Miracle-dz/UniFAR-FTUs huggingface dataset:
The effectiveness and cost effectiveness of telephone counselling and the nicotine patch in a state tobacco quitline

Objectives: State and national tobacco quitlines have expanded rapidly and offer a range of services. We examined the effectiveness and cost effectiveness of offering callers single session versus multisession counselling, with or without free nicotine patches. Methods: This 3×2 randomised trial included 4614 Oregon tobacco quitline callers and compared brief (one 15-minute call), moderate (one 30-minute call and a follow-up call) and intensive (five proactive calls) intervention protocols, with or without offers of free nicotine patches (nicotine replacement therapy, NRT). Blinded staff assessed tobacco use by phone at 12 months. Results: Abstinence odds ratios were significant for moderate (OR = 1.22, CI = 1.01 to 1.48) and intensive (OR = 1.29, CI = 1.07 to 1.56) intervention, and for NRT (OR = 1.58, CI = 1.35 to 1.85). Intent to treat quit rates were as follows: brief no NRT (12%); brief NRT (17%); moderate no NRT (14%); moderate NRT (20%); intensive no NRT (14%); and intensive NRT (21%). Relative to brief no NRT, the added costs for each additional quit was $2467 for brief NRT, $1912 for moderate no NRT, $2109 for moderate NRT, $2641 for intensive no NRT, and $2112 for intensive NRT. Conclusion: Offering free NRT and multisession telephone support within a state tobacco quitline led to higher quit rates, and similar costs per incremental quit, than less intensive protocols.
Optimal task allocation for maximizing reliability in distributed real-time systems

Distributed system has been developed as a platform for huge computations. Reliability is one of the prominent issues in such systems. Many studies have been recently done to improve reliability by proper task allocation in distributed systems, but they have only considered some system constraints such as processing load, memory capacity, and communication rate. In this paper, we consider time constraint in form of task deadline to above-mentioned constraints in order to model and analyze reliability in distributed real-time systems. To maximize reliability besides satisfying the constraints, we proposed a new offline task allocation algorithm. The algorithm is Systematic Memory-based Simulated Annealing (SMSA) which uses a monotonic cooling schedule and limited memory to store recently visited solutions to prevent cycling. In addition, an effective greedy heuristic algorithm intensifies SMSA. For evaluating the algorithm, SMSA is compared with Genetic Algorithm (GA) and Simulated Annealing (SA). Results have shown that in contrast to SA and GA, SMSA obtains satisfactory reliability in reasonable execution time. Meanwhile, SMSA meets all deadlines same as SA and GA. Furthermore, SMSA results have low deviation from average reliability.
Expression of Inducible Nitric Oxide Synthase (iNOS) in Microglia of the Developing Quail Retina

Inducible nitric oxide synthase (iNOS), which produce large amounts of nitric oxide (NO), is induced in macrophages and microglia in response to inflammatory mediators such as LPS and cytokines. Although iNOS is mainly expressed by microglia that become activated in different pathological and experimental situations, it was recently reported that undifferentiated amoeboid microglia can also express iNOS during normal development. The aim of this study was to investigate the pattern of iNOS expression in microglial cells during normal development and after their activation with LPS by using the quail retina as model. iNOS expression was analyzed by iNOS immunolabeling, western-blot, and RT-PCR. NO production was determined by using DAR-4M AM, a reliable fluorescent indicator of subcellular NO production by iNOS. Embryonic, postnatal, and adult in situ quail retinas were used to analyze the pattern of iNOS expression in microglial cells during normal development. iNOS expression and NO production in LPS-treated microglial cells were investigated by an in vitro approach based on organotypic cultures of E8 retinas, in which microglial cell behavior is similar to that of the in situ retina, as previously demonstrated in our laboratory. We show here that amoeboid microglia in the quail retina express iNOS during normal development. This expression is stronger in microglial cells migrating tangentially in the vitreal part of the retina and is downregulated, albeit maintained, when microglia differentiate and become ramified. LPS treatment of retina explants also induces changes in the morphology of amoeboid microglia compatible with their activation, increasing their lysosomal compartment and upregulating iNOS expression with a concomitant production of NO. Taken together, our findings demonstrate that immature microglial cells express iNOS during normal development, suggesting a certain degree of activation. Furthermore, LPS treatment induces overactivation of amoeboid microglia, resulting in a significant iNOS upregulation.
Natural language call routing: towards combination and boosting of classifiers

We describe different techniques to improve natural language call routing: boosting, relevance feedback, discriminative training, and constrained minimization. Their common goal is to reweight the data in order to let the system focus on documents judged hard to classify by a single classifier. These approaches are evaluated with the common vector-based classifier and also with the beta classifier which had given good results in the similar task of E-mail steering. We explore ways of deriving and combining uncorrelated classifiers in order to improve accuracy. Compared to the cosine and beta baseline classifiers, we report an improvement of 49% and 10%, respectively.
Body Area Network Security: A Fuzzy Attribute-Based Signcryption Scheme

Body Area Networks (BANs) are expected to play a major role in the field of patient-health monitoring in the near future. While it is vital to support secure BAN access to address the obvious safety and privacy concerns, it is equally important to maintain the elasticity of such security measures. For example, elasticity is required to ensure that first-aid personnel have access to critical information stored in a BAN in emergent situations. The inherent tradeoff between security and elasticity calls for the design of novel security mechanisms for BANs. In this paper, we develop the Fuzzy Attribute-Based Signcryption (FABSC), a novel security mechanism that makes a proper tradeoff between security and elasticity. FABSC leverages fuzzy Attribute-based encryption to enable data encryption, access control, and digital signature for a patient's medical information in a BAN. It combines digital signatures and encryption, and provides confidentiality, authenticity, unforgeability, and collusion resistance. We theoretically prove that FABSC is efficient and feasible. We also analyze its security level in practical BANs.
Bacteriorhodopsin Folds through a Poorly Organized Transition State

The folding mechanisms of helical membrane proteins remain largely uncharted. Here we characterize the kinetics of bacteriorhodopsin folding and employ φ-value analysis to explore the folding transition state. First, we developed and confirmed a kinetic model that allowed us to assess the rate of folding from SDS-denatured bacteriorhodopsin (bRU) and provides accurate thermodynamic information even under influence of retinal hydrolysis. Next, we obtained reliable φ-values for 16 mutants of bacteriorhodopsin with good coverage across the protein. Every φ-value was less than 0.4, indicating the transition state is not uniquely structured. We suggest that the transition state is a loosely organized ensemble of conformations.
PP2A inhibitors arrest G2/M transition through JNK/Sp1-dependent down-regulation of CDK1 and autophagy-dependent up-regulation of p21

Protein phosphatase 2A (PP2A) plays an important role in the control of the cell cycle. We previously reported that the PP2A inhibitors, cantharidin and okadaic acid (OA), efficiently repressed the growth of cancer cells. In the present study, we found that PP2A inhibitors arrested the cell cycle at the G2 phase through a mechanism that was dependent on the JNK pathway. Microarrays further showed that PP2A inhibitors induced expression changes in multiple genes that participate in cell cycle transition. To verify whether these expression changes were executed in a PP2A-dependent manner, we targeted the PP2A catalytic subunit (PP2Ac) using siRNA and evaluated gene expression with a microarray. After the cross comparison of these microarray data, we identified that CDK1 was potentially the same target when treated with either PP2A inhibitors or PP2Ac siRNA. In addition, we found that the down-regulation of CDK1 occurred in a JNK-dependent manner. Luciferase reporter gene assays demonstrated that repression of the transcription of CDK1 was executed through the JNK-dependent activation of the Sp1 transcription factor. By constructing deletion mutants of the CDK1 promoter and by using ChIP assays, we identified an element in the CDK1 promoter that responded to the JNK/Sp1 pathway after stimulation with PP2A inhibitors. Cantharidin and OA also up-regulated the expression of p21, an inhibitor of CDK1, via autophagy rather than PP2A/JNK pathway. Thus, this present study found that the PP2A/JNK/Sp1/CDK1 pathway and the autophagy/p21 pathway participated in G2/M cell cycle arrest triggered by PP2A inhibitors.
Applying machine learning to event data in soccer

Soccer is the world’s most popular sport but published research in soccer analytics has yet to attain the same level of sophistication as analytics in other professional sports. We hope to discover new knowledge from a soccer ball-event dataset by applying different machine learning techniques. In this thesis we present three experiments that address three interesting questions in soccer that involve game prediction and team style. We approach each question by constructing features using the ball-event data, where an event is a pass, shot, etc., and applying machine learning algorithms. In the first experiment, we construct three models that use different features to predict which team won a given game, without any knowledge of goals. We achieve a top accuracy rate of 0.84 using an L2-regularized logistic regression classifier. We also investigate the feature weights to learn relationships between game events and a team’s chances of success. In the second experiment we try several classifiers to predict which team produced the sequence of ball-events that occurred during a game. Despite the relatively small number of events per game, we achieved an accuracy rate of 0.345 for a 20-team classification task when using a RBF SVM. By learning which sequences are characteristic of teams we are potentially able to discover if successful teams have a common style. We also learn the efficacy of transforming ball-events into predefined symbols. Finally, in the third experiment, we predict which team attempted a given set of passes. We first construct 2D histograms of the locations of the origins of the passes. We then use the histograms as features in a 20-team classification task and discover that teams have characteristic passing styles, by achieving an accuracy rate of 0.735 using a learned K-NN classifier. The results demonstrate that approaching soccer analytics with a machine learning framework is effective. In addition to achieving good classification performance, we are able to discover useful, potentially actionable, knowledge by investigating the models and features that we construct. Thesis Supervisor: John V. Guttag Title: Dugald C. Jackson Professor, Electrical Engineering and Computer Science
Anti-C1q Antibodies as a Follow-Up Marker in SLE Patients

In cross-sectional studies autoantibodies against complement C1q (anti-C1q) were found to be highly associated with active lupus nephritis. The aim of this retrospective study was to determine the value of anti-C1q as follow-up marker of disease activity and renal involvement in patients with systemic lupus erythematosus (SLE). Fifty-two patients with SLE and a minimum of three anti-C1q measurements during follow-up were analyzed. Anti-C1q levels correlated with global disease activity scores. In subgroup analyses, patients without renal involvement did not show a significant correlation between anti-C1q levels and disease activity. In contrast, in patients with renal involvement, anti-C1q levels correlated well with global disease activity. In addition, a positive correlation with the urine protein-to-creatinine ratio and anti-dsDNA antibody levels as well as a negative correlation with complement levels was observed. Anti-C1q antibodies were found to strongly correlate with parameters of SLE disease activity during follow-up, in particular with regard to renal involvement.
Efficacy of repeated 5-fluorouracil needling for failing and failed filtering surgeries based on simple gonioscopic examination

Purpose To evaluate the success rate of a modified bleb needling technique in eyes with previous glaucoma surgery that had elevated intraocular pressure. Methods A retrospective study of 24 eyes of 24 patients that underwent repeated bleb needling performed for failing and failed blebs on slit lamp with 5-fluorouracil (5-FU) injections on demand. This was performed after gonioscopic examination to define levels of filtration block. Results There was significant reduction of mean IOP from 36.91 mmHg to 14.73 mmHg at the final follow-up (P < 0.001). The overall success rate was 92%. Conclusion Repeated needling with adjunctive 5-FU proved a highly effective, safe alternative to revive filtration surgery rather than another medication or surgery.
Enhanced visual functioning in autism: An ALE meta‐analysis

Autistics often exhibit enhanced perceptual abilities when engaged in visual search, visual discrimination, and embedded figure detection. In similar fashion, while performing a range of perceptual or cognitive tasks, autistics display stronger physiological engagement of the visual system than do non‐autistics. To account for these findings, the Enhanced Perceptual Functioning Model proposes that enhanced autistic performance in basic perceptual tasks results from stronger engagement of sensory processing mechanisms, a situation that may facilitate an atypically prominent role for perceptual mechanisms in supporting cognition. Using quantitative meta‐analysis of published functional imaging studies from which Activation Likelihood Estimation maps were computed, we asked whether autism is associated with enhanced task‐related activity for a broad range of visual tasks. To determine whether atypical engagement of visual processing is a general or domain‐specific phenomenon, we examined three different visual processing domains: faces, objects, and words. Overall, we observed more activity in autistics compared to non‐autistics in temporal, occipital, and parietal regions. In contrast, autistics exhibited less activity in frontal cortex. The spatial distribution of the observed differential between‐group patterns varied across processing domains. Autism may be characterized by enhanced functional resource allocation in regions associated with visual processing and expertise. Atypical adult organizational patterns may reflect underlying differences in developmental neural plasticity that can result in aspects of the autistic phenotype, including enhanced visual skills, atypical face processing, and hyperlexia. Hum Brain Mapp, 2011 © 2011 Wiley‐Liss, Inc.
Differential Tangential Expansion as a Mechanism for Cortical Gyrification

Gyrification, the developmental buckling of the cortex, is not a random process—the forces that mediate expansion do so in such a way as to generate consistent patterns of folds across individuals and even species. Although the origin of these forces is unknown, some theories have suggested that they may be related to external cortical factors such as axonal tension. Here, we investigate an alternative hypothesis, namely, whether the differential tangential expansion of the cortex alone can account for the degree and pattern-specificity of gyrification. Using intrinsic curvature as a measure of differential expansion, we initially explored whether this parameter and the local gyrification index (used to quantify the degree of gyrification) varied in a regional-specific pattern across the cortical surface in a manner that was replicable across independent datasets of neurotypicals. Having confirmed this consistency, we further demonstrated that within each dataset, the degree of intrinsic curvature of the cortex was predictive of the degree of cortical folding at a global and regional level. We conclude that differential expansion is a plausible primary mechanism for gyrification, and propose that this perspective offers a compelling mechanistic account of the co-localization of cytoarchitecture and cortical folds.
A National Survey of Canadian Psychiatrists’ Attitudes toward Medical Assistance in Death

Background: Bill C-14 allows for medical assistance in dying (MAID) for patients who have intolerable physical or psychological suffering that occurs in the context of a reasonably foreseeable death. In Canada, psychiatrist support for MAID on the basis of mental illness and beliefs influencing level of support are unknown. The objectives of this research were to 1) determine if psychiatrists are supportive of MAID under certain conditions and on the basis of mental illness and 2) determine what factors are related to psychiatrist support for MAID on the basis of mental illness. Methods: This cross-sectional study was conducted among 528 psychiatrists in Canada using an online survey platform (February 19 to March 11, 2016). Results: The response rate was 20.9% (n = 528). Most psychiatrists supported the legalisation of MAID in some circumstances (72%); however, only 29.4% supported MAID on the basis of mental illness. Factors correlating with decreased support for MAID for mental illness were the belief that MAID for mental illness would change the psychiatrists’ commitment to their patients through enduring suffering, having a personal faith, and having had past patients who would have received MAID for mental illness were it legal but instead went on to recover. Interpretation: This study found that most psychiatrists do not support the legalisation of MAID for mental illness, despite being quite supportive of MAID in general. Objections seemed to be based upon concern for vulnerable patients, personal moral objections, and concern for the effect it would have on the therapeutic alliance.
The role of cognitive control in memory retrievalapplications to schizotypy and schizophrenia

Individuals with schizophrenia have memory impairments. The experiments in this thesis were designed to determine whether these impairments arise from failures of cognitive control operations. Specifically, these experiments examined whether differences in target and non-target left-parietal old/new effects – a proposed neural correlate of strategic control over recollection - are predicted by schizotypy and/or schizophrenia symptomology. Correlations between schizotypy measures or symptom scores and how well people exercise control over retrieval of relevant information would point to one locus for memory problems associated with schizophrenia.
Efficacy and safety of high‐dose chemotherapy with autologous stem cell transplantation in senior versus younger adults with newly diagnosed multiple myeloma

We retrospectively studied 340 fit patients with multiple myeloma (MM) who underwent autologous stem cell transplantation (ASCT). We hypothesized that progression‐free survival (PFS) of older patients was non‐inferior to that of younger patients after ASCT. Our null hypothesis was that the PFS hazard ratio (HR) for a 5‐year increase in age was ≥1.05; the alternative (non‐inferiority) hypothesis was that the HR was ≤1. The observed HR was 0.94 (95% confidence interval [CI] 0.86‐1.03); since the CI upper bound was <1.05, we reject the null hypothesis and conclude that PFS in older patients was at least as good as in younger patients. We cannot reject an analogous null hypothesis for overall survival (HR 1.06 [95% CI 0.94‐1.19]), since the CI upper bound >1.05. Toxicity was similar across ages and transplant‐related mortality was minimal. 28% of subjects <65 versus 45% of those ≥65 received maintenance therapy. In summary, ASCT prolongs PFS equally well in older vs. younger adults. Although we cannot exclude maintenance as a confounder, these data support ASCT for fit seniors with MM.
The Fourier Spectrum Analysis of Optical Feedback Self-Mixing Signal under Weak and Moderate Feedback

The spectrum characteristics of self-mixing signals observed in optical feedback self-mixing interferometry (OFSMI) is studied in this paper. The purpose is to provide guidance for the design of pre-processing techniques for eliminating noise or disturbance. The influence of two important parameters of the OFSMI system on the spectrum, that is, the optical feedback factor and the linewidth enhancement factor, are measured by means of Discrete Fourier transform (DFT). The simulated results show that, at a weak feedback, the OFSMI signals are strictly band limited in nature, with the cut-off frequencies being the vibration frequency and the fringe frequency respectively. Also increasing broadens the bandwidth but has little influence on the bandwidth of the signal. At moderate feedback, the OFSMI signals are still band limited with much higher cut-off frequencies, and there is a considerable spectrum spread over a very wide range in the high frequency end. The presented analysis provides sufficient information for applying signal processing to OFSMI signals such as the filter design.
Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Deep learning thrives with large neural networks and large datasets. However, larger networks and larger datasets result in longer training times that impede research and development progress. Distributed synchronous SGD offers a potential solution to this problem by dividing SGD minibatches over a pool of parallel workers. Yet to make this scheme efficient, the per-worker workload must be large, which implies nontrivial growth in the SGD minibatch size. In this paper, we empirically show that on the ImageNet dataset large minibatches cause optimization difficulties, but when these are addressed the trained networks exhibit good generalization. Specifically, we show no loss of accuracy when training with large minibatch sizes up to 8192 images. To achieve this result, we adopt a hyper-parameter-free linear scaling rule for adjusting learning rates as a function of minibatch size and develop a new warmup scheme that overcomes optimization challenges early in training. With these simple techniques, our Caffe2-based system trains ResNet-50 with a minibatch size of 8192 on 256 GPUs in one hour, while matching small minibatch accuracy. Using commodity hardware, our implementation achieves ~90% scaling efficiency when moving from 8 to 256 GPUs. Our findings enable training visual recognition models on internet-scale data with high efficiency.