Contributions of foot muscles and plantar fascia morphology to foot posture

Background: The plantar foot muscles and plantar fascia differ between different foot postures. However, how each individual plantar structure contribute to foot posture has not been explored. The purpose of this study was to investigate the associations between static foot posture and morphology of plantar foot muscles and plantar fascia and thus the contributions of these structures to static foot posture. Methods: A total of 111 participants were recruited, 43 were classified as having pes planus and 68 as having normal foot posture using Foot Posture Index assessment tool. Images from the flexor digitorum longus (FDL), flexor hallucis longus (FHL), peroneus longus and brevis (PER), flexor hallucis brevis (FHB), flexor digitorum brevis (FDB) and abductor hallucis (AbH) muscles, and the calcaneal (PF1), middle (PF2) and metatarsal (PF3) regions of the plantar fascia were obtained using a Venue 40 ultrasound system with a 5–13 MHz transducer. Results: In order of decreasing contribution, PF3>FHB>FHL>PER>FDB were all associated with FPI and able to explain 69% of the change in FPI scores. PF3 was the highest contributor explaining 52% of increases in FPI score. Decreased thickness was associated with increased FPI score. Smaller cross sectional area (CSA) in FHB and PER muscles explained 20% and 8% of increase in FPI score. Larger CSA of FDB and FHL muscles explained 4% and 14% increase in FPI score respectively. Conclusion: The medial plantar structures and the plantar fascia appear to be the major contributors to static foot posture. Elucidating the individual contribution of multiple muscles of the foot could provide insight about their role in the foot posture

Deep learning-enabled technologies for bioimage analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases

The Production and Decomposition Rate of Mangrove Litter in the Sungai Alam Village, Bengkalis Sub-district, Bengkalis Regency, Riau Province

The research has been done on february 2014 in Sungai Alam Village, Bengkalis Subdistrict,Bengkalis Regency, Riau Province. A research aims to understand the Productionand Decomposition Rate of Mangrove Litter in Sungai Alam Village. There were 3 stationsand 3 plots in each station (station 1 in the mangrove conservation, station 2 in the goodmangrove forest and station 3 in the mangrove exploited wisely). In each plot, 1 net 1 x 1 m2were placed mangrove dominant. This net 2 meters above the mangrove forest floor. The bag(nets 20 cm x 10 cm) was filled with 10 grams litter and used for studying the litterdecomposition rate. The bag were placed in forest floor still be suffused of water. Sampel forlitter production, decomposition rate and water quality were taken once of 7 days (3 times).Results shown the highest litter production was ini St 2 (52,5 gr/m2/day) and thelowest was in the St 1 (25,151 gr/m2/day). Decomposition rate of the litter in each samplingpoints was relatively low in the 1st week and become faster in the 3st week. The highestdecomposition rate litter mangrove (lief, stick, flower or fruit) was in the St III, while thelowest was in the St 1

Klasifikasi Tanaman Obat Berdasarkan Ekstraksi Fitur Morfologi Daun Menggunakan Jaringan Syaraf Tiruan

Abstrak  Indonesia telah lama mengenal dan menggunakan tanaman yang berkhasiat sebagai obat. Dari banyaknya tanaman obat yang ada di dunia, 80% tanaman obat tumbuh di hutan tropika yang berada di Indonesia. Sekitar 28.000 spesies tanaman tumbuh dan 1.000 spesies diantaranya telah digunakan sebagai  tanaman obat. Dengan banyaknya spesies tanaman obat dan tingkat kemiripan yang tinggi dapat menyebabkan kesalahan dalam proses identifikasi jenis tanaman obat. Sehingga dibutuhkan bantuan komputer untuk mengenali jenis tanaman obat tersebut. Tujuan dari penelitian ini adalah untuk mengidentifikasi jenis tanaman obat menggunakan jaringan syaraf tiruan backpropagation berdasarkan ekstraksi fitur morfologi daun. Hasilnya menujukkan bahwa perubahan nilai learning rate mempengaruhi hasil identifikasi jenis tanaman obat berdasarkan fitur morfologi daun. Hasil perhitungan rata-rata nilai recognition rate sebesar 90% untuk data training dan 75,56% untuk data testing terjadi saat learning rate 0,01. Nilai learning rate terbaik untuk identifikasi jenis tanaman obat adalah 0,01 dengan jumlah rata-rata epoch sebesar 11,67 dan MSE sebesar 0,13. Ini menunjukkan bahwa metode ekstraksi fitur morfologi daun dan algoritma jaringan syaraf tiruan backpropagation sangat baik digunakan untuk mengidentifkasi jenis tanaman obat.   Kata Kunci: Ekstraksi Fitur, Jaringan Syaraf Tiruan Backpropagation, Morfologi Daun, Tanaman Obat   Abstract  Indonesia has known and used a nutritious plant as a medicine. most of the medicinal plants in the world that is 80% of medicinal plants grown in tropical forests in Indonesia. the plant grows about 28,000 species and 1,000 species of which have been used as medicinal plants. Many species of medicinal plants with a high degree of similarity can cause errors in the process of identifying medicinal plants. Because the problem was needed computer assistance to recognize the types of medicinal plants. This research proposed to identify species of medicinal plants using backpropagation artificial neural network based on leaf morphological feature extraction. The results showed that changes in the value of learning rate influence the identification of medicinal plant species based on leaf morphology features. The calculation average of recognition rate is 90% for training data and 75.56% for data testing occurs at learning rate 0.01. The best learning rate for plant species identification is 0.01 with epoch average is 11.67 and MSE is 0.13. The results of this research concluded that the leaf morphology feature extraction method and backpropagation artificial neural network algorithm are very well used to identify the types of medicinal plants.   Keywords: Backpropagation Artificial Neural Network, Feature Extraction, Leaf Morphology, Medicinal Plan

Regulatory T Cells Expanded from Hiv-1-Infected Individuals Maintain Phenotype, Tcr Repertoire and Suppressive Capacity

While modulation of regulatory T cell (Treg) function and adoptive Treg transfer are being explored as therapeutic modalities in the context of autoimmune diseases, transplantation and cancer, their role in HIV-1 pathogenesis remains less well defined. Controversy persists regarding their beneficial or detrimental effects in HIV-1 disease, which warrants further detailed exploration. Our objectives were to investigate if functional CD4+ Tregs can be isolated and expanded from HIV-1-infected individuals for experimental or potential future therapeutic use and to determine phenotype and suppressive capacity of expanded Tregs from HIV-1 positive blood and tissue. Tregs and conventional T cell controls were isolated from blood and gut-associated lymphoid tissue of individuals with HIV-1 infection and healthy donors using flow-based cell-sorting. The phenotype of expanded Tregs was assessed by flow-cytometry and quantitative PCR. T-cell receptor ß-chain (TCR-β) repertoire diversity was investigated by deep sequencing. Flow-based T-cell proliferation and chromium release cytotoxicity assays were used to determine Treg suppressive function. Tregs from HIV-1 positive individuals, including infants, were successfully expanded from PBMC and GALT. Expanded Tregs expressed high levels of FOXP3, CTLA4, CD39 and HELIOS and exhibited a highly demethylated TSDR (Treg-specific demethylated region), characteristic of Treg lineage. The TCRß repertoire was maintained following Treg expansion and expanded Tregs remained highly suppressive in vitro. Our data demonstrate that Tregs can be expanded from blood and tissue compartments of HIV-1+ donors with preservation of Treg phenotype, function and TCR repertoire. These results are highly relevant for the investigation of potential future therapeutic use, as currently investigated for other disease states and hold great promise for detailed studies on the role of Tregs in HIV-1 infection.Elizabeth Glaser Pediatric AIDS Foundation (Pediatric HIV Vaccine Program Award MV-00-9-900-1429-0-00)Massachusetts General Hospital. Executive Committee on Research (MGH/ECOR Physician Scientist Development Award)National Institutes of Health (U.S.) (NIH NIAID (KO8 AI074405))National Institutes of Health (U.S.) (NIH NIAID AI074405-03S1)Massachusetts General Hospital (William F. Milton Fund)Harvard University. Center for AIDS Research (CFAR Scholar Award)Massachusetts General Hospital. Center for the Study Inflammatory Bowel Disease (P30DK043351)Harvard University. Center for AIDS Research (NIH funded program (5P30AI060354-09

Bilkent university at TRECVID 2006

We describe our third participation, that includes one high-level feature extraction run, and two manual and one interactive search runs, to the TRECVID video retrieval evaluation. All of these runs have used a system trained on the common development collection. Only visual and textual information were used where visual information consisted of color, texture and edge-based low-level features and textual information consisted of the speech transcript provided in the collection