Miracle-dz/UniFAR-FTUs huggingface dataset:
CMAT: Integrating Convolution Mixer and Self-Attention for Visual Tracking

Convolutional Neural Networks (CNNs) and Transformer are two powerful representation learning techniques for visual tracking. Although CNNs can effectively reduce local redundancy via small-neighborhood convolution operations, their limited receptive fields make it difficult to capture global dependency. Self-attention in Transformer uses patches as the input representation, which can effectively capture long-range dependency. However, blind similarity comparisons between all patches can lead to high redundancy. Is there then a technique that combines well the advantages of both paradigms for visual tracking? In this work, we design a novel backbone network for feature extraction. First, we choose Depthwise Convolution and Pointwise Convolution to build a Convolution Mixer, which effectively separates spatial mixing from channel-wise mixing of information. The Convolution Mixer reduces redundancy in spatial and channel features while increasing receptive field. Then, to exploit the global modeling ability of self-attention, we construct a module by aggregating Convolution Mixer and self-attention. The module shares dominant computational complexity (the square of the channel size) in the first stage. In the second stage, the shift and summation operations are lightweight. Finally, to alleviate the overfitting of the backbone network during training, a dropout layer is added at the end of the module to improve the generalization ability of the network model. Stronger image features are provided for subsequent feature fusion and prediction. The proposed tracker (named CMAT) achieves satisfying tracking performance on ten challenging datasets. In particular, CMAT achieves a 64.1% AUC on LaSOT and a 68.9% AUC on UAV123 while running at 23 FPS.
A Heuristically Accelerated Reinforcement Learning-Based Neurosurgical Path Planner

The steerable needle becomes appealing in the neurosurgery intervention procedure because of its flexibility to bypass critical regions inside the brain; with proper path planning, it can also minimize the potential damage by setting constraints and optimizing the insertion path. Recently, reinforcement learning (RL)-based path planning algorithm has shown promising results in neurosurgery, but because of the trial and error mechanism, it can be computationally expensive and insecure with low training efficiency. In this paper, we propose a heuristically accelerated deep Q network (DQN) algorithm to safely preoperatively plan a needle insertion path in a neurosurgical environment. Furthermore, a fuzzy inference system is integrated into the framework as a balance of the heuristic policy and the RL algorithm. Simulations are conducted to test the proposed method in comparison to the traditional greedy heuristic searching algorithm and DQN algorithms. Tests showed promising results of our algorithm in saving over 50 training episodes, calculating path lengths of 0.35 after normalization, which is 0.61 and 0.39 for DQN and traditional greedy heuristic searching algorithm, respectively. Moreover, the maximum curvature during planning is reduced to 0.046 from 0.139 mm−1 using the proposed algorithm compared to DQN.
Introducing soft topology constraints in deep learning-based segmentation using projected pooling loss

When performing manual segmentations, experts heavily rely on prior anatomical knowledge. Topology is an important prior information due to its stability across patients. Recently, several losses based on persistent homology were proposed to constrain topology. However, such approaches are computationally expensive and complex to implement, in particular in 3D. In this paper, we propose a novel loss function to introduce topological priors in deep learning-based segmentation, which is fast to compute and easy to implement. Our approach was evaluated in several medical datasets (spleen, heart, hippocampus, red nucleus). It allowed reducing topological errors and, in some cases, improving voxel-level accuracy.
Tissue Microarray Lipidomic Imaging Mass Spectrometry Method: Application to the Study of Alcohol-Related White Matter Neurodegeneration

Central nervous system (CNS) white matter pathologies accompany many diseases across the lifespan, yet their biochemical bases, mechanisms, and consequences have remained poorly understood due to the complexity of myelin lipid-based research. However, recent advances in matrix-assisted laser desorption/ionization-imaging mass spectrometry (MALDI-IMS) have minimized or eliminated many technical challenges that previously limited progress in CNS disease-based lipidomic research. MALDI-IMS can be used for lipid identification, semi-quantification, and the refined interpretation of histopathology. The present work illustrates the use of tissue micro-arrays (TMAs) for MALDI-IMS analysis of frontal lobe white matter biochemical lipidomic pathology in an experimental rat model of chronic ethanol feeding. The use of TMAs combines workload efficiency with the robustness and uniformity of data acquisition. The methods described for generating TMAs enable simultaneous comparisons of lipid profiles across multiple samples under identical conditions. With the methods described, we demonstrate significant reductions in phosphatidylinositol and increases in phosphatidylcholine in the frontal white matter of chronic ethanol-fed rats. Together with the use of a novel rapid peak alignment protocol, this approach facilitates reliable inter- and intra-group comparisons of MALDI-IMS data from experimental models and could be extended to human disease states, including using archival specimens.
Quality of Life in Breast Cancer Patients: A Systematic Review of the Qualitative Studies

Objective Quality of life (QoL) in breast cancer patients is still an important topic. Despite numerous quantitative scales, qualitative studies can help to in-depth understand the QoL of breast cancer patients. The purpose of this systematic review was to integrate qualitative studies on the QoL of women with breast cancer. Methods A literature search was performed in electronic databases including PubMed, Scopus, and Web of Science from January 1, 2010 until June 28, 2022 to find out qualitative studies assessing breast cancer patient’s QoL. Two authors independently evaluated methodological quality according to the consolidated criteria for reporting qualitative research (COREQ) checklist. Data were extracted and reported by themes for cancer-free women and patients with metastatic cancer separately. Results In all, 1565 citations were retrieved. After removing 1387 duplicate and irrelevant papers, the full texts of 27 articles were reviewed and finally, 9 were eligible for evaluation. In quality checking of the citations, all articles gained the required quality score. After examining and merging similar topics, nine major themes were extracted. Physical, spiritual, and psychological aspects of QoL were the common issues in cancer-free women (before and after the COVID-19 pandemic) and patients with metastatic cancer. Perception of cancer and social life were the other main concerns in cancer-free women, whereas, in metastatic patients’ overall survival and planning for the future and their children’s life was the focus of interest. Women with metastatic disease showed more vulnerability in coping compared to cancer-free women. Conclusion This review provides an opportunity to have a closer look into the several domains of QoL in women with breast cancer. In-depth information provided by this review might help to develop interventions for patients and their families to support women to cope much better with their life challenges.
Colorectal Polyp Size Classification Using a Siamese Network

Colorectal cancer is one of the leading cause of cancer related deaths with increasing prevalence. One key factor in the likelihood of adenomatous cell differentiation is polyp diameter. There exist a significant cut-off value of 10 mm which clinicians use in diagnosis management. We propose a novel method to classify polyp size into above or below 10 mm classes based on a Siamese network. In a first step, a Siamese networks is trained to build a high dimensional feature embedding extracted for each polyp size. In as second step, we use a k-NN approach to classify polyp sizes based on the distance between the feature embedding of the input image, and the whole embedding space learned by the Siamese network. This method allows for better binary classification of the suband sup10 mm polyp size classes. Our data consist of around 55,000 images from 129 movies classified into various polyp sizes ranging from 1-15 mm. We trained our model on 10,746 images, and tested on 2,688 images equally split into each binary category. We obtained 79.2% in feature classification and 95.7% in polyp size classification.
Unbiased Longitudinal Brain Atlas Creation Using Robust Linear Registration And Log-Euclidean Framework For Diffeomorphisms

We present a new method to create a diffeomorphic longitudinal (4D) atlas composed of a set of 3D atlases each representing an average model at a given age. This is achieved by generalizing atlasing methods to produce atlases unbiased with respect to the initial reference up to a rigid transformation and ensuring diffeomorphic deformations thanks to the Baker-Campbell-Hausdorff formula and the log-Euclidean framework for diffeomorphisms. Subjects are additionally weighted using an asymmetric function to closely match specified target ages. Creating a longitudinal atlas also implies dealing with subjects with large brain differences that can lead to registration errors. This is overcome by a robust rigid registration based on polar decomposition. We illustrate these techniques for the creation of a 4D pediatric atlas, showing their ability to create a temporally consistent atlas.
The Analysis of Designed Experiments with Missing Observations

SUMMARY This paper examines published methods for analysing designed experiments with missing observations. An exact method originated by Wilkinson is shown to be a viable alternative to the more commonly used iterative techniques. It has the advantage that, from a small number of standard analyses of the design using specially constructed data vectors, a full and accurate analysis of the observed data can be obtained. THIS paper examines the relationship between exact and iterative procedures for analysing designed experiments with missing observations. The various techniques are presented in a common mathematical framework. The notation used originates largely from Tocher (1952). For a more detailed survey of the literature, the reader is referred to Hoyle (1971). The occurrence of missing observations destroys the orthogonal or balanced structure usually present in a designed experiment. The incomplete experiment can be analysed by using a standard least-squares method to fit successive sets of parameters corresponding to successive terms in the model. This may prove to be a tedious and possibly inaccurate procedure, especially with large and complicated experiments. A more satisfactory method would be to insert values for the missing observations and to perform the analysis using the structure of the original experiment. Yates (1933) showed that the correct values to insert are those for which, when the formally completed experiment is analysed, the residuals in the missing plots are zero. The most common procedure is to use an iterative approach similar to that proposed by Healy and Westmacott (1956). With this method, initial guesses are inserted for the missing values and, at each iteration, the residuals in the missing plots are subtracted from the current estimates. It can be proved that the residuals in the missing plots must tend to zero and that the residual sum of squares must converge to the correct value. Once these missing-value estimates have been obtained for the full model, the complete analysis is usually performed with these values inserted, the degrees of freedom for the residual sum of squares being reduced by the number of missing values. Exact procedures for analysing such an experiment consist of a series of analyses using specially constructed data vectors. Notable among these procedures are the covariance technique of Bartlett (1937) and the method of Wilkinson (1958a, b). Recent papers on exact methods (Rubin 1972; Haseman and Gaylor, 1973; John and Prescott, 1975) deal mainly with the problem of obtaining missing-value estimates for the full model. Presumably, it is intended that the complete analysis be performed with these estimates inserted for the missing obser- vations. Any method which finds missing-value estimates only for the full model and uses these
MALDI Imaging, a Powerful Multiplex Approach to Decipher Intratumoral Heterogeneity: Combined Hepato-Cholangiocarcinomas as Proof of Concept

Simple Summary To provide a comprehensive molecular characterization of combined hepatocholangiocarcinomas (cHCC-CCA), the MALDI (Matrix Assisted Laser Desorption Ionization) imaging approach was used to acquire high-resolution spatialized molecular profiles directly from fixed tissue sections without a previous labeling step. The heterogeneity of cHCC-CCA was explored through the analysis of whole tumor slides by comparing peptidic profiles and histological and immunophenotypical images. MALDI imaging analysis was able to identify both tumor components (HCC and iCCA) and also small tumor areas not seen at the microscopic level. In addition, the morphological distribution of tryptic peptides of the different immunophenotypic markers was analyzed in silico. Their distribution allowed us to identify areas initially unlabeled by immunohistochemistry (IHC). This study demonstrates that an in silico MALDI imaging approach may improve conventional histological analysis and allow the development of new markers. Abstract Combined hepato-cholangiocarcinomas (cHCC-CCA) belong to the spectrum of primary liver carcinomas, which include hepatocellular carcinomas (HCC) and intrahepatic cholangiocarcinomas (iCCA) at both ends of the spectrum. Mainly due to the high intratumor heterogeneity of cHCC-CCA, its diagnosis and pathological description remain challenging. Taking advantage of in situ non-targeted molecular mapping provided by MALDI (Matrix Assisted Laser Desorption Ionization) imaging, we sought to develop a multiscale and multiparametric morphological approach, integrating molecular and conventional pathological analysis. MALDI imaging was applied to five representative cases of resected cHCC-CCA. Principal component analysis and segmentations with MALDI imaging techniques identified areas related to either iCCA or HCC and also hidden tumor areas not visible microscopically. In addition, the overlap between MALDI segmentation and immunostaining provided a comprehensive description of cHCC-CCA tumor heterogeneity by identifying transitional and micro-metastatic areas. Moreover, a list of peptides derived from in silico digestion was obtained for each immunohistochemical marker and was matched within the peptide peak list acquired by MALDI. Comparison of immunostaining images with ions from in silico digestion revealed an accurate identification of iCCA and HCC areas. Our study provides further evidence on the performance of MALDI imaging in exploring intratumor heterogeneity and offering virtual multiplex immunostaining through a single acquisition.
Molecular-dynamics simulations of the nanoscale Taylor test under extreme loading conditions

A series of molecular-dynamics simulations of the classic Taylor impact test is performed by using a flat-ended monocrystalline nanoscale projectile made of the Lennard-Jones two-dimensional solid. The nanoprojectile striking velocities range from 0.75 to 7 km/s. These atomistic simulations offer insight into nature of fragment distributions and evolution of state parameters. According to the simulation results, the cumulative distribution of fragment sizes in the course of this non-homogeneous fragmentation process for hypervelocity impacts appears to be well represented by the bimodal-exponential distribution commonly observed during high-energy uniform fragmentation events. For more moderate impact velocities, the cumulative distribution of fragment sizes, in addition to the bimodal-exponential part, exhibits a large-fragment tail. Temporal evolutions on instantaneous kinetic temperature, stress and strain invariants are presented and discussed. Scaling relations between temperature/temperature rate and kinematic rates of deformation are suggested.
SpectralMAE: Spectral Masked Autoencoder for Hyperspectral Remote Sensing Image Reconstruction

Accurate hyperspectral remote sensing information is essential for feature identification and detection. Nevertheless, the hyperspectral imaging mechanism poses challenges in balancing the trade-off between spatial and spectral resolution. Hardware improvements are cost-intensive and depend on strict environmental conditions and extra equipment. Recent spectral imaging methods have attempted to directly reconstruct hyperspectral information from widely available multispectral images. However, fixed mapping approaches used in previous spectral reconstruction models limit their reconstruction quality and generalizability, especially dealing with missing or contaminated bands. Moreover, data-hungry issues plague increasingly complex data-driven spectral reconstruction methods. This paper proposes SpectralMAE, a novel spectral reconstruction model that can take arbitrary combinations of bands as input and improve the utilization of data sources. In contrast to previous spectral reconstruction techniques, SpectralMAE explores the application of a self-supervised learning paradigm and proposes a masked autoencoder architecture for spectral dimensions. To further enhance the performance for specific sensor inputs, we propose a training strategy by combining random masking pre-training and fixed masking fine-tuning. Empirical evaluations on five remote sensing datasets demonstrate that SpectralMAE outperforms state-of-the-art methods in both qualitative and quantitative metrics.
Parameters behind "non-parametric" statistics: Kendall's ¿a, Somers' D and median dierences

So-called “non-parametric” statistical methods are often in fact based on population parameters, which can be estimated (with confidence limits) using the corresponding sample statistics. This article reviews the uses of three such parameters, namely Kendall’s ? a, Somers’ D and the Hodges-Lehmann median dierence. Confidence intervals for these are demonstrated using the somersd package. It is argued that confidence limits for these parameters, and their dierences, are more informative than the traditional practice of reporting only P-values. These three parameters are also important in defining other tests and parameters, such as the Wilcoxon test, the area under the receiver operating characteristic (ROC) curve, Harrell’s C, and the Theil median slope.
The Expression of Affective Temperaments in Cystic Fibrosis Patients: Psychopathological Associations and Possible Neurobiological Mechanisms

The aim of this study was to investigate the association between Cystic Fibrosis (CF) and affective temperaments, considering the relevance of ionic balances in neural excitability, as a possible neurobiological basis for temperamental expression. A cross-sectional study involving 55 adult CF patients was conducted. Sociodemographic, clinical and therapeutic characteristics, temperamental and personality dispositions and depressive and anxiety symptoms were evaluated through standardized semi-structured and structured interviews. The majority of the enrolled CF patients were receiving Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) therapy (72.7%), and most of them had hyperthymic temperament predominance (29.1%). Different TEMPS-A (Temperament Evaluation of Memphis, Pisa, Paris, and San Diego Autoquestionnaire) dimensions were not associated with the type of CF phenotype-related mutation or with the use of CFTR-modulator therapy. However, a tendency towards irritability was noted in patients not undergoing CFTR modulator therapy (6.7 ± 4.72 vs. 4.7 ± 4.33; p = 0.13). In light of the limitations imposed by the cross-sectional nature of the study, a hyperthymic temperament was found to be protective against current or lifetime psychopathologic events, whereas the other temperaments were associated with positive psychopathological anamnesis. Based on the measurement of temperament profiles and the study of their associations with clinically relevant variables, we argue that subjecting CF patients to such a temperament assessment could prove beneficial in the transition towards integrated and personalized care.
Effect of Prepartum Magnesium Butyrate Supplementation on Rumination Time, Milk Yield and Composition, and Blood Parameters in Dairy Cows

Simple Summary Prepartum magnesium butyrate (MgB) supplementation was given to dairy cows three weeks before parturition and lactational performance; inflammation-related protein levels, blood energy analytes, and rumination time were compared to Control cows. Milk samples were collected for the first 70 days of lactation, and various blood parameters and rumination activity were also monitored. The MgB-supplemented group yielded more milk compared to the Control during week 1, and had increased milk fat and protein levels over a longer duration of lactation. Blood energy analytes indicated that MgB supplementation had no effects on the negative energy balance that occurs often during the early lactation phase. Whilst no clinical disease was detected in either group of animals during the study, the MgB group was found to have a significantly lower milk somatic cell count and blood concentration of an inflammation-related protein. Rumination activity was greater in the MgB group relative to the Control group due to a depression in rumination activity being overcome. The basis of this is not clear, as feed intake could not be monitored in the current study. Abstract Background: Magnesium butyrate (MgB) supplementation of dairy cows during the three-week close-up period was tested for its effects on blood energy analytes, rumination time, inflammation, and lactation performance. Methods: Daily milk yield was recorded and weekly milk samples collected for the first 70 days of lactation from MgB supplemented (MgB, n = 34), and unsupplemented (Control, n = 31) multiparous Holstein-Friesian cows. During a period from week 3 to week 10 postpartum, blood samples were taken and analyzed for various parameters, and ruminant activity was measured. Results: The MgB group yielded 25.2% more milk than the Control during week 1, and had increased milk fat and protein concentrations over a longer duration. Somatic cell counts (SCC) were decreased in the MgB group independent of days in milk. No differences were observed between groups in terms of plasma non-esterified fatty acids, β-hydroxybutyrate, glucose, or blood iCa levels. The MgB group had lower haptoglobin (Hp) levels during lactation relative to the Control group. Time spent ruminating increased after calving with MgB due to a shorter post calving rumination delay relative to the Control group. Conclusions: Prepartum MgB supplementation improved lactation performance without affecting blood energy analytes. The basis by which MgB also improved rumination activity remains to be determined, as DMI could not be assessed. As MgB lowered SCC and Hp concentrations, it is speculated that MgB may help minimize postpartum inflammatory processes.