Miracle-dz/UniFAR-FTUs huggingface dataset:
Incidence, laboratory diagnosis and predictors of tracheobronchial tuberculosis in patients with pulmonary tuberculosis in Chongqing, China

Tracheobronchial tuberculosis (TBTB) is reported in 10-40% of patients with pulmonary tuberculosis (PTB). Due to its non-specific presentation, the diagnosis and management are frequently delayed. The aim of the present study was to investigate the incidence, predictors and laboratory diagnosis of concomitant TBTB and PTB in Chongqing, China. Bronchoscopy was performed in all patients with newly diagnosed or relapsed PTB in order to detect TBTB between January 2018 and April 2019 in a sub-tertiary hospital in Chongqing, China. The clinical characteristics and laboratory data were analyzed to identify predictors and determine the diagnostic yield of TBTB. A total of 341 (31.4%) of the 1,085 patients with PTB who underwent the bronchoscopic examination presented with concomitant TBTB. The parameters of female sex [odds ratio (OR)=2.57], clinical symptoms (OR=6.26) and atelectasis (OR=4.3) were independent predictors of TBTB. Cough (OR=32.48) and atelectasis (OR=3.14) were independent predictors of TBTB-associated tracheobronchial stenosis. The diagnostic yields of sputum smear, bronchial brush smear, sputum culture, GeneXpert Mycobacterium tuberculosis/rifampicin resistance (GX) using sputum, GX using brushings and in bronchial brush culture used for the diagnosis of TBTB were 44.2, 44.2, 63.5, 57.7, 71.2 and 75%, respectively. GX brushings had higher diagnostic yields compared with sputum or brush smears; however, there was no significant difference between sputum/brushings cultures and GX with sputum. The incidence of TBTB in PTB was 31.4% in Chongqing, China. The parameters of female sex, atelectasis and cough were the major predictors of concomitant TBTB and associated tracheobronchial stenosis. Although GX is an accurate and rapid test to detect TBTB, additional laboratory techniques should also be adopted to improve diagnostic yields in the detection of TBTB in patients with PTB.
Effective action for delta potentials: Spacetime-dependent inhomogeneities and Casimir self-energy

We study the vacuum fluctuations of a quantum scalar field in the presence of a thin and inhomogeneous flat mirror, modelled with a delta potential. Using Heat-Kernel techniques, we evaluate the Euclidean effective action perturbatively in the inhomogeneities (nonperturbatively in the constant background). We show that the divergences can be absorbed into a local counterterm, and that the remaining finite part is in general a nonlocal functional of the inhomogeneities, which we compute explicitly for massless fields in $D=4$ dimensions. For time-independent inhomogeneities, the effective action gives the Casimir self-energy for a partially transmitting mirror. For time-dependent inhomogeneites, the Wick-rotated effective action gives the probability of particle creation due to the dynamical Casimir effect.
ANSH: Multimodal Neuroimaging Database Including MR Spectroscopic Data From Each Continent to Advance Alzheimer’s Disease Research

Alzheimer’s disease (AD) is a devastating neurodegenerative disorder affecting millions of people worldwide. The etiology of AD is not known, and intense research involving multimodal neuroimaging data (e.g., MRI, functional MRI, PET etc.) is extensively used to identify the causal molecular process for AD. In this context, various imaging-based databases accessible to researchers globally, are useful for an independent analysis. Apart from MRI-based brain imaging data, the neurochemical data using magnetic resonance spectroscopy (MRS) provide early molecular processes before the structural or functional changes are manifested. The existing imaging-based databases in AD lack the integration of MRS modality and, thus, limits the availability of neurochemical information to the AD research community. This perspective is an initiative to bring attention to the development of the neuroimaging database, “ANSH,” that includes brain glutathione (GSH), gamma aminobutyric acid (GABA) levels, and other neurochemicals along with MRI-based information for AD, mild cognitive impairment (MCI), and healthy subjects. ANSH is supported by a JAVA-based workflow environment and python providing a simple, dynamic, and distributed platform with data security. The platform consists of two-tiered architecture for data collection and management further supporting quality control, report generation for analyzed data, and data backup with a dedicated storage system. The ANSH database aims to present a single neuroimaging data platform incorporating diverse data types from healthy control and patient groups to provide better insights pertaining to disease progression. This data management platform provides flexible data sharing across users with continuous project monitoring. The development of ANSH platform will facilitate collaborative research and multi-site data sharing across the globe.
Assembly Sequences Based on Multiple Criteria Against Products with Deformable Parts

To generate assembly sequences that robots can easily handle, this study tackled assembly sequence generation (ASG) by considering two tradeoff objectives: (1) insertion conditions and (2) degrees of the constraints affecting the assembled parts. We propose a multi-objective genetic algorithm to balance these two objectives. Furthermore, we extend our previously proposed 3D computer-aided design (CAD)-based method for extracting three types of two-part relationship matrices from 3D models that include deformable parts. The interference between deformable and other parts can be determined using scaled part shapes. Our proposed ASG can produce Pareto-optimal sequences for multi-component models with deformable parts such as rubber bands, rubber belts, and roller chains. We further discuss the limitation and applicability of the generated sequences to robotic assembly.
Single-Trial Inhibition of Anterior Cingulate Disrupts Model-based Reinforcement Learning in a Two-step Decision Task.

The anterior cingulate cortex (ACC) is implicated in learning the value of actions, and thus in allowing past outcomes to influence the current choice. However, it is not clear whether or how it contributes to the two major ways such learning is thought to happen: model-based mechanisms that learn action-state predictions and use these to infer action values; or model-free mechanisms which learn action values directly through reward prediction errors. Having confirmed, using a classical probabilistic reversal learning task, that optogenetic inhibition of ACC neurons on single trials indeed affected reinforcement learning, we examined the consequence of this manipulation in a novel two-step decision task designed to dissociate model-free and model-based learning mechanisms in mice. On the two-step task, silencing spared the influence of the trial outcome but reduced the influence of the experienced state transition. Analysis using reinforcement learning models indicated that ACC inhibition disrupted model-based RL mechanisms.
Dynamics of Microbial Community Structure and Ecological Functions in Estuarine Intertidal Sediments

Microbial communities are important indicators of aquatic ecosystem health, responsible for functional roles sustaining the ecosystem such as nutrient cycling, as well as environmental problems. Urbanized estuaries are vulnerable intersections between terrestrial and marine ecosystems and are susceptible to pressure and perturbation from both natural processes and human activities over time. The response by microbial communities toward changes in the environment should be closely monitored and studied. In this study, microbial communities in brackish intertidal sediments, sampled seasonally over a year along the coast of Yangtze River estuary, were analyzed using 16S rRNA gene sequencing. Potential compositional and functional changes in microbial communities resulting from temporal variation and associated physiochemical gradients in the environment were studied and notable patterns were observed over time. Summer season was recorded with the lowest microbial community α-diversity. For β-diversity, spatiotemporal differentiation in community structures was recorded with seasonal clustering. Proteobacteria was recorded as the most dominant phylum among all sampling sites throughout the year but its relative abundance showed no temporal changes. Instead, relative abundances of phyla Actinobacteria, Chloroflexi, and Verrucomicrobia were found to differ significantly over time, generally favoring the wet Spring and Summer. In contrast, temporal variation was observed for the class Deltaproteobacteria and some of the corresponding sulfate/sulfur-reducing bacterial genera, favoring the dry Autumn and Winter. However, there were no major temporal changes in ecological functions contributed by microbial communities throughout the study period. While results from redundancy analysis showed that the subtle changes in pH in the environment had the greatest impact on microbial community composition, the variation it explained remained relatively low. Alternatively, precipitation, distinguishing the dry and wet seasons of the subtropical monsoon climate zone, was suggested to be another potential key factor influencing microbial community composition and their relative abundances. The present study provides new data and insights on the impact of temporal variation on microbial community composition and ecological function in an urbanized estuarine ecosystem.
Association Between Psoriasis and Dementia: Current Evidence

Background: Psoriasis and dementia are both inflammatory diseases. The association between psoriasis and dementia has rarely been investigated, and the existing results are conflicting. Thus, we conducted this study to evaluate whether an association exists between psoriasis and dementia. Methods: We searched for studies from six databases from inception to July 30, 2020, using subject and free words. RevMan 5.4 was used to calculate the risk ratio (RR) of dementia in the subjects with psoriasis. When heterogeneity was present, a random-effects model was used. Subgroup, sensitivity, and meta-regression analyses were performed using Stata 15.1. Results: Nine studies were identified and included in the study, of which seven that involved a total of 3,638,487 participants were included in the meta-analysis. We found that among the patients with psoriasis (RR: 1.14, 95% confidence interval [CI]: 1.06–1.24, p = 0.0009) and psoriatic arthritis (RR: 2.20, 95% CI: 1.29–3.78, p = 0.004), the proportions of those with non-vascular dementia (RR: 1.13, 95% CI: 1.11–1.15, p < 0.00001) and vascular dementia (RR: 1.41, 95% CI: 1.09–1.82, p = 0.009) were higher than that among the patients without psoriasis. Those with dementia were also more likely to develop psoriasis, and those with severe psoriasis were less likely to die from dementia (RR: 1.88, 95% CI: 0.72–4.90, p = 0.020). The meta-regression analysis did not show any significant sources of heterogeneity. Conclusions: The patients with psoriasis and psoriatic arthritis show high prevalence of different types of dementia. Based on the findings of this study, dementia may not be considered a high-risk factor of death from severe psoriasis. However, identification of this potential risk allows for early intervention, thereby reducing comorbidities and deaths.
Minimizing Glucose Excursions (GEM) With Continuous Glucose Monitoring in Type 2 Diabetes: A Randomized Clinical Trial

Abstract This study aimed to compare conventional medication management of type 2 diabetes (T2D) to medication management in conjunction with a lifestyle intervention using continuous glucose monitoring to minimize glucose excursions. Thirty adults (63% female; mean age, 53.3 years) who were diagnosed with T2D for less than 11 years (mean, 5.6 years), had glycated A1c (HbA1c) ≥ 7.0% (51 mmol/mol) (mean 8.8%, [73 mmol/mol]), and were not using insulin, were randomly assigned in a 1:2 ratio to routine care (RC) or 4 group sessions of glycemic excursion minimization plus real-time CGM (GEMCGM). Assessments at baseline and 5 months included a physical exam, metabolic and lipid panels, a review of diabetes medications, and psychological questionnaires. For the week following assessments, participants wore a blinded activity monitor and completed 3 days of 24-hour dietary recall. A subgroup also wore a blinded CGM. GEMCGM participants significantly improved HbA1c (from 8.9% to 7.6% [74-60 mmol/mol] compared with 8.8% to 8.7% [73-72 mmol/mol] for RC (P = .03). Additionally, GEMCGM reduced the need for diabetes medication (P = .01), reduced carbohydrate consumption (P = .009), and improved diabetes knowledge (P = .001), quality of life (P = .01) and diabetes distress (P = .02), and trended to more empowerment (P = .05) without increasing dietary fat, lipids, or hypoglycemia. Confirming our prior research, GEMCGM appears to be a safe, effective lifestyle intervention option for adults with suboptimally controlled T2D who do not take insulin.
Tensor Train Random Projection

This work proposes a novel tensor train random projection (TTRP) method for dimension reduction, where the pairwise distances can be approximately preserved. Based on the tensor train format, this new random projection method can speed up the computation for high dimensional problems and requires less storage with little loss in accuracy, compared with existing methods (e.g., very sparse random projection). Our TTRP is systematically constructed through a rank-one TT-format with Rademacher random variables, which results in efficient projection with small variances. The isometry property of TTRP is proven in this work, and detailed numerical experiments with data sets (synthetic, MNIST and CIFAR-10) are conducted to demonstrate the efficiency of TTRP.
Unsupervised Deep Learning based Multiple Choices Question Answering: Start Learning from Basic Knowledge

In this paper, we study the possibility of almost unsupervised Multiple Choices Question Answering (MCQA). Starting from very basic knowledge, MCQA model knows that some choices have higher probabilities of being correct than the others. The information, though very noisy, guides the training of an MCQA model. The proposed method is shown to outperform the baseline approaches on RACE and even comparable with some supervised learning approaches on MC500.
On accompanying measures and asymptotic expansions in limit theorems for maximum of random variables

A sequence of accompanying laws is suggested in the limit theorem of B. V. Gnedenko for maximums of independent random variables belonging to maximum domain of attraction of the Gumbel distribution. It is shown that this sequence gives an exponential power rate of convergence whereas the Gumbel distribution gives only a logarithmic rate. As examples, classes of Weibull and log-Weibull type distributions are considered in details. A scale for the Gumbel maximum domain of attraction is suggested as a continuation of the considered two classes.
LemMED: Fast and Effective Neural Morphological Analysis with Short Context Windows

We present LemMED, a character-level encoder-decoder for contextual morphological analysis (combined lemmatization and tagging). LemMED extends and is named after two other attention-based models, namely Lematus, a contextual lemmatizer, and MED, a morphological (re)inflection model. Our approach does not require training separate lemmatization and tagging models, nor does it need additional resources and tools, such as morphological dictionaries or transducers. Moreover, LemMED relies solely on character-level representations and on local context. Although the model can, in principle, account for global context on sentence level, our experiments show that using just a single word of context around each target word is not only more computationally feasible, but yields better results as well. We evaluate LemMED in the framework of the SIMGMORPHON-2019 shared task on combined lemmatization and tagging. In terms of average performance LemMED ranks 5th among 13 systems and is bested only by the submissions that use contextualized embeddings.
Assessing the Effectiveness of a Novel Microscopy Technique in Middle & High School Science Classrooms

Abstract Although research and new technologies have introduced different ways of observing microorganisms, including scanning and electron microscopy, these methods are expensive and require equipment that is typically not found in a middle school classroom. The transmission-through-dye technique (TTD; Gregg et al., 2010), a new optical microscopy method that can be used with current basic light microscopes, relies on the fairly simple mechanism of filtered light passing through a dyed medium to produce an image that reflects cell thickness. With this technique, living microorganisms look bright red against a dark background, and movement can be seen easily among dead microorganisms and debris that show up black. Since the technique is low-cost and easy to implement, it addresses the needs of practitioners and is appropriate for a wide array of school contexts. We describe a three-week, hands-on, inquiry-based unit on TTD microscopy for middle and high school students.
High-Capacity Complex Convolutional Neural Networks For I/Q Modulation Classification

I/Q modulation classification is a unique pattern recognition problem as the data for each class varies in quality, quantified by signal to noise ratio (SNR), and has structure in the complex-plane. Previous work shows treating these samples as complex-valued signals and computing complex-valued convolutions within deep learning frameworks significantly increases the performance over comparable shallow CNN architectures. In this work, we claim state of the art performance by enabling high-capacity architectures containing residual and/or dense connections to compute complex-valued convolutions, with peak classification accuracy of 92.4% on a benchmark classification problem, the RadioML 2016.10a dataset. We show statistically significant improvements in all networks with complex convolutions for I/Q modulation classification. Complexity and inference speed analyses show models with complex convolutions substantially outperform architectures with a comparable number of parameters and comparable speed by over 10% in each case.
From Schools to Scans: A Neuroeducational Approach to Comorbid Math and Reading Disabilities

We bridge two analogous concepts of comorbidity, dyslexia-dyscalculia and reading-mathematical disabilities, in neuroscience and education, respectively. We assessed the cognitive profiles of 360 individuals (mean age 25.79 ± 13.65) with disability in reading alone (RD group), mathematics alone (MD group) and both (comorbidity: MDRD group), with tests widely used in both psychoeducational and neuropsychological batteries. As expected, the MDRD group exhibited reading deficits like those shown by the RD group. The former group also exhibited deficits in quantitative reasoning like those shown by the MD group. However, other deficits related to verbal working memory and semantic memory were exclusive to the MDRD group. These findings were independent of gender, age, or socioeconomic and demographic factors. Through a systematic exhaustive review of clinical neuroimaging literature, we mapped the resulting cognitive profiles to correspondingly plausible neuroanatomical substrates of dyslexia and dyscalculia. In our resulting “probing” model, the complex set of domain-specific and domain-general impairments shown in the comorbidity of reading and mathematical disabilities are hypothesized as being related to atypical development of the left angular gyrus. The present neuroeducational approach bridges a long-standing transdisciplinary divide and contributes a step further toward improved early prediction, teaching and interventions for children and adults with combined reading and math disabilities.
Analyzing the Source and Target Contributions to Predictions in Neural Machine Translation

In Neural Machine Translation (and, more generally, conditional language modeling), the generation of a target token is influenced by two types of context: the source and the prefix of the target sequence. While many attempts to understand the internal workings of NMT models have been made, none of them explicitly evaluates relative source and target contributions to a generation decision. We argue that this relative contribution can be evaluated by adopting a variant of Layerwise Relevance Propagation (LRP). Its underlying ‘conservation principle’ makes relevance propagation unique: differently from other methods, it evaluates not an abstract quantity reflecting token importance, but the proportion of each token’s influence. We extend LRP to the Transformer and conduct an analysis of NMT models which explicitly evaluates the source and target relative contributions to the generation process. We analyze changes in these contributions when conditioning on different types of prefixes, when varying the training objective or the amount of training data, and during the training process. We find that models trained with more data tend to rely on source information more and to have more sharp token contributions; the training process is non-monotonic with several stages of different nature.
Hypoxia pathway proteins regulate the synthesis and release of epinephrine in the mouse adrenal gland

The adrenal gland and its hormones regulate numerous fundamental biological processes; however, the impact of hypoxia signalling on its function remains scarcely understood. Here, we reveal that deficiency of HIF (Hypoxia Inducible Factors) prolyl hydroxylase domain protein-2 (PHD2) in the adrenal medulla of mice results in HIF2α-mediated reduction in phenylethanolamine N-methyltransferase (PNMT) expression, and consequent reduction in epinephrine synthesis. Concomitant loss of PHD2 in renal erythropoietin (EPO) producing cells stimulated HIF2α-driven EPO overproduction, excessive RBC formation (erythrocytosis) and systemic hypoglycaemia. Using mouse lines displaying only EPO-induced erythrocytosis or anaemia, we show that hypo- or hyperglycaemia is necessary and sufficient to respectively enhance or reduce exocytosis of epinephrine from the adrenal gland. Based on these results, we propose that the PHD2-HIF2α axis in the adrenal medulla and beyond regulates both synthesis and release of catecholamines, especially epinephrine. Our findings are also of great significance in view of the small molecule PHD inhibitors being tested in phase III global clinical development trials for use in renal anaemia patients.
Creative Storytelling with Language Models and Knowledge Graphs

Automated story generation is a popular and well-recognized task in the field of natural language processing. The emergence of pre-trained language models based on large Transformer architectures shows the great capability of text generation. However, language models are limited when the generation requires explicit clues within the context. In this research, we study how to combine knowledge graphs with language models, and build a creative story generation system named DICE. DICE uses external knowledge graphs to provide context clues and implicit knowledge to generate coherent and creative stories. The evaluation shows that our approach can effectively inject the knowledge from knowledge graphs into the stories automatically generated by the language model.