Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine learning for classifying chronic kidney disease and predicting creatinine levels using at-home measurements

Background: Chronic kidney disease (CKD) is a global health concern with early detection playing a pivotal role in effective management. Machine learning models demonstrate promise in CKD detection, yet the impact on detection and classification using different sets of clinical features remains under-explored. Methods: In this study, we focus on CKD classification and creatinine prediction using three sets of features; at-home, monitoring, and laboratory. We employ artificial neural networks (ANNs) and random forests (RFs) on a dataset of 400 patients with 25 input features, which we divide into three feature sets. Using 10-fold cross-validation, we calculate metrics such as accuracy, true positive rate (TPR), true negative rate (TNR), and mean squared error. Results: Our results reveal RF achieves superior accuracy (92.5%) in at-home CKD classification over ANNs (82.9%). ANNs achieve a higher TPR (92.0%) but a lower TNR (67.9%) compared with RFs (90.0% and 95.8%, respectively). For monitoring and laboratory features, both methods achieve accuracies exceeding 98%. The R2 score for creatinine regression is approximately 0.3 higher with laboratory features than at-home features. Feature importance analysis identifies key clinical variables hemoglobin and blood urea, and key comorbidities hypertension and diabetes mellitus, in agreement with previous studies. Conclusions: Machine learning models, particularly RFs, exhibit promise in CKD diagnosis and highlight significant features in CKD detection. Moreover, such models may assist in screening a general population using at-home features---potentially increasing early detection of CKD, thus improving patient care and offering hope for a more effective approach to managing this prevalent health condition.
Variational Pressure Image Assimilation for Atmospheric Motion Estimation

The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite images sequences very difficult. In this paper, we face the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose a dynamically consistent atmospheric motion estimator relying on a multi-layer dynamical model. This estimator is based on a weak constraint variational data assimilation scheme and is applied on noisy and incomplete pressure difference observations derived from satellite images. The dynamical model consists in a simplified vorticity-divergence form of a multi-layer shallow-water model. Average horizontal motion fields are estimated for each layer. The performance of the proposed technique is assessed on real world meteorological satellite image sequences.
Chromatin breakdown during necrosis by serum Dnase1 and the plasminogen system.

OBJECTIVE Dnase1-deficient mice with the 129 x C57BL/6 genetic background develop symptoms of systemic lupus erythematosus, such as high titers of antinuclear autoantibodies directed against nucleosomes. In this study we analyzed a potential molecular pathomechanism leading to this autoimmunity, by exploring the influence of extracellular Dnase1 present in serum on the breakdown of chromatin in necrotic cells in vitro. METHODS Human breast adenocarcinoma cells (MCF-7) and other cell lines were subjected to necrosis induced by hydrogen peroxide, streptolysin O, or freeze-thawing. Subsequently, the influence of sera from Dnase1-deficient and wild-type mice as well as the influence of purified enzymes present in the culture medium on the process of necrotic chromatin breakdown was investigated. RESULTS Necrotic chromatin breakdown resembled apoptotic DNA laddering and was catalyzed by serum Dnase1 in conjunction with plasmin. During necrosis, Dnase1 and plasminogen penetrated the cell and accumulated in the cytoplasm and nucleus. Plasminogen bound to the cytoskeleton and nuclear structures, was activated to plasmin by either tissue-type or urokinase-type plasminogen activator, and degraded histone H1, thereby facilitating internucleosomal DNA cleavage by Dnase1. CONCLUSION Our results suggest that serum Dnase1 in cooperation with the plasminogen system guarantees a fast and effective breakdown of chromatin during necrosis by the combined cleavage of DNA as well as of DNA binding proteins. The failure of such a clearance mechanism might lead to antinuclear autoimmunity similar to that observed in the Dnase1-deficient mouse.
Gap Safe screening rules for sparsity enforcing penalties

In high dimensional regression settings, sparsity enforcing penalties have proved useful to regularize the data-fitting term. A recently introduced technique called screening rules propose to ignore some variables in the optimization leveraging the expected sparsity of the solutions and consequently leading to faster solvers. When the procedure is guaranteed not to discard variables wrongly the rules are said to be safe. In this work, we propose a unifying framework for generalized linear models regularized with standard sparsity enforcing penalties such as $\ell_1$ or $\ell_1/\ell_2$ norms. Our technique allows to discard safely more variables than previously considered safe rules, particularly for low regularization parameters. Our proposed Gap Safe rules (so called because they rely on duality gap computation) can cope with any iterative solver but are particularly well suited to (block) coordinate descent methods. Applied to many standard learning tasks, Lasso, Sparse-Group Lasso, multi-task Lasso, binary and multinomial logistic regression, etc., we report significant speed-ups compared to previously proposed safe rules on all tested data sets.
Contributions of remote monitoring to the follow-up of implantable cardioverter–defibrillator leads under advisory

Aims Automatic daily transmission of data from implantable cardioverter–defibrillators (ICDs) enables the remote monitoring of device status and leads function. We report on a 2-year experience with remote monitoring in 40 recipients of high-voltage ICD leads, prone to fracture and under advisory since October 2007. Methods and results The ICDs were remotely monitored as well as systematically interrogated in the ambulatory department every 3 months. The patients were also seen in case of abnormal lead impedance, or other manifestations consistent with lead dysfunction. Over a mean follow-up of 22 ± 4 months after ICD implantation, four lead dysfunctions were suspected because of remotely transmitted oversensing of noise artifacts, abrupt rise in pacing impedance, or both. A lead fracture needing lead replacement was confirmed in three patients (7.5%), two of them before any inappropriate therapy and one after the delivery of three inappropriate shocks. No lead failure was observed in the remaining 36 patients, either at the time of ambulatory visits or during remote monitoring. Conclusion Remote monitoring allowed the early and reliable detection of ICD leads failure without requiring any patient intervention.
A Survey on Legacy and Emerging Technologies for Public Safety Communications

Effective emergency and natural disaster management depend on the efficient mission-critical voice and data communication between first responders and victims. Land mobile radio system (LMRS) is a legacy narrowband technology used for critical voice communications with limited use for data applications. Recently, long term evolution (LTE) emerged as a broadband communication technology that has a potential to transform the capabilities of public safety technologies by providing broadband, ubiquitous, and mission-critical voice and data support. For example, in the United States, FirstNet is building a nationwide coast-to-coast public safety network based on LTE broadband technology. This paper presents a comparative survey of legacy and the LTE-based public safety networks, and discusses the LMRS-LTE convergence as well as mission-critical push-to-talk over LTE. A simulation study of LMRS and LTE band class 14 technologies is provided using the NS-3 open source tool. An experimental study of APCO-25 and LTE band class 14 is also conducted using software-defined radio to enhance the understanding of the public safety systems. Finally, emerging technologies that may have strong potential for use in public safety networks are reviewed.
Alcohol and breast cancer risk: the alcoholism paradox

A population-based cohort study of 36 856 women diagnosed with alcoholism in Sweden between 1965 and 1995 found that alcoholic women had only a small 15% increase in breast-cancer incidence compared to the general female population. It is therefore apparent, contrary to expectation, that alcoholism does not increase breast-cancer risk in proportion to presumed ethanol intake. © 2000 Cancer Research Campaign
Real-time approximate sorting for self shadowing and transparency in hair rendering

When rendering materials represented by high frequency geometry such as hair, smoke or clouds, standard shadow mapping or shadow volume algorithms fail to produce good self shadowing results due to aliasing. Moreover, in all of the aforementioned examples, properly approximating self shadowing is crucial to getting realistic results. To cope with this problem, opacity shadow maps have been used. I.e., an opacity function is rendered into a set of slices parallel to the light-plane. The original Opacity Shadow Map technique [Kim and Neumann 2001] requires the geometry to be rendered once for each slice, making it impossible to render complex geometry into a large set of slices in real time. In this paper we present a method for sorting n line primitives into s number of sub-sets, where the primitives of one set occupy a single slice, in O(nlog(s)), making it possible to render hair into opacity maps in linear time. It is also shown how the same method can be used to roughly sort the geometry in back-to-front order for alpha blending, to allow for transparency. Finally, we present a way of rendering self shadowed geometry using a single 2D opacity map, thereby reducing the memory usage significantly.
The Applicability of Workflow Management Systems for the Preservation of Business Processes

Digital preservation research has increasingly been shifting focus from the preservation of data and static objects to investigate the preservation of complete processes and workflows. Capturing all aspects of a process to be preserved, however, is an extensive and difficult undertaking, as it requires capturing complete software setups with potentially complex setups. Further, the process might use external services that are not easy to capture and monitor. In this paper, we therefore investigate the applicability and usability of using Workflow Management Systems for executing processes in a standard environment where the state of the process can be closely monitored. To this end, we compare three popular workflow management systems. We use a scenario from eScience, implementing a data analysis process, to evaluate the challenges and implications for establishing sustainable and verifyable eScience processes.
Sampled-and-Hold Based Automatic Calibration Modulator for WLAN Transmitter

This paper presents an automatic calibration circuit to correct the I/Q imbalance and DC offset in the modulator of a WLAN transmitter. The impairments are estimated and compensated by a sampled-and-hold negative feedback circuits. The circuit architecture is controlled by an on chip 12 steps state machine. After the calibration, the LO leakage at the modulator output can be suppressed more than 50dB. The side band unwanted signal also can be eliminated 45dB at least. The proposed self-calibrated modulator with quadrature LO generator is implemented in 0.25 mum 1P5M mixed-mode CMOS process. The chip area is 1.2mm times 1.7mm. The total power dissipates 17 mW at 1.1~1.3 GHz from a single 2.5V supply voltage
Identifying and verifying Huntington's disease subtypes: Clinical features, neuroimaging, and cytokine changes

Abstract Aims Huntington's disease (HD) is a progressive neurodegenerative disorder with heterogeneous clinical manifestations. Identifying distinct clinical clusters and their relevant biomarkers could elucidate the underlying disease pathophysiology. Methods Following the Enroll‐HD program initiated in 2018.09, we have recruited 104 HD patients (including 21 premanifest) and 31 health controls at Beijing Tiantan Hospital. Principal components analysis and k‐means cluster analysis were performed to determine HD clusters. Chi‐square test, one‐way ANOVA, and covariance were used to identify features among these clusters. Furthermore, plasma cytokines levels and brain structural imaging were used as biomarkers to delineate the clinical features of each cluster. Results Three clusters were identified. Cluster 1 demonstrated the most severe motor and nonmotor symptoms except for chorea, the lowest whole brain volume, the plasma levels of IL‐2 were higher and significantly associated with cluster 1. Cluster 2 was characterized with the most severe chorea and the largest pallidum volume. Cluster 3 had the most benign motor symptoms but moderate psychiatric problems. Conclusion We have identified three HD clusters via clinical manifestations with distinct biomarkers. Our data shed light on better understanding about the pathophysiology of HD.
Adaptive Object Placement in Object-Based Storage Systems with Minimal Blocking Probability

Object placement is an importance issue in object-based storage system. A dynamic object placement algorithm is presented to minimize the blocking probability in heterogeneous storage systems. To address the online problem without a priori knowledge of workload parameters, or with changing workload, the algorithm employs an adaptive mechanism to estimate the characteristics of workloads. Due to a periodic adjustment of object placement through object replication and migration, the algorithm can dramatically reduce the overall blocking probability. The simulation results show that the performance of the algorithm exceeds that of a greedy algorithm. Furthermore, the adaptive algorithm with object replication and migration always outperforms the one with only migration especially in the situation where objects have large traffic intensities
Breast Milk and Glucose for Pain Relief in Preterm Infants: A Noninferiority Randomized Controlled Trial

OBJECTIVE: The study goal was to compare the efficacy of expressed breast milk (EBM) versus 25% glucose on pain responses of late preterm infants during heel lancing. METHODS: In a noninferiority randomized controlled trial, a total of 113 newborns were randomized to receive EBM (experimental group [EG]) or 25% glucose (control group [CG]) before undergoing heel lancing. The primary outcome was pain intensity (Premature Infant Pain Profile [PIPP]) and a 10% noninferiority margin was established. Secondary outcomes were incidence of cry and percentage of time spent crying and adverse events. Intention-to-treat (ITT) analysis was used. RESULTS: Groups were similar regarding demographics and clinical characteristics, except for birth weight and weight at data collection day. There were lower pain scores in the CG over 3 minutes after lancing (P < .001). A higher number of infants in the CG had PIPP scores indicative of minimal pain or absence of pain (P = .002 and P = .003 on ITT analysis) at 30 seconds after lancing, and the mean difference in PIPP scores was 3 (95% confidence interval: 1.507–4.483). Lower incidence of cry (P = .001) and shorter duration of crying (P = .014) were observed for CG. Adverse events were benign and self-limited, and there was no significant difference between groups (P = .736 and P = .637 on ITT analysis). CONCLUSIONS: Results based on PIPP scores and crying time indicate poorer effects of EBM compared with 25% glucose during heel lancing. Additional studies exploring the vol and administration of EBM and its combination with other strategies such as skin-to-skin contact and sucking are necessary.
Multihop cellular: a new architecture for wireless communications

This work presents a new architecture, multihop cellular network (MCN), for wireless communications. MCN preserves the benefit of conventional single-hop cellular networks (SCN) where the service infrastructure is constructed by fixed bases, and it also incorporates the flexibility of ad-hoc networks where wireless transmission through mobile stations in multiple hops is allowed. MCN can reduce the required number of bases or improve the throughput performance, while limiting path vulnerability encountered in ad-hoc networks. In addition, MCN and SCN are analyzed, in terms of mean hop count, hop-by-hop throughput, end-to-end throughput, and mean number of channels (i.e. simultaneous transmissions) under different traffic localities and transmission ranges. Numerical results demonstrate that the throughput of MCN exceeds that of SCN, the former also increases as the transmission range decreases. The above results can be accounted for by the different orders, linear and square, at which the mean hop count and mean number of channels increase, respectively.
Niche distribution and influence of environmental parameters in marine microbial communities: a systematic review

Associations between microorganisms occur extensively throughout Earth’s oceans. Understanding how microbial communities are assembled and how the presence or absence of species is related to that of others are central goals of microbial ecology. Here, we investigate co-occurrence associations between marine prokaryotes by combining 180 new and publicly available metagenomic datasets from different oceans in a large-scale meta-analysis. A co-occurrence network was created by calculating correlation scores between the abundances of microorganisms in metagenomes. A total of 1,906 correlations amongst 297 organisms were detected, segregating them into 11 major groups that occupy distinct ecological niches. Additionally, by analyzing the oceanographic parameters measured for a selected number of sampling sites, we characterized the influence of environmental variables over each of these 11 groups. Clustering organisms into groups of taxa that have similar ecology, allowed the detection of several significant correlations that could not be observed for the taxa individually.
Better optical triangulation through spacetime analysis

The standard methods for extracting range data from optical triangulation scanners are accurate only for planar objects of uniform reflectance illuminated by an incoherent source. Using these methods, curved surfaces, discontinuous surfaces, and surfaces of varying reflectance cause systematic distortions of the range data. Coherent light sources such as lasers introduce speckle artifacts that further degrade the data. We present a new ranging method based on analyzing the time evolution of the structured light reflections. Using our spacetime analysis, we can correct for each of these artifacts, thereby attaining significantly higher accuracy using existing technology. We present results that demonstrate the validity of our method using a commercial laser stripe triangulation scanner. <<ETX>>
Reasons for the Rising Cesarean Delivery Rates: 1978–1984

This study was undertaken to estimate the cesarean delivery rate in United States hospitals and to determine whether the reasons for rise in this rate have changed since the National Institute of Child Health and Human Development Consensus Development Conference on Cesarean Childbirth in 1979. The cesarean delivery rate steadily increased from 9.1% in 1974 to 14.7% in 1978, and to 21.2% in 1984. One-third of the rise in the cesarean rate from 1974–1978 was due to repeat cesareans, and 9% was due to fetal distress. Since 1978, 47% of the rise in the cesarean rate was attributed to repeat cesareans, and 16% to fetal distress. Less of the recent rise in the cesarean rate was due to dystocia and breech presentation. The rate of cesarean delivery among those with a previous cesarean was 96%. Deliveries complicated by fetal distress increased from 1% in 1978 to 6% in 1984. The incidence of breech presentation dropped by 18% (2.8–2.3%), which may indicate an increase in the successful use of external cephalic version. If the number of vaginal deliveries after cesarean increases in the future, then the overall cesarean delivery rate will decrease or stabilize. Additional efforts should be focused on the diagnostic categories of fetal distress and dystocia, because it is likely that the definitions of these complications are changing to include less severe forms.
Prediction of Pharmacological and Xenobiotic Responses to Drugs Based on Time Course Gene Expression Profiles

More and more people are concerned by the risk of unexpected side effects observed in the later steps of the development of new drugs, either in late clinical development or after marketing approval. In order to reduce the risk of the side effects, it is important to look out for the possible xenobiotic responses at an early stage. We attempt such an effort through a prediction by assuming that similarities in microarray profiles indicate shared mechanisms of action and/or toxicological responses among the chemicals being compared. A large time course microarray database derived from livers of compound-treated rats with thirty-four distinct pharmacological and toxicological responses were studied. The mRMR (Minimum-Redundancy-Maximum-Relevance) method and IFS (Incremental Feature Selection) were used to select a compact feature set (141 features) for the reduction of feature dimension and improvement of prediction performance. With these 141 features, the Leave-one-out cross-validation prediction accuracy of first order response using NNA (Nearest Neighbor Algorithm) was 63.9%. Our method can be used for pharmacological and xenobiotic responses prediction of new compounds and accelerate drug development.
ON THE POISSON EQUATION FOR MARKOV CHAINS : EXISTENCE OF SOLUTIONS AND PARAMETER DEPENDENCEBY PROBABILISTIC

This paper considers the Poisson equation associated with time-homogeneous Markov chains on a countable state space. The discussion emphasizes probabilistic arguments and focuses on three separate issues, namely (i) the existence and uniqueness of solutions to the Poisson equation, (ii) growth estimates and bounds on these solutions and (iii) their parametric dependence. Answers to these questions are obtained under a variety of recurrence conditions, and extensions to non-countable state spaces are outlined. Motivating applications can be found in the theory of Markov decision processes in both its adaptive and non-adaptive formulations, and in the theory of Stochastic Approximations. The results complement available results from Potential Theory for Markov chains, and are therefore of independent interest.
On predicting Twitter trend: Factors and models

In this paper, we predict hashtag trend in Twitter network with two basic issues under investigation, i.e. trend factors and prediction models. To address the first issue, we consider different content and context factors by designing features from tweet messages, network topology, user behavior, etc. To address the second issue, we adopt prediction models that have different combinations of the two basic model properties, i.e. linearity and state-space. Experiments on large Twitter dataset show that both content and context factors can help trend prediction. However, the most relevant factors are derived from user behaviors on the specific trend. Non-linear models are significantly better than their linear counterparts, which can be further slightly improved by the adoption of state-space models.