Initial Template Research Articles

Creek systems in restored coastal wetlands: Morphological evolution and design implications

Saltmarsh restoration such as managed realignment (MR) projects often include excavation of simplified tidal creek networks to improve drainage and marsh functioning, but their design is based on limited evidence. This paper compares the morphological evolution of creek networks in current MR projects in the UK with creek networks in natural saltmarshes, in order to provide improved guidance.The evolution of creek networks was monitored for 2–20 years post-breach at 10 MR sites across the UK by semi-automatically extracting 12 morphological creek parameters from lidar. The rates of creek evolution in MR sites are linked to the initial tidal, morphological and sedimentological conditions using principal component analysis, then compared with power law relationships of morphological equilibrium defined from 13 mature natural saltmarshes.MR creeks evolved into larger, more complex, better distributed systems, with a total creek length and volume statistically similar to their natural counterparts. However, the creek volume remains poorly distributed, with a mean distance between creeks ranging from 33 to 101 m versus 5–15 m for natural mature saltmarshes. MR creeks are also clustered around the breach area, leaving the marsh interior poorly drained. MR creek network morphologies remain strongly influenced by the initial creek template, as evidenced by unnaturally straight creeks inherited from former drainage ditches.A combination of external conditions (i.e., tidal range, sediment concentration in the wider estuary) and local conditions (i.e., site elevation, topographical heterogeneity, soil compaction) controls how easily creeks can form within MR sites. This in turn determines the amount of engineering effort required to help achieve reference site conditions. The end goal of creek design is to create MR sites that closely resemble reference site conditions, however the final design is also likely to be affected by a range of practical factors (e.g. engineering/cost) unique to each site and project.

"We always felt psychologically unstable": A qualitative study of midwives' experiences in providing maternity care during the COVID-19 pandemic in Tanzania.

We explored midwives' experiences and challenges in providing maternity care during the period of the first surge of the COVID-19 pandemic in Dodoma. Phenomenological study design was used to explore midwives' experiences and challenges in providing maternity care during the COVID-19 pandemic in Tanzania. We conducted interviews with a total of 23 midwives, using semi-structured interviews and an audio recorder. Five focus group discussions (FGDs) were conducted. To analyse the data, we used interpretive phenomenological thematic data analysis with NVivo software. To analyse templates, we followed a step-by-step process, starting with familiarizing ourselves with the data, followed by preliminary coding, organizing themes, developing an initial coding template, using the initial template, refining the template and finally applying it to the entire dataset. It was found that three themes and eight subthemes merged in the current study. The main themes were ① mental health distress, ② work environment challenges and ③ isolation of midwives by different groups. Reducing risk and protecting midwives from infectious diseases will improve the workforce, reduce the number of hospital stays, reduce the cost of hospital services, improve birth outcomes and indirectly improve family, community and national economies. Due to the system's failure to provide psychological support, protective environment and isolation rooms for confirmed or suspected cases put midwives at a higher risk of contracting the virus and had to work in a stressful environment. In addition to having the right tools, midwives must also receive emotional and psychological support in order to be at their best. The system must ensure that midwives are ready for uncertain times, such as pandemic infectious disease outbreaks, by providing protective work place environment such as personal protective equipment, psychological support and isolation room for confirmed COVID-19 cases.

Leading primary care under the weight of COVID-19: how leadership was enacted in six australian general practices during 2020.

The COVID-19 pandemic challenged health care delivery globally, providing unique challenges to primary care. Australia's primary healthcare system (primarily general practices) was integral to the response. COVID-19 tested the ability of primary health care to respond to the greater urgency and magnitude than previous pandemics. Early reflections highlighted the critical role of leaders in helping organisations negotiate the pandemic's consequences. This study explores how general practice leadership was enacted during 2020, highlighting how leadership attributes were implemented to support practice teams. We performed secondary analysis on data from a participatory prospective qualitative case study involving six general practices in Melbourne, Victoria, between April 2020 and February 2021. The initial coding template based on Miller et al.'s relationship-centred model informed a reflexive thematic approach to data re-analysis, focused on leadership. Our interpretation was informed by Crabtree et al.'s leadership model. All practices realigned clinical and organisational routines in the early months of the pandemic - hierarchical leadership styles often allowing rapid early responses. Yet power imbalances and exclusive communication channels at times left practice members feeling isolated. Positive team morale and interdisciplinary teamwork influenced practices' ability to foster emergent leaders. However, emergence of leaders generally represented an inherent 'need' for authoritative figures in the crisis, rather than deliberate fostering of leadership. This study demonstrates the importance of collaborative leadership during crises while highlighting areas for better preparedness. Promoting interdisciplinary communication and implementing formal leadership training in crisis management in the general practice setting is crucial for future pandemics.

Planning, Reflecting and Evaluating Participatory Health Research: Adaptation and Piloting of the "Community Based Participatory Research (CBPR) Model" for German-Speaking Countries

Participatory research approaches are becoming increasingly established in both academic and practice settings. The participation of people with varied lived experiences and professional backgrounds can help academia and practitioners to learn from and empower each other. In the exchange of different perspectives, needs and ideas, it is possible to plan, reflect on, implement and evaluate projects in the health sector jointly and with attention to the needs of all stakeholders. The Community Based Participatory Research (CBPR) Model is often used internationally to guide participatory processes. However, an accessible translation has been lacking for application in German-speaking countries. To address this problem, a multidisciplinary working group composed of academic researchers and practitioners came together within the German-speaking Participatory Health Research Network (PartNet) to adapt the CBPR model for German-speaking countries and to test the adapted version with potential users. The adaptation was more than a translation, as the four model components "Contexts", "Partnership Processes", "Intervention & Research" and "Outcomes" as well as their associated contents are not directly applicable to the socio-structural and political contexts of the German-speaking countries. This article describes the process of adapting the model. This includes how translation drafts for German-speaking countries were first discussed in detail and then agreed upon as an initial template for testing in practice. Subsequently, various users reflected on the German-language model based on their experience of testing it in different projects, focusing on accuracy, comprehensibility and applicability. At the same time, the model was presented and discussed at conferences. The diverse feedback was incorporated into further revisions of the model. The result is a German-language version called "Modell für partizipative Gesundheitsforschung (PGF-Modell)".

Comparison of Ethos template-based planning and AI-based dose prediction: General performance, patient optimality, and limitations

PurposeEthos proposes a template-based automatic dose planning (Etb) for online adaptive radiotherapy. This study evaluates the general performance of Etb for prostate cancer, as well as the ability to generate patient-optimal plans, by comparing it with another state-of-the-art automatic planning method, i.e., deep learning dose prediction followed by dose mimicking (DP + DM). MaterialsGeneral performances and capability to produce patient-optimal plan were investigated through two studies: Study-S1 generated plans for 45 patients using our initial Ethos clinical goals template (EG_init), and compared them to manually generated plans (MG). For study-S2, 10 patients which showed poor performances at study-S1 were selected.S2 compared the quality of plans generated with four different methods: 1) Ethos initial template (EG_init_selected), 2) Ethos updated template-based on S1 results (EG_upd_selected), 3) DP + DM, and 4) MG plans. ResultsEG_init plans showed satisfactory performance for dose level above 50 Gy: reported mean metrics differences (EG_init minus MG) never exceeded 0.6 %. However, lower dose levels showed loosely optimized metrics, mean differences for V30Gy to rectum and V20Gy to anal canal were of 6.6 % and 13.0 %. EG_init_selected showed amplified differences in V30Gy to rectum and V20Gy to anal canal: 8.5 % and 16.9 %, respectively. These dropped to 5.7 % and 11.5 % for EG_upd_selected plans but strongly increased V60Gy to rectum for 2 patients. DP + DM plans achieved differences of 3.4 % and 4.6 % without compromising any V60Gy. ConclusionGeneral performances of Etb were satisfactory. However, optimizing with template of goals might be limiting for some complex cases. Over our test patients, DP + DM outperformed the Etb approach.

Transformer-based target tracking algorithm for space-based optoelectronic detection

The target tracking by space-based surveillance systems is difficult due to the long distances, weak energies, fast speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To mitigate the impact of these difficulties, this article proposes a target tracking algorithm based on image processing and Transformer, which employs a two-dimensional Gaussian soft-thresholding method to reduce the image noise, and combines a Laplace operator-weighted fusion method to augment the image, so as to improve the overall quality of the image and increase the accuracy of target tracking. Based on the SiamCAR framework, the Transformer model in the field of natural language processing is introduced, which can be used to enhance the image features extracted from the backbone network by mining the rich temporal information between the initial and dynamic templates. In order to capture the information of the target’s appearance change in the temporal sequence, a template update branch is introduced at the input of the algorithm, which realizes the dynamic update of the templates by constructing a template memory pool, and selecting the best templates for the candidate templates in the memory pool using the cosine similarity-based selection, thus ensuring the robustness of the tracking algorithm. The experimental results that compared with the SiamCAR algorithm and the mainstream algorithms, the TrD-Siam algorithm proposed in this article effectively improves the tracking success rate and accuracy, addressing poor target tracking performance under space-based conditions, and has a good value of application in the field of optoelectronic detection.

Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience.

The large variability in tumor appearance and shape makes manual delineation of the clinical target volume (CTV) time-consuming, and the results depend on the oncologists' experience. Whereas deep learning techniques have allowed oncologists to automate the CTV delineation, multi-site tumor analysis is often lacking in the literature. This study aimed to evaluate the deep learning models that automatically contour CTVs of tumors at various sites on computed tomography (CT) images from objective and subjective perspectives. 577 patients were selected for the present study, including nasopharyngeal (n = 34), esophageal (n = 40), breast-conserving surgery (BCS) (left-sided, n = 71; right-sided, n = 71), breast-radical mastectomy (BRM) (left-sided, n = 43; right-sided, n = 37), cervical (radical radiotherapy, n = 45; postoperative, n = 85), prostate (n = 42), and rectal (n = 109) carcinomas. Manually delineated CTV contours by radiation oncologists are served as ground truth. Four models were evaluated: Flexnet, Unet, Vnet, and Segresnet, which are commercially available in the medical product "AccuLearning AI model training platform". The data were divided into the training, validation, and testing set at a ratio of 5:1:4. The geometric metrics, including Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD), were calculated for objective evaluation. For subjective assessment, oncologists rated the segmentation contours of the testing set visually. High correlations were observed between automatic and manual contours. Based on the results of the independent test group, most of the patients achieved satisfactory quantitative results (DSC > 0.8), except for patients with esophageal carcinoma (DSC: 0.62-0.64). The subjective review indicated that 82.65% of predicted CTVs scored either as clinically accepting (8.68%) or requiring minor revision (73.97%), and no patients were scored as rejected. This experimental work demonstrated that auto-generated contours could serve as an initial template to help oncologists save time in CTV delineation. The deep learning-based auto-segmentations achieve acceptable accuracy and show the potential to improve clinical efficiency for radiotherapy of a variety of cancer.

Evaluation of reverse transcription-polymerase chain reaction and simultaneous amplification and testing for quantitative detection of serum hepatitis B virus RNA

BackgroundChronic hepatitis B virus (HBV) infection is one of the common infectious diseases in the world. HBV covalently closed circular DNA (cccDNA) is the initial template of HBV replication, which can exist in human hepatocytes for a long time and is difficult to be completely removed. It has been shown that HBV RNA can directly respond to the levels and transcriptional activity of cccDNA in hepatocytes and can be used as a surrogate marker of cccDNA transcriptional activity. At present, the detection techniques used for quantitative HBV RNA mainly include reverse transcription quantitative polymerase chain reaction (RT-qPCR) and simultaneous amplification and testing (SAT). MethodsIn this study, we verified the performance of the SAT method for detecting HBV RNA and the clinical effectiveness of SAT and RT-qPCR, and compared the correlation and consistency of the two detection methods for HBV RNA detection. ResultsThe results showed that the limit of detection for HBV RNA by SAT method was 50 copies/mL, with a linear range of 1 × 102-1 × 108 copies/mL. There was no difference in HBV RNA levels detected by the two methods. The correlation and consistency of the results were good, with the coefficient of determination of 0.7787 in HBeAg positive group and 0.8235 in HBeAg negative group. ConclusionsTherefore, this study confirmed that the SAT method and RT-qPCR for detecting HBV RNA have good agreement, which are both reliable methods to detect HBV RNA and can replace each other.

Only Once Attack: Fooling the Tracker With Adversarial Template

Adversarial attacks in visual object tracking aims to fool trackers via injecting invisible perturbations for the video frames. Most adversarial methods advocate generating perturbations for each video frame, but frequent attacks may increase the computational load and the risk of exposure. Unfortunately, less works are about only attacking the initial frame and their attack effects are insufficient. To tackle this, we focus on the initialization phase of tracking and propose an only once attack framework. It can effectively fool the tracker via only generating invisible perturbations for the initial template, rather than each frame. Specifically, considering the tracking mechanism of the Siamese-based trackers, we design the minimum score-based and the minimum IoU-based loss functions. Both of them are used for training the UNet-based perturbation generator instead of the tracker, achieving the non-targeted attack. Additionally, we propose the location and direction offsets as the base attacks of sophisticated targeted attack. Combined with the two basic attacks, the tracker can be easily hijacked to move towards the fake target predefined by users. Extensive experimental results demonstrate that our only once attack framework costs the least number of attacks yet achieves better attack effect, with the maximum drop of 68.7%. The transferability experiments illustrate that our attack framework with good generalization ability can be directly applicable to the CNN-based, Siamese-based, deep discriminative-based and Transformer-based trackers, without retraining.

Facile preparation of S, O-codoped carbon materials from wasted pulping liquid for supercapacitors

Hierarchical porous carbon S, O-doped is prepared by a facile and green way using industrial red pulping liquid as precursor and mild potassium chloride as initial templates to initiate templates regeneration. Replacement reactions occurs between potassium chloride and sodium atoms on precursors to form a more stable sodium chloride template, which is used to adjust pore structure during the etching process. Rich phenolic structures and sulfur groups in precursor result in the prepared porous carbon with many advantages for supercapacitors, including excellent specific surface area (1873.6 m2 g−1), high graphitization degree, hierarchical porosity and rich heteroatom content. The specific capacitance of fabricated samples can successfully achieve 386.9 F g−1. In addition, assembled energy storage device shows a remarkable energy density 8.6 Wh kg−1 at 21 kW kg−1 power density, and after 10,000 cycles, specific capacitance retention rate can be 86.4%. The superior electrochemical performances demonstrate a great potential candidate of industrial wastes for energy storage devices with excellent properties.

AMST2: aggregated multi-level spatial and temporal context-based transformer for robust aerial tracking

Recently, many existing visual trackers have made significant progress by incorporating either spatial information from multi-level convolution layers or temporal information for tracking. However, the complementary advantages of both spatial and temporal information cannot be leveraged when these two types of information are used separately. In this paper, we present a new approach for robust visual tracking using a transformer-based model that incorporates both spatial and temporal context information at multiple levels. To integrate the refined similarity maps through multi-level spatial and temporal encoders, we propose an aggregation encoder. Consequently, the output of the proposed aggregation encoder contains useful features that integrate the global contexts of multi-level spatial and the temporal contexts. The feature we propose offers a contrasting yet complementary representation of multi-level spatial and temporal contexts. This characteristic is particularly beneficial in complex aerial scenarios, where tracking failures can occur due to occlusion, motion blur, small objects, and scale variations. Also, our tracker utilizes a light-weight network backbone, ensuring fast and effective object tracking in aerial datasets. Additionally, the proposed architecture can achieve more robust object tracking against significant variations by updating the features of the latest object while retaining the initial template information. Extensive experiments on seven challenging short-term and long-term aerial tracking benchmarks have demonstrated that the proposed tracker outperforms state-of-the-art tracking methods in terms of both real-time processing speed and performance.

Construction of the MIITRA T1‐weighted and DTI templates of the older adult brain in a common space at 0.5mm resolution

AbstractBackgroundHigh resolution multimodal MRI templates of the older adult brain have the potential to facilitate template‐based studies of aging. As part of the Multichannel Illinois Institute of Technology & Rush university Aging (MIITRA) atlas, the present work developed high quality 0.5mm isotropic resolution T1‐weighted (T1w) and diffusion tensor imaging (DTI) templates in common space using data from a large, diverse, community cohort of non‐demented older adults.Method Data T1w and DTI data were collected using two 3T MRI scanners on 400 non‐demented older adults (50% male;64.9‐98.9 yrs;54% white,43% black) (Bennett et al.,2018; Barnes et al.,2012). Template construction Step1: An unbiased initial T1w template was created by pairwise affine registration (Ridwan et al.,2021).Step2: Spatial normalization was driven by T1w and DTI data alternatively (Wu et al., 2019). All transformations were combined to minimize interpolations and applied to both modalities. A patch‐based tissue‐guided sparse‐representation approach was employed to compute the template (Ridwan et al.,2021).Step3: High spatial resolution templates were created based on principles of super‐resolution (Niaz et al.,2022)The resulting templates are referred to as MIITRA_0.5mm templates. Evaluation T1w and DTI data from 202 ADNI3 non‐demented older adults were used for evaluation.The MIITRA_0.5mm templates were compared to other high resolution templates and the templates including older adults in terms of image sharpness and spatial normalization accuracy of ADNI3 data when each template was used as a reference for normalization.ResultMIITRA_0.5mm templates exhibited higher image sharpness (Fig.1,2). MIITRA_0.5mm templates allowed the highest inter‐subject spatial normalization accuracy across spatially normalized ADNI3 data and required less deformation than other high‐resolution templates (Fig.3,4). It should be noted that 1mm templates require on average less deformation than high‐resolution templates because larger voxels limit local deformations. Finally, the MIITRA_0.5mm templates allowed higher inter‐modality spatial matching of normalized older adult data (Fig.5).ConclusionThis work developed high resolution T1w and DTI multimodal templates of the older adult brain for the MIITRA atlas. The new templates exhibited higher image quality, were more representative of the older adult brain, and allowed higher spatial normalization accuracy compared to other templates. The new templates will be available for download at www.nitrc.org/projects/miitra.

Shape reconstruction for undetectable regions of abdominal organs based on a graph convolutional network

Although computed tomography (CT) and magnetic resonance imaging (MRI) produce high-resolution images, during surgery or radiotherapy, only low-resolution cone-beam CT and low-dimensional X-ray images are usually obtained. Furthermore, because the duodenum and stomach are filled with air, it may be hard to accurately segment their contours, even on high-resolution images. Although denoising and image enhancement can be used to assist in the segmentation and reconstruction of 3D images, some regions of organs may remain difficult to detect. To approach this issue, we propose a graph convolutional network (GCN) to reconstruct organs that are hard to detect on medical images. The method uses a framework to transform an initial template into an estimated shape of the patient’s organs based on previous information attainted from different detectable organs from different patients. To overcome the inaccurate estimation results of triangular surface mesh data with the GCN, we propose a method to add internal vertices and edges, which has the effect of improving the calculation accuracy by enhancing the topological structure of the mesh data. In this study, the organ data of 124 patients who underwent pancreatic tumor treatment were used to verify the predictive effect of the proposed method. The 3D organ contours were defined by board-certified radiation oncologists and used as the ground truth surface meshes. For 100 randomly selected detectable feature vertices, the average Euclidean distance error of the liver was 3.72 mm. The topological enhancement proposed in this study reduced the prediction error of the liver by an average of 0.84 mm.

FAmesh: Generating Frequency Adaptive Meshes from Single Images under 2D Hole Constraints

Reconstructing 3D models from a single image has numerous applications in fields such as VR/AR, medical imaging, and gaming. However, most mesh-based methods are limited by the use of 0-genus initial templates, which makes it difficult to reconstruct 3D meshes with complex topologies. Additionally, existing methods often prioritize reconstructing the overall shape and neglect to study local meshes with varying curvatures, resulting in a lack of correct and detailed local features in the generated meshes. This paper proposes a 3D reconstruction framework that transitions from global to local and incorporates MLP and GCN. The framework introduces a mesh pruning strategy under a 2D hole constraint to restore the correct mesh topology. Moreover, the framework fine-tunes local details by separately learning corresponding mapping functions on high-frequency and low-frequency local extended patches. The experiment with the proposed network on the ShapeNet dataset shows that the network has a CD value of 1.763 and an F-score of 85.40. The results from extensive experiments demonstrate that our proposed method outperforms existing methods in topology correction and local detail reconstruction.

Fluorescence and Colorimetric Analysis of African Swine Fever Virus Based on the RPA-Assisted CRISPR/Cas12a Strategy.

It is well-established that different detection modes are necessary for corresponding applications, which can effectively reduce matrix interference and improve the detection accuracy. Here, we reported a magnetic separation method based on recombinase polymerase amplification (RPA)-assisted clustered regularly interspaced short palindromic repeats (CRISPR)/Cas12a for dual-mode analysis of African swine fever virus (ASFV) genes, including colorimetry and fluorescence. The ASFV gene was selected as the initial RPA template to generate the amplicon. The RPA amplicon was then recognized by CRISPR-associated RNA (crRNA), activating the trans-cleavage activity of Cas12a and leading to the nonspecific cleavage of ssDNA as well as a significant release of alkaline phosphatase (ALP) in the ALP-ssDNA modified magnetic bead. The released ALP can catalyze para-nitrophenyl phosphate to generate para-nitrophenol, resulting in substantial changes in absorbance and fluorescence, both of which can be used for detection with the naked eye. This strategy allows the sensitive detection of ASFV DNA, with a 20 copies/mL detection limit; no cross-reactivity with other viruses was observed. A good linear relationship was obtained in serum. In addition, this sensor displayed 100% specificity and sensitivity for clinical sample analysis. This method integrates the high sensitivity of fluorescence with easy readout of colorimetry and enables a simple, low-cost, and highly sensitive dual-mode detection of viral nucleic acid, thereby providing a broad prospect for the practical application in the diagnosis of virus infection.

Deep-Learning-Based Precision Visual Tracking

In this paper, we target the “precision visual tracking” problem, which is the precise tracking of a target point on a moving object. Compared with the abundant efforts in object-level tracking, which aims at accurately predicting the bounding box (or the mask) of the object, much less attention has been drawn to precision visual tracking. To this end, we present a template tracking framework that consists of two parts, template matching and template updating. For template matching, we trained a deep architecture to directly estimate the projective transformation that deforms the template to the search image. For template updating, we came up with a systematic strategy to update the initial and new templates. To avoid drift build-ups, we incorporate a fast-running dense correspondence matching module into the template update step. The proposed method was extensively tested on both synthetic and real data. To generate the synthetic data, we created a dataset of 480 image sequences. Each image sequence comes with the trajectory ground truth of a target point moving across all the frames. We compared the proposed method with six comparative approaches on this dataset. The tracking accuracy achieved by the proposed method was as follows: around 44% of the tracking errors were less than one pixel, about 78% of them were less than three pixels, and only about 14% of them exceeded five pixels. The proposed method is fast, running at 14 frames-per-second (fps) on [Formula: see text] image sequences on a workstation equipped with a Nvidia RTX 2080Ti graphic card. Qualitative results also show that the proposed method is applicable to real-world image sequences. The related code, pre-trained models, and the test data will be made publicly available at https://github.com/XM-Peng/Precision-Visual-Tracking/ .

Unsupervised Registration Refinement for Generating Unbiased Eye Atlas.

With the confounding effects of demographics across large-scale imaging surveys, substantial variation is demonstrated with the volumetric structure of orbit and eye anthropometry. Such variability increases the level of difficulty to localize the anatomical features of the eye organs for populational analysis. To adapt the variability of eye organs with stable registration transfer, we propose an unbiased eye atlas template followed by a hierarchical coarse-to-fine approach to provide generalized eye organ context across populations. Furthermore, we retrieved volumetric scans from 1842 healthy patients for generating an eye atlas template with minimal biases. Briefly, we select 20 subject scans and use an iterative approach to generate an initial unbiased template. We then perform metric-based registration to the remaining samples with the unbiased template and generate coarse registered outputs. The coarse registered outputs are further leveraged to train a deep probabilistic network, which aims to refine the organ deformation in unsupervised setting. Computed tomography (CT) scans of 100 de-identified subjects are used to generate and evaluate the unbiased atlas template with the hierarchical pipeline. The refined registration shows the stable transfer of the eye organs, which were well-localized in the high-resolution (0.5 mm3) atlas space and demonstrated a significant improvement of 2.37% Dice for inverse label transfer performance. The subject-wise qualitative representations with surface rendering successfully demonstrate the transfer details of the organ context and showed the applicability of generalizing the morphological variation across patients.

Applying Unique Molecular Indices with an Extensive All-in-One Forensic SNP Panel for Improved Genotype Accuracy and Sensitivity

One of the major challenges in forensic genetics is being able to detect very small amounts of DNA. Massively parallel sequencing (MPS) enables sensitive detection; however, genotype errors may exist and could interfere with the interpretation. Common errors in MPS-based analysis are often induced during PCR or sequencing. Unique molecular indices (UMIs) are short random nucleotide sequences ligated to each template molecule prior to amplification. Applying UMIs can improve the limit of detection by enabling accurate counting of initial template molecules and removal of erroneous data. In this study, we applied the FORCE panel, which includes ~5500 SNPs, with a QIAseq Targeted DNA Custom Panel (Qiagen), including UMIs. Our main objective was to investigate whether UMIs can enhance the sensitivity and accuracy of forensic genotyping and to evaluate the overall assay performance. We analyzed the data both with and without the UMI information, and the results showed that both genotype accuracy and sensitivity were improved when applying UMIs. The results showed very high genotype accuracies (>99%) for both reference DNA and challenging samples, down to 125 pg. To conclude, we show successful assay performance for several forensic applications and improvements in forensic genotyping when applying UMIs.

Wrinkles Realistic Clothing Reconstruction by Combining Implicit and Explicit Method

We propose a novel method to achieve robust and wrinkle-realistic clothing reconstruction from a single RGB image. Our approach successfully exploits both advantages of implicit function methods and explicit clothing generative models. The former allows to capture high-frequency wrinkle details and clothing appearance from a single image, and the latter provides reasonable clothing shape prior and structured topology. To utilize the implicit function method to obtain implicit clothing, the key is that we need to automatically segment the clothing region from the reconstructed results and solve its depth ambiguity problem. Therefore, we design a pixel-aligned segmentation method to achieve automatic and complete clothing segmentation. It relies on a lightweight clothing mask network and a simple but effective segmentation strategy. To deal with the depth ambiguity problem, a depth correction function is introduced to significantly remove the uncertainty in the depth direction and recover the correct clothing shape. Further, we utilize explicit clothing generative models to provide reasonable shapes and topologies. It first infers an initial explicit clothing template based on generative models. Then, the implicit clothing is fitted by this explicit template to capture high-frequency deformation, resulting in wrinkle-realistic and structured clothing results. Extensive experiments have proved the validity of our approach with the visual effect and geometry accuracy both reaching the state-of-the-art level in clothing reconstruction from a single image.

Derived Functions for MATLAB Non-linear Solvers – an optimal Stator Slot Design Stopgap

In the three-phase squirrel cage induction motor (SCIM), like other rotating electromechanical machines, the active length of the stator windings lies in the slots. The proper dimensioning of the slots is therefore critical to the design of both the windings and the stator core, so as to attain a high performing machine. The preliminary stage of the SCIM design, if well done, facilitates the refinement and optimization stages; serving as a good take-off point for that final state of the art design. The aim of this article isn’t to come up with a novel optimization method for the SCIM, but to provide another way of obtaining an initial stator slot design template, from where proper design refinement/optimization techniques could obtain very good starting values and be facilitated. Therefore, an attempt is being made here to derive relevant design functions that could be fed into the algorithms of various non-linear solvers such as the Genetic Algorithm (GA), to output the main stator slot dimensions for the SCIM. The final designed SCIM obtained from implementing the derived equations, when compared with the appropriate reference datasheets, showed only minor deviations from the expected performance indices – an outcome deemed satisfactory for a preliminary design attempt. These initial dimensions may in practice be subjected to the appropriate refinement. Depending on the target performance requirement of the motor.