Miracle-dz/UniFAR-FTUs huggingface dataset:
MicroRNA-194 promotes the growth, migration, and invasion of ovarian carcinoma cells by targeting protein tyrosine phosphatase nonreceptor type 12

Ovarian carcinoma is the most lethal gynecologic malignancy among women. Ovarian cancer metastasis is the main reason for poor prognosis. MicroRNAs (miRNAs) have been shown to play an important role in tumorigenesis and metastasis in various cancers by affecting the expression of their targets. In this study, we explored the role of miR-194 in ovarian cancer. Real-time polymerase chain reaction assays showed that miR-194 was significantly upregulated in ovarian cancer tissues. Overexpression of miR-194 in ovarian cancer cells promotes cell proliferation, migration, and invasion; in contrast, inhibition of the expression of miR-194 has the opposite effects. Meanwhile, bioinformatics tools were used to identify protein tyrosine phosphatase nonreceptor type 12 (PTPN12) as a potential target of miR-194. The luciferase assay showed that miR-194 directly binds to the 3′-untranslated region of PTPN12. Western blot analysis and quantitative real-time polymerase chain reaction assay revealed that PTPN12 expression was negatively associated with miR-194 expression in both ovarian cancer tissues and cells. Thus, we conclude that miR-194 targets PTPN12 and functions as an oncogene in ovarian cancer cells. This novel pathway may provide a new insight to explain ovarian cancer development and metastasis.
Distribution-based objectives for Markov Decision Processes

We consider distribution-based objectives for Markov Decision Processes (MDP). This class of objectives gives rise to an interesting trade-off between full and partial information. As in full observation, the strategy in the MDP can depend on the state of the system, but similar to partial information, the strategy needs to account for all the states at the same time. In this paper, we focus on two safety problems that arise naturally in this context, namely, existential and universal safety. Given an MDP A and a closed and convex polytope H of probability distributions over the states of A, the existential safety problem asks whether there exists some distribution Δ in H and a strategy of A, such that starting from Δ and repeatedly applying this strategy keeps the distribution forever in H. The universal safety problem asks whether for all distributions in H, there exists such a strategy of A which keeps the distribution forever in H. We prove that both problems are decidable, with tight complexity bounds: we show that existential safety is PTIME-complete, while universal safety is co-NP-complete. Further, we compare these results with existential and universal safety problems for Rabin's probabilistic finite-state automata (PFA), the subclass of Partially Observable MDPs which have zero observation. Compared to MDPs, strategies of PFAs are not state-dependent. In sharp contrast to the PTIME result, we show that existential safety for PFAs is undecidable, with H having closed and open boundaries. On the other hand, it turns out that the universal safety for PFAs is decidable in EXPTIME, with a co-NP lower bound. Finally, we show that an alternate representation of the input polytope allows us to improve the complexity of universal safety for MDPs and PFAs.
Precision cancer therapy: profiting from tumor specific defects in the DNA damage tolerance system

DNA damage tolerance (DDT) enables replication to continue in the presence of a damaged template and constitutes a key step in DNA interstrand crosslink repair. In this way DDT minimizes replication stress inflicted by a wide range of endogenous and exogenous agents, and provides a critical first line defense against alkylating and platinating chemotherapeutics. Effective DDT strongly depends on damage-induced, site-specific PCNA-ubiquitination at Lysine (K) 164 by the E2/E3 complex (RAD6/18). A survey of The Cancer Genome Atlas (TCGA) revealed a high frequency of tumors presents RAD6/RAD18 bi-allelic inactivating deletions. For instance, 11% of renal cell carcinoma and 5% of pancreatic tumors have inactivating RAD18-deletions and 7% of malignant peripheral nerve sheath tumors lack RAD6B. To determine the potential benefit for tumor-specific DDT defects, we followed a genetic approach by establishing unique sets of DDT-proficient PcnaK164 and -defective PcnaK164R lymphoma and breast cancer cell lines. In the absence of exogenous DNA damage, PcnaK164R tumors grew comparably to their PcnaK164 controls in vitro and in vivo. However, DDT-defective lymphomas and breast cancers were compared to their DDT-proficient controls hypersensitive to the chemotherapeutic drug cisplatin (CsPt), both in vitro and in vivo. CsPt strongly inhibited tumor growth and the overall survival of tumor bearing mice greatly improved in the DDT-defective condition. These insights open new therapeutic possibilities for precision cancer medicine with DNA damaging chemotherapeutics and optimize Next-Generation-Sequencing (NGS)-based cancer-diagnostics, -therapeutics, and -prognosis.
DisguiseNet: A Contrastive Approach for Disguised Face Verification in the Wild

This paper describes our approach for the Disguised Faces in the Wild (DFW) 2018 challenge. The task here is to verify the identity of a person among disguised and impostors images. Given the importance of the task of face verification it is essential to compare methods across a common platform. Our approach is based on VGG-face architecture paired with Contrastive loss based on cosine distance metric. For augmenting the data set, we source more data from the internet. The experiments show the effectiveness of the approach on the DFW data. We show that adding extra data to the DFW dataset with noisy labels also helps in increasing the gen 11 eralization performance of the network. The proposed network achieves 27.13% absolute increase in accuracy over the DFW baseline.
An analysis and a comparative study of cryptographic algorithms used on the Internet of Things (IoT) based on avalanche effect

The world is already on the way of connecting everything (objects) to the internet, from refrigerators to sprinkler systems. This interconnection of objects to the internet is new computing paradigm called the Internet of Things (IoT) networks. The internet of things within the broader domains of security also requires algorithms that are secure and are able to protect the gadgets connected to the internet. There are problems concerning security, especially on the algorithms used on the IoT. This study, we managed to; (1) Collecting existing block cryptographic algorithms (maximum of ten) for authentication and encryption from different devices used on Internet of Things; (2) Analyze their avalanche effect and speed of collected cryptographic algorithms from step one if they give efficient authentication on Internet of Things; (3) Improving their speed and avalanche effect by designing a mathematical model that strengthens the avalanche effect of the algorithms. This was done by using an initial vector XORed with plaintext and final vector XORed with cipher text (4) Testing the new mathematical model if it really enhances the avalanche effect and speed on each and every algorithm tested in step one.
Dynamic Supply Chain Design and Operations Plan for Connected Smart Factories with Additive Manufacturing

Interest in smart factories and smart supply chains has been increasing, and researchers have emphasized the importance and the effects of advanced technologies such as 3D printers, the Internet of Things, and cloud services. This paper considers an innovation in dynamic supply-chain design and operations: connected smart factories that share interchangeable processes through a cloud-based system for personalized production. In the system, customers are able to upload a product design file, an optimal supply chain design and operations plan are then determined based on the available resources in the network of smart factories. The concept of smart supply chains is discussed and six types of flexibilities are identified, namely: design flexibility, product flexibility, process flexibility, supply chain flexibility, collaboration flexibility, and strategic flexibility. Focusing on supply chain flexibility, a general planning framework and various optimization models for dynamic supply chain design and operations plan are proposed. Further, numerical experiments are conducted to analyze fixed, production, and transportation costs for various scenarios. The results demonstrate the extent of the dynamic supply chain design and operations problem, and the large variation in transportation cost.
On the Performance of a Canonical Labeling for Matching Correlated Erdős-Rényi Graphs

Graph matching in two correlated random graphs refers to the task of identifying the correspondence between vertex sets of the graphs. Recent results have characterized the exact information-theoretic threshold for graph matching in correlated Erd\H{o}s-R\'enyi graphs. However, very little is known about the existence of efficient algorithms to achieve graph matching without seeds. In this work we identify a region in which a straightforward $O(n^2\log n)$-time canonical labeling algorithm, initially introduced in the context of graph isomorphism, succeeds in matching correlated Erd\H{o}s-R\'enyi graphs. The algorithm has two steps. In the first step, all vertices are labeled by their degrees and a trivial minimum distance matching (i.e., simply sorting vertices according to their degrees) matches a fixed number of highest degree vertices in the two graphs. Having identified this subset of vertices, the remaining vertices are matched using a matching algorithm for bipartite graphs.
LIFE HISTORY, HABITAT SATURATION AND THE EVOLUTION OF FECUNDITY AND SURVIVAL ALTRUISM

Hamilton's rule provides a general description of the conditions for the evolution of altruism. But altruism can take different forms depending on which life‐history trait is affected by the helping behavior (fecundity vs. survival helping). In particular, these different forms of helping may have very different demographic consequences, which may feed back on evolution. We examine the interplay between various forms of helping and demography in viscous populations with empty sites. A key component of our analysis is the local density of empty sites experienced by a focal individual, which provides a measure of habitat saturation. Habitat saturation is shown to have contrasting effects depending on (1) whether the physiological costs and benefits of helping affect fecundity, survival or both; and (2) whether the costs of helping are paid in a density‐dependent or density‐independent manner. For a given level of habitat saturation and with density‐dependent reproduction, we find that the conditions for the evolution of helping should be more favorable in the survival altruism life cycle with a cost on fecundity, and more stringent in the fecundity altruism life cycle with a cost on survival. More generally, our analysis stresses the importance of taking into account the feedback between population demography, life history, and kin selection when investigating the selective pressures on altruism.
Iterative Multiuser Equalization for Subconnected Hybrid mmWave Massive MIMO Architecture

Millimeter waves and massive MIMO are a promising combination to achieve the multi-Gb/s required by future 5G wireless systems. However, fully digital architectures are not feasible due to hardware limitations, which means that there is a need to design signal processing techniques for hybrid analog-digital architectures. In this manuscript, we propose a hybrid iterative block multiuser equalizer for subconnected millimeter wave massive MIMO systems. The low complexity user-terminals employ pure-analog random precoders, each with a single RF chain. For the base station, a subconnected hybrid analog-digital equalizer is designed to remove multiuser interference. The hybrid equalizer is optimized using the average bit-error-rate as a metric. Due to the coupling between the RF chains in the optimization problem, the computation of the optimal solutions is too complex. To address this problem, we compute the analog part of the equalizer sequentially over the RF chains using a dictionary built from the array response vectors. The proposed subconnected hybrid iterative multiuser equalizer is compared with a recently proposed fully connected approach. The results show that the performance of the proposed scheme is close to the fully connected hybrid approach counterpart after just a few iterations.
Metrics analysis of risk profile: A perspective on business aspects

Risk analysis is process to identify and develop profile of risk. Most risk analysis procedures focuses in technical perspectives, whereas business perspective is less involved. Business impacts are very important to be considered because it is related to business continuity of organization. Therefore, identification of business aspects is needed to be done so that aspects can be used as metrics in risk analysis of IT security threats. This paper proposes metrics of risk analysis in IT security threats based on business aspects. Methods for validating business aspects as metrics are literature analysis, linear regression analysis, correlation analysis and significancy analysis. The defined metrics will be used to develop risk analysis procedure in future work so it is expected to result better output in risk profile.
Deterministic and Approximation Algorithms for Graph Theoretic Problems

Cut-based graph theoretic problems encompass some of the most seminal problems in theoretical computer science, having wide ranging applications within and beyond computing. The standard graph partitioning seeks to partition a graph G = (V,E) into k pieces where k ≤ n = |V | while fulfilling a balance condition on the size of each piece. The goal is to minimize the edges that cross from one piece to another. Unfortunately, this problem and many of its variants are NP-complete. The set of closely related problems includes finding a balanced cut, maximum-cut and sparsest cut in a weighted undirected graph. In this thesis, we study various NP-hard graph partitioning problems. The overarching goals of this thesis are: 1) to survey several breakthrough techniques in convex and continuous optimization (such as linear programming, semidefinite programming and eigenvalue optimization) that have been successfully applied to get provably good solutions for fundamental problems in combinatorial optimization and theoretical computer science and 2) to provide empirical evidence of the quality of approximation ratios obtained by employing such techniques to solve the maximum cut and sparsest cut problems in real world problem instances. the weighted undirected setting is sufficiently general to encapsulate others.
How Robust are Deep Neural Networks?

Convolutional and Recurrent, deep neural networks have been successful in machine learning systems for computer vision, reinforcement learning, and other allied fields. However, the robustness of such neural networks is seldom apprised, especially after high classification accuracy has been attained. In this paper, we evaluate the robustness of three recurrent neural networks to tiny perturbations, on three widely used datasets, to argue that high accuracy does not always mean a stable and a robust (to bounded perturbations, adversarial attacks, etc.) system. Especially, normalizing the spectrum of the discrete recurrent network to bound the spectrum (using power method, Rayleigh quotient, etc.) on a unit disk produces stable, albeit highly non-robust neural networks. Furthermore, using the $\epsilon$-pseudo-spectrum, we show that training of recurrent networks, say using gradient-based methods, often result in non-normal matrices that may or may not be diagonalizable. Therefore, the open problem lies in constructing methods that optimize not only for accuracy but also for the stability and the robustness of the underlying neural network, a criterion that is distinct from the other.
Progressive Neural Networks for Image Classification

The inference structures and computational complexity of existing deep neural networks, once trained, are fixed and remain the same for all test images. However, in practice, it is highly desirable to establish a progressive structure for deep neural networks which is able to adapt its inference process and complexity for images with different visual recognition complexity. In this work, we develop a multi-stage progressive structure with integrated confidence analysis and decision policy learning for deep neural networks. This new framework consists of a set of network units to be activated in a sequential manner with progressively increased complexity and visual recognition power. Our extensive experimental results on the CIFAR-10 and ImageNet datasets demonstrate that the proposed progressive deep neural network is able to obtain more than 10 fold complexity scalability while achieving the state-of-the-art performance using a single network model satisfying different complexity-accuracy requirements.
History And Philosophy Of Biology

Historical and Philosophical Perspectives: Pre-hellenic Science Hellenic Science China, Science Islam, Science Early Christianity Inductive Logic, Works, and Francis Bacon Deductive Logic, Maths, and Renee Descartes The Scientific Revolution The Church and Science Falsifiability: Karl Popper Paradigm: Thomas Kuhn Two Cultures: C P Snow Emergence Physical Sciences: Engineering Mathematics Astronomy Mechanics Alchemy Phlogiston Periodic Table Electricity, Magnetism, and Optics Thermodynamics Geology Biology: Medicine Anatomy Physiology Cell Biology Embryology Microbiology Pharmacology Biochemistry Neurobiology Botany Genetics Paleontology Systematics Evolution Race Information Origin of Life Society and Science: Integrity Informed Consent Faith Art Global Warming Free Will
Recurrence Is Associated With Body Mass Index in Patients Undergoing a Single-Level Lumbar Disc Herniation Surgery

Background The aim of the study was to assess the body mass index (BMI) and other risk factors associated with lumbar disc herniation (LDH) and clinical outcomes, in patients who undergo surgery for single-level LDH. Methods This was a retrospective cohort study, affecting patients that underwent surgery for single-level LDH attending our hospital between July 2009 and January 2016. The mean follow-up period was 3.5 years (1 - 8 years). To maintain adequately sized groups for analysis, level L2-L3 and L3-L4 herniations were grouped as upper disc levels (group A) and level L4-L5 (group B) and L5-S1 (group C) herniations were analyzed individually. Disk herniation was graded on T2-weighted sagittal magnetic resonance images by using a five-point scale. Pain assessment was made using the visual analog scale (VAS). Results Two hundred fifty-six (256) patients met study inclusion criteria. There were 138 males (53.9%) with a mean age of 55.3 ± 12.9 years (range, 30 - 77). The association between A, B and C groups was analyzed, based on criteria such as age, sex, BMI, surgical techniques, diabetes, size of herniated disc, preoperative VAS, length of hospital stay, drop foot on admission, smoking, family history and history of injury to the lumbar spine, location of herniated disc (far lateral) and use of steroids. We found a statistically significant factor between groups in BMI (P = 0.006), family history (P = 0.001), location (far lateral) (P = 0.003) and history of injury to the lumbar spine (P = 0.003). Conclusions There may be an association between severity of disc degeneration and BMI (overweight and obese adults). Furthermore, spine and neurosurgeons should be aware that BMI might be related to patients’ outcome.
Assessment of a respiratory face mask for capturing air pollutants and pathogens including human influenza and rhinoviruses.

Background Prevention of infection with airborne pathogens and exposure to airborne particulates and aerosols (environmental pollutants and allergens) can be facilitated through use of disposable face masks. The effectiveness of such masks for excluding pathogens and pollutants is dependent on the intrinsic ability of the masks to resist penetration by airborne contaminants. This study evaluated the relative contributions of a mask, valve, and Micro Ventilator on aerosol filtration efficiency of a new N95 respiratory face mask. Methods The test mask was challenged, using standardized methods, with influenza A and rhinovirus type 14, bacteriophage ΦΧ174, Staphylococcus aureus (S. aureus), and model pollutants. The statistical significance of results obtained for different challenge microbial agents and for different mask configurations (masks with operational or nonoperational ventilation fans and masks with sealed Smart Valves) was assessed. Results The results demonstrate >99.7% efficiency of each test mask configuration for exclusion of influenza A virus, rhinovirus 14, and S. aureus and >99.3% efficiency for paraffin oil and sodium chloride (surrogates for PM2.5). Statistically significant differences in effectiveness of the different mask configurations were not identified. The efficiencies of the masks for excluding smaller-size (i.e., rhinovirus and bacteriophage ΦΧ174) vs. larger-size microbial agents (influenza virus, S. aureus) were not significantly different. Conclusions The masks, with or without features intended for enhancing comfort, provide protection against both small- and large-size pathogens. Importantly, the mask appears to be highly efficient for filtration of pathogens, including influenza and rhinoviruses, as well as the fine particulates (PM2.5) present in aerosols that represent a greater challenge for many types of dental and surgical masks. This renders this individual-use N95 respiratory mask an improvement over the former types of masks for protection against a variety of environmental contaminants including PM2.5 and pathogens such as influenza and rhinoviruses.
Analysis of Bacterial Community Structure of Activated Sludge from Wastewater Treatment Plants in Winter

Activated sludge bulking is easily caused in winter, resulting in adverse effects on effluent treatment and management of wastewater treatment plants. In this study, activated sludge samples were collected from different wastewater treatment plants in the northern Xinjiang Uygur Autonomous Region of China in winter. The bacterial community compositions and diversities of activated sludge were analyzed to identify the bacteria that cause bulking of activated sludge. The sequencing generated 30087–55170 effective reads representing 36 phyla, 293 families, and 579 genera in all samples. The dominant phyla present in all activated sludge were Proteobacteria (26.7–48.9%), Bacteroidetes (19.3–37.3%), Chloroflexi (2.9–17.1%), and Acidobacteria (1.5–13.8%). Fifty-five genera including unclassified_f_Comamonadaceae, norank_f_Saprospiraceae, Flavobacterium, norank_f_Hydrogenophilaceae, Dokdonella, Terrimonas, norank_f_Anaerolineaceae, Tetrasphaera, Simplicispira, norank_c_Ardenticatenia, and Nitrospira existed in all samples, accounting for 60.6–82.7% of total effective sequences in each sample. The relative abundances of Saprospiraceae, Flavobacterium, and Tetrasphaera with the respective averages of 12.0%, 8.3%, and 5.2% in bulking sludge samples were higher than those in normal samples. Filamentous Saprospiraceae, Flavobacterium, and Tetrasphaera multiplied were the main cause for the sludge bulking. Redundancy analysis (RDA) indicated that influent BOD5, DO, water temperature, and influent ammonia had a distinct effect on bacterial community structures.