Miracle-dz/UniFAR-FTUs huggingface dataset:
Promoting Mental Health Resource Use on Campus by “Trying Something New”

Purpose: To design and test a persuasive health promotion campaign that aligns with the qualities of trying something new for the first time. Given that a majority of students have not previously sought/considered professional mental health assistance before, the hypothesis tested in this study asked whether a campaign that takes this into account is effective with this audience. Design: Participants viewed an online informational message (n = 84), information message plus first-time experience banner (n = 99), or 1 of 4 full campaigns, each depicting a student story and photo about a first-time experience (moving from home [n = 48], skydiving [n = 52], acting in a play [n = 48], and exercising with personal trainer [n = 48]). Measures: Visual poster items: appeal (visually pleasing, 7 items, α = .92), support (value of poster, 5 items, α = .86) and behavioral intention items: engagement (participant seek help/pay attention, 3 items, α = .86), relevance (content as relevant, 3 items, α = .84), and judgment (judgment of others for not seeking help, 2 items, α = .87). Participants: College students (N = 380). Results: In comparison to information-only messages, framing mental health help seeking as a first-time experience was linked with increased appeal, support, and engagement (Minformationonly = 2.79 [standard deviation, SD = 1.34], Minformationplusbanner = 3.25 [SD = 1.23], Mfullcampaign = 4.07 [SD = 1.28], P < .001, Minformationonly = 4.38 [SD = 1.47], Minformationplusbanner = 4.92 [SD = 1.21], Mfullcampaign = 4.57 [SD = 1.26], P = .014, and Minformationonly = 3.13 [SD = 1.76], Minformationplusbanner = 3.56 [SD = 1.48], Mfullcampaign = 4.02 [SD = 1.42], P < .001, respectively). As anticipated, the full campaign garnered the highest affect and engagement scores. When comparing the 4 first-time experiences, there were main effects on support and engagement (Mtrain = 5.06 [SD = 1.17], Mplane = 4.27 [SD = 1.28], Mhome = 4.59 [SD = 1.19], Mplay = 4.38 [SD = 1.29], P = .009 and Mtrain = 4.50 [SD = 1.27], Mplane = 3.75 [SD = 1.43], Mhome = 4.01 [SD = 1.49], Mplay = 3.84 [SD = 1.39], P = .042, respectively), with the novel experience of “working with a personal trainer” rated highest. Conclusion: Findings from this study have implications for the design of health promotion materials on college campuses. Specifically, campaigns that frame seeking help for mental health as a new experience potentially increase student engagement in this behavior. A key finding from the present study is that a campaign in which this behavior is linked to a familiar form of interpersonal help seeking (personal training) can create receptivity to the stigmatized issue of mental health help seeking.
HESDK: A Hybrid Approach to Extracting Scientific Domain Knowledge Entities

We investigate a variant of the problem of automatic keyphrase extraction from scientific documents, which we define as Scientific Domain Knowledge Entity (SDKE) extraction. Keyphrases are noun phrases important to the documents themselves. In contrast, an SDKE is text that refers to a concept and can be classified as a process, material, task, dataset etc. A SDKE represents domain knowledge, but is not necessarily important to the document it is in. Supervised keyphrase extraction algorithms using non-sequential classifiers and global measures of informativeness (PMI, tf-idf) have been used for this task. Another approach is to use sequential labeling algorithms with local context from a sentence, as done in the named entity recognition. We show that these two methods can complement each other and a simple merging can improve the extraction accuracy by 5-7 percentiles. We further propose several heuristics to improve the extraction accuracy. Our preliminary experiments suggest that it is possible to improve the accuracy of the sequential learner itself by utilizing the predictions of the non-sequential model.
Classifying Short Unstructured Data Using the Apache Spark Platform

People worldwide use Twitter to post updates about the events that concern them directly or indirectly. Study of these posts can help identify global events and trends of importance. Similarly, E-commerce applications organize their products in a way that can facilitate their management and satisfy the needs and expectations of their customers. However, classifying data such as tweets or product descriptions is still a challenge. These data are described by short texts, containing in their vocabulary abbreviations of sentences, emojis, hashtags, implicit codes, and other non-standard usage of written language. Consequently, traditional text classification techniques are not effective on these data. In this paper, we describe our use of the Spark platform to implement two classification strategies to process large data collections, where each datum is a short textual description. One of our solutions uses an associative classifier, while the other is based on a multiclass Logistic Regression classifier using Word2Vec as a feature selection and transformation technique. Our associative classifier captures the relationships among words that uniquely identify each class, and Word2Vec captures the semantic and syntactic context of the words. In our experimental evaluation, we compared our solutions, as well as Spark MLlib classifiers. We assessed effectiveness, efficiency, and memory requirements. The results indicate that our solutions are able to effectively classify the millions of data instances composed of thousands of distinct features and classes, found in our digital libraries.
Latent Network Features and Overlapping Community Discovery via Boolean Intersection Representations

We propose a new latent Boolean feature model for complex networks that capture different types of node interactions and network communities. The model is based on a new concept in graph theory, termed the Boolean intersection representation of a graph, which generalizes the notion of an intersection representation. We mostly focus on one form of Boolean intersection, termed cointersection, and describe how to use this representation to deduce node feature sets and their communities. We derive several general bounds on the minimum number of features used in cointersection representations and discuss graph families for which exact cointersection characterizations are possible. Our results also include algorithms for finding optimal and approximate cointersection representations of a graph.
A Scalable In-Memory Logic Synthesis Approach Using Memristor Crossbar

Because of their resistive switching properties and ease of controlling the resistive states, memristors have been proposed in nonvolatile storage as well as logic design applications. Memristors can be fabricated in a crossbar and suitable voltages applied to the row and column nanowires to control their states. This makes it possible to move toward new non-von Neumann-type architectures, usually referred to as in-memory computing, where logic operations can be performed directly on the storage fabric. In this paper, a scalable design flow for in-memory computing has been proposed, where a given multioutput logic function is synthesized as a netlist of NOT/NOR gates and then mapped to the crossbar using the Memristor-Aided loGIC (MAGIC) design style. The memristors corresponding to the primary inputs are initialized a priori. Subsequently, the required gate operations are performed by applying suitable row and column voltages in sequence. Two alternate mapping schemes have been analyzed. The switching characteristics of MAGIC NOR gates have been evaluated using circuit simulation under the Cadence Virtuoso environment. Experimental evaluation on ISCAS’85 benchmarks reports the average improvements of 27.7%, 34.6%, and 26.2%, respectively over a recently published work with respect to the number of memristors, number of cycles, and total energy dissipation, respectively. It may be noted that the energy consumption of the gates used in the proposed approach (NOT and NOR) is significantly higher than that using CMOS technology.
Erratum for Panayiotou et al., “Viperin Restricts Zika Virus and Tick-Borne Encephalitis Virus Replication by Targeting NS3 for Proteasomal Degradation”

28 Flaviviruses are arthropod-borne viruses that constitute a major global health problem, 29 with millions of human infections annually. Their pathogenesis ranges from mild illness 30 to severe manifestations such as hemorrhagic fever and fatal encephalitis. Type I 31 interferons (IFNs) are induced in response to viral infection, and stimulate the expression 32 of interferon-stimulated genes (ISGs), including that encoding viperin (virus-inhibitory 33 protein, endoplasmic reticulum-associated, IFN-inducible), which shows antiviral activity 34 against a broad spectrum of viruses including several flaviviruses. Here we describe a 35 novel antiviral mechanism exerted by viperin against two prominent flaviviruses, tick- 36 borne encephalitis virus (TBEV) and Zika virus (ZIKV). Viperin was found to interact 37 and co-localize with the structural proteins pre-membrane (prM) and envelope (E) of 38 TBEV, as well as the non-structural (NS) proteins NS2A, NS2B, and NS3. Interestingly, 39 viperin expression reduced the NS3 protein level, and the stability of the other interacting 40 viral proteins, but only in the presence of NS3. We also found that although viperin 41 interacted with NS3 of mosquito-borne flaviviruses (ZIKV, Japanese encephalitis virus, 42 and yellow fever virus), only ZIKV was sensitive to the antiviral effect of viperin. This 43 sensitivity correlated with viperin ’ s ability to induce proteasome-dependent degradation 44 of NS3. ZIKV and TBEV replication was rescued completely when NS3 was 45 overexpressed, suggesting that the viral NS3 is the specific target of viperin. In summary, 46 we present here a novel antiviral mechanism of viperin that is selective for specific 47 viruses in the genus Flavivirus , affording the possibility of new drug targets that can be 48 used for therapeutic intervention.
Resilience-Aware Frequency Tuning for Neural-Network-Based Approximate Computing Chips

Unlike conventional ICs, approximate computing chips are less sensitive to hardware errors. This fascinating feature can be utilized to improve the performance of chip design and even change the timing closure procedure of digital circuit design flow. In this paper, we study the potential of resilience-aware circuit clocking scheme, and demonstrate the methodology with advanced neural network (NN)-based accelerator. We propose a novel timing analysis and frequency setting method for NN-based approximate computing circuits based on in-field NN retraining. With the proposed iterative retiming-and-retraining framework, NN-based accelerator can be retrained to operate safely at aggressive operating frequencies compared with the frequency decided purely by statistical timing analysis or Monto Carlo analysis. For nanometer process technology with increasing threats of timing errors induced by process variation, noises, and so on, our retiming-and-retraining method enables higher circuit operating frequency and enables dynamic precision/frequency adjustment for approximate computing circuits. We evaluate the methodology with both the neural and deep learning accelerators in experiments. The experimental results show that timing errors in neural circuits can be effectively tamed for different applications, so that the circuits can operate at higher clocking rates under the specified quality constraint or be dynamically scaled to work at a wide range of frequency states with only minor accuracy losses.
Determining Source–Destination Connectivity in Uncertain Networks: Modeling and Solutions

Determination of source–destination connectivity in networks has long been a fundamental problem, where most existing works are based on deterministic graphs that overlook the inherent uncertainty in network links. To overcome such limitation, this paper models the network as an uncertain graph, where each edge <inline-formula> <tex-math notation="LaTeX">$e$ </tex-math></inline-formula> exists independently with some probability <inline-formula> <tex-math notation="LaTeX">$p(e)$ </tex-math></inline-formula>. The problem examined is that of determining whether a given pair of nodes, a source <inline-formula> <tex-math notation="LaTeX">$s$ </tex-math></inline-formula> and a destination <inline-formula> <tex-math notation="LaTeX">$t$ </tex-math></inline-formula>, are connected by a path or separated by a cut. Assuming that during each determining process we are associated with an underlying graph, the existence of each edge can be unraveled through edge testing at a cost of <inline-formula> <tex-math notation="LaTeX">$c(e)$ </tex-math></inline-formula>. Our goal is to find an optimal strategy incurring the minimum expected testing cost with the expectation taken over all possible underlying graphs that form a product distribution. Formulating it into a combinatorial optimization problem, we first characterize the computational complexity of optimally determining source–destination connectivity in uncertain graphs. Specifically, through proving the NP-hardness of two closely related problems, we show that, contrary to its counterpart in deterministic graphs, this problem cannot be solved in polynomial time unless P = NP. Driven by the necessity of designing an exact algorithm, we then apply the Markov decision process framework to give a dynamic programming algorithm that derives the optimal strategies. As the exact algorithm may have prohibitive time complexity in practical situations, we further propose two more efficient approximation schemes compromising the optimality. The first one is a simple greedy approach with linear approximation ratio. Interestingly, we show that naive as it is, and it enjoys significantly better performance guarantee than some other seemingly more sophisticated algorithms. Second, by harnessing the submodularity of the problem, we further design a more elaborate algorithm with better approximation ratio. The effectiveness of the proposed algorithms is justified through extensive simulations on three real network data sets, from which we demonstrate that the proposed algorithms yield strategies with smaller expected cost than conventional heuristics.
Comparison of two decellularized dermal equivalents

Immunologically inert allogeneic acellular dermal scaffolds provide a matrix with molecular architecture close to native tissues, which synthetic scaffolds cannot. Not all nature‐derived scaffolds possess the same biological and physical properties. The different properties of scaffolds supporting cellular growth used for manufacturing tissue engineered grafts could lead to different implantation results. The scaffold properties should be carefully considered in order to meet the expected outcomes of tissue engineered grafts. In this report, we evaluated the cellular growth on AlloDerm® and Allopatch, 2 acellular scaffolds derived from human cadaver skin, using a fabricated 3D organotypic culture with primary human oral keratinocytes to produce an ex vivo produced oral mucosa equivalent (EVPOME). A well stratified epithelium could be constructed on both scaffolds. AlloDerm® and Allopatch EVPOMEs were also implanted into severe combined immunodeficiency mice to compare the ingrowth of blood vessels into the dermal component of the two EVPOMEs. Blood vessel counts were 3.3 times higher (p = .01) within Allopatch EVPOMEs than within AlloDerm® EVPOMEs. An oral and skin keratinocyte co‐culture, separated by a physical barrier to create a cell‐free zone, was used to evaluate cell migration on AlloDerm® and Allopatch. Slower cell migration was observed on Allopatch than on AlloDerm®.
Comparing early liver graft function from heart beating and living‐donors: A pilot study aiming to identify new biomarkers of liver injury

The liver and kidney functions of recipients of liver transplantation (LT) surgery with heart beating (HBD, n = 13) or living donors (LD, n = 9) with different cold ischemia times were examined during the neohepatic phase for the elimination of rocuronium bromide (ROC, cleared by liver and kidney) and tranexamic acid (TXA, cleared by kidney). Solid phase micro‐extraction and LC–MS/MS was applied to determine the plasma concentrations of ROC and TXA, and creatinine was determined by standard laboratory methods. Metabolomics and the relative expressions of miR‐122, miR‐148a and γ‐glutamyltranspeptidase (GGT), liver injury biomarkers, were also measured. The ROC clearance for HBD was significantly lower than that for LD (0.147 ± 0.052 vs. 0.265 ± 0.148 ml·min−1·g−1 liver) after intravenous injection (0.6 mg·kg−1). The clearance of TXA, a compound cleared by glomerular filtration, given as a 1 g bolus followed by infusion (10 mg·kg−1·h−1), was similar between HBD and LD groups (~ 1 ml·min−1·kg−1). The TXA clearance in both groups was lower than the GFR, showing a small extent of hepatorenal coupling. The miR‐122 and miR‐148a expressions were similar for the HBD and LD groups, whereas GGT expression was significantly increased for HBD. The lower ROC clearance and the higher GGT levels in the HBD group of longer cold ischemia times performed worse than the LD group during the neophase. Metabololmics further showed clusters of bile acids, phospholipids and lipid ω‐oxidation products for the LD and HBD groups. In conclusion, ROC CL and GGT expression, and metabolomics could serve as sensitive indices of early graft function. Copyright © 2017 John Wiley & Sons, Ltd.
The cutaneous lipid composition of bat wing and tail membranes: a case of convergent evolution with birds

The water vapour permeability barrier of mammals and birds resides in the stratum corneum (SC), the outermost layer of the epidermis. The molar ratio and molecular arrangement of lipid classes in the SC determine the integrity of this barrier. Increased chain length and polarity of ceramides, the most abundant lipid class in mammalian SC, contribute to tighter packing and thus to reduced cutaneous evaporative water loss (CEWL). However, tighter lipid packing also causes low SC hydration, making it brittle, whereas high hydration softens the skin at the cost of increasing CEWL. Cerebrosides are not present in the mammalian SC; their pathological accumulation occurs in Gaucher's disease, which leads to a dramatic increase in CEWL. However, cerebrosides occur normally in the SC of birds. We tested the hypothesis that cerebrosides are also present in the SC of bats, because they are probably necessary to confer pliability to the skin, a quality needed for flight. We examined the SC lipid composition of four sympatric bat species and found that, as in birds, their SC has substantial cerebroside contents, not associated with a pathological state, indicating convergent evolution between bats and birds.
Factors associated with sleep duration in Brazilian high school students

ABSTRACT The aim of this study was to investigate the factors associated with short sleep duration on southern Brazilian high school students. Our study was comprised of 1,132 adolescents aged 14 to 19 years, enrolled in public high schools in São José, Brazil. The students answered a questionnaire about working (work and workload), health perception, smoking, school schedule, sleep (duration and daytime sleepiness), and socio-demographics data. The results showed that more than two thirds of adolescent workers had short sleep duration (76.7%), and those with a higher workload (more than 20 hours) had a shorter sleep duration (7.07 hours) compared to non-workers (7.83 hours). In the analysis of factors associated with short sleep duration, adolescents who worked (OR = 2.12, 95% CI 1.53 to 2.95) were more likely to have short sleep duration compared to those who did not work. In addition, older adolescents (17–19 years) and students with poor sleep quality were 40% and 55% more likely to have short sleep duration compared to younger adolescents (14–16 years) and students with good sleep quality, respectively. Adolescents with daytime sleepiness were more likely to have short sleep duration (OR = 1.49, 95% CI 1.06 to 2.07) compared to those without excessive daytime sleepiness. In addition students of the morning shift (OR = 6.02, 95% CI 4.23 to 8.57) and evening shift (OR = 2.16, 95% CI 1.45 to 3.22) were more likely to have short sleep duration compared to adolescents of the afternoon shift. Thereby adolescents who are workers, older, attended morning and evening classes and have excessive daytime sleepiness showed risk factors for short sleep duration. In this sense, it is pointed out the importance of raising awareness of these risk factors for short sleep duration of students from public schools from São José, located in southern Brazil.
Agents+Control: A Methodology for CPSs

The development of Cyber-Physical Systems (CPSs) is challenging because the cyber part (software) has to control, through sensors/actuators accessed by means of a suitable network infrastructure, a physical plant which can require the fulfillment of timing constraints. This paper proposes a methodology based on a control-centric agent framework, for the development of CPSs. The approach is original and enables model continuity, i.e., the same model can be exploited along all the development stages of a system, from analysis based on simulation down to real-time implementation and execution. For demonstration purposes, a case study concerned with optimizing power management in an electric power grid is reported.
Water-like anomalies as a function of tetrahedrality

Significance Water is the most common and yet least understood material on Earth. Despite its simplicity, water tends to form tetrahedral order locally by directional hydrogen bonding. This structuring is known to be responsible for a vast array of unusual properties, e.g., the density maximum at 4 °C, which play a fundamental role in countless natural and technological processes, with the Earth’s climate being one of the most important examples. By systematically tuning the degree of tetrahedrality, we succeed in continuously interpolating between water-like behavior and simple liquid-like behavior. Our approach reveals what physical factors make water so anomalous and special even compared with other tetrahedral liquids. Tetrahedral interactions describe the behavior of the most abundant and technologically important materials on Earth, such as water, silicon, carbon, germanium, and countless others. Despite their differences, these materials share unique common physical behaviors, such as liquid anomalies, open crystalline structures, and extremely poor glass-forming ability at ambient pressure. To reveal the physical origin of these anomalies and their link to the shape of the phase diagram, we systematically study the properties of the Stillinger–Weber potential as a function of the strength of the tetrahedral interaction λ. We uncover a unique transition to a reentrant spinodal line at low values of λ, accompanied with a change in the dynamical behavior, from non-Arrhenius to Arrhenius. We then show that a two-state model can provide a comprehensive understanding on how the thermodynamic and dynamic anomalies of this important class of materials depend on the strength of the tetrahedral interaction. Our work establishes a deep link between the shape of the phase diagram and the thermodynamic and dynamic properties through local structural ordering in liquids and hints at why water is so special among all substances.