Miracle-dz/UniFAR-FTUs huggingface dataset:
Increased Fear Memory and Glutamatergic Modulation in Compulsive Drinker Rats Selected by Schedule-Induced Polydipsia

Compulsive behavior is observed in several neuropsychiatric disorders such as obsessive-compulsive disorder (OCD), anxiety, depression, phobia, and schizophrenia. Thus, compulsivity has been proposed as a transdiagnostic symptom with a highly variable pharmacological treatment. Recent evidence shows that glutamate pharmacotherapy may be of benefit in impaired inhibitory control. The purpose of the present study was: first, to test the comorbidity between compulsivity and other neuropsychiatric symptoms on different preclinical behavioral models; second, to assess the therapeutic potential of different glutamate modulators in a preclinical model of compulsivity. Long Evans rats were selected as either high (HD) or low (LD) drinkers corresponding with their water intake in schedule-induced polydipsia (SIP). We assessed compulsivity in LD and HD rats by marble burying test (MBT), depression by forced swimming test (FST), anxiety by elevated plus maze (EPM) and fear behavior by fear conditioning (FC) test. After that, we measured the effects of acute administration (i.p.) of glutamatergic drugs: N-Acetylcysteine (NAC; 25, 50, 100 and 200 mg/kg), memantine (3.1 and 6.2 mg/kg) and lamotrigine (15 and 30 mg/kg) on compulsive drinking on SIP. The results obtained showed a relation between high compulsive drinking on SIP and a higher number of marbles partially buried in MBT, as well as a higher percentage of freezing on the retrieval day of FC test. We did not detect any significant differences between LD and HD rats in FST, nor in EPM. The psychopharmacological study of glutamatergic drugs revealed that memantine and lamotrigine, at all doses tested, decreased compulsive water consumption in HD rats compared to LD rats on SIP. NAC did not produce any significant effect on SIP. These results indicate that the symptom clusters of different forms of compulsivity and phobia might be found in the compulsive phenotype of HD rats selected by SIP. The effects of memantine and lamotrigine in HD rats point towards a dysregulation in the glutamatergic signaling as a possible underlying mechanism in the vulnerability to compulsive behavior on SIP. Further studies on SIP, could help to elucidate the therapeutic role of glutamatergic drugs as a pharmacological strategy on compulsive spectrum disorders.
Computer Modeling of the Obtaining Nanostructures Process Under the Action of Laser Radiation on Steel

The conditions for the formation of nanostructures in the surface layer of steels with different carbon content (steel 20, 40, 45, 40Cr, U8 and U12), which determine the required temperature (500-1500 K) and the rate of increase (more than 107 K/s). The zones of formation of nanostructures depending on the heat flux density on the time of action of the ionizing radiation are determined. It is shown that it is necessary to take into account the rate of temperature rise and the probability of thermoelastic destruction due to the action of temperature stresses. In low speed temperature rise formed micro and submicrostructure that has been confirmed experimentally.
Elaborations on Corallopyronin A as a Novel Treatment Strategy Against Genital Chlamydial Infections

Ascending Chlamydia trachomatis infection causes functional damage to the fallopian tubes, which may lead to ectopic pregnancy and infertility in women. Treatment failures using the standard regimens of doxycycline and azithromycin have been observed. We tested the polyketide-derived α-pyrone antibiotic Corallopyronin A (CorA) that inhibits the bacterial DNA dependent RNA polymerase and has strong activity against various extracellular and some intracellular bacteria. Extensive testing in cell culture infection models and in an ex vivo human fallopian tube model under different oxygen concentrations was performed to assess the anti-chlamydial efficacy of CorA at physiological conditions. CorA showed high efficacy against C. trachomatis (MICN/H: 0.5 μg/mL for serovar D and L2), C. muridarum (MICN/H: 0.5 μg/mL), and C. pneumoniae (MICN/H: 1 μg/mL) under normoxic (N) and hypoxic (H) conditions. Recoverable inclusion forming units were significantly lower already at 0.25 μg/mL for all tested chlamydiae. CorA at a concentration of 1 μg/mL was also effective against already established C. trachomatis and C. pneumoniae infections (up to 24 h.p.i.) in epithelial cells, while efficacy against C. muridarum was limited to earlier time points. A preliminary study using a C. muridarum genital infection model revealed corresponding limitations in the efficacy. Importantly, in an ex vivo human fallopian tube model, the growth of C. trachomatis was significantly inhibited by CorA at concentrations of 1–2 μg/mL under normoxic and hypoxic conditions. The overall high efficacies of CorA against C. trachomatis in cell culture and an ex vivo human fallopian tube model under physiological oxygen concentrations qualifies this drug as a candidate that should be further investigated.
RNA Regulatory Networks as a Control of Stochasticity in Biological Systems

The discovery that the non-protein coding part of human genome, dismissed as “junk DNA,” is actively transcripted and carries out crucial functions is probably one of the most important discoveries of the past decades. These transcripts are becoming the rising stars of modern biology. In this review, we have casted a new light on RNAs. We have placed these molecules in the context of life origins, evolution with a big emphasize on the “RNA networks” concept. We discuss how this view can help us to understand the global role of RNA networks in modern cells, and can change our perception of the cell biology and therapy. Finally, although high-throughput methods as well as traditional case-to-case studies have laid the groundwork for our current knowledge of transcriptomes, we would like to discuss new strategies that are better suited to uncover and tackle these integrated and complex RNA networks.
Development of a Computerized Adaptive Testing for Internet Addiction

Internet addiction disorder has become one of the most popular forms of addiction in psychological and behavioral areas, and measuring it is growing increasingly important in practice. This study aimed to develop a computerized adaptive testing to measure and assess internet addiction (CAT-IA) efficiently. Four standardized scales were used to build the original item bank. A total of 59 polytomously scored items were finally chosen after excluding 42 items for failing the psychometric evaluation. For the final 59-item bank of CAT-IA, two simulation studies were conducted to investigate the psychometric properties, efficiency, reliability, concurrent validity, and predictive validity of CAT-IA under different stopping rules. The results showed that (1) the final 59 items met IRT assumptions, had high discrimination, showed good item-model fit, and were without DIF; and (2) the CAT-IA not only had high measurement accuracy in psychometric properties but also sufficient efficiency, reliability, concurrent validity, and predictive validity. The impact and limitations of CAT-IA were discussed, and several suggestions for future research were provided.
Time-Course Effects of Acute Aflatoxin B1 Exposure on Hepatic Mitochondrial Lipids and Oxidative Stress in Rats

Aflatoxins are secondary metabolites of certain Aspergillus species, that contaminate staple foods, particularly in developing countries. Aflatoxin B1 (AFB1) is the most toxic and common of the major types of aflatoxins. AFB1 is hepatotoxic and has been implicated in increasing the risk of hepatocellular carcinoma (HCC). We have previously shown that subacute exposure to AFB1 for 7 days disrupts hepatic lipids; therefore, this study determined the time-course effects of acute aflatoxin exposure on hepatic mitochondrial lipids and oxidative stress. To achieve this, thirty male albino rats were randomly assigned to six groups. The groups received an oral dose of 1 mg/kg body weight AFB1 or vehicle only (controls) for one, four, or seven days, respectively. Twenty-four hours after the last dose, the animals were sacrificed and liver excised. Mitochondria and cytosolic fractions were obtained from the liver after which lipids (cholesterol, triacylglycerols) were determined in the mitochondria while biomarkers of oxidative stress (glutathione, glutathione transferase (GST), glutathione peroxidase (GPx), glutathione reductase, nitric oxide (NO), malonaldehyde (MDA), thioredoxin reductase (TR), and superoxide dismutase (SOD) were determined spectrophotometrically in the mitochondria and cytosolic fractions. The expression of genes (Nrf2, Acc, Nqo1, and HmgCoa) were determined using quantitative RT-PCR. Results showed that AFB1 significantly increased mitochondrial cholesterol at day seven (treatment vs. control, p = 0.016). It also increased the concentrations of NO and MDA at day one and day seven while the activity of GPx and concentration of GSH were increased at day seven (p = 0.030) and day one (p = 0.025) alone, respectively, compared to control. The activities of cytosolic GR (p = 0.014), TR (p = 0.046) and GST (p = 0.044) were increased at day seven. AFB1 significantly increased the expression of Nrf2 (p = 0.029) and decreased the expression of Acc (p = 0.005) at day one. This study revealed that AFB1 disrupts hepatic mitochondrial lipids and antioxidant capacity. These changes were dependent on the timing of exposure and did not follow a linear time-course trend. These alterations could be part of the hepatic mitochondria response mechanism to acute AFB1 toxicity.
Dynamic load balancing in WDM packet networks with and without wavelength constraints

We develop load balancing algorithms for WDM-based packet networks where the average traffic between nodes is dynamically changing. In WDM-based packet networks, routers are connected to each other using wavelengths (lightpaths) to form a logical network topology. The logical topology may be reconfigured by rearranging the lightpaths connecting the routers. Our algorithms reconfigure the logical topology to minimize the maximum link load. In this paper, we develop iterative reconfiguration algorithms for load balancing that track rapid changes in the traffic pattern. At each reconfiguration step, our algorithms make only a small change to the network topology hence minimizing the disruption to the network. We study the performance of our algorithms under several dynamic traffic scenarios and show that our algorithms perform near optimally. We further show that these large reconfiguration gains are achievable in systems with a limited number of wavelengths.
Attitudes and Beliefs About 12-Step Groups Among Addiction Treatment Clients and Clinicians: Toward Identifying Obstacles to Participation

Participation in 12-step groups (12SG) during and after formal treatment has been associated with positive outcome among substance users. However, the effectiveness of 12SG may be limited by high attrition rates and by low participation, areas on which there has been little research. Clinicians play an important role in fostering 12-step participation, and the insights which they develop in their practice can greatly contribute to informing the research process. Yet, little is known about clinicians’ attitudes about 12-step groups or about their experiences in referring clients. This study surveyed clients (N = 101) and clinicians (N = 102) in outpatient treatment programs to examine 12-step-related attitudes and to identify potential obstacles to participation. Data collection was conducted between 05, 2001 and 01, 2002 in New York City. Both client and clinician samples were primarily African-American and Hispanic; 32% of clients reported substance use in the previous month, with crack and marijuana cited most frequently as the primary drug problem. On average, clinicians had worked in the treatment field for 8 years. Both staff and clients viewed 12SG as a helpful recovery resource. Major obstacles to participation centered on motivation and readiness for change and on perceived need for help, rather than on aspects of the 12-step program often cited as points of resistance (e.g., religious aspect and emphasis on powerlessness). Clinicians also frequently cited convenience and scheduling issues as possible obstacles to attending 12SG. Clinical implications of these findings are discussed, including the importance of fostering motivation for change, the need to assess clients’ beliefs about and experiences with 12SG on a case-by-case basis, and to find a good fit between clients’ needs and inclinations on the one hand, and the tools and support available within 12-step groups on the other.
Super instanton counting and localization

We study the super instanton solution in the gauge theory with U$(n_{+}| n_{-})$ gauge group. Based on the ADHM construction generalized to the supergroup theory, we derive the instanton partition function from the super instanton moduli space through the equivariant localization. We derive the Seiberg-Witten geometry and its quantization for the supergroup gauge theory from the instanton partition function, and study the connection with classical and quantum integrable systems. We also argue the brane realization of the supergroup quiver gauge theory, and possible connection to the non-supergroup quiver gauge theories.
Constant-time navigation in four-dimensional nested simplicial meshes

We consider a recursive decomposition of a four-dimensional hypercube into a hierarchy of nested 4-dimensional simplexes, that we call pentatopes. The paper presents an algorithm for finding the neighbors of a pentatope along its five tetrahedral faces in constant time. To this aim, we develop a labeling technique for nested pentatopes that enables their identification by using location codes. The constant-time behavior is achieved through bit manipulation operations, thus avoiding traversing the simplicial hierarchy via pointer following. We discuss an application of this representation to multi-resolution representations of four-dimensional scalar fields. Extracting adaptive continuous approximations of the scalar field from such a model requires generating conforming meshes, i.e., meshes in which the pentatopes match along their tetrahedral faces. Our neighbor finding algorithm enables computing face-adjacent pentatopes efficiently.
Latent Unexpected and Useful Recommendation

Providing unexpected recommendations is an important task for recommender systems. To do this, we need to start from the expectations of users and deviate from these expectations when recommending items. Previously proposed approaches model user expectations in the feature space, making them limited to the items that the user has visited or expected by the deduction of associated rules, without including the items that the user could also expect from the latent, complex and heterogeneous interactions between users, items and entities. In this paper, we define unexpectedness in the latent space rather than in the feature space and develop a novel Latent Convex Hull (LCH) method to provide unexpected recommendations. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed model that significantly outperforms alternative state-of-the-art unexpected recommendation methods in terms of unexpectedness measures while achieving the same level of accuracy.
Personalized Query Auto-Completion Through a Lightweight Representation of the User Context

Query Auto-Completion (QAC) is a widely used feature in many domains, including web and eCommerce search, suggesting full queries based on a prefix typed by the user. QAC has been extensively studied in the literature in the recent years, and it has been consistently shown that adding personalization features can significantly improve the performance of QAC. In this work we propose a novel method for personalized QAC that uses lightweight embeddings learnt through fastText. We construct an embedding for the user context queries, which are the last few queries issued by the user. We also use the same model to get the embedding for the candidate queries to be ranked. We introduce ranking features that compute the distance between the candidate queries and the context queries in the embedding space. These features are then combined with other commonly used QAC ranking features to learn a ranking model. We apply our method to a large eCommerce search engine (eBay) and show that the ranker with our proposed feature significantly outperforms the baselines on all of the offline metrics measured, which includes Mean Reciprocal Rank (MRR), Success Rate (SR), Mean Average Precision (MAP), and Normalized Discounted Cumulative Gain (NDCG). Our baselines include the Most Popular Completion (MPC) model as well as a ranking model without our proposed features. The ranking model with the proposed features results in a $20-30\%$ improvement over the MPC model on all metrics. We obtain up to a $5\%$ improvement over the baseline ranking model for all the sessions, which goes up to about $10\%$ when we restrict to sessions that contain the user context. Moreover, our proposed features also significantly outperform text based personalization features studied in the literature before, and adding text based features on top of our proposed embedding based features results only in minor improvements.
Who wrote this book? A challenge for e-commerce

Modern e-commerce catalogs contain millions of references, associated with textual and visual information that is of paramount importance for the products to be found via search or browsing. Of particular significance is the book category, where the author name(s) field poses a significant challenge. Indeed, books written by a given author might be listed with different authors’ names due to abbreviations, spelling variants and mistakes, among others. To solve this problem at scale, we design a composite system involving open data sources for books, as well as deep learning components, such as approximate match with Siamese networks and name correction with sequence-to-sequence networks. We evaluate this approach on product data from the e-commerce website Rakuten France, and find that the top proposal of the system is the normalized author name with 72% accuracy.
Impact of Artificial Intelligence on Businesses: from Research, Innovation, Market Deployment to Future Shifts in Business Models

The fast pace of artificial intelligence (AI) and automation is propelling strategists to reshape their business models. This is fostering the integration of AI in the business processes but the consequences of this adoption are underexplored and need attention. This paper focuses on the overall impact of AI on businesses - from research, innovation, market deployment to future shifts in business models. To access this overall impact, we design a three-dimensional research model, based upon the Neo-Schumpeterian economics and its three forces viz. innovation, knowledge, and entrepreneurship. The first dimension deals with research and innovation in AI. In the second dimension, we explore the influence of AI on the global market and the strategic objectives of the businesses and finally, the third dimension examines how AI is shaping business contexts. Additionally, the paper explores AI implications on actors and its dark sides.
A wireless, inexpensive optical tracker for the CAVE/sup TM/

CAVE/sup TM/ displays offer many advantages over other virtual reality (VR) displays, including a large, unencumbered viewing space. Unfortunately, the typical tracking subsystems used with CAVE/sup TM/ displays tether the user and lessen this advantage. We have designed a simple, low-cost foot tracker that is wireless, leaving the user free to move. The tracker can be assembled for less than $200 US, and achieves an accuracy of /spl plusmn/10 cm at a 20-Hz sampling rate. We have tested the prototype with two applications: a visualization supporting close visual inspection, and a walkthrough of the campus. Although the tracking was convincing, it was clear that the tracker's limitations make it less than ideal for applications requiring precise visual inspection. However the freedom of motion allowed by the tracker was a compelling supplement to our campus walkthrough, allowing users to stroll and look around corners.
Remove-Win: a Design Framework for Conflict-free Replicated Data Collections

Internet-scale distributed systems often replicate data within and across data centers to provide low latency and high availability despite node and network failures. Replicas are required to accept updates without coordination with each other, and the updates are then propagated asynchronously. This brings the issue of conflict resolution among concurrent updates, which is often challenging and error-prone. The Conflict-free Replicated Data Type (CRDT) framework provides a principled approach to address this challenge. This work focuses on a special type of CRDT, namely the Conflict-free Replicated Data Collection (CRDC), e.g. list and queue. The CRDC can have complex and compound data items, which are organized in structures of rich semantics. Complex CRDCs can greatly ease the development of upper-layer applications, but also makes the conflict resolution notoriously difficult. This explains why existing CRDC designs are tricky, and hard to be generalized to other data types. A design framework is in great need to guide the systematic design of new CRDCs. To address the challenges above, we propose the Remove-Win Design Framework. The remove-win strategy for conflict resolution is simple but powerful. The remove operation just wipes out the data item, no matter how complex the value is. The user of the CRDC only needs to specify conflict resolution for non-remove operations. This resolution is destructed to three basic cases and are left as open terms in the CRDC design skeleton. Stubs containing user-specified conflict resolution logics are plugged into the skeleton to obtain concrete CRDC designs. We demonstrate the effectiveness of our design framework via a case study of designing a conflict-free replicated priority queue. Performance measurements also show the efficiency of the design derived from our design framework.
Charge-to-Spin Conversion by the Rashba–Edelstein Effect in Two-Dimensional van der Waals Heterostructures up to Room Temperature

The proximity of a transition-metal dichalcogenide (TMD) to graphene imprints a rich spin texture in graphene and complements its high-quality charge/spin transport by inducing spin–orbit coupling (SOC). Rashba and valley-Zeeman SOCs are the origin of charge-to-spin conversion mechanisms such as the Rashba–Edelstein effect (REE) and spin Hall effect (SHE). In this work, we experimentally demonstrate for the first time charge-to-spin conversion due to the REE in a monolayer WS2-graphene van der Waals heterostructure. We measure the current-induced spin polarization up to room temperature and control it by a gate electric field. Our observation of the REE and the inverse of the effect (IREE) is accompanied by the SHE, which we discriminate by symmetry-resolved spin precession under oblique magnetic fields. These measurements also allow for the quantification of the efficiencies of charge-to-spin conversion by each of the two effects. These findings are a clear indication of induced Rashba and valley-Zeeman SOC in graphene that lead to the generation of spin accumulation and spin current without using ferromagnetic electrodes. These realizations have considerable significance for spintronic applications, providing accessible routes toward all-electrical spin generation and manipulation in two-dimensional materials.