Miracle-dz/UniFAR-FTUs huggingface dataset:
Pax6 regulation of Sox9 in the mouse retinal pigmented epithelium controls its timely differentiation and choroid vasculature development

ABSTRACT The synchronized differentiation of neuronal and vascular tissues is crucial for normal organ development and function, although there is limited information about the mechanisms regulating the coordinated development of these tissues. The choroid vasculature of the eye serves as the main blood supply to the metabolically active photoreceptors, and develops together with the retinal pigmented epithelium (RPE). Here, we describe a novel regulatory relationship between the RPE transcription factors Pax6 and Sox9 that controls the timing of RPE differentiation and the adjacent choroid maturation. We used a novel machine learning algorithm tool to analyze high resolution imaging of the choroid in Pax6 and Sox9 conditional mutant mice. Additional unbiased transcriptomic analyses in mutant mice and RPE cells generated from human embryonic stem cells, as well as chromatin immunoprecipitation and high-throughput analyses, revealed secreted factors that are regulated by Pax6 and Sox9. These factors might be involved in choroid development and in the pathogenesis of the common blinding disease: age-related macular degeneration (AMD). Summary: In the developing retinal pigmented epithelium (RPE), Pax6 inhibits Sox9 expression and thereby regulates RPE-choroid differentiation. Their mutation causes choroid pathology and implicates novel secreted factors as involved in blinding diseases.
Self-management behaviors in World Trade Center rescue and recovery workers with asthma

ABSTRACT Background: Asthma is a major source of morbidity among World Trade Center (WTC) rescue and recovery workers. While physical and mental health comorbidities have been associated with poor asthma control, the potential role and determinants of adherence to self-management behaviors (SMB) among WTC rescue and recovery workers is unknown. Objectives: To identify modifiable determinants of adherence to asthma self-management behaviors in WTC rescue and recovery worker that could be potential targets for future interventions. Methods: We enrolled a cohort of 381 WTC rescue and recovery workers with asthma. Sociodemographic data and asthma history were collected during in-person interviews. Based on the framework of the Model of Self-regulation, we measured beliefs about asthma and controller medications. Outcomes included medication adherence, inhaler technique, use of action plans, and trigger avoidance. Results: Medication adherence, adequate inhaler technique, use of action plans, and trigger avoidance were reported by 44%, 78%, 83%, and 47% of participants, respectively. Adjusted analyses showed that WTC rescue and recovery workers who believe that they had asthma all the time (odds ratio [OR]: 2.37; 95% confidence interval [CI]: 1.38–4.08), that WTC-related asthma is more severe (OR: 1.73; 95% CI: 1.02–2.93), that medications are important (OR: 12.76; 95% CI: 5.51–29.53), and that present health depends on medications (OR: 2.39; 95% CI: 1.39–4.13) were more likely to be adherent to their asthma medications. Illness beliefs were also associated with higher adherence to other SMB. Conclusions: Low adherence to SMB likely contributes to uncontrolled asthma in WTC rescue and recovery workers. Specific modifiable beliefs about asthma chronicity, the importance of controller medications, and the severity of WTC-related asthma are independent predictors of SMB in this population. Cognitive behavioral interventions targeting these beliefs may improve asthma self-management and outcomes in WTC rescue and recovery workers. Key message: This study identified modifiable beliefs associated with low adherence to self-management behaviors among World Trade Center rescue and recovery rescue and recovery workers with asthma which could be the target for future interventions. Capsule summary: Improving World Trade Center-related asthma outcomes will require multifactorial approaches such as supporting adherence to controller medications and other self-management behaviors. This study identified several modifiable beliefs that may be the target of future efforts to support self-management in this patient population.
Update of the S1 guidelines on the definition and treatment of primary hyperhidrosis

Focal hyperhidrosis can have a considerable impact on social and occupational activities and be associated with significant impairment in quality of life. Primary (idiopathic) hyperhidrosis is neither caused by systemic disorders nor by external factors. It usually occurs in areas characterized by a high density of sweat glands such as the axillae and hands. The diagnosis of focal hyperhidrosis is based on history and clinical findings. Supplementary diagnostic tests include gravimetry (measurement of the amount of sweat) and the iodine starch test (which shows the area of sweating). Treatment options consist of topical agents, botulinum toxin A injections, iontophoresis, treatment with radiofrequency, microwaves, or ultrasound as well as surgical procedures (axillary suction curettage, sympathectomy). Systemic agents such as anticholinergics may also be used. Selection of the most appropriate therapeutic approach depends on the site affected, prior treatment as well as the patient's requests.
Ex-post IR Dynamic Auctions with Cost-per-Action Payments

Motivated by online ad auctions, we consider a repeated auction between one seller and many buyers, where each buyer only has an estimation of her value in each period until she actually receives the item in that period. The seller is allowed to conduct a dynamic auction but must guarantee ex-post individual rationality. In this paper, we use a structure that we call credit accounts to enable a general reduction from any incentive compatible and ex-ante individual rational dynamic auction to an approximate incentive compatible and ex-post individually rational dynamic auction with credit accounts. Our reduction obtains stronger individual rationality guarantees at the cost of weaker incentive compatibility. Surprisingly, our reduction works without any common knowledge assumption. Finally, as a complement to our reduction, we prove that there is no non-trivial auction that is exactly incentive compatible and ex-post individually rational under this setting.
PLASTIC: Prioritize Long and Short-term Information in Top-n Recommendation using Adversarial Training

Recommender systems provide users with ranked lists of items based on individual's preferences and constraints. Two types of models are commonly used to generate ranking results: long-term models and session-based models. While long-term models represent the interactions between users and items that are supposed to change slowly across time, session-based models encode the information of users' interests and changing dynamics of items' attributes in short terms. In this paper, we propose a PLASTIC model, Prioritizing Long And Short-Term Information in top-n reCommendation using adversarial training. In the adversarial process, we train a generator as an agent of reinforcement learning which recommends the next item to a user sequentially. We also train a discriminator which attempts to distinguish the generated list of items from the real list recorded. Extensive experiments show that our model exhibits significantly better performances on two widely used real-world datasets.
Simultaneous Clustering and Ranking from Pairwise Comparisons

When people make decisions with a number of ideas, designs, or other kinds of objects, one attempt is probably to organize them into several groups of objects and to prioritize them according to some preference. The grouping task is referred to as clustering and the prioritizing task is called as ranking. These tasks are often outsourced with the help of human judgments in the form of pairwise comparisons. Two objects are compared on whether they are similar in the clustering problem, while the object of higher priority is determined in the ranking problem. Our research question in this paper is whether the pairwise comparisons for clustering also help ranking (and vice versa). Instead of solving the two tasks separately, we propose a unified formulation to bridge the two types of pairwise comparisons. Our formulation simultaneously estimates the object embeddings and the preference criterion vector. The experiments using real datasets support our hypothesis; our approach can generate better neighbor and preference estimation results than the approaches that only focus on a single type of pairwise comparisons.
Changes in Patient Safety Culture in Palestinian Public Hospitals: Impact of Quality and Patient Safety Initiatives and Programs

Objectives To assess the changes in the patient safety culture between 2011 and 2016 after the implementation of patient safety initiative in Palestinian public hospitals. Methods A cross-sectional quantitative design employed using the Hospital Survey on Patient Safety Culture to collect data. Participants were 1,229 clinical and nonclinical employees from all public hospitals in the West Bank. Results Significant improvements were observed in patient safety culture with positive responses to 10 (83.3%) composite categories and 36 (86.0%) items of the Hospital Survey on Patient Safety Culture since the baseline survey in 2011 (P < 0.05). The increase in the percent of positive responses to the survey composites ranged from 9.1% (P < 0.001) to “Frequency of events reported” to 3.8% for “Teamwork across hospital units” (P < 0.001). Significant decrease in positive responses to “Staffing” (11.4%) was observed (P < 0.05); no significant change was observed in “Organizational learning—continuous improvement” (P > 0.05). Most participants (70.5%) rated patient safety level in their units/hospital as ‘Excellent/Very good’, with an increase of 6.3% from the baseline (P < 0.001); no significant increase in the number of reported events in the past 12 months was observed (P < 0.05). Conclusions Compared with the baseline survey results, patients’ safety initiatives and quality improvement programs had positive effect on the safety culture in public Palestinian hospitals.
The Treponema denticola PAS Domain-Containing Histidine Kinase Hpk2 Is a Heme Binding Sensor of Oxygen Levels

Periodontal disease affects nearly 60% of the global adult population. Its costs to individuals, and to society as a whole, are enormous. As periodontal disease develops, there is a shift in the composition of the oral microbial community. The bacteria that become dominant are able to cause significant damage to the tissues that support the teeth, leading to tooth loss. Treponema denticola is one of the keystone pathogens associated with periodontal disease. An earlier study demonstrated that the Hpk2 and Rrp2 proteins play an important role in adaptive responses. Here, we explore the role of specific Hpk2 amino acids in environmental sensing and function, using structural analyses and site-directed mutagenesis. ABSTRACT Periodontal disease (PD) results from a shift in the composition of the microbial community of the subgingival crevice. As the bacterial population transitions from Gram-positive bacteria to predominantly Gram-negative anaerobes and spirochetes, dramatic changes occur in the physiological and immunological environment at diseased sites. Treponema denticola thrives in periodontal pockets, indicating that it has a unique ability to adapt to changing environmental conditions. Hpk2 (tde1970), a Per-Arnt-Sim motif (PAS) domain-containing histidine kinase (HK), is part of the T. denticola Hpk2-Rrp2 (tde1969) two-component regulatory (TCR) system. This TCR system is growth phase regulated and has been postulated to play a key role in adaptive responses. In this study, we employ predictive structural analyses and site-directed mutagenesis to investigate the functional role of specific amino acid residues located within the Hpk2 PAS domain. Specific substitutions impacted autophosphorylation (AP), phosphotransfer (PT), oligomerization, and hemin binding. The AP, PT, hemin binding, and oligomerization potential of some mutated Hpk2 proteins differed under aerobic versus anaerobic reaction conditions. The data presented here suggest that the regulatory activity of Hpk2 is linked to diatomic gas levels. In a broader sense, this study highlights the importance of studying proteins produced by anaerobes under conditions that approximate the environment in which they thrive. IMPORTANCE Periodontal disease affects nearly 60% of the global adult population. Its costs to individuals, and to society as a whole, are enormous. As periodontal disease develops, there is a shift in the composition of the oral microbial community. The bacteria that become dominant are able to cause significant damage to the tissues that support the teeth, leading to tooth loss. Treponema denticola is one of the keystone pathogens associated with periodontal disease. An earlier study demonstrated that the Hpk2 and Rrp2 proteins play an important role in adaptive responses. Here, we explore the role of specific Hpk2 amino acids in environmental sensing and function, using structural analyses and site-directed mutagenesis.
CSNN: An Augmented Spiking based Framework with Perceptron-Inception

Spiking Neural Networks (SNNs) represent and transmit information in spikes, which is considered more biologically realistic and computationally powerful than the traditional Artificial Neural Networks. The spiking neurons encode useful temporal information and possess highly anti-noise property. The feature extraction ability of typical SNNs is limited by shallow structures. This paper focuses on improving the feature extraction ability of SNNs in virtue of powerful feature extraction ability of Convolutional Neural Networks (CNNs). CNNs can extract abstract features resorting to the structure of the convolutional feature maps. We propose a CNN-SNN (CSNN) model to combine feature learning ability of CNNs with cognition ability of SNNs. The CSNN model learns the encoded spatial temporal representations of images in an event-driven way. We evaluate the CSNN model on the handwritten digits images dataset MNIST and its variational databases. In the presented experimental results, the proposed CSNN model is evaluated regarding learning capabilities, encoding mechanisms, robustness to noisy stimuli and its classification performance. The results show that CSNN behaves well compared to other cognitive models with significantly fewer neurons and training samples. Our work brings more biological realism into modern image classification models, with the hope that these models can inform how the brain performs this high-level vision task.
A^3NCF: An Adaptive Aspect Attention Model for Rating Prediction

Current recommender systems consider the various aspects of items for making accurate recommendations. Different users place different importance to these aspects which can be thought of as a preference/attention weight vector. Most existing recommender systems assume that for an individual, this vector is the same for all items. However, this assumption is often invalid, especially when considering a user's interactions with items of diverse characteristics. To tackle this problem, in this paper, we develop a novel aspect-aware recommender model named A$^3$NCF, which can capture the varying aspect attentions that a user pays to different items. Specifically, we design a new topic model to extract user preferences and item characteristics from review texts. They are then used to 1) guide the representation learning of users and items, and 2) capture a user's special attention on each aspect of the targeted item with an attention network. Through extensive experiments on several large-scale datasets, we demonstrate that our model outperforms the state-of-the-art review-aware recommender systems in the rating prediction task.
Functional Partitioning of Ontologies for Natural Language Query Completion in Question Answering Systems

Query completion systems are well studied in the context of information retrieval systems that handle keyword queries. However, Natural Language Interface to Databases (NLIDB) systems that focus on syntactically correct and semantically complete queries to obtain high precision answers require a fundamentally different approach to the query completion problem as opposed to IR systems. To the best of our knowledge, we are first to focus on the problem of query completion for NLIDB systems. In particular, we introduce a novel concept of functional partitioning of an ontology and then design algorithms to intelligently use the components obtained from functional partitioning to extend a state-of-the-art NLIDB system to produce accurate and semantically meaningful query completions in the absence of query logs. We test the proposed query completion framework on multiple benchmark datasets and demonstrate the efficacy of our technique empirically.
Mining Streaming and Temporal Data: from Representation to Knowledge

In this big-data era, vast amount of continuously arriving data can be found in various fields, such as sensor networks, network management, web and financial applications. To process such data, algorithms are usually challenged by its complex structure and high volume. Representation learning facilitates the data operation by providing a condensed description of patterns underlying the data. Knowledge discovery based on the new representations will then be computationally efficient, and to certain extent be more effective due to the removal of noise and irrelevant information in the step of representation learning. In this paper, we will briefly review state-of-the-art techniques for extracting representation and discovering knowledge from streaming and temporal data, and demonstrate their performance at addressing several real application problems.
DMC: A Distributed Model Counter

We present and evaluate DMC, a distributed model counter for propositional CNF formulae based on the state-of-the-art sequential model counter D4. DMC can take advantage of a (possibly large) number of sequential model counters running on (possibly heterogeneous) computing units spread over a network of computers. For ensuring an efficient workload distribution, the model counting task is shared between the model counters following a policy close to work stealing. The number and the sizes of the messages which are exchanged by the jobs are kept small. The results obtained show DMC as a much more efficient counter than D4, the distribution of the computation yielding large improvements for some benchmarks. DMC appears also as a serious challenger to the parallel model counter CountAntom and to the distributed model counter dCountAntom.
Robust Multi-view Representation: A Unified Perspective from Multi-view Learning to Domain Adaption

Multi-view data are extensively accessible nowadays thanks to various types of features, different view-points and sensors which tend to facilitate better representation in many key applications. This survey covers the topic of robust multi-view data representation, centered around several major visual applications. First of all, we formulate a unified learning framework which is able to model most existing multi-view learning and domain adaptation in this line. Following this, we conduct a comprehensive discussion across these two problems by reviewing the algorithms along these two topics, including multi-view clustering, multi-view classification, zero-shot learning, and domain adaption. We further present more practical challenges in multi-view data analysis. Finally, we discuss future research including incomplete, unbalance, large-scale multi-view learning. This would benefit AI community from literature review to future direction.
Glycine Relieves Intestinal Injury by Maintaining mTOR Signaling and Suppressing AMPK, TLR4, and NOD Signaling in Weaned Piglets after Lipopolysaccharide Challenge

This study was conducted to envaluate whether glycine could alleviate Escherichia coli lipopolysaccharide (LPS)-induced intestinal injury by regulating intestinal epithelial energy status, protein synthesis, and inflammatory response via AMPK, mTOR, TLR4, and NOD signaling pathways. A total of 24 weanling piglets were randomly allotted to 1 of 4 treatments: (1) non-challenged control; (2) LPS-challenged control; (3) LPS + 1% glycine; (4) LPS + 2% glycine. After 28 days feeding, piglets were injected intraperitoneally with saline or LPS. The pigs were slaughtered and intestinal samples were collected at 4 h postinjection. The mRNA expression of key genes in these signaling pathways was measured by real-time PCR. The protein abundance was measured by Western blot analysis. Supplementation with glycine increased jejunal villus height/crypt depth ratio. Glycine also increased the jejunal and ileal protein content, RNA/DNA ratio, and jejunal protein/DNA ratio. The activities of citroyl synthetase in ileum, and α-ketoglutarate dehydrogenase complex in jejunum, were increased in the piglets fed diets supplemented with glycine. In addition, glycine decreased the jejunal and ileal phosphorylation of AMPKα, and increased ileal phosphorylation of mTOR. Furthermore, glycine downregulated the mRNA expression of key genes in inflammatory signaling. Meanwhile, glycine increased the mRNA expression of negative regulators of inflammatory signaling. These results indicate that glycine supplementation could improve energy status and protein synthesis by regulating AMPK and mTOR signaling pathways, and relieve inflammation by inhibiting of TLR4 and NOD signaling pathways to alleviate intestinal injury in LPS-challenged piglets.
Observation of highly stable and symmetric lanthanide octa-boron inverse sandwich complexes

Significance Lanthanide borides constitute an important class of materials with wide industrial applications, but clusters of lanthanide borides have been rarely investigated. It is of great interest to study these nanosystems, which may provide molecular-level understanding of the emergence of new properties and provide insight into designing new boride materials. We have produced lanthanide boride clusters and probed their electronic structure and chemical bonding. Two Ln2B8− clusters are presented, and they are found to possess inverse sandwich structures. The neutral Ln2B8 complexes are found to possess D8h symmetry with strong Ln–B8 bonding. A unique (d–p)δ bond is found to be important for the Ln–B8–Ln interactions. The Ln2B8 inverse sandwich complexes broaden the structural chemistry of the lanthanide elements and provide insights into bonding in lanthanide boride materials. While boron forms a wide range of metal borides with important industrial applications, there has been relatively little attention devoted to lanthanide boride clusters. Here we report a joint photoelectron spectroscopy and quantum chemical study on two octa-boron di-lanthanide clusters, Ln2B8− (Ln = La, Pr). We found that these clusters form highly stable inverse sandwich structures, [Ln–B8–Ln]−, with strong Ln and B8 bonding via interactions between the Ln 5d orbitals and the delocalized σ and π orbitals on the B8 ring. A (d–p)δ bond, involving the 5dδ and the antibonding π orbital of the B8 ring, is observed to be important in the Ln–B8 interactions. The highly symmetric inverse sandwich structures are overwhelmingly more stable than any other isomers. Upon electron detachment, the (d–p)δ orbitals become half-filled, giving rise to a triplet ground state for neutral La2B8. In addition to the two unpaired electrons in the (d–p)δ orbitals upon electron detachment, the neutral Pr2B8 complex also contains two unpaired 4f electrons on each Pr center. The six unpaired spins in Pr2B8 are ferromagnetically coupled to give rise to a septuplet ground state. The current work suggests that highly magnetic Ln…B8…Ln inverse sandwiches or 1D Ln…B8…Ln nanowires may be designed with novel electronic and magnetic properties.