Miracle-dz/UniFAR-FTUs huggingface dataset:
Mesenteric Lymph Node Transplantation in Mice to Study Immune Responses of the Gastrointestinal Tract

Mesenteric lymph nodes (mLNs) are sentinel sites of enteral immunosurveillance and immune homeostasis. Immune cells from the gastrointestinal tract (GIT) are constantly recruited to the mLNs in steady-state and under inflammatory conditions resulting in the induction of tolerance and immune cells activation, respectively. Surgical dissection and transplantation of lymph nodes (LN) is a technique that has supported seminal work to study LN function and is useful to investigate resident stromal and endothelial cell biology and their cellular interactions in experimental disease models. Here, we provide a detailed protocol of syngeneic mLN transplantation and report assays to analyze effective mLN engraftment in congenic recipients. Transplanted mLNs allow to study T cell activation and proliferation in preclinical mouse models. Donor mLNs proved viable and functional after surgical transplantation and regenerated blood and lymphatic vessels. Immune cells from the host completely colonized the transplanted mLNs within 7-8 weeks after the surgical intervention. After allogeneic hematopoietic cell transplantation (allo-HCT), adoptively transferred allogeneic CD4+ T cells from FVB/N (H-2q) mice homed to the transplanted mLNs in C57BL/6 (H-2b) recipients during the initiation phase of acute graft-versus-host disease (aGvHD). These CD4+ T cells retained full proliferative capacity and upregulated effector and gut homing molecules comparable to those in mLNs from unmanipulated wild-type recipients. Wild type mLNs transplanted into MHCII deficient syngeneic hosts sufficed to activate alloreactive T cells upon allogeneic hematopoietic cell transplantation, even in the absence of MHCII+ CD11c+ myeloid cells. These data support that orthotopically transplanted mLNs maintain physiological functions after transplantation. The technique of LN transplantation can be applied to study migratory and resident cell compartment interactions in mLNs as well as immune reactions from and to the gut under inflammatory and non-inflammatory conditions.
Rapid Terrain Assessment for Earthquake-Triggered Landslide Susceptibility With High-Resolution DEM and Critical Acceleration

Earthquake ground motion often triggers landslides in mountainous areas. A simple, robust method to quickly evaluate the terrain’s susceptibility of specific locations to earthquake-triggered landslides is important for planning field reconnaissance and rescues after earthquakes. Different approaches have been used to estimate coseismic landslide susceptibility using Newmark’s sliding block model. This model requires an estimate of the landslide depth or thickness, which is a difficult parameter to estimate. We illustrate the use of Newmark sliding block’s critical acceleration for a glaciated valley affected by the 2015 Gorkha earthquake in Nepal. The landslide data came from comparing high-resolution pre- and post-earthquake digital elevation models (DEMs) derived from Spot 6/7 images. The areas where changes were detected provided an inventory of all the landslides triggered by the earthquake. The landslide susceptibility was modeled in a GIS environment using as inputs the pre-earthquake terrain and slope angles, the peak ground acceleration from the 2015 Gorkha earthquake, and a geological map. We exploit the depth information for the landslides (obtained by DEM difference) to apply the critical acceleration model. The spatial distribution of the predicted earthquake-triggered landslides matched the actual landslides when the assumed landslide thickness in the model is close to the median value of the actual landslide thickness (2.6 m in this case). The landslide predictions generated a map of landslide locations close to those observed and demonstrated the applicability of critical acceleration for rapidly creating a map of earthquake-triggered landslides.
A mercapto functionalized magnetic Zr-MOF by solvent-assisted ligand exchange for Hg2+ removal from water

A facile preparation of magnetic MOF composites with flexible mercapto group pendants in their pores is realized through solvent-assisted ligand exchange (SALE) under mild conditions in water, and terephthalate in UIO-66 is replaced with mercaptoacetic acid (MAA). After the SALE processing, the crystallinity, porosity, and superparamagnetism of the mercapto functionalized magnetic Zr-MOF (MFC-S) are well maintained, and the obtained MFC-S exhibits a higher adsorption capacity (282 mg g−1) for Hg2+ than that of the magnetic Zr-MOF (MFC-O). Furthermore, the prepared MFC-S can be regenerated easily by liquid desorption after adsorption of metal ions and be reused more than 5 times without obvious decrease in removal performance. The proposed SALE method for the preparation of mercapto functionalized magnetic MOF composites (MFCs) is easy-to-operate and low cost, providing not only a reliable porous MFC platform for the removal of metal ions, but also a simple and efficient synthesis route for the preparation of functionalized MFCs which cannot be achieved through conventional approaches.
Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases—PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.
Asymmetric Underlying Mechanisms of Relation-Based and Property-Based Noun–Noun Conceptual Combination

Conceptual combination is a fundamental human cognitive ability by which people can experience infinite thinking by artfully combining finite knowledge. For example, one can instantly combine “cactus” and “fish” together as “prickly fish” even if one has never previously heard of a “cactus fish. ” Although two major combinatorial types—property and relational combinations—have been identified, the underlying processes of each remain elusive. This study investigates the asymmetric processing mechanisms underlying property and relational combinations by examining differential semantic activation during noun–noun conceptual combination. Across two experiments utilizing each combinatorial process as semantic priming and implementing a lexical decision task immediately after combination, we measure and compare the semantic activation patterns of intrinsic and extrinsic semantic features in these two combinatorial types. We found converging evidence that property and relational combinations involve asymmetric semantic information and entail distinct processing mechanisms. In property combination, the intrinsic feature in the modifier concept showed greater activation than the semantic feature of the same dimension in the head concept. In contrast, in relational combination, the extrinsic semantic feature in the head concept and the whole modifier concept showed similar levels of activation. Moreover, our findings also showed that these patterns of semantic activation occurred only when the combinatorial process was complete, indicating that accessing the same lexical-semantic information is not sufficient to observe asymmetric patterns. These findings demonstrate that property combination involves replacing a specific semantic feature of the head noun with that of the modifier noun, whereas relational combination involves completing the semantic feature of the head noun with the whole modifier concept. We discuss the implications of these findings, research limitations, and future research directions.
Contribution of Low-Level Acoustic and Higher-Level Lexical-Semantic Cues to Speech Recognition in Noise and Reverberation

Masking noise and reverberation strongly influence speech intelligibility and decrease listening comfort. To optimize acoustics for ensuring a comfortable environment, it is crucial to understand the respective contribution of bottom-up signal-driven cues and top-down linguistic-semantic cues to speech recognition in noise and reverberation. Since the relevance of these cues differs across speech test materials and training status of the listeners, we investigate the influence of speech material type on speech recognition in noise, reverberation and combinations of noise and reverberation. We also examine the influence of training on the performance for a subset of measurement conditions. Speech recognition is measured with an open-set, everyday Plomp-type sentence test and compared to the recognition scores for a closed-set Matrix-type test consisting of syntactically fixed and semantically unpredictable sentences (c.f. data by Rennies et al., J. Acoust. Soc. America, 2014, 136, 2642–2653). While both tests yield approximately the same recognition threshold in noise in trained normal-hearing listeners, their performance may differ as a result of cognitive factors, i.e., the closed-set test is more sensitive to training effects while the open-set test is more affected by language familiarity. All experimental data were obtained at a fixed signal-to-noise ratio (SNR) and/or reverberation time set to obtain the desired speech transmission index (STI) values of 0.17, 0.30, and 0.43. respectively, thus linking the data to STI predictions as a measure of pure low-level acoustic effects. The results confirm the consistent difference between robustness to reverberation observed in the literature between the matrix type sentences and the Plomp-type sentences, especially for poor and medium speech intelligibility. The robustness of the closed-set matrix type sentences against reverberation disappeared when listeners had no a priori knowledge about the speech material (sentence structure and words used), thus demonstrating the influence of higher-level lexical-semantic cues in speech recognition. In addition, the consistent difference between reverberation- and noise-induced recognition scores of everyday sentences for medium and high STI conditions and the differences between Matrix-type and Plomp-type sentence scores clearly demonstrate the limited utility of the STI in predicting speech recognition in noise and reverberation.
Measuring Privileged Identity in Educational Environments: Development and Validation of the Privileged Identify Exploration Scale

The present study describes the development and validation of an instrument to measure defensive reactions individuals display in difficult dialogues while exploring privileged identities and interacting across difference. The increased focus on difficult dialogues when exploring privileged social identities in educational environments points to a need for the Privileged Identity Exploration Scale (PIE-S). The Privileged Identity Exploration Model (PIE) (Watt, College Student Affairs Journal., 2007, 26, 114–126; Watt et al., Counselor Education and Supervision., 2009, 49, 86–105) identifies eight defensive reactions. Using exploratory and confirmatory factor analysis, we identified and confirmed four constructs of privileged identity exploration that students exhibit when interacting across social differences, the PIE Scale (PIE-S). We provide a brief overview of the development of the PIE-S, as well as future directions for research and applications to training and facilitation in various educational settings.
Commentary: Unbiasing Genome-Based Analyses of Selection: An Example Using Iconic Shark Species

Department of Biology, Trustee Science Center, Drury University, Springfield, MO, United States, 2 Population Medicine and Diagnostic Sciences, Cornell College of Veterinary Medicine, Cornell University, Ithaca, NY, United States, 3 School of Natural Sciences, California State University Monterey Bay, Seaside, CA, United States, Department of Biological Sciences, Clemson University, Clemson, SC, United States, 5 Save Our Seas Shark Research Center, Nova Southeastern University, Dania Beach, FL, United States, Guy Harvey Oceanographic Center, Halmos College of Natural Sciences and Oceanography, Nova Southeastern University, Ft. Lauderdale, FL, United States, 7 Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal, Department of Biology, Faculty of Sciences, University of Porto, Porto, Portugal, Guy Harvey Research Institute, Nova Southeastern University, Dania Beach, FL, United States
Survey On Aspect Based Sentiment Analysis Using Machine Learning Techniques

; Web 2.0 facilitates the expression of views through diverse Internet applications which serve as a rich source of information. The textual expressions have latent information that when processed and analysed reveal the sentiment of the user/people. This is known as sentiment analysis, which is the process of computationally extracting the opinions and viewpoints from textual data and it is also known as opinion mining, review mining or attitude mining, etc. Aspect-level sentiment analysis is one among the three main types of sentiment analysis, where granule level processing takes place in which the different aspects of entities are harnessed to identify the sentiment orientations. The emergence of machine learning and deep learning techniques has made a striking mark towards aspect-oriented sentiment analysis. This paper presents a survey and review of different works from the recent literature on aspect-based sentiment analysis done using machine learning techniques.
Separation of Hexane Isomers in Metal-Organic Frameworks (MOFs)

The main goals of this research are the synthesis of new specific adsorbents named Metal-Organic Frameworks (MOFs) for the separation of hexane isomers in order to improve the octane number of the gasoline. The separation of hexane isomers is actually performed using the conventional Total Isomerization Processes (TIP) with zeolite 5A which isolates only «non-normal paraffins». In order to improve and to test other alternatives, flexible and rigid frameworks were synthesized, performing a set of breakthrough curves with hexane isomers nHEX, 3MP, 22DMB and 23DMB with the purpose of obtaining adsorption equilibrium isotherms and further analysis of their performances in order to find new frameworks that offer better results. This concerned first the rigid frameworks UiO-66(Zr) functionalized with the functional groups –Br, –NO2, and –NH2; the mesoporous MIL-100(Cr) and its functionalized analogue MIL-100(Cr) grafted with alkylamines, the microporous Ti MOF MIL-125 functionalized with the functional group –NH2 and the iron tetracarboxylate MIL-127(Fe). The flexible frameworks were the Zn imidazolate ZIF-8, the iron(III) dicarboxylates MIL-53(Fe) functionalized with the functional groups –(CF3)2 and –2CH3 and the MIL-88 functionalized with the functional group –2CF3. The characterization of these crystalline adsorbents was achieved by X-Ray Powder Diffraction (XRPD), Infra-Red spectroscopy (IR), Thermogravimetric Analysis (TGA) and nitrogen surface area measurement. MIL-53(Fe)–(CF3)2 and ZIF-8 demonstrated molecular sieve effects with interesting and promossing behaviour for hexane isomers separation.
SMART ENERGY MANAGEMENT SYSTEM

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - This paper describes a smart campus management system (SCEMS) that will reduce the energy consumption of all electrically powered devices in a campus environment by using a mobile application, a Bluetooth Low Energy Beacons (BLE Beacon), Microcontroller, different sensors. BLE Beacons and mobile application determines whether a user enters or exits the room. Sensors will determine the position of the user inside the room, includes whether the user is walking or not. This paper enables the automatic control of electrical appliances thus reducing electricity costs. The proposed system will cut off the power of appliances when the user is not around them. There by reducing the energy consumption and ensures a cost effective energy usage
Lane- Free Microscopic Simulation for Connected and Automated Vehicles

This paper presents the ongoing development of the microscopic TrafficFluid-Sim simulator, aimed primarily for Connected and Automated Vehicles (CAVs) under a novel lane-free traffic paradigm. In particular, TrafficFluid-Sim builds on the SUMO simulation infrastructure to model traffic environments featuring two novel vehicle characteristics: (i) Vehicles can be located at any arbitrary lateral position within the road boundaries; and (ii) Vehicles may exert, based on their automated driving and connectivity capabilities, “vehicle nudging” to other surrounding vehicles. As such, TrafficFluid-Sim enables simulation of novel CAV movement strategies for various types of road infrastructure and is an indispensable tool for the design, testing and evaluation of the characteristics of a future CAV traffic flow as an efficient artificial fluid, as envisaged by the ongoing TrafficFluid ERC project.
A Study on Exploitable DRDoS Amplifiers in Europe

—One of the best-known cyber attacks, distributed denial of service (DDoS), is evolving. It has become much more malefic and effective with the use of amplification power of reflected messages. This attack is known as the distributed reflected denial of service (DRDoS) or the amplification attack. Attackers abuse UDP-based protocols’ connectionless property for this attack and achieve an attack volume of hundreds of Gbps. The attack occurs by botnets’ spoofing a victim’s IP address and demanding some service from unhardened servers. Attackers generally prefer protocols that have high a “amplification factor” such as NTP and Memcached, or protocols where it is hard to differentiate legal requests from malicious ones, such as DNS. At this point, an important defensive strategy against these attacks is to harden servers not to play a role as amplifiers. In this paper, we carried out a detailed research of servers in 41 European countries and focused on three UDP-based protocols most commonly abused by attackers: DNS, NTP, and Memcached. We searched these servers by automatic regional scans and analyzed whether they have been hardened against DRDoS attacks.
Providing Feedback and UI Context to Programmers Writing Web Automation Scripts

—To more efﬁciently and effectively perform frequent, tedious, or inaccessible web tasks, web users can leverage web automation macros to programmatically click, type, and perform other page operations. Developers commonly use automation libraries such as Selenium to write custom web automation scripts, but this comes with challenges. A developer must identify robust navigation and element selection logic that works across a variety of inputs and website pages. In particular it can be challenging to choose meaningful and robust CSS selectors that query the appropriate user interface (UI) elements from the DOM. We believe developers need better tools for understanding the effects their script has on a website. We built a prototype web automation IDE that embeds UI snapshots, provides live feedback on CSS selectors, and presents these across different scenarios so the programmer can assess script robustness. We will demo our prototype to get feedback and to discuss the challenges and opportunities of UI automation.
Determinants of phytoplankton size structure in warm, shallow lakes

ŞEYDA ERDOĞAN1,2,*, MERYEM BEKLİOĞLU2,3,*, ELENA LITCHMAN4,5,*, ELIZABETH T. MILLER6, ETİ E. LEVİ2,7, TUBA BUCAK2,8 AND ÜLKÜ NİHAN TAVŞANOĞLU2,9 department of biology, faculty of science and art, yozgat bozok university, erdoğan akdağ kampüsü atatürk yolu 7. km, 66900 yozgat, turkey, limnology laboratory, department of biological sciences, middle east technical university, universiteler mahallesi, dumlupinar bulvarı, no. 1, 06800, ankara, turkey, ecosystem research and implementation centre (ekosam), department of biological sciences, middle east technical university universiteler mahallesi, dumlupinar bulvarı, no. 1, 06800 ankara, turkey, w.k. kellogg biological station, michigan state university, 3700 east gull lake drive, hickory corners, mi 49060, usa, program in ecology, evolutionary biology and behavior, michigan state university, 293 farm lane, east lansing, mi 48840, usa. , university of oregon, department of biology, institute of ecology and evolution, 272 onyx bridge, eugene, or 97403, usa, aarhus university, department of bioscience, nordre ringgade 1, 8000, silkeborg, denmark, the nature conservation centre (dkm), çığdem mahallesı, 1594, 06460, ankara, turkey and Çankırı karatekin university, eldivan vocational school of health services, department environmental health program, Çay mahallesı gazıosmanpaşa caddesi no:4 18100, Çankırı, turkey