Miracle-dz/UniFAR-FTUs huggingface dataset:
What have we Learned from Necosad? Practical Implications for Peritoneal Dialysis Patients

Correspondence to: J.C. Korevaar, Department of Clinical Epidemiology & Biostatistics (J1B-209), Academic Medical Center, University of Amsterdam, P.O. Box 22660, 1100 DD Amsterdam, The Netherlands. j.korevaar@amc.uva. nl Received 4 November 2005; accepted 23 June 2006. a A.J. Apperloo, J.A. Bijlsma, M. Boekhout, W.H. Boer, H.R. Buller, F.Th. de Charro, C.W.H. de Fijter, C.J. Doorenbos, W.J. Fagel, G.W. Feith, L.A.M. Frenken, P.G. G. Gerlag, J.P.M.C. Gorgels, W. Grave, R.M. Huisman, K.J. Jager, K. Jie, W.A. H. Koning-Mulder, M.I. Koolen, T.K. Kremer Hovinga, A.T. J. Lavrijssen, A.J. Luik, K.J. Parlevliet, M.H. M. Raasveld, M.J.M. Schonck, M.M.J. Schuurmans, C.E. H. Siegert, C.A. Stegeman, P. Stevens, J.G. P. Thijssen, R.M. Valentijn, M. van Buren, M.A. van den Dorpel, P.J.M. van der Boog, J. van der Meulen, F.M. van der Sande, A. van Es, J.A. C.A. van Geelen, G.H. Vastenburg, C.A. Verburgh, H.H. Vincent, P.F. Vos. The Netherlands Cooperative Study on the Adequacy of Dialysis (NECOSAD), a large prospective cohort study, was started in 1997. A majority of Dutch dialysis centers participated in this study. The aims of NECOSAD were to monitor the quality and adequacy of dialysis treatment and to provide evidence for optimal treatment of dialysis patients. Within NECOSAD, a sizeable variation in clinical and treatment characteristics was observed. These differences were due partly to differences in case-mix but mainly to differences in treatment regimes between centers. Due to these differences in usual care, we were able to study similar patients who obtained a different treatment regime. At this moment, several important issues regarding optimal dialysis treatment have been studied: the effect of the start of dialysis treatment, adequate dialysis dose, and evaluation of international guidelines, with special attention to comparisons between hemodialysis (HD) and peritoneal dialysis (PD) patients. All end-stage renal disease (ESRD) patients included in NECOSAD were new on dialysis treatment and it had to be their first renal replacement therapy. Clinical and treatment characteristics and data on important patient outcomes, such as mortality, morbidity, and health-related quality of life (HRQOL) were collected at 6-month intervals. Patients were followed until death, transplantation, or refusal of the patient to continue participation. Patient inclusion and follow-up are, at this time, still going on. By August 2005, 1983 patients had been included; 71% started with HD and 29% with PD. Median followup time was 25 months (interquartile range: 12 – 45 months) and maximum follow-up was 8.5 years. Over 400 patients have a follow-up period of at least 4 years. So far, 763 (39%) patients have died, 494 (25%) have received a kidney transplant, and 10% have refused further participation. Two-year survival of all included patients is 75%; 2-year survival is 80% for patients starting with PD and 73% for patients starting with HD.
CO2/HCO3−- and Calcium-regulated Soluble Adenylyl Cyclase as a Physiological ATP Sensor*

Background: The affinity of soluble adenylyl cyclase (sAC) for its substrate ATP suggested that it might be sensitive to fluctuations in ATP. Results: In sAC-overexpressing and glucose-responsive cells, sAC-generated cAMP reflects intracellular ATP levels. Conclusion: sAC can be an ATP sensor inside cells. Significance: sAC serves as a metabolic sensor via regulation by three cellular metabolites: ATP, bicarbonate, and calcium. The second messenger molecule cAMP is integral for many physiological processes. In mammalian cells, cAMP can be generated from hormone- and G protein-regulated transmembrane adenylyl cyclases or via the widely expressed and structurally and biochemically distinct enzyme soluble adenylyl cyclase (sAC). sAC activity is uniquely stimulated by bicarbonate ions, and in cells, sAC functions as a physiological carbon dioxide, bicarbonate, and pH sensor. sAC activity is also stimulated by calcium, and its affinity for its substrate ATP suggests that it may be sensitive to physiologically relevant fluctuations in intracellular ATP. We demonstrate here that sAC can function as a cellular ATP sensor. In cells, sAC-generated cAMP reflects alterations in intracellular ATP that do not affect transmembrane AC-generated cAMP. In β cells of the pancreas, glucose metabolism generates ATP, which corresponds to an increase in cAMP, and we show here that sAC is responsible for an ATP-dependent cAMP increase. Glucose metabolism also elicits insulin secretion, and we further show that sAC is necessary for normal glucose-stimulated insulin secretion in vitro and in vivo.
Design and Implementation of SDN-Based Underwater Acoustic Sensor Networks With Multi-Controllers

Underwater acoustic sensor networks (UASNs) provide new opportunities for exploring oceans and consequently improving our understanding of the underwater world. UASNs usually rely on hardware infrastructure with poor flexibility and versatility. Compared with wireless sensor networks, UASNs are quite expensive to manufacture and deploy. Due to the unique data format, protocols, and service constraints of various applications, UASNs are typically deployed in a redundant manner, which not only leads to waste but also causes serious interference due to the presence of multiple signals in the same underwater region. Software-defined networking (SDN) provides an innovative means of improving the flexibility of underwater systems. In this paper, we present an SDN-based UASN framework, followed by the design of a clustering method in which learning automata and degree-constrained connected dominating sets are employed. We then propose a load balancing mechanism involving multiple controllers, based on the consistent hashing algorithm. Finally, we describe a simulation program (called UASNs hypervisor) that we developed and implemented to assess the network survival time, bit-error rate, and computational complexity. The experimental results show that the UASN was improved significantly. This work provides important theoretical and technical support for the implementation of SDN-based UASNs.
Learning causality and causality-related learning: some recent progress.

much data there are for training, there always exist cases that the training data cannot cover. How to deal with the long tail problem poses a significant challenge to deep learning. By resorting to deep learning alone, this problem would be hard to solve. Language data is by nature symbol data, which is different from vector data (real-valued vectors) that deep learning normally utilizes. Currently, symbol data in language are converted to vector data and then are input into neural networks, and the output from neural networks is further converted to symbol data. In fact, a large amount of knowledge for natural language processing is in the form of symbols, including linguistic knowledge (e.g. grammar), lexical knowledge (e.g. WordNet) and world knowledge (e.g. Wikipedia). Currently, deep learning methods have not yet made effective use of the knowledge. Symbol representations are easy to interpret and manipulate and, on the other hand, vector representations are robust to ambiguity and noise. How to combine symbol data and vector data and how to leverage the strengths of both data types remain an open question for natural language processing. There are complex tasks in natural language processing, which may not be easily realized with deep learning alone. For example, multi-turn dialogue amounts to a very complicated process. It involves language understanding, language generation, dialogue management, knowledge base access and inference. Dialoguemanagement can be formalized as a sequential decision process and reinforcement learning can play a critical role. Obviously, combination of deep learning and reinforcement learning could be potentially useful for the task, which is beyond deep learning itself. In summary, there are still a number of open challenges with regard to deep learning for natural language processing. Deep learning, when combined with other technologies (reinforcement learning, inference, knowledge), may further push the frontier of the field.
Lean Metabolic Syndrome: An Emerging Concept

The prevalence of metabolic syndrome (MetS) in India is reported to be up to 29.7%, quite high as compared to other south-east Asian countries. [1] The concept of MS was to identify a clustering of cardiovascular (CV) risk factors which predisposed subjects to risk of developing cardiovascular disease (CVD) and type 2 diabetes (T2DM), so that early effective preventive measures can be implemented. The original accepted criteria for the MetS were based on risk prediction in the non-Asian Indian populations. Therefore, there is a continuous debate regarding the optimum definition which is most sensitive and universally applicability in different populations like Asians. National Cholesterol Education Program Adult Treatment Panel III recommends that three of five clinical and/or biochemical abnormalities should be present to satisfy labeling of MetS, whereas the International Diabetes Federation (IDF) definition requires abdominal obesity as an obligatory criterion and presence of at least two other abnormal criteria. The recent data by Mukhopadhyay et al. in the original article “Lean metabolic syndrome: A concept or a reality? ” suggests that even the current IDF Asian Indian cut-offs of waist circumference (WC) may not be sensitive for predicting the risk of metabolic disorders in all segments of our population. [2]
The Breakdown of Skeletal Muscle in Dairy Cows During Peak Lactation

The decline in fertility in dairy cows is of international concern. Since 1950, milk production demands have increased while first service rates of conception have decreased. It is unclear why fertility has decreased, however current dairy management practice requires cows to be kept on a tight yearly calving schedule to ensure maximum milk production over the lifetime of the cow. The current postulate suggests that this regime places a high metabolic burden on the cows, which in turn requires breakdown of tissues such as fat and muscle to provide substrates to meet the increased energy demands of lactation. Immediately after calving, dairy cows enter a state of negative energy balance (EB), as they cannot consume enough energy to sustain lactation. During this period of negative EB, fat is mobilised in the form of nonesterified fatty acids to help supply the body with the extra energy it needs, but fat mobilisation decreases after four weeks while cows remain in a state of negative EB for several more weeks. It is unclear whether or not muscle breakdown occurs and plays a role in the restoration of EB in lactating dairy cows. I hypothesized that the breakdown of muscle does occur in cows during peak lactation, and that it occurs to a greater extent in cows producing higher amounts of milk. Dairy cows from three strains, NZL, NZH and OSH, representing cows with differing milk production abilities (low, intermediate and high, respectively), were studied for 12 weeks postpartum. Blood was drawn at
Invited Synthesis Paper : Implications of rising atmospheric carbon dibxide concentration for rangelands FL

Extensive ragelends and other vegetario” types that we know today formed while atmospheric carbon dioxide (CO3 confentratian was kw (50 to 75% of today’s cmtceotmtio”). Fmsil fuel bum@ and deforestation and other land use changes during the last 200 yep~ have incwased CO, mncentrntio” by about 30’5, to tbe present 360 parts per million (ppm). Atmospheric CO, will continue to rise during the next century, possibly to Q)“ce”tratioos that are unprecedented for the last several million year%. Much of the p”tenttal impwta”ce of co, co”ce”tmtio” to vegetation derives from its influence on plant carbon balance and water relations. Plants grow by assimilating CO2 that diEuses into leaves through stomata1 pores. Inevitably associated with CO2 uptake is tmospimtional loss of water vapor through stomata Traospimtio” rates usually decline as CO2 increases, while, in many plants, photosynthesis and growth increase. These “primary” responses to CO2 can lead to a multitude of changes at the plant and emsystem levels, ranging from alteration of the cbemical armposition of plant tissues to changes in csosystem function and tbe species composition of plant commuoitieg The direct physiological responses of plants to CO2 snd expsioo of these respooses at higher scales differ amoag species and gmwi”g cooditions. Gmwtb response to CO* is usually highest io rapidly-growing plants that quickly export the carbohydrates formed in leaves and use them for storage or “ew growth axad allocate a high proportion of fired carbon to produce leaves. Growth is also m”re responsive to CO2 in plants with the Cj (most woody plants and ‘cool-season’ g rasses) than c, pbotosy”thetic pathway (most ‘warm-seas”“’ grasses). These and other dit%reoces among species could lead to changes in the compositio” of rangeland vegetation, but generaliitions are dficnlt. 0” many rangelands, species abundances are detemained more by morphological and pbenological attributes that influence plant access to esseotbd resources like nitmge” and light and reaction to fire, grazing, and other disturbances than by physiological traits that are sensitive to CO2 coocentratio”. Species aomposi562 tio” probably will be most responsive t” CO2 o” moderately watedimited and disturbed rangelands where multiple pmitive effects of CO2 on plant water relations can be expressed sod mmpetiti”” for light is minimized. Greatest initiinl changes in species composition likely will occur DO C&,C, grasslands and at the transition between grasslads and woodlands. PLant pmdnctio” should also inwease on water-limited rangelands, bet CO, may have little innuence on production when nutrient elements like nitrogen are severely limitiog.
Classification of Sparsity Patterns and Performance Evaluation in OLAP Systems (データベースシステム研究報告 夏のデータベースワークショップ DBWS2002)

In fact, input data of OLAP is usually very sparse and it causes data explosion phenomenon in precomputation of summary tables. With very sparse data, space for all possible cells defined as combinations of dimension members in multidimensional cube has to be assigned although they have no actual data. Therefore, this takes a huge amount of space, regardless of the storage efficiency or the database technology used and decreases response time of multi-dimensional queries [7,8]. Several approaches have been presented for handling sparsity of OLAP like chunk-based array structure [9,10], composite dimension method [11] and sparsedense split method [12]. Although these techniques can handle the sparse data with a fixed pattern, they have no general solution to the mixed type of sparsity patterns. However, actual data in applications of OLAP represents complex sparsity patterns so more generalized approaches have to be considered. OLAP performs the applications that interact with users and require fast response time over large amounts of data of data warehouse. Because these applications need a lot of multi-dimensional aggregation, it can take many hours or even days to run if executed directly on the raw data. Therefore, various kinds of precomputation techniques are proposed to handle this performance problem. However, sparse data of OLAP causes the data explosion problem in precomputation process and decreases the performance of OLAP. In this paper, we discuss the sparsity of OLAP data and the performance bottleneck problem. As a fundamental research on sparsity handling problem, we define the sparsity patterns and develop an automated data generation program according to the sparsity patterns. We evaluate the performance of two OLAP products, MS SQL Server 2000 Analysis Services and Pilot DSS (Decision Support Suite) with the generated data. Focusing on the sparsity handling ability, evaluation results also are presented. In this paper, we investigate sparsity of OLAP data and related performance problem. For the fundamental research to the generalized sparsity controlling methods, we define the sparsity patterns of OLAP data, especially in two and three dimensional data model. In addition, an automated data generator according to the defined sparsity pattern is implemented. Using generated test data, we evaluate two commercial OLAP products, MS SQL Server 2000 Analysis Services and Pilot DSS, we analyze the performance of commercial OLAP products with various sparsity patterns
Energy-Efficient SRAM Design with Data-Aware Dual-Modes L0T Storage Cell for CNN Processors

Energy Efficient CNN processors for IOT (Internet of Things) terminals are becoming more and more attractive. For these battery-powered applications, the ultra-low-power circuits design is of great importance. Because memory accessing has become the main power bottleneck of a system, this paper proposes a new single-ended-read l 0T SRAM cell based on the statistical characteristic of data of CNN algorithms. In the proposed storage cells of SRAM, pull-up and pull-down paths are both included for two read modes for different data distributions of ‘0^{\prime}/^{\prime}1^{\prime}, which are precharge mode and predischarge mode. Moreover, much less energy is consumed while reading bit ‘1’ in precharge mode or reading bit ‘0’ in predischarge mode. The operation mode is determined by the proportion of two kinds of bits stored in one column. Results on different datasets and algorithms show an up to 87% reduction in switching energy of bit line, compared with conventional SRAM cells in SMIC 65nm low-leakage CMOS technology, with little extra area cost.
The Detection of Fimbrial Pathogenic Genes in E. coli Strains Isolated from Patients with Urinary Tract Infection

هدیکچ فده و هنیمز : قـفوم نویـسازینولک رد لـحارم نیرت یساسا ءزج ینابزیم حوطس هب لاصتا ییاناوت دشاب یم یبورکیم ياهنژوتاپ . ياه هیوس E.coli يداـیز دادـعت ياراد يراردا هاگتـسد تـنوفع هدـننکداجیا عون هیربمیف لثم هیربمیف I ، P ،هیربمیف S ،هیربمیف FIC ،هیربمیف Dr یـم لاـیربمیفا ياـه نیسهدا و هیربمیف دنشاب . تنوفع دلوم یلک ایشرشا رد اه هیربمیف هدننک دک ياه نژ یناوارف نییعت فده اب هعلاطم نیا رد يراردا تسا هدش ماجنا نارهت . یسررب شور : قیقحت نیا رد 363 هنومن یـط رد يراردا هاگتـسد تـنوفع هب لاتبم دارفا زا يراردا ياهلاس 89 87 دـش يروآ عـمج نارـهت برـغ ینیلاب ياههاگشیامزآ زا . ییایمیـشویب ياهـشور زا هدافتـسا اـب درادناتسا E.coli ندوب 200 دیدرگ دییات هنومن . سپ جارختـسا زا DNA شور هـب اـه هـنومن زا Boiling ، ایربمیف ياهنژ روضح fim ,sfa ,pap ,foc ,afa شور هب PCR تفرگرارق یسررب دروم . هتفای اه : هـیربمیف ياـهنژ عویش نازیم pap ,sfa, afa, foc, fim نیـب رد 200 بـیترت هـب هیوـس 188 ) 94 (% ، 34 ) 17 (% ، 20 ) 10 (% ، 62 ) 31 (% ، 71 ) 35,5 (% یعت دیدرگ نی . يریگ هجیتن : نژ هک داد ناشن هعلاطم نیا جیاتن fim و pap هـیریمیف هدـننک دک ياهنژ نیرتعیاش دشاب یم نارهت رهش رد يراردا تنوفع زا هدش ادج ياه یلک ایشرشا رد . رد یتاـعلاطا دـناوت یـم هلئسـم نیا تیریدـم ،يراردا تـنوفع یسانش بیسآرد اهنآ تیمها اب هطبار UTI هار و دـیلوت دـننامه یناـمرد ياـهراک دروآ مهارف نیون ياه نسکاو . يدیلک ياه هژاو : یلک ایشرشا،هیربمیف ، يراردا تنوفع یمظان یلع رایداتسا ، هورگ يژولویب ، شناد هاگ دازآ یملاسا دحاو نباکنت
Scheduling in Real-Time Systems

Introduction. 1. Basic Concepts. Real--time applications. Basic notions for real--time task scheduling. 2. Scheduling of Independent Tasks. Basic on--line algorithms for periodic tasks. Hybrid task sets scheduling. 3. Scheduling of Dependent Tasks. Tasks for precedence relationships. Tasks sharing critical resources. 4. Scheduling Schemes for Handling Overload. Scheduling techniques in overload conditions. Handling real--time tasks with varying timing parameters. Handling overload conditions for hybrid task sets. 5. Multiprocessor Scheduling. Introduction. First results and comparison with uniprocessor scheduling. Multiprocessor scheduling anomalies. Schedulability conditions. Scheduling algorithms. 6. Joint Scheduling of Tasks and Messages in Distributed Systems. Overview of distributed real--time systems. Task allocation in real--time distributed systems. Real--time traffic. Message scheduling. Conclusion. 7. Packet Scheduling in Networks. Introduction. Network and traffic models. Service disciplines. Work--conserving service disciplines. Non--work--conserving service disciplines. 8. Software Environment. Real--time operating system and real--time kernel. Real--time languages. Real--time middleware. Summary of scheduling capabilities of standardized components. 9. Case Studies. Real--time acquisition and analysis of rolling mill signals. Embedded real--time application: Mars Pathfinder mission. Distributed automotive application.
African literatures in English : East and West

Preface. Patronage and the development of East and West African English writing. 1. 18th and 19th Century Slave Narratives of the Black Diaspora 2. 19th and Early 20th Century Histories, Travel Accounts, Political Writing and the Beginnings of Fiction 3. 19th and 20th Century Missionary-Writing Testements and Writings by Africans Produced Under Missionary Auspices and Control 4. 20th Century Secular Patronage: Literature, Education and Control 5. 20th Century Popular Culture and English Writing in Africa Dominant themes and Patterns in East and West African Writing in English 6. Alternatives: Contrasting Themes and Patterns in Post-Independence Africa Writing in English 7. Self-Criticism and Post-Independence Disillusion 8. Opposition, Resistance and Decolonisation 9. Internal Discord. Alternative Voices in East and West African English Writing. 10. Minority Voices: English Writing in Non-Anglophone East and West African Countries 11. Women's Voices: Gendered Pasts, Liberated Futures 12. New Transcultural Voices in East and West African Writing References. Chronology of Literary and Historical Events General Bibliography Individual Authors: Notes on Biography, Works and Criticism
BEWARE THE WILD GOOSE CHASE: THE EFFECT OF EX ANTE RISK ON ENTREPRENEURIAL DECISION-MAKING

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