Miracle-dz/UniFAR-FTUs huggingface dataset:
PROS: should immunotherapy be incorporated in the treatment of oncogene-driven lung cancer?

Transl Lung Cancer Res 2018;7(Suppl 3):S283-S286 tlcr.amegroups. com Immune checkpoint inhibitors (ICPIs) targeting the programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1) axis have dramatically expanded the therapeutic armamentarium for non-small cell lung cancer (NSCLC) and are now standard treatments for patients with advanced disease (1). However, most patients with NSCLC do not respond to ICPIs, an observation motivating ongoing research efforts to identify reliable predictive biomarkers. So far, such efforts have largely centred on PD-L1 expression, although several other biomarkers are currently under investigation. A high tumour mutational burden (TMB), as defined by at least 10 mutations per megabase, has been recently identified as a possible predictive biomarker for progression-free survival (PFS) with an ICPIs combination (nivolumab plus ipilimumab) as compared to chemotherapy in the first-line treatment of patients with NSCLC (2). This finding theoretically further challenges the role of ICPIs in patients with targetable oncogenic drivers, such as epidermal growth factor receptor mutations (EGFRmut) and anaplastic lymphoma kinase rearrangements (ALK+), that is rather considered an “oligoclonal disease” (3-5). Within this group of patients, ICPIs have been understudied as they were excluded or minimally represented within randomised trials with ICPIs. Here we examine some preclinical, translational and clinical data suggesting that ICPIs should be reasonably not excluded a priori in these patients. Pre-clinical data showed that the EGFR oncogenic signaling may promote PD-L1 expression and treatment with PD1 inhibitors could enhance tumour responses in EGFRmutated models (6,7). Indeed, EGFR-mutated tumors are often characterized by a high PD-L1 expression (8). However, other translational studies demonstrated that EGFR-mutated NSCLC is characterized by a low TMB (9) and a low rate of associated tumour infiltrating lymphocytes (TILs) (10), that could reduce the immune-dependence of these tumors. However, some changes could occur along the course of the disease and should be considered. PD-L1 overexpression and high rate of TILs were simultaneously detected in the tumour microenvironment of only 1/57 tyrosine kinase inhibitor (TKI)-naïve but in 5/57 TKI-resistant EGFR-mutated 283-286
Comparison of minimally invasive cardiac surgery incisions: Periareolar approach in female patients

Objective: All innovations in cardiac surgery provide us with new techniques to perform surgery through smaller incisions with less invasive and best cosmetic results. After promising results in minimally invasive cardiac surgery (MICS), pain and cosmetic appearance became important end points, especially for female patients. In the current study, we intended to evaluate the surgical results and cosmetic satisfaction with the periareolar and submammary incision types in cardiac surgery. Methods: Ninety-four female patients underwent MICS between July 2013 and March 2018. MICS was performed in 62 patients via periareolar incision and in 32 patients via submammarian incision. We investigated the incision size, wound infection, pain levels by using a postoperative standard pain-level questionnaire, the postoperative scar size, and patient satisfaction using a postoperative patient questionnaire. Results: Periareolar incision size was smaller than the submammary incision (Group A: 5.6±0.6 vs. Group B: 6.7±0.8, p=0.001). Four patients from Group B had superficial wound infection (p=0.01). Patients who underwent MICS via periareolar incision and submammary incision had similar pain level (p=0.2). The scar tissue was smaller in size and postoperatively healed better in the following days for the patients with periareolar incision due to the elastic structure of breast tissue. (Group A: 4.3±0.4 vs. Group B: 5.3±0.2, p=0.001). Conclusion: Our study suggests that the periareolar approach would be more aesthetic, show better healing, and have a smaller scar size in female patients.
Fast Non-Bayesian Poisson Factorization for Implicit-Feedback Recommendations

This work explores non-negative matrix factorization based on regularized Poisson models for recommender systems with implicit-feedback data. The properties of Poisson likelihood allow a shortcut for very fast computation and optimization over elements with zero-value when the latent-factor matrices are non-negative, making it a more suitable approach than squared loss for very sparse inputs such as implicit-feedback data. A simple and embarrassingly parallel optimization approach based on proximal gradients is presented, which in large datasets converges 2-3 orders of magnitude faster than its Bayesian counterpart (Hierarchical Poisson Factorization) fit through variational inference techniques, and 1 order of magnitude faster than implicit-ALS fit with the Conjugate Gradient method.
Log-concave polynomials II: high-dimensional walks and an FPRAS for counting bases of a matroid

We design an FPRAS to count the number of bases of any matroid given by an independent set oracle, and to estimate the partition function of the random cluster model of any matroid in the regime where 0<q<1. Consequently, we can sample random spanning forests in a graph and estimate the reliability polynomial of any matroid. We also prove the thirty year old conjecture of Mihail and Vazirani that the bases exchange graph of any matroid has edge expansion at least 1. Our algorithm and proof build on the recent results of Dinur, Kaufman, Mass and Oppenheim who show that a high dimensional walk on a weighted simplicial complex mixes rapidly if for every link of the complex, the corresponding localized random walk on the 1-skeleton is a strong spectral expander. One of our key observations is that a weighted simplicial complex X is a 0-local spectral expander if and only if a naturally associated generating polynomial pX is strongly log-concave. More generally, to every pure simplicial complex with positive weights on its maximal faces, we can associate to X a multiaffine homogeneous polynomial pX such that the eigenvalues of the localized random walks on X correspond to the eigenvalues of the Hessian of derivatives of pX.
My Watch Says I'm Busy: Inferring Cognitive Load with Low-Cost Wearables

To prevent undesirable effects of attention grabbing at times when a user is occupied with a difficult task, ubiquitous computing devices should be aware of the user's cognitive load. However, inferring cognitive load is extremely challenging, especially when performed without obtrusive, expensive, and purpose-built equipment. In this study we examine the potential for inferring one's cognitive load using merely cheap wearable sensing devices. We subject 25 volunteers to varying cognitive load using six different Primary tasks. In parallel, we collect physiological data with a cheap device, extract features, and then construct machine learning models for cognitive load prediction. As metrics for the load we use one subjective measure, the NASA Task Load Index (NASA-TLX), and two objective measures: task difficulty and reaction time. The leave-one-subject-out evaluation shows a significant influence of the task type and the chosen cognitive load metric on the prediction accuracy.
On a generalization of iterated and randomized rounding

We give a general method for rounding linear programs that combines the commonly used iterated rounding and randomized rounding techniques. In particular, we show that whenever iterated rounding can be applied to a problem with some slack, there is a randomized procedure that returns an integral solution that satisfies the guarantees of iterated rounding and also has concentration properties. We use this to give new results for several classic problems where iterated rounding has been useful.
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation

We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT). This loss compares original inputs to reconstructed inputs, obtained by back-translating translation hypotheses into the input language. We leverage differentiable sampling and bi-directional NMT to train models end-to-end, without introducing additional parameters. This approach achieves small but consistent BLEU improvements on four language pairs in both translation directions, and outperforms an alternative differentiable reconstruction strategy based on hidden states.
Multi-armed Bandits with Compensation

We propose and study the known-compensation multi-arm bandit (KCMAB) problem, where a system controller offers a set of arms to many short-term players for $T$ steps. In each step, one short-term player arrives to the system. Upon arrival, the player greedily selects an arm with the current best average reward and receives a stochastic reward associated with the arm. In order to incentivize players to explore other arms, the controller provides proper payment compensation to players. The objective of the controller is to maximize the total reward collected by players while minimizing the compensation. We first give a compensation lower bound $\Theta(\sum_i {\Delta_i\log T\over KL_i})$, where $\Delta_i$ and $KL_i$ are the expected reward gap and Kullback-Leibler (KL) divergence between distributions of arm $i$ and the best arm, respectively. We then analyze three algorithms to solve the KCMAB problem, and obtain their regrets and compensations. We show that the algorithms all achieve $O(\log T)$ regret and $O(\log T)$ compensation that match the theoretical lower bound. Finally, we use experiments to show the behaviors of those algorithms.
Tangshen Formula Treatment for Diabetic Kidney Disease by Inhibiting Racgap1-stata5-Mediated Cell Proliferation and Restoring miR-669j-Arntl-Related Circadian Rhythm

Background The aim of this study was to investigate the underlying mechanisms of Tangshen formula (TSF) for treatment of diabetic kidney disease (DKD). Material/Methods Microarray dataset GSE90842 was collected from the Gene Expression Omnibus database, including renal cortical tissues from normal control (NC), DKD, and DKD mice given TSF for 12 weeks (TSF) (n=3). Differentially-expressed genes (DEGs) were identified using LIMMA method. A protein-protein interaction (PPI) network was constructed using data from the STRING database followed by module analysis. The Mirwalk2 database was used to predict the underlying miRNAs of DEGs. Function enrichment analysis was performed using the DAVID tool. Results A total of 2277 and 2182 genes were identified as DEGs between DKD and NC or TSF groups, respectively. After overlap, 373 DEGs were considered as common in 2 comparison groups. Function enrichment indicated common DEGs were related to cell proliferation (Asf1b, anti-silencing function 1B histone chaperone; Anln, anillin, actin-binding protein; Racgap1, Rac GTPase activating protein 1; and Stat5, signal transducer and activator of transcription 5) and circadian rhythm (Arntl, aryl hydrocarbon receptor nuclear translocator-like). Racgap1 was considered as a hub gene in the PPI network because it could interact with Asf1b, Anln, and Stat5. Arntl was regulated by miR-669j in the miRNA-DEGs network and this miRNA was also a DEG in 2 comparisons. Conclusions TSF may be effective for DKD by inhibiting Racgap1-stata5-mediated cell proliferation and restoring miR-669j-Arntl-related circadian rhythm.
Relation Mention Extraction from Noisy Data with Hierarchical Reinforcement Learning

In this paper we address a task of relation mention extraction from noisy data: extracting representative phrases for a particular relation from noisy sentences that are collected via distant supervision. Despite its significance and value in many downstream applications, this task is less studied on noisy data. The major challenges exists in 1) the lack of annotation on mention phrases, and more severely, 2) handling noisy sentences which do not express a relation at all. To address the two challenges, we formulate the task as a semi-Markov decision process and propose a novel hierarchical reinforcement learning model. Our model consists of a top-level sentence selector to remove noisy sentences, a low-level mention extractor to extract relation mentions, and a reward estimator to provide signals to guide data denoising and mention extraction without explicit annotations. Experimental results show that our model is effective to extract relation mentions from noisy data.
Evaluation of implant fit and frontal plane alignment after bi-compartmental knee arthroplasty using patient-specific instruments and implants

Introduction The goals of successful bi-compartmental knee arthroplasty are to achieve correct fit and positioning of the implant, while appropriately correcting the mechanical alignment of the leg after surgery. As these requirements are not always reliably fulfilled using off-the-shelf implant systems, newer approaches for bi-compartmental resurfacing have been explored. Material and methods In this article we report the radiographic results of 30 patients with anteromedial osteoarthritis (OA) who were treated with a novel patient-specific fixed-bearing bi-compartmental knee resurfacing system using custom-made implants and instruments. Utilizing standardized pre- and postoperative radiographic analyses (based on anterior-posterior and lateral, anterior-posterior weight-bearing full-length radiographs, patella skyline views and preoperative computed tomography (CT) scanning) implant fit and positioning as well as correction of the mechanical axis (hip-knee-ankle angle, HKA) were determined. Results On average, HKA was corrected from 173.4 ±3.47° preoperatively to 179.4 ±2.85° postoperatively. The coronal femoro-tibial angle was corrected on average 5.61°. The preoperative tibial slope measured on lateral views was 6.38 ±2.4°, while the average slope in the CT-based planning protocol (iView) was 6.14 ±2.40°. Postoperative lateral tibial slope was determined to be 5.77 ±1.97°. The thickness of the posterior femoral cuts was measured intraoperatively and, in all cases, corresponded well to the targeted thickness of the cuts provided by the iView. The joint line was preserved in all cases and the average Insall-Salvati index was 1.078 ±0.11 pre- and 1.072 ±0.11 postoperatively. The fit of the implant components measured by over- or underhang was excellent throughout (< 1.01 mm). Conclusions Custom-made bicompartmental knee arthroplasty can ensure optimized fitting and positioning of the implant with restoration of the leg axis. These implants could be considered as an alternative primary solution for knee surgeons treating bi-compartmental disease.
Advances in capsulorhexis

Purpose of review Continuous curvilinear manual capsulorhexis is currently the standard of cataract surgery. In the past several years, new technologies have been developed to improve the consistency and safety of capsulorhexis creation. This article reviews the most recent technologies in capsulotomy formation and their advantages and disadvantages. Recent findings Guidance devices, femtosecond laser capsulotomy and precision pulse capsulotomy improve the centration, circularity and precision of anterior capsulorhexis and capsulotomy. These developments show particular promise for complex cataract surgeries, though clinical data on the refractive outcomes and complication rates of these technologies are currently limited and warrant additional investigation. Summary New technological advances in capsulorhexis help surgeons achieve a more ideal capsulotomy geometry. Whether this translates into more predictable refractive outcomes and safer surgeries remains an area of future study.
Probing the modularity of megasynthases by rational engineering of a fatty acid synthase Type I

Modularity is a fundamental property of megasynthases such as polyketide synthases (PKSs). In this study, we exploit the close resemblance between PKSs and animal fatty acid synthase (FAS) to re‐engineer animal FAS to probe the modularity of the FAS/PKS family. Guided by sequence and structural information, we truncate and dissect animal FAS into its components, and reassemble them to generate new PKS‐like modules as well as bimodular constructs. The novel re‐engineered modules resemble all four common types of PKSs and demonstrate that this approach can be a powerful tool to deliver products with higher catalytic efficiency. Our data exemplify the inherent plasticity and robustness of the overall FAS/PKS fold, and open new avenues to explore FAS‐based biosynthetic pathways for custom compound design.
Modeling Stated Preference for Mobility-on-Demand Transit: A Comparison of Machine Learning and Logit Models

Logit models are usually applied when studying individual travel behavior, i.e., to predict travel mode choice and to gain behavioral insights on traveler preferences. Recently, some studies have applied machine learning to model travel mode choice and reported higher out-of-sample predictive accuracy than traditional logit models (e.g., multinomial logit). However, little research focuses on comparing the interpretability of machine learning with logit models. In other words, how to draw behavioral insights from the high-performance "black-box" machine-learning models remains largely unsolved in the field of travel behavior modeling. This paper aims at providing a comprehensive comparison between the two approaches by examining the key similarities and differences in model development, evaluation, and behavioral interpretation between logit and machine-learning models for travel mode choice modeling. To complement the theoretical discussions, the paper also empirically evaluates the two approaches on the stated-preference survey data for a new type of transit system integrating high-frequency fixed-route services and ridesourcing. The results show that machine learning can produce significantly higher predictive accuracy than logit models. Moreover, machine learning and logit models largely agree on many aspects of behavioral interpretations. In addition, machine learning can automatically capture the nonlinear relationship between the input features and choice outcomes. The paper concludes that there is great potential in merging ideas from machine learning and conventional statistical methods to develop refined models for travel behavior research and suggests some new research directions.
Calmify: Measuring the Effectiveness of Personalized Meditation Techniques Using Mobile Technologies

In recent years, the number of people suffering from stress and anxiety disorders is growing dramatically, reaching over 40 million adults in the United States alone. The meditation, amongst many other relaxation techniques, is emerging as a popular practice for dealing with stress and anxiety. However, so far meditation and its effect on well-being have been mostly measured based on subjective feedback or in some cases in laboratory settings where bulky and expensive devices are used for capturing physiological data, therefore, unfit for daily life uses. In this paper, we present the early design of Calmify, a mobile application, which is aimed to measure the effectiveness of personalized meditation techniques using a smartphone and off-the-shelf wearable devices.
Lactobacillus crispatus BC5 Interferes With Chlamydia trachomatis Infectivity Through Integrin Modulation in Cervical Cells

Lactobacilli play a crucial role in maintaining the ecological equilibrium of the vaginal niche, preventing the colonization of exogenous microorganisms. Although many studies have discussed the mechanisms displayed by lactobacilli in counteracting several urogenital pathogens, a few data are available on the interaction between lactobacilli and Chlamydia trachomatis. This study aimed to elucidate the molecular bases of the interaction among vaginal lactobacilli, the sexually transmitted pathogen C. trachomatis and the epithelial cervical cells. We evaluated the in vitro activity of 15 Lactobacillus strains, belonging to different species (i.e., L. crispatus, L. gasseri, L. vaginalis), against C. trachomatis. In particular, we evaluated the capability of lactobacilli cells to interfere with C. trachomatis infection in HeLa cells, by exclusion assays. Lactobacilli significantly reduced C. trachomatis infectivity, being L. crispatus the most active species. Although a dose-dependent effect was noticed, a significant antagonistic activity was maintained even at lower doses. As other Gram-positive bacteria (i.e., Streptococcus agalactiae, Enterococcus faecalis, and Bacillus subtilis) failed to interfere with C. trachomatis infectivity, Lactobacillus activity proved to be specific. The potential mechanism of protection was investigated in Lactobacillus crispatus BC5, chosen as the model strain. The incubation of HeLa cell line with BC5 cells induced important modifications in the epithelial plasma membrane, by altering lipid composition and α5 integrin subunit exposure. When α5 integrin subunits were masked by a specific blocking antibody or ITGA5 gene expression was silenced, Chlamydia infection was significantly reduced. It follows that α5 integrin subunit is crucial for the pathogen infection process, and the anti-Chlamydia activity can be directly linked to membrane properties modifications in cervical cells. The three Gram-positive bacteria used as controls failed to modify the expression of α5β1 integrin. In conclusion, we identified a potential molecular mechanism at the basis of the protection exerted by L. crispatus BC5 against C. trachomatis, getting insights into the role of the cervico-vaginal microbiota for the woman’s health.
Self-specific processing in the meditating brain: a MEG neurophenomenology study

Abstract Self-specific processes (SSPs) specify the self as an embodied subject and agent, implementing a functional self/nonself distinction in perception, cognition, and action. Despite recent interest, it is still undetermined whether SSPs are all-or-nothing or graded phenomena; whether they can be identified in neuroimaging data; and whether they can be altered through attentional training. These issues are approached through a neurophenomenological exploration of the sense-of-boundaries (SB), the fundamental experience of being an ‘I’ (self) separated from the ‘world' (nonself). The SB experience was explored in collaboration with a uniquely qualified meditation practitioner, who volitionally produced, while being scanned by magnetoencephalogram (MEG), three mental states characterized by a graded SB experience. The results were then partly validated in an independent group of 10 long-term meditators. Implicated neural mechanisms include right-lateralized beta oscillations in the temporo-parietal junction, a region known to mediate the experiential unity of self and body; and in the medial parietal cortex, a central node of the self's representational system. The graded nature as well as the trainable flexibility and neural plasticity of SSPs may hold clinical implications for populations with a disturbed SB.