INTELEGENSIA MENENTUKAN PRESTASI BELAJAR MAHASISWA PRODI D III KEBIDANAN

Prestasi belajar merupakan faktor penentu kesuksesan mahasiswa di masa depannya. Kemampuan mahasiswa di perguruan tinggi menjadi perhatian bagi perusahaan dalam rantai pasokan lulusan pasar tenaga kerja (end user).  Penelitian ini bertujuan untuk mengetahui faktor apakah yang paling dominan terhadap prestasi belajar. Desain penelitian ini adalah Cross Sectional. Hasil penelitian menunjukkan bahwarerata indeks prestasi mahasiswa mahasiswa prodi D III Kebidanan semester III dan V adalah sebesar 3,514 dengan skor intelensia rerata sebesar 122, 27 yang tergolong very superior. Ada perbedaan nilai indeks prestasi mahasiswa  antara asal sekolah negeri dan swasta dengan P value (0,021), antara hasil psikotest yang disarankan dengan yang tidak disarankan (0,001), antara yang berminat dan tidak berminat menjadi bidan (P= 0,001). Tidak ada perbedaan nilai Indeks Prestasi mahasiswa antara mahasiswa yang berasal dari jurusan IPA atau IPS, jalur seleksi melalui Penelusuran Minat Dan Prestasi (PMDP) dan sipenmaru, penerapan SCL atau tidak, motivasi tinggi atau rendah dan sarana PBM dan peran PA yang mendukung atau mendukung terhadap prestasi belajar mahasiswa.Intelegensia merupakan faktor yang paling dominan dalam menentukan prestasi belajar mahasiswa D III Kebidanan. Seleksi mahasiswa baru tetap dapat dilakukan melalui dua jalur yaitu jalur PMDP dan test Sipenmaru. Test Psikotestdapat dipertimbangkan sebagai salah satu aspek penilaian

Polymyxin and lipopeptide antibiotics: membrane- targeting drugs of last resort

The polymyxin and lipopeptide classes of antibiotics are membrane-targeting drugs of last resort used to treat infections caused by multi-drug-resistant pathogens. Despite similar structures, these two antibiotic classes have distinct modes of action and clinical uses. The polymyxins target lipopolysaccharide in the membranes of most Gram-negative species and are often used to treat infections caused by carbapenem-resistant species such as Escherichia coli , Acinetobacter baumannii and Pseudomonas aeruginosa . By contrast, the lipopeptide daptomycin requires membrane phosphatidylglycerol for activity and is only used to treat infections caused by drug-resistant Gram-positive bacteria such as methicillin-resistant Staphylococcus aureus and vancomycin-resistant enterococci. However, despite having distinct targets, both antibiotic classes cause membrane disruption, are potently bactericidal in vitro and share similarities in resistance mechanisms. Furthermore, there are concerns about the efficacy of these antibiotics, and there is increasing interest in using both polymyxins and daptomycin in combination therapies to improve patient outcomes. In this review article, we will explore what is known about these distinct but structurally similar classes of antibiotics, discuss recent advances in the field and highlight remaining gaps in our knowledge

Subsampling effects in neuronal avalanche distributions recorded in vivo

Background Many systems in nature are characterized by complex behaviour where large cascades of events, or avalanches, unpredictably alternate with periods of little activity. Snow avalanches are an example. Often the size distribution f(s) of a system's avalanches follows a power law, and the branching parameter sigma, the average number of events triggered by a single preceding event, is unity. A power law for f(s), and sigma=1, are hallmark features of self-organized critical (SOC) systems, and both have been found for neuronal activity in vitro. Therefore, and since SOC systems and neuronal activity both show large variability, long-term stability and memory capabilities, SOC has been proposed to govern neuronal dynamics in vivo. Testing this hypothesis is difficult because neuronal activity is spatially or temporally subsampled, while theories of SOC systems assume full sampling. To close this gap, we investigated how subsampling affects f(s) and sigma by imposing subsampling on three different SOC models. We then compared f(s) and sigma of the subsampled models with those of multielectrode local field potential (LFP) activity recorded in three macaque monkeys performing a short term memory task. Results Neither the LFP nor the subsampled SOC models showed a power law for f(s). Both, f(s) and sigma, depended sensitively on the subsampling geometry and the dynamics of the model. Only one of the SOC models, the Abelian Sandpile Model, exhibited f(s) and sigma similar to those calculated from LFP activity. Conclusions Since subsampling can prevent the observation of the characteristic power law and sigma in SOC systems, misclassifications of critical systems as sub- or supercritical are possible. Nevertheless, the system specific scaling of f(s) and sigma under subsampling conditions may prove useful to select physiologically motivated models of brain function. Models that better reproduce f(s) and sigma calculated from the physiological recordings may be selected over alternatives