Miracle-dz/UniFAR-FTUs huggingface dataset:
An Ex Vivo Fermentation Screening Platform to Study Drug Metabolism by Human Gut Microbiota

Colon microbiota-based drug metabolism has received little attention thus far in the process of drug development, whereas the role of gut microbiota in clinical safety and efficacy of drugs has become more clear. Many of these studies have been performed using animal studies, but the translational value of these data with respect to drug pharmacokinetics, efficacy, and safety is largely unknown. To investigate human colon microbiota-mediated drug metabolism, we applied a recently developed ex vivo fermentation screening platform, in which human colonic microbiota conditions are simulated. A set of 12 drugs (omeprazole, simvastatin, metronidazole, risperidone, sulfinpyrazone, sulindac, levodopa, dapsone, nizatidine, sulfasalazine, zonisamide, and acetaminophen) was incubated with human colon microbiota under strictly anaerobic conditions, and samples were analyzed using high-performance liquid chromatograph–UV–high-resolution mass spectrometry analysis. The human microbiota in the fermentation assay consisted of bacterial genera regularly encountered in human colon and fecal samples and could be reproducibly cultured in independent experiments over time. In addition, fully anaerobic culture conditions could be maintained for 24 hours of incubation. Five out of the 12 included drugs (sulfasalazine, sulfinpyrazone, sulindac, nizatidine, and risperidone) showed microbiota-based biotransformation after 24 hours of incubation in the ex vivo fermentation assay. We demonstrated that drug metabolites formed by microbial metabolism can be detected in a qualitative manner and that the data are in accordance with those reported earlier for in vivo metabolism. In conclusion, we present a research tool to investigate human colon microbiota-based drug metabolism that may be applied to enable translatability of microbiota-based drug metabolism.
Collecting Diverse Natural Language Inference Problems for Sentence Representation Evaluation

We present a large-scale collection of diverse natural language inference (NLI) datasets that help provide insight into how well a sentence representation captures distinct types of reasoning. The collection results from recasting 13 existing datasets from 7 semantic phenomena into a common NLI structure, resulting in over half a million labeled context-hypothesis pairs in total. We refer to our collection as the DNC: Diverse Natural Language Inference Collection. The DNC is available online at https://www.decomp. net, and will grow over time as additional resources are recast and added from novel sources.
Activity Classification in Independent Living Environment with JINS MEME Eyewear

The population of older adults relative to the total population is rising rapidly worldwide, and this contributes to an increased burden on healthcare systems. Older adults with complex needs are often limited in their ability to perform basic daily activities, and they may require task-specific supports. With continuous health-monitoring systems, the ability to recognize people's activities in their homes can enable automated assisted living systems, caregivers and clinicians to provide suitable adaptive care. With the advent of miniaturized sensing technology, which can be wearable, it is now possible to collect and store data on different aspects of human movement under realistic independent living conditions.In our most recent Smart Condo™ study, twenty-six participants spent one two-hour session in the one-bedroom living environment, either alone or in pairs, and performed a scripted protocol of activities of daily living. Twelve of these participants wore the commercial smart eyewear device JINS MEME, which collected electrooculography, accelerometer and gyroscope data throughout their sessions. In this paper, we describe our method for offline classification of the participants' activities. We show that this method yields equal or better results with a variety of activities compared to approaches that involve more restrictive wearable device setups. The results demonstrate the suitability of JINS MEME for recognition of activities of daily living and identify limitations associated with the current model.
An Operation Sequence Model for Explainable Neural Machine Translation

We propose to achieve explainable neural machine translation (NMT) by changing the output representation to explain itself. We present a novel approach to NMT which generates the target sentence by monotonically walking through the source sentence. Word reordering is modeled by operations which allow setting markers in the target sentence and move a target-side write head between those markers. In contrast to many modern neural models, our system emits explicit word alignment information which is often crucial to practical machine translation as it improves explainability. Our technique can outperform a plain text system in terms of BLEU score under the recent Transformer architecture on Japanese-English and Portuguese-English, and is within 0.5 BLEU difference on Spanish-English.
Model Predictive Path Integral Control for Car Driving with Dynamic Cost Map

Path planning in a complex dynamic environment is one of the key subsystems in an autonomous vehicle. This paper presents an extension of Model Predictive Path Integral (MPPI) control method which is able to take moving objects into account while path planning and driving. To obtain real-time performance, cost map update with respect to dynamic objects both as basic MPPI is implemented as a set of concurrent processes using CUDA technology. The algorithm’s performance is demonstrated on a model of a stock car in a simulation
Influence of Exercise Time of Day on Salivary Melatonin Responses.

BACKGROUND Sleep deprivation negatively affects cognition, pain, mood, metabolism, and immunity, which can reduce athletic performance. Melatonin facilitates sleepiness and may be affected by the proximity of exercise to sleep. PURPOSE To evaluate the influence of exercise time of day on salivary melatonin (s-melatonin) responses. METHODS Twelve regularly exercising men (age 20.75 [0.62] y, height 1.75 [0.04] m, mass 73.63 [10.43] kg, and maximal oxygen consumption 57.72 [6.11] mL/kg/min) participated in a randomized, crossover design. Subjects completed 3 protocols-morning exercise (09:00 h), afternoon exercise (16:00 h), and no exercise (CON)-at least 5 d apart. Exercise sessions consisted of 30 min of steady-state running at 75% of maximal oxygen consumption. Saliva was collected via passive drool at 20:00, 22:00, and 03:00 h following all sessions. RESULTS Repeated-measures analysis of variance revealed significant time (P = .001) and condition (P = .026) effects for melatonin. Levels of s-melatonin were significantly increased at 03:00 h compared with 20:00 and 22:00 h for all conditions. Post hoc analyses revealed that s-melatonin at 22:00 h was significantly higher after morning exercise (16.5 [7.5] pg/mL) compared with afternoon exercise (13.7 [6.1] pg/mL) sessions (P = .03), whereas neither exercise condition significantly differed from the control (P > .05). CONCLUSIONS It appears that exercising in the afternoon may blunt melatonin secretion compared with morning exercise. If sleep is an issue, morning exercise may be preferable to afternoon exercise.
Features of Long YBCO Josephson Junctions Fabricated by Preliminary Topology Mask

We present the results of an experimental and theoretical study of YBCO long Josephson junctions on bicrystal YSZ substrates under the influence of an external magnetic field and under the combined action of a magnetic field and microwave radiation. Samples of YBCO films have been fabricated using the original technology of a preliminary topology mask, leading to critical current densities of order <inline-formula><tex-math notation="LaTeX"> ${\text{0.6}}\,{{\text{MA}\cdot \text{cm}}}^{-2}$</tex-math></inline-formula> at a temperature of 6 K. Detailed measurements of the samples were made in the temperature range of 6–90 K, and the wide variety of subharmonic Shapiro steps has been observed and investigated depending on the magnetic field.
Comparison of GPR and Capacitance Probe laboratory experiments in sandy soils

The integration of different techniques for the estimation of the volumetric water content θ in low-loss sandy soils may allow to obtain more reliable measure, after a proper evaluation of the techniques limits and their pros and cons. In particular, the integration of direct laboratory measurements performed on samples $\theta$ values measured) with geophysical data collected on a soil column using a Ground Penetrating Radar (GPR) as well as a Capacitance Probe (CP), allowed us to compare the results and evaluate their accuracy. Our experimental measures, performed on two typical sandy soil outcropping in Central Italy, show that the GPR reflected pulses provide similar permittivity $(\varepsilon_{\mathrm{r}})$ values for both soils at very low θ. The measured $\varepsilon_{r}$ values seem to progressively differ by increasing the soil moisture of the two sands. The CP shows a clear difference of measured permittivity already at lower soil moisture. As θ values in the media increase approaching the soil saturation, the CP $\varepsilon_{r}$ values measured on both the two soils show a larger difference. In conclusion, the comparison between GPR and CP measures in two selected sands under controlled condition $\pmb{(0.05\ < \theta < 0.3)}$, shows that the latter tends to overestimate $\varepsilon_{\mathrm{r}}$ on the entire range investigated. Nevertheless, if a specific laboratory calibration is carried out, as in the present work, reliable $\theta$ values estimations can be obtained by both methods. Other measurement techniques will be tested and compared in further experiments; moreover, the calibration and integration of GPR and CP is advised not only in laboratory studies, but also to better constrain possible field applications.
On Effectiveness of Compressed File Transfers to/from the Cloud: An Experimental Evaluation

An increasing reliance on cloud and distributed processing of scientific and big data in commercial, academic, and government institutions necessitate new approaches to optimize file transfers. Lossless data compression and decompression is essential in improving the overall effectiveness of file transfers between edge devices and the cloud by increasing communication throughput, reducing connection latency, making effective use of cloud storage, and reducing costs. This paper experimentally evaluates effectiveness of common and emerging general-purpose compression utilities for file transfers between edge devices and the cloud. The utilities are evaluated in terms of throughput and costs during representative file transfers between a workstation and the cloud, while varying LAN network conditions. The results show that the optimal compressed transfer modes improve both upload and download throughputs. For uploads, the peak improvements range from 5.16 to 25.6 times relative to uncompressed file uploads, and from 1.33 to 17.4 times relative to the default compressed uploads. For downloads, the peak improvements range from 3.82 to 19.57 times relative to uncompressed downloads, and from 1.8 to 13.8 times relative to the default compressed downloads. In addition, the best performing compressed transfer modes reduce the costs related to cloud computing.
Synchronized path following for LTI systems and closed paths: Laboratory implementation

This paper examines a synchronized path following control design problem for a collection of homogenous, minimum phase, linear time-invariant systems assigned closed curves in their output space. In the first part of the paper we define a path following normal form for LTI systems. We isolate the role that the zero dynamics play in determining the feasibility of using the path following normal form for control design. In the second part of the paper we leverage recent results from the literature on synchronizing LTI systems to develop synchronizing controllers. The main result is a distributed feedback law that drives all the agents to their respective paths and ensures path invariance while simultaneously synchronizing their positions along the path. Laboratory results are presented to illustrate the effectiveness of the proposed approach.
A Proportionate NLMS Algorithm for the Identification of Sparse Bilinear Forms

Proportionate-type algorithms are designed to exploit the sparseness character of the systems to be identified, in order to improve the overall convergence of the adaptive filters used in this context. However, when the parameter space is large, the system identification problem becomes more challenging. In this paper, we focus on the identification of bilinear forms, where the bilinear term is defined with respect to the impulse responses of a spatiotemporal model. In this framework, we develop a proportionate normalized least-mean-square algorithm tailored for the identification of such bilinear forms. Simulation results indicate the good performance of the proposed algorithm, in terms of both convergence rate and computational complexity.
Understanding usual care for patients with multimorbidity: baseline data from a cluster-randomised trial of the 3D intervention in primary care

Objectives Recent evidence has highlighted the high prevalence and impact of multimorbidity, but the evidence base for improving management is limited. We have tested a new complex intervention for multimorbidity (the 3D model). The paper describes the baseline characteristics of practices and patients in order to establish the external validity of trial participants. It also explores current ‘usual primary care’ for multimorbidity, against which the 3D intervention was tested. Design Analysis of baseline data from patients in a cluster-randomised controlled trial and additional data from practice staff. Setting Primary care in the UK. Participants Patients with multimorbidity (n=5253) and 154 practice staff. Primary and secondary outcome measures Using surveys and routinely available data, we compared the characteristics of participating and non-participating practices and participating and non-participating eligible patients. Baseline questionnaire data from patient participants was used to examine participant illness burden, treatment burden and perceptions of receiving patient-centred care. We obtained data about usual care preintervention from practice staff using questionnaires and a structured pro forma. Results Participating practices were slightly larger, in less deprived areas, and with slightly higher scores for patient satisfaction compared with non-participating practices. Patients with dementia or learning difficulties were likely to be excluded by their general practitioners, but comparison of participants with non-participants identified only minor differences in characteristics, suggesting that the sample was otherwise representative. Patients reported substantial illness burden, and an important minority reported high treatment burden. Although patients reported relatively high levels of satisfaction with care, many reported not having received potentially important components of care. Conclusion This trial achieved good levels of external validity. Although patients were generally satisfied with primary care services, there was significant room for improvement in important aspects of care for multimorbidity that are targeted by the 3D intervention. Trial registration number ISRCTN06180958; Post-results.
The Role of Hepatocyte Growth Factor (HGF) in Insulin Resistance and Diabetes

In obesity, insulin resistance (IR) and diabetes, there are proteins and hormones that may lead to the discovery of promising biomarkers and treatments for these metabolic disorders. For example, these molecules may impair the insulin signaling pathway or provide protection against IR. Thus, identifying proteins that are upregulated in IR states is relevant to the diagnosis and treatment of the associated disorders. It is becoming clear that hepatocyte growth factor (HGF) is an important component of the pathophysiology of IR, with increased levels in most common IR conditions, including obesity. HGF has a role in the metabolic flux of glucose in different insulin sensitive cell types; plays a key role in β-cell homeostasis; and is capable of modulating the inflammatory response. In this review, we discuss how, and to what extent HGF contributes to IR and diabetes pathophysiology, as well as its role in cancer which is more prevalent in obesity and diabetes. Based on the current literature and knowledge, it is clear that HGF plays a central role in these metabolic disorders. Thus, HGF levels could be employed as a biomarker for disease status/progression, and HGF/c-Met signaling pathway modulators could effectively regulate IR and treat diabetes.
Fractional Derivatives of Online Handwriting: A New Approach of Parkinsonic Dysgraphia Analysis

Parkinson's disease (PD) is the second most frequent neurodegenerative disorder. One typical hallmark of PD is disruption in execution of practised skills such as handwriting. This paper introduces a new methodology of kinematic features calculation based on fractional derivatives applied on PD handwriting. Discrimination power of basic kinematic features (velocity, acceleration, jerk) was evaluated by classification analysis (using support vector machines and random forests). For this purpose, 30 PD patients and 36 healthy controls were enrolled. In comparison with results reported in other works, the newly designed features based on fractional derivatives increased classification accuracy by 8 % in univariate analysis and by 10 % when employing the multivariate one. This study reveals an impact of fractional derivatives based features in analysis of Parkinsonic dysgraphia.
SABE: A Selective Attribute-based Encryption for an Efficient Threshold Multi-level Access Control

: With the emergence of decentralized systems and distributed infrastructures, access control to outsourced data becomes more complex, as it should be ﬂexible and distinguishable among users with different access rights. In this paper, we present SABE, a Selective Attribute-based Encryption scheme, as a new threshold multi-level access control mechanism based on an original use of attribute based encryption schemes. Our proposal is multi-fold. First, it ensures ﬁne-grained access control, supporting multi-security levels with respect to different granted access privileges for each outsourced data ﬁle. Second, SABE is proven secure against selective non-adaptive chosen ciphertext attacks in the generic group model. Third, our construction is proven to provide efﬁcient processing and communication complexities, compared to most closely related schemes.
In Vitro Osteogenesis Stimulation via Nano-Hydroxyapatite/Carbon Nanotube Thin Films on Biomedical Stainless Steel

We evaluated the electrophoretic deposition of nanohydroxyapatite/superhydrop hilic multiwalled carbon nanotube composites (nHAp/MWCNT) onto stainless steel biomedical alloys for applications in bone tissue engineering. First, nHAp/MWCNT composites were dispersed into 0.042 mol·L−1 of Ca(NO3)2·4H2O + 0.025 mol·L−1 NH4H2PO4 electrolytes (pH = 4.8) at two different concentrations. Next, a voltage of −2 V was applied using 316L stainless steel as a working electrode (0.27 cm2), a high-purity platinum coil wire was used as the auxiliary electrode, and an Ag/AgCl (3 M) electrode was used as the reference electrode. The nHAp/MWCNT composites were characterized by transmission electron microscopy. The deposited nHAp and nHAp/MWCNT films were characterized by profilometry, scanning electron microscopy, X-ray diffractometry and Raman spectroscopy. Human osteoblast cells were cultivated with the different materials and in vitro cytotoxicity was evaluated using lactate dehydrogenase (LDH) assay. The osteogenesis process was evaluated by mRNA levels of the three genes that are directly related to bone repair: Alkaline Phosphatase, Osteopontin and Osteocalcin. We showed that rough, crystalline apatite thin films containing phases of nHAp were successfully deposited onto 316L stainless steel alloys. Also, we noticed that nHAp/MWCNT thin films deposited onto 316L stainless steel alloys upregulated the expression of important genes related to bone mineralization and maturation. Our results strongly support the possibility of this new alternative to modify the surface of metallic biomedical alloys to promote bone tissue regeneration.
Coercive Control Between Intimate Partners: An Application to Nonfatal Strangulation

Nonfatal strangulation between intimate partners represents an extreme controlling form of violent behavior, increasing the risk that intimate partner violence (IPV) becomes lethal. Guided by Dutton and Goodman’s conceptualization of coercive control, the present research explored the relation between death threats and subsequent nonfatal strangulation to amplify the credibility of those threats, using a large sample of IPV perpetrators (n = 6,488). Logistic regression analyses determined the relation between overt threats to a partner’s life during an initial incident arrest and subsequent nonfatal strangulation postincident arrest, accounting for perpetrator characteristics and assessed risk. Results showed the highly gendered nature of this violent behavior, noting that men were significantly more likely than women to persist in nonfatal strangulation. Given the potential lethality of this violent behavior, the analysis also explored whether treatment service recommendations (family violence education, counseling, and mental health evaluation) mitigate these patterns. These preliminary findings support the further exploration of treatment and intervention efforts for reducing nonfatal strangulation.
An Overview of the Quartz Modelling and Simulation Framework

Quartz is a modelling and simulation framework enabling the development and the execution of models based on the Parallel Discrete Event System Specification (PDEVS) formalism. In this paper we give an overview of the tool by giving details on its design and features. An experimental comparison shows that Quartz yields performances comparable to what is observed with aDEVS, one of the most efficient tool implemented with a compiled language. We present our efforts to provide dedicated expressions and verification facilities to the modeler and give examples of application domains that motivated this software. Namely, agent-based modeling through the Dynamic Parallel Discrete Event Multi-Agent (DPDEMAS) specification and multicomponent modeling with the multiPDEVS formalism.
Operator Awareness in Human–Robot Collaboration Through Wearable Vibrotactile Feedback

In industrial scenarios, requiring human–robot collaboration, the understanding between the human operator and his/her robot coworker is paramount. On the one side, the robot has to detect human intentions, and on the other side, the human needs to be aware of what is happening during the collaborative task. In this letter, we address the first issue by predicting human behavior through a new recursive Bayesian classifier, exploiting head, and hand tracking data. Human awareness is tackled by endowing the human with a vibrotactile ring that sends acknowledgments to the user during critical phases of the collaborative task. The proposed solution has been assessed in a human–robot collaboration scenario, and we found that adding haptic feedback is particularly helpful to improve the performance when the human–robot cooperation task is performed by nonskilled subjects. We believe that predicting operator's intention and equipping him/her with wearable interface, able to give information about the prediction reliability, are essential features to improve performance in a human–robot collaboration in industrial environments.