Miracle-dz/UniFAR-FTUs huggingface dataset:
Characterization of mRNA Expression and Endogenous RNA Profiles in Bladder Cancer Based on The Cancer Genome Atlas (TCGA) Database

Background Bladder cancer is a multifactorial disease with increasing incidence and mortality. Genetic alterations and altered expressions of mRNAs, long non-coding RNAs (lncRNAs), and miRNAs have been shown to play important roles in the tumorigenesis of bladder cancer. However, the functions of key RNAs and their regulatory network in bladder cancer are still to be elucidated. Material/Methods RNA profiles were downloaded from The Cancer Genome Atlas (TCGA) database. The differentially expressed mRNAs, lncRNAs, and miRNAs in bladder cancer were acquired through analyses of data from 414 bladder cancer tissues and 19 normal bladder tissues. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis was performed by using “DAVID6.8” and the R package “ClusterProfile”. Protein–protein interaction and competing endogenous RNA (ceRNA) networks were constructed by using “STRING” database and Cytoscape 3.6. 2. Based on the clinical data and Cox regression, a prognosis model was established, and survival analysis was performed. Results A total of 1819 mRNAs, 659 lncRNAs, and 160 miRNAs were identified as significantly differentially expressed in bladder cancer of which 52 mRNAs, 58 lncRNAs, and 22 miRNAs were incorporated in the ceRNA network. CFL2 and TPM2 were found to be downregulated and showed significant correlation to each other in bladder cancer. HOXB5 and 6 lncRNAs (ADAMTS9-AS1, AC112721.1, LINC00460, AC110491.1, LINC00163, and HCG22) were strongly associated with high-grade, disease stages, and overall survival. Conclusions In this study, we have identified differentially expressed mRNAs, lncRNAs, and miRNAs in bladder cancer which were strongly associated with oncogenesis and prognosis. Further experimental studies are necessary to validate these results.
Declarative Recursive Computation on an RDBMS

We explore the close relationship between the tensor-based computations performed during modern machine learning, and relational database computations. We consider how to make a very small set of changes to a modern RDBMS to make it suitable for distributed learning computations. Changes include adding better support for recursion, and optimization and execution of very large compute plans. We also show that there are key advantages to using an RDBMS as a machine learning platform. In particular, DBMSbased learning allows for trivial scaling to large data sets and especially large models, where different computational units operate on different parts of a model that may be too large to fit into RAM.
Printable Thermoelectric Materials and Applications

Thermoelectric (TE) materials have been of great importance for the capability of direct energy conversion between heat and electricity. In the past decades, people have developed various methods to enhance the performance of the TE materials, which is characterized in terms of the dimensionless figure of merit of zT. Combined with the advanced techniques of printing, the printable and flexible TE materials have been obtained, which can greatly broaden the application of the TE materials, thus improve the overall energy conversion efficiency. Most importantly, the flexible high performance TE materials-based device can reduce the consume of unrecoverable resources. In this review, we would like to discuss the strategies of enhancing the performance of TE materials in details. Then, the advanced techniques of printing of TE materials-based device will be involved. Finally, we would suggest some promising methods to enhance the thermoelectric properties of TE materials and the potential applications in future.
Chelator-assisted phytoextraction of arsenic, cadmium and lead by Pteris vittata L. and soil microbial community structure response

Abstract Using biodegradable chelators to assist in phytoextraction may be an effective approach to enhance the heavy-metal remediation efficiencies of plants. A pot experiment was conducted to investigate the effects of ethylenediamine disuccinic acid (EDDS), citric acid (CA), and oxalic acid (OA) on the growth of the arsenic (As) hyperaccumulator Pteris vittata L. , its arsenic (As), cadmium (Cd), and lead (Pb) uptake and accumulation, and soil microbial responses in multi-metal(loid)-contaminated soil. The addition of 2.5-mmol kg−1 OA (OA-2.5) produced 26.7 and 14.9% more rhizoid and shoot biomass, respectively compared with the control, while EDDS and CA treatments significantly inhibited plant growth. The As accumulation in plants after the OA-2.5 treatment increased by 44.2% and the Cd and Pb accumulation in plants after a 1-mmol kg−1 EDDS treatment increased by 24.5 and 19.6%, respectively. Soil urease enzyme activities in OA-2.5 treatment were significantly greater than those in the control and other chelator treatments (p < 0.05). A PCR–denatured gradient gel electrophoresis analysis revealed that with the addition of EDDS, CA and OA enhanced soil microbial diversity. It was concluded that the addition of OA-2.5 was suitable for facilitating phytoremediation of soil As and did not have negative effects on the microbial community.
Rescue of Retinal Degeneration in rd1 Mice by Intravitreally Injected Metformin

Retinitis pigmentosa (RP) is a progressive hereditary retinal degenerative disease in which photoreceptor cells undergo degeneration and apoptosis, eventually resulting in irreversible loss of visual function. Currently, no effective treatment exists for this disease. Neuroprotection and inflammation suppression have been reported to delay the development of RP. Metformin is a well-tested drug used to treat type 2 diabetes, and it has been reported to exert beneficial effects in neurodegenerative diseases, such as Parkinson’s disease and Alzheimer’s disease. In the present study, we used immunofluorescence staining, electroretinogram (ERG) recordings and RNA-Seq to explore the effects of metformin on photoreceptor degeneration and its mechanism in rd1 mice. We found that metformin significantly reduced apoptosis in photoreceptors and delayed the degeneration of photoreceptors and rod bipolar cells in rd1 mice, thus markedly improving the visual function of rd1 mice at P14, P18, and P22 when tested with a light/dark transition test and ERG. Microglial activation in the outer nuclear layer (ONL) of the retina of rd1 mice was significantly suppressed by metformin. RNA-Seq showed that metformin markedly downregulated inflammatory genes and upregulated the expression of crystallin proteins, which have been demonstrated to be important neuroprotective molecules in the retina, revealing the therapeutic potential of metformin for RP treatment. αA-crystallin proteins were further confirmed to be involved in the neuroprotective effects of metformin in a Ca2+ ionophore-damaged 661W photoreceptor-like cell line. These data suggest that metformin exerts a protective effect in rd1 mice via both immunoregulatory and new neuroprotective mechanisms.
SF2523: Dual PI3K/BRD4 Inhibitor Blocks Tumor Immunosuppression and Promotes Adaptive Immune Responses in Cancer

Macrophages (MΘs) are key immune infiltrates in solid tumors and serve as major drivers behind tumor growth, immune suppression, and inhibition of adaptive immune responses in the tumor microenvironment (TME). Bromodomain and extraterminal (BET) protein, BRD4, which binds to acetylated lysine on histone tails, has recently been reported to promote gene transcription of proinflammatory cytokines but has rarely been explored for its role in IL4-driven MΘ transcriptional programming and MΘ-mediated immunosuppression in the TME. Herein, we report that BET bromodomain inhibitor, JQ1, blocks association of BRD4 with promoters of arginase and other IL4-driven MΘ genes, which promote immunosuppression in TME. Pharmacologic inhibition of BRD4 using JQ1 and/or PI3K using dual PI3K/BRD4 inhibitor SF2523 (previously reported by our group as a potent inhibitor to block tumor growth and metastasis in various cancer models) suppresses tumor growth in syngeneic and spontaneous murine cancer models; reduces infiltration of myeloid-derived suppressor cells; blocks polarization of immunosuppressive MΘs; restores CD8+ T-cell activity; and stimulates antitumor immune responses. Finally, our results suggest that BRD4 regulates the immunosuppressive myeloid TME, and BET inhibitors and dual PI3K/BRD4 inhibitors are therapeutic strategies for cancers driven by the MΘ-dependent immunosuppressive TME.
Resource Scheduling In Cloud Using Bee Algorithm For Heterogeneous Environment

The concept of cloud computing is gaining large exposure now, due to which, every industry aims at leasing resources and complete their jobs by the services provided by the cloud computing. The important service it provides is the Infrastructure as Service (IaaS) which helps company to lease computer resources in terms of memory or processing capacity. The major factor which determines the efficiency of resource utilization lies in scheduling of resources present at that point of time in a cloud. In this paper we will show how to effectively allocate the resource using one of the nature inspired algorithm, Bee algorithm.
The Study of a Classification Technique for Numeric Gaze-Writing Entry in Hands-Free Interface

Recently, many applications are developed in numerous domains with various environments. Since some environments require hands-free applications, new technology is needed for the input interfaces other than the mouse and keyboard. Therefore, to meet the needs, many researchers have begun to investigate the gaze and voice for the input technology. In particular, there are many approaches to render virtual keyboards with the gaze. However, since the virtual keyboards hide the screen space, this technique can only be applied in limited environments. In this paper, we propose a classification technique for gaze-written numbers as the hands-free interface. Since the gaze-writing is less accurate compared to the virtual keyboard typing, we apply the convolutional neural network (CNN) deep learning algorithm to recognize the gaze-writing and improve the classification accuracy. Besides, we create new gaze-writing datasets for training, gaze MNIST (gMNIST), by modifying the MNIST data with features of the gaze movement patterns. For the evaluation, we compare our approach with the basic CNN structures using the original MNIST dataset. Our study will allow us to have more options for the input interfaces and expand our choices in hands-free environments.
Human activity can influence the gut microbiota of Darwin's finches in the Galapagos Islands

The gut microbiota of animal hosts can be influenced by environmental factors, such as unnatural food items that are introduced by humans. Over the past 30 years, human presence has grown exponentially in the Galapagos Islands, which are home to endemic Darwin's finches. Consequently, humans have changed the environment and diet of Darwin's finches, which in turn, could affect their gut microbiota. In this study, we compared the gut microbiota of two species of Darwin's finches, small ground finches (Geospiza fuliginosa) and medium ground finches (Geospiza fortis), across sites with and without human presence, where finches prefer human‐processed and natural food, respectively. We predicted that: (a) finch microbiota would differ between sites with and without humans due to differences in diet, and (b) gut microbiota of each finch species would be most similar where finches have the highest niche overlap (areas with humans) compared to the lowest niche overlap (areas without humans). We found that gut bacterial community structure differed across sites and host species. Gut bacterial diversity was most distinct between the two species at the site with human presence compared to the site without human presence, which contradicted our predictions. Within host species, medium ground finches had lower bacterial diversity at the site with human presence compared to the site without human presence and bacterial diversity of small ground finches did not differ between sites. Our results show that the gut microbiota of Darwin's finches is affected differently across sites with varying human presence.
Nusinersen in later-onset spinal muscular atrophy

Objective To report results of intrathecal nusinersen in children with later-onset spinal muscular atrophy (SMA). Methods Analyses included children from a phase 1b/2a study (ISIS-396443-CS2; NCT01703988) who first received nusinersen during that study and were eligible to continue treatment in the extension study (ISIS-396443-CS12; NCT02052791). The phase 1b/2a study was a 253-day, ascending dose (3, 6, 9, 12 mg), multiple-dose, open-label, multicenter study that enrolled children with SMA aged 2–15 years. The extension study was a 715-day, single-dose level (12 mg) study. Time between studies varied by participant (196–413 days). Assessments included the Hammersmith Functional Motor Scale–Expanded (HFMSE), Upper Limb Module (ULM), 6-Minute Walk Test (6MWT), compound muscle action potential (CMAP), and quantitative multipoint incremental motor unit number estimation. Safety also was assessed. Results Twenty-eight children were included (SMA type II, n = 11; SMA type III, n = 17). Mean HFMSE scores, ULM scores, and 6MWT distances improved by the day 1,150 visit (HFMSE: SMA type II, +10.8 points; SMA type III, +1.8 points; ULM: SMA type II, +4.0 points; 6MWT: SMA type III, +92.0 meters). Mean CMAP values remained relatively stable. No children discontinued treatment due to adverse events. Conclusions Nusinersen treatment over ∼3 years resulted in motor function improvements and disease activity stabilization not observed in natural history cohorts. These results document the long-term benefit of nusinersen in later-onset SMA, including SMA type III. Clinicaltrials.gov identifier NCT01703988 (ISIS-396443-CS2); NCT02052791 (ISIS-396443-CS12). Classification of evidence This study provides Class IV evidence that nusinersen improves motor function in children with later-onset SMA.
Growth of zinnia, Italian ryegrass, and alfalfa and their remediation effects in diesel oil-contaminated soils

Abstract Our objective in this study was to compare the growth of zinnia, Italian ryegrass, and alfalfa, and their remediation effects in oil-contaminated soils. The soils were prepared by mixing 2, 4, or 8% diesel oil by weight with soil. The plant height and dry weights of shoots and roots were highest for zinnia in the 2 and 4% oil treatments, and highest for Italian ryegrass in the 8% oil treatment. The reduction ratios in soil total petroleum hydrocarbons concentration (TPH) for 3 plants were lower in the 4 and 8% oil treatments than those in the 2% treatment. The reduction ratios for Italian ryegrass and zinnia contaminated with 2, 4, and 8% diesel oil treatments were significantly higher than those for alfalfa and the non-cultivation treatment at 45 days after sowing, and there were no significant differences in reduction ratios between Italian ryegrass and zinnia. The reduction ratio of soil TPH concentration brought about by zinnia was also comparable to that of Italian ryegrass. Therefore, we conclude that zinnia shows growth and remediation effects that are equivalent to those of Italian ryegrass, in soils contaminated with less than 8% oil.
Branched Hamiltonians and time translation symmetry breaking in equations of the Liénard type

Shapere and Wilczek [Phys. Rev. Lett. 109, 160402 and 200402 (2012)] have recently described certain singular Lagrangian systems which display spontaneous breaking of time translation symmetry. We begin by considering the standard Liénard equation for which a Lagrangian is constructed by using the method of Jacobi Last Multiplier. The velocity dependence of the Lagrangian is such that the momentum may exhibit multi-valuedness, thereby leading to the so-called branched Hamiltonian. Next, with a quadratic velocity dependence in the Liénard equation, one can construct a Hamiltonian description involving a position-dependent mass. We compute the Lagrangian and Hamiltonian of this system and show that the canonical Hamiltonian is single valued. However, we find that up to a constant shift, the square of this Hamiltonian describes systems giving rise to spontaneous time translation symmetry breaking provided the potential function is negative.
Microdialysis Workflow for Metabotyping Superficial Pathologies: Application to Burn Injury

Burn injury can be a devastating traumatic injury, with long-term personal and social implications for the patient. The many complex local and disseminating pathological processes underlying burn injury’s clinical challenges are orchestrated from the site of injury and develop over time, yet few studies of the molecular basis of these mechanisms specifically explore the local signaling environment. Those that do are typically destructive in nature and preclude the collection of longitudinal temporal data. Burn injury therefore exemplifies a superficial temporally dynamic pathology for which experimental sampling typically prioritizes either specificity to the local burn site or continuous collection from circulation. Here, we present an exploratory approach to the targeted elucidation of complex, local, acutely temporally dynamic interstitia through its application to burn injury. Subcutaneous microdialysis is coupled with ultraperformance liquid chromatography–mass spectrometry (UPLC–MS) analysis, permitting the application of high-throughput metabolomic profiling to samples collected both continuously and specifically from the burn site. We demonstrate this workflow’s high yield of burn-altered metabolites including the complete structural elucidation of niacinamide and uric acid, two compounds potentially involved in the pathology of burn injury. Further understanding the metabolic changes induced by burn injury will help to guide therapeutic intervention in the future. This approach is equally applicable to the analysis of other tissues and pathological conditions, so it may further improve our understanding of the metabolic changes underlying a wide variety of pathological processes.
A Multi-Classifier Network-Based Crypto Ransomware Detection System: A Case Study of Locky Ransomware

Ransomware is a type of advanced malware that has spread rapidly in recent years, causing significant financial losses for a wide range of victims, including organizations, healthcare facilities, and individuals. Modern host-based detection methods require the host to be infected first in order to identify anomalies and detect the malware. By the time of infection, it can be too late as some of the system’s assets would have been already exfiltrated or encrypted by the malware. Conversely, the network-based methods can be effective in detecting ransomware attacks, as most ransomware families try to connect to command and control servers before their harmful payloads are executed. Therefore, a careful analysis of ransomware network traffic can be one of the key means for early detection. This paper demonstrates a comprehensive behavioral analysis of crypto ransomware network activities, taking Locky, one of the most serious families, as a case study. A dedicated testbed was built, and a set of valuable and informative network features were extracted and classified into multiple types. A network-based intrusion detection system was implemented, employing two independent classifiers working in parallel on different levels: packet and flow levels. The experimental evaluation of the proposed detection system demonstrates that it offers high detection accuracy, low false positive rate, valid extracted features, and is highly effective in tracking ransomware network activities.
The Australian Journal of Mathematical Analysis and Applications

In this paper, an exposition of the central concept of global analysis on a Riemannan manifold is given. We extend the theory of smooth vector fields from open subsets of Euclidean space to Riemannan manifolds. Specifically, we prove that a Riemannian manifold admits a unique solution for a system of ordinary differential equations generated by the flow of smooth tangent vectors. The idea of partial differential equations on Riemannian manifold is highlighted on the unit sphere.
Coordinated Power Control of Electric Vehicles for Grid Frequency Support: MILP-Based Hierarchical Control Design

Frequency regulation is one of the most crucial ancillary services that strives to maintain the demand and supply in smart grid setup. The deviations in grid’s frequency can be managed efficiently by adjusting the power generation and consumption of supply and demand sides, respectively. Traditionally, frequency support is provided using conventional generators but their usage leads to the emission of harmful gases, degraded heat rate, and associated wear and tear. However, electric vehicles (EVs) can play a significant role in managing demand and supply imbalances in the near future; with their penetration expected to reach 400 billion by 2020. Moreover, EVs have large charging and discharging capacities due to which they can provide instantaneous frequency support. Motivated by these factors, in this paper, a power management scheme has been presented to leverage the participation of EVs for secondary frequency regulation. The proposed scheme uses a 2-level hierarchical control mechanism to attain the following objectives: 1) to minimize the frequency deviations at the grid level; 2) to support bi-directional vehicle-to-grid in accordance with users’ power requirements; 3) to generate an optimal schedule for EV’s charging and discharging needs; 4) to reduce battery degradation; and 5) to maximize EV’s revenue. Using these objectives, the problem of frequency support has been formulated as a mixed integer linear programming problem. The proposed scheme has been evaluated using Mosek solver on real-time data acquired from PJM and CAISO. The results obtained demonstrate the effectiveness of the proposed scheme for providing frequency support in comparison to the existing scheme.
Language Specificity in Phonetic Cue Weighting: Monolingual and Bilingual Perception of the Stop Voicing Contrast in English and Spanish

Abstract Background/Aims: This work examines the perception of the stop voicing contrast in Spanish and English along four acoustic dimensions, comparing monolingual and bilingual listeners. Our primary goals are to test the extent to which cue-weighting strategies are language-specific in monolinguals, and whether this language specificity extends to bilingual listeners. Methods: Participants categorized sounds varying in voice onset time (VOT, the primary cue to the contrast) and three secondary cues: fundamental frequency at vowel onset, first formant (F1) onset frequency, and stop closure duration. Listeners heard acoustically identical target stimuli, within language-specific carrier phrases, in English and Spanish modes. Results: While all listener groups used all cues, monolingual English listeners relied more on F1, and less on closure duration, than monolingual Spanish listeners, indicating language specificity in cue use. Early bilingual listeners used the three secondary cues similarly in English and Spanish, despite showing language-specific VOT boundaries. Conclusion: While our findings reinforce previous work demonstrating language-specific phonetic representations in bilinguals in terms of VOT boundary, they suggest that this specificity may not extend straightforwardly to cue-weighting strategies.