Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards the Prediction of Rearrest during Out-of-Hospital Cardiac Arrest

A secondary arrest is frequent in patients that recover spontaneous circulation after an out-of-hospital cardiac arrest (OHCA). Rearrest events are associated to worse patient outcomes, but little is known on the heart dynamics that lead to rearrest. The prediction of rearrest could help improve OHCA patient outcomes. The aim of this study was to develop a machine learning model to predict rearrest. A random forest classifier based on 21 heart rate variability (HRV) and electrocardiogram (ECG) features was designed. An analysis interval of 2 min after recovery of spontaneous circulation was used to compute the features. The model was trained and tested using a repeated cross-validation procedure, on a cohort of 162 OHCA patients (55 with rearrest). The median (interquartile range) sensitivity (rearrest) and specificity (no-rearrest) of the model were 67.3% (9.1%) and 67.3% (10.3%), respectively, with median areas under the receiver operating characteristics and the precision–recall curves of 0.69 and 0.53, respectively. This is the first machine learning model to predict rearrest, and would provide clinically valuable information to the clinician in an automated way.
Effect of seasonal variations on the content of some osmolytes of Aleppo pine and holm oak

Abstract The forest of Chettaba is of great ecological importance for the Constantine region. It is characterized by a very heterogeneous forest cover, the dominance of forest species (Pinus halepensis and Quercus ilex) and an advanced stage of degradation. Vegetation in this region reflects the climate. The summer is dry and hot while the winter is rainy and cold. Indeed, the combination of rain and temperature directly influences the physiology of the vegetation in this forest. The main objective of this work is to characterize the different responses of Aleppo pine and holm oak seedlings and to specify the biochemical variations under the seasonal effect. Biochemical assays of proline, soluble sugars, total proteins and chlorophyll were quantified in the different organs of two species for each of the years 2018–2019. The results obtained show very significant accumulation of total soluble sugars and proline in holm oak and Aleppo pine leaves compared to stems and roots during the winter season (520 ± 20 and 370 ± 17.98 µmol/mg DM respectively). Contrary to the previous results, the highest total protein levels were recorded during the summer with levels of 0.666 ± 0.116 and 1,626 ± 0.107 mg/g FM. Parallel to the accumulation of these three solutes, a gradual decrease in the chlorophyll pigment content was recorded in winter and summer.
Myogenin is an essential regulator of adult myofibre growth and muscle stem cell homeostasis

Growth and maintenance of skeletal muscle fibres depend on coordinated activation and return to quiescence of resident muscle stem-cells (MuSCs). The transcription factor Myogenin (Myog) regulates myocyte fusion during development, but its role in adult myogenesis remains unclear. In contrast to mice, myog−/− zebrafish are viable, but have hypotrophic muscles. By isolating adult myofibres with associated MuSCs we found that myog−/− myofibres have severely reduced nuclear number, but increased myonuclear domain size. Expression of fusogenic genes is decreased, pax7 upregulated, MuSCs are fivefold more numerous and mis-positioned throughout the length of myog−/− myofibers instead of localising at myofibre ends as in wild-type. Loss of Myog dysregulates mTORC1 signalling, resulting in an ‘alerted’ state of MuSCs, which display precocious activation and faster cell cycle entry ex vivo, concomitant with myod upregulation. Thus, beyond controlling myocyte fusion, Myog influences the MuSC:niche relationship, demonstrating a multi-level contribution to muscle homeostasis throughout life.
Information Transfer in Linear Multivariate Processes Assessed through Penalized Regression Techniques: Validation and Application to Physiological Networks

The framework of information dynamics allows the dissection of the information processed in a network of multiple interacting dynamical systems into meaningful elements of computation that quantify the information generated in a target system, stored in it, transferred to it from one or more source systems, and modified in a synergistic or redundant way. The concepts of information transfer and modification have been recently formulated in the context of linear parametric modeling of vector stochastic processes, linking them to the notion of Granger causality and providing efficient tools for their computation based on the state–space (SS) representation of vector autoregressive (VAR) models. Despite their high computational reliability these tools still suffer from estimation problems which emerge, in the case of low ratio between data points available and the number of time series, when VAR identification is performed via the standard ordinary least squares (OLS). In this work we propose to replace the OLS with penalized regression performed through the Least Absolute Shrinkage and Selection Operator (LASSO), prior to computation of the measures of information transfer and information modification. First, simulating networks of several coupled Gaussian systems with complex interactions, we show that the LASSO regression allows, also in conditions of data paucity, to accurately reconstruct both the underlying network topology and the expected patterns of information transfer. Then we apply the proposed VAR-SS-LASSO approach to a challenging application context, i.e., the study of the physiological network of brain and peripheral interactions probed in humans under different conditions of rest and mental stress. Our results, which document the possibility to extract physiologically plausible patterns of interaction between the cardiovascular, respiratory and brain wave amplitudes, open the way to the use of our new analysis tools to explore the emerging field of Network Physiology in several practical applications.
Patient No-Show Prediction: A Systematic Literature Review

Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research.
Approximating Requirement Cut via a Configuration LP

We consider the Requirement Cut problem, where given an undirected graph G = ( V, E ) equipped with non-negative edge weights c : E → R + , and g groups of vertices X 1 , . , X g ⊆ V each equipped with a requirement r i , the goal is to ﬁnd a collection of edges F ⊆ E , with total minimum weight, such that once F is removed from G in the resulting graph every X i is broken into at least r i connected components. Requirement Cut captures multiple classic cut problems in graphs, e.g., Multicut , Multiway Cut , Min k -Cut , Steiner k -Cut , Steiner Multicut , and Multi-Multiway Cut . Nagarajan and Ravi [Algoritmica‘10] presented an approximation of O (log n log R ) for the problem, which was subsequently improved to O (log g log k ) by Gupta, Nagarajan and Ravi [Operations Research Letters‘10] (here R = P g i =1 r i and k = | ∪ gi =1 X i | ). We present an approximation of O ( X log R √ log k log log k ) for Requirement Cut (here X = max i =1 ,.,g {| X i |} ). Our approximation in general is incomparable to the above mentioned previous results, however when all groups are not too large, i.e., X = o (cid:0) ( √ log k log g ) / (log R log log k ) (cid:1) , it is better. Our algorithm is based on a new conﬁguration linear programming relaxation for the problem, which is accompanied by a remarkably simple randomized rounding procedure.
Genomic and Epigenetic Instability in Colorectal Cancer

: Colorectal Cancer (CRC) is the third most common cancer in men and the second most common cancer in women worldwide. Both genetic and epigenetic alterations are common in CRC and are the driving force of tumorigenesis. Chromosomal instability, microsatellite instability and CpG island methylator phenotype pathways are responsible for genetic instability in colorectal cancer. Chromosomal instability pathway consists of activation of proto-oncogenes and inactivation of tumor suppression genes and Loss of Heterozogosity (LOH). In this review, we discuss genetic and epigenetic phenomena that can be suggested as biomarkers in colorectal cancer
Logical modeling of dendritic cells in vitro differentiation from human monocytes unravels novel transcriptional regulatory interactions

Dendritic cells are the major specialized antigen-presenting cells, thereby connecting innate and adaptive immunity. Because of their role in establishing adaptive immunity, they have been used as targets for immunotherapy. Monocytes can differentiate into dendritic cells in vitro in the presence of colony-stimulating factor 2 (CSF2) and interleukin 4 (IL4), activating four signalling pathways (MAPK, JAK/STAT, NFKB, and PI3K). However, the transcriptional regulation responsible for dendritic cell differentiation from monocytes (moDCs) remains unknown. By curating scientific literature on moDCs differentiation, we established a preliminary logical model that helped us identify missing information for the activation of genes responsible for this differentiation, including missing targets for key transcription factors (TFs). Using ChIP-seq and RNA-seq data from the Blueprint consortium, we defined active and inactive promoters, together with differentially expressed genes in monocytes, moDCs, and macrophages (which correspond to an alternative cell fate). We then used this functional genomic information to predict novel targets for the identified TFs. We established a second logical model integrating this information, which enabled us to recapitulate the main established facts regarding moDCs differentiation. Prospectively, the resulting model should be useful to develop novel immunotherapies based on moDCs regulatory network.
Unsupervised Learning For Sequence-to-sequence Text-to-speech For Low-resource Languages

Recently, sequence-to-sequence models with attention have been successfully applied in Text-to-speech (TTS). These models can generate near-human speech with a large accurately-transcribed speech corpus. However, preparing such a large data-set is both expensive and laborious. To alleviate the problem of heavy data demand, we propose a novel unsupervised pre-training mechanism in this paper. Specifically, we first use Vector-quantization Variational-Autoencoder (VQ-VAE) to ex-tract the unsupervised linguistic units from large-scale, publicly found, and untranscribed speech. We then pre-train the sequence-to-sequence TTS model by using the pairs. Finally, we fine-tune the model with a small amount of paired data from the target speaker. As a result, both objective and subjective evaluations show that our proposed method can synthesize more intelligible and natural speech with the same amount of paired training data. Besides, we extend our proposed method to the hypothesized low-resource languages and verify the effectiveness of the method using objective evaluation.
Nitrogen concentrations and proportions of ammonium and nitrate in the nutrition and growth of yellow passion fruit seedlings

Abstract Despite the importance of nitrogen (N) supply to plants, there are still doubts concerning the optimal relations of ammonium and nitrate in the nutrition of yellow passion fruit seedlings. This study aims to evaluate the interaction between nitrogen concentrations and ammonium and nitrate proportions in the nutrition, growth, and dry matter production of passion fruit seedlings grown in a substrate with a nutrient solution. The experiment was conducted in a greenhouse in randomized complete block design with three replications in a 4 × 5 factorial design, consisting of four N concentrations (2.5, 5.0, 10.0 and 20.0 mmol L−1) and five ammonium proportions (0, 25, 50, 75 and 100% in relation to the total N supply). At 60 days after transplanting, green color index; accumulation of N, potassium, calcium, and magnesium in roots and shoots; stem diameter; leaf area; root length; nitrogen use efficiency (NUE); and dry matter of roots and shoots were evaluated. For the formation of seedlings of yellow passion fruit, the nutrient solution should have 13 mmol L−1 of N, with 40% of this nutrient in the form of ammonium. The passion fruit is a plant tolerant to ammonium. However, a critical concentration above 5.7 mmol L−1 of NH4+ in the nutrient solution decreases absorption of cations, NUE, and production of dry matter.
Mechanical ventilation in disaster situations: A new paradigm using the AGILITIES Score System.

BACKGROUND The failure of life-critical systems such as mechanical ventilators in the wake of a pandemic or a disaster may result in death, and therefore, state and federal government agencies must have precautions in place to ensure availability, reliability, and predictability through comprehensive preparedness and response plans. METHODS All 50 state emergency preparedness response plans were extensively examined for the attention given to the critically injured and ill patient population during a pandemic or mass casualty event. Public health authorities of each state were contacted as well. RESULTS Nine of 51 state plans (17.6 percent) included a plan or committee for mechanical ventilation triage and management in a pandemic influenza event. All 51 state plans relied on the Centers for Disease Control and Prevention Flu Surge 2.0 spreadsheet to provide estimates for their influenza planning. In the absence of more specific guidance, the authors have developed and provided guidelines recommended for ventilator triage and the implementation of the AGILITIES Score in the event of a pandemic, mass casualty event, or other catastrophic disaster. CONCLUSIONS The authors present and describe the AGILITIES Score Ventilator Triage System and provide related guidelines to be adopted uniformly by government agencies and hospitals. This scoring system and the set of guidelines are to be used in disaster settings, such as Hurricane Katrina, and are based on three key factors: relative health, duration of time on mechanical ventilation, and patients' use of resources during a disaster. For any event requiring large numbers of ventilators for patients, the United States is woefully unprepared. The deficiencies in this aspect of preparedness include (1) lack of accountability for physical ventilators, (2) lack of understanding with which healthcare professionals can safely operate these ventilators, (3) lack of understanding from where additional ventilator resources exist, and (4) a triage strategy to provide ventilator support to those patients with the greatest chances of survival.
Effect of Paclitaxel and hyperthermia combination on the proliferation,apoptosis in nasopharygeal carcinoma CNE-1 cell lines

【Objective】To examine the expression of Paclitaxel combined hyperthermia against on the prolifera-tion and apoptosis in nasopharygeal carcinoma CNE-1 cell lines. 【Methods】The cells were divided into 15 groups according to the different temperature of hyperthermia(39℃,41℃ and 43℃) and the different concentration of pa-clitaxel.5 groups heating at 37℃ with the different concentration of Paclitaxel were designed.The CNE-1 cell lines was treated with paclitaxel alone for 48 hours and then heated for 30 minutes.The cell proliferation rate of CNE-1 cell lines was counted with MTT assay,colony formation test,cell morphology and agarose gel electrophoresis have been developed for quantitative evaluation of the proliferation of CNE-1 cell lines and cytotoxicity of taxo with or without hyperthermia were used for the assessment of apoptosis rates after treatment.The appearance changes and apoptosis cells. 【Result】Compared with the same dosage of Paclitaxel but different temperature,the cell prolifera-tion rate in the group heating at 41℃ decreased significantly(P 0.05). The proliferation rate of CNE-1 cell in 39℃ and 43℃ groups decreased significantly compared with the 37℃ control group(P 0.01). The morphologic changes of apoptosis,when cells were treated with Paclitaxel for 48 hours,DNA ladder with agarose gel electrophoresis was observed,and the intensity of DNA ladder was enhanced in adose-and time-dependent pattern.DNA fragment ap-peared in cells treated with 0.1 nmol/L Paclitaxel. 【Conclusion】Paclitaxel has obvious inhibitive effect on CNE-1 at 41℃ with different concentrations of Paclitaxel.While hyperthermia and Paclitaxel agents can individually induce apoptosis and anti-proliferation effect,they may offer synergistic benefits when used concurrently.The proliferation ability of CNE-1 cell line is highest at 39℃ or 43℃ combined with 0.01 nmol/L of Paclitaxel.
Physiologically Based Pharmacokinetic Modeling of Central Nervous System Pharmacokinetics of CDK4/6 Inhibitors to Guide Selection of Drug and Dosing Regimen for Brain Cancer Treatment

A better understanding of the human central nervous system (CNS) pharmacokinetics is critical to the selection of the right drug and refinement of dosing regimen for more effective treatment of primary and metastatic brain cancer. Using the physiologically‐based pharmacokinetic (PBPK) modeling approach, we systematically compared the CNS pharmacokinetics of three cyclin D‐cyclin dependent kinase 4 and 6 (CDK4/6) inhibitors (ribociclib, palbociclib, and abemaciclib) in patients with cancer. A PBPK model platform was developed and verified for predicting plasma and CNS pharmacokinetics. Target engagement ratio (TER), defined as the ratio of the average steady‐state unbound drug brain concentration to the in vitro half‐maximal inhibitory concentration (IC50) for CDK4/6 inhibition, was used as a crude predictor of efficacy. As compared with ribociclib and palbociclib, abemaciclib penetrated into the human brain to a larger extent, but at a slower rate, and was retained in the brain longer. Following the standard dosing regimens, the predicted CDK4/6 TERs were 26/5.2 for abemaciclib, 2.4/0.62 for ribociclib, and 0.36/0.27 for palbociclib. Simulations suggested that abemaciclib achieved comparable TERs following twice daily or daily dosing; ribociclib may sufficiently inhibit both CDK4 and CDK6 at the maximum tolerated dose; whereas, palbociclib achieved TERs < 0.5 even at a dose 50% higher than the standard dose. In conclusion, the PBPK modeling, supported by available preclinical and clinical evidence, suggests that abemaciclib is the best CDK4/6 inhibitor for brain cancer treatment, whereas palbociclib is not recommended. The model refined dosing regimen is 300 mg daily on a 4‐weeks‐on schedule for abemaciclib, and 900 mg daily on a 3‐weeks‐on/1‐week‐off schedule for ribociclib.
Evolutionary biology in silico: explorations of adaptation in artificial populations

The complexity, detail and diversity of life has intrigued and excited both scientist and layperson alike. How such intricate design, as that commonly found in biological life, could have come about is the subject of millennia of thought. Charles Darwin’s (1859) theory of evolution (co-discovered by Alfred Wallace) proposes a simple mechanism of change across generations, and provides a testable explanation of such design. Although united on the validity of evolution, scientists remain divided on many open questions, including the adaptive nature of traits that benefit the group at the cost of individual fitness — that is, biological altruism. Interpreting the progress of evolutionary history from the fossil record is fraught with challenges. The large gaps between fossilization events hide the transitions between species, and much of the details of the organisms themselves are lost in the process. While we can conduct experiments on living organisms in the laboratory, the temporal and spatial scales involved in evolution in nature do not lend themselves to easily controllable and repeatable experiments. The recent field of artificial life (ALife) (Langton, 1989) provides new exciting potentials for experimental exploration of evolution. Within the software medium we can create simulations of populations of artificial organisms, which are subject to evolutionary forces, existing in an artificial environment, competing with each other for the opportunity to survive and reproduce. By incorporating such simulation techniques into the studies of evolutionary biology we greatly enhance our experimental repertoire. In this thesis I show that ALife provides new experimental techniques to explore open questions in evolutionary biology. In particular, I consider questions on the adaptive nature of groups and biological altruism. Firstly, I examine the relationship between the two popular explanations of altruism, group and kin selection, demonstrating a common dependence on inclusive fitness (Hamilton, 1964). Next, I investigate an extreme form of biological altruism, genetically programmed aging. Commonly thought to be non- adaptive (Medawar, 1952; Williams, 1957; Kirkwood and Holliday, 1979), I develop my own hypothesis of adaptive aging for the sake of group diversity, confirming it in simulated worlds of hosts and parasites. Lastly, I test, and find support for, Eldredge and Gould’s (1972) claim that punctuated equilibrium, a pervasive phenomena in the fossil record, provides the foundations for a species/group selection mechanism.
Concealed role of red blood cells in pathogenesis of pulmonary arterial hypertension: Decreased red blood cell nitric oxide generation and effect of Rho-Kinase inhibitor fasudil.

BACKGROUND Pulmonary arterial hypertension (PAH) is a devastating disease characterized with alterations in pulmonary vasculature yielding increased pulmonary arterial resistance. Emerging evidences suggest important regulatory roles of red blood cells (RBCs) on nitric oxide (NO) bioavailability, mainly by modulating their endothelial nitric oxide synthase (eNOS) enzyme activity. OBJECTIVE The aim of the present study was to evaluate the alterations in RBC eNOS activity and intracellular NO generation under PAH conditions and the modulatory effects of Rho-Kinase (ROCK) inhibitors. METHODS RBCs were isolated from patients with PAH and age-matched healthy subjects and were analyzed for their eNOS activity and NO generation capacity under the conditions of the presence or absence of ROCK inhibitor, fasudil. Phosphotidylserine (PS) exposure was also defined. RESULTS eNOS activity and intracellular NO generation were found to be lower in RBC from PAH patients. ROCK inhibitor increased basal eNOS activity and NO generation capacity of RBC from PAH patients to healty conditions.PS exposure levels were also higher in RBC from PAH patients. CONCLUSIONS This study provides first evidences for decreased RBC eNOS activity due to its ROCK mediated negative regulation under PAH conditions. Considering increased ROCK activity contributes to progression of PAH, ROCK inhibition influences NO bioavailability through RBC eNOS, in addition to endothelial eNOS.