Miracle-dz/UniFAR-FTUs huggingface dataset:
An Origin of the Huge Far-Infrared Luminosity of Starburst Mergers

Recently Taniguchi and Ohyama found that the higher 12CO to 13CO integrated intensity ratios at a transition J=1-0, R=I(12CO)/I(13CO) ≳ 20, in a sample of starburst merging galaxies such as Arp 220 are mainly attributed to the depression of 13CO emission with respect to 12CO. Investigating the same sample of galaxies analyzed by Taniguchi and Ohyama, we find that there is a tight, almost linear correlation between the dust mass and 13CO luminosity. This implies that dust grains are also depressed in the high-R starburst mergers, leading to the higher dust temperature (Td) in them because of the relative increase in the radiation density. Nevertheless, the average dust mass (Md) of the high-R starburst mergers is significantly higher than that of non-high-R galaxies. This is naturally understood because the galaxy mergers could accumulate a lot of dust grains from their progenitor galaxies together with the supply of dust grains formed newly in the star-forming regions. Since L(FIR) ∝ MdT5d given the dust emissivity law Sν ∝ λ−1, the increases in both Md and Td explain well why the starburst mergers are so bright in the far-infrared. We discuss the important role that the superwind activity plays in destroying dust grains as well as dense gas clouds in the central region of mergers.
New tools for “hot-wiring” clathrin-mediated endocytosis with temporal and spatial precision

Clathrin-mediated endocytosis (CME) is the major route of receptor internalization at the plasma membrane. Analysis of constitutive CME is difficult because the initiation of endocytic events is unpredictable. When and where a clathrin-coated pit will form and what cargo it will contain are difficult to foresee. Here we describe a series of genetically encoded reporters that allow the initiation of CME on demand. A clathrin-binding protein fragment (“hook”) is inducibly attached to an “anchor” protein at the plasma membrane, which triggers the formation of new clathrin-coated vesicles. Our design incorporates temporal and spatial control by the use of chemical and optogenetic methods for inducing hook–anchor attachment. Moreover, the cargo is defined. Because several steps in vesicle creation are bypassed, we term it “hot-wiring. ” We use hot-wired endocytosis to describe the functional interactions between clathrin and AP2. Two distinct sites on the &bgr;2 subunit, one on the hinge and the other on the appendage, are necessary and sufficient for functional clathrin engagement.
IEEE 802.11ay-Based mmWave WLANs: Design Challenges and Solutions

Millimeter-wave (mmWave) with large spectrum available is considered as the most promising frequency band for future wireless communications. The IEEE 802.11ad and IEEE 802.11ay operating on 60 GHz mmWave are the two most expected wireless local area network (WLAN) technologies for ultra-high-speed communications. For the IEEE 802.11ay standard still under development, there are plenty of proposals from companies and researchers who are involved with the IEEE 802.11ay task group. In this survey, we conduct a comprehensive review on the medium access control layer (MAC) related issues for the IEEE 802.11ay, some cross-layer between physical layer and MAC technologies are also included. We start with MAC related technologies in the IEEE 802.11ad and discuss design challenges on mmWave communications, leading to some MAC related technologies for the IEEE 802.11ay. We then elaborate on important design issues for IEEE 802.11ay. Specifically, we review the channel bonding and aggregation for the IEEE 802.11ay, and point out the major differences between the two technologies. Then, we describe channel access and channel allocation in the IEEE 802.11ay, including spatial sharing and interference mitigation technologies. After that, we present an in-depth survey on beamforming training, beam tracking, single-user multiple-input-multiple-output beamforming, and multi-user multiple-input-multiple-output beamforming. Finally, we discuss some open design issues and future research directions for mmWave WLANs. We hope that this paper provides a good introduction to this exciting research area for future wireless systems.
Strategies and challenges in eliciting immunity to melanoma

Summary: The ability of CD8+ T cells to recognize melanoma tumors has led to the development of immunotherapeutic approaches that use the antigens CD8+ T cells recognize. However, clinical response rates have been disappointing. Here we summarize our work to understand the mechanisms of self‐tolerance that limit responses to currently utilized antigens and our approach to identify new antigens directly tied to malignancy. We also explore several aspects of the anti‐tumor immune response induced by peptide‐pulsed dendritic cells (DCs). DCs differentially augment the avidity of recall T cells specific for self‐antigens and overcome a process of aberrant CD8+ T‐cell differentiation that occurs in tumor‐draining lymph nodes. DC migration is constrained by injection route, resulting in immune responses in localized lymphoid tissue, and differential control of tumors depending on their location in the body. We demonstrate that CD8+ T‐cell differentiation in different lymphoid compartments alters the expression of homing receptor molecules and leads to the presence of systemic central memory cells. Our studies highlight several issues that must be addressed to improve the efficacy of tumor immunotherapy.
Heterologous Expression of Mycobacterial Esx Complexes in Escherichia coli for Structural Studies Is Facilitated by the Use of Maltose Binding Protein Fusions

The expression of heteroligomeric protein complexes for structural studies often requires a special coexpression strategy. The reason is that the solubility and proper folding of each subunit of the complex requires physical association with other subunits of the complex. The genomes of pathogenic mycobacteria encode many small protein complexes, implicated in bacterial fitness and pathogenicity, whose characterization may be further complicated by insolubility upon expression in Escherichia coli, the most common heterologous protein expression host. As protein fusions have been shown to dramatically affect the solubility of the proteins to which they are fused, we evaluated the ability of maltose binding protein fusions to produce mycobacterial Esx protein complexes. A single plasmid expression strategy using an N-terminal maltose binding protein fusion to the CFP-10 homolog proved effective in producing soluble Esx protein complexes, as determined by a small-scale expression and affinity purification screen, and coupled with intracellular proteolytic cleavage of the maltose binding protein moiety produced protein complexes of sufficient purity for structural studies. In comparison, the expression of complexes with hexahistidine affinity tags alone on the CFP-10 subunits failed to express in amounts sufficient for biochemical characterization. Using this strategy, six mycobacterial Esx complexes were expressed, purified to homogeneity, and subjected to crystallization screening and the crystal structures of the Mycobacterium abscessus EsxEF, M. smegmatis EsxGH, and M. tuberculosis EsxOP complexes were determined. Maltose binding protein fusions are thus an effective method for production of Esx complexes and this strategy may be applicable for production of other protein complexes.
Systemic Neutrophil Depletion Modulates the Migration and Fate of Transplanted Human Neural Stem Cells to Rescue Functional Repair

The interaction of transplanted stem cells with local cellular and molecular cues in the host CNS microenvironment may affect the potential for repair by therapeutic cell populations. In this regard, spinal cord injury (SCI), Alzheimer's disease, and other neurological injuries and diseases all exhibit dramatic and dynamic changes to the host microenvironment over time. Previously, we reported that delayed transplantation of human CNS-derived neural stem cells (hCNS-SCns) at 9 or 30 d post-SCI (dpi) resulted in extensive donor cell migration, predominantly neuronal and oligodendrocytic donor cell differentiation, and functional locomotor improvements. Here, we report that acute transplantation of hCNS-SCns at 0 dpi resulted in localized astroglial differentiation of donor cells near the lesion epicenter and failure to produce functional improvement in an all-female immunodeficient mouse model. Critically, specific immunodepletion of neutrophils (polymorphonuclear leukocytes) blocked hCNS-SCns astroglial differentiation near the lesion epicenter and rescued the capacity of these cells to restore function. These data represent novel evidence that a host immune cell population can block the potential for functional repair derived from a therapeutic donor cell population, and support targeting the inflammatory microenvironment in combination with cell transplantation after SCI. SIGNIFICANCE STATEMENT The interaction of transplanted cells with local cellular and molecular cues in the host microenvironment is a key variable that may shape the translation of neurotransplantation research to the clinical spinal cord injury (SCI) human population, and few studies have investigated these events. We show that the specific immunodepletion of polymorphonuclear leukocyte neutrophils using anti-Ly6G inhibits donor cell astrogliosis and rescues the capacity of a donor cell population to promote locomotor improvement after SCI. Critically, our data demonstrate novel evidence that a specific host immune cell population can block the potential for functional repair derived from a therapeutic donor cell population.
Implementing vocal tract length normalization in the MLLR framework

Vocal Tract Length Normalization (VTLN) and Maximum Likelihood Linear Regression (MLLR) are two approaches to reduce the degradation in speech recognition performance caused by variation of speakers. This paper derives a novel efficient adaptation algorithm from the two techniques. Based on prior knowledge of usual VTLN, an approximate constrained-form linear transformation is obtained. The transformation is learned using EM algorithm and then applied in the MLLR setting. Experiments of three tasks are performed on an isolated word recognition system. Experimental results shows that with several adaptation words, WER is decreased greatly.
Estimating defectiveness of source code: A predictive model using GitHub content

Two key contributions presented in this paper are: i) A method for building a dataset containing source code features extracted from source files taken from Open Source Software (OSS) and associated bug reports, ii) A predictive model for estimating defectiveness of a given source code. These artifacts can be useful for building tools and techniques pertaining to several automated software engineering areas such as bug localization, code review, and recommendation and program repair. In order to achieve our goal, we first extract coding style information (e.g. related to programming language constructs used in the source code) for source code files present on GitHub. Then the information available in bug reports (if any) associated with these source code files are extracted. Thus fetched un(/ semi)-structured information is then transformed into a structured knowledge base. We considered more than 30400 source code files from 20 different GitHub repositories with about 14950 associated bug reports across 4 bug tracking portals. The source code files considered are written in four programming languages (viz., C, C++, Java, and Python) and belong to different types of applications. A machine learning (ML) model for estimating the defectiveness of a given input source code is then trained using the knowledge base. In order to pick the best ML model, we evaluated 8 different ML algorithms such as Random Forest, K Nearest Neighbour and SVM with around 50 parameter configurations to compare their performance on our tasks. One of our findings shows that best K-fold (with k=5) cross-validation results are obtained with the NuSVM technique that gives a mean F1 score of 0.914.
Evaluating Spatial Overlap and Relatedness of White-tailed Deer in a Chronic Wasting Disease Management Zone

Wildlife disease transmission, at a local scale, can occur from interactions between infected and susceptible conspecifics or from a contaminated environment. Thus, the degree of spatial overlap and rate of contact among deer is likely to impact both direct and indirect transmission of infectious diseases such chronic wasting disease (CWD) or bovine tuberculosis. We identified a strong relationship between degree of spatial overlap (volume of intersection) and genetic relatedness for female white-tailed deer in Wisconsin’s area of highest CWD prevalence. We used volume of intersection as a surrogate for contact rates between deer and concluded that related deer are more likely to have contact, which may drive disease transmission dynamics. In addition, we found that age of deer influences overlap, with fawns exhibiting the highest degree of overlap with other deer. Our results further support the finding that female social groups have higher contact among related deer which can result in transmission of infectious diseases. We suggest that control of large social groups comprised of closely related deer may be an effective strategy in slowing the transmission of infectious pathogens, and CWD in particular.
Next Basket Recommendation with Neural Networks

One crucial task in recommendation is to predict what a user will buy next given her shopping history. In this paper, we propose a novel neural network to complete this task. The model consists of an embedding layer, a hidden layer and an output layer. Firstly, the distributed representations of the user and the items bought before are obtained and used to form a feature vector by the embedding layer. Then the hidden layer transforms the feature vector to another space by a non-linear operator. Finally, the softmax operator is adopted to output the probabilities of next items. We can see that the model elegantly involves both the user's general interest and the sequential dependencies between items for prediction. Experimental results on two real datasets prove the effectiveness of our model.
Efficacy and tolerability of short-term duloxetine treatment in adults with generalized anxiety disorder: A meta-analysis

Objective To investigate the efficacy and tolerability of duloxetine during short-term treatment in adults with generalized anxiety disorder (GAD). Methods We conducted a comprehensive literature review of the PubMed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, and ClinicalTrials databases for randomized controlled trials(RCTs) comparing duloxetine or duloxetine plus other antipsychotics with placebo for the treatment of GAD in adults. Outcome measures were (1) efficacy, assessed by the Hospital Anxiety and Depression Scale(HADS) anxiety subscale score, the Hamilton Rating Scale for Anxiety(HAM-A) psychic and somatic anxiety factor scores, and response and remission rates based on total scores of HAM-A; (2) tolerability, assessed by discontinuation rate due to adverse events, the incidence of treatment emergent adverse events(TEAEs) and serious adverse events(SAEs). Review Manager 5.3 and Stata Version 12.0 software were used for all statistical analyses. Results The meta-analysis included 8 RCTs. Mean changes in the HADS anxiety subscale score [mean difference(MD) = 2.32, 95% confidence interval(CI) 1.77–2.88, P<0.00001] and HAM-A psychic anxiety factor score were significantly greater in patients with GAD that received duloxetine compared to those that received placebo (MD = 2.15, 95%CI 1.61–2.68, P<0.00001). However, there was no difference in mean change in the HAM-A somatic anxiety factor score (MD = 1.13, 95%CI 0.67–1.58, P<0.00001). Discontinuation rate due to AEs in the duloxetine group was significantly higher than the placebo group [odds ratio(OR) = 2.62, 95%CI 1.35–5.06, P = 0.004]. The incidence of any TEAE was significantly increased in patients that received duloxetine (OR = 1.76, 95%CI 1.36–2.28, P<0.0001), but there was no significant difference in the incidence of SAEs (OR = 1.13, 95%CI 0.52–2.47, P = 0.75). Conclusion Duloxetine resulted in a greater improvement in symptoms of psychic anxiety and similar changes in symptoms of somatic anxiety compared to placebo during short-term treatment in adults with GAD and its tolerability was acceptable.
Influence of fixed orthodontic treatment on the menstrual cycle of adult females: A prospective longitudinal study.

OBJECTIVE To investigate the influence of fixed orthodontic treatment on the menstrual cycle, including menstrual cycle length (MCL) and duration of menstrual bleeding (DMB), in adult female patients. MATERIALS AND METHODS This was a prospective longitudinal study conducted in Chengdu, China. A total of 164 adult women with normal menstrual cycles were recruited in the study, with 79 patients undergoing orthodontic treatment and 85 serving as controls. Data of MCL, DMB, and accompanying symptoms were collected over six consecutive menstrual cycles in each participant. Student's t test, Chi-square test, Moses extreme reaction test, and repeated measures analysis of variance were used for statistical analysis. RESULTS The MCL of the first menstrual cycle (T1) was significantly elongated by 2.1 ± 0.5 days compared with baseline (P = .003, 95% CI [-3.7, -0.5]). Variability of MCL of the orthodontic group at T1 was also significantly greater (range, 15-46 days) than that of the control group (range, 24-36 days) (P < .05). No significant difference in MCL was found in the subsequent five menstrual cycles (T2-T6) compared with baseline, and no significant differences in DMB or other accompanying symptoms were observed throughout the study. CONCLUSION Fixed orthodontic treatment may influence the MCL of adult females in the first month after bonding, but showed no effect on DMB or subsequent MCL through the follow-ups.
Approximate convex decomposition based localization in wireless sensor networks

Accurate localization in wireless sensor networks is the foundation for many applications, such as geographic routing and position-aware data processing. An important research direction for localization is to develop schemes using connectivity information only. These schemes primary apply hop counts to distance estimation. Not surprisingly, they work well only when the network topology has a convex shape. In this paper, we develop a new Localization protocol based on Approximate Convex Decomposition (ACDL). It can calculate the node virtual locations for a large-scale sensor network with arbitrary shapes. The basic idea is to decompose the network into convex subregions. It is not straight-forward, however. We first examine the influential factors on the localization accuracy when the network is concave such as the sharpness of concave angle and the depth of the concave valley. We show that after decomposition, the depth of the concave valley becomes irrelevant. We thus define concavity according to the angle at a concave point, which can reflect the localization error. We then propose ACDL protocol for network localization. It consists of four main steps. First, convex and concave nodes are recognized and network boundaries are segmented. As the sensor network is discrete, we show that it is acceptable to approximately identify the concave nodes to control the localization error. Second, an approximate convex decomposition is conducted. Our convex decomposition requires only local information and we show that it has low message overhead. Third, for each convex subsection of the network, an improved Multi-Dimensional Scaling (MDS) algorithm is proposed to compute a relative location map. Fourth, a fast and low complexity merging algorithm is developed to construct the global location map. Our simulation on several representative networks demonstrated that ACDL has localization error that is 60%-90% smaller as compared with the typical MDS-MAP algorithm and 20%-30% smaller as compared to a recent state-of-the-art localization algorithm CATL.
3D model dynamic cutting technology based on game engine

Although games is famous for its high interactivity, but in some special area of applications, such as educational games [1], the interactivity of games is still not enough. For example, the 3D objects in game scene cannot be cut off in real. This paper proposes a real-time 3D objects cutting technology in games, which can cut any arbitrary 3D objects at any angle into two parts. Applying with this technology, game players can do a number of more realistic cutting operations. This technology is suitable in areas which need real cutting operations, such as educational games, mechanical simulation, and medical surgery teaching and so on.
Minutiae-based enhanced fingerprint verification assessment relaying on image quality factors

In this paper we evaluate authentication performance of the minutiae-based fingerprint automatic recognition system, previously proposed D. Simon-Zorita, et al. (2001) and recently completed, with the new large fingerprint image database, MCYT J. Ortega-Garcia, et al. (2002). The scheme includes: image enhancement, characteristic extraction and pattern recognition. The design of this database permits to analyse the influence of the variability factors appearing in the image acquisition phase. We focus in two factors: the finger position over the acquisition sensor, and the quality of the acquired fingerprint images. The analysis is accomplished in cases of supervised and nonsupervised databases. Score normalization is presented as an effective technique to improve the fingerprint verification system performance, yielding highly competitive EERs.
Mobile robot navigation based on fuzzy discrete event systems

Abstract Abstract Recently, several approaches for the control of fuzzy discrete event systems (FDES) have been proposed. First results towards the use of FDES in mobile robot navigation have also been presented, which however mainly build on sensory information processing. In this paper, we develop a methodology to compute control actions for the navigation of a mobile robot based on distributed FDES. The FDES description permits to take into account possible uncertainties in sensory information and enables a prediction of the future behavior of the robot depending on potential control actions. This prediction can then be used to select the most appropriate control action in each time instant. Our approach is tested by simulations of a mobile robot that encounters unknown obstacles on a pre-defined path.