Miracle-dz/UniFAR-FTUs huggingface dataset:
Human Polymorphisms in the Glutathione Transferase Zeta 1/Maleylacetoacetate Isomerase Gene Influence the Toxicokinetics of Dichloroacetate

Dichloroacetate (DCA), a chemical relevant to environmental science and allopathic medicine, is dehalogenated by the bifunctional enzyme glutathione transferase zeta (GSTz1)/maleylacetoacetate isomerase (MAAI), the penultimate enzyme in the phenylalanine/tyrosine catabolic pathway. The authors postulated that polymorphisms in GSTz1/MAAI modify the toxicokinetics of DCA. GSTz1/MAAI haplotype significantly affected the kinetics and biotransformation of 1,2‐13C‐DCA when it was administered at either environmentally (μg/kg/d) or clinically (mg/kg/d) relevant doses. GSTz1/MAAI haplotype also influenced the urinary accumulation of potentially toxic tyrosine metabolites. Atomic modeling revealed that GSTz1/MAAI variants associated with the slowest rates of DCA metabolism induced structural changes in the enzyme homodimer, predicting protein instability or abnormal protein‐protein interactions. Knowledge of the GSTz1/MAAI haplotype can be used prospectively to identify individuals at potential risk of DCA's adverse side effects from environmental or clinical exposure or who may exhibit aberrant amino acid metabolism in response to dietary protein.
What drives the comparability effect of mandatory IFRS adoption?

We investigate the effects of mandatory International Financial Reporting Standards (IFRS) adoption on the comparability of financial accounting information. Using a set of alternative comparability measures, our results suggest that the overall comparability effect of mandatory IFRS adoption is marginal. We hypothesize that firm-level heterogeneity in IFRS compliance explains the limited comparability effect. To test this conjecture, we first hand-collect data on IFRS compliance for a sample of German and Italian firms and find that firm-, region-, and country-level incentives systematically shape IFRS compliance. We then use these compliance determinants to explain the variance in the comparability effect of mandatory IFRS adoption and find that only firms with high compliance incentives experience substantial increases in comparability. Moreover, we document that firms from countries with tighter reporting enforcement experience larger IFRS comparability effects, and that public firms adopting IFRS become less comparable to local GAAP private firms from the same country.
Viable Group A Streptococci in Macrophages during Acute Soft Tissue Infection

Background Group A streptococcal severe soft tissue infections, such as necrotizing fasciitis, are rapidly progressive infections associated with high mortality. Group A streptococcus is typically considered an extracellular pathogen, but has been shown to reside intracellularly in host cells. Methods and Findings We characterized in vivo interactions between group A streptococci (GAS) and cells involved in innate immune responses, using human biopsies (n = 70) collected from 17 patients with soft tissue infections. Immunostaining and in situ image analysis revealed high amounts of bacteria in the biopsies, even in those collected after prolonged antibiotic therapy. Viability of the streptococci was assessed by use of a bacterial viability stain, which demonstrated viable bacteria in 74% of the biopsies. GAS were present both extracellularly and intracellularly within phagocytic cells, primarily within macrophages. Intracellular GAS were predominantly noted in biopsies from newly involved tissue characterized by lower inflammation and bacterial load, whereas purely extracellular GAS or a combination of intra- and extracellular GAS dominated in severely inflamed tissue. The latter tissue was also associated with a significantly increased amount of the cysteine protease streptococcal pyrogenic exotoxin SpeB. In vitro studies confirmed that macrophages serve as reservoirs for viable GAS, and infection with a speB-deletion mutant produced significantly lower frequencies of cells with viable GAS following infection as compared to the wild-type bacteria. Conclusions This is the first study to demonstrate that GAS survive intracellularly in macrophages during acute invasive infections. This intracellular presence may have evolved as a mechanism to avoid antibiotic eradication, which may explain our finding that high bacterial load is present even in tissue collected after prolonged intravenous antibiotic therapy. This new insight into the pathogenesis of streptococcal soft tissue infections highlights a need for alternative therapeutic strategies.
Fog computing: Data analytics and cloud distributed processing on the network edges

The term fog computing was coined in 2012. However, the concept of pushing data and application logic to the network edges is not a novelty. Similar proposals were observed with edge computing, from the early 2000s, and cloudlets, from 2009. In fact, the cloudlet concept is a subset of edge computing applied to mobile networks and the fog concept is a subset of edge computing applied to Internet of Things (IoT). This paper demystifies these concepts and provides a comprehensive survey of references from academia and industry. It analyzes the terminology and dimensions of performance, security, and governance, based on a taxonomy proposed and presented in the paper. In addition we provide a thorough analysis of related topics, identifying the main research areas correlated to edge computing. Finally, we draw conclusions regarding the state of the art and the future of edge computing.
Short paper: Experimental analysis of misbehavior detection and prevention in VANETs

Vehicular Ad-hoc Networks (VANETs) aim to increase, among others, traffic safety and efficiency by warning and informing the driver about road events and hazards. Due to their direct impact on drivers' safety, external and internal attacks have to be prevented. While authentication prevents most of the external attacks, internal attackers are still able to misuse the system and inject fake - but authenticated - messages. Therefore, misbehavior detection and prevention mechanisms are required to mitigate such attacks. In this paper we provide a categorization of internal attackers to identify most relevant attack variants. Instead of using simulations, as done by most related works, we use an implementation on real vehicles to demonstrate the feasibility of location-based attacks. Especially, we demonstrate that a malware application installed on a vehicle can provoke false warnings on benign vehicles that are within the attacker's communication range. This exemplary attack is possible due to insufficiently specified VANET security standards. By using our proposed countermeasures, we show that this internal attack is detected and blocked, preventing false driver warnings.
Selecting a Customization Strategy Under Competition: Mass Customization, Targeted Mass Customization, and Product Proliferation

Customization requires not only an implementation of proper manufacturing systems but also a proper strategy regarding when firms should offer customized products and what the nature of customization should be. This paper questions 1)whether customization is better than no customization, and, if so, 2) what kind of customization strategy firms should adopt under competition. We find that customization is not optimal when the cost of soliciting customer preference information is sufficiently high. When competing firms choose to customize, we show that firms target only certain customer segments with customized products. We also find that the optimal customization strategy may require firms to offer only a few discrete product varieties. Despite the concern that customization may initiate price wars because customization reduces product differentiation, we find that customization does not escalate the price competition, because aggressive price competition exacerbates cannibalization. Although customers within the product line of a firm are charged higher prices, we show that on average customers are better off when firms adopt customization. However, unless the customization is quite cheap, when firms choose to customize, we find that firms cannot generate more profits than when firms offer only a single product
Preliminary Investigation on the Effect of Speed Variation to Pressure Distribution for Flat, Medium and High Foot Arch

This paper presents the investigation on the types of foot arch affecting plantar pressure distribution during running. In this study, 20 participants of different arch types volunteered. The participants were required to run on the treadmill at three different speed with the FSR sensors placed under the sole of the shoes. Two sensors used, one at forefoot and one at rear foot. Only left foot was examined through this study. The data were analysed to compare the foot pressure of individuals during running and to analyse the effect of speed on the voltage measured with different arch types. The rear foot showed higher pressure value compared to the fore foot.
Directional Based Graphical Authentication Method with Shoulder Surfing Resistant

Authentication has been advanced by implying advancement in security such as cryptography to protect password against data leaking and sniffing; captcha to prevent robot system; Secure Sockets Layer designed to protect users against phishing website and many other solution invented for different types of vulnerabilities. Although there are other methods suggested for preventing shoulder surfing (i.e. exposure of password to nearby observing-person), the techniques still have some drawback. Some of the proposed techniques also required high computational process with high range of possible answer. This paper proposed a shoulder-surfing-proof graphical based authentication with direction scheme that combines technique adapted from Passfaces scheme and a selection of direction. User is required to choose four faces and one direction to serve as the secret authentication. Besides shoulder surfing, the proposed method also caters security in data transmission storage as hashing is applied along the communication. This technique has been proven to demonstrate the robustness, security strength and the functionality advantages of the system. The result from the security testing shows that image based passwords is more secure because the possibility of the image to be guessed is very low.
Classification in Devanagari Script using Elliptical Region-wise Features

In this article, an attempt has been made to develop a system for classification of online handwritten text and non-text data from within a single online handwritten document in the most popular Indic script-Devanagari. As per our knowledge, no recognized work exists for handwritten text and non-text document classification in online mode in any Indic script. To develop this system an elliptical region-wise feature extraction approach has been proposed in this article. In this approach, each online stroke information of text and non-text documents is divided into smaller elliptical regions by constructing several concentric ellipses around the stroke. Each elliptical region is further divided into several sub-regions before extracting various structural and directional features of stroke portions from each sub region. These features are then studied in Hidden Markov Model (HMM) based classification platform. The efficiency of the present system has been measured on a self-generated dataset and it has provided promising result.
Identifying Sub-documents in a Composite Scanned Document Using Naive Bayes, Levenshtein Distance and Domain Driven Knowledge Base

This paper presents an approach to classification of scanned documents by identifying the title of the document using Naïve Bayes classifier, the predicted title is then validated using a domain driven knowledge base. Levenshtein distance is used to mitigate errors arising from the OCR (optical character recognition) algorithm. This approach produced significantly better results than using the Naïve Bayes classifier by itself. This study contributes resources to the intelligent processing of real estate documents in the form of rich domain specific knowledge base.
Water-only fasting and an exclusively plant foods diet in the management of stage IIIa, low-grade follicular lymphoma

Follicular lymphoma (FL), the second most common non-Hodgkin's lymphoma (NHL), is well characterised by a classic histological appearance and an indolent course. Current treatment protocols for FL range from close observation to immunotherapy, chemotherapy and/or radiotherapies. We report the case of a 42-year-old woman diagnosed by excisional biopsy with stage IIIa, grade 1 FL. In addition to close observation, the patient underwent a medically supervised, 21-day water-only fast after which enlarged lymph nodes were substantially reduced in size. The patient then consumed a diet of minimally processed plant foods free of added sugar, oil and salt (SOS), and has remained on the diet since leaving the residential facility. At 6 and 9-month follow-up visits, the patient's lymph nodes were non-palpable and she remained asymptomatic. This case establishes a basis for further studies evaluating water-only fasting and a plant foods, SOS-free diet as a treatment protocol for FL.
A Smart Home Appliance Control System for Physically Disabled People

The construction and conversion of ordinary homes into “smart homes” has seen a tremendous rise in recent years. This can be ascribed to technologies such as the Internet of Things, sensors, smart phones, smart appliances, cloud computing, and digital assistants such as Amazon Alexa, Google Home, Google Assistant, Apple Siri, and Microsoft Cortana. At the outset, smart homes were built to enhance the quality of life for ordinary nondisabled persons. Impressively, we have seen smart home residents reaping the benefits of security, energy saving, and the ability to control their lighting, HVAC (heating, ventilation, and air conditioning), door locks, and coffee makers while they are in their space of comfort, for example in bed or siting on a couch. However, most smart home devices are not designed with people with disabilities and limited range of movement in mind. Of course, being able to control home devices using smart technology could be a tremendous benefit to people with physical disabilities and the older persons. This paper presents a system that uses smart plugs, smart cameras, smart power strips and a digital assistant such as Amazon Alexa, Google Home, Google Assistant, Apple Siri, or Microsoft Cortana to capture voice commands, from a person with physical disabilities, spoken in a much more natural way to control ordinary home electrical appliances in order to turn them on or off, with minimal exertion.
Leveraging Deep Learning to Improve the Performance Predictability of Cloud Microservices

Performance unpredictability is a major roadblock towards cloud adoption, and has performance, cost, and revenue ramifications. Predictable performance is even more critical as cloud services transition from monolithic designs to microservices. Detecting QoS violations after they occur in systems with microservices results in long recovery times, as hotspots propagate and amplify across dependent services. We present Seer, an online cloud performance debugging system that leverages deep learning and the massive amount of tracing data cloud systems collect to learn spatial and temporal patterns that translate to QoS violations. Seer combines lightweight distributed RPC-level tracing, with detailed low-level hardware monitoring to signal an upcoming QoS violation, and diagnose the source of unpredictable performance. Once an imminent QoS violation is detected, Seer notifies the cluster manager to take action to avoid performance degradation altogether. We evaluate Seer both in local clusters, and in large-scale deployments of end-to-end applications built with microservices with hundreds of users. We show that Seer correctly anticipates QoS violations 91% of the time, and avoids the QoS violation to begin with in 84% of cases. Finally, we show that Seer can identify application-level design bugs, and provide insights on how to better architect microservices to achieve predictable performance.
Energy Efficient Medium Access Control Protocol for Wireless Sensor Networks

Medium Access Control (MAC) protocol plays vital role for improving the efficiency of wireless sensor networks (WSNs). Thus, several energy efficient MAC protocols are introduced for better outcomes. This paper introduces an energy efficient Medium Access Control (EE-MAC) protocol for WSNs. EE-MAC handles idle listening and congestion problem to preserve the energy. EE-MAC uses semi synchronous low duty cycle that takes less time for channel access. As a result, higher data delivery rate is expected, and extra energy is preserved. Evaluation of EE-MAC is performed using network simulator-3 (ns3) then compared with X-MAC (low duty cycles energy efficient protocol) and low power listening (LPL). We compared BN-MAC with MAC hybrid protocols: Speck-MAC, Boarder Node MAC (BN-MAC), advertisement-based MAC (A-MAC), Adaptive Duty Cycle SMAC (ADC-SMAC) and low power real-time MAC (LPR-MAC) protocol. Based on initial experimental results, we demonstrate that EE-MAC protocol saves additional 2% to 4.5% energy as compared with other MAC protocols.
Design of Hybrid CMOS Non-Volatile SRAM Cells in 130nm RRAM Technology

Static Random-Access Memories (SRAMs) are very common in today’s chips industry thanks to their speed and power consumption but they are classified as volatile memory. Non-Volatile SRAMs (NVSRAMs) combine SRAM features with non-volatility. This combination has the advantage to retain data after power off or in the case of power failure, enabling energy-efficient and reliable systems under frequent power-off conditions. This paper presents a detailed overview on Resistive RAM-based NVSRAM structures, with deep looking on the ability to store and restore data. After reviewing the designs, a comparison in terms of speed, power consumption and design complexity is presented for 2 NVSRAM memory cells (6T2R and a 10T1R) implemented in a 130-nm high voltage CMOS technology from STMicroelectronics.
Gesture Recognition Based on Tri-Axis Accelerometer Using 1D Gabor Filters

In this paper, a novel gesture recognition framework based on tri-axis accelerometer mounted on a cell phone is proposed. Since the length of acceleration signals is variable according to different gestures and different subjects' input speed, most of recognition algorithms cannot be used. To solve this problem, we propose a novel resampling method by combining decimation and interpolation. After that, we propose 1D Gabor coefficients of acceleration signals as features. However, the dimensionality of Gabor feature space used in gesture recognition is very high. We adopt a two-stage method PCA plus LDA to select and compress the Gabor feature. The Classifier we used is Multi-class Support Vector Machine. The average recognition results of seventeen complex gestures using the proposed Gabor feature are effective. The performance of experimental results show that gesture-based interaction can be used as a novel human computer interaction for consumer electronics and mobile devices.
Honeybees: combining replication and evasion for mitigating base-station jamming in sensor networks

By violating MAC-layer protocols, the jamming attack aims at blocking successful communication among wireless nodes. Wireless sensor networks (WSNs) are highly vulnerable to jamming because of reliance on shared wireless medium, constrained per-sensor resources, and high risk of sensor compromise. Moreover, base stations of WSNs are single points of failure and, thus, attractive jamming targets. To tackle base-station jamming, replication of base stations as well as jamming evasion, by relocation to unjammed locations, have been proposed. In this paper, we propose Honeybees, an energy-aware defense framework against base-station jamming attack in WSNs. Honeybees efficiently combines replication and evasion to allow WSNs to continue delivering data for a long time during a jamming attack. We present three defense strategies: reactive, proactive, and hybrid, in the context of multi-hop WSN deployment. Through simulation, we show the interaction of these strategies with different attack tactics as well as the effect of system and attack parameters. We found that our honeybees framework struck an energy-efficient balance between replication and evasion that outperformed both separate mechanisms. Specifically, hybrid honeybees outperformed replication and evasion at low and intermediate number of attackers and gracefully degraded to high attack intensity
Investigating Polaron Formation in Anatase and Brookite TiO2 by Density Functional Theory with Hybrid-Functional and DFT + U Methods

Anatase and brookite are robust materials with enhanced photocatalytic properties. In this study, we used density functional theory (DFT) with a hybrid functional and the Hubbard on-site potential methods to determine electron- and hole-polaron geometries for anatase and brookite and their energetics. Localized electron and hole polarons were predicted not to form in anatase using DFT with hybrid functionals. In contrast, brookite formed both electron and hole polarons. The brookite electron-polaronic solution exhibits coexisting localized and delocalized states, with hole polarons mainly dispersed on two-coordinated oxygen ions. Hubbard on-site potential testing over the wide 4.0–10 eV range revealed that brookite polarons are formed at U = 6 eV, while anatase polarons are formed at U = 8 eV. The brookite electron polaron was always localized on a single titanium ion under the Hubbard model, whereas the hole polaron was dispersed over four oxygen atoms, consistent with the hybrid DFT studies. The anatase electron polarons were dispersed at lower on-site potentials but were more localized at higher potentials. Both methods predict that brookite has a higher driving force for the formation of polarons than anatase.
A novel structural position-specific scoring matrix for the prediction of protein secondary structures

MOTIVATION The precise prediction of protein secondary structure is of key importance for the prediction of 3D structure and biological function. Although the development of many excellent methods over the last few decades has allowed the achievement of prediction accuracies of up to 80%, progress seems to have reached a bottleneck, and further improvements in accuracy have proven difficult. RESULTS We propose for the first time a structural position-specific scoring matrix (SPSSM), and establish an unprecedented database of 9 million sequences and their SPSSMs. This database, when combined with a purpose-designed BLAST tool, provides a novel prediction tool: SPSSMPred. When the SPSSMPred was validated on a large dataset (10,814 entries), the Q3 accuracy of the protein secondary structure prediction was 93.4%. Our approach was tested on the two latest EVA sets; accuracies of 82.7 and 82.0% were achieved, far higher than can be achieved using other predictors. For further evaluation, we tested our approach on newly determined sequences (141 entries), and obtained an accuracy of 89.6%. For a set of low-homology proteins (40 entries), the SPSSMPred still achieved a Q3 value of 84.6%. AVAILABILITY The SPSSMPred server is available at http://cal.tongji.edu.cn/SPSSMPred/ CONTACT lith@tongji.edu.cn