Miracle-dz/UniFAR-FTUs huggingface dataset:
Mechanisms and Therapy for Cancer Metastasis to the Brain

Advances in chemotherapy and targeted therapies have improved survival in cancer patients with an increase of the incidence of newly diagnosed brain metastases (BMs). Intracranial metastases are symptomatic in 60–70% of patients. Magnetic resonance imaging (MRI) with gadolinium is more sensitive than computed tomography and advanced neuroimaging techniques have been increasingly used in the detection, treatment planning, and follow-up of BM. Apart from the morphological analysis, the most effective tool for characterizing BM is immunohistochemistry. Molecular alterations not always reflect those of the primary tumor. More sophisticated methods of tumor analysis detecting circulating biomarkers in fluids (liquid biopsy), including circulating DNA, circulating tumor cells, and extracellular vesicles, containing tumor DNA and macromolecules (microRNA), have shown promise regarding tumor treatment response and progression. The choice of therapeutic approaches is guided by prognostic scores (Recursive Partitioning Analysis and diagnostic-specific Graded Prognostic Assessment-DS-GPA). The survival benefit of surgical resection seems limited to the subgroup of patients with controlled systemic disease and good performance status. Leptomeningeal disease (LMD) can be a complication, especially in posterior fossa metastases undergoing a “piecemeal” resection. Radiosurgery of the resection cavity may offer comparable survival and local control as postoperative whole-brain radiotherapy (WBRT). WBRT alone is now the treatment of choice only for patients with single or multiple BMs not amenable to surgery or radiosurgery, or with poor prognostic factors. To reduce the neurocognitive sequelae of WBRT intensity modulated radiotherapy with hippocampal sparing, and pharmacological approaches (memantine and donepezil) have been investigated. In the last decade, a multitude of molecular abnormalities have been discovered. Approximately 33% of patients with non-small cell lung cancer (NSCLC) tumors and epidermal growth factor receptor mutations develop BMs, which are targetable with different generations of tyrosine kinase inhibitors (TKIs: gefitinib, erlotinib, afatinib, icotinib, and osimertinib). Other “druggable” alterations seen in up to 5% of NSCLC patients are the rearrangements of the “anaplastic lymphoma kinase” gene TKI (crizotinib, ceritinib, alectinib, brigatinib, and lorlatinib). In human epidermal growth factor receptor 2-positive, breast cancer targeted therapies have been widely used (trastuzumab, trastuzumab-emtansine, lapatinib-capecitabine, and neratinib). Novel targeted and immunotherapeutic agents have also revolutionized the systemic management of melanoma (ipilimumab, nivolumab, pembrolizumab, and BRAF inhibitors dabrafenib and vemurafenib).
Aggregating Descriptors with Local Gaussian Metrics

Recently, large-scale image classification has made a remar kable progress because of the significant advancement in the representation of imag e features. To realize scalable systems that can handle millions of training sampl es and tens of thousands of categories, it is crucially important to develop di scriminative image signatures that are compatible to linear classifiers. One of the promising approaches to realize this is to encode high-level statistics of local f eatures. Many state-ofthe-art large-scale systems are following this approach an d have made remarkable progress over the past few years. However, while first-o rder statistics are frequently used in many methods, the power of higher-order s tatistics has not received much attention. In this work, we propose an e fficient method to exploit the second-order statistics of local features. For each visual word, the local features o f training samples are modeled with a Gaussian, and descriptors from two images are compared using a Fisher vector with respect to the Gaussian. In experiments, we how the promising performance of our method.
Community Detection With Side Information: Exact Recovery Under the Stochastic Block Model

The community detection problem involves making inferences about node labels in a graph, based on observing the graph edges. This paper studies the effect of additional, nongraphical side information on the phase transition of exact recovery in the binary stochastic block model with <inline-formula><tex-math notation="LaTeX">$n$</tex-math> </inline-formula> nodes. When side information consists of noisy labels with error probability <inline-formula> <tex-math notation="LaTeX">$\alpha$</tex-math></inline-formula>, it is shown that phase transition is improved if and only if <inline-formula><tex-math notation="LaTeX">$\log (\frac{1-\alpha }{\alpha })=\Omega (\log (n))$</tex-math> </inline-formula>. When side information consists of revealing a fraction <inline-formula><tex-math notation="LaTeX"> $1-\epsilon$</tex-math></inline-formula> of the labels, it is shown that phase transition is improved if and only if <inline-formula><tex-math notation="LaTeX">$\log (1/\epsilon)=\Omega (\log (n))$</tex-math></inline-formula>. For a more general side information consisting of <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> features, two scenarios are studied, first, <inline-formula><tex-math notation="LaTeX">$K$</tex-math> </inline-formula> is fixed while the likelihood of each feature with respect to corresponding node label evolves with <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula>, and, second, the number of features <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula> varies with <inline-formula> <tex-math notation="LaTeX">$n$</tex-math></inline-formula> but the likelihood of each feature is fixed. In each case, we find when side information improves the exact recovery phase transition and by how much. In the process of deriving inner bounds, a variation of an efficient algorithm is proposed for community detection with side information that uses a partial recovery algorithm combined with a local improvement procedure.
Automatic estimation of semantic relatedness for sentences using machine learning

Because of the growth of interest in natural language processing, semantic relatedness between words already has gotten a lot of attention in research. Since the ability to cope with natural language automatically is getting better, many natural language processing task can already process sentences. There has also been an increasing interest for semantic relatedness between sentences. The main research question of this paper is: Which features can be automatically extracted from a natural language sentence that represents a good overview of the semantic meaning of a sentence? This can be tested by trying to predict the amount of semantic relatedness between sentences. For training and evaluation the SICK (Sentences Involving Compositional Knowledge) corpus is used. This corpus consists of 9,927 sentence pairs and an average human judgement score on relatedness between 1 and 5. The sentences in the corpus avoid the use of named entities and encyclopedic information, as well as usage of the past tense. Therefore we can avoid a lot of preprocessing, and we can focus on the task at hand. The barebone of the system is a random forest regressor. This regressor builds a model based on a lot of features that are extracted from many different models and representations of the original sentence pairs. Most of the features used are based on models built by the C&C tools and Boxer. From these tools the lemmatization, POS tags, a logical model and a Discourse Representation Structure are used. Other data used isWordNet data, the paraphrases database and two large text corpora (Wikipedia and the English Gigaword corpus). The models that are generated are analyzed by a python script, and properties that might contain semantic information are extracted. After this extraction the overlap of a sentence pair for this property is calculated, the result of this is the value of this feature that is inserted in our regression model. The Final system uses 18 features in total. Ranging from very simple features as sentence length and word overlap, to much more complex features as synset overlap and a Compositional Distributional Semantic Model (CDSM). The combination of all features results in a pearson correlation of 0.820, and a mean square error of 0.334. Paraphrasing can improve the precision of almost all features. The result of the total model with all features can improve to a pearson correlation of 0.837 and a mean square error of 0.322. This is a state of the art result compared to other systems built for the same task. For many possible applications this is already a usable result.
PoW, PoS, & Hybrid protocols: A Matter of Complexity?

In a previous paper, it was discussed whether Bitcoin and/or its blockchain could be considered a complex system and, if so, whether a chaotic one, a positive response raising concerns about the likelihood of Bitcoin/blockchain entering a chaotic regime, with catastrophic consequences for financial systems based on it. This paper intends to simplify and extend that analysis to other PoW, PoS, and hybrid protocol-based cryptocurrencies. As before, this study was carried out with the help of Information Theory of Complex Systems, in general, and Crutchfield's Statistical Complexity measure, in particular. This paper is a work-in-progress. We intend to uncover some other measures that capture the qualitative notion of complexity of systems that can be applied to these cryptocurrencies to compare with the results here obtained.
Mapping PDTB-style connective annotation to RST-style discourse annotation

Penn Discourse Treebank and Rhetorical Structure Theory annotation account for different aspects of discourse structure, but to some extent, their analyses also correspond to each other. For a corpus annotated with both types of information, we describe a procedure for mapping systematically from the ﬁrst layer to the second. In this way, we can observe commonalities and differences in the annotations of discourse structure between the two approaches. The method also allows for a data-driven mapping of coherence relations from one taxonomy to another with a suitable independently-annotated corpus
Sublinear Bounds for a Quantitative Doignon-Bell-Scarf Theorem

The recent paper "A quantitative Doignon-Bell-Scarf Theorem" by Aliev et al. generalizes the famous Doignon-Bell-Scarf Theorem on the existence of integer solutions to systems of linear inequalities. Their generalization examines the number of facets of a polyhedron that contains exactly $k$ integer points in $\mathbb{R}^n$. They show that there exists a number $c(n,k)$ such that any polyhedron in $\mathbb{R}^n$ that contains exactly $k$ integer points has a relaxation to at most $c(n,k)$ of its inequalities that will define a new polyhedron with the same integer points. They prove that $c(n,k) = O(k2^n)$. In this paper, we improve the bound asymptotically to be sublinear in $k$. We also provide lower bounds on $c(n,k)$, along with other structural results. For dimension $n=2$, our bounds are asymptotically tight to within a constant.
PLANT REGENERATION FROM MESOPHYLL PROTOPLASTS OF DIHAPLOID AND TETRAPLOID POTATO LINES ( SOLANUM TUBEROSUM L . )

Regeneration of plants from potato (Solanum tuberosum L.) mesophyll protoplasts of a tetraploid cultivar Desirée and a dihaploid line 687061/1 is reported. In vitro grown plants were used for protoplast isolation. Two different culture protocols were applied for the mesophyll protoplasts of potato cv. Desirée using W-S-S media. The number of plants regenerated was rather low i.e. 15 plants for the first and 60 plants for the second protocol and the time needed to reach entire plants quite long for both protocols (16 and 14 months respectively). The efficiency of plant regeneration was good for the dihaploid line 687061/1 using the same media. A number of 628 plants were regenerated after 12 months in culture.
Tracking through singularities using sliding mode differentiators

In this work, an alternative solution to the tracking problem for a SISO nonlinear dynamical system exhibiting points of singularity is given. An inversion-based controller is synthesized using the Fliess generalized observability canonical form associated to the system. This form depends on the input and its derivatives. For this purpose, a robust exact differentiator is used for estimating the control derivatives signals with the aim of defining a control law depending on such control derivative estimates and on the system state variables. This control law is such that, when applied to the system, bounded tracking error near the singularities is guaranteed.
Acceleration profile of an acrobatic act during training and shows using wearable technology

Abstract The purpose of this study was to describe the mechanical characteristics of a trampoline circus act and its individual tracks performed in training and shows using a tri-axial accelerometer. A track is an artist’s specific role within a choreographed act. Seven male acrobats performed their trampoline act during training and shows while wearing a triaxial accelerometer and reported ratings of perceived exertion (RPE) after each trial. Average acceleration (AVG), root mean square (RMS), root mean to the fourth (RM4), time spent in specific acceleration ranges and RPE were measured/recorded from training and show acts. Paired t-tests compared dependent variables between training and show. Acceleration AVG, RMS and RM4 were significantly higher (p < 0.05) in training than show. RPE was significantly higher (p < 0.05) in show than training. No significant differences existed in time spent in any of the acceleration ranges between training and show. GPS devices have been used to manage workloads in field sports but are inoperable in theatres. But, inertial measurements may be an effective alternative to describe mechanical demands in theatre or arena environments. Wearable technology may be useful to coaches to improve understanding of track demands to manage artist workloads.
The influence of CT based attenuation correction on PET/CT registration: an evaluation study

We are currently developing a PET/CT based navigation system for guidance of biopsies and radiofrequency ablation (RFA) of early stage hepatic tumors. For these procedures, combined PET/CT data can potentially improve current interventions. The diagnostic efficacy of biopsies can potentially be improved by accurately targeting the region within the tumor that exhibits the highest metabolic activity. For RFA procedures the system can potentially enable treatment of early stage tumors, targeting tumors before structural abnormalities are clearly visible on CT. In both cases target definition is based on the metabolic data (PET), and navigation is based on the spatial data (CT), making the system highly dependent upon accurate spatial alignment between these data sets. In our institute all clinical data sets include three image volumes: one CT, and two PET volumes, with and without CT-based attenuation correction. This paper studies the effect of the CT-based attenuation correction on the registration process. From comparing the pairs of registrations from five data sets we observe that the point motion magnitude difference between registrations is on the same scale as the point motion magnitude in each one of the registrations, and that visual inspection cannot identify this discrepancy. We conclude that using non-rigid registration to align the PET and CT data sets is too variable, and most likely does not provide sufficient accuracy for interventional procedures.
The Importance of Ph on the Biotransformation of Bromoxynil by Microbacterium Imperiale Cbs 498-74 Resting Cells

The Importance of pH on the Biotransformation of Bromoxynil by Microbacterium imperiale CBS 498-74 Resting Cells Fabrizia Pasquarelli, Natchari Chuchat Agata Spera, Laura Cantarella, Maria Cantarella Department of Industrial and Information Engineering and Economics, University of L'Aquila, Via Giovanni Gronchi n.18Nucleo industriale di Pile, 67100 L'Aquila, Italy. Department of Civil and Mechanical Engineering, University of Cassino and Southern Lazio, via G. Di Biasio, 43, 03043 Cassino, Italy. maria.cantarella@univaq.it
The Use of Impression Management Strategies to Manage Stock Market Reactions to IT Failures

In this work we show how organizational impression management strategies can influence stock market reactions to information technology (IT) failures. We combine the resource-based view of the firm with organizational impression management strategies to analyze what strategies work with different types and causes of IT failures. We perform an event study on a sample of 214 IT failures over eight years and find that a firm’s choice of organizational impression management strategy has a significant effect on a firm’s market value. On average, over $212 million in market value can be saved with the correct impression management strategy. For implementation failures, we find that assertive strategies are better than defensive strategies. Conversely, we find that for operational failures, defensive strategies are superior. Furthermore, we examine failures caused by human error and discuss the impacts. This research provides new theoretical insights to the resource-based view of the firm.
Autologous Platelet-Derived Growth Factors (Platelet-Rich Plasma [PRP])

Bone, Cartilage and Ligament Graft Substitutes Breast Reconstruction Following Mastectomy or Lumpectomy Electrical Stimulation Therapy and Home Devices Hyperbaric and Topical Oxygen Therapies Lumbar Fusion for Spinal Instability and Degenerative Disc Conditions, Including Sacroiliac Fusion Miscellaneous Musculoskeletal Procedures Negative Pressure Wound Therapy/Vacuum-Assisted Closure (VAC) for Nonhealing Wounds Plantar Fasciitis Treatments Scar Revision Stem Cell Transplantation: Non-cancer Disorders Tissue-Engineered Skin Substitutes
Sativex® as add-on therapy vs. further optimized first-line ANTispastics (SAVANT) in resistant multiple sclerosis spasticity: a double-blind, placebo-controlled randomised clinical trial

Abstract Purpose/aim: To evaluate the efficacy of tetrahydrocannabinol (THC):cannabidiol (CBD) oromucosal spray (Sativex®) as add-on therapy to optimised standard antispasticity treatment in patients with moderate to severe multiple sclerosis (MS) spasticity. Methods: Sativex® as add-on therapy vs. further optimised first-line ANTispastics (SAVANT) was a two-phase trial. In Phase A, eligible patients received add-on THC:CBD spray for 4 weeks to identify initial responders [≥20% improvement from baseline in spasticity 0–10 numerical rating scale (NRS) score]. Following washout, eligible initial responders were randomised to receive THC:CBD spray or placebo for 12 weeks (double-blinded, Phase B). Optimisation of underlying antispasticity medications was permitted in both groups across all study periods. Results: Of 191 patients who entered Phase A, 106 were randomised in Phase B to receive add-on THC:CBD spray (n = 53) or placebo (n = 53). The proportion of clinically relevant responders after 12 weeks (≥30% NRS improvement; primary efficacy endpoint) was significantly greater with THC:CBD spray than placebo (77.4 vs. 32.1%; p < 0.0001). Compared with placebo, THC:CBD spray also significantly improved key secondary endpoints: changes in mean spasticity NRS (p < 0.0001), mean pain NRS (p = 0.0013), and mean modified Ashworth’s scale (p = 0.0007) scores from Phase B baseline to week 12. Adverse events, when present, were mild/moderate and without new safety concerns. Conclusions: Add-on THC:CBD oromucosal spray provided better and clinically relevant improvement of resistant MS spasticity compared with adjusting first-line antispasticity medication alone.
Multilingualization of Question Answering Using Universal Dependencies

This paper investigates the capability of the syntax representation framework of Universal Dependencies to build multilingual applications. In a multilingual question answering system, the dependency structures are used as the input to the downstream components that can be shared for multiple languages. The experiment on the question answering pipeline demonstrates that the dependency structures commonly designed for multiple languages work better than conventional language-dependent representations, even for the Japanese language which has very different structures from those of English and Spanish.
Running Head: DEVELOPMENT OF SCHEMA EFFECTS Neural activation patterns during retrieval of schema-related memories: Differences and commonalities between children and adults

Schemas represent stable properties of individuals’ experiences, and allow to classify new events as being congruent or incongruent with existing knowledge. Research with adults indicates that the prefrontal cortex (PFC) is involved in memory retrieval of schema-related information. However, developmental differences between children and adults in the neural correlates of schema-related memories are not well understood. One reason for this is the inherent confound between schema-relevant experience and maturation, as both are related to time. To overcome this limitation, we used a novel paradigm that experimentally induces, and then probes for task-relevant knowledge during encoding of new information. Thirty-one children aged 8–12 years and 26 young adults participated in the experiment. While successfully retrieving schema-congruent events, children showed less medial PFC activity than adults. In addition, medial PFC activity during successful retrieval correlated positively with children’s age. While successfully retrieving schema-incongruent events, children showed stronger hippocampus (HC) activation as well as weaker connectivity between the striatum and the dorsolateral PFC than adults. These findings were corroborated by an exploratory full-factorial analysis investigating age differences in the retrieval of schemacongruent versus schema-incongruent events, comparing the two conditions directly. Consistent with the findings of the separate analyses, two clusters, one in the medial PFC, one in the HC, were identified that exhibited a memory x congruency x age group interaction. In line with the two-component model of episodic memory development, the present findings point to an age-related shift from a more HC-bound processing to an increasing recruitment of prefrontal brain regions in the retrieval of schema-related events.
Document Exploration with Topic Modeling : Designing Interactive Visualizations to Support Effective Analysis Workflows

We present a series of published and ongoing projects, on developing interactive visualizations for document exploration using statistical topic models. Taking a user-centric approach, we engage analysts working with real-world datasets, identify bottlenecks in their analysis workflow, and respond by developing appropriate visual presentations, interactions, and algorithms to address the users’ needs. We first review visual analysis tools produced by the authors of this paper. While these tools have been previously published in various venues, here we summarize their collective contributions, report on user feedbacks received since the deployment of the tools, and identity potential areas of improvement. We then present various visualization components currently under development, and report on informal feedbacks received from early prototypes. Effective visual designs often need to be paired with appropriate algorithmic development. We highlight potential areas of collaboration between machine learning and visualization researchers, to address the present barriers to analysis. Finally, we outline how a completed system may enable evaluations of topic modeling based on task performance.