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                <text>Ajay Shah, Allen A Champagne, Joshua Del Papa, Jay Toor, Jeremie Larouche, Markku T Nousiainen</text>
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                <text>Implementasi Algoritma K-Neares Neighbor  dalam Pengenalan Aksara Jawa Berbasis Android  untuk Anak Sekolah Dasar</text>
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                <text>Ajib Susanto, Elkaf Rahmawan Pramudya, Gilang Raharjito, Muslih Muslih</text>
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                <text>Teknologi saat ini berkembang semakin pesat sehingga memudahkan dunia pendidikan dalam melakukan proses belajar mengajar seperti kondisi saat ini yang sedang terkendala oleh wabah Covid-19 secara global sehingga siswa dan guru melakukan proses belajar mengajar di rumah. Berbagai aplikasi platform menawarkan proses pembelajaran online baik lewat learning management system (LMS), media sosial maupun jejaring sosial, akan tetapi beberapa materi perlu dilakukan pendekatan dan media khusus salah satu adalah materi pengenalan aksara Jawa bagi anak-anak Sekolah Dasar (SD) membutuhkan sarana latihan dan penjelasan yang menyenangkan. Penelitian ini mengusulkan aplikasi pembelajaran aksara Jawa dengan menggunakan Algoritma K-NN untuk belajar mengenal aksara Jawa dan mendeteksi kecocokan dalam penulisan aksara Jawa yang dikemas dalam aplikasi Android yang menarik dan menyenangkan. Hasil pengujian K-NN berdasarkan nilai k yang paling akurat adalah k = 5 dengan hasil berhasil 54,9% dan hasil gagal 45,1% dan diperoleh pengujian kebenaran siswa dalam mengenal aksara Jawa adalah 86%.</text>
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                <text>2020</text>
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                <text>pembelajaran, android, sekolah dasar, KNN, aksara Jawa</text>
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                <text>Jurnal Informatika dan Rekayasa Perangkat Lunak</text>
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                <text>Universitas Wahid Hasyim</text>
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                <text>Technology</text>
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              <name>Title</name>
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                  <text>Coronavirus</text>
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              <name>Description</name>
              <description>An account of the resource</description>
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                  <text>Dominio científico: Coronavirus</text>
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                <text>Post-traumatic stress symptoms in hemodialysis patients with MERS-CoV exposure</text>
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                <text>Ajin Cho, Hong-Seock Lee, Young Ki Lee, Hee Jung Jeon, Hayne Cho Park, Da Wun Jeong, Yang Gyun Kim, Sangho Lee, Chang-Hee Lee, Kyung Don Yoo, Ae Kyeong Wong</text>
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                <text>Abstract Background Post-traumatic stress symptoms can occur in patients with medical illness. During the Middle East Respiratory Syndrome (MERS) outbreak in South Korea in 2015, some dialysis patients in three centers who were incidentally exposed to patients or medical staff with confirmed MERS-CoV infection were isolated to interrupt the spread of the infection. We aimed to investigate post-traumatic stress symptoms and risk factors among these patients. Materials and methods In total, 116 hemodialysis (HD) patients in contact with MERS-CoV-confirmed subjects were isolated using three strategies, namely, single room isolation, cohort isolation, and self-quarantine. We used the Impact of Event Scale-Revised-Korean (IES-R-K) to examine post-traumatic stress symptoms at 12 months after the isolation period. Results Of the 116 HD patients, 27 were lost to follow-up. Of the 89 patients, 67 (75.3%) completed the questionnaires. Single room isolation was used on 40 (58.8%) of the patients, cohort isolation on 20 (29.4%), and self-imposed quarantine on 8 (11.8%). In total, 17.9% of participants (n = 12) reported post-traumatic stress symptoms exceeding the IES-R-K’s cutoff point (≧18). Prevalence rates of IES-R-K ≧18 did not differ significantly according to isolation method. However, isolation duration was linearly associated with the IES-R-K score (standardized β coefficient − 0.272, P = 0.026). Scores in Avoidance, Emotional numbing and Dissociation subscale were higher in patients with longer isolation period. Conclusion MERS was a traumatic experience for quarantined HD patients. IES-R-K scores were not significantly different by isolation methods. However, short isolation was associated with post-traumatic stress symptoms.</text>
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                <text>2020</text>
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                <text>Post-traumatic stress symptom, Hemodialysis, Middle East respiratory syndrome</text>
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                <text>DOI: 10.1186/s13030-020-00181-z</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
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                <text>BioPsychoSocial Medicine</text>
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                <text>BMC</text>
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            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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                <text>Neurosciences. Biological psychiatry. Neuropsychiatry</text>
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                <text>EN</text>
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                  <text>Dominio científico: Coronavirus</text>
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                <text>Porcine Epidemic Diarrhea Virus among Farmed Pigs, Ukraine</text>
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                <text>Akbar Dastjerdi, John Carr, Richard J. Ellis, Falko Steinbach, Susanna Williamson</text>
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                <text>An outbreak of porcine epidemic diarrhea occurred in the summer of 2014 in Ukraine, severely affecting piglets</text>
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                <text>2015</text>
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                <text>Porcine epidemic diarrhea, coronavirus, Ukraine, porcine epidemic diarrhea virus, Viruses, pigs</text>
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                <text>DOI: 10.3201/eid2112.150272</text>
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                <text>Emerging Infectious Diseases</text>
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                <text>Infectious and parasitic diseases, Medicine</text>
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                <text>Conundrums of the second line health care workers during the time of a pandemic:  analytical solutions based on the experience from a developing country</text>
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                <text>Akhil Paul</text>
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            <description>An account of the resource</description>
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                <text>The COVID 19 pandemic has affected the entire world, and it is continuing to spread morbidity, mortality, and chaos. The second line of health care workers who are not treating the COVID 19 infected patients also plays a significant role during this pandemic by treating other diseases and screening for COVID 19 infection. Nevertheless, many of them still struggle to identify and understand their role, lost between the idea of self-protection and principles of medical ethics. This article is trying to break the ice by solving a few common conundrums based on India's experiences.</text>
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                <text>DOI: 10.1136/bmjdrc-2020-001450</text>
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                <text>BMJ open diabetes research &amp; care</text>
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                <text>The recent COVID-19 pandemic, which broke at the end of the year 2019 in Wuhan, China, has infected more than 98.52 million people by today (January 23, 2021) with over 2.11 million deaths across the globe. To combat the growing pandemic on urgent basis, there is need to design effective solutions using new techniques that could exploit recent technology, such as machine learning, deep learning, big data, artificial intelligence, Internet of Things, for identification and tracking of COVID-19 cases in near real time. These technologies have offered inexpensive and rapid solution for proper screening, analyzing, prediction and tracking of COVID-19 positive cases. In this paper, a detailed review of the role of AI as a decisive tool for prognosis, analyze, and tracking the COVID-19 cases is performed. We searched various databases including Google Scholar, IEEE Library, Scopus and Web of Science using a combination of different keywords consisting of COVID-19 and AI. We have identified various applications, where AI can help healthcare practitioners in the process of identification and monitoring of COVID-19 cases. A compact summary of the corona virus cases are first highlighted, followed by the application of AI. Finally, we conclude the paper by highlighting new research directions and discuss the research challenges. Even though scientists and researchers have gathered and exchanged sufficient knowledge over last couple of months, but this structured review also examined technological perspectives while encompassing the medical aspect to help the healthcare practitioners, policymakers, decision makers, policymakers, AI scientists and virologists to quell this infectious COVID-19 pandemic outbreak.</text>
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                <text>2021</text>
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                <text>infectious diseases, covid-19, SARS-CoV-2, machine learning, deep learning, drug discovery, Disease prediction</text>
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                <text>10.1007/s12539-021-00431-w</text>
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                <text>Interdisciplinary sciences, computational life sciences</text>
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                <text>What reinfections mean for COVID-19.</text>
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              <elementText elementTextId="50906">
                <text>Akiko Iwasaki</text>
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              <elementText elementTextId="50909">
                <text>The Lancet. Infectious diseases</text>
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                <text>Pyrogallol Structure in Polyphenols is Involved in Apoptosis-induction on HEK293T and K562 Cells</text>
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              <elementText elementTextId="6109">
                <text>Akiko Saito, Shinya Mitsuhashi, Hiroshi Shima, Noriyuki Nakajima, Makoto Ubukata</text>
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              <elementText elementTextId="6110">
                <text>As multiple mechanisms account for polyphenol-induced cytotoxicity, the development of structure-activity relationships (SARs) may facilitate research on cancer therapy. We studied SARs of representatives of 10 polyphenol structural types: (+)-catechin (1), (-)-epicatechin (2), (-)-epigallocatechin (3), (-)-epigallocatechin gallate (4), gallic acid (5), procyanidin B2 (6), procyanidin B3 (7), procyanidin B4 (8), procyanidin C1 (9), and procyanidin C2 (10). Amongst them, the polyphenols containing a pyrogallol moiety (3-5) showed the most potent cytotoxicic activity. These compounds evoked a typical DNA-laddering phenomenon in HEK293T, which indicated that the induction of apoptosis at least partly mediates their cytotoxic activity. Anti-oxidative capacity of compounds 3-5 were comparable to those of the trimers 9 and 10, which were not cytotoxic. Therefore, we suggest that pyrogallol moiety is important for fitting of polyphenols to their putative target molecule(s) in non-oxidative mechanism.</text>
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              <elementText elementTextId="6111">
                <text>2008</text>
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                <text>cytotoxicity, apoptosis, Pyrogallol, polyphenol, structure-activity relationship</text>
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                <text>DOI: 10.3390/molecules13122998</text>
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                <text>Molecules</text>
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                <text>MDPI AG</text>
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            <name>Coverage</name>
            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
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              <elementText elementTextId="6116">
                <text>Organic chemistry</text>
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            </elementTextContainer>
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              <name>Title</name>
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                  <text>Coronavirus</text>
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              </elementTextContainer>
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              <name>Description</name>
              <description>An account of the resource</description>
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                  <text>Dominio científico: Coronavirus</text>
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      <name>Text</name>
      <description>A resource consisting primarily of words for reading. Examples include books, letters, dissertations, poems, newspapers, articles, archives of mailing lists. Note that facsimiles or images of texts are still of the genre Text.</description>
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        <name>Dublin Core</name>
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          <element elementId="50">
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                <text>Polymorphic edge detection (PED): two efficient methods of polymorphism detection from next-generation sequencing data</text>
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            <name>Creator</name>
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              <elementText elementTextId="18202">
                <text>Akio Miyao, Jianyu Song Kiyomiya, Keiko Iida, Koji Doi, Hiroshi Yasue</text>
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                <text>Abstract Background Accurate detection of polymorphisms with a next generation sequencer data is an important element of current genetic analysis. However, there is still no detection pipeline that is completely reliable. Result We demonstrate two new detection methods of polymorphisms focusing on the Polymorphic Edge (PED). In the matching between two homologous sequences, the first mismatched base to appear is the SNP, or the edge of the structural variation. The first method is based on k-mers from short reads and detects polymorphic edges with k-mers for which there is no match between target and control, making it possible to detect SNPs by direct comparison of short-reads in two datasets (target and control) without a reference genome sequence. The second method is based on bidirectional alignment to detect polymorphic edges, not only SNPs but also insertions, deletions, inversions and translocations. Using these two methods, we succeed in making a high-quality comparison map between rice cultivars showing good match to the theoretical value of introgression, and in detecting specific large deletions across cultivars. Conclusions Using Polymorphic Edge Detection (PED), the k-mer method is able to detect SNPs by direct comparison of short-reads in two datasets without genomic alignment step, and the bidirectional alignment method is able to detect SNPs and structural variations from even single-end short-reads. The PED is an efficient tool to obtain accurate data for both SNPs and structural variations. Availability The PED software is available at: https://github.com/akiomiyao/ped.</text>
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            <name>Date</name>
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                <text>2019</text>
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          <element elementId="49">
            <name>Subject</name>
            <description>The topic of the resource</description>
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              <elementText elementTextId="18205">
                <text>NGS, mutation, Polymorphism, indel, SV</text>
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              <elementText elementTextId="18206">
                <text>DOI: 10.1186/s12859-019-2955-6</text>
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            <name>Source</name>
            <description>A related resource from which the described resource is derived</description>
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              <elementText elementTextId="18207">
                <text>BMC Bioinformatics</text>
              </elementText>
            </elementTextContainer>
          </element>
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            <name>Publisher</name>
            <description>An entity responsible for making the resource available</description>
            <elementTextContainer>
              <elementText elementTextId="18208">
                <text>BMC</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="38">
            <name>Coverage</name>
            <description>The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant</description>
            <elementTextContainer>
              <elementText elementTextId="18209">
                <text>Biology (General), Computer applications to medicine. Medical informatics</text>
              </elementText>
            </elementTextContainer>
          </element>
          <element elementId="44">
            <name>Language</name>
            <description>A language of the resource</description>
            <elementTextContainer>
              <elementText elementTextId="18210">
                <text>EN</text>
              </elementText>
            </elementTextContainer>
          </element>
        </elementContainer>
      </elementSet>
    </elementSetContainer>
  </item>
</itemContainer>
