LLukas22/scidocs huggingface dataset:
Lovastatin attenuates ionizing radiation-induced normal tissue damage in vivo.

BACKGROUND AND PURPOSE
HMG-CoA-reductase inhibitors (statins) are widely used lipid-lowering drugs. Moreover, they have pleiotropic effects on cellular stress responses, proliferation and apoptosis in vitro. Here, we investigated whether lovastatin attenuates acute and subchronic ionizing radiation-induced normal tissue toxicity in vivo.


MATERIALS AND METHODS
Four hours to 24h after total body irradiation (6Gy) of Balb/c mice, acute pro-inflammatory and pro-fibrotic responses were analyzed. To comprise subchronic radiation toxicity, mice were irradiated twice with 2.5Gy and analyses were performed 3weeks after the first radiation treatment. Molecular markers of inflammation and fibrosis as well as organ toxicities were measured.


RESULTS
Lovastatin attenuated IR-induced activation of NF-kappaB, mRNA expression of cell adhesion molecules and mRNA expression of pro-inflammatory and pro-fibrotic marker genes (i.e. TNFalpha, IL-6, TGFbeta, CTGF, and type I and type III collagen) in a tissue- and time-dependent manner. gammaH2AX phosphorylation stimulated by IR was not affected by lovastatin, indicating that the statin has no major impact on the induction of DNA damage in vivo. Radiation-induced thrombopenia was significantly alleviated by lovastatin.


CONCLUSIONS
Lovastatin inhibits both acute and subchronic IR-induced pro-inflammatory and pro-fibrotic responses and cell death in normal tissue in vivo. Therefore, lovastatin might be useful for selectively attenuating acute and subchronic normal tissue damage caused by radiotherapy.
Medical subdomain classification of clinical notes using a machine learning-based natural language processing approach

BACKGROUND
The medical subdomain of a clinical note, such as cardiology or neurology, is useful content-derived metadata for developing machine learning downstream applications. To classify the medical subdomain of a note accurately, we have constructed a machine learning-based natural language processing (NLP) pipeline and developed medical subdomain classifiers based on the content of the note.


METHODS
We constructed the pipeline using the clinical NLP system, clinical Text Analysis and Knowledge Extraction System (cTAKES), the Unified Medical Language System (UMLS) Metathesaurus, Semantic Network, and learning algorithms to extract features from two datasets - clinical notes from Integrating Data for Analysis, Anonymization, and Sharing (iDASH) data repository (n = 431) and Massachusetts General Hospital (MGH) (n = 91,237), and built medical subdomain classifiers with different combinations of data representation methods and supervised learning algorithms. We evaluated the performance of classifiers and their portability across the two datasets.


RESULTS
The convolutional recurrent neural network with neural word embeddings trained-medical subdomain classifier yielded the best performance measurement on iDASH and MGH datasets with area under receiver operating characteristic curve (AUC) of 0.975 and 0.991, and F1 scores of 0.845 and 0.870, respectively. Considering better clinical interpretability, linear support vector machine-trained medical subdomain classifier using hybrid bag-of-words and clinically relevant UMLS concepts as the feature representation, with term frequency-inverse document frequency (tf-idf)-weighting, outperformed other shallow learning classifiers on iDASH and MGH datasets with AUC of 0.957 and 0.964, and F1 scores of 0.932 and 0.934 respectively. We trained classifiers on one dataset, applied to the other dataset and yielded the threshold of F1 score of 0.7 in classifiers for half of the medical subdomains we studied.


CONCLUSION
Our study shows that a supervised learning-based NLP approach is useful to develop medical subdomain classifiers. The deep learning algorithm with distributed word representation yields better performance yet shallow learning algorithms with the word and concept representation achieves comparable performance with better clinical interpretability. Portable classifiers may also be used across datasets from different institutions.
Links between mothers' and fathers' perceptions of infant temperament and coparenting.

This study examined the contributions of infant temperament, marital functioning, and the division of parenting on the quality of the coparenting relationship for couples parenting 6-month-old infants. Marital functioning was assessed prenatally. When infants were 6 months old, infant temperamental characteristics (i.e., distress to limits, distress to novelty, and soothability), division of parenting, and coparenting were rated by parents. Results indicated that the reactivity dimension of temperament was only associated with reduced coparenting quality if other stressors were present and these effects were different for mothers and fathers. Mothers who perceived their infants as more reactive only reported more negative coparenting if their infants were also not easily soothed or if mothers were dissatisfied with how parenting tasks were divided and performed given their prior expectations. Whereas fathers reported more negative coparenting when faced with a more reactive infant and they reported a low quality marital relationship.
Radiation Bursts from Particles in the Field of Compact, Impenetrable, Astrophysical Objects

The radiation emitted by charged, scalar particles in a Schwarzschild field with maximal acceleration corrections is calculated classically and in the tree approximation of quantum field theory. In both instances the particles emit radiation that has characteristics similar to those of gamma-ray bursters.
Overview of the lunar laser communications demonstration

For one month in late 2013, NASA’s Lunar Laser Communication Demonstration (LLCD) successfully demonstrated for the first time duplex laser communications between a satellite in lunar orbit, the Lunar Atmosphere and Dust Environment Explorer (LADEE), and ground stations on the Earth. It constituted the longest-range laser communication link ever built and demonstrated the highest communication data rates ever achieved to or from the Moon. The system included the development of a novel space terminal, a novel ground terminal, two major upgrades of existing ground terminals, and a capable and flexible ground operations infrastructure. This presentation will give an overview of the system architecture and the several terminals, basic operations of the system, and a brief discussion of the results. Keywords-Free-space optical communications, laser communications, lasercom, photon counting receiver, lunar laser communications demonstration, moon, lunar
Dietary fat and prostate cancer survival

Objectives: Relationships between dietary factors and the incidence of prostate cancer have emerged from epidemiologic and laboratory studies, but there are no published data on the relationship between diet and disease progression among prostate cancer patients. We studied the association between dietary fat intake and prostate cancer survival. Methods: We prospectively followed 384 men diagnosed with prostate cancer between 1990 and 1992 in the Quebec City area. On average 3 months following diagnosis, trained nutritionists interviewed the men on their usual diet using a diet history questionnaire. Deaths during follow-up were documented through record linkage with the provincial mortality file and review of hospital records. Cox proportional hazard models were used to estimate the relative risk of dying from prostate cancer associated with terciles of fat intake, expressed as percent of dietary energy, while controlling for prognostic factors and total energy. Results: The median duration of follow-up was 5.2 years. During the follow-up period 32 patients died of prostate cancer and 39 died of other causes. After controlling for grade, clinical stage, initial treatment, age and total energy intake, we found that saturated fat consumption was significantly associated with disease-specific survival (two-sided p-value = 0.008). Compared to men in the lower tercile of saturated fat, those in the upper tercile had three times the risk of dying from prostatecancer (relative risk = 3.1, 95% confidence interval: 1.3–7.7). Conclusions: Our findings suggest that saturated fat is related to disease-specific survival and that, if this relation is causal, a moderate reduction of its intake might reduce the risk of dying from prostate cancer.
Parallel Mining of Association Rules

We consider the problem of mining association rules on a shared-nothing multiprocessor. We present three algorithms that explore a spectrum of trade-oos between computation, communication, memory usage, synchronization, and the use of problem-speciic information. The best algorithm exhibits near perfect scaleup behavior, yet requires only minimal overhead compared to the current best serial algorithm.
Radar high resolution range & micro-Doppler analysis of human motions

In radar imaging it is well known that relative motion or deformation of parts of illuminated objects induce additional features in the Doppler frequency spectra. These features are called micro-Doppler effect and appear as sidebands around the central Doppler frequency. They can provide valuable information about the structure of the moving parts and may be used for identification purposes [1].
End-of-life nursing education consortium: 5 years of educating graduate nursing faculty in excellent palliative care.

Since January 2001, over 4,500 nurses, representing all 50 U.S. states and the District of Columbia, have attended 1 of 50 national End-of-Life Nursing Education Consortium (ELNEC) train-the-trainer courses. Of the 4,500 nurses who have attended a national ELNEC course, 300 graduate nursing faculty members participated in one of four National Cancer Institute (NCI) grant-funded courses, ELNEC-Graduate, that focused on the needs of faculty teaching graduate nursing students. These nursing faculty members represent every state in the United States and 278 (63%) out of 438 graduate nursing programs. The final NCI-funded ELNEC-Graduate course took place in June 2006. Due to the continued need for this education, additional courses were offered at the 2007 American Association of Colleges of Nursing (AACN) master's conference, and another course was presented in February 2008 at the same AACN meeting. The purpose of this paper is to describe the history of the ELNEC-Graduate project and to demonstrate its impact in empowering graduate nursing faculty members to improve their teaching methods and strategies regarding end-of-life (EOL)/palliative care education. Because of ELNEC-Graduate, graduate nursing faculty members are better equipped to provide this education to their students so that these students are prepared to care for patients and their families experiencing EOL/palliative care issues.
Fast and precise Fourier transforms

Many applications of fast Fourier transforms (FFT’s), such as computer tomography, geophysical signal processing, high-resolution imaging radars, and prediction filters, require high-precision output. An error analysis reveals that the usual method of fixed-point computation of FFT’s of vectors of length2 leads to an average loss of/2 bits of precision. This phenomenon, often referred to as computational noise, causes major problems for arithmetic units with limited precision which are often used for real-time applications. Several researchers have noted that calculation of FFT’s with algebraic integers avoids computational noise entirely, see, e.g., [1]. We will combine a new algorithm for approximating complex numbers by cyclotomic integers with Chinese remaindering strategies to give an efficient algorithm to compute -bit precision FFT’s of length . More precisely, we will approximate complex numbers by cyclotomic integers in [ 2 2 ] whose coefficients, when expressed as polynomials in 2 2 , are bounded in absolute value by some integer . For fixed our algorithm runs in time (log( )), and produces an approximation with worst case error of (1 2 ). We will prove that this algorithm has optimal worst case error by proving a corresponding lower bound on the worst case error of any approximation algorithm for this task. The main tool for designing the algorithms is the use of the cyclotomic units, a subgroup of finite index in the unit group of the cyclotomic field. First implementations of our algorithms indicate that they are fast enough to be used for the design of low-cost high-speed/highprecision FFT chips.
[Multidetector computed tomography coronarography: from studies to everyday life].

INTRODUCTION
Multidetector computed tomography coronarography is a promising tool, offering a non-invasive anatomic evaluation of coronary arteries. The great majority of studies conducted upon it are single-center studies, and have reported results based upon a highly selected patient population. Our aim was to determine its diagnostic accuracy among an unselected population in multicenter studies.


METHODS
Fifty-two patients were included in a non-randomised, retrospective study. Patients underwent multidetector computed tomography coronarography (16, 40 and 64 slices), in ten different centers (community hospitals or private centres), for clinical suspicion of coronary stenoses. The diagnostic accuracy for detecting significant coronary stenoses (> or =50%) was determined in comparison with conventional coronarography.


RESULTS
The sensitivity, specificity, positive and negative predictive values and diagnostic accuracy were 91.4%, 17.7%, 69.6%, 50.0%, and 67.3%, respectively, in a patient-based analysis, and 55.3%, 85.6%, 30.3%, 94.4% and 82.5% in a segment-based analysis.


CONCLUSION
The results of this study indicate that routine implementation of multidetector computed tomography coronarography is limited by a high false-positive rate, when performed among an unselected population of patients with a high pretest probability of having coronary stenoses, in centres with variable expertises. Its place within the range of diagnostic tools has yet to be determined by large multicenter studies, before being subject to precise recommendations framing its routine clinical application.
A Collaborative Filtering Recommendation Algorithm Based on Item Classification

Collaborative filtering systems represent services of personalized that aim at predicting a user’s interest on some items available in the application systems. With the development of electronic commerce, the number of users and items grows rapidly, resulted in the sparsity of the user-item rating dataset. Poor quality is one major challenge in collaborative filtering recommender systems. Sparsity of users’ ratings is the major reason causing the poor quality and the traditional similarity measure methods make poor in this situation. To address this issue, this paper proposes a collaborative filtering recommendation algorithm based on the item classification to pre-produce the ratings. This approach classifies the items to predict the ratings of the vacant values where necessary, and then uses the item-based collaborative filtering to produce the recommendations. The collaborative filtering recommendation method based on item classification prediction can alleviate the sparsity problem of the user-item rating dataset, and can provide better recommendation than traditional collaborative filtering.
Neuromorphic photonic networks using silicon photonic weight banks

Photonic systems for high-performance information processing have attracted renewed interest. Neuromorphic silicon photonics has the potential to integrate processing functions that vastly exceed the capabilities of electronics. We report first observations of a recurrent silicon photonic neural network, in which connections are configured by microring weight banks. A mathematical isomorphism between the silicon photonic circuit and a continuous neural network model is demonstrated through dynamical bifurcation analysis. Exploiting this isomorphism, a simulated 24-node silicon photonic neural network is programmed using “neural compiler” to solve a differential system emulation task. A 294-fold acceleration against a conventional benchmark is predicted. We also propose and derive power consumption analysis for modulator-class neurons that, as opposed to laser-class neurons, are compatible with silicon photonic platforms. At increased scale, Neuromorphic silicon photonics could access new regimes of ultrafast information processing for radio, control, and scientific computing.
Interpretable Features for the Activity Prediction of Short Antimicrobial Peptides Using Fuzzy Logic

Antimicrobial peptides are a promising class of substances for overcoming multidrug resistant bacteria. In a previous study, high-throughput screening of short peptides for antimicrobial activity against Pseudomonas aeruginosa was performed and the resulting data with 1609 peptides was analyzed using quantitative structure–activity relationship models (QSAR) with excellent prediction power. To avoid non-interpretable black-box behavior of the QSAR models, new features based on fuzzy logic and molecular descriptors were introduced. They were used for comprehensive analysis and visualization. The new features provide good interpretability and are able to differentiate between active and inactive peptides. The statistical relevance of this differentiation was shown using a Wilcoxon rank sum test. The best compromise between activity prediction and interpretability was found for fuzzy terms of the Hopp-Woods scale and the Isoelectric point. A visualization of two of these terms enables an in-depth understanding of regions with active and inactive peptides and the identification of outliers. In addition, we generated rules to explain typical amino acid distributions in active peptides. These rules can be used to increase the probability of finding active peptides in new peptide libraries, which can improve the speed of finding leading substances for drug development against resistant bacteria.
Standard 12-lead electrocardiography measures predictive of increased appropriate therapy in implantable cardioverter defibrillator recipients.

AIMS
Identification of patients most likely to benefit from implantable cardioverter defibrillator (ICD) implant remains a complex challenge. This study aimed to investigate the utility of measures derived from standard 10 s 12-lead electrocardiogrphy (ECG) without complex signal processing in predicting appropriate therapy in an ICD population.


METHODS AND RESULTS
We examined 108 ICD patients for primary (n = 32) and secondary prevention (n = 76). Baseline clinical data and characteristics of QRS complex, T-wave, and heart rate from standard 12-lead ECG were examined and related to the occurrence of subsequent appropriate therapy. Over a mean follow-up of 29 ± 11 months, 44% of patients received appropriate therapy. Patients with depressed heart rate variability (HRV) (≤6.5%) were 2.68 [95% confidence interval (CI) 1.21-5.90, P = 0.015] times more likely to receive appropriate therapy than patients with HRV >6.5%. In patients with bundle branch block (BBB), large QRS dispersion of >39 ms was associated with 2.88 times risk (95% CI 1.24-6.71, P = 0.014) of experiencing appropriate therapy than those with QRS dispersion <39 ms. In patients without BBB, reduced maximum T-wave amplitude (<0.4 mV) were 3.82 times (95% CI 1.63-8.93, P = 0.002) more likely to receive appropriate therapy compared with those with maximum T-wave amplitude >0.4 mV. History of atrial arrhythmia [hazard ratio (HR) = 2.30, 95% CI 1.29-4.12, P = 0.005] and secondary prevention (HR = 2.55, 95% CI 1.14-5.71, P = 0.022) were also predictive of device therapy.


CONCLUSION
Measurements from standard 12-lead ECG were predictive of appropriate therapy in a heterogeneous ICD population. Incorporation of 12-lead ECG parameters such as these into risk stratification models may improve our ability to select patients for ICD implantation.
An Isomorphism Between Substructural Proofs and Deterministic Finite Automata

Proof theory is a powerful tool for understanding computational phenomena, as most famously exemplified by the Curry–Howard isomorphism between intuitionistic logic and the simply-typed λ-calculus. In this paper, we identify a fragment of intuitionistic linear logic with least fixed points and establish a Curry–Howard isomorphism between a class of proofs in this fragment and deterministic finite automata. Proof-theoretically, closure of regular languages under complementation, union, and intersection can then be understood in terms of cut elimination. We also establish an isomorphism between a different class of proofs and subsequential string transducers. Because prior work has shown that linear proofs can be seen as session-typed processes, a concurrent semantics of transducer composition is obtained for free. 1998 ACM Subject Classification F.4.1 Mathematical Logic; F.1.1 Models of Computation
Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy

BACKGROUND
Strong evidence shows that physical inactivity increases the risk of many adverse health conditions, including major non-communicable diseases such as coronary heart disease, type 2 diabetes, and breast and colon cancers, and shortens life expectancy. Because much of the world's population is inactive, this link presents a major public health issue. We aimed to quantify the eff ect of physical inactivity on these major non-communicable diseases by estimating how much disease could be averted if inactive people were to become active and to estimate gain in life expectancy at the population level.


METHODS
For our analysis of burden of disease, we calculated population attributable fractions (PAFs) associated with physical inactivity using conservative assumptions for each of the major non-communicable diseases, by country, to estimate how much disease could be averted if physical inactivity were eliminated. We used life-table analysis to estimate gains in life expectancy of the population.


FINDINGS
Worldwide, we estimate that physical inactivity causes 6% (ranging from 3·2% in southeast Asia to 7·8% in the eastern Mediterranean region) of the burden of disease from coronary heart disease, 7% (3·9-9·6) of type 2 diabetes, 10% (5·6-14·1) of breast cancer, and 10% (5·7-13·8) of colon cancer. Inactivity causes 9% (range 5·1-12·5) of premature mortality, or more than 5·3 million of the 57 million deaths that occurred worldwide in 2008. If inactivity were not eliminated, but decreased instead by 10% or 25%, more than 533 000 and more than 1·3 million deaths, respectively, could be averted every year. We estimated that elimination of physical inactivity would increase the life expectancy of the world's population by 0·68 (range 0·41-0·95) years.


INTERPRETATION
Physical inactivity has a major health eff ect worldwide. Decrease in or removal of this unhealthy behaviour could improve health substantially.


FUNDING
None.