Miracle-dz/UniFAR-FTUs huggingface dataset:
The Error Probability of Maximum-Likelihood Decoding over Two Deletion Channels

This paper studies the problem of reconstructing a word given several of its noisy copies. This setup is motivated by several applications, among them is reconstructing strands in DNA-based storage systems. Under this paradigm, a word is transmitted over some fixed number of identical independent channels and the goal of the decoder is to output the transmitted word or some close approximation. The main focus of this paper is the case of two deletion channels and studying the error probability of the maximum-likelihood (ML) decoder under this setup. First, it is discussed how the ML decoder operates. Then, we observe that the dominant error patterns are deletions in the same run or errors resulting from alternating sequences. Based on these observations, it is derived that the error probability of the ML decoder is roughly $\frac{3q-1}{q-1}p^2$, when the transmitted word is any $q$-ary sequence and $p$ is the channel's deletion probability. We also study the cases when the transmitted word belongs to the Varshamov Tenengolts (VT) code or the shifted VT code. Lastly, the insertion channel is studied as well. These theoretical results are verified by corresponding simulations.
GABAA Receptor Ligands Often Interact with Binding Sites in the Transmembrane Domain and in the Extracellular Domain—Can the Promiscuity Code Be Cracked?

Many allosteric binding sites that modulate gamma aminobutyric acid (GABA) effects have been described in heteropentameric GABA type A (GABAA) receptors, among them sites for benzodiazepines, pyrazoloquinolinones and etomidate. Diazepam not only binds at the high affinity extracellular “canonical” site, but also at sites in the transmembrane domain. Many ligands of the benzodiazepine binding site interact also with homologous sites in the extracellular domain, among them the pyrazoloquinolinones that exert modulation at extracellular α+/β− sites. Additional interaction of this chemotype with the sites for etomidate has also been described. We have recently described a new indole-based scaffold with pharmacophore features highly similar to pyrazoloquinolinones as a novel class of GABAA receptor modulators. Contrary to what the pharmacophore overlap suggests, the ligand presented here behaves very differently from the identically substituted pyrazoloquinolinone. Structural evidence demonstrates that small changes in pharmacophore features can induce radical changes in ligand binding properties. Analysis of published data reveals that many chemotypes display a strong tendency to interact promiscuously with binding sites in the transmembrane domain and others in the extracellular domain of the same receptor. Further structural investigations of this phenomenon should enable a more targeted path to less promiscuous ligands, potentially reducing side effect liabilities.
The Application of Pollen as a Functional Food and Feed Ingredient—The Present and Perspectives

Pollen is recognized as an excellent dietary supplement for human nutrition, which is why it can be found in different forms on the market (granules, capsules, tablets, pellets, and powders). But, the digestibility of pollen’s nutrients is strongly affected by the presence of a pollen shell, which can decrease the bioavailability of nutrients by 50% and more. Since consumers have become more aware of the benefits of a healthy diet and the necessity to improve pollen digestibility, different pollen-based functional food products have been developed and extensive studies were done to estimate the beneficial effects of pollen-based feed on animal growth, health, and rigor mortise stage. Considering the positive effects of pollen nutrients and phytometabolites on human and animal health, the aim of this paper was to give an overview of recent achievements in the application of pollen in the formulation of functional food and animal diets. Special attention was paid to the effects of pollen’s addition on the nutritional, functional, techno-functional, and sensory properties of the new formulated food products. Anti-nutritional properties of pollen were also discussed. This review points out the benefits of pollen addition to food and feed and the possible directions in the further development of functional food and feed for the wellbeing of everyone.
Integrating Activity-Based Geographic Information and Long-Term Remote Sensing to Characterize Urban Land Use Change

The land use structure is a key component to understand the complexity of urban systems because it provides a snapshot of urban dynamics and how people use space. This paper integrates socially sensed activity data with a remotely sensed land cover product in order to infer urban land use and its changes over time. We conducted a case study in the Washington D.C.–Baltimore metropolitan area to identify the pattern of land use change from undeveloped to developed land, including residential and non-residential uses for a period covering 1986–2008. The proposed approach modeled physical and behavioral features of land parcels from a satellite-based impervious surface cover change product and georeferenced Tweets, respectively. A model assessment with random forests classifiers showed that the proposed classification workflow could classify residential and non-residential land uses at an accuracy of 81%, 4% better than modeling the same land uses from physical features alone. Using the timestamps of the impervious surface cover change product, the study also reconstructed the timeline of the identified land uses. The results indicated that the proposed approach was capable of mapping detailed land use and change in an urban region, and represents a new and viable way forward for urban land use surveying that could be especially useful for surveying and tracking changes in cities where traditional approaches and mapping products (i.e., from remote sensing products) may have a limited capacity to capture change.
Anaphase‐promoting complex/cyclosome‐Cdc‐20 promotes Zwint‐1 degradation

ZW10 interactor (Zwint‐1) is an important component of the centromere and can recruit the dynamic protein kinase and dynein to promote chromosome movement and regulate the spindle assembly checkpoint (SAC). Zwint‐1 activity is tightly regulated during the cell cycle. However, how the stability of Zwint‐1 is regulated has not been clarified. Here, we show that the relative levels of Zwint‐1 expression gradually decreased with the progression of cell cycling and decline sharply during mitotic exit. Treatment with cycloheximide reduced the levels of Zwint‐1 while treatment with MG132 to inhibit endogenous ubiquitin‐proteasome elevated the levels of Zwint‐1 in HEK293T cells or Hela cells. Such data suggest that Zwint‐1 may be degraded by endogenous ubiquitin‐proteasome. Furthermore, induction of cell‐division cycle protein 20 (Cdc20) overexpression decreased the levels of Zwint‐1, which was abrogated by MG132 treatment. In contrast, Cdc20 silencing promoted the accumulation of Zwint‐1. in vivo ubiquitination assay revealed that Cdc20 promoted the formation of Zwint‐1 and ubiquitin‐proteasome conjugates. Cotransfection with Cdc20 and wild‐type Zwint‐1, but not Zwint‐1ΔD‐box, reduced the levels of Zwint‐1. Immunoprecipitation and western blot analyses showed that Cdc20 interacted with wild‐type Zwint‐1, but not Zwint‐1ΔD‐box although both Zwint‐1 and Zwint‐1ΔD‐box overexpression did not induce mitotic arrest. Collectively, our data indicated that Zwint‐1 was ubiquitinated by anaphase‐promoting complex/cyclosome (APC/C)‐Cdc20 in a D‐box‐dependent manner. Therefore, the APC/C‐Cdc20 controls the stability of Zwint‐1, ensuring accurate regulation of the spindle assembly during the cell cycling in HEK293T cells.
Association between the Mother’s Social Cognition and the Child’s Social Functioning in Kindergarten: The Mediating Role of the Child’s Social Cognition

Children’s ability to adjust to the social rules and expectations in the educational environment is of major concern to researchers and practitioners alike. Accordingly, the main purpose of the present study was to examine predictors of children’s social functioning in kindergarten with a specific focus on (a) maternal factors and (b) children’s social cognition. Using a multi-method (self-reports and direct assessments), multi-informant (child, mother, teacher) design, we collected data from 301 kindergarten children and their mothers tapping the mother’s social cognition (general and child-related) and parenting style, and children’s social cognition (social information processing) and functioning in kindergarten. We found direct associations between the mother and child’s social cognition, between the mother’s authoritarian parenting style and her child’s less competent social cognition and behavior, and between the child’s social cognition and social functioning. Finally, as hypothesized, we found a number of interesting mediated effects. Most notably, we found that the association between the mother’s social cognition (her tendency to attribute hostile intent to unknown others) and the child’s social cognition (his/her tendency to generate less competent responses) is fully mediated by the mother’s higher levels of authoritarian parenting style. The important theoretical and clinical implications of our findings are discussed.
High Speed Crop and Weed Identification in Lettuce Fields for Precision Weeding

Precision weeding can significantly reduce or even eliminate the use of herbicides in farming. To achieve high-precision, individual targeting of weeds, high-speed, low-cost plant identification is essential. Our system using the red, green, and near-infrared reflectance, combined with a size differentiation method, is used to identify crops and weeds in lettuce fields. Illumination is provided by LED arrays at 525, 650, and 850 nm, and images are captured in a single-shot using a modified RGB camera. A kinematic stereo method is utilised to compensate for parallax error in images and provide accurate location data of plants. The system was verified in field trials across three lettuce fields at varying growth stages from 0.5 to 10 km/h. In-field results showed weed and crop identification rates of 56% and 69%, respectively. Post-trial processing resulted in average weed and crop identifications of 81% and 88%, respectively.
Satellite Altimetry and Tide Gauge Observed Teleconnections between Long-Term Sea Level Variability in the U.S. East Coast and the North Atlantic Ocean

Rising sea levels amplify the threat and magnitude of storm surges in coastal areas. The U.S. east coast region north of Cape Hatteras has shown a significant sea level rise acceleration and is believed to be a “hot-spot” for accelerating tidal flooding. To better understand the forcing mechanism of long-term regional sea level change, in order to more efficiently implement local sea level rise adaptation and mitigation measures, this work investigated the teleconnections between low-frequency sea level variability in the coastal region north of Cape Hatteras and the subpolar/tropical North Atlantic Ocean by using tide gauge measurements, satellite altimetry data and a sea level reconstruction dataset. The correlation analysis demonstrates that the tide-gauge measured sea level variability in the area north of Cape Hatteras is highly and positively correlated with that observed by satellite altimetry in the subpolar and tropical North Atlantic between 1993 and 2002. Over the following decade (2003–2012), the phase of the teleconnection in the subpolar region was reversed and the spatio-temporal correlation in the tropical North Atlantic was enhanced. Furthermore, the positive correlation in the region north of Cape Hatteras’s near shore area is strengthened, while the negative correlation in the Gulf Stream front region is weakened. The North Atlantic Oscillation and Atlantic Multidecadal Oscillation, which affect variations of the Atlantic Meridional Overturning Circulation and Gulf Stream, were shown to have significant impacts on the decadal changes of the teleconnections. Coherent with satellite altimetry data, the reconstructed sea level dataset in the 20th century exhibits similar spatial correlation patterns with the Atlantic Meridional Overturning Circulation, North Atlantic Oscillation and Atlantic Multidecadal Oscillation indices.
Indoor Layout Estimation by 2D LiDAR and Camera Fusion

This paper presents an algorithm for indoor layout estimation and reconstruction through the fusion of a sequence of captured images and LiDAR data sets. In the proposed system, a movable platform collects both intensity images and 2D LiDAR information. Pose estimation and semantic segmentation is computed jointly by aligning the LiDAR points to line segments from the images. For indoor scenes with walls orthogonal to floor, the alignment problem is decoupled into top-down view projection and a 2D similarity transformation estimation and solved by the recursive random sample consensus (R-RANSAC) algorithm. Hypotheses can be generated, evaluated and optimized by integrating new scans as the platform moves throughout the environment. The proposed method avoids the need of extensive prior training or a cuboid layout assumption, which is more effective and practical compared to most previous indoor layout estimation methods. Multi-sensor fusion allows the capability of providing accurate depth estimation and high resolution visual information.
Combining Progressive Rethinking and Collaborative Learning: A Deep Framework for In-Loop Filtering

In this paper, we aim to address issues of <xref rid="deqn1" ref-type="disp-formula">(1)</xref> joint spatial-temporal modeling and <xref rid="deqn2" ref-type="disp-formula">(2)</xref> side information injection for deep-learning based in-loop filter. For <xref rid="deqn1" ref-type="disp-formula">(1)</xref>, we design a deep network with both progressive rethinking and collaborative learning mechanisms to improve quality of the reconstructed intra-frames and inter-frames, respectively. For intra coding, a Progressive Rethinking Network (PRN) is designed to simulate the human decision mechanism for effective spatial modeling. Our designed block introduces an additional inter-block connection to bypass a high-dimensional informative feature before the bottleneck module across blocks to review the complete past memorized experiences and <italic>rethinks progressively</italic>. For inter coding, the current reconstructed frame interacts with reference frames (peak quality frame and the nearest adjacent frame) <italic>collaboratively</italic> at the feature level. For <xref rid="deqn2" ref-type="disp-formula">(2)</xref>, we extract both intra-frame and inter-frame side information for better context modeling. A coarse-to-fine partition map based on HEVC partition trees is built as the intra-frame side information. Furthermore, the warped features of the reference frames are offered as the inter-frame side information. Our PRN with intra-frame side information provides 9.0% BD-rate reduction on average compared to HEVC baseline under All-intra (AI) configuration. While under Low-Delay B (LDB), Low-Delay P (LDP) and Random Access (RA) configuration, our PRN with inter-frame side information provides 9.0%, 10.6% and 8.0% BD-rate reduction on average respectively. Our project webpage is <uri>https://dezhao-wang.github. io/PRN-v2/</uri>.
Anal cancer screening and Pap testing acceptability among HIV‐positive men who have sex with men populations

We read with interest the publication by Torodova et al. [1] which evaluated anal carcinoma screening in male and female HIV patients at an interdisciplinary HIV therapy centre. The study has a number of strengths including a large, representative clinical population of HIV-positive patients (predominantly men who have sex with men (MSM)).
Can Behavioral "Nudges" Improve Compliance? The Case of Colombia Social Protection Contributions

The Government of Colombia imposes a variety of taxes that must be paid by individual wage earners, called in their entirety “social protection contributions”. Since 2007 individual payments have been collected using an on-line mechanism. In order to improve compliance, the Government used a controlled field experiment in which various “pop-up messages” were sent to individuals when making their on-line payments, as behavioral “nudges”. We examine the impact of these nudges on individual reporting behavior. We find mixed evidence that these messages increased compliance rates relative to a control group that received a so-called “neutral” message. However, we also demonstrate that the use as the control group of individuals receiving a so-called “neutral” message creates considerable bias; that is, the receipt of any message of any type clearly influences behavior. Instead, we show that the appropriate control group should be individuals who receive no message at all. When this control group is used, we find that self-employed individuals generally increase their contributions; individuals who are making declarations on behalf of all employees in their company are less likely to respond to messages in a systematic way.
Right for the Wrong Scientific Reasons: Revising Deep Networks by Interacting with their Explanations

Deep neural networks have shown excellent performances in many real-world applications. Unfortunately, they may show "Clever Hans"-like behavior---making use of confounding factors within datasets---to achieve high performance. In this work we introduce the novel learning setting of "explanatory interactive learning" (XIL) and illustrate its benefits on a plant phenotyping research task. XIL adds the scientist into the training loop such that she interactively revises the original model via providing feedback on its explanations. Our experimental results demonstrate that XIL can help avoiding Clever Hans moments in machine learning and encourages (or discourages, if appropriate) trust into the underlying model.
Reduced sensitivity from pooled urine, pharyngeal and rectal specimens when using a molecular assay for the detection of chlamydia and gonorrhoea near the point of care.

Background:The aim of this study was to compare the performance of pooled self-collected urogenital, pharyngeal and anorectal specimens to that of individual specimen results for the molecular detection of Chlamydia trachomatis (CT) and Neisseria gonorrhoeae (NG) near the point of care (POC) for diagnostic sensitivity. Methods: Clients (mostly men who have sex with men) attending an urban community testing service and three sex-on-premises venues in Brisbane, Australia, were offered CT and NG testing by trained lay providers. Participants provided three self-collected specimens (urine, pharyngeal and rectal) for testing by GeneXpert (Cepheid, Sunnyvale, CA, USA). If any of the individual specimens from a participant were positive, all three specimens were pooled and retested. Results: Of the 388 participants who provided three individual anatomical specimens, 76 (19.6%) were found to be positive for CT and/or NG at one or more sites. The pooling approach failed to detect five CT rectal and four NG pharyngeal infections. The overall performance (sensitivity) of the pooling approach compared with individual specimen testing and Cohen's κ were 90.0% and 0.86 respectively for CT and 89.7% and 0.89 respectively for NG. Conclusions: Reduced sensitivity was observed when using pooled specimens for the detection of CT and NG using GeneXpert near the POC, similar to results reported in laboratory-based CT and NG pooling studies. These data suggest specimen pooling is feasible near to the POC, potentially saving time and costs when screening at-risk populations for CT and NG. Our data also suggest a reduction in pooled urine could improve overall test sensitivity.
Switching Structured Prediction for Simple and Complex Human Activity Recognition

Automatic human activity recognition is an integral part of any interactive application involving humans (e.g., human–robot interaction systems). One of the main challenges for activity recognition is the diversity in the way individuals often perform activities. Furthermore, changes in any of the environment factors (i.e., illumination, complex background, human body shapes, viewpoint, etc.) intensify this challenge. In addition, there are different types of activities that robots need to interpret for seamless interaction with humans. Some activities are short, quick, and simple (e.g., sitting), while others may be detailed/complex, and spread throughout a long span of time (e.g., washing mouth). In this article, we recognize the activities within the context of graphical models in a sequence-labeling framework based on skeleton data. We propose a new structured prediction strategy based on probabilistic graphical models (PGMs) to recognize both types of activities (i.e., complex and simple). These activity types are often spanned in very diverse subspaces in the space of all possible activities, which would require different model parameterizations. In order to deal with these parameterization and structural breaks across models, a category-switching scheme is proposed to switch over the models based on the activity types. For parameter optimization, we utilize a distributed structured prediction technique to implement our model in a distributed setting. The method is tested on three widely used datasets (CAD-60, UT-Kinect, and Florence 3-D) that cover both activity types. The results illustrate that our proposed method is able to recognize simple and complex activities while the previous work concentrated on only one of these two main types.
Neuregulin 1/ErbB4 signaling attenuates neuronal cell damage under oxygen-glucose deprivation in primary hippocampal neurons

The hippocampus is one of the most important brain areas of cognition. This region is particularly sensitive to hypoxia and ischemia. Neuregulin-1 (NRG1) has been shown to be able to protect against focal cerebral ischemia. The aim of the present study was to investigate the neuroprotective effect of NRG1 in primary hippocampal neurons and its underlying mechanism. Our data showed oxygen-glucose deprivation (OGD)-induced cytotoxicity and overexpression of ErbB4 in primary hippocampal neurons. Moreover, pretreatment with NRG1 could inhibit OGD-induced overexpression of ErbB4. In addition, NRG1 significantly attenuated neuronal death induced by OGD. The neuroprotective effect of NRG1 was blocked in ischemic neurons after pretreatment with AG1478, an inhibitor of ErbB4, but not after pretreatment with AG879, an inhibitor of ErbB2. These results indicate an important role of ErbB4 in NRG1-mediated neuroprotection, suggesting that endogenous ErbB4 might serve as a valuable therapeutic target for treating global cerebral ischemia.
Deep Residual Flow for Novelty Detection

The effective application of neural networks in the real-world relies on proficiently detecting out-of-distribution examples. Contemporary methods seek to model the distribution of feature activations in the training data for adequately distinguishing abnormalities, and the state-of-the-art method uses Gaussian distribution models. In this work, we present a novel approach that improves upon the state-of-the-art by leveraging an expressive density model based on normalizing flows. We introduce the residual flow, a novel flow architecture that learns the residual distribution from a base Gaussian distribution. Our model is general, and can be applied to any data that is approximately Gaussian. For novelty detection in image datasets, our approach provides a principled improvement over the state-of-the-art. Specifically, we demonstrate the effectiveness of our method in ResNet and DenseNet architectures trained on various image datasets. For example, on a ResNet trained on CIFAR-100 and evaluated on detection of out-of-distribution samples from the ImageNet dataset, holding the true positive rate (TPR) at $95\%$, we improve the true negative rate (TNR) from $56.7\%$ (current state-of-the-art) to $77.5\%$ (ours).