, detection, and comprehensive structural analysis is a challenging task due to the structural diversity of GSL molecules. In this work, GSL subclasses are isolated from human plasma using an optimized monophasic ethanol–water solvent system capable to recover a broad range of GSL species. Obtained deproteinized plasma is subsequently purified and concentrated by C18-based solid-phase extraction (SPE). The hydrophilic interaction liquid chromatography (HILIC) coupled to electrospray ionization linear ion trap tandem mass spectrometry (ESI-LIT-MS/MS) is used for GSL analysis in the human plasma extract. Our results provide an in-depth profiling and structural characterization of glycosphingolipid and some phospholipid subclasses identified in the human plasma based on their retention times and the interpretation of tandem mass spectra. The structural composition of particular lipid species is readily characterized based on the detailed interpretation of MS and MS/MS spectra and further confirmed by specific fragmentation behavior following predictable patterns, which yields to the unambiguous identification of 154 GSL species within 7 lipid subclasses and 77 phospholipids representing the highest number of GSL species ever reported in the human plasma. The developed HILIC-ESI-MS/MS method can be used for further clinical and biological research of GSL in the human blood or other biological samples.

This paper surveys estimates of the transmission features of the novel coronavirus, and then proposes a model to address sample-selection bias in estimated determinants of infection. Containment assumptions of the infection forecasting models depend on assumed effects of policies and self-regulating behavior. In the commons dilemma of the pandemic, the perceived ‘low risks’ of unregulated marginal choices do not reflect the full social cost, implying non-pharmaceutical interventions (NPI) to reduce mortality can enhance social welfare. As more economic activity renews with liftings of restrictive NPI (RNPI), a critical question concerns the ability of milder NPI (MNPI) and voluntary precautions to mitigate the risk of greater infections and deaths while also limiting the pandemic’s economic damage and its social costs. Ineffective NPI could lead to continued COVID-19 waves and new types of crises, worsened expectations and delayed economic recoveries. From the central range of surveyed estimates of transmission and alternative herd-immunity-threshold estimates, a ‘worst-case’ virus guidepost suggests eventual deaths of around 25 to 41 million worldwide and 1.1 to 1.7 million in the U.S. needed to reach herd immunity with no vaccine or treatment. The most optimistic study surveyed (theoretical model from a non-reviewed preprint study) combined with the low end of the range of the estimated mortality rate suggests 6 to 9 million deaths worldwide and 250 to 370 thousand in the U.S. to reach herd immunity. Successes in the mix of NPI, treatments, and vaccine can limit the eventual global death toll of the virus. Improved estimation models for forecasting and decision making may assist in better targeting the local timings and mix of NPI. Diagnostic tests for the virus have been largely limited to symptomatic cases, causing possible sample selection bias. A recursive bivariate probit model of infection and testing is proposed along with several possible applications from cross-section or panel-data estimation. Multiple potential explanatory variables, data sources, and estimation needs are specified and discussed.

The integration of drone technology into healthcare logistics presents a significant opportunity to enhance the speed, reliability, and efficiency of medical sample transportation. This study inves-tigates the current advancements in drone technology, focusing on its application in the rapid and secure delivery of medical samples, particularly in urban and remote regions where traditional transportation methods often face challenges. Utilizing a combination of recent technological de-velopments such as AI-driven navigation systems, real-time monitoring, and secure payload management, the study examines how drones can mitigate logistical barriers like traffic conges-tion and geographical isolation. The research methodology includes a comprehensive review of recent case studies from various regions, illustrating the practical applications and benefits of drones in healthcare. The results demonstrate a substantial reduction in transportation time and costs, along with improved accessibility to healthcare services in underserved areas. The study concludes that, while challenges such as regulatory hurdles and privacy concerns remain, the on-going advancements in drone technology and supportive regulatory frameworks have the poten-tial to revolutionize medical logistics, ultimately improving patient outcomes and healthcare de-livery.

The European Union's recent decision to renew the authorization for the use of glyphosate until 15 December 2033 stimulates the scientific discussion all around the world regarding its toxicity or otherwise for humans. Glyphosate is a chemical used by millions of tons in the last 50 years worldwide to drying weeds in cultivated fields, greenhouses, roadsides. Concern has been raised in many quarters about the possible presence in the food chain and the consequent adverse effects on health: both aspects that argue in favor of toxicity and those that instead may indicate limited toxicity of glyphosate are here discussed. The great debate that has been generated requires further investigations and field measurements to understand its fate once dispersed in the environment and its concentration in the food chain. Hence the need for validated analytical methods that are available to analysts in the field. In the present review, methods for the analytical determination of glyphosate and its main metabolite, AMPA, are discussed, with a specific focus on chromatographic techniques applied to cereal products. The experimental procedures are explained in any detail, including cleanup, derivatization, and instrumental conditions to give the laboratories involved enough information to proceed with the implementation of this line of analysis. The prevalent chromatographic methods used are LC-MS/MS, GC-MS/SIM, and GC-MS/MS but sufficient indications are also given to those laboratories that wish to use the simpler HPLC-FLD, HPLC-UV, GC-NPD or GC-FPD techniques for screening purposes. Concentrations from literature measured in wheat, corn, barley, rye, oats, soybean, and cereal-based foods are reported together with the regulatory status in various parts of the world and the accumulation mechanism. As for the accumulation in cereals, available data show that glyphosate tends to accumulate more in wholemeal flours than in refined ones, that its concentration in the product strictly depends on the treatment period (the closer it is to the time of harvesting, the higher the concentration) and that in cold climates the herbicide tends to persist in the soil for a long time.

The present paper reports an alternative solution of the measurement problem in quantum theory. The measurement problem can be characterized as that the U-procedure verses the R-procedure. The R-procedure is tested by the Yes/No measurement originally proposed by von Neuman and discussed in details by Penrose. We propose a novel stochastic sampling method to tackle the measurement paradox. Each testing sample produces a pair of Yes-number c and No-number d, which in turn generates a sample phase with respect to the exponential form of (c+id). All the sample phases forms a group G_R. Taking G_R as the sampling potential, combined with the dynamic phase group G_U，we establish a unified model of U-procedure and R-procedure for the evolution of wavefunction. Based on the Born rule, the probability of a sample is given by the squared magnitude of （c+id）. Some metaproperties of the new U-R model, such as natural transformation, consistency, and completeness, are presented. The present work provides a new picture of quantum mechanics and alike. The measurement problem widely exists in dynamic analysis, higher cognition, and large data modeling. Thus, the work reported here has a wide range of applications. Keywords: The measurement problem; U-R procedures; the Yes/No type measurement; stochastic sampling; sample phase; Born probability.

In this paper, we will extend the expected value of the function w.r.t the uniform probability measure on sets measurable in the Caratheodory sense to be finite for a larger class of functions, since the set of all measurable functions with infinite or undefined expected values forms a prevalent subset of the set of all measurable functions, which means "almost all" measurable functions have infinite or undefined expected values. Before we define the specific problem in section 2, we will outline some preliminary definitions. We'll then define the specific problem (along with a partial solution in section 3) to visualize the complete solution. Along the way, we will ask a series of questions to clarify our understanding of the paper.

In recent times, voice assistants have become a part of our day-to-day lives, allowing information retrieval by voice synthesis, voice recognition, and natural language processing. These voice assistants can be found in many modern-day devices such as Apple, Amazon, Google, and Samsung. This project is primarily focused on Virtual Assistance in Natural Language Processing. Natural Language Processing is a form of AI that helps machines understand people and create feedback loops. This project will use deep learning to create a Voice Recognizer and use Commonvoice and data collected from the local community for model training using Google Colaboratory. After recognizing a command, the AI assistant will be able to perform the most suitable actions and then give a response. The motivation for this project comes from the race and gender bias that exists in many virtual assistants. The computer industry is primarily dominated by the male gender, and because of this, many of the products produced do not regard women. This bias has an impact on natural language processing. This project will be utilizing various open-source projects to implement machine learning algorithms and train the assistant algorithm to recognize different types of voices, accents, and dialects. Through this project, the goal to use voice data from underrepresented groups to build a voice assistant that can recognize voices regardless of gender, race, or accent. Increasing the representation of women in the computer industry is important for the future of the industry. By representing women in the initial study of voice assistants, it can be shown that females play a vital role in the development of this technology. In line with related work, this project will use first-hand data from the college population and middle-aged adults to train voice assistants to combat gender bias.

Ecosystem surveillance monitoring is critical to managing natural resources and especially so under changing environments. Despite this importance, the design and implementation of monitoring programs across large temporal and spatial scales has been hampered by the lack of appropriately standardised methods and data streams. To address this gap, we outline a surveillance monitoring method based on permanent plots and voucher samples suited to rangeland environments around the world that is repeatable, cost-effective, appropriate for large-scale comparisons and adaptable to other global biomes. The method provides comprehensive data on vegetation composition and structure along with soil attributes relevant to plant growth, delivered as a combination of modules that can be targeted for different purposes or available resources. Plots are located in a stratified design across vegetation units, landforms and climates to enhance continental and global comparisons. Changes are investigated through revisits. Vegetation is measured to inform on composition, cover and structure. Samples of vegetation and soils are collected and tracked by barcode labels and stored long-term for subsequent analysis. Technology is used to enhance the accuracy of field methods, including differential GPS r plot locations, instrument based Leaf Area Index (LAI) measures, and three dimensional photo-panoramas for advanced analysis. A key feature of the method is the use of electronic field data collection to enhance data delivery into a publicly-accessible database.Our method is pragmatic, whilst still providing consistent data, information and samples on key vegetation and soil attributes. The method is operational and has been applied at more than 704 field locations across the Australian rangelands as part of the Ecosystem Surveillance program of the Terrestrial Ecosystem Research Network (TERN). The methodology enables continental analyses, and has been tested in communities broadly representative of rangelands globally, with components being applicable to other biomes. Here we also recommend the consultative process and guiding principles that drove the development of this method as an approach for development of the method into other biomes. The consistent, standardised and objective method enables continental, and potentially global analyses than were not previously possible with disparate programs and datasets.

Unmetamorphosed carbonaceous chondrites provide important information concerning the formation and evolution of organic matter, such as amino acids. However, terrestrial contamination remains a valid concern when investigating the organic inventory of meteorites that have fallen to Earth. Accordingly, JAXA’s Hayabusa2 and NASA’s OSIRIS-REx have been launched with the task of returning uncontaminated C-type asteroid material to Earth. The successful Hayabusa2 mission has a very limited sample size (5.4 g). Therefore, many conventional compound specific techniques will struggle to detect amino acids above detection limit with available sample amounts (~several mg to 10’s of mg) being much smaller than those typically used. Here a novel method utilizing ultrahigh performance liquid chromatography-Orbitrap-mass spectrometry is validated and applied to very small meteorite samples, thus providing an approach that can overcome the small sample size constraints of sample return missions. The method is highly sensitive, enabling the detection of amino acids in the carbonaceous chondrites Murchison (2.28 mg) and Orgueil (2.30 mg). Furthermore, quantitation was possible for many of the detected amino acids in Murchison and Orgueil. Using the data presented here, both the amino acid reservoirs of Murchison and Orgueil and the potential application of this method to sample return samples are discussed.

Background: Reliable HPV testing and genotyping are essential for quality assurance in HPV-based primary screening, disease management and for monitoring the impact of HPV vaccination. The clinical validation of HPV molecular diagnostic assays has significantly contributed to these objectives, however little emphasis has been placed in assuring sample quality. This study aims to assess the accuracy of sample cellularity assessment using C-C Motif Chemokine Receptor 5 (CCR5) gene target as a marker of sample adequacy in molecular diagnostics. Methods: Jurkat cell line samples were counted both using Thoma cell-counting chamber and Fluorescent-Activated Cell Sorting (FACS). Jurkat cell line samples at three different concentrations were subsequently evaluated using the OncoPredict HPV Quality Control (QC) real-time PCR assay, employing CCR5 for molecular cellularity quantification. Results: Cellularity values obtained were comparable across the 3 different methods for all dilutions of the cell line tested. Conclusions: The results obtained from this study show that CCR5 represents a promising molecular marker for the accurate quantification of sample cellularity, confirming its use as a reliable sample adequacy control, thus reducing the risk of “false negative” results.

The ISO 2859 and ISO 3951 series provide acceptance sampling procedures for lot inspection, allowing both sample size and acceptance rule to be determined, starting from a specific value either for the consumer or producer risk. However, insufficient resources often make it difficult to implement “ISO sampling plans.” In cases where the sample size is determined by external constraints, the focus shifts from determining sample size to determining consumer and producer risks. Moreover, if the sample size is very low (e.g. one single item), prior information should be included in the statistical analysis. For this reason, it makes sense to work within a Bayesian theoretical framework, such as that described in JCGM 106. Accordingly, the approach from JCGM 106 is adopted and broadened so as to allow application to lot inspection. The discussion is based on a “real-life” example of lot inspection on the basis of a single item. Starting from simple assumptions, expressions for both the prior and posterior distributions are worked out, and it is shown how the concepts from JCGM 106 can be reinterpreted in the context of lot inspection. Conceptual differences regarding the definition of consumer and producer risks in JCGM 106 and in the ISO acceptance sampling standards are elucidated and a numerical example is provided.

Background and objectives: Only a few studies analyzed the physical activity level of elderly people living alone in local communities and evaluated the relationship between it and mental health. The purpose of this study was to investigate the relationship between physical activity and depression in the elderly living alone and to provide basic data for the prevention of depression in the elderly. Materials and Methods: We analyzed 256 elderly people living alone aged 65 years or older who completed the 2014 Korea National Health and Nutrition Examination Survey. Depression was defined as a score of 10 or higher using Patient Health Questionnaire-10(PHQ-9). This study investigated walking per week, days of muscular strength exercise performance in the past one week, days of flexibility exercise in the past one week, mean hours in a sitting position per day, the numbers of days and hours conducting a high intensity physical activity in the past one week, and numbers of days and hours conducting a medium intensity physical activity in the past one week to define physical activity. Our study presented prevalence odds ratios (pOR) and 95% confidence interval (CI) by using complex sample logistic regression analysis in order to identify the relationship between physical activity and depression. Results: The results of complex sample logistic regression analysis showed that flexibility exercise was significantly related to depression (p <0.05). On the other hand, the mean hours in a sitting position per day, aerobic physical activity, walking, and muscular strength exercise were not significantly related to geriatric depression. Conclusions: The results of our study implied that persistent flexibility exercise might be more effective to maintain a healthy mental status than muscular strength exercise. A longitudinal study is required to prove the causal relationship between physical activity and depression in the old age.

The high performance of liquid chromatography(HPLC) has long been an indispensable tool for vigilance and analysis across range disciplines such as pharmaceuticals, environmental monitoring, food safety attributes etc. We will discuss about HPLC method development and validation, types of liquid chromatography methods focusing on proper use of a mobile phase like an eluent selection, how to optimize the conditions as well take it through its various procedures required for approval from regulatory bodies. Strategies to improve method robustness, sensitivity and reproducibility are highlighted. The future breakthroughs in HPLC technology and some innovative trends have also been speculated to limit this fundamental methodology.

The Mann-Whitney effect is a measure for comparing survival distributions between two groups. The Mann-Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann-Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent each other, the traditional expression of the Mann-Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann-Witney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands (https://nkosuke.shinyapps.io/shiny_survival/). Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

Over the last few years, Earth Observation (EO) data has shifted towards increased use to produce official statistics, particularly in the agriculture sector. National statistics offices worldwide, including in Asia and the Pacific, are expanding their use of EO data to produce agricultural statistics such as crop classification, yield estimation, irrigation mapping, and crop loss estimation. The advances in image classification, such as pixel-based and phenology-based classifications, and machine learning create new opportunities for researchers to analyze EO data applied to agriculture statistics. However, it requires the ground truth (GT) data because classification result mainly depends on the quality of GT. Therefore, in this study, we introduced a random sampling approach to design and collect GT data using EO imagery and ancillary data. As a result of data collection, GT data improve the algorithms and validates classification results. Nevertheless, despite the importance of GT data, they are rarely disseminated as a data product in themselves. Thus, this results in an untapped opportunity to share GT data as a global public good, and improved use of survey and census data as a source of GT data.

Recent studies on seafloor mapping have presented different modelling methods for the automatic classification of seafloor sediments. However, most of these studies have applied these models to seafloor data with appropriate number of ground-truth samples, which raises the question whether these methods are applicable to studies with smaller numbers of ground-truth data. In this study, we aim to address this issue by conducting sediment class-specific predictions using ensemble modelling to map areas with limited or without ground-truth data and combined with hydro-acoustic datasets. The resulting class-specific maps were then assembled into one map, where the most probable class was assigned to the appropriate location. Our approach was able to predict sediment classes without bias to the class with more ground-truth data and produced reliable seafloor sediment distributions maps that can be used for seafloor monitoring. Sediment shifts of a heterogenous seafloor in the Sylt Outer Reef, German North Sea were also assessed to understand the sediment dynamics in the area. The analyses of sediment shifts showed that the western area of the Sylt Outer Reef is highly active, and the results of the analyses assisted in providing recommendations on future seafloor monitoring activities.

In this work, we have studied microstructure and unusual ferromagnetic behavior in cobalt (Co)-implanted epitaxial tin dioxide (SnO2) films. The study was aimed to provide a comprehensive understanding of the interplay between cobalt implant distribution, crystal defects, such as oxygen vacancies, and magnetic properties in Co-implanted SnO2 films, which has potential applications in spintronics and memristor devices. The scanning electron microscopy (SEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), vibrating sample magnetometry (VSM), differential thermo-magnetic analysis (DTMA), and ferromagnetic resonance (FMR) were employed for investigations of SnO2 films implanted with 40 keV Co+ ions at room or the elevated temperatures of substrate. The experimental data showed that the Co ion implantation with high fluence of 1.0 1017 ions/cm2 induces significant changes in the microstructure of SnO2 films, leading to the appearance of ferromagnetism with the Curie temperature significantly above room temperature. Strong influence of implantation temperature and subsequent high-temperature annealing in air or in vacuum on magnetic properties of Co-implanted SnO2 films were established. Moreover, the chemical effect of ethanol on FMR spectra were observed. The obtained results are discussed within the model of two magnetic layers with different concentration and valence states of the implanted cobalt and with high content of oxygen vacancies.

Safe sample transport is of great importance for infectious diseases diagnostics. Various treatments and buffers are used to inactivate pathogens in diagnostic samples. At the same time, adequate sample preservation, particularly of nucleic acids, is essential to allow an accurate laboratory diagnosis. For viruses with single-stranded RNA genomes of positive polarity, such as foot-and-mouth disease virus (FMDV), however, naked full-length viral RNA can itself be infectious. In order to assess the risk of infection from inactivated FMDV samples, two animal experiments were performed. In the first trial, six cattle were injected with FMDV RNA (isolate A22/IRQ/24/64) into the tongue epithelium. All animals developed clinical disease within two days and FMDV was reisolated from serum and saliva samples. In the second trial, another group of six cattle was exposed to FMDV RNA by instilling it on the tongue and spraying it into the nose. The animals were observed for 10 days after exposure. All animals remained clinically unremarkable and virus isolation as well as FMDV genome detection in serum and saliva were negative. No transfection reagent was used for any of the animal inoculations. In conclusion, cattle can be infected by injection with naked FMDV RNA, but not by non-invasive exposure to the RNA. Inactivated FMDV samples that contain full-length viral RNA carry only a negligible risk of infecting animals.

Fluidic larger-scale integration (LSI) resides at the heart of comprehensive sample-to-answer automation and parallelization of assay panels for frequent and ubiquitous bioanalytical testing in decentralized the point-of-use / point-of-care settings. This paper develops a novel “digital twin” strategy with an emphasis on rotational, centrifugo-pneumatic flow control. The underlying model systematically connects retention rates of rotationally actuated valves as a key element of LSI to experimental input parameters; for the first time, the concept of band widths in frequency space as the decisive quantity characterizing operationally robustness is introduced, a set of quantitative performance metrics guiding algorithmic optimization of disc layouts is defined, and the engineering principles of advanced, logical flow control and timing are elucidated. Overall, the digital twin enables efficient design for automating multiplexed bioassay protocols on such “Lab-on-a-Disc” (LoaD) systems featuring high packing density, reliability, configurability, modularity and manufacturability to eventually minimize cost, time and risk of development and production.

The aim of this study is to obtain the Bayes estimators and the maximum likelihood estimators (MLEs) for the unknown parameters of the Rayleigh Weibull (RW) distribution based on progres-sive type-II censored samples. The approximate Bayes estimators are calculated using the idea of Lindley and Tierney-Kadane's approximation method under the squared-error loss function when the Bayes estimators are not handed in explicit forms. In this study, the approximate Bayes esti-mates are compared with the maximum likelihood estimates in the aspect of the estimated risks (ERs) using Monte Carlo simulation. In addition, the coverage probabilities of the parametric bootstrap estimates are calculated. Real lifetime data sets belonging to the cancer types as bladder cancer, head and neck cancer, and leukemia are used to illustrate the emprical results belonging to the approximate Bayes estimates, the maximum likelihood estimates, and the parametric bootstrap intervals.

There is significant need for development of a new environmentally friendly, extraction-free sample collection medium that can effectively preserve and protect genetic material for point-of-care, and/or self-collection, home-collection, and mail-back testing. Systematic Evolution of Ligands by Exponential Enrichment (SELEX) was used to create anti-ribonuclease (RNase) Deoxyribonucleic acid (DNA) aptamers against purified RNase A conjugated to paramagnetic carboxylated beads. Following 8 rounds of SELEX carried out under various stringency conditions, e.g., selection using Xtract-Free™ (XF) specimen collection medium, and elevated ambient temperature of 28 ºC, a panel of 5 aptamers was chosen following bioinformatic analysis using next-generation sequencing. The efficacy of aptamer inactivation of RNase was assessed by monitoring Ribonucleic acid (RNA) integrity by fluorometric and real-time RT-PCR analysis. Inclusion of aptamers in reaction incubations resulted in an 8,800 to 11,200-fold reduction of RNase activity, i.e., digestion of viral RNA compared to control. Thus, anti-RNase aptamers integrated into XF collection medium as well as other commercial reagents and kits have great potential for ensuring quality intact RNA for subsequent genomic analysis.