Number Of Samples Research Articles

Proton spot dose estimation based on positron activity distributions with neural network.

Positron emission tomography (PET) has been investigated for its ability to reconstruct proton-induced positron activity distributions in proton therapy. This technique holds potential for range verification in clinical practice. Recently, deep learning-based dose estimation from positron activity distributions shows promise for in vivo proton dose monitoring and guided proton therapy. This study evaluates the effectiveness of three classical neural network models, recurrent neural network (RNN), U-Net, and Transformer, for proton dose estimating. It also investigates the characteristics of these models, providing valuable insights for selecting the appropriate model in clinical practice. Proton dose calculations for spot beams were simulated using Geant4. Computed tomography (CT) images from four head cases were utilized, with three for training neural networks and the remaining one for testing. The neural networks were trained with one-dimensional (1D) positron activity distributions as inputs and generated 1D dose distributions as outputs. The impact of the number of training samples on the networks was examined, and their dose prediction performance in both homogeneous brain and heterogeneous nasopharynx sites was evaluated. Additionally, the effect of positron activity distribution uncertainty on dose prediction performance was investigated. To quantitatively evaluate the models, mean relative error (MRE) and absolute range error (ARE) were used as evaluation metrics. The U-Net exhibited a notable advantage in range verification with a smaller number of training samples, achieving approximately 75% of AREs below 0.5mm using only 500 training samples. The networks performed better in the homogeneous brain site compared to the heterogeneous nasopharyngeal site. In the homogeneous brain site, all networks exhibited small AREs, with approximately 90% of the AREs below 0.5mm. The Transformer exhibited the best overall dose distribution prediction, with approximately 92% of MREs below 3%. In the heterogeneous nasopharyngeal site, all networks demonstrated acceptable AREs, with approximately 88% of AREs below 3mm. The Transformer maintained the best overall dose distribution prediction, with approximately 85% of MREs below 5%. The performance of all three networks in dose prediction declined as the uncertainty of positron activity distribution increased, and the Transformer consistently outperformed the other networks in all cases. Both the U-Net and the Transformer have certain advantages in the proton dose estimation task. The U-Net proves well suited for range verification with a small training sample size, while the Transformer outperforms others at dose-guided proton therapy.

Identification and bioinformatic characterization of a serum miRNA signature for early detection of laryngeal squamous cell carcinoma

BackgroundThe growing understanding of cancer biology and the establishment of new treatment modalities has not yielded the expected results in terms of survival for Laryngeal Squamous Cell Cancer (LSCC). Early diagnosis, as well as prompt identification of patients with high risk of relapse would ensure greater chance of therapeutic success. However, this goal remains a challenge due to the absence of specific biomarkers for this neoplasm.MethodsSerum samples from 45 LSCC patients and 23 healthy donors were collected for miRNA expression profiling by TaqMan Array analysis. Additional 20 patients and 42 healthy volunteers were included for the validation set, reaching an equal number of clinical samples for each group. The potential diagnostic ability of the such identified three-miRNA signature was confirmed by ROC analysis. Moreover, each miRNA was analyzed for the possible correlation with HNSCC patients’ survival and TNM status by online databases Kaplan–Meier (KM) plotter and OncomiR. In silico analysis of common candidate targets and their network relevance to predict shared biological functions was finally performed by PANTHER and GeneMANIA software.ResultsWe characterized serum miRNA profile of LSCC patients identifying a novel molecular signature, including miR-223, miR-93 and miR-532, as circulating marker endowed with high selectivity and specificity. The oncogenic effect and the prognostic significance of each miRNA was investigated by bioinformatic analysis, denoting significant correlation with OS. To analyse the molecular basis underlying the pro-tumorigenic role of the signature, we focused on the simultaneously regulated gene targets—IL6ST, GTDC1, MAP1B, CPEB3, PRKACB, NFIB, PURB, ATP2B1, ZNF148, PSD3, TBC1D15, PURA, KLF12—found by prediction tools and deepened for their functional role by pathway enrichment analysis. The results showed the involvement of 7 different biological processes, among which inflammation, proliferation, migration, apoptosis and angiogenesis.ConclusionsIn conclusion, we have identified a possible miRNA signature for early LSCC diagnosis and we assumed that miR-93, miR-223 and miR-532 could orchestrate the regulation of multiple cancer-related processes. These findings encourage the possibility to deepen the molecular mechanisms underlying their oncogenic role, for the desirable development of novel therapeutic opportunities based on the use of short single-stranded oligonucleotides acting as non-coding RNA antagonists in cancer.

The Effect of Electronic Word of Mouth (EWOM) and Fear of Missing Out (FOMO) on Impulse Buying in Shopee Moderated Self-Control

The main objective of this study is to examine the effect of Electronic Word of Mouth (EWOM) and Fear of Missing Out (FOMO) on self-controlled moderated impulse buying. The number of samples was 120 young generation who categorized as Gen Z. This study showed that EWOM and self-control had an effect on impulse buying on Shopee e-commerce, while FOMO had no effect on impulse buying. The self-control variable is not able to moderate the influence between EWOM on impulse buying and the influence between FOMO on impulse buying.

Synthetic Malware Using Deep Variational Autoencoders and Generative Adversarial Networks

The effectiveness of detecting malicious files heavily relies on the quality of the training dataset, particularly its size and authenticity. However, the lack of high-quality training data remains one of the biggest challenges in achieving widespread adoption of malware detection by trained machine and deep learning models. In response to this challenge, researchers have made initial strides by employing generative techniques to create synthetic malware samples. This work utilizes deep variational autoencoders (VAE) and generative adversarial networks (GAN) to produce malware samples as opcode sequences. The generated malware opcodes are then distinguished from authentic opcode samples using machine and deep learning techniques as validation methods. The primary objective of this study was to compare synthetic malware generated using VAE and GAN technologies. The results showed that neither approach could create synthetic malware that could deceive machine learning classification. However, the WGAN-GP algorithm showed more promise by requiring a higher number of synthetic malware samples in the train set to effectively be detected, proving ita better approach in synthetic malware generation.

A Data and Model-Driven Clutter Suppression Method for Airborne Bistatic Radar Based on Deep Unfolding

Space–time adaptive processing (STAP) based on sparse recovery achieves excellent clutter suppression and target detection performance, even with a limited number of available training samples. However, most of these methods face performance degradation due to grid mismatch, which impedes their application in bistatic clutter suppression. Some gridless methods, such as atomic norm minimization (ANM), can effectively address grid mismatch issues, yet they are sensitive to parameter settings and array errors. In this article, the authors propose a data and model-driven algorithm that unfolds the iterative process of atomic norm minimization into a deep network. This approach establishes a concrete and systematic link between iterative algorithms, extensively utilized in signal processing, and deep neural networks. This methodology not only addresses the challenges associated with parameter settings in traditional optimization algorithms, but also mitigates the lack of interpretability issues commonly found in deep neural networks. Moreover, due to more rational parameter settings, the proposed algorithm achieves effective clutter suppression with fewer iterations, thereby reducing computational time. Finally, extensive simulation experiments demonstrate the effectiveness of the proposed algorithm in clutter suppression for airborne bistatic radar.

The Relationship between Housewives' Knowledge and Attitudes towards Clean and Healthy Living Behavior

Aims: The research aims to determine the relationship between knowledge and attitudes of housewives towards clean and healthy living behaviour in Intan Lestari Housing, Bekasi City in 2024 Introduction: Clean and healthy living behaviour can be done anywhere, such as at home, work, health facilities, or in public places. Maintaining a healthy and clean home environment is very important because the home is a place for family members to fulfil each other's needs and build relationships with each other. Study Design: The approach used is a cross-sectional design, which is a type of observational research in which the relationship between variables is observed and studied simultaneously over a certain period. Place and Duration of Study: This research was carried out in the period January 2024 to February 2024. The data collection and retrieval process were carried out online via the Google Form platform which was distributed to housewives at Intan Housing, Bekasi, West Java. Methodology: The population in this study were all housewives in the Intan Lestari Bekasi Housing Complex. The samples used in this research were all housewives in the Intan Lestari Housing Bekasi who lived in the RW 022 area. To calculate the number of samples that would be used as respondents in the research, the researcher used the Slovin formula, obtaining a total sample of 93 housewives. Results: From the research results, data was obtained that of the 93 housewife respondents, there were 62 people (67%) had good abilities; there were 51 people (55%) had an attitude in the good category; In terms of implementing a Clean and Healthy Lifestyle (PHBS), there are 63 people (68%) who implement PHBS well. The results of the bivariate analysis to see the relationship between housewives' knowledge of PHBS using SPSS obtained a p-value of 0.005 < sig 0.05, meaning that there was a significant relationship between housewives' knowledge of PHBS behaviour, as well as the bivariate test of the relationship between attitudes towards PHBS and obtained a p-value of 0.015 < sig 0.05, which means there is a significant relationship between housewives' attitudes towards PHBS. Conclusion: From the research results, it can be concluded that a person's good knowledge and attitudes are very significant in influencing clean and healthy lifestyle habits (PHBS), meaning that a habitual pattern for a clean and healthy life requires good knowledge and attitudes.

Resurgence of Dengue Virus Serotype 2: Findings from the 2023 Bangladesh Outbreak.

Bangladesh experienced the largest and deadliest dengue outbreak in 2023, after the virus had reappeared in the country 2 decades earlier. A total of 1,705 people died in Bangladesh, representing the highest case fatality rate (0.5%) due to dengue in the world for that year. The severity of dengue infection is to some extent related to the emergence of new circulating serotypes. To identify the possible predominant serotype in 2023, the reverse transcription polymerase chain reaction-based identification technique was used on stored serum samples of suspected dengue patients during the period between July and December 2023. The overall result of molecular serotyping showed that dengue virus (DENV-2) reappeared as the predominant serotype (74.1%), followed by a moderate number of samples with DENV-1 (19.8%) and DENV-3 (6.1%), in 2023. However, DENV-1 was found to be dominant in a few rural areas of Cox's Bazar districts. During the 2019 outbreak, DENV-3 was the dominant serotype, which seemed to be replaced by the DENV-2 serotype; this may have impacted the increased case fatality in 2023.

Pretreatment of Hybrid Ceramics Using Ho: YAG, Low-Level Laser Therapy Activated Malachite Green, and Non-Thermal Plasma on Surface Roughness, Bond Strength, and Color Change, SEM and EDX Analysis

The study aimed to assess the effects of different surface conditionings on hybrid ceramics (HBC). Hydrofluoric acid was combined with a silane (HFA+S), low-level laser therapy activated Malachite green (LLLT-MG), Ho: YAG laser, and non-thermal plasma (NTP) as surface conditioning methods for HBC. Eighty-four HBC discs were prepared and divided into four groups according to surface conditioning methods. The total number of samples (n = 21) for each group was further split into two for the non-thermocycling and thermocycling subgroups. After surface treatment, all samples were examined to study the effect of color change and surface roughness. The shear bond strength (SBS) test of HBC was performed on thermo-cycled samples. Statistical analysis using ANOVA with Tukey post hoc was performed to observe any significant difference among tested groups, p > 0.05. The HFA+S and Ho: YAG surface-treated samples showed higher SBS than other surface-treated samples due to higher surface roughness. All surface conditioning methods, except NTP, induced noticeable color change, making them less suitable for aesthetical purposes in clinical settings. Overall, surface conditioning methods are critical in affecting shear bond strength through surface roughness and color change.

Effects of Pilate Mat Exercises on Agility and Flexibility of Athletes - A Pilot Study

Background-Athletes are always looking for ways to get better at what they do and get hurt less frequently. Movement efficiency and total physical capability are influenced by flexibility and agility, which are essential elements of athletic success. Pilates mat movements have a reputation for enhancing body awareness, flexibility, and core strength. Despite its growing popularity, the impact of incorporating Pilates into athletes' training routines, particularly concerning flexibility and agility, remains largely unexplored. Objective-The purpose of this study was to evaluate the agility and flexibility of athletes who included Pilates mat movements in their training regimen to a control group who continued with their regular training regimen. Methods-Two groups, Group A (experimental) and Group B (control), each with fifteen participants, were randomly selected from among the thirty participants. Group B adhered to their regular training program without making any alterations, Group A engaged in Pilates mat exercises three days per week for six weeks, following informed consent obtained from each participant prior to the intervention. Agility and flexibility were assessed using the 4 x 10-meter shuttle run test and the sit and reach test before and after a six-week intervention period. Results and Conclusion-Compared to Group B, Group A showed noticeably greater flexibility and agility after the intervention. With a standard deviation of improvement of 2.90, the experimental group (Group A) demonstrated an average improvement in the sit and reach test score from 21.5 to 27.7. Group B, the control group, saw very little improvement; mean scores varied greatly, going from 22.18 to 22.20. Group A's mean time in the 4 x 10-meter shuttle run test increased from 23.3 to 21, while Group B's mean time only marginally improved from 21.53 to 22.07. These results imply that adding Pilates mat exercises to athletes' training regimens can increase their flexibility and agility, which may help them perform better on the field and avoid injuries. To confirm these results and investigate the long-term advantages of Pilates for athletes, more studies with bigger sample numbers and longer intervention times are necessary.

Exponentially improved efficient machine learning for quantum many-body states with provable guarantees

Solving the ground state and the ground-state properties of quantum many-body systems is generically a hard task for classical algorithms. For a family of Hamiltonians defined on an m-dimensional space of physical parameters, the ground state and its properties at an arbitrary parameter configuration can be predicted via a machine learning protocol up to a prescribed prediction error ɛ, provided that a sample set (of size N) of the states can be efficiently prepared and measured. In a recent work [Huang , ], a rigorous guarantee for such a generalization was proved. Unfortunately, an exponential scaling for the provable sample complexity, N=mO(1ɛ), was found to be universal for generic gapped Hamiltonians. This result applies to the situation where the dimension of the parameter space is large, while the scaling with the accuracy is not an urgent factor, not entering the realm of more precise learning and prediction. In this work, we consider an alternative relevant scenario, where the effective dimension m is a finite, not necessarily large constant, while the scaling with the prediction error becomes the central concern. By jointly preserving the fundamental properties of density matrices in the learning protocol and utilizing the continuity of quantum states in the parameter range of interest, we rigorously obtain a polynomial sample complexity for predicting quantum many-body states and their properties, with respect to the prediction error ɛ and the number of qubits, n, with N=poly(ɛ−1,n,ln1δ), where poly denotes a polynomial function, and (1−δ) is the probability of success. Moreover, if restricted to learning local quantum-state properties, the number of samples can be further reduced to N=poly(ɛ−1,lnnδ). Numerical demonstrations confirm our findings, and an alternative approach utilizing statistical learning theory with reproducing kernel Hilbert space achieves consistent results. The mere continuity assumption indicates that our results are not restricted to gapped Hamiltonian systems and properties within the same phase. Published by the American Physical Society 2024

Cr (VI)-induced ribosomal DNA copy number variation is associated with semen quality impairment: Evidence from human to animal study

ObjectivesThis study aimed to analyze the possible role of rDNA copy number variation in the association between hexavalent chromium [Cr (VI)] exposure and semen quality in semen donors and further confirm this association in mice. MethodsIn this cross-sectional study, whole blood and semen samples were collected from 155 semen donors in the Zhejiang Human Sperm Bank from January 1st to April 31st, 2021. Adult C57BL/6 J male mice were treated with different doses of Cr (VI) (0, 10, or 15 mg/kg b.w./day). Semen quality, including semen volume, total spermatozoa count, sperm concentration, progressive motility, and total motility, were analyzed according to the WHO laboratory manual. Cr concentration was detected using inductively coupled plasma mass spectrometry. The rDNA copy number was measured using qPCR. ResultsIn semen donors, whole blood Cr concentration was negatively associated with semen concentration and total sperm counts. Semen 5 S and 45 S rDNA copy numbers were negatively associated with whole blood Cr concentration and whole blood 5.8 S rDNA copy number was negatively associated with semen Cr concentration. In mice, Cr (VI) damaged testicular tissue, decreased semen quality, and caused rDNA copy number variation. Semen quality was related to the rDNA copy number in whole blood, testicular tissue, and semen samples in mice. ConclusionCr (VI) was associated with decreased semen quality in semen donors and mice. Our findings suggest an in-depth analysis of the role of the rDNA copy number variation in the Cr (VI)-induced impairment of semen quality.

Journey to the centre of the ovale: a novel, combined percutaneous fluoroscopic and image-guided approach to biopsy a Meckel’s cave lesion.

Introduction: The percutaneous Hartel’s technique is a well-described approach to targeting lesions in the Meckel’s cave. It is used as the standard approach for all percutaneous trigeminal neuralgia procedures for accessing the Gasserian ganglion through the foramen ovale. It has also been described to biopsy lesions in the Meckel’s cave and cavernous sinus using fluoroscopic guidance however there were a significant number of non-diagnostic samples. No one to date has described a combined fluoroscopic and image-guided approach to improve safety and accuracy. Methods: The patient had a 3-month history of left-sided facial numbness and a left VIth palsy causing diplopia and a squint. The CT of the chest, abdomen and pelvis was unremarkable as were the serum tumour markers. We describe the novel approach of using a percutaneous biopsy needle to obtain a cytological sample of a Meckel’s cave lesion using a combination of X-ray and EM image-guidance with use of StealthStation® S8® System (Medtronic Sofamor Danek, Memphis, USA). The need for a craniotomy and its associated morbidity and technical challenges was therefore obviated. Results: The procedure was performed under general anaesthesia and eight core samples were sent to neuro-pathology for analysis. The lesion was histologically confirmed to be lymphoma and the patient subsequently received oncological treatment. The patient had no immediate or post-operative complications and the use of the aforementioned combined approach improved safety and accuracy of targeting the lesion in real time. Conclusion: A combined percutaneous fluoroscopic and image-guided approach to biopsy a Meckel’s cave lesion is recommended.

High-Resolution PM10 Estimation Using Satellite Data and Model-Agnostic Meta-Learning

Characterizing the spatial distribution of particles smaller than 10 μm (PM10) is of great importance for air quality management yet is very challenging because of the sparseness of air quality monitoring stations. In this study, we use a model-agnostic meta-learning-trained artificial neural network (MAML-ANN) to estimate the concentrations of PM10 at 60 m × 60 m spatial resolution by combining satellite-derived aerosol optical depth (AOD) with meteorological data. The network is designed to regress from the predictors at a specific time to the ground-level PM10 concentration. We utilize the ANN model to capture the time-specific nonlinearity among aerosols, meteorological conditions, and PM10, and apply MAML to enable the model to learn the nonlinearity across time from only a small number of data samples. MAML is also employed to transfer the knowledge learned from coarse spatial resolution to high spatial resolution. The MAML-ANN model is shown to accurately estimate high-resolution PM10 in Beijing, with coefficient of determination of 0.75. MAML improves the PM10 estimation performance of the ANN model compared with the baseline using pre-trained initial weights. Thus, MAML-ANN has the potential to estimate particulate matter estimation at high spatial resolution over other data-sparse, heavily polluted, and small regions.

An Adaptive Noisy Label-Correction Method Based on Selective Loss for Hyperspectral Image-Classification Problem

Due to the intricate terrain and restricted resources, hyperspectral image (HSI) datasets captured in real-world scenarios typically contain noisy labels, which may seriously affect the classification results. To address this issue, we work on a universal method that rectifies the labels first and then trains the classifier with corrected labels. In this study, we relax the common assumption that all training data are potentially corrupted and instead posit the presence of a small set of reliable data points within the training set. Under this framework, we propose a novel label-correction method named adaptive selective loss propagation algorithm (ASLPA). Firstly, the spectral–spatial information is extracted from the hyperspectral image and used to construct the inter-pixel transition probability matrix. Secondly, we construct the trusted set with the known clean data and estimate the proportion of accurate labels within the untrusted set. Then, we enlarge the trusted set according to the estimated proportion and identify an adaptive number of samples with lower loss values from the untrusted set to supplement the trusted set. Finally, we conduct label propagation based on the enlarged trusted set. This approach takes full advantage of label information from the trusted and untrusted sets, and moreover the exploitation on the untrusted set can adjust adaptively according to the estimated noise level. Experimental results on three widely used HSI datasets show that our proposed ASLPA method performs better than the state-of-the-art label-cleaning methods.

Study of the Karachay Population Based on the Analysis of Ten Polymorphic DNA Loci

Genetic structure of Karachai population has been studied based on analysis of 10 autosomal DNA markers (diallelicand multiallelic): CCR5∆32, ACE, D7S23(KM19) STR/THOI, STR/FABP, STR/IVS6a, VNTR/PAH, VNTR/DAT1, VNTR/eNOS VNTR/APOB. The total number of sample makes up 485 individuals who are residents of five Karachai regions: Karachaevsky, Prikubansky, Malokarachayevsky, Ust-Dzhegutinsky and the city of Cherkessk, the capital of the Republic of Karachaevo-Cherkessia. Analysis of allele’s frequency of autosomal DNA markers in Karachay geographic subgroups shows considerable genetic differentiation between them. The highest level of genetic diversity for Karachay people on dialle system is set at the locus ID/ACE, Hobs = 0.513, on multi-allele system isat the locus STR/THOI, Hobs = 0.792. The average value of the observed heterozygosity per locus is 0.466, varying from 0.441 in Ust-Dzhegutinsky to 0.503 in Cherkessk. The level of genetic differences between Karachai groups (FST = 0.007) is inside the variance defined in the previously studied peoples, Mari (FST = 0.0024), Udmurt (FST = 0.0048), Chuvash (FST = 0.006), Tatars (FST = 0.0075) and Bashkir (FST = 0.008).

The Transformation of Shape Movement Structure in the Drawings of Wasma Al-Agha

Formal formations, linear arrangements, variations in spaces, color contrasts, proximity and spacing between shapes, and even the repetition of shapes in some cases, and differences in sizes, make the painting a full-fledged compositional dimension. Therefore, we find through that importance a pressing need to conduct a study that sheds light on an important aspect of the formation represented by the experience of the artist Wasma Al-Agha. From this starting point, the researcher found a logical justification to address the subject of this research, to study and deepen the idea of shape movement and its relationship to the paintings of the artist Wasma Al-Agha. Through the foregoing, the researcher was able to formulate the following question: To what extent does the structure of shape movement transform in the drawings of Wasma Al-Agha?As the current research aims to understand the mechanisms of formation, types of shapes, and their movement in the drawings of the artist. Therefore, the researcher relied on the descriptive-analytical method (descriptive analysis) due to its suitability for the subject of the current study and what it allows for in terms of analysis procedures in order to achieve the research goal. The sample was selected using the intentional selection method for the representative sample of the research community, where the researcher identified his research models (3) models from the original community. Also the research came out with a set of results, the most important of which are: The artist tried to represent a sad and dramatic story of the suffering of immigrants in the Iraqi provinces, as in sample number (1). She relied on her own style in embodying the shapes to convey the scene with an illusory movement of the shapes, like an old inherited novel that she tells through colors and shapes, as in all the samples. As for the research Conclusions: 1. The artist relied on multiple pure colors that express a festive dimension that evokes joy in the viewer's soul. She depicted the Baghdadi scene that was repeated in all her paintings and her representation of the Iraqi woman as an old inherited novel that she tells through colors and shapes.

How many Monte Carlo samples are needed for probabilistic cost-effectiveness analyses?

ObjectiveProbabilistic sensitivity analysis (PSA) is conducted to account for the uncertainty in cost and effect of decision options under consideration. PSA involves obtaining a large sample of input parameter values (N) to estimate the expected cost and effect of each alternative in the presence of parameter uncertainty. When the analysis involves using stochastic models (e.g., individual-level models), the model is further replicated P times for each sampled parameter set. We study how N and P should be determined. MethodsWe show that PSA could be structured such that P can be an arbitrary number (say, P=1). To determine N, we derive a formula based on Chebyshev’s inequality such that the error in estimating the incremental cost-effectiveness ratio (ICER) of alternatives (or equivalently, the willingness-to-pay value at which the optimal decision option changes) is within a desired level of accuracy. We described two methods to confirmed, visually and quantitatively, that the N informed by this method results in ICER estimates within the specified level of accuracy. ResultsWhen N is arbitrarily selected, the estimated ICERs could be substantially different from the true ICER (even as P increases), which could lead misleading conclusions. Using a simple resource allocation model, we demonstrate that the proposed approach can minimize the potential for this error. ConclusionsThe number of parameter samples in probabilistic CEAs should not be arbitrarily selected. We describe three methods to ensure that enough parameter samples are used in probabilistic CEAs.

The Association Between Energy Adequacy Level And Physical Activity With Nutri-tional Status In Adolescents At Walisongo High School, Ketanggungan

Growth during adolescence is rapid due to the development of muscle mass and fat tissue as well as hormonal changes that affect the nutritional needs of adolescents. The purpose of this study was to determine how the nutritional status of adolescents and the level of energy adequacy and physical activity relate to each other. This type of research uses a cross-sectional design. The number of samples taken with simple random sampling technique in this study was 152 people. Data collection of energy adequacy level using SQ-FFQ, physical activity data with PAL questionnaire, and nutritional status of respondents obtained from measurements of BW and TB. The results showed that most participants had an adequate level of energy sufficiency (65.1%) according to the Spearman Rank test, physical activity was mostly in the moderate category (63.8%), and the majority had good nutritional status (80.9%). The results of the Spearman Rank test showed that there was a significant correlation between the level of energy adequacy and nutritional status (p value = 0.045) and there was a significant correlation between physical activity and nutritional status (p value = 0.021). The conclusion is that there is a significant correlation between the level of energy adequacy and physical activity with nutritional status in adolescents at Walisongo Ketanggungan High School. It is recommended for the school to collaborate with the health department or local health center to make preventive and promotive efforts related to nutritional status problems through education about General Guidelines for Balanced Nutrition (PUGS).

BSMH: Cross‐dataset object detection based on box‐separated multiple‐head

AbstractCross‐dataset object detection methods can adapt to the needs of rapid category expansion in object detection tasks. However, these methods are prone to generate dataset‐aware errors with false alarm objects. This study is aimed to address these issues. A box‐separated multiple‐head module and box‐separated loss function based on the YOLOv8 network are devised to achieve cross‐dataset object detection. Additionally, a sameclass‐aware fusion module to avoid gradient conflicts due to cross‐category conflicts is developed. A multiple‐head fusion module is devised to reduce the number of false alarm objects caused by dataset‐aware errors. A global class‐aware sampler is also designed to adapt to the impact of the imbalanced number of categories and training samples across datasets. The effectiveness of the box‐separated multiple‐head module is verified using cross‐datasets built using the COCO, WiderFace, WiderPerson, and OpenImages V4 datasets. Extensive experiments demonstrate the efficiency and precision of the proposed method.

Switching Vancomycin Monitoring From Trough Concentration to Area Under the Curve Estimation by Bayesian Forecasting: A Short Communication on a Cost-Benefit Study in Resource-Limited Settings.

This study was conducted to evaluate the cost-benefit indicators of a vancomycin monitoring protocol based on area under the curve estimation using commercial Bayesian software. This quasi-experimental study included patients who were aged >18 years with a vancomycin prescription for >24 hours. Patients who were terminally ill or those with acute kidney injury (AKI) ≤24 hours were excluded. During the preintervention period, doses were adjusted based on the trough concentration target of 15-20 mg/L, whereas the postintervention period target was 400-500 mg × h/L for the area under the curve. The medical team was responsible for deciding to stop the antimicrobial prescription without influence from the therapeutic drug monitoring team. The main outcomes were the incidence of AKI and length of stay. Cost-benefit simulation was performed after statistical analysis. There were 96 patients in the preintervention group and 110 in the postintervention group. The AKI rate decreased from 20% (n = 19) to 6% (n = 6; P = 0.003), whereas the number of vancomycin serum samples decreased from 5 (interquartile range: 2-7) to 2 (interquartile range: 1-3) examinations per patient (P < 0.001). The mean length of hospital stay for patients was 26.19 days after vancomycin prescription, compared with 17.13 days for those without AKI (P = 0.003). At our institution, the decrease in AKI rate and reduced length of stay boosted yearly savings of up to US$ 369,000 for 300 patients receiving vancomycin therapy. Even in resource-limited settings, a commercial Bayesian forecasting-based protocol for vancomycin is important for determining cost-benefit outcomes.