Miracle-dz/UniFAR-FTUs huggingface dataset:
Detection and Identification of Bacillus cereus, Bacillus cytotoxicus, Bacillus thuringiensis, Bacillus mycoides and Bacillus weihenstephanensis via Machine Learning Based FTIR Spectroscopy

The Bacillus cereus group comprises genetical closely related species with variable toxigenic characteristics. However, detection and differentiation of the B. cereus group species in routine diagnostics can be difficult, expensive and laborious since current species designation is linked to specific phenotypic characteristic or the presence of species-specific genes. Especially the differentiation of Bacillus cereus and Bacillus thuringiensis, the identification of psychrotolerant Bacillus mycoides and Bacillus weihenstephanensis, as well as the identification of emetic B. cereus and Bacillus cytotoxicus, which are both producing highly potent toxins, is of high importance in food microbiology. Thus, we investigated the use of a machine learning approach, based on artificial neural network (ANN) assisted Fourier transform infrared (FTIR) spectroscopy, for discrimination of B. cereus group members. The deep learning tool box of Matlab was employed to construct a one-level ANN, allowing the discrimination of the aforementioned B. cereus group members. This model resulted in 100% correct identification for the training set and 99.5% correct identification overall. The established ANN was applied to investigate the composition of B. cereus group members in soil, as a natural habitat of B. cereus, and in food samples originating from foodborne outbreaks. These analyses revealed a high complexity of B. cereus group populations, not only in soil samples but also in the samples from the foodborne outbreaks, highlighting the importance of taking multiple isolates from samples implicated in food poisonings. Notable, in contrast to the soil samples, no bacteria belonging to the psychrotolerant B. cereus group members were detected in the food samples linked to foodborne outbreaks, while the overall abundancy of B. thuringiensis did not significantly differ between the sample categories. None of the isolates was classified as B. cytotoxicus, fostering the hypothesis that the latter species is linked to very specific ecological niches. Overall, our work shows that machine learning assisted (FTIR) spectroscopy is suitable for identification of B. cereus group members in routine diagnostics and outbreak investigations. In addition, it is a promising tool to explore the natural habitats of B. cereus group, such as soil.
CEDR: Contextualized Embeddings for Document Ranking

Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this work, we investigate how two pretrained contextualized language models (ELMo and BERT) can be utilized for ad-hoc document ranking. Through experiments on TREC benchmarks, we find that several ex-sting neural ranking architectures can benefit from the additional context provided by contextualized language models. Furthermore, we propose a joint approach that incorporates BERT's classification vector into existing neural models and show that it outperforms state-of-the-art ad-hoc ranking baselines. We call this joint approach CEDR (Contextualized Embeddings for Document Ranking). We also address practical challenges in using these models for ranking, including the maximum input length imposed by BERT and runtime performance impacts of contextualized language models.
Bioenergetic basis for the increased fatigability with ageing

The mechanisms for the age‐related increase in fatigability during dynamic exercise remain elusive. We tested whether age‐related impairments in muscle oxidative capacity would result in a greater accumulation of fatigue causing metabolites, inorganic phosphate (Pi), hydrogen (H+) and diprotonated phosphate (H2PO4−), in the muscle of old compared to young adults during a dynamic knee extension exercise. The age‐related increase in fatigability (reduction in mechanical power) of the knee extensors was closely associated with a greater accumulation of metabolites within the working muscle but could not be explained by age‐related differences in muscle oxidative capacity. These data suggest that the increased fatigability in old adults during dynamic exercise is primarily determined by age‐related impairments in skeletal muscle bioenergetics that result in a greater accumulation of metabolites.
Nanospot angle-resolved photoemission study of Bernal-stacked bilayer graphene on hexagonal boron nitride: Band structure and local variation of lattice alignment

Hexagonal boron nitride (hBN) is the supporting substrate of choice for two-dimensional material devices because it is atomically flat and chemically inert. However, due to the small size of mechanically exfoliated hBN flakes, electronic structure studies of 2D materials supported by hBN using angle-resolved photoemission spectroscopy (ARPES) are challenging. Here we investigate the electronic band structure of a Bernal-stacked bilayer graphene sheet on a hexagonal boron nitride (BLG/hBN) flake using nanospot ARPES (nanoARPES). By fitting high-resolution energy vs. momentum electronic band spectra, we extract the tight-binding parameters for BLG on hBN. In addition, we reveal spatial variations of the alignment angle between BLG and hBN lattices via inhomogeneity of the electronic bands near the Fermi level. We confirmed these findings by scanning tunneling microscopy measurements obtained on the same device. Our results from spatially resolved nanoARPES measurements of BLG/hBN heterostructures are instrumental for understanding experiments that utilize spatially averaging techniques such as electronic transport and optical spectroscopy.
TwitterMancer: Predicting Interactions on Twitter Accurately

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain "higher-dimensional" triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at this https URL. Keywords: Graph mining, machine learning, social media, social networks
Surgical site infections: reanalysis of risk factors.

BACKGROUND Surgical site infections (SSI) are the most common nosocomial infection in surgical patients, accounting for 38% of all such infections, and are a significant source of postoperative morbidity resulting in increased hospital length of stay and increased cost. During 1986-1996 the Center for Disease Control and Prevention's National Nosocomial Infections Surveillance system reported 15,523 SSI following 593,344 operations (2.6%). Previous studies have documented patient characteristics associated with an increased risk of SSI, including diabetes, tobacco or steroid use, obesity, malnutrition, and perioperative blood transfusion. In this study we sought to reevaluate risk factors for SSI in a large cohort of noncardiac surgical patients. METHODS Prospective data (NSQIP) were collected on 5031 noncardiac surgical patients at the Veteran's Administration Maryland Healthcare System from 1995 to 2000. All preoperative risk factors were evaluated as independent predictors of surgical site infection. RESULTS The mean age of the study cohort was 61 plus minus 13. SSI occurred in 162 patients, comprising 3.2% of the study cohort. Gram-positive organisms were the most common bacterial etiology. Multiple logistic regression analysis documented that diabetes (insulin- and non-insulin-dependent), low postoperative hematocrit, weight loss (within 6 months), and ascites were significantly associated with increased SSI. Tobacco use, steroid use, and chronic obstructive pulmonary disease (COPD) were not predictors for SSI. CONCLUSION This study confirms that diabetes and malnutrition (defined as significant weight loss 6 months prior to surgery) are significant preoperative risk factors for SSI. Postoperative anemia is a significant risk factor for SSI. In contrast to prior analyses, this study has documented that tobacco use, steroid use, and COPD are not independent predictors of SSI. Future SSI studies should target early preoperative intervention and optimization of patients with diabetes and malnutrition.
Unsupervised label noise modeling and loss correction

Despite being robust to small amounts of label noise, convolutional neural networks trained with stochastic gradient methods have been shown to easily fit random labels. When there are a mixture of correct and mislabelled targets, networks tend to fit the former before the latter. This suggests using a suitable two-component mixture model as an unsupervised generative model of sample loss values during training to allow online estimation of the probability that a sample is mislabelled. Specifically, we propose a beta mixture to estimate this probability and correct the loss by relying on the network prediction (the so-called bootstrapping loss). We further adapt mixup augmentation to drive our approach a step further. Experiments on CIFAR-10/100 and TinyImageNet demonstrate a robustness to label noise that substantially outperforms recent state-of-the-art. Source code is available at https://git.io/fjsvE and Appendix at https://arxiv.org/abs/1904.11238.
Continuous Glucose Monitoring: A Possible Aid for Detecting Hypoglycemic Events during Insulin Tolerance Tests

The combined pituitary function test evaluates the anterior pituitary gland, while the insulin tolerance test evaluates growth hormone deficiencies. However, successful stimulation requires achieving an appropriate level of hypoglycemia. Close medical supervision for glucose monitoring is required during hypoglycemia induction and the test is often very tedious. In addition, a capillary blood sugar test (BST) and serum glucose levels may differ greatly. An alternative approach may be utilizing a continuous glucose-monitoring (CGM) system. We provide three cases in which CGM was successfully used alongside a standard BST and serum glucose levels during the combined pituitary function test to better detect and induce hypoglycemia. Three participants who were diagnosed with multiple pituitary hormone deficiencies during childhood were re-evaluated in adulthood; a Dexcom G6 CGM was used. The CGM sensor glucose and BST levels were simultaneously assessed for glycemic changes and when adequate hypoglycemia was reached during the combined pituitary function test. The CGM sensor glucose, BST, and serum glucose levels showed similar glucose trends in all three patients. A Bland–Altman analysis revealed that the CGM underestimated the BST values by approximately 9.68 mg/dL, and a Wilcoxon signed-rank test showed that the CGM and BST measurements significantly differed during the stimulation test (p = 0.003). Nevertheless, in all three cases, the CGM sensor mimicked the glycemic variability changes in the BST reading and assisted in monitoring appropriate hypoglycemia nadir. Thus, CGM can be used as a safe aid for clinicians to use during insulin tolerance tests where critical hypoglycemia is induced.
The Relationship Between Cognitive Dysfunction and Symptom Dimensions Across Schizophrenia, Bipolar Disorder, and Major Depressive Disorder

Background: Cognitive dysfunction is considered a core feature among schizophrenia (SZ), bipolar disorder (BD), and major depressive disorder (MDD). Despite abundant literature comparing cognitive dysfunction among these disorders, the relationship between cognitive dysfunction and symptom dimensions remains unclear. The study aims are a) to identify the factor structure of the BPRS-18 and b) to examine the relationship between symptom domains and cognitive function across SZ, BD, and MDD. Methods: A total of 716 participants [262 with SZ, 104 with BD, 101 with MDD, and 249 healthy controls (HC)] were included in the study. One hundred eighty participants (59 with SZ, 23 with BD, 24 with MDD, and 74 HC) completed the MATRICS Consensus Cognitive Battery (MCCB), and 507 participants (85 with SZ, 89 with BD, 90 with MDD, and 243 HC) completed the Wisconsin Card Sorting Test (WCST). All patients completed the Brief Psychiatric Rating Scale (BPRS). Results: We identified five BPRS exploratory factor analysis (EFA) factors (“affective symptoms,” “psychosis,” “negative/disorganized symptoms,” “activation,” and “noncooperation”) and found cognitive dysfunction in all of the participant groups with psychiatric disorders. Negative/disorganized symptoms were the most strongly associated with cognitive dysfunctions across SZ, BD, and MDD. Conclusions: Our findings suggest that cognitive dysfunction severity relates to the negative/disorganized symptom domain across SZ, BD, and MDD, and negative/disorganized symptoms may be an important target for effective cognitive remediation in SZ, BD, and MDD.
Shared factory: A new production node for social manufacturing in the context of sharing economy

Manufacturing industry is heading towards socialization, interconnection and platformization. Motivated by the infiltration of sharing economy usage in manufacturing, this article addresses a new factory model – shared factory, and provides a theoretical architecture and some actual cases for manufacturing sharing. Concepts related to three kinds of shared factories which deal, respectively, with sharing production-orders, manufacturing-resources and manufacturing-capabilities are defined accordingly. These three kinds of shared factory modes can be used for building correspondent sharing manufacturing ecosystems. On the basis of sharing economic analysis, we identify feasible key-enabled technologies for configuring and running a shared factory. At the same time, opportunities and challenges of enabling the shared factory are also analysed in detail. In fact, shared factory, as a new production node, enhances the sharing nature of social manufacturing paradigm, fits the needs of light assets and gives us a new chance to use socialized manufacturing resources. It can be drawn that implementing a shared factory would reach a ‘win-win’ way through production value-added transformation and social innovation.
Ray Interference: a Source of Plateaus in Deep Reinforcement Learning

Rather than proposing a new method, this paper investigates an issue present in existing learning algorithms. We study the learning dynamics of reinforcement learning (RL), specifically a characteristic coupling between learning and data generation that arises because RL agents control their future data distribution. In the presence of function approximation, this coupling can lead to a problematic type of 'ray interference', characterized by learning dynamics that sequentially traverse a number of performance plateaus, effectively constraining the agent to learn one thing at a time even when learning in parallel is better. We establish the conditions under which ray interference occurs, show its relation to saddle points and obtain the exact learning dynamics in a restricted setting. We characterize a number of its properties and discuss possible remedies.
On the Curious Pulsation Properties of the Accreting Millisecond Pulsar IGR J17379–3747

We report on the Neutron Star Interior Composition Explorer (NICER) monitoring campaign of the 468 Hz accreting millisecond X-ray pulsar IGR J17379–3747. From a detailed spectral and timing analysis of the coherent pulsations we find that they show a strong energy dependence, with soft thermal emission lagging about 640 μs behind the hard, Comptonized emission. Additionally, we observe uncommonly large pulse fractions, with measured amplitudes in excess of 20% sinusoidal fractional amplitude across the NICER passband and fluctuations of up to ∼70%. Based on a phase-resolved spectral analysis, we suggest that these extreme properties might be explained if the source has an unusually favorable viewing geometry with a large magnetic misalignment angle. Due to these large pulse fractions, we were able to detect pulsations down to quiescent luminosities ( erg ). We discuss these low-luminosity pulsations in the context of transitional millisecond pulsars.
Modeling of Beam Loss Induced Quenches in the LHC Main Dipole Magnets

The full energy exploitation of the Large Hadron Collider (LHC), a planned increase of the beam energy beyond the present 6.5 TeV, will result in more demanding working conditions for the superconducting dipoles and quadrupoles operating in the machine. It is hence crucial to analyze, understand, and predict the quench levels of these magnets for the required values of current and generated magnetic fields. A one-dimensional multi-strand electro-thermal model has been developed to analyze the effect of beam-losses heat deposition. Critical elements of the model are the ability to capture heat and current distribution among strands, and heat transfer to the superfluid helium bath. The computational model has been benchmarked against experimental values of LHC quench limits measured at 6.5 TeV for the Main Bending dipole magnets.
haploidentical hematopoietic stem cell transplantation without total body irradiation for pediatric acute leukemia: a single-center experience

: Hematopoietic stem cell transplantation (HSCT) is a promising method for therapy of pediatric patients with acute leukemia. However, less availability of matched donors limited its wide application. Recently, haploidentical HSCT has become a great resource. Here, we have retrospectively reported our experience of 20 pediatric patients with acute leukemia who underwent haploidentical HSCT without total body irradiation (TBI) myeloablative regimen in our center from November 2007 to June 2014. All the patients attained successful HSCT engraftment in terms of myeloid and platelet recovery. Thirteen patients developed grade I–IV acute graft-versus-host disease (a-GVHD). The incidence of grade I–II a-GVHD, grade III–IV a-GVHD, and chronic GVHD (c-GVHD) was 45%, 20%, and 25%, respectively. The mean myeloid and platelet recovery time was 13.20 ± 2.41 and 19.10 ± 8.37 days. The median follow-up time was 43.95 ± 29.26 months. During the follow-up, three patients died. The overall survival (OS) rate was 85%. The present study indicated that haploidentical HSCT without TBI myeloablative regimen significantly improved the OS rate of pediatric patients with acute leukemia.
Ag Alloying and KF Treatment Effects on Low Bandgap CuInSe2 Solar Cells

Silver alloying and KF post-deposition treatments are explored as approaches to increase the efficiency of low bandgap CuInSe<sub>2</sub> (CIS) solar cells. The KF treatment prevents a loss in <italic>V</italic><sub>OC</sub> when CIS has a thinner CdS layer, and also increases the carrier concentration of these devices. Compared with CIS devices, Ag alloyed CIS (ACIS) devices have lower efficiencies due to a lower carrier concentration which decreases <italic>V</italic><sub>OC</sub>. When ACIS is exposed to the KF treatment, the resulting devices have reduced <italic>V</italic><sub>OC</sub> and fill factor (<italic>FF</italic>) compared with untreated devices; however, thinning the CdS layer with KF-treated ACIS improves <italic>V</italic><sub>OC</sub>. ACIS+KF films show an unidentified compound in Raman spectra and the resulting solar cells have a dominant interface recombination mechanism and light-to-dark crossover. An electron microscopy study of the growth and coverage of CdS reveals that KF-treated samples in this study have faster CdS growth and ACIS+KF has complete CdS coverage with less than half of the normal deposition time. Improved coverage of CdS layers on KF-treated samples is also verified by X-ray photoelectron spectroscopy. Finally, using a heat treatment and an anti-reflection layer enables CIS+KF solar cells to obtain a high 16.0% efficiency for CIS with a <italic>V</italic><sub>OC</sub> = 526 mV and <italic>J</italic><sub>SC</sub> = 41.0 mA/cm<sup>2</sup>.
Notch Signaling Affects Oral Neoplasm Cell Differentiation and Acquisition of Tumor-Specific Characteristics

Histopathological findings of oral neoplasm cell differentiation and metaplasia suggest that tumor cells induce their own dedifferentiation and re-differentiation and may lead to the formation of tumor-specific histological features. Notch signaling is involved in the maintenance of tissue stem cell nature and regulation of differentiation and is responsible for the cytological regulation of cell fate, morphogenesis, and/or development. In our previous study, immunohistochemistry was used to examine Notch expression using cases of odontogenic tumors and pleomorphic adenoma as oral neoplasms. According to our results, Notch signaling was specifically associated with tumor cell differentiation and metaplastic cells of developmental tissues. Notch signaling was involved in the differentiation of the ductal epithelial cells of salivary gland tumors and ameloblast-like cells of odontogenic tumors. However, Notch signaling was also involved in squamous metaplasia, irrespective of the type of developmental tissue. In odontogenic tumors, Notch signaling was involved in epithelial–mesenchymal interactions and may be related to tumor development and tumorigenesis. This signaling may also be associated with the malignant transformation of ameloblastomas. Overall, Notch signaling appears to play a major role in the formation of the characteristic cellular composition and histological features of oral neoplasms, and this involvement has been reviewed here.
The measurement of participant-reported memory across diverse populations and settings: a systematic review and meta-analysis of the Multifactorial Memory Questionnaire

ABSTRACT The Multifactorial Memory Questionnaire (MMQ) is a participant-reported measure of memory satisfaction, ability, and strategy use. Initially validated with healthy older adults, it has since been used in many different populations and settings for a variety of purposes. We conducted a systematic review and meta-analysis of the measurement properties of the MMQ across multiple, diverse studies. Methods: The study was designed using the Consensus-Based Standards for the Selection of Health Measurement Instruments and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We calculated aggregate statistics and evaluated the methodological quality of 29 studies retrieved from PsycINFO, MEDLINE, EMBASE, and Web of Science. Results: Analyses revealed high-quality evidence for internal consistency, stability, measurement error, convergent validity, and known-groups validity of the three MMQ scales. There was moderate-quality evidence for responsiveness and structural validity, with some studies identifying separate factors for internal and external memory strategy use. Measurement properties were similar across languages, participant samples, and study designs. Conclusions: The MMQ is a valid, reliable, and responsive measure across diverse settings and populations. Future research is needed to determine whether more detailed information can be obtained from the scales, specifically, internal versus external strategy use.