Miracle-dz/UniFAR-FTUs huggingface dataset:
Pharmacological evaluation of ethanolic extract of Rhizophora mucronata flower against streptozotocin-induced diabetic nephropathy in experimental animals

Diabetic nephropathy is a long-term complication of diabetic mellitus. Many experimental evidences suggest that persistent hyperglycemia generates intracellular reactive oxygen species (ROS), which is involved of free radicals in the pathogenesis of diabetes and more importantly in the development of diabetic complications. The objective of the present investigation was to evaluate the antidiabetic activity and protective effect of diabetic-induced nephropathy of ethanolic extract of Rhizophora mucronata flowers (ERMF) on streptozotocin (STZ) generated oxidative stress induced diabetic nephropathy in rats. Forty Wistar rats were indiscriminately distributed into normal control, diabetic untreated and diabetic treated with ERMF (250 and 500 mg/kg per body weight). Experimental diabetic nephropathy was induced by a single intraperitoneal (i.p) administration of STZ 60 mg/kg body weight (b.w.). The body weight, plasma glucose, creatinine, urea, uric acid and total protein along with oxidative stress parameters were examined in different groups of experimental animals. Histopathological examination was also performed for observing cellular changes on kidney tissues. Elevated level of blood sugar and urea whereas lower uric acid, creatinine, and total protein were detected in the STZ-diabetic rats, which were significantly attenuated after ERMF (250 and 500 mg/kg per body weight) administration, dosedependently. Even various oxidative stress parameters were also drastically changed in a STZ-diabetic rats which were improved by ERMF (250 and 500 mg/kg per body weight). Histopathological study of kidney was supported the potential protective effect of ERMF (250 and 500 mg/kg per body weight). The activity elicited by the extract might be due to its ability to activate antioxidant enzymes. The findings suggest the potential use of the ERMF (250 and 500 mg/kg per body weight) as a novel therapeutically useful nephroprotective, agent.
I. 서론-본문 큰제목

We can define as retaliatory crime avenge crime for crime statement, testimony, state of victimized, witness etc. of crimers related. In case of Republic of Korea, damages of retaliatory crimes are every year increasing and increasing ratio is high, Nevertheless, opposition for retaliatory is incomplete realities. This study is going to search for the opposition method with scrutinize of regulation for retaliary crimes.
Myeloid‐derived suppressor cells (MDSCs) and tumor‐associated macrophages (TAMs) produce CCL22 which selectively recruits regulatory T‐cells (Tregs) to the tumor microenvironment

Tumor‐induced immune suppression involves the accumulation of immune suppressive infiltrates in the microenvironment. We demonstrated an increased number of Tregs in Lewis Lung Carcinoma (LLC). High levels of CCR4 on Tregs lead us to hypothesize that CCR4‐chemokines are secreted by LLC and selectively recruit Tregs. ELISA shows a large increase in CCL22 secretion by LLC bearing tissue. Lack of CCL22 in primary cultures suggests that CCL22 is NOT produced by the tumor, but rather by some other component of the microenvironment. Migration assays showed that normal, and to a greater extent, LLC‐bearing tissue selectively recruits Tregs. Neutralization of CCL22 significantly reduced the selective Treg migration. Fractionated normal and LLC bearing tissue shows that while NK, CD31+, and CD11c+ cells all produce CCL22, only the CD11b+ population secretes higher levels in LLC bearing tissue. This is magnified by the increased number of CD11b+ cells found in LLC bearing tissue. Of the CD11b+ cells, two populations emerge. One population is absent in normal lung tissue and expresses F4/80, suggesting that these are TAMs. The other population expresses Gr‐1, which is consistent with MDSCs. Together, our data suggests that other immune suppressive phenotypes such as TAMS and MDSCs play an active role in Treg recruitment to the microenvironment through the production of CCL22.
Antimicrobial Substance Produced by Lactobacillus reuterit

Lactobacillus reuteri converts glycerol into a potent cell growth inhibitor. This substance, termed reuterin, inlhibits the growth of gram-positive and gram-negative bacteria as well as yeasts, fungi, and protozoa. Semipreparative chromatography was used to purify reuterin, and Fourier transform infrared spectroscopy and liquid chromatography-mass spectrometry were used to establish the molecular weight as well as the molecular functionality of the reuterin molecule. Nuclear magnetic resonance studies of purified reuterin carried out with deuterium oxide confirmed the presence of two three-carbon compounds, I8-hydroxypropionaldehyde and the corresponding hydrated acetal, and a six-carbon cyclic dimer of the aldehyde. Further nuclear magnetic resonance studies with deuterated methanol revealed that in this solvent the compound existed as a three-carbon compound in a methoxy form. Trimethylsilyl derivatives of reuterin were analyzed by gas chromatography-mass spectrometry, and a molecule was identified which had a molecular weight corresponding to a disilylated dimeric structure. On the basis of the above information, reuterin was determined to be an equilibrium mixture of monomeric, hydrated monomeric, and cyclic dimeric forms of l-hydroxypropionaldehyde. This was subsequently confirmed by chemical synthesis.
Manipulation of birefringence in spin-VCSELs

Data centers play an important role in the ongoing demand for higher data rates. Here intensity-modulated vertical-cavity surface-emitting lasers (VCSELs) are the emitters of choice. The intensity dynamics resonance is currently limited to around 30 GHz. Using the much faster polarization dynamics in VCSELs can be a promising alternative. The polarization dynamics resonance is mainly determined by the frequency difference of the two orthogonal linearly polarized modes, which is defined by the birefringence. We have experimentally investigated mechanisms for birefringence manipulation from large-scale down to on-chip solutions. Polarization oscillations with frequencies in excess of 200 GHz have been observed.
Light field displays for near eye VR and AR

A light field display for near eye viewing to reduce the human eye vergence-accommodation conflict (VAC) existing in most stereoscopic VR devices is designed and demonstrated . The 3D perception of the resulting light field is verified by using eye models. The Light Field Displays for Near Eye has verified by focal adjustable camera, and proved its function for free of VAC.
The Impact of No-Loan Program Participation on the Likelihood of Graduate School Enrollment Among Low-Income, First-Generation Students

Abstract Previous research shows that low-income and first-generation college students are less likely to obtain the benefits associated with attending graduate school. No-loan programs, which typically administer financial aid through institutional grants, are designed to improve access and success among students from low-income backgrounds, but we know very little about the influence of no-loan programs after students enroll and eventually graduate from college. This study examines the impact of no-loan program participation on graduate school enrollment by leveraging a novel institutional dataset and applying regression discontinuity, difference-in-differences, and propensity score weighting approaches. Results indicate that no-loan program participation has a positive and relatively consistent impact on graduate school enrollment among low-income and first-generation students.
Toward a Unified Framework for Cognitive Maps

In this study, we integrated neural encoding and decoding into a unified framework for spatial information processing in the brain. Specifically, the neural representations of self-location in the hippocampus (HPC) and entorhinal cortex (EC) play crucial roles in spatial navigation. Intriguingly, these neural representations in these neighboring brain areas show stark differences. Whereas the place cells in the HPC fire as a unimodal function of spatial location, the grid cells in the EC show periodic tuning curves with different periods for different subpopulations (called modules). By combining an encoding model for this modular neural representation and a realistic decoding model based on belief propagation, we investigated the manner in which self-location is encoded by neurons in the EC and then decoded by downstream neurons in the HPC. Through the results of numerical simulations, we first show the positive synergy effects of the modular structure in the EC. The modular structure introduces more coupling between heterogeneous modules with different periodicities, which provides increased error-correcting capabilities. This is also demonstrated through a comparison of the beliefs produced for decoding two- and four-module codes. Whereas the former resulted in a complete decoding failure, the latter correctly recovered the self-location even from the same inputs. Further analysis of belief propagation during decoding revealed complex dynamics in information updates due to interactions among multiple modules having diverse scales. Therefore, the proposed unified framework allows one to investigate the overall flow of spatial information, closing the loop of encoding and decoding self-location in the brain.
Tactical Decisions for Lane Changes or Lane Following? Development of a Study Design for Automated Driving

Overtaking slower vehicles on a highway usually involves lane changes. This paper examines a large number of non-automated as well as automated lane changes on the basis of two datasets. The focus is on the relationship between the velocity of the preceding vehicle being overtaken and the target velocity of the vehicle involved in changing lane and overtaking. Based on this, a study design is developed and should enable human-centered investigation of the preferred points in time for automated lane changes. In order to identify further characteristics of an automated journey that can influence the preferred lane change behavior, expert interviews were conducted, to be taken into consideration in the study design. According to this, non-driving related tasks play an essential role in the proposed driving study.
Sniffing visible light communication through walls

Visible light communication (VLC) is gaining a significant amount of interest as a new paradigm to meet rapidly increasing demands on wireless capacity required by a digitalized world. VLC is considered as a secure wireless communication scheme because VLC signals can be easily constrained within physical boundaries. In this paper, for the first time, we show that VLC is not as secure as people thought: VLC can be sniffed through walls! The key principle behind this is that in VLC transmissions, a VLC transmitter not only emits visible light signals but also leaks out 'side channel RF signals'. The leaked RF signals can be sniffed by a receiver to decode the VLC transmissions even the receiver is blocked (e.g., by walls) from the VLC transmitter. In this work, we establish a theoretical model to quantify the amplitude of the leaked RF signal and verify the model with comprehensive experiments. We design and implement a VLC sniffing system including receiver coil design, signal processing and frame decoding, spanning across hardware and software. Field studies show that with a cheap receiver design, our system can simultaneously sniff transmissions from multiple VLC transmitters 6.4 meters away with a 14 cm concrete wall in between, where the distance exceeds the communication range of most state-of-the-art VLC systems. By simply twining a wired earphone on the arm, we can sniff the VLC transmission 1.9 meters away.
Bringing hybrid analog-digital beamforming to commercial MU-MIMO wifi networks

Commercial off-the-shelf (COTS) IEEE 802.11ac WiFi systems only use a low number of antennas. This limits the multi-user MIMO (MU-MIMO) performance and, hence, the throughput of such systems, especially in dense environments. We present a unique solution based on hybrid digital-analog (HDA) beamforming to overcome the limitation of COTS WiFi hardware and effectively increase the MU-MIMO gain and to unlock the potential of MU-MIMO in WiFi. We use COTS WiFi hardware in combination with a self-developed beamforming module and novel algorithms for the HDA approach, leveraging the MU-MIMO precoding of the WiFi system. The implemented control and signal processing software is fully transparent to the WiFi part, i.e., no protocol changes are needed. We demonstrate the increase in MU-MIMO gain using unmodified COTS end-user terminals.
Scenario-based specification of security protocols and transformation to security model checkers

Security protocols ensure secure communication between and within systems such as internet services, factories, and smartphones. As evidenced by numerous successful attacks against popular protocols such as TLS, designing protocols securely is a tedious and error-prone task. Model checkers greatly aid protocol verification, yet any single model checker is oftentimes insufficient to check a protocol's security in full. Instead, engineers are forced to maintain multiple overlapping and hopefully non-contradicting and non-diverging specifications, one per model-checking tool---an error-prone task. To address this problem, this paper presents VICE, a scenario-based approach to security-protocol verification. It provides a visual modeling language based for specifying security protocols independent of the model checker. It then automatically transforms the relevant fragments of these models into equivalent inputs to multiple model checkers. In result, VICE completely relieves the security engineer from choosing and specifying queries via a fully automatic generation of all necessary queries. Through a case study involving real-world specifications of eight security protocols, we show that VICE is applicable in practice.
SpiroSonic: monitoring human lung function via acoustic sensing on commodity smartphones

Respiratory diseases have been a significant public health challenge. Efficient disease evaluation and monitoring call for daily spirometry tests, as an effective way of pulmonary function testing, out of clinic. This requirement, however, is hard to be satisfied due to the large size and high costs of current spirometry equipments. In this paper, we present SpiroSonic, a new system design that uses commodity smartphones to support complete, accurate yet reliable spirometry tests in regular home settings with various environmental and human factors. SpiroSonic measures the humans' chest wall motion via acoustic sensing and interprets such motion into lung function indices, based on the clinically validated correlation between them. We implemented SpiroSonic as a smartphone app, and verified SpiroSonic's monitoring error over healthy humans as <3%. Clinical studies further show that SpiroSonic reaches 5%-10% monitoring error among 83 pediatric patients. Given that the error of in-clinic spirometry is usually around 5%, SpiroSonic can be reliably used for disease tracking and evaluation out of clinic.
Relationship of genetic polymorphisms in thymidylate synthase and alcohol drinking with risk of colorectal cancer

OBJECTIVE:To investigate influences of two important functionally polymorphisms (5'-UTR and 3'-UTR) in thymidylate synthase (TS) gene and their combinative effects with drinking on the susceptibility of colorectal cancer(CRC). METHODS:A hospital-base,case-control study consisting of 300 incident colorectal cancer cases and 300 controls matched on age and sex was conducted to investigate the association between polymorphisms in TS and susceptibility to colorectal cancer. Genotypes of TS were analyzed by polymerase chain reaction restriction fragment length polymorphism methods. RESULTS:TS5'-UTR 2R/2R showed decreaed risk to CRC with the OR value of 0.35(95%CI:0.12-0.98). Interactions were found between the extent of smoking and TS 5'-UTR and TS 3'-UTR (P=0.006; P=0.001),but in smokers bigger than 16 pack year with TS 3'-UTR gene significantly decreased the risk CRC by 0.17 (95%CI:0.05-0.56). The interation between time limit of drinking and TS 5'-UTR was found (P=0.04). CONCLUSION:The protective factors of CRC is TS 5'-UTR 2R/2R genotype,and there are interactions between TS polymorphisms and smoking or drinking.
RTPTorrent: An Open-source Dataset for Evaluating Regression Test Prioritization

The software engineering practice of automated testing helps programmers find defects earlier during development. With growing software projects and longer-running test suites, frequency and immediacy of feedback decline, thereby making defects harder to repair. Regression test prioritization (RTP) is concerned with running relevant tests earlier to lower the costs of defect localization and to improve feedback. Finding representative data to evaluate RTP techniques is non-trivial, as most software is published without failing tests. In this work, we systematically survey a wide range of RTP literature regarding whether their dataset uses real or synthetic defects or tests, whether they are publicly available, and whether datasets are reused. We observed that some datasets are reused, however, many projects study only few projects and these rarely resemble real-world development activity. In light of these threats to ecological validity, we describe the construction and characteristics of a new dataset, named RTPTorrent, based on 20 open-source Java programs. Our dataset allows researchers to evaluate prioritization heuristics based on version control meta-data, source code, and test results from fine-grained, automated builds over 9 years of development history. We provide reproducible baselines for initial comparisons and make all data publicly available. We see this as a step towards better reproducibility, ecological validity, and long-term availability of studied software in the field of test prioritization.
Recommending the Video to Watch Next: An Offline and Online Evaluation at YOUTV.de

The task “recommend a video to watch next? ” has been in the focus of recommender systems’ research for a long time. However, adequately exploiting the clues hidden in the sequences of actions of user sessions in order to reveal users’ short-term intentions moved only recently into the focus of research. Based on a real-world application scenario, in this paper, we propose a Markov Chain-based transition probability matrix to efficiently reveal the short-term preferences of individuals. We experimentally evaluated our proposed method by comparing it against state-of-the-art algorithms in an offline as well as a live evaluation setting. In both cases our method not only demonstrated its superiority over its competitors, but exposed a clearly stronger engagement of users on the platform. In the online setting, our method improved the click-through rate by up to 93.61%. This paper therefore contributes real-world evidence for improving the recommendation effectiveness, by considering sequence-awareness, since capturing the short-term preferences of users is crucial in the light of items with a short life span such as tv programs (news, tv shows, etc.).
SDR receiver using commodity wifi via physical-layer signal reconstruction

With the explosive increase in wireless devices, physical-layer signal analysis has become critically beneficial across distinctive domains including interference minimization in network planning, security and privacy (e.g., drone and spycam detection), and mobile health with remote sensing. While SDR is known to be highly effective in realizing such services, they are rarely deployed or used by the end-users due to the costly hardware ~1K USD (e.g., USRP). Low-cost SDRs (e.g., RTL-SDR) are available, but their bandwidth is limited to 2-3 MHz and operation range falls well below 2.4 GHz - the unlicensed band holding majority of the wireless devices. This paper presents SDR-Lite, the first zero-cost, software-only software defined radio (SDR) receiver that empowers commodity WiFi to retrieve the In-phase and Quadrature of an ambient signal. With the full compatibility to pervasively-deployed WiFi infrastructure (without any change to the hardware and firmware), SDR-Lite aims to spread the blessing of SDR receiver functionalities to billions of WiFi users and households to enhance our everyday lives. The key idea of SDR-Lite is to trick WiFi to begin packet reception (i.e., the decoding process) when the packet is absent, so that it accepts ambient signals in the air and outputs corresponding bits. The bits are then reconstructed to the original physical-layer waveform, on which diverse SDR applications are performed. Our comprehensive evaluation shows that the reconstructed signal closely reassembles the original ambient signal (>85% correlation). We extensively demonstrate SDR-Lite effectiveness across seven distinctive SDR receiver applications under three representative categories: (i) RF fingerprinting, (ii) spectrum monitoring, and (iii) (ZigBee) decoding. For instance, in security applications of drone and rogue WiFi AP detection, SDR-Lite achieves 99% and 97% accuracy, which is comparable to USRP.
Crosswalk Cooperation: A Phone-Integrated Driver-Vehicle Cooperation Approach to Predict the Crossing Intentions of Pedestrians in Automated Driving

While the implementation of automated driving under well-defined circumstances such as highways is possible today, more complex and dynamic environments such as urban areas remain challenging. In particular, the behavior prediction of other road users like pedestrians can be difficult for automated vehicles. We suggest a cooperative approach to overcome this system limitation. More specifically, the system can rely on the users’ interpretation of the situation to clarify ambiguity. This study explores how passengers can be used to disambiguate pedestrian crossing behavior. Because passengers likely use their phones while traveling in an automated vehicle, we displayed a live view of the traffic scenes as well as a cooperation request to ask about the intentions of the pedestrian at the crosswalk. A preliminary evaluation of usability shows that this approach provides promising results. Participants also reported trusting the system and showed willingness to help an automated vehicle.
ImRec: Learning Reciprocal Preferences Using Images

Reciprocal Recommender Systems are recommender systems for social platforms that connect people to people. They are commonly used in online dating, social networks and recruitment services. The main difference between these and conventional user-item recommenders that might be found on, for example, a shopping service, is that they must consider the interests of both parties. In this study, we present a novel method of making reciprocal recommendations based on image data. Given a user’s history of positive and negative preference expressions on other users images, we train a siamese network to identify images that fit a user’s personal preferences. We provide an algorithm to interpret those individual preference indicators into a single reciprocal preference relation. Our evaluation was performed on a large real-world dataset provided by a popular online dating service. Based on this, our service significantly improves on previous state-of-the-art content-based solutions, and also out-performs collaborative filtering solutions in cold-start situations. The success of this model provides empirical evidence for the high importance of images in online dating.