Miracle-dz/UniFAR-FTUs huggingface dataset:
A glycolysis-related gene signatures in diffuse large B-Cell lymphoma predicts prognosis and tumor immune microenvironment

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of lymphoma which that highly aggressive and heterogeneous. Glycolysis has been implicated in the regulation of tumor microenvironment (TME) and development. In this study, we aimed to establish a glycolysis-related prognostic model for the risk stratification, prognosis prediction, and immune landscape evaluation in patients with DLBCL. Methods: Three independent datasets GSE181063, GSE10846, and GSE53786 containing gene expression profiles and clinical data were downloaded from the Gene Expression Omnibus (GEO) database. The glycolysis-related prognostic model was developed with Cox and Least Absolute Shrinkage and Selector Operation (LASSO) regression and validated. A nomogram integrating clinical factors and glycolytic risk scores was constructed. The composition of the TME was analyzed with the ESTIMATE algorithm and single-sample gene set enrichment analysis (ssGSEA). Results: A glycolytic risk model containing eight genes was developed. The area under the receiver operating characteristic (ROC) curve (AUC) for the 1-, 3-, and 5-year was 0.718, 0.695, and 0.688, respectively. Patients in the high-risk group had significantly lower immune scores, elevated tumor purity, and poorer survival compared with those in the low-risk group. The nomogram constructed based on glycolytic risk score, age, Eastern Cooperative Oncology Group performance status (ECOG-PS), use of rituximab, and cell of origin (COO) displayed better prediction performance compared with the International Prognostic Index (IPI) in DLBCL. The glycolytic risk score was negatively correlated with the infiltration level of activated CD8 T cells, activated dendritic cells, natural killer cells, and macrophages and immune checkpoint molecules including PD-L2, CTLA4, TIM-3, TIGIT, and B7-H3. Conclusion: These results suggested that the glycolytic risk model could accurately and stably predict the prognosis of patients with DLBCL and might unearth the possible explanation for the glycolysis-related poor prognosis.
Towards a Handwritten Text Interpretation Framework for Ancient Spanish Manuscripts

Handwritten Text Recognition is an extensively studied research topic. We implement a widely known binarization method in order to preprocess handwritten text images eﬃciently and accurately, acquiring adequate binary black-white images for later recognition processes. Afterwards, the characters present in the documents are used to train and evaluate deep-learning mechanisms for the recognition task. Our framework provides good source images for the recognition phase in terms of noise removal and processing of low contrast images. Besides, the process of character recognition is also improved by means of deep-learning techniques.
INTRINSIC STABILIZER REDUCTION AND GENERALIZED DONALDSON–THOMAS INVARIANTS

Abstract Let $\sigma $ be a stability condition on the bounded derived category $D^b({\mathop{\mathrm {Coh}}\nolimits } W)$ of a Calabi–Yau threefold W and $\mathcal {M}$ a moduli stack parametrizing $\sigma $ -semistable objects of fixed topological type. We define generalized Donaldson–Thomas invariants which act as virtual counts of objects in $\mathcal {M}$ , fully generalizing the approach introduced by Kiem, Li and the author in the case of semistable sheaves. We construct an associated proper Deligne–Mumford stack $\widetilde {\mathcal {M}}^{\mathbb {C}^{\ast }}$ , called the $\mathbb {C}^{\ast }$ -rigidified intrinsic stabilizer reduction of $\mathcal {M}$ , with an induced semiperfect obstruction theory of virtual dimension zero, and define the generalized Donaldson–Thomas invariant via Kirwan blowups to be the degree of the associated virtual cycle $[\widetilde {\mathcal {M}}]^{\mathrm {vir}} \in A_0(\widetilde {\mathcal {M}})$ . This stays invariant under deformations of the complex structure of W. Applications include Bridgeland stability, polynomial stability, Gieseker and slope stability.
Routing Protocols for WBAN

The emergence of wireless body area network (WBAN) technology has brought hope and dawn to solve the problems of population aging, various chronic diseases, and medical facility shortage. The increasing demand for real-time applications in such networks stimulates many research activities. Our aim is to provide a tutorial to introduce WBAN routing proto-cols. We classify, and compare the advantages and disadvantages of various routing proto-cols. We also address Emergency health issues and suggest how it can be improved.
NeuroCARE: A generic neuromorphic edge computing framework for healthcare applications

Highly accurate classification methods for multi-task biomedical signal processing are reported, including neural networks. However, reported works are computationally expensive and power-hungry. Such bottlenecks make it hard to deploy existing approaches on edge platforms such as mobile and wearable devices. Gaining motivation from the good performance and high energy-efficiency of spiking neural networks (SNNs), a generic neuromorphic framework for edge healthcare and biomedical applications are proposed and evaluated on various tasks, including electroencephalography (EEG) based epileptic seizure prediction, electrocardiography (ECG) based arrhythmia detection, and electromyography (EMG) based hand gesture recognition. This approach, NeuroCARE, uses a unique sparse spike encoder to generate spike sequences from raw biomedical signals and makes classifications using the spike-based computing engine that combines the advantages of both CNN and SNN. An adaptive weight mapping method specifically co-designed with the spike encoder can efficiently convert CNN to SNN without performance deterioration. The evaluation results show that the overall performance, including the classification accuracy, sensitivity and F1 score, achieve 92.7, 96.7, and 85.7% for seizure prediction, arrhythmia detection and hand gesture recognition, respectively. In comparison with CNN topologies, the computation complexity is reduced by over 80.7% while the energy consumption and area occupation are reduced by over 80% and over 64.8%, respectively, indicating that the proposed neuromorphic computing approach is energy and area efficient and of high precision, which paves the way for deployment at edge platforms.
Recent Advances in the Synthesis and Applications of Nitrogen-Doped Graphene

As the focus of much attention in multi-disciplinary fields such as physics, chemistry, biomedicine, and materials science, graphene has the following limitations which impede their widespread applications: 1) the gapless electronic structure of graphene would retard their optoelectronic applications, 2) the high surface energy of graphene nanosheets causes them to readily aggregate, consequently losing their unique properties, and 3) the inert surface of graphene makes it difficult to combine with other materials. In order to realize more widespread applications of graphene, it is essential to functionalize graphene physically or chemically, and graphene functionalization is a broad subject being undergoing an intense study. This is because the functionalization cannot only retain the unique intrinsic properties of graphene to a certain extent but also impart new structures and properties to the functionalized graphene. Doping with heteroatoms is one of the most hot-topic research areas regarding the functionalization of graphene, which leads to the breakage of the original symmetry and ordered honeycomb structure and to the rearrangement of the crystal structure of graphene. Compared to other non-metal heteroatoms, nitrogen has a size closer to carbon, revealing a higher compatibility of nitrogen with the lattice structure of graphene. Hence, nitrogen can be more easily doped into the graphene lattices, producing nitrogen-doped graphene (NG) that is more stable in comparison with other heteroatom-doped graphene. More importantly, the incorporation of nitrogen would enhance the electronegativity of graphene materials, attributed to the generated N-C bond where the adjacent carbon atoms are endowed with more positive charges. The enhancement of the electronegativity facilitates catalytic redox reactions. These characteristics of NG lead the research and applications of NG to become an important direction in various fields. This review article summarizes various NG preparation methods in recent years, and compares the merits and demerits of these preparation methods. In addition, the applications of NG in catalysis, supercapacitors, photocatalysis, biosensing, and antibacterial, etc., are reviewed, and the bottleneck in the current stage and the future prospect are also pointed out. The review paper presented here paves the way for the development of more high-performance NG-based materials for addressing both fundamental and technical problems and challenges in both scientific and industrial communities.
Bira Atıkları ve Değerlendirme Yöntemleri

Atık yönetimi, biracılık sektörü de dahil olmak üzere tüm gıda endüstrisi için çevre bileşenlerinin korunması, kirliliğin önlenmesi ve ekonomik üretim için kritik bir öneme sahiptir. Çevre kirliliği, dünya nüfusundaki artış ve buna bağlı olarak daha verimli üretim yöntemlerinin geliştirilmek zorunda olması gibi nedenlerle atık ve yan ürünlerin geri kazanılması ve bu atıklardan katma değeri yüksek ürünlerin üretilmesi zorunlu hale gelmektedir. Bira üreticileri, atıkların yönetimi için ülkeler tarafından yürürlüğe sokulan çevre ile ilgili yasal düzenlemelere uymak zorunda olup, bu konuda ciddi yatırımlar yapmaktadırlar. Bira sanayi için olumsuz bir maliyet unsuru olan organik yapıdaki bira sanayi atıkları, biyoteknolojik süreçler için ucuz maliyeti ve zengin kimyasal kompozisyonu ile gelecek vaat eden bir ham madde kaynağını oluşturmaktadır. Bu çalışmada, bira üretimi sonucunda ortaya çıkan çeşitli atık ve yan ürünler tanımlanmış ve bu atıklar ile yan ürünlerin biyoteknolojik yöntemlerle değerlendirilme olanakları incelenmiştir. Waste management is a critical and important issue for the food industry related to the issues of environmental protection, pollution prevention and process economy. Utilization of waste and by-products for the production of high value-added products becomes compulsory due to economic and environmental issues. Since brewery industry generates large amounts of organic wastes, breweries make investments for the management of wastes according to the environmental regulations forced by the rules of the countries. Although brewery waste is a negative cost factor for brewery industry, its rich content of complex carbohydrates, nitrogen and minerals opens avenues to the production of biotechnological products such as biofuel, enzymes, organic acids and bioactive compounds. In this review, different types of brewery waste and by products are defined and alternative valorization methods and utilization strategies in biotechnological processes are explained.
Association between fatigue and cytokine profiles in patients with ischemic stroke

Background Chronic fatigue is a common symptom after a stroke. Studies suggested that chronic fatigue is caused by inflammatory or immunological processes but data are limited and contradictory. Thus, the present study aimed to identify specific biomarkers associated with fatigue in post-stroke patients and replicated the findings in a population-based study. Methods We investigated associations between 39 circulating biomarkers of inflammation and fatigue in 327 patients after an ischemic stroke included in the Stroke Cohort Augsburg (SCHANA) study and the “Metabolism, Nutrition and Immune System in Augsburg” (MEIA) study (n = 140). The Fatigue Assessment Scale (FAS) was used to assess the severity of fatigue. The serum concentrations of the biomarkers were measured using the Bio-Plex Pro™ Human Cytokine Screening Panel (Bio-Rad, USA). Multiple linear regression models adjusted for possible confounders were used to examine associations. Results In patients with stroke, SCGFb was inversely associated [−1.67, 95% confidence interval (CI) (−3.05; −0.29) p = 0.018], and in healthy subjects, G-CSF was positively associated [1.56, 95% CI (0.26; 2.87), p = 0.020] with an increasing FAS-score, while SCF was positively related in both samples [1.84, 95% CI (0.27; 3.42), p = 0.022 and 1.40, 95% CI (0.29; 2.52), p = 0.015]. However, after correction for multiple testing, all of these associations lost statistical significance. Conclusion The present findings suggested an association between the growth factor SCF and fatigue. Future research on cytokines as possible markers of fatigue should focus on a longitudinal design including a sufficiently large number of study participants to enable testing associations between certain cytokines and sub-groups of chronic fatigue.
A REVIEW ON PERSON RE-IDENTIFICATION TECHNIQUES

: In investigation, person re-identification is also a tough task of matching persons determined from utterly completely different camera views. It is necessary applications in AI, threat detection, human trailing and activity analysis. Person re-identification is also a tough analysis topic as a result of partial occlusions, low resolution images and massive illumination changes. Also, person determined from utterly completely different camera views has very important variations on poses and viewpoints. This paper summarizes the challenges related to the person re-identification jointly discuss varied techniques utilized person re-identification.
UDC 33 . 02 : 005 . 342 JEL Classification : А 10 , D 41 , L 20 , M 11 , М 21 DOI : 10 . 15587 / 2312-8372 . 2018

Об’єктом дослідження є економічні процеси, які забезпечують створення належних умов формування конкурентоспроможності підприємства на засадах стратегічного планування. Найбільш проблемними моментами при формуванні конкурентоспроможності є формулювання її критеріїв з позиції сильних і слабких сторін потенціалу підприємства, а також можливостей і загроз, які воно може мати в зовнішньому середовищі. А також, труднощі дослідження рівня негативного впливу зовнішніх чинників конкурентоспроможності об’єктивного характеру. Дослідження проведені із застосуванням діалектичного підходу, згідно якого сукупність кількісних та якісних характеристик підприємства в єдності з економічними відносинами, розглядається як умова збільшення обсягів виробництва конкурентоспроможної продукції, на засадах стратегічного планування. За допомогою комплексного підходу вибудовано концептуальну модель впливу стратегічного планування на рівень конкурентоспроможності підприємства. В ході дослідження визначено, що ринковий механізм, в умовах якого функціонують підприємства, не в змозі створити конкуренцію чи конкурентне середовище, оскільки виробництво продукту здійснюється не ринком, а господарюючими суб’єктами. Відтак, конкуренцію доцільно розглядати, як основоположний засіб реакції ринку, на необхідність врегулювання ситуації протистояння на ньому. Конкурентоспроможність підприємства доцільно трактувати як сукупність його якісно-кількісних ресурсних параметрів та чинників, які за співставимими умовами, або наближених до них, з іншими підприємствами забезпечують виробництво конкурентоспроможної продукції. Авторами удосконалено трактування терміну «стратегічне планування». Стратегічне планування – це системний, чітко скоординований шлях до управління змінами, з метою досягнення гармонійного розвитку підприємства та формування спільного бачення його майбутнього. Це взаємоузгоджений механізм окреслення та визначення проблем, погодження реалістичних цілей, завдань і стратегій, реалізація яких сприятиме їх вирішенню та формуванню конкурентоспроможності підприємства. Визначено основні професійні компетенції головного менеджера, які відображають інтегрований результат взаємодії його фахових навиків та безпосередньо впливають на ефективність формування стратегічного плану. Ключові слова: конкурентне становище підприємства на ринку, конкурентоспроможність підприємства, стратегічне планування, професійні компетенції менеджера. Pavlenchyk N. , Horbonos F. , Pavlenchyk A. , Skrynkovskyy R.
Divergent coupling mechanism of precipitation on plant community multifunction across alpine grassland on the Tibetan Plateau

Introduction It is essential to understand plant adaptive strategies on plant stoichiometric traits at the species level rather than at the community level under various environmental conditions across the Tibetan Plateau (TP). Methods Here, plant community function and edaphic and meteorological factors were collected at 111 sites along an extensive water–heat gradient during the peak growing season in 2015. Community-weighted mean trait (CWM) was introduced to illuminating dynamics of the functional trait at the community level. Results Our results indicated that plant functional traits, including CWM-leaf total carbon (CWM_LTC), CWM-leaf total nitrogen (CWM_LTN), and CWM-leaf total phosphorus (CWM_LTP), showed similar and comparatively marked increases from alpine meadow (AM) to alpine steppe (AS). Moreover, since the tightly coordinated variation among each plant functional trait of AM was higher than that of AS, a more stable coupling mechanism of these plant functional traits could be observed in AM under a long-term evolutionary habit. Specifically, there was higher annual mean precipitation (AMP) in AM than that in AS significantly (P < 0.01), and AMP was significantly correlated with soil moisture and soil total phosphorus in AM. Generally, our findings suggest that precipitation determines divergent coupling plant community function in both AS and AM.
Consideration the Effect of Exchange Rate Volatility on Agricultural Products Export Price, the Case Study of Iran’s Saffron

Iran as the major producer and exporter of Saffron in the world has devoted a considerable production, cultivated area andworld export value of Saffron to itself. In this study, the effects of short and long-run fluctuations of exchange rate were considered on Saffron export price by Autoregressive Distributed Lag model (ARDL). Results showed that the fluctuations of exchange rate have affected Saffron export price more than other variables under study. The effect of exchange rate and quantity of export, on Saffron export price was positive and significant in long-run. Furthermore there was no significant relationship between export price and domestic production of Saffron. According to finding, some policies such as control and stabilization exchange rate come into sight constructive in order to increase Saffron export.
Convolutional Neural Network for Link Prediction Based on Subgraphs in Social Networks

Link Prediction (LP) in social networks (SN) is referred to as predicting the likelihood of a link formation in SNs in the near future. There are several types of SNs that are available such as human interaction network, biological network, protein-toprotein interaction network, and so on. Earlier LP researches used heuristics methods, including Common Neighbors, Resource Allocation, and many other similarity score methods. Even though heuristics methods perform better in some types of SNs, their performance is limited in other types of SNs. Finding the best heuristics for a given type of SN is a trial and error process. Recent state-of-the-art research, WLNM and SEAL showed that with deep learning techniques and subgraphing, the heuristics selection could be automated and increase the accuracy of LP. However, WLNM and SEAL have some limitations and still having performance lack in some types of SNs. The objective of this paper is to introduce a novel framework that overcomes the limitations of state-of-the-art methods and improves the accuracy of LP over various types of social networks. We propose a Link Prediction framework called PLACN that analyzes common neighbors based subgraphs using deep learning technique to predict links. PLACN is equipped with two new algorithms that are a subgraph extraction algorithm that efficiently extracts common neighbors of targeted nodes and a proposed new node labeling algorithm based on hop number and average path weight that creates consistent node orders over subgraphs. In addition to the algorithms, we derived a formula based on network properties to find an optimal number node for a given SN. PLACN converts the LP problem into an Image Classification problem and utilizes a Convolutional Neural Network to classify the links. We tested the proposed PLACN on seven different types of real-work networks and compared the performance against heuristics, latent methods, and state-of-the-art methods. Our results show that PLACN outperformed the compared Link Prediction methods while reaching above 96% AUC in tested benchmark social networks.
Direct Capture of CO2 from Air & Scale-Up it for Sustainable Developments

Environmentalists and climate specialists today recognize need for techniques that remove CO2 from atmosphere to mitigate changes to the global climate caused by over a century of expanding anthropogenic CO2 emissions. Traditional modes of carbon capture; such as pre-combustion and postcombustion CO2 capture from large point sources can help to slow the rate of increase of atmospheric CO2 concentration. However, the direct removal of CO2 from the air, or “direct air capture” (DAC), can actually reduce the global atmospheric CO2 concentration. The past decade has seen a steep rise in use of chemical sorbents that are cycled through sorption and desorption for CO2 removal from ultra-dilute gases such as air. This paper provides a historical overview of the field of DAC, along with an exhaustive description of the use of chemical sorbents and its alternative uses, applications in CO2 Capturing. When developing technologies, there are number of steps includes; the development of commercial process, optimization of process, scale-up from the bench to a pilot plant, and from the pilot plant to the full scale process. Reasons for these critical steps include; understanding potential waste streams, examination of macro-processes, process interactions, process variations, process controls, development of standard operating procedures, etc. Solvents and solid sorbents that interact strongly with CO2 are described, including basic solvents, supported amine and ammonium materials, and “metal organic frameworks” (MOFs), as the primary classes of chemical sorbents. Hypothetical processes for the deployment of such sorbents are discussed, concluded that many new materials could play role in emerging DAC techniques. Index Terms :– CO2, Air-Pollution, DAC, MOFs, Kyoto Protocol, Global Warming, Scale-UP, & Sustainable Development.