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In this paper, we analyze the Reddit social network with the goal of identifying “highly interconnected” subreddits. Intuitively, a subreddit is highly interconnected if the users in the subreddit interact a lot with users from other subreddits in the Reddit ecosystem. To identify the highly interconnected subreddits, we used the communication patterns of the users on the Reddit platform. We definde an “interconnectedness score” that was obtained from user interactions across subreddits. This score was used to identify the highly interconnected subreddits. We also leveraged the interactions among users within the subreddits to identify implicit leader–follower relationships within them. Intuitively, an implicit leader in a subreddit is someone who receives a lot of attention from other users, who are the followers. We inferred the implicit leaders using only the responses they received on their posts from other users in the subreddit. Finally, we studied the role played by these implicit leaders within the interconnected subreddits using the idea of a “leaderness score”. For the analysis, we used data obtained from Reddit in 2022 with a custom-built crawler. We analyzed a total of 125,000 subreddits for this work and identified the group of highly interconnected subreddits using the idea of the interconnectedness score. We manually evaluated the content of the posts on the identified interconnected subreddits in order to understand the nature of these subreddits. Our analysis showed that the highly interconnected subreddits discuss content considered to be “not safe/suitable for work” (NSFW). We also observed that though these subreddits were highly interconnected among themselves, they were sparsely connected with other non-NSFW subreddits. Furthermore, we found that the implicit leaders in these subreddits drove majority of the conversations in these groups. These results are socially significant as they can be used to make online social networks safe for the underage population. Thus, our results can be used for enforcing age-based restrictions on access to these NSFW subreddits. Finally, our results also open up the possibility of moderating the content on these subreddits by enforcing content moderation rules on the implicit leaders who drive the conversation in these groups. Finally, though these results are specific to Reddit, the insights obtained from this analysis can be used for analyzing other large-scale online social networks with similar goals to this study.

Deep Neural Networks (DNNs) have become the tool of choice for machine learning practitioners today. One important aspect of designing a neural network is the choice of the activation function to be used at the neurons of the different layers. In this work, we introduce a four-output activation function called the Reflected Rectified Linear Unit (RReLU) activation which considers both a feature and its negation during computation. Our activation function is \"sparse\", in that only two of the four possible outputs are active at a given time. We test our activation function on the standard MNIST and CIFAR-10 datasets, which are classification problems, as well as on a novel Computational Fluid Dynamics (CFD) dataset which is posed as a regression problem. On the baseline network for the MNIST dataset, having two hidden layers, our activation function improves the validation accuracy from 0.09 to 0.97 compared to the well-known ReLU activation. For the CIFAR-10 dataset, we use a deep baseline network that achieves 0.78 validation accuracy with 20 epochs but overfits the data. Using the RReLU activation, we can achieve the same accuracy without overfitting the data. For the CFD dataset, we show that the RReLU activation can reduce the number of epochs from 100 (using ReLU) to 10 while obtaining the same levels of performance.

Toll like receptor 4 (TLR4), a pathogen-associated molecular pattern (PAMP) receptor, is known to exert inflammation in various cases of microbial infection, cancer and autoimmune disorders. However, any such involvement of TLR4 in Chikungunya virus (CHIKV) infection is yet to be explored. Accordingly, the role of TLR4 was investigated towards CHIKV infection and modulation of host immune responses in the current study using mice macrophage cell line RAW264.7, primary macrophage cells of different origins and in vivo mice model. The findings suggest that TLR4 inhibition using TAK-242 (a specific pharmacological inhibitor) reduces viral copy number as well as reduces the CHIKV-E2 protein level significantly using p38 and JNK-MAPK pathways. Moreover, this led to reduced expression of macrophage activation markers like CD14, CD86, MHC-II and pro-inflammatory cytokines (TNF, IL-6, MCP-1) significantly in both the mouse primary macrophages and RAW264.7 cell line, in vitro . Additionally, TAK-242-directed TLR4 inhibition demonstrated a significant reduction of percent E2-positive cells, viral titre and TNF expression in hPBMC-derived macrophages, in vitro . These observations were further validated in TLR4-knockout (KO) RAW cells. Furthermore, the interaction between CHIKV-E2 and TLR4 was demonstrated by immuno-precipitation studies, in vitro and supported by molecular docking analysis, in silico. TLR4-dependent viral entry was further validated by an anti-TLR4 antibody-mediated blocking experiment. It was noticed that TLR4 is necessary for the early events of viral infection, especially during the attachment and entry stages. Interestingly, it was also observed that TLR4 is not involved in the post-entry stages of CHIKV infection in host macrophages. The administration of TAK-242 decreased CHIKV infection significantly by reducing disease manifestations, improving survivability (around 75%) and reducing inflammation in mice model. Collectively, for the first time, this study reports TLR4 as one of the novel receptors to facilitate the attachment and entry of CHIKV in host macrophages, the TLR4-CHIKV-E2 interactions are essential for efficient viral entry and modulation of infection-induced pro-inflammatory responses in host macrophages, which might have translational implication for designing future therapeutics to regulate the CHIKV infection.

Due to the great achievements in the field of optimization of the design of cyclone separators, non-standard solutions are sought to increase their performance. Therefore, in this study, we consider the impact of different cone and cylinder height variants on the performance of cyclone separators. Additionally, we propose non-standard shapes for these sections. Three different heights: H/D = 0.5, 1.0, and 1.5, with D (the main cyclone body diameter), are analyzed. Since the cone is one of the most important geometrical entities, three different shapes viz. a straight (conventional) profile, a concave profile as well as a convex profile are also taken into account. Cyclone performance is rated at three different inlet velocities viz. Uin = 10 m/s, 15 m/s, and 20 m/s. Hence, a total of 27 simulations have been performed using the Reynolds stress model. It becomes apparent from the present study that the pressure loss is lowest in the convex variant, whereas the separation efficiency is better in the conventional design. Furthermore, an increase in the length of the cylindrical section reduces pressure drop with a mild decrease in the collection efficiency in all variants.

Background Transient receptor potential ankyrin 1 (TRPA1) channels are known to be actively involved in various pathophysiological conditions, including neuronal inflammation, neuropathic pain, and various immunological responses. Heat shock protein 90 (Hsp90), a cytoplasmic molecular chaperone, is well-reported for various cellular and physiological processes. Hsp90 inhibition by various molecules has garnered importance for its therapeutic significance in the downregulation of inflammation and are proposed as anti-cancer drugs. However, the possible role of TRPA1 in the Hsp90-associated modulation of immune responses remains scanty. Results Here, we have investigated the role of TRPA1 in regulating the anti-inflammatory effect of Hsp90 inhibition via 17-(allylamino)-17-demethoxygeldanamycin (17-AAG) in lipopolysaccharide (LPS) or phorbol 12-myristate 13-acetate (PMA) stimulation in RAW 264.7, a mouse macrophage cell lines and PMA differentiated THP-1, a human monocytic cell line similar to macrophages. Activation of TRPA1 with Allyl isothiocyanate (AITC) is observed to execute an anti-inflammatory role via augmenting Hsp90 inhibition-mediated anti-inflammatory responses towards LPS or PMA stimulation in macrophages, whereas inhibition of TRPA1 by 1,2,3,6-Tetrahydro-1,3-dimethyl-N-[4-(1-methylethyl)phenyl]-2,6-dioxo-7 H-purine-7-acetamide,2-(1,3-Dimethyl-2,6-dioxo-1,2,3,6-tetrahydro-7 H-purin-7-yl)-N-(4-isopropylphenyl)acetamide (HC-030031) downregulates these developments. LPS or PMA-induced macrophage activation was found to be regulated by TRPA1. The same was confirmed by studying the levels of activation markers (major histocompatibility complex II (MHCII), cluster of differentiation (CD) 80 (CD80), and CD86, pro-inflammatory cytokines (tumor necrosis factor (TNF) and interleukin 6 (IL-6)), NO (nitric oxide) production, differential expression of mitogen-activated protein kinase (MAPK) signaling pathways (p-p38 MAPK, phospho-extracellular signal-regulated kinase 1/2 (p-ERK 1/2), and phosphor-stress-activated protein kinase/c-Jun N-terminal kinase (p-SAPK/JNK)), and induction of apoptosis. Additionally, TRPA1 has been found to be an important contributor to intracellular calcium levels toward Hsp90 inhibition in LPS or PMA-stimulated macrophages. Conclusion This study indicates a significant role of TRPA1 in Hsp90 inhibition-mediated anti-inflammatory developments in LPS or PMA-stimulated macrophages. Activation of TRPA1 and inhibition of Hsp90 has synergistic roles towards regulating inflammatory responses associated with macrophages. The role of TRPA1 in Hsp90 inhibition-mediated modulation of macrophage responses may provide insights towards designing future novel therapeutic approaches to regulate various inflammatory responses.

In this paper we consider the problem of \"end-to-end\" digital camera identification by considering sequence of images obtained from the cameras. The problem of digital camera identification is harder than the problem of identifying its analog counterpart since the process of analog to digital conversion smooths out the intrinsic noise in the analog signal. However it is known that identifying a digital camera is possible by analyzing the camera's intrinsic sensor artifacts that are introduced into the images/videos during the process of photo/video capture. It is known that such methods are computationally intensive requiring expensive pre-processing steps. In this paper we propose an end-to-end deep feature learning framework for identifying cameras using images obtained from them. We conduct experiments using three custom datasets: the first containing two cameras in an indoor environment where each camera may observe different scenes having no overlapping features, the second containing images from four cameras in an outdoor setting but where each camera observes scenes having overlapping features and the third containing images from two cameras observing the same checkerboard pattern in an indoor setting. Our results show that it is possible to capture the intrinsic hardware signature of the cameras using deep feature representations in an end-to-end framework. These deep feature maps can in turn be used to disambiguate the cameras from each another. Our system is end-to-end, requires no complicated pre-processing steps and the trained model is computationally efficient during testing, paving a way to have near instantaneous decisions for the problem of digital camera identification in production environments. Finally we present comparisons against the current state-of-the-art in digital camera identification which clearly establishes the superiority of the end-to-end solution.

Wearable devices are becoming more and more prevalent in our daily lives as people become more curious about how well they are doing in monitoring, improving, or maintaining their health and fitness. Fitness trackers and smartwatches have become almost ubiquitous, so these devices have begun to play a critical role in forensic investigations. In this paper, the authors conducted a forensic analysis of the controlling applications for three popular fitness bands and smartwatches (i.e., Amazon Halo, Garmin Connect, and Mobvoi) on an Android smartphone device to (1) provide forensic investigators with a road-map of forensically relevant data that are stored within these applications and (2) highlight any privacy concerns that the stored data within these applications may present to the applications’ users. Our findings indicate that the three fitness applications store a wealth of user data. In particular, the Amazon Halo app stores daily, weekly, and monthly activity-related data for at least the last 13 days. The user’s Tone Analysis results were also recovered. The Garmin Connect application also records detailed user activity information, as it was possible to recover the last 15 days worth of user activity data. The Garmin Connect user’s general location was also determined via the application’s weather notification feature. Lastly, the Mobvoi application records all data points from the time the device is first used until the last time the device is used. These data points may include heart rates taken every 5 min and step counts. Our findings highlight the possibility of collecting personally identifiable information about users of these devices and apps, including their profile information, habits, location, and state of mind. These findings would be pertinent to forensic investigators in the event that these or similar applications are part of an investigation.

In this paper we present a large scale, passive positioning system that can be used for approximate localization in Global Positioning System (GPS) denied/spoofed environments. This system can be used for detecting GPS spoofing as well as for initial position estimation for input to other GPS free positioning and navigation systems like Terrain Contour Matching (TERCOM). Our Location inference through Frequency Modulation (FM) Signal Integration and estimation (LoSI) system is based on broadcast FM radio signals and uses Received Signal Strength Indicator (RSSI) obtained using a Software Defined Radio (SDR). The RSSI thus obtained is used for indexing into an estimated model of expected FM spectrum for the entire United States. We show that with the hardware for data acquisition, a single point resolution of around 3 miles and associated algorithms, we are capable of positioning with errors as low as a single pixel (more precisely around 0.12 mile). The algorithm uses a large-scale model estimation phase that computes the expected FM spectrum in small rectangular cells (realized using geohashes) across the Contiguous United States (CONUS). We define and use Dominant Channel Descriptor (DCD) features, which can be used for positioning using time varying models. Finally we use an algorithm based on Euclidean nearest neighbors in the DCD feature space for position estimation. The system first runs a DCD feature detector on the observed spectrum and then solves a subset query formulation to find Inference Candidates (IC). Finally, it uses a simple Euclidean nearest neighbor search on the ICs to localize the observation. We report results on 1500 points across Florida using data and model estimates from 2015 and 2017. We also provide a Bayesian decision theoretic justification for the nearest neighbor search.

The title molecules are sought in connection with various synthetic applications. The aliphatic fluorous alcohols R f n CH 2 OH (R f n = CF 3 (CF 2 ) n –1 ; n = 11, 13, 15) are converted to the triflates R f n CH 2 OTf (Tf 2 O, pyridine; 22–61%) and then to R f n CH 2 I (NaI, acetone; 58–69%). Subsequent reactions with NaOCl/HCl give iodine(III) dichlorides R f n CH 2 ICl 2 ( n = 11, 13; 33–81%), which slowly evolve Cl 2 . The ethereal fluorous alcohols CF 3 CF 2 CF 2 O(CF(CF 3 )CF 2 O) x CF(CF 3 )CH 2 OH ( x = 2–5) are similarly converted to triflates and then to iodides, but efforts to generate the corresponding dichlorides fail. Substrates lacking a methylene group, R f n I, are also inert, but additions of TMSCl to bis(trifluoroacetates) R f n I(OCOCF 3 ) 2 appear to generate R f n ICl 2 , which rapidly evolve Cl 2 . The aromatic fluorous iodides 1,3-R f6 C 6 H 4 I, 1,4-R f6 C 6 H 4 I, and 1,3-R f10 C 6 H 4 I are prepared from the corresponding diiodides, copper, and R f n I (110–130 °C, 50–60%), and afford quite stable R f n C 6 H 4 ICl 2 species upon reaction with NaOCl/HCl (80–89%). Iodinations of 1,3-(R f6 ) 2 C 6 H 4 and 1,3-(R f8 CH 2 CH 2 ) 2 C 6 H 4 (NIS or I 2 /H 5 IO 6 ) give 1,3,5-(R f6 ) 2 C 6 H 3 I and 1,2,4-(R f8 CH 2 CH 2 ) 2 C 6 H 3 I (77–93%). The former, the crystal structure of which is determined, reacts with Cl 2 to give a 75:25 ArICl 2 /ArI mixture, but partial Cl 2 evolution occurs upon work-up. The latter gives the easily isolated dichloride 1,2,4-(R f8 CH 2 CH 2 ) 2 C 6 H 3 ICl 2 (89%). The relative thermodynamic ease of dichlorination of these and other iodine(I) compounds is probed by DFT calculations.

Despite intensive effort, the majority of the annotated Mycobacterium tuberculosis genome consists of genes encoding proteins of unknown or poorly understood function. For example, there are seven conserved hypothetical proteins annotated as homologs of pyridoxine 5'-phosphate oxidase (PNPOx), an enzyme that oxidizes pyridoxine 5'-phosphate (PNP) or pyridoxamine 5'-phosphate (PMP) to form pyridoxal 5'-phosphate (PLP). We have characterized the function of Rv2607 from Mycobacterium tuberculosis H37Rv and shown that it encodes a PNPOx that oxidizes PNP to PLP. The k(cat) and K(M) for this reaction were 0.01 s(-1) and 360 µM, respectively. Unlike many PNPOx enzymes, Rv2607 does not recognize PMP as a substrate.