Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative study of evaluation of the oral stereognostic ability between diabetic and nondiabetic complete denture wearers with and without denture

Background: The diabetic subjects would have impaired oral stereognostic ability (OSA) compared with normal subjects due to diabetic neuropathy and microcirculatory disturbances. This study was conducted to compare the OSA between diabetic and nondiabetic complete denture wearers with and without denture. Materials and Methods: In this in vivo study the present comparative study comprised of seventy edentulous subjects (36 males and 34 females), aged from 35 to 84 rehabilitated with complete dentures (among them 35 were diabetic and 35 subjects were nondiabetic complete denture wearer). The OSA tests were conducted using acrylic test samples of 12 shaped forms, which were placed in patient's mouth for a given period of time for identification and scored according to three-point scale as OSA score and the identification time was also recorded. The data obtained were analyzed using Chi-square test, t-test, and Pearson's correlation coefficient (P < 0.05). Results: In this study, diabetic complete denture wearers got the mean OSA score of 12.43 ± 3.93 without dentures, which was lower than nondiabetic complete denture wearer group (14.82 ± 4.44). There was a significant difference (P = 0.020*) in the identification of test pieces. Conclusion: Within limitations of this study, diabetic complete denture wearers showed decreased OSA than nondiabetic subjects, particularly it was significant while not wearing dentures. Oral stereognosis may be used as one of the clinical aids in predicting patient's performance to a prosthesis. Based on their response, we can educate the patient about the prognosis.
Nuclear Receptors in the Pathogenesis and Management of Inflammatory Bowel Disease

Nuclear receptors (NRs) are ligand-dependent transcription factors that regulate the transcription of target genes. Previous epidemiological and genetic studies have documented the association of NRs with the risk of inflammatory bowel disease (IBD). Although the mechanisms of action of NRs in IBD have not been fully established, accumulating evidence has demonstrated that NRs play complicated roles in regulating intestinal immunity, mucosal barriers, and intestinal flora. As one of the first-line medications for the treatment of IBD, 5-aminosalicylic acid (5-ASA) activates peroxisome proliferator-activated receptor gamma (PPARγ) to attenuate colitis. The protective roles of rifaximin and rifampicin partly depend on promoting pregnane X receptor (PXR) expression. The aims of this review are to discuss the roles of several important NRs, such as PPARγ, PXR, vitamin D receptor (VDR), farnesoid X receptor (FXR), and RAR-related orphan receptor gammat (RORγt), in the pathogenesis of IBD and management strategies based on targeting these receptors.
Efficacy of a new N-(n-butyl) thiophosphoric triamide formulation in reducing ammonia volatilization from urea-based fertilizers

Abstract: N-(n-Butyl)thiophosphoric triamide (NBPT) has been reported to reduce ammonia volatilization from surface-applied urea and urea ammonium nitrate (UAN). A new NBPT formulation (ARM U™, 18% NBPT) that contains a polymer allowing for lower application rate of NBPT was evaluated for its efficacy relative to Agrotain® (30% NBPT) and Arborite® (24% NBPT). Trials consisted of (i) a greenhouse study that compared two rates of ARM U-treated urea (360 and 540 mg NBPT kg-1 urea) with Arborite- or Agrotain-treated urea (480 and 600 mg NBPT kg-1 urea, respectively) and (ii) a field study that compared urea and UAN treated with either ARM U (360 mg NBPT kg-1 urea) or Agrotain (600 mg NBPT kg-1 urea) at two sites. Static chambers fitted with acid-charged discs were used to measure ammonia volatilization at six or seven dates over 28 d. In the greenhouse study, ammonia volatilization was reduced by 96% with either ARM U or Agrotain and 95% with Arborite. In the field study, ARM U and Agrotain reduced ammonia volatilization from urea by 80% and 66%, respectively, across sites. Similarly, ammonia volatilization from UAN was reduced by 46% and 60% with ARM U and Agrotain, respectively. Despite the lower NBPT application rates with ARM U, ammonia reduction by ARM U, Agrotain, and Arborite was not significantly different. The addition of ARM U to urea and UAN enabled lower application rate of NBPT without compromising its efficacy.
The Handbook of Symptom-Oriented Neurology

One can easily infer that the subject-oriented medical school curricula implicitly presume that patients arrive in the consultation office with a diagnostic label attached. Such may actually be the situation in universities and referral centers and consequently the subject-oriented books would be the appropriate references to use. In real life, however, clinical practitioners are faced with patients presenting with generic problems and the physician has to work his way through an analysis algorithm of a specialty in which he may not have received full training. The symptom-oriented or problem-oriented approach has a lot of merit and is worth adopting by medical schools. References based on this approach are few and far between, even in this age of publications explosion. The second edition of "The Handbook of Symptom-Oriented Neurology" comes as a welcome addition to the endeavor of bringing this approach into wider use. It is touted as a quick reference to help primary care physicians in handling neurological emergencies. It is well organized and clearly written. It fulfills the promise of being an easy reference where a particular question can be looked up quickly. The line drawings are quite clear, self-explanatory and very helpful in presenting the technique of clinical examination or demonstrating clinical signs. A purist may see some chapters as a vestige of the subject-oriented approach. The chapters on multiple sclerosis, diseases of the cervical spine and infections of the nervous system are examples of labels that rarely come as a presenting complaint to the primary care physician. What I find particularly interesting is the chapter on emergencies and the appendix on symptom-oriented neurologic examination. The latter should probably replace, or merge with the first chapter on neurologic examination to avoid repetition. The book is cut in a size that fits in a coat pocket but its 600 plus pages makes it a little bulky. The way technology is advancing, it may become possible to publish the next edition on an IC card to fit in an expansion slot of one of the pocket or palm-top minicomputers. Although the stated target readers are the primary care physicians, it will be a useful manual as well for the house staff in neurology training programs and medical residents on neurology rotations.
Confinement‐Induced Ordering and Self‐Folding of Cellulose Nanofibrils

Cellulose is a pervasive polymer, displaying hierarchical lengthscales and exceptional strength and stiffness. Cellulose's complex organization, however, also hinders the detailed understanding of the assembly, mesoscopic properties, and structure of individual cellulose building blocks. This study combines nanolithography with atomic force microscopy to unveil the properties and structure of single cellulose nanofibrils under weak geometrical confinement. By statistical analysis of the fibril morphology, it emerges that confinement induces both orientational ordering and self‐folding of the fibrils. Excluded volume simulations reveal that this effect does not arise from a fibril population bias applied by the confining slit, but rather that the fibril conformation itself changes under confinement, with self‐folding favoring fibril's free volume entropy. Moreover, a nonstochastics angular bending probability of the fibril kinks is measured, ruling out alternating amorphous–crystalline regions. These findings push forward the understanding of cellulose nanofibrils and may inspire the design of functional materials based on fibrous templates.
Obesity Indices in relation to Lipid Abnormalities among Medical University Students in Zahedan, South-East of Iran

Background: There is no statement on the ability of obesity indices in prediction of lipid abnormalities among young adults. The present study was conducted to determine the ability of obesity indices as predictors of lipid abnormalities among a group of young adults. Materials and Methods: A total of 353 medical university students aged 18–25 years (188 males and 165 females) participated in this cross-sectional study in 2014. Weight, height, waist circumference (WC), and hip circumference were measured to calculate obesity indices including body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR). Serum total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG) were measured by commercially available kits and were applied to calculate low-density lipoprotein cholesterol (LDL-C) and atherogenic parameters including LDL-C/ HDL-C ratio, TC/HDL-C ratio, non-HDL-C, and atherogenic index of plasma (AIP). Results: Subjects with BMI ≥25 kg/m2 had a greater value of all lipid profiles and atherogenic parameters (P < 0.05) except for HDL-C (P > 0.05) compared to subjects with BMI <25 kg/m2. In logistic regression model, BMI ≥25 kg/m2 was significantly associated with the highest odds for elevated TC (odds ratio [OR] = 7.67, P = 0.003), LDL-C (OR = 3.24, P = 0.01), TC:HDL-C (OR = 4.98, P = 0.01), and non-HDL-C(OR = 4.32, P = 0.001) in males, as well as high values of TG (OR = 8.80, P = 0.002), LDL-C:HDL-C (OR = 3.64, P = 0.01), and AIP (OR = 9.65, P < 0.001) in females. In terms of central obesity indices, males with WC ≥102 cm and females with WC ≥88 cm had the highest odds of increased LDL-C:HDL-C (OR = 6.71, P = 0.01) and TC:HDL-C (OR = 3.25, P = 0.050), respectively. In addition, females with WHtR ≥0.50 had the highest odds of high TC (OR = 3.56, P = 0.02) and non-HDL-C (OR = 2.70, P = 0.02). Conclusions: Overall, the findings of the present study showed that BMI was a stronger index for prediction of classical lipid parameters and atherogenic parameters than central obesity indices in medical students.
Using detection theory and molecular computation to understand signal processing in living cells

Living cells need to constantly sense their environment, make decision and communicate with other cells. We can try to understand these cellular functions from two angles. We can understand them from the angle of molecular biology and identify all the chemical species, chemical reactions and transport mechanisms that realise these functions. Alternatively, we can understand them from an information processing perspective where cells use networks of chemical reactions (which are also known as molecular circuits) as the substrate to execute algorithms for computation and communication. In this paper, we focus on the cellular signal processing task of discriminating persistent signals from transient ones. We show that we can understand this task by using statistical detection theory, timescale separation and approximate analog molecular computation.
All Your Alexa Are Belong to Us: A Remote Voice Control Attack against Echo

Voice controlled system becomes increasingly popular these days due to the convenient and natural control over lots of functionalities and smart devices. Amazon Echo, designed around Alexa, is capable of controlling smart devices such as locks, sending emails, making phone calls, and even bridging the gap between online services such as Twitter, Facebook, etc. Previously, researchers demonstrated that by carefully crafting obfuscated commands or transmitting commands over ultrasound carrier, voice controlled systems can be compromised without people's awareness. However, those researches require the target voice controlled systems to be close enough to their speaker or ultrasound transducer. In this paper, we proposed REEVE (REmotE VoicE control) attack that can manipulate Amazon Alexa remotely, e.g., via signal broadcasting to compromise radio, TV, speaker, etc. It works on behalf of the attackers to operate various commands beneficial to them. By analyzing more than 15,000 Alexa skills and 600 IFTTT Applets related to Alexa, we found that more than 100 of them can be used to attack Echo. We also thoroughly scrutinized the attack surface of Echo's voice control and conducted security analysis based on different consequences.
Population Game Based Energy and Time Aware Task Offloading for Large Amounts of Competing Users

Computation offloading is envisioned as a promising solution to resource scarcity problem on mobile devices. Mobile users can offload computation intensive tasks to remote cloud with stronger capabilities. In order to execute tasks in cloud, mobile users have to upload computational data through cellular networks. When large amounts of mobile users in the same cell attempt to offload mobile tasks through the base station, the communication latencies for data transmissions may be high due to limited bandwidth resources. However, since the task completion times are constrained by hard deadlines, this restricts the feasible set of computational tasks that can be uploaded. In this paper, we propose a population game based approach to achieve efficient computation offloading for large amounts of competing mobile users, where each user is aimed to minimize his energy consumption. This game is subject to the task execution deadlines, user specific data rates, and the competition over the shared communication channel. We analyze the evolutionary dynamic of the game and show that the game always admits a Nash equilibrium. We then design a computation offloading mechanism that can achieve a Nash equilibrium of the game. Numerical results demonstrate that the proposed mechanism can achieve efficient computation offloading performance and scale well as the system size increases.
Detection and Channel Equalization with Deep Learning for Low Resolution MIMO Systems

Deep learning (DL) provides a framework for designing new communication systems that embrace practical impairments. In this paper, we present an exploration of DL as applied to design the physical layer for MIMO systems with low resolution analog-to-digital converters. The application of DL is nontrivial thanks to the severe nonlinear distortion caused by quantization and the large dimensional MIMO channel. We investigate network architectures for channel estimation and detection. The channel estimation results indicate that the adopted DL architectures lead to good results in the large signal-to-noise ratio (SNR) regime, but are outperformed by state-of-the-art iterative message passing algorithms. For decoding, we adopted a multilabel classification architecture with implicit equalization and output size scaling linearly with the number of data symbols to be estimated. While feasible for high MIMO dimensions, the adopted DL architecture for decoding converged only for relatively small MIMO dimensions. A main conclusion of our paper is that DL still has potential but more efficient architectures are required, given the convergence problems associated with time-varying channels and 1-bit quantization.
Joint Offloading and Resource Allocation in Mobile Edge Computing Systems: An Actor-Critic Approach

Offloading computationally intensive tasks from user equipments (UEs) to mobile edge computing (MEC) servers is a promising technique to boost up the computational capacity of UEs. However, MEC will incur extra energy consumption and time delays, which motivates the deployment of energy harvesting (EH) small cell networks with MEC in mobile networks. Due to the complexity of such networks, it is challenging to effectively allocate resources for UEs. In this paper, we investigate the offloading decision, wireless and computational resources allocation problem in energy harvesting (EH) small cell networks with MEC. Different from existing literatures, our research focuses on improving mobile operators' revenue by maximizing the amount of the offloaded tasks while decreasing the energy expenditure and time-delays. Besides, queues are created at the MEC server side to store the un-executed tasks in a time slot, which is used as a punishment in our utility function to avoid serious delay. Considering the varying lengths of queues, the states of EH-batteries of small base stations (SBSs) and down-link channels, the above problem is modeled as a Markov decision process (MDP). Since the states and actions in the MDP are infinite, an online and on-policy actor-critic with eligibility traces algorithm is proposed to resolve the problem. Simulation results show the proposed algorithm has superior performances compared with the policy-gradient algorithm and Q- learning.
Deep Learning-Based Big Data-Assisted Anomaly Detection in Cellular Networks

5G is envisioned to have an artificial intelligence (AI)-empowerment to efficiently plan, manage and optimize the extremely complex network by leveraging colossal amount of data (big data) generated at different levels of the network architecture. Cell outages and congestion pose serious threat to the network management. Sleeping cell is a special case of cell outage in which the cell provides inferior services to its users. This peculiar behavior of the cell is particularly challenging to detect as it disguises itself from the network monitoring entity. Inadequate accuracy and high false alarms are two major constraints of state-of-the-art approaches for the anomaly-sleeping cell and surge in user traffic activity that may lead to congestion-detection in cellular networks. This implies squandering of scarce resources which ultimately results in increased operational expenditure (OPEX) while disrupting network's quality of service (QoS) and user's quality of experience (QoE). Inspired from the prominent success of deep learning (DL) technology in machine learning domain, this is the first study that applies DL for the detection of abovementioned anomalies. We utilized, and did a comprehensive study of, L-layer deep feedforward neural network fueled by real call detail record (CDR) dataset (big data) and achieved 94.6% accuracy with 1.7% false positive rate (FPR), that are remarkable improvements and overcome the limitations of the previous studies. The preliminary results elucidate the feasibility and preeminence of our proposed anomaly detection framework.
Quantized Non-Bayesian Quickest Change Detection with Energy Harvesting

This paper focuses on the analysis of an optimal sensing and quantization strategy in a multi-sensor network where each individual sensor sends its quantized log-likelihood information to the fusion center (FC) for non-Bayesian quickest change detection. It is assumed that the sensors are equipped with a battery/energy storage device of finite capacity, capable of harvesting energy from the environment. The FC is assumed to have access to either non-causal or causal channel state information (CSI) and energy state information (ESI) from all the sensors while performing the quickest change detection. The primary observations are assumed to be generated from a sequence of random variables whose probability distribution function changes at an unknown time point. The objective of the detection problem is to minimize the average detection delay of the change point with respect to a lower bound on the rate of false alarm. In this framework, the optimal sensing decision and number of quantization bits for information transmission can be determined with the constraint of limited available energy due to finite battery capacity. This optimization is formulated as a stochastic control problem and is solved using dynamic programming algorithms for both non-causal and causal CSI and ESI scenario. A set of non-linear equations is also derived to determine the optimal quantization thresholds for the sensor log-likelihood ratios, by maximizing an appropriate Kullback-Leibler (KL) divergence measure between the distributions before and after the change. A uniform threshold quantization strategy is also proposed as a simple sub-optimal policy. The simulation results indicate that the optimal quantization is preferable when the number of quantization bits is low as its performance is significantly better compared to its uniform counterpart in terms of average detection delay. For the case of a large number of quantization bits, the performance benefits of using the optimal quantization as compared to its uniform counterpart diminish, as expected.
Estimating the Extrema of Large-Scale RFID Systems

In some large-scale RFID systems where tags carry values, the extrema are critical statistics. We consider estimating the extrema, i.e., estimating the maximum and minimum values simultaneously. A straightforward approach is to perform binary search on the possible values, each time requesting tags with values in a certain range to respond. We show that this approach is suboptimal due to the interframe overhead between two frames in practical RFID systems. We propose a class of protocols to find the minimum value or maximum value separately, and show how to select the best protocol according to the hardware parameters of RFID systems. We then revise the protocol to estimate the minimum and maximum values simultaneously, and give the optimal parameters. Extensive simulations show that our protocol gives the smallest estimation error within any allocated time.
DC-Bias Allocation in Cooperative VLC Networks via Joint Information and Energy Transfer

In order to meet the new escalating demand for high data rate services and applications, visible light communication (VLC) has emerged as a promising solution for the fifth-generation (5G) wireless networks and beyond. Consider a VLC network, where multiple access points (APs) serve both energy-harvesting users (EHUs), i.e., users which harvest energy from light intensity, and information-users (IUs), i.e., users which gather data information. The performance of the system becomes a function of the direct current (DC) bias values allocated to each AP. After adopting a zero-forcing (ZF) precoding approach to cancel the inter-cell interference, the paper formulates the problem of maximizing the network harvested energy subject to individual harvested energy and data rate constraints at the EHUs and IUs, respectively, so as to determine the DC bias of every AP. The paper then proposes solving such a difficult non-convex optimization problem using an iterative approach. The proposed algorithm uses well-chosen approximations of the objective and constraints functions, and compensates for the approximations using proper outer-loop updates. The paper further proposes a sub optimal heuristic which provides a feasible, yet simple, solution to the problem. Numerical results illustrate the convergence of our proposed algorithms, and highlight the significant performance improvement of the proposed algorithm as compared to the proposed baseline approach.