Divergent evolution of developmental timing in the neocortex revealed by marsupial and eutherian transcriptomes

Only mammals evolved a neocortex, which integrates sensory-motor and cognitive functions. Significant diversifications in the cellular composition and connectivity of the neocortex occurred between the two main Therian groups: marsupials and eutherians. However, the developmental mechanisms underlying these diversifications are largely unknown. Here, we compared the neocortical transcriptomes of Sminthopsis crassicaudata, a mouse-sized marsupial, with those of eutherian mice at two developmentally equivalent timepoints corresponding to deeper and upper layer neuron generation. Enrichment analyses revealed more mature gene networks in marsupials at the early stage, which reverted at the later stage, suggesting a more precocious but protracted neuronal maturation program relative to birth timing of cortical layers. We ranked genes expressed in different species and identified important differences in gene expression rankings between species. For example, genes known to be enriched in upper-layer cortical projection neuron subtypes, such as Cux1, Lhx2 and Satb2, likely relating to corpus callosum emergence in eutherians. These results show molecular heterochronies of neocortical development in Theria, and highlight changes in gene expression and cell type composition that may underlie neocortical evolution and diversification.

Any neuron can perform linearly non-separable computations

Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturating dendrites. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with dendrites has more computational capacity than without. Because any neuron has one or more layer, and all dendrites do saturate, we show that any dendrited neuron can implement linearly non-separable computations.

Any neuron can perform linearly non-separable computations

Multiple studies have shown how dendrites enable some neurons to perform linearly non-separable computations. These works focus on cells with an extended dendritic arbor where voltage can vary independently, turning dendritic branches into local non-linear subunits. However, these studies leave a large fraction of the nervous system unexplored. Many neurons, e.g. granule cells, have modest dendritic trees and are electrically compact. It is impossible to decompose them into multiple independent subunits. Here, we upgraded the integrate and fire neuron to account for saturating dendrites. This artificial neuron has a unique membrane voltage and can be seen as a single layer. We present a class of linearly non-separable computations and how our neuron can perform them. We thus demonstrate that even a single layer neuron with dendrites has more computational capacity than without. Because any neuron has one or more layer, and all dendrites do saturate, we show that any dendrited neuron can implement linearly non-separable computations.

Biphasic roles of hedgehog signaling in the production and self-renewal of outer radial glia in the ferret cerebral cortex

The neocortex, the center for higher brain function, emerged in mammals and expanded in the course of evolution. The expansion of outer radial glia (oRGs) and intermediate progenitor cells (IPCs) plays key roles in the expansion and consequential folding of the neocortex. Therefore, understanding the mechanisms of oRG and IPC expansion is important for understanding neocortical development and evolution. By using mice and human cerebral organoids, we previously revealed that hedgehog (HH) signaling expands oRGs and IPCs. Nevertheless, it remained to be determined whether HH signaling expanded oRGs and IPCs in vivo in gyrencephalic species, in which oRGs and IPCs are naturally expanded. Here, we show that HH signaling is necessary and sufficient to expand oRGs and IPCs in ferrets, a gyrencephalic species, through conserved cellular mechanisms. HH signaling increases oRG-producing division modes of ventricular radial glia (vRGs), oRG self-renewal, and IPC proliferation. Notably, HH signaling affects vRG division modes only in an early restricted phase before superficial-layer neuron production peaks. Beyond this restricted phase, HH signaling promotes oRG self-renewal. Thus, HH signaling expands oRGs and IPCs in two distinct but continuous phases during cortical development.

MiR-137 and miR-122, two outer subventricular zone-enriched non-coding RNAs, regulate basal progenitor expansion and neuronal differentiation

Cortical expansion in the primate brain relies on the presence and the spatial enlargement of multiple germinal zones during development and on a prolonged developmental period. In contrast to other mammals, which have two cortical germinal zones, the ventricular zone (VZ) and subventricular zone (SVZ), gyrencephalic species display an additional germinal zone, the outer subventricular zone (OSVZ), which role is to increase the number and types of neurons generated during corticogenesis. How the OSVZ emerged during evolution is poorly understood but recent studies suggest a role for non-coding RNAs, which allow tight regulations of transcriptional programs in time and space during development (Dehay et al. 2015; Arcila et al., 2014). Here, using in vivo functional genetics, single-cell RNA sequencing, live imaging and electrophysiology to assess progenitor and neuronal properties in mice, we identify two ferret and human OSVZ-enriched microRNAs (miR), miR-137 and miR-122, which regulate key cellular features associated with cortical expansion. MiR-137 promotes basal progenitor self-replication and superficial layer neuron fate, while miR-122 slows down neuronal differentiation pace. Together, these findings support a cell-type specific role for miR-mediated transcriptional regulation in cortical expansion.

Desain Sistem Informasi Cerdas pada Smart Grid Berbasis Internet of Things dan Artificial Neural Network

Smart grid merupakan sistem kelistrikan yang memungkinkan pengguna untuk melakukan proses menjual dan membeli daya listrik. Pada penelitian ini dirancang model smart grid dengan sumber daya dari listrik PLN dan panel surya yang terhubung dengan beban. Beban yang digunakan memiliki daya maksimal 40 W dan panel surya yang digunakan memiliki kapasitas 100 Wp. ESP32 digunakan sebagai perangkat Internet of Things, yang digunakan sebagai pengukur dan pengontrol daya listrik yang akan dijual atau dibeli. Raspberry Pi digunakan sebagai web server pengolah data dari smart grid. Aplikasi “Smart Grid Dikti” merupakan aplikasi berbasis android yang dapat digunakan untuk melakukan pemantauan serta pengaturan dalam sistem smart grid tersebut. Aplikasi android tersebut telah diuji coba dengan metode Black Box, dengan hasil pengujian 100% berhasil. Kecerdasan buatan berbasis Artificial Neural Network (ANN) dengan metode backpropagation diimplementasikan dalam sistem smart grid yang berfungsi sebagai pengaturan otomatis dalam proses jual dan beli daya listrik. ANN yang digunakan memiliki 3 input, 2 layer neuron, 3 output, dan masing-masing layer memiliki 4 neuron yang diimplementasikan ke dalam bahasa Python. Setelah pelatihan sebanyak 11.000 kali, didapatkan Root Mean Square Error (RMSE) sebesar 0,12151 dan pada saat uji coba didapatkan RMSE sebesar 0,10500 dengan akurasi rata-rata sebesar 89,50%.

FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces

In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis. They are widely adopted in synthesizing facial images which brings potential security concerns to humans as the fakes spread and fuel the misinformation. However, robust detectors of these AI-synthesized fake faces are still in their infancy and are not ready to fully tackle this emerging challenge. In this work, we propose a novel approach, named FakeSpotter, based on monitoring neuron behaviors to spot AI-synthesized fake faces. The studies on neuron coverage and interactions have successfully shown that they can be served as testing criteria for deep learning systems, especially under the settings of being exposed to adversarial attacks. Here, we conjecture that monitoring neuron behavior can also serve as an asset in detecting fake faces since layer-by-layer neuron activation patterns may capture more subtle features that are important for the fake detector. Experimental results on detecting four types of fake faces synthesized with the state-of-the-art GANs and evading four perturbation attacks show the effectiveness and robustness of our approach.

Extracellular matrix-inducing Sox9 promotes both basal progenitor proliferation and gliogenesis in developing neocortex

Neocortex expansion is largely based on the proliferative capacity of basal progenitors (BPs), which is increased by extracellular matrix (ECM) components via integrin signaling. Here we show that the transcription factor Sox9 drives expression of ECM components and that laminin 211 increases BP proliferation in embryonic mouse neocortex. We show that Sox9 is expressed in human and ferret BPs and is required for BP proliferation in embryonic ferret neocortex. Conditional Sox9 expression in the mouse BP lineage, where it normally is not expressed, increases BP proliferation, reduces Tbr2 levels and induces Olig2 expression, indicative of premature gliogenesis. Conditional Sox9 expression also results in cell-non-autonomous stimulation of BP proliferation followed by increased upper-layer neuron production. Our findings demonstrate that Sox9 exerts concerted effects on transcription, BP proliferation, neuron production, and neurogenic vs. gliogenic BP cell fate, suggesting that Sox9 may have contributed to promote neocortical expansion.

PDK1 Regulates Transition Period of Apical Progenitors to Basal Progenitors by Controlling Asymmetric Cell Division

The six-layered neocortex consists of diverse neuron subtypes. Deeper-layer neurons originate from apical progenitors (APs), while upper-layer neurons are mainly produced by basal progenitors (BPs), which are derivatives of APs. As development proceeds, an AP generates two daughter cells that comprise an AP and a deeper-layer neuron or a BP. How the transition of APs to BPs is spatiotemporally regulated is a fundamental question. Here, we report that conditional deletion of phoshpoinositide-dependent protein kinase 1 (PDK1) in mouse developing cortex achieved by crossing Emx1Cre line with Pdk1fl/fl leads to a delayed transition of APs to BPs and subsequently causes an increased output of deeper-layer neurons. We demonstrate that PDK1 is involved in the modulation of the aPKC-Par3 complex and further regulates the asymmetric cell division (ACD). We also find Hes1, a downstream effecter of Notch signal pathway is obviously upregulated. Knockdown of Hes1 or treatment with Notch signal inhibitor DAPT recovers the ACD defect in the Pdk1 cKO. Thus, we have identified a novel function of PDK1 in controlling the transition of APs to BPs.

Empirical model for quantification of confidentiality in OO system

The coupling or aggregation binds together the different entities or components within the system. An external process when takes or try to take the control of the system will be assisted in its action if the underlying system is highly coupled. A highly coupled design degrades the ability of software to defend against exploitation. Thus from a software developer’s point of view, we must provide so much security at design time that no one outside the system should be able to access in unauthorized way. It is to insure that information leakage is minimal (if not zero as is desired theoretically). This research work done quantitatively, describes the ability of object oriented coupling metrics to predict faulty classes. There are two major section of this paper. One section covers the ability of multi layer neuron perceptron model for prediction of faulty classes and in other section we have proposed and validated a statistical model for confidentiality using data set of dif-ferent releases of apache velocity project so as to quantify the effects of coupling on confidentiality of system.