Miracle-dz/UniFAR-FTUs huggingface dataset:
Obtaining an Extract Rich in Phenolic Compounds from Olive Pomace by Pressurized Liquid Extraction

The olive oil industry produces large volumes of wastes, which are also potential sources of bioactive compounds by developing healthy and/or functional foods. Extraction of phenolic compounds from the residues of the olive oil is mainly carried out with solvents. However, there is currently a growing public awareness about the use of organic solvents in food processing, which has pointed out the need for the application of clean technologies such as pressurized liquid extraction (PLE). Therefore, the aim of this research was to optimize the phenolic compound extraction from olive pomace by PLE, establishing the qualitative and quantitative phenolic profile by HPLC-ESI-TOF/MS. The extraction design to recover phenolics from olive pomace demonstrates a great compositional variability of PLE extracts obtained under different experimental conditions. Indeed, quantitative results have pointed out the selectivity of PLE extraction when this technique is applied to the treatment of olive pomace. PLE-optimized conditions showed higher total phenolic compound content than conventional extraction (1659 mg/kg d.w. and 281.7 mg/kg d.w., respectively). Among these phenolics, the quantity of secoiridoids and flavonoids in the optimized PLE extract was three and four times higher than in conventional extracts. Furthermore, optimal PLE conditions allowed to obtain an enriched hydroxytyrosol extract which was not detected in the conventional one.
Diffuse intrinsic pontine glioma: molecular landscape and emerging therapeutic targets.

PURPOSE OF REVIEW Diffuse intrinsic pontine glioma (DIPG) is a fatal childhood brainstem malignancy. Despite advances in understanding of the molecular underpinnings of the tumor in the past decade, the dismal prognosis of DIPG has thus far remained unchanged. This review seeks to highlight promising therapeutic targets within three arenas: DIPG cell-intrinsic vulnerabilities, immunotherapeutic approaches to tumor clearance, and microenvironmental dependencies that promote tumor growth. RECENT FINDINGS Promising therapeutic strategies from recent studies include epigenetic modifying agents such as histone deacetylase inhibitors, bromodomain and extra-terminal motif bromodomain inhibitors, and CDK7 inhibitors. Tumor-specific immunotherapies are emerging. Key interactions between DIPG and normal brain cells are coming to light, and targeting critical microenvironmental mechanisms driving DIPG growth in the developing childhood brain represents a new direction for therapy. SUMMARY Several strategies are being evaluated in early clinical trials. However, we suspect that a multifaceted therapeutic approach utilizing cell-intrinsic, microenvironmental, and immunotherapeutic targets will be necessary for eradicating DIPG.
Vitamin D is not required for adaptive immunity to listeria

Although ex vivo research suggests that vitamin D may play a role in innate and adaptive immunity, clear in vivo evidence is lacking. We have tested whether severe vitamin D deficiency alters the ability of mice to resist infection by Listeria. Our results show that vitamin D deficiency does not affect the LD50 of naïve mice in response to Listeria. To study the adaptive immune response, the LD50 for Listeria‐immunized mice was determined for vitamin D‐deficient and vitamin D‐sufficient mice. Although the LD50 clearly increased by immunization with inactivated Listeria, there was no effect of vitamin D deficiency on survival of mice infected with wild‐type Listeria. Thus, in this model of adaptive immunity, we could find no evidence of a role for vitamin D.
Impact of Chronic Osteoarthritis of Knee Joint on Postural Stability and Functional Mobility in Women

Background: Earlier studies have demonstrated that static postural control is worse in patients with knee osteoarthritis (OA), whereas little information is available about the dynamic balance and impact of OA on functional mobility. Purposes: The primary purpose of this study was to investigate whether balance as assessed by static postural control under different test conditions and dynamic standing are impaired in subjects with knee OA. Secondary purpose was to investigate whether functional mobility is impaired in subjects with knee OA compared with age, gender and body mass index (BMI) matched control group. Finally, investigate the correlation between pain, physical function and postural stability, functional mobility. Methods: Thirty female subjects with knee OA and 30 normal control females participated in this study, modified clinical test of sensory interaction on balance (mCTSIB) ,limit of stability (LOS), step up/over (SUO) and sit to stand (STS) tests were assessed by Balance Master System. Results: Compared with age, gender and BMI control group, the patients with knee OA had statistical significant greater sway under different test conditions except under firm condition with eye opened (P=0.29). There were statistical significant reductions in directional control, movement velocity and increase in reaction time (P=0.0001). Regarding variables of SUO there were statistical significant increases in exerted force and time required to get up and over stair. In addition there were statistical significant differences in weight transfer, sway velocity and rising index (P= 0.033, 0.018 and 0.027) respectively. Finally negative correlation (r=-.479) was found only between pain and ability of patients to progress toward target. Conclusion: The results of the present study suggested that older women with knee OA present worse performance in functional mobility and require a longer period of time and exert more force and less directional control during balance tests when compared with age, gender and BMI matched controls.
An Empirical Study of Domain Adaptation for Unsupervised Neural Machine Translation

Domain adaptation methods have been well-studied in supervised neural machine translation (NMT). However, domain adaptation methods for unsupervised neural machine translation (UNMT) have not been well-studied although UNMT has recently achieved remarkable results in some specific domains for several language pairs. Besides the inconsistent domains between training data and test data for supervised NMT, there sometimes exists an inconsistent domain between two monolingual training data for UNMT. In this work, we empirically show different scenarios for unsupervised domain-specific neural machine translation. Based on these scenarios, we propose several potential solutions to improve the performances of domain-specific UNMT systems.
Correlates of loneliness in older persons living with HIV

ABSTRACT Loneliness poses a significant risk for morbidity and mortality in the context of older adulthood. Research shows that older persons living with HIV (PLWH) often face increased and complex vulnerability in terms of physical and psychosocial needs which may promote loneliness. The current study sought to identify correlates of loneliness in a sample of 146 older PLWH (age ≥50) recruited from an outpatient HIV clinic in Atlanta, GA. Participants completed a survey on loneliness, depression, HIV-related stigma, social network size, HIV-disclosure status, disease burden, and demographics. HIV biomarkers were abstracted from electronic medical records. Participants were predominantly male (60%) and African American (86%). Twelve percent (n = 17) reported past homelessness/unstable housing. Multivariable modeling revealed that depression and HIV-related stigma explained 41% of the variance in loneliness, above and beyond the effects of past homelessness/unstable housing and disease burden (R 2 = 0.41, F(7, 138) = 13.76, p < . 001). Findings suggest that targeting HIV-related stigma and depression may reduce loneliness in older PLWH, but more studies are needed to elucidate causal pathways. A greater understanding of the mechanisms by which loneliness affects health among older PLWH could help better inform efforts to improve health in this patient population.
Inhaled corticosteroid suppression of cathelicidin drives dysbiosis and bacterial infection in chronic obstructive pulmonary disease

Inhaled corticosteroids promote bacterial infection in chronic obstructive pulmonary disease by suppressing the antimicrobial peptide cathelicidin. Corticosteroid-driven bacterial load Patients with chronic obstructive pulmonary disease (COPD) have increased susceptibility to bacterial infections that can have deleterious consequences on disease outcome. The mechanisms modulating the susceptibility to infections in COPD are poorly understood. Inhaled corticosteroids (ICS) are standard treatment for COPD. Here, Singanayagam and colleagues show an association between ICS use and increased bacterial proliferation in lungs. In vitro and in vivo studies using human lung samples and mouse models demonstrated that the effect of ICS on bacterial infection was mediated by inhibition of the antimicrobial peptide cathelicidin. Blocking cathelicidin cleavage reduced the increased bacterial load associated with ICS administration in mice. Bacterial infection commonly complicates inflammatory airway diseases such as chronic obstructive pulmonary disease (COPD). The mechanisms of increased infection susceptibility and how use of the commonly prescribed therapy inhaled corticosteroids (ICS) accentuates pneumonia risk in COPD are poorly understood. Here, using analysis of samples from patients with COPD, we show that ICS use is associated with lung microbiota disruption leading to proliferation of streptococcal genera, an effect that could be recapitulated in ICS-treated mice. To study mechanisms underlying this effect, we used cellular and mouse models of streptococcal expansion with Streptococcus pneumoniae, an important pathogen in COPD, to demonstrate that ICS impairs pulmonary clearance of bacteria through suppression of the antimicrobial peptide cathelicidin. ICS impairment of pulmonary immunity was dependent on suppression of cathelicidin because ICS had no effect on bacterial loads in mice lacking cathelicidin (Camp−/−) and exogenous cathelicidin prevented ICS-mediated expansion of streptococci within the microbiota and improved bacterial clearance. Suppression of pulmonary immunity by ICS was mediated by augmentation of the protease cathepsin D. Collectively, these data suggest a central role for cathepsin D/cathelicidin in the suppression of antibacterial host defense by ICS in COPD. Therapeutic restoration of cathelicidin to boost antibacterial immunity and beneficially modulate the lung microbiota might be an effective strategy in COPD.
Surface markers: An identity card of endothelial cells

All endothelial cells have the common characteristic that they line the vessels of the blood circulatory system. However, endothelial cells display a large degree of heterogeneity in the function of their location in the vascular tree. In this article, we have summarized the expression patterns of a number of well‐accepted endothelial surface markers present in normal microvascular endothelial cells, arterial and venous endothelial cells, lymphatic endothelial cells, tumor endothelial cells, and endothelial precursor cells.
Decentralized Resource Auctioning for Latency-Sensitive Edge Computing

The prevalence of Internet of Things (IoT) in contemporary settings has induced systems composed of heterogeneous devices, computing infrastructures, and cloud services. New paradigms have emerged where computational resources are managed closer to IoT end-devices, within a general theme of decoupling from the cloud. This is because meeting application demands must occur at runtime, in the face of uncertainty and in a decentralized manner. Taking advantage of available resources closer to devices calls for novel resource allocation techniques that comply with latency, privacy and decentralization demands of IoT applications. To this end, we propose a novel decentralized resource management technique and accompanying technical framework for the deployment of latency-sensitive IoT applications on edge devices. Our technique is inspired from the functionality of an auction house and has two objectives; (i) find a deployment mapping for an arbitrary application, compliant with its individual resource requirements and latency constraints, (ii) facilitate privacy, as each device participates at their own will, based on its own availability and privacy preferences. Our approach ensures seamless deployment at runtime, assuming no design-time knowledge of device resources or network topology.
DMS-SLAM: A General Visual SLAM System for Dynamic Scenes with Multiple Sensors

Presently, although many impressed SLAM systems have achieved exceptional accuracy in a real environment, most of them are verified in the static environment. However, for mobile robots and autonomous driving, the dynamic objects in the scene can result in tracking failure or large deviation during pose estimation. In this paper, a general visual SLAM system for dynamic scenes with multiple sensors called DMS-SLAM is proposed. First, the combination of GMS and sliding window is used to achieve the initialization of the system, which can eliminate the influence of dynamic objects and construct a static initialization 3D map. Then, the corresponding 3D points of the current frame in the local map are obtained by reprojection. These points are combined with the constant speed model or reference frame model to achieve the position estimation of the current frame and the update of the 3D map points in the local map. Finally, the keyframes selected by the tracking module are combined with the GMS feature matching algorithm to add static 3D map points to the local map. DMS-SLAM implements pose tracking, closed-loop detection and relocalization based on static 3D map points of the local map and supports monocular, stereo and RGB-D visual sensors in dynamic scenes. Exhaustive evaluation in public TUM and KITTI datasets demonstrates that DMS-SLAM outperforms state-of-the-art visual SLAM systems in accuracy and speed in dynamic scenes.
Multi-Stage and Multi-Customer Assortment Optimization With Inventory Constraints

We consider an assortment optimization problem where a customer chooses a single item from a sequence of sets shown to her, while limited inventories constrain the items offered to customers over time. In the special case where all of the assortments have size one, our problem captures the online stochastic matching with timeouts problem. For this problem, we derive a polynomial-time approximation algorithm which earns at least 1-ln(2-1/e), or 0.51, of the optimum. This improves upon the previous-best approximation ratio of 0.46, and furthermore, we show that it is tight. For the general assortment problem, we establish the first constant-factor approximation ratio of 0.09 for the case that different types of customers value items differently, and an approximation ratio of 0.15 for the case that different customers value each item the same. Our algorithms are based on rounding an LP relaxation for multi-stage assortment optimization, and improve upon previous randomized rounding schemes to derive the tight ratio of 1-ln(2-1/e).
Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors

Objective To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup. Materials and Methods Two-hundred four patients with LGGs from our institutional cohort were allocated to training (n = 136) and test (n = 68) sets. Postcontrast T1-weighted images, T2-weighted images, and fluid-attenuated inversion recovery images were analyzed to extract 250 radiomics features. Various machine learning classifiers were trained using the radiomics features to predict the glioma grade. The trained classifiers were internally validated on the institutional test set and externally validated on a separate cohort (n = 99) from The Cancer Genome Atlas (TCGA). Classifier performance was assessed by determining the area under the curve (AUC) from receiver operating characteristic curve analysis. An identical process was performed in the nonenhancing LGG subgroup (institutional training set, n = 73; institutional test set, n = 37; and TCGA cohort, n = 37) to predict the glioma grade. Results The performance of the best classifier was good in the internal validation set (AUC, 0.85) and fair in the external validation set (AUC, 0.72) to predict the LGG grade. For the nonenhancing LGG subgroup, the performance of the best classifier was good in the internal validation set (AUC, 0.82), but poor in the external validation set (AUC, 0.68). Conclusion Radiomics feature-based classifiers may be useful to predict LGG grades. However, radiomics classifiers may have a limited value when applied to the nonenhancing LGG subgroup in a TCGA cohort.
Measuring Patent Claim Generation by Span Relevancy

Our goal of patent claim generation is to realize "augmented inventing" for inventors by leveraging latest Deep Learning techniques. We envision the possibility of building an "auto-complete" function for inventors to conceive better inventions in the era of artificial intelligence. In order to generate patent claims with good quality, a fundamental question is how to measure it. We tackle the problem from a perspective of claim span relevancy. Patent claim language was rarely explored in the NLP field. It is unique in its own way and contains rich explicit and implicit human annotations. In this work, we propose a span-based approach and a generic framework to measure patent claim generation quantitatively. In order to study the effectiveness of patent claim generation, we define a metric to measure whether two consecutive spans in a generated patent claims are relevant. We treat such relevancy measurement as a span-pair classification problem, following the concept of natural language inference. Technically, the span-pair classifier is implemented by fine-tuning a pre-trained language model. The patent claim generation is implemented by fine-tuning the other pre-trained model. Specifically, we fine-tune a pre-trained Google BERT model to measure the patent claim spans generated by a fine-tuned OpenAI GPT-2 model. In this way, we re-use two of the state-of-the-art pre-trained models in the NLP field. Our result shows the effectiveness of the span-pair classifier after fine-tuning the pre-trained model. It further validates the quantitative metric of span relevancy in patent claim generation. Particularly, we found that the span relevancy ratio measured by BERT becomes lower when the diversity in GPT-2 text generation becomes higher.
Percutaneous Unilateral Biportal Endoscopic Spine Surgery Using a 30-Degree Arthroscope in Patients With Severe Lumbar Spinal Stenosis

Supplemental Digital Content is available in the text. Background: Unilateral biportal endoscopic surgery (UBESS) for severe lumbar central canal stenosis (LCCS) remains challenging. Objective: To describe the use of UBESS with a 30-degree arthroscope in patients with severe LCCS. Materials and Methods: Working and viewing portals were created in each unilateral paravertebral area at the target interlaminar level. After ensuring the visual field with a 30-degree arthroscope, effective tissue removal was possible through safe access to the bilateral hypertrophic yellow ligament with minimal osteotomy. The authors evaluated 58 patients and analyzed the clinical outcomes using the visual analog scale, Macnab criteria, and self-predicted walking distance. Results: The visual analog scale scores for low back and leg pains decreased from 7.1 to 1.9 and from 7.9 to 1.6, respectively, at 18 months after the procedure. According to the Macnab criteria, “excellent,” “good,” and “fair” results were obtained in 51.7%, 41.4%, and 6.9% subjects, respectively. Before surgery, the subjects could walk a mean of 305.8±468.1 m. After surgery, 43.1% of the patients could walk for >1 hour, whereas the remaining patients could walk 1521.8±1831.1 m. Conclusion: UBESS using a 30-degree arthroscope can be an efficient and safe intervention in patients with severe LCCS.
Conceptual Pacts , Syntactic Priming , and Referential Form

Interlocutors repeat each other’s referential forms, but theories of coordination in dialogue explain this repetition differently. One influential theory is Clark’s (1996) collaborative joint activity framework, which implicates negotiated conceptual pacts; another is Pickering & Garrod’s (2004) Interactive Alignment Model, which implicates automatic syntactic priming. This experiment examines the relative contributions of conceptual pacts and syntactic priming to repetition in an unscripted referential dialogue between naïve participants. The results show that syntactic priming is only strong when the referent is a consistent part of the situation model throughout the dialogue. In addition, syntactic repetition due to a conceptual pact was stronger than local syntactic priming, even when an explanation based on lexical repetition was ruled out. Finally, there was an association between communicative success and local syntactic priming. We conclude that syntactic priming has a small but significant effect on successful communication in dialogue, while conceptual pacts have a much larger impact on referential form.
Effects of Acupuncture on Neurological Disease in Clinical- and Animal-Based Research

Neurological disease, including Alzheimer’s disease (AD), Parkinson’s disease (PD), which were caused by abnormalities in the nervous system involves the accumulation of false proteins, neurotransmitter abnormalities, neuronal apoptosis, etc. As an alternative supplementary medicine (ASM), acupuncture plays an important role in the treatment of neurological diseases. In this review article, we summarized the current evidence for the treatment efficacy of acupuncture in AD and PD from the perspective of clinical trials and animal model. Acupuncture can inhibit the accumulation of toxic proteins in neurological diseases, modulate energy supply based on glucose metabolism, depress neuronal apoptosis, etc., and exert a wide range of neuroprotective effects.
High-definition tDCS to the right temporoparietal junction modulates slow-wave resting state power and coherence in healthy adults.

The right temporoparietal junction (rTPJ) is a multisensory integration hub that is increasingly utilised as a target of stimulation studies exploring its rich functional network roles and potential clinical applications. Whilst transcranial direct current stimulation (tDCS) is frequently employed in such studies, there is still relatively little known regarding its local and network neurophysiological effects, particularly at important non-motor sites such as the rTPJ. The current study applied either anodal, cathodal, or sham high-definition tDCS (HD-tDCS) to the rTPJ of 53 healthy participants and used offline electroencephalography (EEG) to assess the impacts of stimulation on resting state (eyes open and eyes closed) band power and coherence. Temporoparietal and central region delta power was increased after anodal stimulation (the latter trend only), whereas cathodal stimulation increased frontal region delta and theta power. Increased coherence between right and left temporoparietal regions was also observed after anodal stimulation. All significant effects occurred in the eyes open condition. These findings are discussed with reference to domain general and mechanistic theories of rTPJ function. Low frequency oscillatory activity may exert long-range inhibitory network influences that enable switching between and integration of endogenous/exogenous processing streams.