Miracle-dz/UniFAR-FTUs huggingface dataset:
A Small Molecule TrkB Neurotrophin Receptor Partial Agonist as Possible Treatment for Experimental Nonarteritic Anterior Ischemic Optic Neuropathy

ABSTRACT Purpose Brain-derived neurotrophic factor (BDNF) and activation of its high affinity receptor tropomyosin kinase (Trk) B promote retinal ganglion cells (RGCs) survival following injury. In this study, we tested the effects of LM22A-4, a small molecule TrkB receptor-specific partial agonist, on RGC survival in vitro and in experimental nonarteritic anterior ischemic optic neuropathy (AION), the most common acute optic neuropathy in those older than 50 years. Methods We assessed drug effects on immunopanned, cultured RGCs and calculated RGC survival and assessed TrkB receptor activation by mitogen-activated protein (MAP) kinase translocation. To assess effects in vivo, we induced murine AION and treated the animals with one intravitreal injection and three-week systemic treatment. We measured drug effects using serial spectral-domain optical coherence tomography (OCT) and quantified retinal Brn3A+ RGC density three weeks after ischemia. Results In vitro, LM22A-4 significantly increased the survival of cultured RGCs at day 2 (95% CI control: 8.4–13.6; LM22A-4: 23.7–30.3; BDNF: 24.3–29.9; P ≤ 0.0001), similar to the effect of the endogenous TrkB receptor ligand BDNF. There was also significant nuclear and cytoplasmic translocation of MAP kinase (95% CI control: 0.9–6.8; LM22A-4: 38.8–84.4; BDNF: 64.0–93.0; P = 0.0002), a known downstream event of TrkB receptor activation. Following AION, LM22A-4 treatment led to significant preservation of the ganglion cell complex (95% CI: AION-PBS: 66.8–70.7%; AION-LM22A-4: 70.0–73.1; P = 0.03) and total retinal thickness (95% CI: AION-PBS: 185–196%; AION-LM22A-4: 195–203; P = 0.002) as measured by OCT compared with non-treated eyes. There was also significant rescue of the Brn3A+ RGC density on morphometric analysis of whole mount retinae (95% CI control: 2360–2629; AION-PBS: 1647–2008 cells/mm2; AION-LM22A-4: 1958–2216 cells/mm2; P = 0.02). Conclusions TrkB receptor partial agonist LM22A-4 promoted survival of cultured RGCs in vitro by TrkB receptor activation, and treatment in vivo led to increased survival of RGCs after optic nerve ischemia, providing support that LM22A-4 may be effective therapy to treat ischemic optic neuropathy. Abbreviations AION: anterior ischemic optic neuropathy, BDNF: Brain-derived neurotrophic factor, GCC: ganglion cell complex, MAP: mitogen-activated protein, OCT: spectral-domain optical coherence tomography, OD: right eye, ON: optic nerve, ONH: optic nerve head, OS: left eye, RGC: retinal ganglion cell; Trk: tropomyosin kinase
Fundamental concepts of reactive control for autonomous drones

Autonomous drones represent a new breed of mobile computing system. Compared to smartphones and connected cars that only opportunistically sense or communicate, drones allow motion control to become part of the application logic. The efficiency of their movements is largely dictated by the low-level control enabling their autonomous operation based on high-level inputs. Existing implementations of such low-level control operate in a time-triggered fashion. In contrast, we conceive a notion of reactive control that allows drones to execute the low-level control logic only upon recognizing the need to, based on the influence of the environment onto the drone operation. As a result, reactive control can dynamically adapt the control rate. This brings fundamental benefits, including more accurate motion control, extended lifetime, and better quality of service in end-user applications. Based on 260+ hours of real-world experiments using three aerial drones, three different control logic, and three hardware platforms, we demonstrate, for example, up to 41% improvements in motion accuracy and up to 22% improvements in flight time.
Role of Mitochondria in Regulating Lutein and Chlorophyll Biosynthesis in Chlorella pyrenoidosa under Heterotrophic Conditions

The green alga Chlorella pyrenoidosa can accumulate lutein and chlorophyll under heterotrophic conditions. We propose that the mitochondrial respiratory electron transport chain (mRET) may be involved in this process. To verify this hypothesis, algal cells were treated with different mRET inhibitors. The biosynthesis of lutein and chlorophyll was found to be significantly stimulated by salicylhydroxamic acid (SHAM), whereas their contents substantially decreased after treatment with antimycin A and sodium azide (NaN3). Proteomic studies revealed profound protein alterations related to the redox and energy states, and a network was proposed: The up-regulation of peroxiredoxin reduces oxidized glutathione (GSSG) to reduced glutathione (GSH); phosphoenolpyruvate carboxykinase (PEPCK) catalyzes the conversion of oxaloacetic acid to phosphoenolpyruvate, and after entering the methylerythritol phosphate (MEP) pathway, 4-hydroxy-3-methylbut-2-en-1yl diphosphate synthase reduces 2-C-methyl-d-erythritol-2,4-cyclodiphosphate (ME-Cpp) to 1-hydroxy-2-methyl-2-(E)-butenyl 4-diphosphate (HMBPP), which is closely related to the synthesis of lutein; and coproporphyrinogen III oxidase and ChlI play important roles in the chlorophyll biosynthetic pathway. These results supported that for the heterotrophic C. pyrenoidosa, the signaling, oriented from mRET, may regulate the nuclear genes encoding the enzymes involved in photosynthetic pigment biosynthesis.
Ostracism drives group moralization and extreme group behavior

ABSTRACT Sharing beliefs, particularly moral beliefs, is a way to establish social connections. We hypothesized that ostracism leads people who are high in the need to belong to adhere to the moral beliefs of an ingroup, and that moralizing the beliefs of one’s group increases the willingness to endorse extreme behavior on behalf of the group. Across two studies, participants were ostracized or included, rated the moral relevance of their group values, and indicated their endorsement of extreme behavior on behalf of the group. Across studies, ostracism increased group moralization in participants high in the need to belong. In Study 2, group moralization translated into endorsement of extreme behavior. Our findings suggest that morality serves a binding function that may be channeled into extreme behaviors. (120 words)
Brain-Permeant and -Impermeant Inhibitors of Fatty Acid Amide Hydrolase Synergize with the Opioid Analgesic Morphine to Suppress Chemotherapy-Induced Neuropathic Nociception Without Enhancing Effects of Morphine on Gastrointestinal Transit

Opioid-based therapies remain a mainstay for chronic pain management, but unwanted side effects limit therapeutic use. We compared efficacies of brain-permeant and -impermeant inhibitors of fatty acid amide hydrolase (FAAH) in suppressing neuropathic pain induced by the chemotherapeutic agent paclitaxel. Paclitaxel produced mechanical and cold allodynia without altering nestlet shredding or marble burying behaviors. We compared FAAH inhibitors that differ in their ability to penetrate the central nervous system for antiallodynic efficacy, pharmacological specificity, and synergism with the opioid analgesic morphine. (3′-(aminocarbonyl)[1,1′-biphenyl]- 3-yl)-cyclohexylcarbamate (URB597), a brain-permeant FAAH inhibitor, attenuated paclitaxel-induced allodynia via cannabinoid receptor 1 (CB1) and cannabinoid receptor 2 (CB2) mechanisms. URB937, a brain-impermeant FAAH inhibitor, suppressed paclitaxel-induced allodynia through a CB1 mechanism only. 5-[4-(4-cyano-1-butyn-1-yl)phenyl]-1-(2,4-dichlorophenyl)-N-(1,1-dioxido-4-thiomorpholinyl)-4-methyl-1H-pyrazole-3-carboxamide (AM6545), a peripherally restricted CB1 antagonist, fully reversed the antiallodynic efficacy of N-cyclohexyl-carbamic acid, 3′-(aminocarbonyl)-6-hydroxy[1,1′- biphenyl]-3-yl ester (URB937) but only partially reversed that of URB597. Thus, URB937 suppressed paclitaxel-induced allodynia through a mechanism that was dependent upon peripheral CB1 receptor activation only. Antiallodynic effects of both FAAH inhibitors were reversed by N-(piperidin-1-yl)-5-(4-iodophenyl)-1-(2,4-dichlorophenyl)-4-methyl-1H-pyrazole-3-carboxamide (AM251). Antiallodynic effects of URB597, but not URB937, were reversed by 6-iodo-2-methyl-1-[2-(4-morpholinyl)ethyl]-1H-indol-3-yl](4-methoxyphenyl)methanone (AM630). Isobolographic analysis revealed synergistic interactions between morphine and either URB597 or URB937 in reducing paclitaxel-induced allodynia. A leftward shift in the dose-response curve of morphine antinociception was observed when morphine was coadministered with either URB597 or URB937, consistent with morphine sparing. However, neither URB937 nor URB597 enhanced morphine-induced deficits in colonic transit. Thus, our findings suggest that FAAH inhibition may represent a therapeutic avenue to reduce the overall amount of opioid needed for treating neuropathic pain with potential to reduce unwanted side effects that accompany opioid administration.
Discriminative Kernel Matrix for Domain Adaptation

In this paper, we investigate the unsupervised domain transfer learning in which there is no label in the target samples while the source samples are all labeled. In our approach the target and source samples are transferred to a new domain and each target sample is constructed by from the linear combination of the source samples in the new transformed domain. The low-rank and sparse constraints are imposed on the reconstruction coefficient matrix which maintains the local and global structure of the samples in the transferred domain. In this paper, the information content of the reconstruction coefficient matrix is utilized in order to consider the discriminative ability of the source samples. Here, we utilize the max-margin classifier in which the kernel matrix is defined using the reconstruction coefficient matrix. To evaluate the proposed method, it is applied on Office and Caltech-256 datasets. The experimental results show that our proposed approach is performed better than the state-of-the-art approaches.
Circular RNAs: Methodological challenges and perspectives in cardiovascular diseases

Circular RNAs are generated by back‐splicing of precursor‐mRNAs. Although they have been known for many years, only recently we have started to appreciate their widespread expression and their regulatory functions in a variety of biological processes. Not surprisingly, circular RNA dysregulation and participation in the pathogenic mechanisms have started to emerge in many instances, including cardiovascular diseases. Detection, differential expression analysis and validation are the three critical points for the characterization of any RNA, and circular RNAs are no exception. Their characteristics, however, generate several problems that are yet to be completely addressed, and literature still lacks comprehensive definitions of well‐defined best practices. We present a map of the current knowledge regarding circular RNAs and the critical issues limiting our understanding of their regulation and function. The goal was to provide the readers with the tools to critically decide which of the many approaches available is most suitable to their experimental plan. Although particularly focused on cardiovascular diseases, most critical issues concerning circular RNAs are common to many other fields of investigation.
MATESC: Metadata-Analytic Text Extractor and Section Classifier for Scientific Publications

This paper addresses the task of extracting free-text sections from scientific PDF documents, and specifically the problem of formatting disparity among different publications, by analysing their metadata. For the purpose of extracting procedural knowledge in the form of recipes from papers, and for the application domain of nanomaterial synthesis, we present Metadata-Analytic Text Extractor and Section Classifier (MATESC), a heuristic rule-based pattern analysis system for text extraction and section classification from scientific literature. MATESC extracts text spans and uses metadata features such as spatial layout location, font type, and font size to create grouped blocks of text and classify them into groups and subgroups based on rules that characterize specific paper sections. The main purpose of our tool is to facilitate information and semantic knowledge extraction across different domain topics and journal formats. We measure the accuracy of MATESC using string matching algorithms to compute alignment costs between each section extracted by our tool and manually-extracted sections. To test its transferability across domains, we measure its accuracy on papers that are relevant to the papers that were used to determine our rule-based methodology and also on random papers crawled from the web. In the future, we will use natural language processing to improve paragraph grouping and classification.
Improved Block-based Technique using SURF and FAST Keypoints Matching for Copy-Move Attack Detection

Due to the advancement of image manipulation tool or techniques, the copy-move attack detection from digital images has become the challenging and active research area. This paper proposes an improved block-based technique for copy-move attack detection using Speeded Up Robust Features (SURF) and Features from Accelerated Segment Test (FAST) keypoint matching. In the first phase of this technique, the image is divided into non-overlapping blocks and SURF descriptors are extracted from each block. These descriptors are matched using 2NN procedure and match blocks are identified. In the second phase, large blocks are constituted by concatenating the neighboring blocks of each matching block. Thereafter, from each large block FAST features points are extracted and matched using 2NN. Finally, the affine transform is applied to remove the outliers if any. The proposed technique is tested using MICC-F220 and MICC-F2000 standard datasets and it yields better performance in comparison with state of the art techniques.
Fairness-Aware Pattern-Based Multiple Access for Multi-Group Multicast Systems

In this paper, we propose pattern-based multicast transmission in a multi-group environment with fairness-aware resource allocation. Traditionally, pattern division multiple access (PDMA) determines the allocation of available radio resources to individual users. In the proposed work, we develop a scheme to assign unique patterns or signatures to multicast groups sharing the same radio resources. When allocating resources to a group, we deal with channel state information (CSI) for all users within the multicast group as to optimize the fairness parameter representing the achievable data rates of different users. In our scheme, to differentiate signals from various multicast groups, we consider resource blocks spanning multiple aspects of signal separation. Within this paper, we demonstrate the principles of the proposed scheme by working with code and frequency allocation. Specifically, PDMA is developed to improve the throughput for each multicast group while maximizing fairness among the groups. Simulation results demonstrate that the proposed PDMA has comparable performance to that of orthogonal frequency-division multiple access (OFDMA) multicasting in terms of aggregate data rate. However, PDMA outperforms OFDMA in terms of inter-group fairness, which makes it a suitable candidate for multi-group multicast transmissions.
On-Chip Digital Temperature Sensor for CMOS Image Sensor

The Digital Temperature Sensors relies on two main parts: temperature sensor with analogue output and analogue-to-digital converter. The analogue temperature sensor architecture developed in this work consists on having a circuit with MOSFETs producing an output voltage proportional to absolute temperature the most linear possible. This voltage is then converted to a 10 bit digital value by a ramp run-up analogue-to-digital converter. The project was developed in a standard 130nm CMOS process. As result the digital temperature sensor has an area of 0.016mm2 and an accuracy of $\pm 0.175^{\circ}\text{C}$ from −20 °C to 120 °C.
Insulator Thickness Scaling: I-V Characteristics of 1D Nanowire MOSFETs and TFETs

This work provides a comparison of the changes in performance of a gate-all-around GAA carbon based tunnel field effect transistor p-i-n TFET and metal-oxide-semiconductor field effect transistor n-i-n MOSFET with respect to insulator thickness $\pmb{t_{ox}}$ values. The performance variations are prominent in the p-i-n TFET than n-i-n MOSFET structure; $\pmb{g_{d}}$ and $\pmb{g_{m}}$ are significantly degraded with $\pmb{t_{ox}}$. The results are considered based on current spectrum on energy band diagram of the device in detail. Due to 1D quantum transport characteristics of CNTs the results could be applicable to any device with the same structure including 1D nanowire FET.
Self-Organizing Localization With Adaptive Weights for Wireless Sensor Networks

Self-organizing localization is a crucial feature of wireless sensor networks that cannot employ positioning techniques such as the global positioning system. If the number of anchor nodes is insufficient, it is difficult to estimate each node’s location using the anchor nodes’ information by employing the existing algorithms, the localization problem becoming complicated. In this paper, we propose a recursive self-organizing localization scheme, solely based on the neighbors’ connectivity information. This scheme utilizes a mass-spring-relaxation algorithm in which each node finds its location by iteratively balancing the geometric relationships with neighboring nodes until the system reaches an equilibrium state. We propose a simple distance correction factor to consider the accuracy of distance measurements, and adopt the adaptive step size control based on the gradient method to improve the system stability. Our simulations show that the proposed scheme improves the system performance in terms of convergence speed, system stability, and estimation accuracy.
Adapting session based recommendation for features through transfer learning

This industry talk covers the deep learning architecture developed at Realtor.com to recommend real estate listings to our userbase. The recommendation of homes is a different problem than most other domains both in the sense that listings are unique and that there are additional geographic and time constraints that increase the sparsity of interactions and make recommendation of individual listings more challenging. In particular time on market in a hot area can be limited to weeks or even days, and listing cold-start is critical to providing up to date market information. Thankfully the structured feature data for listings is incredibly rich and provides a framework from which to map listings into a meaningful vector space. User first impressions are also incredibly important in this highly competitive field, and offline recommendation or models that don't adapt during the users session are less desirable. In order to solve this recommendation problem we have developed a model based off of session based recommendation [1]. The architecture utilizes state of the art techniques from Natural Language Processing, including the AWD-LSTM language model developed by Salesforce [2]. To solve for cold-start of listings a structured data based denoising autoencoder was adapted from the methodology described in the winning entry of the Puerto Segurno Safe Driver Kaggle Competition [3]. This model is not used in the common way of generating fixed feature vectors, but rather the entire head of the autoencoder model, from the feature inputs to the middle layer commonly used as the vector output, is first trained to encode listing features, and then becomes the input to the AWD-LSTM architecture. This style of transfer learning is common in Computer Vision, and has recently been utilized in NLP to achieve state of the art results for text classification [4]. By including the head we are able to further optimize the listing encoder network and embeddings to take user interactions into account. As in traditional session based recommendation users are represented as the sequence of listings that they view, however those listings are fed into the model as the sequence of features. The final system consists of several components. The first attempts to calculate and maintain the users' feature vector and model hidden weights in near realtime, providing a representation for the user within the system. This representation is used by several downstream components, most notably the search rerank and recommendation modules which calculate users' interest in listings both in the context of the output of more traditional elasticsearch queries via cosine similarity of user/listing vectors and through approximate nearest neighbor vector space searches for relevant listings which form the input set for a pointwise scoring model trained on time on listing as done by YouTube [5].
Group Interaction Frontiers in Technology

Analysis of group interaction and team dynamics is an important topic in a wide variety of fields, owing to the amount of time that individuals typically spend in small groups for both professional and personal purposes, and given how crucial group cohesion and productivity are to the success of businesses and other organizations. This fact is attested by the rapid growth of fields such as People Analytics and Human Resource Analytics, which in turn have grown out of many decades of research in social psychology, organizational behaviour, computing, and network science, amongst other fields. The goal of this workshop is to bring together researchers from diverse fields related to group interaction, team dynamics, people analytics, multi-modal speech and language processing, social psychology, and organizational behaviour.
A Cloud-assisted 57.6 Kbps Image Sensor Communication System Using a Smartphone Camera

A Cloud-assisted image sensor communication (IS-C) system is designed and implemented. A 16 16 LED array and a smartphone camera are adopted as the transmitter and receiver respectively. The LED array is refreshed at a rate of 60 Hz, and each element contains 5-bit coded information by applying color intensity modulation (CIM) to take full advantage of color, intensity and spatial dimensions. The smartphone camera captures images with 120 fps frame rate and sends them wirelessly to a personal cloud for signal demodulation. The restored information is sent back to the smartphone to display. After tackling unstable frame rate, synchronization, color crosstalk, and nonlinearity by proper system design and optimization, the system achieves a data rate of 57.6 kbps at 1m.
A 13.56 MHz CMOS High-Efficiency Active Rectifier With Dynamically Controllable Comparator for Biomedical Wireless Power Transfer Systems

A 13.56-MHz active rectifier with a dynamically controllable common-gate comparator for wirelessly powered supplied implantable medical devices is presented in this paper. The proposed active rectifier is composed of two comparator-controlled PMOS and two cross-coupled NMOS for achieving high-voltage and high-power conversion efficiency. The dynamically controllable common-gate comparator is implemented for high input level and low static power consumption of the rectifier. An active self-body bias is implemented to eliminate the body effect of the power PMOS. An SR latch is adopted for the comparator to control the offset and to stabilize the output pulse. The proposed rectifier is fabricated in a standard 0.13-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> (3.3-V device) CMOS process with 0.102-mm<sup>2</sup> active area. The measured input and output ranges under 500-<inline-formula> <tex-math notation="LaTeX">$\Omega $ </tex-math></inline-formula> output load are 2.14–3.6 V and 2–3.46 V, respectively. The measured power conversion efficiencies versus output load (0.1–1 <inline-formula> <tex-math notation="LaTeX">${\text{k}}\Omega $ </tex-math></inline-formula>) under 3.6 V ac input are 88%–91.9%, and the voltage conversion efficiencies are 86%–96.4%.