Miracle-dz/UniFAR-FTUs huggingface dataset:
A Study of Microbiological analysis and its sensitivity pattern of Postoperative Wound Infections

Nosocomial infection constitutes a major public health problem worldwide today. SSI are the second most common nosocomial infections (Biadglegne et al., 2009; Nichols, 2001; Insan et al., 2013). These remain a complication of surgical procedure resulting in increase morbidity, mortality and cost (Insan et al., 2013). One of the major problems faced by patients in now a day is to deal with the post surgical infections, as most of these are being caused by multiple drug resistant bacteria (Raza and Ranabhat, 2013). The most common types of nosocomial infections that occur in a hospital set up are surgical wounds and other soft tissue infections, urinary tract infections, respiratory and blood stream infections (Insan et al., 2013). SSIs occurs when a bacteria is present within a wound. The bacteria may be transferred by contact from surgeons or nurse s hands, the bacterial could be airborne during surgery, and or the patient may come in contact with bacteria after surgery (NINSS, 2001). According to Nichols (2001) infection varies from surgeon to surgeon, hospital to hospital, from one surgical procedure to another, and most importantly from one patient to another.
‘Be prepared! : motivational interviewing as pre-treatment in chronic pain rehabilitation

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Cross Sectional Study on Health Problems among Elderly inmates of Old age Homes in Urban areas of Chennai, India

Introduction: Elderly age also known as senior citizens are increasing in number in Developing countries. The care of the elderly aged is slowly shifting from the family to community level old age homes for those who are financially poor, lacking family care or the destitute. The health problems of the elderly in most of the developing countries who are institutionalized for shelter, health care, rehabilitation and recreation are not known adequately. This study is planned to estimate the problems among old age homes in urban area. Objective: To find prevalence of common Health problems among inmates of Old age homes in North zone of Chennai. Materials and Methods: A Cross sectional study was done among 450 elders in 19 old age homes during April to December 2012 for eight months. Subjects were selected by Simple random technique and the data were collected using standardized pre tested questionnaire after getting consent from the elders. The diagnosis made by clinical examination and some were confirmed by medical records possessed by the subjects. Results : Elders of 60-69 years constituted the maximum percentage of 47%. Number of females is more than the number of males and most of the elders were from upper lower socio economic status. The prevalence of individual health problems was Visual problems 67% followed by Hypertension 54%, Depression 45%, Arthritis 43% , Diabetes mellitus 32% and Hearing problems 24%. The overall prevalence of Visual impairment and Blind was 46 % and 21 % respectively. Conclusion: The prevalence of health problems among the inmates of old age homes are high and periodic health check up to identify the morbid conditions at the early stage and adequate treatment will lead them to have better quality of life.
Themis: Fair and Efficient GPU Cluster Scheduling for Machine Learning Workloads

Modern distributed machine learning (ML) training workloads benefit significantly from leveraging GPUs. However, significant contention ensues when multiple such workloads are run atop a shared cluster of GPUs. A key question is how to fairly apportion GPUs across workloads while ensuring overall cluster efficiency. We find that established cluster scheduling disciplines that provide instantaneous fair share of resources are a poor fit because of ML workloads' unique attributes. ML jobs are typically long running, have coarse grained tasks that need to be gang-scheduled, and their performance is sensitive to tasks' relative placement. These properties cannot be captured by existing fair sharing schemes. We propose Themis, a new scheduling framework for ML training workloads. It's GPU allocation policy enforces that ML workloads complete in a finish-time fair manner, a new notion we introduce. To capture placement sensitivity and ensure efficiency, Themis uses a two-level scheduling architecture where ML workloads bid on available resources that are offered in an auction run by a central arbiter. Our auction design allocates GPUs to winning bids by trading off efficiency for fairness in the short term but compensating for finish-time fairness in the long term. Our evaluation on a number of machine learning models shows that Themis can ensure greater fairness while providing more efficient allocations compared to state-of-the-art schedulers.
Primary Grade Children’s Capacity to Understand Microevolution: The Power of Leveraging Their Fruitful Intuitions and Engagement in Scientific Practices

We investigated second and third graders’ capacity to understand microevolution, given a learning progression leveraging intuitions to build more adequate explanations, problematizing core ideas within scientific practices, and in-depth study of a single domain (botany or animal behavior). The intervention was implemented in both researcher-taught summer school and regular school-year classrooms in two consecutive years, allowing refinement of instruction mid-course, as well as comparison of learning outcomes. A case study examined how the pedagogy functioned within a strategically selected segment of curriculum enactment. Analysis of pre- and post-instruction interviews, using mean scaled estimates and maximum learning progression levels, revealed significant conceptual advancements for all cohorts across both sites and years. After Year I, a majority of children in researcher-taught and regular classrooms could predict and explain the impact of traits’ differential survival advantage on next-generation shifts in relative frequency given particular environmental change. After Year II, the majority in each cohort formed normative predictions and explanations of population changes across multiple generations. This pattern reflects a basic understanding of microevolution, providing evidence of the capacity of primary grade children to understand abstract ideas and, more generally, to benefit from much more conceptually and epistemically ambitious curriculum than current science standards recommend.
HEPATITIS C VIRUS INFECTION IN HEMODIALYSIS PATIENTS: COMPARISON OF THE SURABAYA DIALYSIS CENTER AND JUNTENDO UNIVERSITY HOSPITAL DIALYSIS CENTRE

Hepatitis C virus infection is highly prevalence in chronic hemodialysis (HD) patients. The present study will compare prevalence of HCV positive population in difference countries where there are great contrasts in and diversity of care available to patients who have end stage renal disease. All serum samples of the 100 patients were tested for HCV antibodies, using third-generation enzyme immunoassay. The prevalence of anti-HCV was correlated with a history of blood transfusion and with duration of hemodialysis. HCV prevalences were 88% of Surabaya group and 6% of Juntendo Group, respectively. In Surabaya Group, prevalence of HCV positive was high and the risk factors are not only those of the Juntendo Group, but also a combination of poor living conditions, frequent blood transfusions, and lack of adherence. Much needs to be studied about the role of universal screening and effective techniques for primary prevention in Surabaya Group
Interdependent and Multi-Subject Privacy: Threats, Analysis and Protection

In Alan Westin’s generally accepted definition of privacy [1], he describes it as an individual’s right “to control, edit, manage, and delete information about them[selves] and decide when, how, and to what extent information is communicated to others. ” Therefore, privacy is an individual and independent human right. The great Mahatma Gandhi once said that “interdependence is and ought to be as much the ideal of man as selfsufficiency. Man is a social being. ” To ensure this independent right to inherently social beings, it will be difficult, if not impossible. This is especially true as today’s world is highly interconnected, technology evolves rapidly, data sharing is increasingly abundant, and regulations do not provide sufficient guidance in the realm of interdependency. In this thesis, we explore the topic of interdependent privacy from an adversarial point of view by exposing threats, as well as from an end-user point of view, by exploring awareness, preferences and privacy protection needs. First, we quantify the effect of co-locations on location privacy, considering an adversary such as a social-network operator that has access to this information: Not only can a user be localized due to her reported locations and mobility patterns, but also due to those of her friends (and the friends of her friends and so on). We formalize this problem and propose effective inference algorithms that substantially reduce the complexity of localization attacks that make use of co-locations. Our results show that an adversary can effectively incorporate co-locations in attacks to substantially reduce users’ location privacy; this exposes a real and severe threat. Second, we investigate the interplay between the privacy risks and the social benefits of users when sharing (co-)locations on OSNs. We propose a game-theoretic framework for analyzing users’ strategic behaviors. We conduct a survey of Facebook users and quantify their benefits of sharing vs. viewing information and their preference for privacy vs. benefits. Our survey exposes deficits in users’ awareness of privacy risks in OSNs. Our results further show how users’ individual preferences influence, sometimes in a negative way, each other’s decisions. Third, we consider various types of interdependent and multi-subject data (photo, colocation, genome, etc.) that often have privacy implications for data subjects other than the uploader, yet can be shared without their consent or awareness. We propose a system for sharing such data in a consensual and privacy-preserving manner. We implement it in the case of photos, by relying on image-processing and cryptographic techniques, as well as on a two-tier architecture. We conduct a survey of Facebook users; it indicates that there is interest in such a system, and that users have increasing privacy concerns due to prejudice or discrimination that they have been or could still easily be exposed
AUTOIMMUNE COMPLICATIONS IN CHRONIC LYMPHOCYTIC LEUKEMIA: A SINGLE CENTER EXPERIENCE

Introduction: Chronic lymphocytic leukemia (CLL) is characterized by the progressive accumulation of B cells with mature appearance and a distinctive immunophenotype in peripheral blood, bone marrow, lymph nodes and other lymphoid tissues. Autoimmune complications are common in CLL and by far the most common manifestation is autoimmune hemolytic anemia (AIHA), followed by immune thrombocytopenia (ITP) and pure red cell aplasia (PRCA). The study was carried was carried out to see the spectrum of autoimmune complications in patients diagnosed with CLL. Materials and Methods: This cross sectional study was carried out to describe the clinical presentation, haematological parameters and complications of CLL in patients coming to Shaikh Zayed Hospital (SZH), Lahore over a period of 6 years from June 2002 to July 2008. CLL cases were diagnosed according to the National Cancer Institute (NCI) criteria. The collected data was entered into SPSS version 13 for analysis. Results: Thirty one patients were diagnosed with CLL and in 6 (19.4%) patients the disease was complicated by AIHA and in 1 (3.2%) patient by ITP at presentation. Conclusion: Autoimmune cytopenias were observed in 22% of 31 patients. Autoimmune hemolytic anaemia was more common than ITP, no patients of PRCA were recognized.
Outpatient Management of Deep Vein Thrombosis: a Model with a Breakdown of the Continuum of Care

Objectives For emergency department (ED) patients with deep venous thrombosis (DVT) treated as outpatients, to determine treatment duration, complications and follow-up arrangements, in particular thrombophilia screening. Methods Explicit, retrospective medical record review of patients with an ED discharge diagnosis of DVT over a three-year period. Data collected included demographics, location of DVT, duration of treatment, complications, hospital admission, mortality, follow-up arrangements and referral for thrombophilia screening. Main outcomes were the duration of treatment, rate of complications and requirement for hospital admission, follow-up arrangements and referral for thrombophilia screening for patients treated as outpatients. Results Two hundred and fourteen patients with DVT were identified, of whom 74 [35%] were treated as outpatients. For this group, the median duration of treatment was eight days. There was one death from advanced neoplasia, two proven pulmonary emboli (PE) [2.7%, 95% CI 0.74–9.3%] and one suspected PE [1.4%, 95% CI 0.24–7.3%]. One patient had a recurrence of DVT within three months [1.4%, 95% CI 0.24–7.3%]. Few patients received specialist follow-up [25/74, 34%]. In 20 cases [27%], thrombophilia screening was neither performed nor recommended, despite no obvious cause for the DVT. Conclusion Outpatient treatment of selected DVT is safe, although there is a small incidence of non-fatal PE. Follow-up and screening arrangements were sub-optimal in the study sample and treatment duration was long. An ambulatory care model with direct physician control is recommended to improve follow-up arrangements, monitor service performance and ensure that the service evolves with new evidence and processes.
Bias and beyond in digital trace data

Large-scale digital trace data from sources such as social media platforms, emails, purchase records, browsingbehavior, and sensors in mobile phones are increasingly used for business decision-making, scientificresearch, and even public policy. However, these data do not give an unbiased picture of underlying phenomena.In this thesis, I demonstrate some of the ways in which large-scale digital trace data, despite itsrichness, has biases in who is represented, what sorts of actions are represented, and what sorts of behaviorsare captured. I present three critiques, demonstrating respectively that geotagged tweets exhibit heavy geographicand demographic biases, that social media platforms’ attempts to guide user behavior are successfuland have implications for the behavior we think we observe, and that sensors built into mobile phones likeBluetooth and WiFi measure proximity and co-location but not necessarily interaction as has been claimed.In response to these biases, I suggest shifting the scope of research done with digital trace data away from attemptsat large-sample statistical generalizability and towards studies that situate knowledge in the contextsin which the data were collected. Specifically, I present two studies demonstrating alternatives to complementeach of the critiques. In the first, I work with public health researchers to use Twitter as a means ofpublic outreach and intervention. In the second, I design a study using mobile phone sensors in which Iuse sensor data and survey data to respectively measure proximity and sociometric choice, and model therelationship between the two.
Approaches for studying allostery using network theory

Allostery is the process whereby binding of a substrate at a site other than the active site modulates the function of a protein. Allostery is thus one of the myriad of biological processes that keeps cells under tight regulatory control, specifically one that acts at the level of the protein rather than through changes in gene transciption or translation of mRNA. Despite over 50 years of investigation, allostery has remained a difficult phenomenon to elucidate. Structural changes are often too subtle for many experimental methods to capture and it has become increasingly obvious that a range of timescales are involved, from extremely fast picoto nanosecond local fluctuations all the way up to the millisecond or even second timescales over which the biological effects of allostery are observed. As a result, computational methods have arisen to become a powerful means of studying allostery, aided greatly by the staggering increases in computational power over the last 70 years. A field that has experienced a surge in interest over the last 20 years or so is network theory, perhaps stimulated by the development of the internet and the Web, two examples of immensely important networks in our everyday life. One of the reasons for the popularity of networks in modelling is their comparative simplicity: a network consists of nodes, representing a set of objects in a system, and edges, that capture the relations between them. In this thesis, we both apply existing ideas andmethods from network theory and develop new computational network methods to study allostery in proteins. We attempt to tackle this problem in three distinct ways, each representing a protein using a different form of a network. Our initial work follows on logically from previous work in the group, representing proteins as graphs where atoms are nodes and bonds are energy weighted edges. In effect we disregard the 3-dimensional structure of the protein and instead focus on how the bond connectivity can be used to explain potential long range communication between allosteric and active sites in a multimeric protein. We then focus on a class of protein models known as elastic network models, in which our edges now correspond to mechanical Hooke springs between either atoms or residues, in order to attempt to understand the physical, mechanistic basis of allostery.
Architecting Effectual Computation for Machine Learning Accelerators

Inference efficiency is the predominant design consideration for modern machine learning accelerators. The ability of executing multiply-and-accumulate (MAC) significantly impacts the throughput and energy consumption during inference. However, MAC operation suffers from significant ineffectual computations that severely undermines the inference efficiency and must be appropriately handled by the accelerator. The ineffectual computations are manifested in two ways: first, zero values as the input operands of the multiplier, waste time and energy but contribute nothing to the model inference; second, zero bits in nonzero values occupy a large portion of multiplication time but are useless to the final result. In this article, we propose an ineffectual-free yet cost-effective computing architecture, called split-and-accumulate (SAC) with two essential bit detection mechanisms to address these intractable problems in tandem. It replaces the conventional MAC operation in the accelerator by only manipulating the essential bits in the parameters (weights) to accomplish the partial sum computation. Besides, it also eliminates multiplications without any accuracy loss, and supports a wide range of precision configurations. Based on SAC, we propose an accelerator family called Tetris and demonstrate its application in accelerating state-of-the-art deep learning models. Tetris includes two implementations designed for either high performance (i.e., cloud applications) or low power consumption (i.e., edge devices), respectively, contingent to its built-in essential bit detection mechanism. We evaluate our design with Vivado HLS platform and achieve up to $6.96\times $ performance enhancement, and up to $55.1\times $ energy efficiency improvement over conventional accelerator designs.
An Analysis of the Internal Organization of Facebook Groups

With the rapid development and growth of online social networks (OSNs), researchers have been pushed forward to improve the knowledge of these complex networks by analyzing several aspects, such as the types of social media, the structural properties of the network, or the interaction patterns among users. In particular, a relevant effort has been devoted to the study and identification of cohesive groups of users in OSNs (also referred as communities) because they are the basic building block of each OSN. While several research works on groups in OSNs have mainly focused on identifying the types of groups and the contents created by their members, the analysis of internal organizations of such groups remains unexplored due to the lack of real data sets containing information about such groups, about their members, and the interactions among them. In this article, we compensate for this shortcoming by studying the main properties of groups defined in OSNs, taking as reference use cases 40 real Facebook groups of different categories that account for a total of about 500.000 users. In particular, we exploit interaction patterns among users and social network analysis to uncover interesting aspects related to the internal organization of groups. Experimental results reveal that the majority of the collected groups exhibit an internal structure where members can be clustered in four subgroups according to the level of tie strength of the relations they have. Furthermore, clusters identified on Facebook groups can provide relevant information about the importance of users within such groups.
The Developement of Learning-Tools Oriented Learning Problem Based to Improve Ability Solving Problem and Self Studying of Students SMA Negeri 1 Kualuh Selatan

The objective of this study is to expose : 1) validity of learning-tools oriented learning of problem-based to develop; 2) be practical learning-tools oriented learning problem base to develop; 3) the effectiveness of learning-tools of learning oriented problem based to develop; 4) improvement capability problem solving in those students adopting a learning-tools oriented learning problem based to develop. In the study was taken that (1) the learningtools in developed was valid to the total average of validity RPP = 4.60, students book = 4.45, teachers’ book = 4.47. LKS = 4.52, test ability to solve problem was in valid enough and valid in reliability of 0.910, and questionnaire in selfstudy was in valid category and valid sufficient with reliability of 0.97; (2 )the learningtools in development meets a practical criterion viewed from a) teachers’ assessment in category easily to execute; b) students’ valuation was in highly easy to conduct; and c) how to execute the learning is in the best category; 3) the learning-tools meets the an effective criterion; 4) average rise ability in solving problem on student from the test 1 through test II was 0.29 point with improvementfor full-program to study classically was 15.12%.