Faktor Konsentrasi Pb, Cd, Cu, Ni, Zn Dalam Sedimen Perairan Pesisir Kota Dumai

Anthropological pollution threat to coastal and marine ecosystems Dumai City is intensifying with the acceleration of industrial development in various sectors. Aquatic ecosystem as a final address pollution supply of land will bear the burden of heavy metal contamination. Has been studied heavy metal concentrations in water, sediment and fish. The research aims to identify the heavy metal pollutants travel on ecosystem components and evaluate the security status of fish as a public consumption. Results showed that bottom sediment has accumulated Pb, Cd, Cu, Ni and Zn as indicated by Factor Concentration (doubling), consecutive 25-27, 13-18, 9-38, 74-93 and 34-162 times the heavy metal body of water. In fish flesh Gulama (Pseudociena amoyensis) undetected Pb, Cd, Cu and Zn, consecutive from 4.07 to 5.52; 0.09 to 0.19; 0.13 to 0.29 and from 5.64 to 7.56 tg / g. This value is still below the safe limit consumption of fish when referring to the standard DG POM. Keyword: Concetration Factor, heavy metal, sediment Ancaman pencemaran antropologis terhadap ekosistem pesisir dan laut Kota Dumai semakin meningkat sejalan dengan percepatan pembangunan industri berbagai sektor. Ekosistem perairan sebagai alamat akhir pencemaran daratan akan menanggung beban pasokan cemaran logam berat. Telah dilakukan penelitian konsentrasi logam berat pada badan air, sedimen dan ikan. Penelitian bertujuan menemukenali perjalanan pencemar logam berat pada komponen ekosistem dan mengevaluasi status keamanan ikan sebagai konsumsi masyarakat. Hasil penelitian menunjukkan bahwa sedimen dasar telah mengakumulasi Pb, Cd, Cu, Ni dan Zn yang ditunjukkan dengan Faktor Konsentrasi (pelipatgandaan), berturut turut 25-27, 13-18, 9-38, 74-93 dan 34-162 kali lipat dari logam berat badan air. Pada daging ikan Gulama (Pseudociena amoyensis) terdeteksi Pb, Cd, Cu dan Zn, berturut turut 4,07-5,52; 0,09-0,19; 0,13-0,29 dan 5,64-7,56 µg/g. Nilai ini masih dibawah batas aman konsumsi ikan bila merujuk standar Dirjen POM

Ferromagnetic/superconducting proximity effect in La0.7Ca0.3MnO3 / YBa2Cu3O7 superlattices

We study the interplay between magnetism and superconductivity in high quality YBa2Cu3O7 (YBCO) / La0.7Ca0.3MnO3(LCMO)superlattices. We find evidence for the YBCO superconductivity depression in presence of the LCMO layers. We show that due to its short coherence length superconductivity survives in the YBCO down to much smaller thickness in presence of the magnetic layer than in low Tc superconductors. We also find that for a fixed thickness of the superconducting layer, superconductivity is depressed over a thickness interval of the magnetic layer in the 100 nm range. This is a much longer length scale than that predicted by the theory of ferromagnetic/superconducting proximity effect.Comment: 10 pages + 5 figures, submitted to Phys. Rev.

The preparation of HEMA-MPC films for ocular drug delivery

There is a need to prolong drug residence time using a biocompatible formulation in the subconjunctival space after surgery to treat glaucoma. Drug releasing discs were prepared with 2-(hydroxyethyl)methacrylate (HEMA) and 2-methacryloyl-oxyethyl phosphorylcholine (MPC). The ratio of bound water (Wb) to free water (Wf) ratio increased from 1:0.3 to 1:6.8 with increasing MPC (0 to 50%, w/w). The optimal balance between water content, SR and mechanical strength were obtained with 10% MPC (w/w) hydrogels. Water-alcohol mixtures were examined to facilitate loading of poorly soluble drugs, and they showed greater hydrogel swelling than either water or alcohol alone. The SR was 1.2 ± 0.02 and 3.3 ± 0.1 for water and water:ethanol (1:1) respectively. HEMA-MPC (10%) discs were loaded with dexamethasone using either water:ethanol (1:1) or methanol alone. Drug release was examined in an outflow rig model that mimics the subconjunctival space in the eye. Dexamethasone loading increased from 0.3 to 1.9 mg/disc when the solvent was changed from water:ethanol (1:1) to methanol with the dexamethasone half-life (t½) increasing from 1.9 to 9.7 days respectively. These encouraging results indicate that HEMA-MPC hydrogels have the potential to sustain the residence time of a drug in the subconjunctival space of the eye

Trans−cis Switching Mechanisms in Proline Analogues and Their Relevance for the Gating of the 5-HT3 Receptor

Trans-cis isomerization of a proline peptide bond is a potential mechanism to open the channel of the 5-HT3 receptor. Here, we have used the metadynamics method to theoretically explore such a mechanism. We have determined the free energy surfaces in aqueous solution of a series of dipeptides of proline analogues and evaluated the free energy difference between the cis and trans isomers. These theoretical results were then compared with data from mutagenesis experiments, in which the response of the 5-HT3 receptor was measured when the proline at the apex of the M2-M3 transmembrane domain loop was mutated. The strong correlation between the experimental and the theoretical data supports the existence of a trans-cis proline switch for opening the 5-HT3 receptor ion channel

Federated learning enables big data for rare cancer boundary detection.

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing

Author Correction: Federated learning enables big data for rare cancer boundary detection.

10.1038/s41467-023-36188-7NATURE COMMUNICATIONS14

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives