(Q)SARs to Predict Environmental Toxicities: Current Status and Future Needs

The current state of the art of (Quantitative) Structure-Activity Relationships ((Q)SARs) to predict environmental toxicity is assessed along with recommendations to develop these models further. The acute toxicity of compounds acting by the non-polar narcotic mechanism of action can be well predicted, however other approaches, including read-across, may be required for compounds acting by specific mechanisms of action. The chronic toxicity of compounds to environmental species is more difficult to predict from (Q)SARs, with robust data sets and more mechanistic information required. In addition, the toxicity of mixtures is little addressed by (Q)SAR approaches. Developments in environmental toxicology including Adverse Outcome Pathways (AOPs) and omics responses should be utilised to develop better, more mechanistically relevant, (Q)SAR models

Lessons Learned from Read-Across Case Studies for Repeated-Dose Toxicity

A series of case studies designed to further acceptance of read-across predictions, especially for chronic health-related endpoints, have been evaluated with regard to the knowledge and insight they provide. A common aim of these case studies was to examine sources of uncertainty associated with read-across. While uncertainty is related to the quality and quantity of the read across endpoint data, uncertainty also includes a variety of other factors, the foremost of which is uncertainty associated with the justification of similarity and quantity and quality of data for the source chemical(s). This investigation has demonstrated that the assessment of uncertainty associated with a similarity justification includes consideration of the information supporting the scientific arguments and the data associated with the chemical, toxicokinetic and toxicodynamic similarity. Similarity in chemistry is often not enough to justify fully a read-across prediction, thus, for chronic health endpoints, toxicokinetic and/or toxicodynamic similarity is essential. Data from New Approach Methodology(ies) including high throughput screening, in vitro and in chemico assay and in silico tools, may provide critical information needed to strengthen the toxicodynamic similarity rationale. In addition, it was shown that toxicokinetic (i.e., ADME) similarity, especially metabolism, is often the driver of the overall uncertainty

Interpretation of QSAR Models: Mining Structural Patterns Taking into Account Molecular Context.

The study focused on QSAR model interpretation. The goal was to develop a workflow for the identification of molecular fragments in different contexts important for the property modelled. Using a previously established approach - Structural and physicochemical interpretation of QSAR models (SPCI) - fragment contributions were calculated and their relative influence on the compounds' properties characterised. Analysis of the distributions of these contributions using Gaussian mixture modelling was performed to identify groups of compounds (clusters) comprising the same fragment, where these fragments had substantially different contributions to the property studied. SMARTSminer was used to detect patterns discriminating groups of compounds from each other and visual inspection if the former did not help. The approach was applied to analyse the toxicity, in terms of 40 hour inhibition of growth, of 1984 compounds to Tetrahymena pyriformis. The results showed that the clustering technique correctly identified known toxicophoric patterns: it detected groups of compounds where fragments have specific molecular context making them contribute substantially more to toxicity. The results show the applicability of the interpretation of QSAR models to retrieve reasonable patterns, even from data sets consisting of compounds having different mechanisms of action, something which is difficult to achieve using conventional pattern/data mining approaches

Quantitative structure-activity relationships of comparative toxicity to aquatic organisms.

Quantitative Structure-Activity relationship (QSAR) attempt statistically to relate the physico-chemical properties of a molecule to its biological activity. A QSAR analysis was performed on the toxicities of up to 75 organic chemicals to two aquatic species, Photobacterium phospherum (known as the Microtox test), and the fathead minnow. To model the toxicities 49 physico-chemical and structural parameters were produced including measures of hydrophobicity, molecular size and electronic effects from techniques such as computational chemistry and the use of molecular connectivity indices. These were reduced to a statistically more manageable number by cluster analysis, principal component analysis, factor analysis, and canonical correlation analysis. The de-correlated data were then used to form relationships with the toxicities. All the techniques were validated using a testing set. Some good predictions of toxicity came from regression analysis of the original de-correlated variables. Although successful in simplifying the complex data matrix, principal component analysis, factor analysis, and canonical content analysis were disappointing as predictors of toxicity. The performance of each of the statistical techniques is discussed. The inter-species relationships of toxicity between four Commonly utilised aquatic endpoints, fathead minnow 96 hour IC50, Microtox 5 minute EC50, Daphnia magna 48 hour IC50, and Tetrahymena pyriformis 60 hour IG50, were investigated. Good relationships was found between the fathead minnow and both T. pyriformis and D. magna toxicities indicating that these species could be used to model fish toxicity. The outliers from individual relationships were assessed in order to elucidate if any molecular features may be causing greater relative toxicity in one species as compared to another. It is concluded that in addition to the intrinsic differences between species, the greater length of the test time for any species may result in increases bioaccumulation, metabolism, and detoxification of certain chemical classes. The relationships involving fish toxicity were moderately improved by the addition of a hydrophobic parameter

Penggunaan model teams games tournament (TGT) untuk meningkatkan hasil belajar siswa pada mata pelajaran matematika materi operasi hitung perkalian di kelas IV SDN 100303 Pargarutan

Latar Belakang masalah penelitian ini adalah persentase hasil belajar siswa belum mencapai nilai Kriteria Ketuntasan Minimum (KKM) yang telah ditetapkan oleh sekolah pada materi pembelajaran matematika materi Operasi Hitung Perkalian di kelas IV SDN 100303 Pargarutan. Penyebabnya adalah siswa beranggapan bahwa pembelajaran matematika itu pembelajaran yang sulit dan membosankan, kurangnya interaksi antara siswa dan guru yang menyebabkan siswa pasif, dan kurang paham dalam mengerjakan soal materi operasi hitung perkalian. Rumusan masalah dalam penelitian ini adalah apakah dengan penggunaan Teams Games Tournament dapat meningkatkan hasil belajar pada mata pelajaran Matematika materi operasi hitung perkalian siswa kelas IV SDN 100303 Pargarutan. Tujuan dari penelitian ini adalah untuk mengetahui peningkatan hasil belajar siswa dengan penggunaan Teams Games Tournament pada mata pelajaran matematika materi operasi hitung perkalian siswa kelas IV SDN 100303 Pargarutan. Jenis penelitian ini adalah Penelitian Tindakan Kelas (PTK) dengan tahapan-tahapan yaitu: perencanaan, tindakan, observasi, dan refleksi. Subjek dalam penelitian ini adalah siswa kelas IV SDN 100303 Pargarutan yang berjumlah 14 siswa yang terdiri dari 8 laki-laki 6 perempuan. Penelitian ini dilakukan dalam dua siklus yaitu siklus I dan siklus II, setiap siklus terdiri dari dua pertemuan. Teknik pengumpulan data yang di gunakan pada penelitian ini adalah tes dan observasi. Indikator keberhasilan pada penelitian ini adalah apabila nilai rata-rata dan persentase ketuntasan telah tercapai sesuai yang ditetapkan. Hasil penelitian ini menunjukkan bahwa hasil belajar pada siswa pada pembelajaran matematika setiap siklus meningkat. Pada tes awal nilai rata-rata siswa yaitu 50,62 (25%), kemudian pada siklus I nilai rata-rata siswa dari 56,85 (37,5%) , pada siklus II dari 71,85 (68,75%) menjadi 76,25 (81,25%). Selain itu data yang didapatkan dari hasil observasi yang dilakukan sebelum diberikan tindakan awalnya siswa kurang aktif dalam kegiatan pembelajaran dimana masih didominasi oleh guru, tetapi setelah dilakukannya tindakan dengan menggunakan model pembelajaran Teams Games Tournament siswa sudah lebih aktif dalam kegiatan pembelajaran baik itu bertanya, mengerjakan soal, berpartisipasi dalam kelompok dan bermain game

Hubungan Antara Lingkungan Kerja dengan Kinerja Guru SD YPSA (Yayasan Pendidikan Syafiyyatul Amaliyyah)

Dalam upaya membuktikan hipotesis tersebut, digunakan metode analisis data korelasi Product Moment, dimana berdasarkan pengolahan data, diperoleh hasil-hasil sebagai berikut 1. Terdapat hubungan positif yang sangat signifikan antara lingkungan kerja non fisik dengan kinerja. Hasil ini dibuktikan dengan koefisien korelasi. Artinya semakin baik lingkungan kerja non fisik, maka semakin tinggi kinerja, sebaliknya semakin buruk lingkungan kerja non fisik, maka semakin rendah kinerja. Berdasarkan hasil ini, maka hipotesis yang telah diajukan dalam penelitian ini, dinyatakan diterima 2. Llingkungan kerja non fisik memberikan pengaruh sebesar 16% terhadap kinerja seorang guru. Dari hasil ini maka diketahui bahwa masih terdapat 87% pengaruh dari faktor lain terhadap kinerja, dimana faktor lain tersebut dalam penelitian ini tidak dilihat, diantaranya adalah kemampuan, motivasi, pengetahuan pekerjaan, tingkat pendidikan, persepsi, tujuan, nilai-nilai, keahlian, kompetisi, lingkungan sosial atau tekanan situasi, umur, jenis kelamin, masa, dan jabatan atau keterlibatan kerja. 3. Bahwa diketahui lingkungan kerja non fisik dinyatakan cenderung baik. Hasil ini diketahui dengan melihat perbandingan nilai rata-rata empirik dengan nilai rata-rata hipotetik, dimana nilai rata-rata empirik lebih besar dari nilai rata-rata hoptetik dan selsih kedua nilai rata-rata tersebut tidak melebihi bilangan SD. Kinerja para guru tergolong pada kategori baik, karena nilai rata-rata yang diperoleh sebesar 82,083 berada pada rentang atau interval nilai 70 sampai 8

Development of an In Silico Profiler for Respiratory Sensitisation

In this article, we outline work that led the QSAR and Molecular Modelling Group at Liverpool John Moores University to be jointly awarded the 2013 Lush Science Prize. Our research focuses around the development of in silico profilers for category formation within the Adverse Outcome Pathway paradigm. The development of a well-defined chemical category allows toxicity to be predicted via read-across. This is the central approach used by the OECD QSAR Toolbox. The specific work for which we were awarded the Lush Prize was for the development of such an in silico profiler for respiratory sensitisation. The profiler was developed by an analysis of the mechanistic chemistry associated with covalent bond formation in the lung. The data analysed were collated from clinical reports of occupational asthma in humans. The impact of the development of in silico profilers on the Three Rs is also discussed

Investigation of the Verhaar scheme for predicting acute aquatic toxicity: improving predictions obtained from Toxtree ver. 2.6

Assessment of the potential of compounds to cause harm to the aquatic environment is an integral part 8 of the REACH legislation. To reduce the number of vertebrate and invertebrate animals required for 9 this analysis alternative approaches have been promoted. Category formation and read-across have 10 been applied widely to predict toxicity. A key approach to grouping for environmental toxicity is the 11 Verhaar scheme which uses rules to classify compounds into one of four mechanistic categories. 12 These categories provide a mechanistic basis for grouping and any further predictive modelling. A 13 computational implementation of the Verhaar scheme is available in Toxtree v2.6. The work 14 presented herein demonstrates how modifications to the implementation of Verhaar between version 15 1.5 and 2.6 of Toxtree have improved performance by reducing the number of incorrectly classified 16 compounds. However, for the datasets used in this analysis, version 2.6 classifies more compounds as 17 outside of the domain of the model. Further amendments to the classification rules have been 18 implemented here using a post-processing filter encoded as a KNIME workflow. This results in fewer 19 compounds being classified as outside of the model domain, further improving the predictivity of the 20 scheme. The utility of the modification described herein is demonstrated through building quality, 21 mechanism-specific Quantitative Structure Activity Relationship (QSAR) models for the compounds 22 within specific mechanistic categories

Meningkatkan Mutu Pendidikan melalui Manajemen Peserta Didik

Mutu merupakan proses terstruktur untuk memperbaiki keluaran yang dihasilkan. Bagi setiap institusi mutu adalah agenda utama dan tugas yang paling penting dan tedapat dua hal yang perlu diperhatikan yaitu kualitas dan kuantitas. Banyak lembaga pendidikan yang mengharapkan keberhasilan baik dari segi kualitas maupun dalam segi kuantitas seorang peserta didik. Artikel ini menggunakan metode studi pustaka atau yang biasa disebut studi literatur. Penelitian ini dilakukan dengan metode pengumpulan data dari berbagai rujukan melalui beberapa buku, majalah yang berkaitan dengan tujuan untuk mengungkapkan berbagai teori yang berhubungan dengan permasalahan yang sedang dihadapi atau diteliti