Miracle-dz/UniFAR-FTUs huggingface dataset:
Impact of Aurora kinase A and B expression on response to neoadjuvant chemotherapy and patient outcome in muscle-invasive bladder cancer (MIBC).

393 Background: Identification of robust biomarkers that predict likelihood of benefit from neoadjuvant chemotherapy (NAC) in patients with MIBC remains an unmet need. Previous studies have implicated tumor overexpression of aurora kinases A (AURKA) and B (AURKB) as a mechanism of chemo-resistance. Because overexpression of AURKA has also emerged as a potential biomarker for detection of high-risk urothelial carcinoma, we sought to characterize the expression of AURKA, AURKB with clinical outcomes in MIBC patients who received NAC and to test the hypothesis that tumor overexpression of AURKA and AURKB would predict for residual MIBC. Methods: 47 patients with MIBC who received NAC prior to cystectomy were retrospectively identified. Immunohistochemistry for AURKA and AURKB was performed on pre-treatment diagnostic transurethral resection of bladder tumors and matched cystectomy specimens. Logistic regression models were estimated to determine the impact of pre-NAC expression on pathologic staging at cystectomy. Receiver Operator Characteristic curves (ROC) were calculated to assess diagnostic predictive ability of AURKA and AURKB. AURKA and AURKB were assessed for association with relapse-free survival (RFS) and overall survival (OS) using Kaplan-Meier techniques and Cox proportional hazards models. Results: 22 of 47 [46.8%] patients had residual muscle-invasive (ypT2-4) urothelial carcinoma after NAC. Neither baseline tumor expression of AURKA (ROC = 0.54, p = 0.71) nor AURKB (ROC = 0.46, p = 0.98) predicted for ypT2-4 status. However, baseline expression of AURKA above the 75th percentile for this cohort, determined by the percentage of tumor cells positive, was associated with an inferior RFS, [HR = 3.79, [1.40, 10.26] p = 0.005] and OS, [HR = 5.84, [2.14, 15.98], p < 0.001]. Similar trends for worse survival outcomes were also observed for high AURKB levels. Conclusions: Although baseline tumor AURKA and AURKB expression did not predict for pathologic residual MIBC after NAC, high expression of AURKA and AURKB predicted for inferior RFS and OS. Further evaluation of AURKA and AURKB as potential biomarkers and therapeutic targets in MIBC is warranted.
Forecasting Power Grid States for Regional Energy Markets with Deep Neural Networks

An increasing amount of volatile infrastructure, such as solar power plants, wind power plants, battery storages, or electric vehicles, will be added to the distribution grid. Such volatile infrastructure adds strain to the existing infrastructure and may lead to high transmission load on lines and corrupt power grid states. One way to resolve these issues is to establish regional energy markets. Such energy markets try to coordinate energy consumption or production regionally, based on estimates of future power grid states, thus reducing stress on the power grid. For these markets, it is crucial to know possible threatening grid states in advance to allow the market operators to keep the power grid in a healthy grid state with the help of regionally consumed or produced energy. In this work, we propose three different approaches to forecast future grid states with deep neural networks based on a learned numerical weather prediction representation. We evaluate these approaches with experimental results and discuss the remaining challenges that have to be addressed to enable regional energy markets. Furthermore, we provide the dataset to allow other researchers to reimplement our work.
Biotechnology and Bioengineering

Funding information Ministerio de Economía y Competitividad, Grant/Award Number: PCIN‐2016‐102; Ministerio de Educación y Formación Profesional, Grant/Award Number: FPU14/ 05457 Abstract Proteinaceous organic wastes are suitable substrates to produce high added‐value products in anaerobic mixed‐culture fermentations. In these processes, the stoichiometry of the biotransformation depends highly on operational conditions such as pH or feeding characteristics and there are still no tools that allow the process to be directed toward those products of interest. Indeed, the lack of product selectivity strongly limits the potential industrial development of these bioprocesses. In this work, we developed a mathematical metabolic model for the production of volatile fatty acids from protein‐rich wastes. In particular, the effect of pH on the product yields is analyzed and, for the first time, the observed changes are mechanistically explained. The model reproduces experimental results at both neutral and acidic pH and it is also capable of predicting the tendencies in product yields observed with a pH drop. It also offers mechanistic insights into the interaction among the different amino acids (AAs) of a particular protein and how an AA might yield different products depending on the relative abundance of other AAs. Particular emphasis is placed on the utility of this mathematical model as a process design tool and different examples are given on how to use the model for this purpose.
Biotechnology and Bioengineering

Correspondence Maja Vodopivec, Kolodvorska cesta 27, 1234 Mengeš, Slovenia. Email: maja.vodopivec@novartis.com Abstract Established bioprocess monitoring is based on quick and reliable methods, including cell count and viability measurement, extracellular metabolite measurement, and the measurement of physicochemical qualities of the cultivation medium. These methods are sufficient for monitoring of process performance, but rarely give insight into the actual physiological states of the cell culture. However, understanding of the latter is essential for optimization of bioprocess development. Our study used LC‐MS metabolomics as a tool for additional resolution of bioprocess monitoring and was designed at three bioreactors scales (10 L, 100 L, and 1,000 L) to gain insight into the basal metabolic states of the Chinese hamster ovary (CHO) cell culture during fed‐batch. Metabolites characteristics of the four growth stages (early and late exponential phase, stationary phase, and the phase of decline) were identified by multivariate analysis. Enriched metabolic pathways were then established for each growth phase using the CHO metabolic network model. Biomass generation and nucleotide synthesis were enriched in early exponential phase, followed by increased protein production and imbalanced glutathione metabolism in late exponential phase. Glycolysis became downregulated in stationary phase and amino‐acid metabolism increased. Phase of culture decline resulted in rise of oxidized glutathione and fatty acid concentrations. Intracellular metabolic profiles of the CHO fed‐batch culture were also shown to be consistent with scale and thus demonstrate metabolomic profiling as an informative method to gain physiological insight into the cell culture states during bioprocess regardless of scale.
Interferon-α and -β inhibit the in vitro differentiation of immunocompetent human dendritic cells from CD14+ precursors

Dendritic cell (DC) precursors and immature DC reside in epithelium where they encounter pathogens and cytokines, which stimulate their differentiation. We hypothesized that type-I interferons (IFN- and -β), cytokines that are produced early in the innate immune response against viruses and some bacteria, may influence DC differentiation and function. To examine this possibility, we used an in vitro model of DC differentiation in which initial culture of human CD14+monocytes with granulocyte–macrophage colony-stimulating factor (GM-CSF) and interleukin (IL)-4 generates immature DC, and subsequent culture with tumor necrosis factor (TNF)- drives the final development into mature DC. We found in this model that IFN-/β, added from the initiation of the culture on, significantly reduced the survival and altered the morphology and differentiation of DC. TNF-–dependent maturation of IFN-β–treated immature DC led to cells with reduced expression of CD1a, CD40, CD54, and CD80 when compared with mature DC controls. IFN-/β–treated DC further had a reduced capacity to induce naive Th-cell proliferation through allostimulation or anti-CD3 monoclonal antibody stimulation. In addition, IFN-/β–treated DC secreted less IL-12 upon stimulation with Staphylococcus aureus Cowan strain or with CD4+ T cells, and this decrease correlated directly with their inability to support CD4+ T-cell secretion of IFN-γ, even though T-cell lymphotoxin production was unaffected. These findings indicate that type-I IFNs can influence the generation of acquired immune responses by modifying T-helper cell differentiation through the regulation of DC differentiation and function.
Durable and Effectiveness of Immunotherapy in the Treatment of Metastatic Gastroesophageal Junction Cancer

Citation: Ferreira CGM, Camilo GB, Dias EN, de Oliveira BL, Schanuel CM, et al. (2020) Durable and Effectiveness of Immunotherapy in the Treatment of Metastatic Gastroesophageal Junction Cancer. Int J Immunol Immunother 7:053. doi.org/10.23937/2378-3672/1410053 Accepted: July 07, 2020: Published: July 09, 2020 Copyright: © 2020 Ferreira CGM, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
A remark on conceptual metaphor and scalarity

It is cross-linguistically very common to use locative prepositions as modifiers of numerals in pre-nominal position. As noted by Corver and Zwarts (2006), combinations of prepositions and numerals typically involve prepositions that are compatible with the vertical axis. For instance, English uses under n and over n to be synonymous to fewer than n and more than n, respectively. Similarly, Romanian uses the locative sub 20, as in (2), from Corver and Zwarts (2006).
EXPERT-CONSENSUS AND EVIDENCE-BASED GUIDELINES FOR THE ASSESSMENT OF FLOW MEDIATED DILATION (FMD) IN HUMANS

Endothelial dysfunction is involved in the development of atherosclerosis, which precedes asymptomatic structural vascular alterations as well as clinical manifestations of cardiovascular disease (CVD). Endothelial function can be assessed non-invasively using the flow-mediated dilation (FMD) technique. FMD represents an endothelium-dependent, largely nitric oxide (NO)-mediated dilatation of conduit arteries in response to an imposed increase in blood flow and shear stress. FMD is affected by cardiovascular (CV) risk factors, relates to coronary artery endothelial function, and independently predicts CVD outcome. Accordingly, FMD is a tool for examining the pathophysiology of CVD and possibly identifying subjects at increased risk for future CV events. Moreover, it has merit in examining the acute and long-term impact of physiological and pharmacological interventions in humans. Despite concerns about its reproducibility, the available evidence shows that highly reliable FMD measurements can be achieved when specialised laboratories follow standardised protocols. For this purpose, updated expert-consensus guidelines for the performance of FMD are presented, which are based on critical appraisal of novel technical approaches, development of analysis software, and studies exploring the physiological principles underlying the technique. Uniformity in FMD performance will i. improve comparability between studies, ii. contribute to construction of reference values, and iii. offer an easy accessible and early marker of atherosclerosis that could complement clinical symptoms of structural arterial disease and facilitate early diagnosis and prediction of CVD outcomes.
Listeriosis in a peri-urban area: Cultural and molecular characterization of Listeria monocytogenes isolated from encephalitic goats

Background and Aim: Listeriosis in food animals bears a significant threat to human health. Detailed investigations into the cause facilitate proper management of the disease. This study reports the cultural, pathological, and molecular characterization of Listeria monocytogenes isolated from encephalitic goats from peri-urban Guwahati, Assam. Materials and Methods: Out of nine suspected samples, five positive isolates of L. monocytogenes were subjected to bacteriological, biochemical, and molecular tests. The genus and species-specific L. monocytogenes 16S rRNA and prs genes were amplified by polymerase chain reaction (PCR) to yield 1200 and 370 bp sized products, respectively. The encephalitic form of the disease was characterized by circling movement, high fever, and terminal recumbence. Results: All the five isolates were confirmed to be L. monocytogenes based on PCR amplification of genus and species-specific 16S rRNA and prs gene products. The isolates were sensitive to ciprofloxacin, oxytetracycline (OTC), and norfloxacin, but resistant to doxycycline and erythromycin. A high dose of OTC was used in a goat at the early stage of clinical symptom and the animal recovered clinically. Conclusion: Listeriosis in goats could pose a significant public health threat as the meat (occasionally milk) or meat products from goats are widely consumed by the people of Assam. Understanding the molecular epidemiological aspects of L. monocytogenes infections of food animal species should, therefore, be the priority in this part of the country.
UvA-DARE ( Digital Academic Repository ) Combinatorial auction design for real-estate markets

This paper describes a combinatorial auction design for real-estate markets where floor space (in a multi-storeyed building) is to be allocated to various interested parties. Our design is based on lab experiments that provide guidance in choices regarding pricing, feedback, and activity rules. In addition, the allocation needs to satisfy several municipal and building regulations. We show how these regulations can be included in an integer program that is used to solve the winner determination problem, and discuss its computational complexity. Finally, we report on a practical application of this design, where over one hundred bidders took part in the first combinatorial auction for housing space, in a newly erected building in Amsterdam (the Netherlands).
Democracy, Redistribution, and Inequality: Evidence from the English Poor Law

This paper tests whether inequality mediates the effect of democratization on government redistribution. An 1894 democratic reform to councils that provided social insurance in Britain is used as the treatment event in a difference-in-difference analysis. The reform removed institutional features- a graduated franchise, property qualifications, the absence of a secret ballot, and the participation of unelected magistrates- that helped landowners seize control of spending on poor relief after the 1832 Great Reform Act. The results support theories arguing that inequality strengthens elite opposition to democratization: more unequal districts experienced greater increases in government expenditure following the democratic reform.
Simple Face Thermal Features for Gender Discrimination

A very simple approach is proposed for gender discrimination using thermal infrared images of the persons' face. The selected features are actually based on the mean value of the pixels of specific locations on the face. It is proved that the discrimination is feasible either by simple visualization in the feature space or by using a relatively simple neural network for this purpose. All cases of persons from the used database, males and females, are correctly distinguished based on the mean value of the employed locations of the face. Classification results are verified using two conventional approaches, namely: a. the simplest possible neural network so that generalization is achieved along with successful discrimination between all persons and b. the leave-one-out approach to demonstrate the classification performance on unknown persons using the simplest classifiers possible.
Unsupervised Deep Imputed Hashing for Partial Cross-modal Retrieval

Cross-modal retrieval, given the data of one specific modality as a query, aims to search the relevant data in other modalities. Recently, cross-modal hashing has attracted much attention due to its high efficiency and low storage cost. Its main idea is to approximate the cross-modality similarity via binary codes. This kind of method works well when the cross-modal data is completely observed. However, the real-world application usually avoids this situation, where part of the information is unobserved in some modality. Such partial multimodal data will result in the lack of pairwise information and then destroy the performance of cross-modal hashing. In this paper, we proposed a novel unsupervised cross-modal hashing approach, named as Unsupervised Deep Imputed Hashing (UDIH). It is a two-stage learning strategy. Firstly, the unobserved pairwise data is imputed by the proposed generators. Then a neural network with weighted triplet loss is applied on the correlation graph to learn the hashing code in the Hamming space for each modality, where the correlation graph is constructed with the aid of augmented data. UDIH has the ability to preserve the semantic consistency and difference among data objects. The extensive experimental results have shown that the proposed method outperforms the state-of-the-art methods on two benchmark datasets (MIRFlickr and NUS-WIDE). The source code could be available at https://github.com/AkChen/UDIH
Particle Trajectories in the BBM Approximation

The regularized version of the KdV equation called the BBM equation has been used as a model equation for long waves in shallow water. This model is used to study the behavior of the waves at the surface of the inviscid fluid. In this paper, the particle paths are computed numerically with the aid of the velocity fields found in connection with the exact solutions of the BBM equation. In line with this concept, we examine the solitary wave and the periodic traveling wave solutions. For comparison purposes, we compute the closed-form solutions of the particle trajectories analytically.
On the Linear Distinguishing Attack against ZUC-256 Stream Cipher

At FSE 2020, a linear distinguishing attack is presented against the ZUC-256 stream cipher based on the 32-bit word with a data/time complexity of about 2 236 . 38 . In this paper, we re-evaluate the complexity of this attack and discuss the applicability of such a distinguishing attack in 5G application scenarios, where each keystream frame is limited to 20000, and up to 2 32 bits. To assure a high success probability close to 1, it is shown that the precise time complexity of the distinguishing attack is 2 253 . 93 basic operations with a data complexity of 2 241 . 38 bits keystream, which is far beyond the keystream length limit in 5G application settings in the single-frame setting. Besides, we also consider the multiple-frame scenario where a long keystream could be formed by concatenating many short keystream frames generated from diﬀerent (Key, IV) pairs. We show that even in such a strong model of distinguishing attacks, the reported bias will not exist in 5G application scenarios and the linear distinguishing attack will not work due to the fact that the long linear combination relation derived from the polynomial multiple of the LFSR in ZUC-256 over GF(2 31 − 1), which has been veriﬁed in experiments. It is concluded that the ZUC-256 stream cipher oﬀers the full 256-bit security in 5G application scenarios.
Combining Reinforcement Learning and Rule-based Method to Manipulate Objects in Clutter

Picking up the clustered objects is always a challenging task in robot research field. And reinforcement learning enables robot to adapt to different tasks through plenty of attempts. To reduce the complexity of strategy learning, we propose a framework for robots to pick up the objects in clutter on table based on deep reinforcement learning and rule-based method. To manipulate the objects on table, we mainly divide the robot actions into two categories: one is pushing that uses the reinforcement learning method, while the other one is grasping that is inferred by image morphological processing. The pushing action can separate the stacking objects, create a robust grasp point for the following grasp. The grasp detect algorithm determines if there is a suitable grasp point. Judging on the result of pushing, the grasp detect algorithm will return a reward for pushing learning. Taking images as input, our framework can keep a high grasp rate with low computational complexity, which makes it achieve clutter clearing quickly.
Determining the optimal puncture site of CT-guided transthoracic needle aspiration biopsy for the diagnosis of tuberculosis

Background The purpose of this study was to determine the optimal puncture site of computed tomography (CT)-guided transthoracic needle aspiration biopsy for the diagnosis of tuberculosis (TB) by the comparison of clinical and radiological characteristics of TB patients stratified to different histopathological results. Methods We retrospectively analysed the data of clinically diagnosed TB patients with negative laboratory results between July 2016 and June 2018. Biopsy specimens were obtained from patients for Ziehl-Neelsen (Z-N) staining and TB-DNA. Results For the 356 TB patients, the positive rate of TB-DNA was 70.9%, which was significantly higher than that of Z-N staining (46.4%, P<0.001). The positive rate of lesions from upper lobe (76.4%, 155/203) was significantly higher than that from lower lobe (63.1%, 89/141, P=0.008). The mean of ΔCT density for positive histologic group (12.84±6.81 HU) was lower than that for negative histologic group (28.32±9.82 HU, P<0.001). ROC curve analysis revealed that a density-based cut-off value of 20.5 HU should be set as the cut-off values for determining the optimal puncture site. Conclusions Our data demonstrates that the molecular diagnostics has superiority over Z-N staining for detecting MTB from lung aspirates. The lung biopsies from upper lobe were more likely to yield positive histologic results than those from lower lobe. In addition, the enhancement of 20.5 HU by CT scans should be set as the cut-off values for determining the optimal puncture site that would facilitate an efficient diagnosis of pulmonary TB.
Evolvability

Living organisms function in accordance with complex mechanisms that operate in different ways depending on conditions. Darwin's theory of evolution suggests that such mechanisms evolved through variation guided by natural selection. However, there has existed no theory that would explain quantitatively which mechanisms can so evolve in realistic population sizes within realistic time periods, and which are too complex. In this article, we suggest such a theory. We treat Darwinian evolution as a form of computational learning from examples in which the course of learning is influenced only by the aggregate fitness of the hypotheses on the examples, and not otherwise by specific examples. We formulate a notion of evolvability that distinguishes function classes that are evolvable with polynomially bounded resources from those that are not. We show that in a single stage of evolution monotone Boolean conjunctions and disjunctions are evolvable over the uniform distribution, while Boolean parity functions are not. We suggest that the mechanism that underlies biological evolution overall is “evolvable target pursuit”, which consists of a series of evolutionary stages, each one inexorably pursuing an evolvable target in the technical sense suggested above, each such target being rendered evolvable by the serendipitous combination of the environment and the outcomes of previous evolutionary stages.