Miracle-dz/UniFAR-FTUs huggingface dataset:
The tyranny of difference: exploring attitudes to the role of the consumer academic in teaching students of mental health nursing

Abstract Background: Consumer participation in mental health service delivery is now a policy expectation. Negative attitudes of health professionals towards collaboration with consumers have been identified as a major barrier to policy implementation. Consumers contributing to the education of nurses and other health positions have been identified as an effective strategy, particularly when consumers occupy academic positions. Attitudes of nurse and consumer academics to the consumer academic role remain under-researched. Aims: To explore the implementation of a consumer academic position from the perspectives of the broader academic team. Methods: Qualitative exploratory research was undertaken to give voice to different perspectives of the implementation of a consumer academic position. In-depth interviews were conducted with nurse academics, the consumer academic and the research team. Results: Thematic data analysis revealed five main themes: seeking a united perspective; who can provide a consumer perspective? How accurate is consumer perspective? One consumer, one opinion, one way, one delivery; bias and poor portrayal of nurses. Conclusions: Marked divergence in views and opinions was evident in terms of support for the role and its perceived value. Further investigation of factors facilitating successful implementation is required and strategies identified to facilitate mutual understandings and goal setting.
Part II: presentation, diagnosis, classification, treatment, and prevention of stress fractures in female athletes

ABSTRACT Objectives: Stress fractures (SFx) occur as the result of repetitive loads over short periods of time, which leads to micro-damage of the bone through cortical resorption, ultimately leading to fracture. They are a common injury in female athletes and often cause significant morbidity. The goal of this study is to review the presentation, diagnosis, classification, treatment, and prevention of SFx in female athletes. Results: A thorough history, physical exam, and appropriate imaging can facilitate early diagnosis of stress fracture (SFx) and faster resolution of symptoms with more conservative management. The female athlete triad is an especially important factor that contributes to the increased risk of SFx in females. The continuum of stress injuries ranges from mild microfailure to complete fracture, which has resulted in the development of newer grading schemas through MRI and radiographic findings. Stress fractures are also classified as low- or high-risk according to anatomic location, as blood supply and applied forces at different locations affect the likelihood of fracture propagation, displacement, delayed union, or non-union. Conclusions: The ability to screen for at-risk athletes is paramount in preventing SFx. Recognition and prompt treatment of the female athlete triad requires a multidisciplinary approach in order to restore energy balance, correct menstrual irregularities, and improve bone health. This review provides a basis for understanding how to identify and treat stress fractures, which may allow treating physicians to diagnose this condition earlier and minimize any associated morbidity.
Long-term impairment of neurovascular coupling following experimental subarachnoid hemorrhage

CO2-reactivity and neurovascular coupling are sequentially lost within the first 24 h after subarachnoid hemorrhage (SAH). Whether and when these impairments recover is not known. Therefore, we investigated the reactivity of pial and intraparenchymal vessels by in vivo two-photon microscopy one month after experimental SAH. C57BL/6 mice were subjected to either sham surgery or SAH by filament perforation. One month later, cerebral blood flow following CO2-challenge and forepaw stimulation was assessed by laser Doppler fluxmetry. Diameters of pial and intraparenchymal arterioles were quantified by in vivo two-photon microscopy. One month after SAH, pial and parenchymal vessels dilated in response to CO2. Neurovascular coupling was almost completely absent after SAH: vessel diameter did not change upon forepaw stimulation compared to a 20% increase in sham-operated mice. The current results demonstrate that neurovascular function differentially recovers after SAH: while CO2-reactivity normalizes within one month after SAH, neurovascular coupling is still absent. These findings show an acute and persistent loss of neurovascular coupling after SAH that may serve as a link between early brain injury and delayed cerebral ischemia, two distinct pathophysiological phenomena after SAH that were so far believed not to be directly related.
The “MS-ROM/IFAST” Model, a Novel Parallel Nonlinear EEG Analysis Technique, Distinguishes ASD Subjects From Children Affected With Other Neuropsychiatric Disorders With High Degree of Accuracy

Background and Objective. In a previous study, we showed a new EEG processing methodology called Multi-Scale Ranked Organizing Map/Implicit Function As Squashing Time (MS-ROM/IFAST) performing an almost perfect distinction between computerized EEG of Italian children with autism spectrum disorder (ASD) and typically developing children. In this study, we assessed this system in distinguishing ASD subjects from children affected with other neuropsychiatric disorders (NPD). Methods. At a psychiatric practice in Texas, 20 children diagnosed with ASD and 20 children diagnosed with NPD were entered into the study. Continuous segments of artifact-free EEG data lasting 10 minutes were entered in MS-ROM/IFAST. From the new variables created by MS-ROM/IFAST, only 12 has been selected according to a correlation criterion. The selected features represent the input on which supervised machine learning systems (MLS) acted as blind classifiers. Results. The overall predictive capability in distinguishing ASD from other NPD cases ranged from 93% to 97.5%. The results were confirmed in further experiments in which Italian and US data have been combined. In this analysis, the best MLS reached 95.0% global accuracy in 1 out of 3 classes distinction (ASD, NPD, controls). This study demonstrates the value of EEG processing with advanced MLS in the differential diagnosis between ASD and NPD cases. The results were not affected by age, ethnicity and technicalities of EEG acquisition, confirming the existence of a specific EEG signature in ASD cases. To further support these findings, it was decided to test the behavior of already trained neural networks on 10 Italian very young ASD children (25-37 months). In this test, 9 out of 10 cases have been correctly recognized as ASD subjects in the best case. Conclusions. These results confirm the possibility of an early automatic autism detection based on standard EEG.
Development and Validation of a Deep Learning Algorithm for Mortality Prediction in Selecting Patients With Dementia for Earlier Palliative Care Interventions

Key Points Question How does a deep learning algorithm using patient demographic information and longitudinal clinical notes to predict mortality risk perform as a proxy indicator for identifying patients with dementia who need palliative care? Findings In this cohort study, for a validation data set of 2692 adult patients with dementia, mortality prediction models reached an area under the receiver operating characteristic curve of 0.978 for predicting death in 6 months, 0.956 for 1 year, and 0.943 for 2 years. Meaning Deep learning appears to show promising results in mortality risk stratification in patients with dementia.
Modification of lactoferrin by peroxynitrite reduces its antibacterial activity and changes protein structure

Lactoferrin (LF) is a multifunctional protein that plays important physiological roles as one of the most concentrated proteins in many human and other mammalian fluids and tissues. In particular, LF provides antibacterial properties to human milk, saliva, and tear fluid. LF also protects against stress‐induced lipid peroxidation at inflammation sites through its iron‐binding ability. Previous studies have shown that LF can be efficiently nitrated via biologically relevant mediators such as peroxynitrite (ONOO−), which are also present at high intracellular concentrations during inflammation and nitrosative stress. Here, we examine changes in antibacterial properties and structure of LF following ONOO− treatment. The reaction induces nitration of tyrosine and tryptophan residues, which are commonly used as biomarker molecules for several diseases. Treatment with ONOO− at a 10/1 M ratio of ONOO− to tyrosine inhibited all antibacterial activity exhibited by native LF. Secondary structural changes in LF were assessed using circular dichroism spectroscopy. Nitration products with and without the addition of Fe3+ show significant reduction in alpha‐helical properties, suggesting partial protein unfolding. Iron‐binding capacity of LF was also reduced after treatment with ONOO−, suggesting a decreased ability of LF to protect against cellular damage. LC‐MS/MS spectrometry was used to identify LF peptide fragments nitrated by ONOO−, including tyrosine residue Y92 located in the iron‐binding region. These results suggest that posttranslational modification of LF by ONOO− could be an important pathway to exacerbate infection, for example, in inflamed tissues and to reduce the ability of LF to act as an immune responder and decrease oxidative damage.
Antichemotactic and Antifungal Action of the Essential Oils from Cryptocarya aschersoniana, Schinus terebinthifolia, and Cinnamomum amoenum

The purpose of this work was to determine the chemical composition and evaluate the antichemotactic, antioxidant, and antifungal activities of the essential oil obtained from the species Cryptocarya aschersoniana Mez, Cinnamomum amoenum (Ness & Mart.) Kosterm. , and Schinus terebinthifolia Raddi, as well as the combination of C. aschersoniana essential oil and terbinafine against isolates of dermatophytes. Allo‐aromadendrene, bicyclogermacrene, and germacrene B were identified as major compounds in essential oils. The essential oil of C. aschersoniana shown 100 % inhibitory effect on leukocyte migration at the concentration of 10 μg/mL while S. terebinthifolia oil presented 80.1 % inhibitory effect at the same concentration. Only S. terebinthifolia oil possessed free‐radical‐scavenging activity which indicates its antioxidant capacity. The essential oils were also tested against fungal isolates of dermatophyte species (Trichophyton rubrum, Trichophyton mentagrophytes, Microsporum canis and Microsporum gypseum), resulting in MIC ranging from 125 μg/mL to over 500 μg/mL. C. aschersoniana oil combined with terbinafine resulted in an additive interaction effect. In this case, the essential oil may act as a complement to conventional therapy for the topical treatment of superficial fungal infections, mainly because it is associated with an anti‐inflammatory effect.
Self-Evaluation Dimensions of Criminal Activity and Prospects Simulation of Persons Serving Custodial Sentence

The article presents the self-evaluation indicators of the causes of criminal behavior committed by persons serving a custodial sentence at X institution, focusing on imprisonment and modeling of life prospects after release from prison. The main idea of the study is to reveal the subjective self-evaluation experience of persons serving custodial sentence with regard to criminal activity and simulation of future prospects. The scientific discussion of the article presents incarcerated persons’ (N = 58) subjective self-evaluation data collected during the qualitative research and their theoretical-practical interpretation. Convicted persons’ subjective self-evaluation and prospects simulation data enable stating certain features showing the discrepancy between their disposition and achievement of resocialization goals. This confirms the insufficiently interiorized reasonableness of punishment, personal perception, and realized motivation to change.
Statistical Learning Theoretical Foundations Overview for Big Data Predictive Analytic

This paper presents a learning machine overview for Big Data Predictive Analytic. Produced data, in this decade, become bigger and bigger than ever. They have to be analysed and processed in order to extract relevant knowledge to make predictive analytic. Learning machines comes at this stage to estimate predictors based on observed historical data. Learning algorithms performance and data quantity evolution must be parallel to keep tolerable performance. This parallelism is one of main challenges of Big Data field. For that reason, this work introduces the basic theoretical foundations of learning machines to push researchers to design new algorithms taking the data amount and performance aspect in consideration.
Highly-sensitive As3+ detection using electrodeposited nanostructured MnOx and phase evolution of the active material during sensing.

A simple, one-step electrodeposition approach has been used to fabricate MnOx on indium doped tin oxide (ITO) substrate for highly sensitive As3+ detection. We report an experimental limit of detection of 1 ppb through anodic stripping voltammetry (ASV) with selectivity to As3+ in the presence of 10 times higher concentrations of several metal ions. Additionally, we report the simultaneous phase evolution of active material occurring through multiple stripping cycles, wherein MnO/Mn2O3 eventually converts to Mn3O4; as a result of change in the oxidation states of manganese. This occurs with concomitant changes in morphology. Change in the electronic property (increased charge transfer resistance) of the material due to sensing results in eventual decrease in sensitivity after multiple stripping cycles. In a nutshell, this paper reports stripping voltammetry-induced change in morphology and phase of as-prepared Mn-based electrodes during As sensing.
Genome Sequences of Three Cluster C Mycobacteriophages, Bipolarisk, Bread, and FudgeTart

Three mycobacteriophages, Bipolarisk, Bread, and FudgeTart, were isolated from enriched soil samples found in Crete, NE. All three phages are lytic, belong to subcluster C1, and infect Mycobacterium smegmatis mc2155. The structures of the three genomes are similar, with slight variations in gene number and content. ABSTRACT Three mycobacteriophages, Bipolarisk, Bread, and FudgeTart, were isolated from enriched soil samples found in Crete, NE. All three phages are lytic, belong to subcluster C1, and infect Mycobacterium smegmatis mc2155. The structures of the three genomes are similar, with slight variations in gene number and content.
Endlicheria bracteolata (Meisn.) Essential Oil as a Weapon Against Leishmania amazonensis: In Vitro Assay

The difficulties encountered and the numerous side effects present in the treatment of cutaneous leishmaniasis have encouraged the research for new compounds that can complement or replace existing treatment. The growing scientific interest in the study of plants, which are already used in folk remedies, has led our group to test Endlicheria bracteolata essential oil against Leishmania amazonensis. Several species of the Lauraceae family, or their compounds, have relevant antiprotozoal activities Therefore, the biological potential on L. amazonensis forms from the essential oil of Endlicheria bracteolata leaves was verified for the first time in that work. The antileishmanial activity was evaluated against promastigotes and intracellular amastigotes, and cytotoxicity were performed with J774.G8, which were incubated with different concentrations of E. bracteolata essential oil. Transmission electron microscopy and flow cytometry were performed with E. bracteolata essential oil IC50. Promastigote forms showed E. bracteolata essential oil IC50 of 7.945 ± 1.285 µg/mL (24 h) and 6.186 ± 1.226 µg/mL (48 h), while for intracellular amastigote forms it was 3.546 ± 1.184 µg/mL (24 h). The CC50 was 15.14 ± 0.090 µg/mL showing that E. bracteolata essential oil is less toxic to macrophages than to parasites. Transmission electron microscopy showed that E. bracteolata essential oil treatment is capable of inducing mitochondrial damage to promastigote and intracellular amastigote forms, while flow cytometry showed ΔѰm disruption in treated parasites. These results could bring about new possibilities to develop products based on E. bracteolata essential oil to treat cutaneous leishmaniasis, especially for people who cannot receive the conventional therapy.
Intelligent network intrusion detection using an evolutionary computation approach

Faculty of Health, Engineering and Science School of Computer and Security Science Doctor of Philosophy Intelligent Network Intrusion Detection Using an Evolutionary Computation Approach by Samaneh Rastegari With the enormous growth of users’ reliance on the Internet, the need for secure and reliable computer networks also increases. Availability of effective automatic tools for carrying out different types of network attacks raises the need for effective intrusion detection systems. Generally, a comprehensive defence mechanism consists of three phases, namely, preparation, detection and reaction. In the preparation phase, network administrators aim to find and fix security vulnerabilities (e.g., insecure protocol and vulnerable computer systems or firewalls), that can be exploited to launch attacks. Although the preparation phase increases the level of security in a network, this will never completely remove the threat of network attacks. A good security mechanism requires an Intrusion Detection System (IDS) in order to monitor security breaches when the prevention schemes in the preparation phase are bypassed. To be able to react to network attacks as fast as possible, an automatic detection system is of paramount importance. The later an attack is detected, the less time network administrators have to update their signatures and reconfigure their detection and remediation systems. An IDS is a tool for monitoring the system with the aim of detecting and alerting intrusive activities in networks. These tools are classified into two major categories of signature-based and anomaly-based. A signature-based IDS stores the signature of known attacks in a database and discovers occurrences of attacks by monitoring and comparing each communication in the network against the database of signatures. On the other hand, mechanisms that deploy anomaly detection have a model of normal behaviour of system and any significant deviation from this model is reported as anomaly.
Structural insight into metallocofactor maturation in carbon monoxide dehydrogenase

The nickel-dependent carbon monoxide dehydrogenase (CODH) employs a unique heterometallic nickel–iron–sulfur cluster, termed the C-cluster, to catalyze the interconversion of CO and CO2. Like other complex metalloenzymes, CODH requires dedicated assembly machinery to form the fully intact and functional C-cluster. In particular, nickel incorporation into the C-cluster depends on the maturation factor CooC; however, the mechanism of nickel insertion remains poorly understood. Here, we compare X-ray structures (1.50–2.48 Å resolution) of CODH from Desulfovibrio vulgaris (DvCODH) heterologously expressed in either the absence (DvCODH−CooC) or presence (DvCODH+CooC) of co-expressed CooC. We find that the C-cluster of DvCODH−CooC is fully loaded with iron but does not contain any nickel. Interestingly, the so-called unique iron ion (Feu) occupies both its canonical site (80% occupancy) and the nickel site (20% occupancy), with addition of reductant causing further mismetallation of the nickel site (60% iron occupancy). We also demonstrate that a DvCODH variant that lacks a surface-accessible iron–sulfur cluster (the D-cluster) has a C-cluster that is also replete in iron but lacks nickel, despite co-expression with CooC. In this variant, all Feu is in its canonical location, and the nickel site is empty. This D-cluster–deficient CODH is inactive despite attempts to reconstitute it with nickel. Taken together, these results suggest that an empty nickel site is not sufficient for nickel incorporation. Based on our findings, we propose a model for C-cluster assembly that requires both CooC and a functioning D-cluster, involves precise redox-state control, and includes a two-step nickel-binding process.
BioWolf: A Sub-10-mW 8-Channel Advanced Brain–Computer Interface Platform With a Nine-Core Processor and BLE Connectivity

Advancements in digital signal processing (DSP) and machine learning techniques have boosted the popularity of brain–computer interfaces (BCIs), where electroencephalography is a widely accepted method to enable intuitive human–machine interaction. Nevertheless, the evolution of such interfaces is currently hampered by the unavailability of embedded platforms capable of delivering the required computational power at high energy efficiency and allowing for a small and unobtrusive form factor. To fill this gap, we developed BioWolf, a highly wearable (40 mm × 20 mm × 2 mm) BCI platform based on Mr. Wolf, a parallel ultra low power system-on-chip featuring nine RISC-V cores with DSP-oriented instruction set extensions. BioWolf also integrates a commercial 8-channel medical-grade analog-to-digital converter, and an ARM-Cortex M4 microcontroller unit (MCU) with bluetooth low-energy connectivity. To demonstrate the capabilities of the system, we implemented and tested a BCI featuring canonical correlation analysis (CCA) of steady-state visual evoked potentials. The system achieves an average information transfer rate of 1.46 b/s (aligned with the state-of-the-art of bench-top systems). Thanks to the reduced power envelope of the digital computational platform, which consumes less than the analog front-end, the total power budget is just 6.31 mW, providing up to 38 h operation (65 mAh battery). To our knowledge, our design is the first to explore the significant energy boost of a parallel MCU with respect to single-core MCUs for CCA-based BCI.
Low computational cost classifiers for ECG diagnosis using neural networks

We investigate and compare a number of computationally efficient classifiers for categorising the Frank lead ECG as normal or one of six disease conditions using a neural network expert system. These include a power spectral density estimate, and two discrete wavelets, the Daubechies wavelet of order 10 (db 10) and the Symlet wavelet of order 8 (sym8) applied to a single beat of the X, Y and Z Frank leads. Simple statistical parameters derived from these transforms and from reconstructed filtered signals were used as inputs to a neural network with no hidden units and a softmax output stage. We used multiple runs of 10 fold cross validation to obtain estimates of classifier performance. Best results were obtained for the db 10 parameters when age and sex were also added. Overall accuracy was 68.8/spl plusmn/0.6%. These results are comparable to those derived from neural nets trained with over 229 scalar parameters (70.9/spl plusmn/0.6%) and were derived at much lower computational cost. The methods derived can be easily implemented in real time using a DSP processor.
HEURISTICS AND USABILITY GUIDELINES FOR THE CREATION AND EVALUATION OF FUN IN VIDEO GAMES

This study examines the implicit and explicit heuristics and usability evaluation processes utilized by a leading game developer. Five people from a single game team, each contributing in a different way to the game development process, were observed for one business day and interviewed. At the time of this study, the participating game team was at the tail end of their first month of prototyping in the pre-production phase of development. The data collected and literature reviewed combine to suggest that instituting more formal usability evaluation processes could be helpful to the game development process. The heuristics created in this study are a starting point for the construction of a standard list of game heuristics for use by the game development community.