Alteration of brain network topology in HIV-associated neurocognitive disorder: A novel functional connectivity perspective

HIV is capable of invading the brain soon after seroconversion. This ultimately can lead to deficits in multiple cognitive domains commonly referred to as HIV-associated neurocognitive disorders (HAND). Clinical diagnosis of such deficits requires detailed neuropsychological assessment but clinical signs may be difficult to detect during asymptomatic injury of the central nervous system (CNS). Therefore neuroimaging biomarkers are of particular interest in HAND. In this study, we constructed brain connectivity profiles of 40 subjects (20 HIV positive subjects and 20 age-matched seronegative controls) using two different methods: a non-linear mutual connectivity analysis approach and a conventional method based on Pearson's correlation. These profiles were then summarized using graph-theoretic methods characterizing their topological network properties. Standard clinical and laboratory assessments were performed and a battery of neuropsychological (NP) tests was administered for all participating subjects. Based on NP testing, 14 of the seropositive subjects exhibited mild neurologic impairment. Subsequently, we analyzed associations between the network derived measures and neuropsychological assessment scores as well as common clinical laboratory plasma markers (CD4 cell count, HIV RNA) after adjusting for age and gender. Mutual connectivity analysis derived graph-theoretic measures, Modularity and Small Worldness, were significantly (p < 0.05, FDR adjusted) associated with the Executive as well as Overall z-score of NP performance. In contrast, network measures derived from conventional correlation-based connectivity did not yield any significant results. Thus, changes in connectivity can be captured using advanced time-series analysis techniques. The demonstrated associations between imaging-derived graph-theoretic properties of brain networks with neuropsychological performance, provides opportunities to further investigate the evolution of HAND in larger, longitudinal studies. Our analysis approach, involving non-linear time-series analysis in conjunction with graph theory, is promising and it may prove to be useful not only in HAND but also in other neurodegenerative disorders

Pelatihan Strategi Pemasaran Kreatif Bagi Kelompok Petani Ubi Jalar Desa Balesari Kabupaten Malang pada Masa Pandemi Covid 19

Dusun Segelan desa Balesari Kecamatan Ngajum ini merupakan satu-satunya dusun penghasil ubi jalar khas gunung Kawi. Pada saat ini pemerintah Republik Indonesia telah menetapkan PSBB (Pembatasan Sosial Berskala Besar) dan PPKM (Pemberlakukan Pembatasan Kegiatan Masyarakat) secara mikro ditingkat desa. Dampaknya wilayah wisata gunung Kawi ditutup hampir selama dua tahun, sehingga memberikan dampak ekonomi secara signifikan karena penduduk menjual ubi jalarnya hanya di gunung Kawi. Tujuan dilakukan pelatihan ini yaitu membantu menganalisis teknik pemasaran dan menambah nilai jual produk ubi jalar melalui online. Pelatihan dilaksanakan pada hari Senin tanggal 08 Februari 2021 di dusun Segelan, Desa Balesari, Ngajum, Kabupaten Malang. Adapun jumlah peserta yang menghadiri pelatihan kali ini yaitu 30 petani ubi jalar, Mantri Tani, dan Petugas Penyuluh Lapangan (PPL) desa Balesari. Metode yang digunakan yaitu metode purposive sampling dengan memberikan kusioner dan wawancara teknik snowball.&nbsp; Hasil analisis dari kuisioner menunjukkan bahwa: (1) Petani ubi jalar mau bekerjasama dengan kelompok karang taruna untuk memasarkan ubi jalar secara online, (2) Petani ubi jalar bersedia untuk mengolah ubi jalar menjadi beberapa olahan makanan lainnya yang inovatif. Harapan pasca dari pelatihan ini yaitu para petani ubi jalar lebih semangat lagi bertani ubi jalar, sedangkan untuk dan karang taruna lebih kreatif dan inovatif lagi di dalam proses pengolahan ubi jalar dan teknik pemasaran secara online. Kata kunci: Pelatihan, Pemasaran online, Ubi jala

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) &gt;0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare