Miracle-dz/UniFAR-FTUs huggingface dataset:
Advances in Cardiovascular Imaging Cardiac Involvement in Patients With Muscular Dystrophies Magnetic Resonance Imaging Phenotype and Genotypic Considerations

Muscular dystrophy (MD) connotes a heterogeneous group of inherited disorders characterized by progressive wasting and weakness of the skeletal muscles. In several forms of MD, cardiac dysfunction occurs, and cardiac disease may even be the predominant manifestation of the underlying genetic myopathy. Cardiologists may be unfamiliar with these diseases owing to their low incidence; also, significant advances in respiratory care have only recently unmasked cardiomyopathy as a significant cause of death in MD. 1
Matrix representations of trellises and enumerating trellis pseudocodewords

Tail-biting trellises and their pseudocodewords are very important for modern decoding techniques like iterative decoding. We introduce a useful matrix representation of trellises, give its fundamental properties, and use it to enumerate and describe the distribution of trellis pseudocodewords. We give several examples, a couple of which lead to important open problems. Next, we prove that the pseudocodeword weight enumerator introduced in [2] always satisfies a recurrence equation, and, for certain trellises including the Golay trellis given in [3], that it is invariant under generalized MacWilliams transformations, allowing invariant theory to be used for computing it. Computation for the Golay trellis shows then that pseudocodewords of period at most 4 must have AWGN pseudoweight at least 8.
Caveolae preservation in the characterization of human neuromuscular disease

We have examined freeze‐fracture replicas and conventional thinsection images of rat myofibers prepared by perfusion and by conventional immersion fixation protocols, and myofibers of normal and dystrophic human myofibers prepared by similar immersion fixation methods. In both rat and human myofibers, the size and distribution of caveolae was found to differ substantially according to (1) the method of glutaraldehyde exposure, (2) the depth of the myofiber from the surface exposed to the fixative, and (3) if surgically bisected, the distance from the cut end of the myofiber. Conventional immersion fixation resulted in unavoidable but predictable alterations in sarcolemmal caveolae. These reproducible artifacts of fixation technique substantially complicate the use of caveolae as reliable markers for the characterization of human neuromuscular disease.
25-OH-Vitamin D and procalcitonin levels after correction of acute hyperglycemia

Background Hyperglycemia is a common complication of diabetes melitis (DM) and in the absence of metabolic decompensation is a common finding in the Emergency Department (ED). We aimed to evaluate the 25 OH Vit D [25(OH)D] and procalcitonin (PCT) levels during hyperglycemia and after normalization of blood glucose. Material/Methods The study included 88 patients over the age of 18 years who presented with acute hyperglycemia at the Hacettepe University Department of Emergency Medicine. Euglycemia was obtained within 6–12 hours and serum samples were taken from patients on admission and 6 hours after normalization of blood glucose. Along with plasma glucose, plasma 25(OH)D and PCT levels were measured using ELISA. Results There were 88 (45 males) patients, with a median age of 60.0±13.9 years. Serum 25(OH)D levels increased in all patients after normalization of blood glucose, and serum PCT levels decreased in the whole group. This decrease was independent of type of diabetes or presence of infection. Conclusions We demonstrated an increase in 25(OH)D after normalization of blood glucose, and a decrease in PCT in patients with hyperglycemia. This effect was independent of the type of diabetes and presence of infection. Further studies are needed to evaluate the faster link between metabolic abnormalities, vitamin D, PCT, and inflammation.
Transfer Learning and On-Fly Data Augmentation for Scene UnderstandingUsing InceptionResNet

Deep learning models require large amounts of data to achieve good results. However, most datasets consist of images taken from similar angles, brightness levels, and orientations, which do not reflect the diverse reality of scenes. To address this issue, data augmentation techniques are employed to generate images that mimic actual scenarios, thereby increasing the training data for the model. In this paper, we propose an on-the-fly data augmentation approach that enhances the dataset while minimizing the need for additional storage by not saving augmented images to disk. We evaluate different pretrained and trained-from-scratch Convolutional Neural Network (CNN) models on benchmark scene datasets (Scene15 and MIT67), and our results demonstrate that fine-tuning the InceptionResNetV2 model achieves competitive performance compared to state-of-the-art methods on these datasets with accuracy of 95% and 86% respectively. This research contributes to creating more realistic scene representations through data augmentation while optimizing disk space usage. Furthermore, we highlight the effectiveness of data augmentation as a regularization technique by reducing loss. The findings presented in this paper provide valuable insights for scene understanding tasks and have implications for various applications such as education, healthcare systems, autonomous vehicles, and domestic robot navigation.
Secretion of Sucrase by Leishmania donovani

ABSTRACT. Leishmania donovani promastigotes were collected, washed, resuspended in buffer, and assayed for sucrase activity. No activity was observed in the intact washed cells, but activity was measurable when the cells were permeabilized with Triton X‐100. Intracellular sucrase activity was highest in promastigotes grown at pH 7.4, somewhat lower in promastigotes grown at pH 5.5, and significantly lower in “amastigotes” grown at pH 5.5. No trehalase, lactase, or maltase activities were observed. Assay of the medium in which the cells had grown showed that most the sucrase activity was extracellular, i.e. was secreted into the medium during growth.
A sequential Riesz-like criterion for the Riemann hypothesis

Let c k : = ∑ j − 0 k ( − 1 ) j ( k j ) ( 1 / ζ ( 2 j + 2 ) ) . We prove that the Riemann hypothesis is equivalent to c k ≪ k − 3 / 4 + e for all e > 0 ; furthermore, we prove that c k ≪ k − 3 / 4 implies that the zeros of ζ ( s ) are simple. This is closely related to M. Riesz's criterion which states that the Riemann hypothesis is equivalent to ∑ k = 1 ∞ ( ( − 1 ) k + 1 x k / ( k − 1 ) ! ζ ( 2 k ) ) ≪ x 1 / 4 + e as x → + ∞ , for all e > 0 .
Improving Document Classification by Integrating Human-Crafted Semantic Knowledge

Document classification is a pivotal task in various domains, warranting the development of robust algorithms. Among these, the Bidirectional Encoder Representations from Transformers (BERT) algorithm, introduced by Google, has proven to perform well when fine-tuned for the task at hand. Leveraging transformer architecture, BERT demonstrates stellar language understanding capabilities. However, the integration of BERT with a range of approaches has shown potential for further enhancing classification accuracy. This paper investigates three techniques that leverage semantic understanding to improve the performance of document classification models trained with BERT. First, we balance corpora afflicted by imbalanced training data distributions. Next, we substitute words and phrases with semantically similar terms to create “synthetic documents;” thereby shifting the focus from individual words to semantic meaning. Finally, we retrain our model on a dataset composed of “synthetic documents” with heavier weights given to classes with commonly misclassified documents during the initial round of training. These approaches emphasize the significance of semantic comprehension in document classification, as the meaning of words and phrases often relies on contextual cues. Our findings demonstrate the efficacy of these techniques and highlight the importance of incorporating semantic knowledge in document classification algorithms. On our labeled testing set of news articles from BBC News, BERT performs with a baseline macro F1 score of 0.902. Incorporating the three techniques, we were able to improve the macro F1 score to 0.951.
Emerging cytokine networks in osteosarcoma

Osteosarcoma is the most prevalent primary bone cancer. Although it has a global impact of approximately 1 to 3 cases/million a year, its etiopathophysiology is still unknown. Moreover, the immune response to its development is very individual as well as variable. Particular emphasis has been placed on the study of the immunoregulatory role of cytokines in osteosarcoma. In this scenario, newly identified cytokine networks have emerged. A deep understanding on the complex interplay of cytokines in osteosarcoma may have prognostic importance. In this review, we seek to highlight the classic roles of cytokines in osteosarcoma, to describe the role of new players in the emerging cytokine networks, and to discuss the therapeutic targets that encompass the complexity and implications of this network. Key words: Cytokines, Immunology, Osteosarcoma.
Case studies and mathematical models of ecological speciation. 2. Palms on an oceanic island

A recent study of a pair of sympatric species of palms on the Lord Howe Island is viewed as providing probably one of the most convincing examples of sympatric speciation to date. Here we describe and study a stochastic, individual‐based, explicit genetic model tailored for this palms system. Overall, our results show that relatively rapid (< 50 000 generations) colonization of a new ecological niche, and sympatric or parapatric speciation via local adaptation and divergence in flowering periods are theoretically plausible if (i) the number of loci controlling the ecological and flowering period traits is small; (ii) the strength of selection for local adaptation is intermediate; and (iii) an acceleration of flowering by a direct environmental effect associated with the new ecological niche is present. We discuss patterns and time‐scales of ecological speciation identified by our model, and we highlight important parameters and features that need to be studied empirically in order to provide information that can be used to improve the biological realism and power of mathematical models of ecological speciation.
Neurostimulation for Parkinson's disease with early motor complications.

BACKGROUND Subthalamic stimulation reduces motor disability and improves quality of life in patients with advanced Parkinson's disease who have severe levodopa-induced motor complications. We hypothesized that neurostimulation would be beneficial at an earlier stage of Parkinson's disease. METHODS In this 2-year trial, we randomly assigned 251 patients with Parkinson's disease and early motor complications (mean age, 52 years; mean duration of disease, 7.5 years) to undergo neurostimulation plus medical therapy or medical therapy alone. The primary end point was quality of life, as assessed with the use of the Parkinson's Disease Questionnaire (PDQ-39) summary index (with scores ranging from 0 to 100 and higher scores indicating worse function). Major secondary outcomes included parkinsonian motor disability, activities of daily living, levodopa-induced motor complications (as assessed with the use of the Unified Parkinson's Disease Rating Scale, parts III, II, and IV, respectively), and time with good mobility and no dyskinesia. RESULTS For the primary outcome of quality of life, the mean score for the neurostimulation group improved by 7.8 points, and that for the medical-therapy group worsened by 0.2 points (between-group difference in mean change from baseline to 2 years, 8.0 points; P=0.002). Neurostimulation was superior to medical therapy with respect to motor disability (P<0.001), activities of daily living (P<0.001), levodopa-induced motor complications (P<0.001), and time with good mobility and no dyskinesia (P=0.01). Serious adverse events occurred in 54.8% of the patients in the neurostimulation group and in 44.1% of those in the medical-therapy group. Serious adverse events related to surgical implantation or the neurostimulation device occurred in 17.7% of patients. An expert panel confirmed that medical therapy was consistent with practice guidelines for 96.8% of the patients in the neurostimulation group and for 94.5% of those in the medical-therapy group. CONCLUSIONS Subthalamic stimulation was superior to medical therapy in patients with Parkinson's disease and early motor complications. (Funded by the German Ministry of Research and others; EARLYSTIM ClinicalTrials.gov number, NCT00354133.).
A Portrait of Child Poverty in Germany

This paper offers a descriptive portrait of income poverty among children in Germany between the early 1980s and 2001, with a focus on developments since unification in 1991. Data from the German Socio-Economic Panel are used to estimate poverty rates, rates of entry to and exit from poverty, and the duration of time spent in and out of poverty. The analysis focuses upon comparisons between East and West Germany, by family structure, and citizenship status. Child poverty rates have drifted upward since 1991, and have been increasing more than the rates for the overall population since the mid-1990s. In part these changes are due to increasing poverty among children from households headed by non-citizens. Children in single parent households are by all measures at considerable risk of living in poverty. There are also substantial differences in the incidence of child poverty and its dynamics between East and West Germany.
Genome-Wide Association Study Evaluating Lipoprotein-Associated Phospholipase A2 Mass and Activity at Baseline and After Rosuvastatin Therapy

Background—Lipoprotein-associated phospholipase A2 (Lp-PLA2) is a proinflammatory enzyme bound to low-density lipoprotein cholesterol and other circulating lipoproteins. Two measures of Lp-PLA2, mass and activity, are associated with increased cardiovascular risk. Data are sparse regarding genetic determinants of Lp-PLA2 mass and activity, and no prior data are available addressing genetic determinants of statin-induced changes for this proinflammatory biomarker. Methods and Results—We performed a genome-wide association study of Lp-PLA2 mass and activity at baseline and after 12 months of rosuvastatin therapy (20 mg/d) among 6851 participants of European ancestry from the Justification for Use of Statins in Prevention: an Intervention Trial Evaluating Rosuvastatin (JUPITER) and performed replication in a meta-analysis of 13 664 participants from the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) Consortium. Novel associations were identified and replicated at MS4A4E and TMEM49 for baseline Lp-PLA2 activity with genome-wide significant joint P values (P=2.0×10−11 and P=2.9×10−9, respectively). In addition, genome-wide associations (P<5×10−8) were identified and replicated for baseline Lp-PLA2 mass at CETP and for Lp-PLA2 activity at the APOC1-APOE and PLA2G7 loci. Among 2673 statin-allocated participants, both Lp-PLA2 mass and activity were reduced by >30% and low-density lipoprotein cholesterol by 50% after 12 months of statin therapy (P<0.001 for both). Variants in ABCG2 and LPA were associated with change in statin-induced Lp-PLA2 activity at genome-wide significance but were substantially attenuated after adjustment for statin-induced changes in lipid levels. Conclusions—Genome-wide significant associations at MS4A4E and TMEM49 may reflect novel influences on circulating levels of Lp-PLA2 activity. In addition, genome-wide significant associations with rosuvastatin-induced change in Lp-PLA2 activity were observed in ABCG2 and LPA, likely because of their impact on statin-induced low-density lipoprotein cholesterol lowering.
R-MAX - A General Polynomial Time Algorithm for Near-Optimal Reinforcement Learning

R-MAX is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-MAX, the agent always maintains a complete, but possibly inaccurate model of its environment and acts based on the optimal policy derived from this model. The model is initialized in an optimistic fashion: all actions in all states return the maximal possible reward (hence the name). During execution, it is updated based on the agent's observations. R-MAX improves upon several previous algorithms: (1) It is simpler and more general than Kearns and Singh's E3 algorithm, covering zero-sum stochastic games. (2) It has a built-in mechanism for resolving the exploration vs. exploitation dilemma. (3) It formally justifies the ``optimism under uncertainty'' bias used in many RL algorithms. (4) It is simpler, more general, and more efficient than Brafman and Tennenholtz's LSG algorithm for learning in single controller stochastic games. (5) It generalizes the algorithm by Monderer and Tennenholtz for learning in repeated games. (6) It is the only algorithm for learning in repeated games, to date, which is provably efficient, considerably improving and simplifying previous algorithms by Banos and by Megiddo.
A Memory Efficient Run-time Re-configurable Convolution IP Core for Deep Neural Networks Inference on FPGA Devices

Modern computer vision applications are vastly dependent on convolutional neural networks (CNNs) in order to perform feature extractions, dimensionality reduction, etc. There are a series of arithmetic calculations markedly dominated by multiply-accumulate (MAC) operations in a single convolutional layer which is directly dependent on the kernel size. In this work, we propose a design method for a run-time re-configurable convolution IP core which could be implemented for any layer of a CNN having variable kernel sizes and verified for different dimensions, 5 × 5 for LeNet-5 and 11 × 11 for AlexNet respectively. The proposed accelerator is 6.25 x and 12.1 x faster than basic MAC operation for LeNet-5 and AlexNet respectively. The design has been compared with existing architecture and has improved performance by 2.46x G(FL)OPS/s per MAC module. Our proposed design has run-time re-configuration support with a scheduler logic which empowers to realize the design for any specific layer, kernel dimension and channels for respectively with efficient resource utilization.
Evaluating Usage of WSMO and OWL-S in Semantic Web Services

Applying ontologies is the most promising approach to semantically enrich Web services. To facilitate this, two efforts contributed the most in enabling the creation of ontologies: OWL-S from the US and WSMO in Europe. These two compete and promote their ontologies from the design perspective, reflecting their inventors' bias but not offering much help to Web service developers using them. To bypass existing biases and enable evaluation of ontologies expressed in these two languages, this paper provides a study of the two important facilitators, OWL-S and WSMO, surveying their usage in several SWS Projects and identifying their respective and outstanding gaps. The paper then proposes a set of evaluation criteria for usage measurement on the two prominent SWS ontologies.
Utilizing a Hybrid Geometric Operator for AIFS Data in Renewable-Energy-Source Classification

In this study, a new operator is announced in the domain of Ambiguous Intuitionistic Fuzzy Set (AIFS) theory. As AIFS handle uncertainties more effectually than Fuzzy Sets (FS) and intuitionistic FS (IFS). Generating a scheme for classifying Renewable Energy Sources (RESs) in the AIFS background is the main goal. A new aggregation operator called Ambiguous-Intuitionistic-Hybrid-Geometric (AIFHG) operator is offered to fuse the AIFS information in decision science. With the help of this operator, a decision method for effectively addressing the RES-selection problem within the context of AIFS is established and successfully implemented. The comparison analysis is also included to validate the efficacy of the proposed framework.
Low Polynomial Exclusion of Planar Graph Patterns

The celebrated grid exclusion theorem states that for every h‐vertex planar graph H, there is a constant ch such that if a graph G does not contain H as a minor then G has treewidth at most ch . We are looking for patterns of H where this bound can become a low degree polynomial. We provide such bounds for the following parameterized graphs: the wheel (ch=O(h)) , the double wheel (ch=O(h2·log2h)) , any graph of pathwidth at most 2 (ch=O(h2)) , and the yurt graph (ch=O(h4)) .
Residual Weighted Learning for Estimating Individualized Treatment Rules

ABSTRACT Personalized medicine has received increasing attention among statisticians, computer scientists, and clinical practitioners. A major component of personalized medicine is the estimation of individualized treatment rules (ITRs). Recently, Zhao et al. proposed outcome weighted learning (OWL) to construct ITRs that directly optimize the clinical outcome. Although OWL opens the door to introducing machine learning techniques to optimal treatment regimes, it still has some problems in performance. (1) The estimated ITR of OWL is affected by a simple shift of the outcome. (2) The rule from OWL tries to keep treatment assignments that subjects actually received. (3) There is no variable selection mechanism with OWL. All of them weaken the finite sample performance of OWL. In this article, we propose a general framework, called residual weighted learning (RWL), to alleviate these problems, and hence to improve finite sample performance. Unlike OWL which weights misclassification errors by clinical outcomes, RWL weights these errors by residuals of the outcome from a regression fit on clinical covariates excluding treatment assignment. We use the smoothed ramp loss function in RWL and provide a difference of convex (d.c.) algorithm to solve the corresponding nonconvex optimization problem. By estimating residuals with linear models or generalized linear models, RWL can effectively deal with different types of outcomes, such as continuous, binary, and count outcomes. We also propose variable selection methods for linear and nonlinear rules, respectively, to further improve the performance. We show that the resulting estimator of the treatment rule is consistent. We further obtain a rate of convergence for the difference between the expected outcome using the estimated ITR and that of the optimal treatment rule. The performance of the proposed RWL methods is illustrated in simulation studies and in an analysis of cystic fibrosis clinical trial data. Supplementary materials for this article are available online.
Nonuniform Sampling Walls in Wideband Signal Detection

This work shows the existence of sampling walls in detection of wideband signals from Bernoulli nonuniform sampling (BNS) in the presence of noise uncertainty. A sampling wall is defined as the sampling density below which the target error probabilities, i.e., the missed detection and false alarm probabilities, cannot be guaranteed at a given signal to noise ratio (SNR) regardless the number of acquired samples. The BNS is adopted because it exhibits good tradeoff properties between complexity and performance. It is shown that BNS suffers from noise enhancement, which translates into a whitening effect in the correlation of the legacy signal. Contrarily to the existing literature, the signal detection problem is addressed without having to reconstruct neither the signal nor its spectrum. More specifically, the optimal low SNR detector is formulated as a generalized likelihood ratio test (GLRT) to exploit the available side information of the problem, i.e., the noise variance, the sampling density and the legacy signal autocorrelation. By deriving the asymptotic performance of the GLRT in the presence of noise uncertainty, explicit expressions for sampling walls are obtained as a function of the legacy signal occupancy, the SNR and the noise uncertainty. Further, numerical results are provided to assess the behavior of the sampling walls and signal detection performance.