Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring and Assessing the Degradation Rate of Magnesium-Based Artificial Bone In Vitro Using a Wireless Magnetoelastic Sensor

A magnetoelastic-based (MB) sensor was employed as a novel method to monitor and assess the degradation rate of magnesium-based artificial bone (MBAB) in vitro, which can be used as an implant to repair a bone defect, providing a quantitative method to depict the degradation rate of MBAB. MBABs were fabricated by the Pro/Engineering software and a precision machine tool using high-purity (HP) magnesium. The MB sensor was embedded in the neutral surface of MBAB by an unharmful quick adhesive, forming the MB sensor-embedded MBAB (EMBAB). The modified simulated body fluid (MSBF) media (PH = 7.4), mimicking the human internal environment, and the NaOH media (PH = 12), accelerating EMBAB’s degradation, were used to immerse the EMBAB for 15 days at 37 °C. The EMBAB was then tested daily on a self-developed experimental platform to monitor the relative output power under a 100 N external force. The results showed that the relative output power of the sensing coil gradually increased with the EMBAB’s degradation. The degradation rate of the EMBAB could be calculated on the basis of the changes of the relative output power caused by the MB sensor and of the degradation time. With the EMBAB’s degradation, an increasing strain directly worked on the MB sensor, significantly changing the value of the relative output power, which means that the EMBAB was characterized by a quick degradation rate. During the 15 days of the experiment, the degradation rates on the 7th and 15th days were 0.005 dbm/day and 0.02 dbm/day, and 0.02 dbm/day and 0.04 dbm/day in MSBF and alkaline media, respectively. Therefore, the MB sensor provides a wireless and passive method to monitor and assess the degradation rate of bone implants in vitro.
Realtime Novel View Synthesis with Eigen-Texture Regression

Realtime novel view synthesis, which generates a novel view of a real object or scene in realtime, enjoys a wide range of applications including augmented reality, telepresence, and immersive telecommunication. Image-based rendering (IBR) with rough geometry can be done using only an off-the-shelf camera and thus can be used by many users. However, IBR from images in the wild (e.g., lighting condition changes or the scene contains objects with specular surfaces) has been a tough problem due to color discontinuity; IBR with rough geometry picks up appropriate images for a given viewpoint, but the image used for a rendering unit (a face or pixel) switches when the viewpoint moves, which may cause noticeable changes in color. We use the eigen-texture technique, which represents images for a certain face using a point in the eigenspace. We propose to regress a new point in this space, which moves smoothly, given a viewpoint so that we can generate an image whose color smoothly changes according to the point. Our regressor is based on a neural network with a single hidden layer and hyperbolic tangent nonlinearity. We demonstrate the advantages of our IBR approach using our own datasets as well as publicly available datasets for comparison.
Extreme lateral transodontoid approach to the ventral craniocervical junction: cadaveric dissection and case illustrations.

OBJECTIVESurgical treatment of pathological processes involving the ventral craniocervical junction (CCJ) traditionally involves anterior and posterolateral skull base approaches. In cases of bilateral extension, when lesions extend beyond the midline to the contralateral side, a unilateral corridor may result in suboptimal resection. In these cases, the lateral extent of the tumor will prevent extirpation of the lesion via anterior surgical approaches. The authors describe a unilateral operative corridor developed along an extreme lateral trajectory to the anterior aspect of the clival and upper cervical dura, allowing exposure and resection of tumor on the contralateral side. This approach is used when the disease involves the bone structures inherent to stability at the anterior CCJ.METHODSTo achieve exposure of the ventral CCJ, an extreme lateral transcondylar transodontoid (ELTO) approach was performed with transposition of the ipsilateral vertebral artery, followed by drilling of the C1 anterior arch. Resection of the odontoid process allowed access to the contralateral component of lesions across the midline to the region of the extracranial contralateral vertebral artery, maximizing resection.RESULTSExposure and details of the surgical procedure were derived from anatomical cadavers. At the completion of cadaveric dissection, morphometric measurements of the relevant anatomical landmarks were obtained. Illustrative case examples for approaching ventral CCJ chordomas via the ELTO approach are presented.CONCLUSIONSThe ELTO approach provides a safe and direct surgical corridor to treat complex lesions at the ventral CCJ with bilateral extension through a single operative corridor. This approach can be combined with other lateral approaches or posterior infratemporal approaches to remove more extensive lesions involving the rostral clivus, jugular foramen, and temporal bone.
Affibody-Based PET Imaging to Guide EGFR-Targeted Cancer Therapy in Head and Neck Squamous Cell Cancer Models

In head and neck squamous cell cancer, the human epidermal growth factor receptor 1 (EGFR) is the dominant signaling molecule among all members of the family. So far, cetuximab is the only approved anti-EGFR monoclonal antibody used for the treatment of head and neck squamous cell cancer, but despite the benefits of adding it to standard treatment regimens, attempts to define a predictive biomarker to stratify patients for cetuximab treatment have been unsuccessful. We hypothesized that imaging with EGFR-specific radioligands may facilitate noninvasive measurement of EGFR expression across the entire tumor burden and allow for dynamic monitoring of cetuximab-mediated changes in receptor expression. Methods: EGFR-specific Affibody molecule (ZEGFR:03115) was radiolabeled with 89Zr and 18F. The radioligands were characterized in vitro and in mice bearing subcutaneous tumors with varying levels of EGFR expression. The protein dose for imaging studies was assessed by injecting 89Zr-deferoxamine-ZEGFR:03115 (2.4–3.6 MBq, 2 μg) either together with or 30 min after increasing amounts of unlabeled ZEGFR:03115 (1, 5, 10, 15, and 20 μg). PET images were acquired at 3, 24, and 48 h after injection, and the image quantification data were correlated with the biodistribution results. The EGFR expression and biodistribution of the tracer were assessed ex vivo by immunohistochemistry, Western blot, and autoradiography. To downregulate the EGFR level, treatment with cetuximab was performed, and 18F-aluminium fluoride-NOTA-ZEGFR:03115 (12 μg, 1.5–2 MBq/mouse) was used to monitor receptor changes. Results: In vivo studies demonstrated that coinjecting 10 μg of nonlabeled molecules with 89Zr-deferoxamine-ZEGFR:03115 allows for clear tumor visualization 3 h after injection. The radioconjugate tumor accumulation was EGFR-specific, and PET imaging data showed a clear differentiation between xenografts with varying EGFR expression levels. A strong correlation was observed between PET analysis, ex vivo estimates of tracer concentration, and receptor expression in tumor tissues. Additionally, 18F-aluminium fluoride-NOTA-ZEGFR:03115 could measure receptor downregulation in response to EGFR inhibition. Conclusion: ZEGFR:03115-based radioconjugates can assess different levels of EGFR level in vivo and measure receptor expression changes in response to cetuximab, indicating a potential for assessment of adequate treatment dosing with anti-EGFR antibodies.
Deep Network Uncertainty Maps for Indoor Navigation

Most mobile robots for indoor use rely on 2D laser scanners for localization, mapping and navigation. These sensors, however, cannot detect transparent surfaces or measure the full occupancy of complex objects such as tables. Deep Neural Networks have recently been proposed to overcome this limitation by learning to estimate object occupancy. These estimates are nevertheless subject to uncertainty, making the evaluation of their confidence an important issue for these measures to be useful for autonomous navigation and mapping. In this work we approach the problem from two sides. First we discuss uncertainty estimation in deep models, proposing a solution based on a fully convolutional neural network. The proposed architecture is not restricted by the assumption that the uncertainty follows a Gaussian model, as in the case of many popular solutions for deep model uncertainty estimation, such as Monte-Carlo Dropout. We present results showing that uncertainty over obstacle distances is actually better modeled with a Laplace distribution. Then, we propose a novel approach to build maps based on Deep Neural Network uncertainty models. In particular, we present an algorithm to build a map that includes information over obstacle distance estimates while taking into account the level of uncertainty in each estimate. We show how the constructed map can be used to increase global navigation safety by planning trajectories which avoid areas of high uncertainty, enabling higher autonomy for mobile robots in indoor settings.
Nitrogen isotope signature evidences ammonium deprotonation as a common transport mechanism for the AMT-Mep-Rh protein superfamily

Natural nitrogen isotopic signature reveals deprotonation during ammonium transport across living organisms. Ammonium is an important nitrogen (N) source for living organisms, a key metabolite for pH control, and a potent cytotoxic compound. Ammonium is transported by the widespread AMT-Mep-Rh membrane proteins, and despite their significance in physiological processes, the nature of substrate translocation (NH3/NH4+) by the distinct members of this family is still a matter of controversy. Using Saccharomyces cerevisiae cells expressing representative AMT-Mep-Rh ammonium carriers and taking advantage of the natural chemical-physical property of the N isotopic signature linked to NH4+/NH3 conversion, this study shows that only cells expressing AMT-Mep-Rh proteins were depleted in 15N relative to 14N when compared to the external ammonium source. We observed 15N depletion over a wide range of external pH, indicating its independence of NH3 formation in solution. On the basis of inhibitor studies, ammonium transport by nonspecific cation channels did not show isotope fractionation but competition with K+. We propose that kinetic N isotope fractionation is a common feature of AMT-Mep-Rh–type proteins, which favor 14N over 15N, owing to the dissociation of NH4+ into NH3 + H+ in the protein, leading to 15N depletion in the cell and allowing NH3 passage or NH3/H+ cotransport. This deprotonation mechanism explains these proteins’ essential functions in environments under a low NH4+/K+ ratio, allowing organisms to specifically scavenge NH4+. We show that 15N isotope fractionation may be used in vivo not only to determine the molecular species being transported by ammonium transport proteins, but also to track ammonium toxicity and associated amino acids excretion.
DICER1 regulates antibacterial function of epididymis by modulating transcription of β-defensins

Abstract DICER1 is a key enzyme responsible for the maturation of microRNAs. Recent evidences suggested that DICER1 and microRNAs expressed in epididymis were involved in the control of male fertility. However, the exact mechanism remains to be elucidated. Here, we created a mouse line by targeted disruption of Dicer1 gene in the principal cells of distal caput epididymis. Our data indicated that a set of β-defensin genes were downregulated by DICER1 rather than by microRNAs. Moreover, DICER1 was significantly enriched in the promoter of β-defensin gene and controlled transcription. Besides, the antibacterial ability of the adult epididymis significantly declined upon Dicer1 deletion both in vitro and in vivo. And a higher incidence of reproductive defect was observed in middle-aged Dicer1−/− males. These results suggest that DICER1 plays an important role in transcription of β-defensin genes, which are associated with the natural antibacterial properties in a microRNA-independent manner, and further impacts the male fertility.
The effects of AER and eGFR on outcomes of CVD in patients with T2DM in an urban community over 8 years of multifactorial treatment: the Beijing Communities Diabetes Study 18

Objective It is well known that diabetic kidney disease is a risk factor for cardiovascular diseases (CVD) in patients with type 2 diabetes mellitus (T2DM). In this study, the effects of urine albumin excretion rate (AER) and estimated glomerular filtration rate (eGFR) on CVD outcomes were analyzed in a population of T2DM. Methods The study was carried out using recorded information of a cohort study. A total of 1,914 patients with T2DM with no prevalent CVD were enrolled in an 8 years prospective study and received multifactorial intervention. The risk of CVD outcomes was assessed according to chronic kidney disease staging, which was categorized using AER (mg/d) and eGFR (mL/min/1.73 m2). The effects of AER and eGFR on risk of CVD onset were also analyzed. Results During the follow-up period (median 6.8 years), 71 CVD events occurred. At baseline, those with AER ≥300 mg/d and coexisting eGFR 60–89 mL/min/1.73 m2 or <60 mL/min/1.73 m2 showed increased risk for CVD outcomes when compared with “no chronic kidney disease” (AER <30 mg/d and eGFR ≥90 mL/min/1.73 m2). The increased CVD risk was observed in patients who progressed to AER ≥30 mg/d during the follow-up period, whereas patients who progressed to eGFR <90 mL/min/1.73 m2 alone showed no increased CVD risk. During the follow-up period, after multifactorial intervention, 8.7% patients with microalbuminuria and 1.8% patients with overt nephropathy reversed to normoalbuminuria or microalbuminuria. Conclusion AER is a more sensitive predictor than eGFR for CVD outcomes in T2DM patients. Overt nephropathy can be reversed after multifactorial intervention.
Risk Factors for Cement Loosening after Vertebroplasty for Osteoporotic Vertebral Fracture with Intravertebral Cleft: A Retrospective Analysis

Study Design Retrospective case-control study. Purpose To evaluate the primary outcomes and radiographic results of percutaneous vertebroplasty (PVP) in patients with singlelevel osteoporotic vertebral fracture (OVF) with intravertebral cleft (IVC) to identify the risk factors for cement loosening after PVP. Overview of Literature PVP is a widely accepted method for managing painful OVF; however, cement loosening occasionally occurs with poor outcomes. Methods This retrospective study involved 195 patients treated with PVP for single-level OVF with IVC. Six months thereafter, the primary outcomes were evaluated using the Visual Analog Scale (VAS) for back pain and the modified Oswestry Disability Index. Computed tomography was conducted to detect cement loosening. Possible risk factors, such as age, sex, wedging angle, intravertebral instability, Parkinson’s disease, spinous process fracture, ankylosing spinal hyperostosis, split vertebrae, and adjacent intervertebral vacuum, were assessed. Results Forty-nine patients (25%) experienced cement loosening 6 months after PVP. The mean VAS scores were significantly higher in patients with cement loosening than in those without (50 vs. 26 mm, respectively; p <0.01). Cement loosening was closely associated with intravertebral instability (odds ratio [OR], 1.20; 95% confidence interval [CI], 1.04–1.40; p =0.015), Parkinson’s disease (OR, 54.31; 95% CI, 4.47–659.53; p =0.002), spinous process fracture (OR, 7.11; 95% CI, 1.65–30.60; p =0.009), and split vertebrae (OR, 11.59; 95% CI, 1.64–82.02; p =0.014). Conclusions Patients with cement loosening experienced worse back pain than those without cement loosening. The important risk factors that influenced cement loosening after PVP were high intravertebral instability, Parkinson’s disease, spinous process fracture, and split vertebrae.
Modeling and Analysis of Human Group Dynamics

In this work we provide mathematical and computational tools that allow us to model the “physics” of human group behavior: how the relationship between the individuals’ characteristics, interaction dynamics, and environmental factors translates into changes in the behavior of the group. We characterize the group dynamics for four specific situations, and show the importance of these models to improve the performance of task-solving groups in organizational management, to design strategies that prevent high-risk behaviors, and to motivate collective action and cooperation in situations that lead to social dilemmas.
Saliency Detection by Compactness Diffusion

Most existing methods of salient object segmentation only focus on foreground cues such as contrast, or background cues such as boundary connectivity. Another problem is that they have used redundant information to generate an acceptable saliency map such as variances in different color spaces, multi-scale features and so on. In this paper, we propose saliency detecting with a diffusion model; use optimal seeds generated from foreground statistic cue, i.e., the compactness. Each superpixel is considered as a node and a fully connected graph is constructed to calculate the global compactness of each node. Then the local connected graph is constructed by only considering adjacent nodes, and compactness is diffused by applying a quadratic energy model to generate a coarse saliency map. After that, boundary prior is combined with the coarse saliency map for further eliminating the background. Experiments on three benchmark datasets including MSRA 1000, ECSSD and DUT-OMRON show that compared with other seven stateof-the-art methods, our model achieves stable and excellent performance. Parametric sensitivity analysis and time consumption are given to prove that the proposed method is stable and efficient.
Adaptive Gaussian Process Approximation for Bayesian Inference with Expensive Likelihood Functions

Abstract We consider Bayesian inference problems with computationally intensive likelihood functions. We propose a Gaussian process (GP)–based method to approximate the joint distribution of the unknown parameters and the data, built on recent work (Kandasamy, Schneider, & Póczos, 2015). In particular, we write the joint density approximately as a product of an approximate posterior density and an exponentiated GP surrogate. We then provide an adaptive algorithm to construct such an approximation, where an active learning method is used to choose the design points. With numerical examples, we illustrate that the proposed method has competitive performance against existing approaches for Bayesian computation.
Achieving Low-Latency Mobile Edge Computing by Uplink and Downlink Decoupled Access in HetNets

Despite the physical proximity of computationally-enhanced Base Stations (BSs) in Mobile Edge Computing (MEC) systems, achieving an end-to-end low-latency for computations offloading is still a critical design problem. This is because the offloading of computational tasks via the MEC or cloudlet servers entails the use of uplink and downlink radio links that are usually assumed to be coupled to a single BS. However, for heterogeneous networks, a new architectural paradigm whereby uplink and downlink are not associated with the same BS is proposed and seen to provide gains in network throughput. Motivated by such gains, and by using typical results from stochastic geometry, we formulate the offloading latency for the MEC-based scheme with decoupled UL/DL association, or decoupled access, and compare its performance to the conventional coupled or single association scheme. Despite the extra backhaul delay necessary for the communication between the two serving BSs in UL and DL, the MEC-based offloading scheme with decoupled association is still capable of providing a fairly lower offloading latency as compared to the conventional offloading scheme with coupled association.
Control of Postprandial Hyperglycemia in Type 1 Diabetes by 24-Hour Fixed-Dose Coadministration of Pramlintide and Regular Human Insulin: A Randomized, Two-Way Crossover Study

OBJECTIVE Healthy pancreatic β-cells secrete the hormones insulin and amylin in a fixed ratio. Both hormones are lacking in type 1 diabetes, and postprandial glucose control using insulin therapy alone is difficult. This study tested the pharmacodynamic effects of the amylin analog pramlintide and insulin delivered in a fixed ratio over a 24-h period. RESEARCH DESIGN AND METHODS Patients with type 1 diabetes were stabilized on insulin pump therapy with insulin lispro before a randomized, single-masked, two-way crossover, 24-h inpatient study in which regular human insulin was administered with pramlintide or placebo using separate infusion pumps in a fixed ratio (9 μg/unit). Meal content and timing and patient-specific insulin doses were the same with each treatment. The primary outcome measure was change in mean glucose by continuous glucose monitoring (CGM). Profiles of laboratory-measured glucose, insulin, glucagon, and triglycerides were also compared. RESULTS Mean 24-h glucose measured by CGM was lower with pramlintide versus placebo (8.5 vs. 9.7 mmol/L, respectively; P = 0.012) due to a marked reduction of postprandial increments. Glycemic variability was reduced, and postprandial glucagon and triglycerides were also lower with pramlintide versus placebo. Gastrointestinal side effects were more frequent during use of pramlintide; no major hypoglycemic events occurred with pramlintide or placebo. CONCLUSIONS Coadministration of fixed-ratio pramlintide and regular human insulin for 24 h improved postprandial hyperglycemia and glycemic variability in patients with type 1 diabetes. Longer studies including dose titration under daily conditions are needed to determine whether this regimen could provide long-term improvement of glycemic control.
Do laws impact opioids consumption? A breakpoint analysis based on Italian sales data

Purpose In Italy, where the adoption of opioid analgesics in pain management has been historically poor, an increase in opioids consumption occurred between 2000 and 2015. The aim of this study is to assess, through specific time series analyses for trend changes, the impact of different intervening factors – such as the availability of new drugs, the observance of clinical guidelines, changes in prescription regulations, and in reimbursement policies – on opioids sales to community pharmacies in Italy, focusing on the time period 2000–2010. Materials and methods Five opioids were considered: codeine, tramadol, buprenorphine, morphine, and fentanyl. The analysis is based on sales data collected at wholesale distributors. For each one of the five drugs, time series of the number of Defined Daily Doses per thousand inhabitants per day in the period 2000–2010 were analyzed, and an estimation of breakpoints was performed using segmented linear regression. Results Drug sales underwent a sharp increase in 2000–2010, although on different scales. Segmented regression analysis highlighted different potential breakpoints, corresponding to either a significant change in value and/or in slope. Sales of the five opioids were affected by at least one relevant event, often due to a synergy of regulatory, marketing, and technological factors. The effect of reimbursement changes has proved important. Conclusion Between 2000 and 2010, regulatory, technological, and reimbursement changes significantly influenced opioid sales to community pharmacies in Italy. The sales of relatively new drug products seem to be less influenced by changes in reimbursement and regulatory policies than that of more established products, suggesting that physicians are more comfortable with “old” drugs, since their clinical use is supported by established clinical guidelines and protocols.