Metformin ameliorates the age‐related changes of d‐galactose administration in ovariectomized mice

Metformin (Met) has been shown to have pleiotropic effects such as neuroprotective, antioxidant, and anti‐inflammatory properties making that a potential candidate for the treatment of central nervous system (CNS) disorders. This study was designed to investigate the possible effect of Met on the d ‐galactose (d ‐gal)‐induced aging in ovariectomized mice. The female mice underwent bilateral ovariectomy. d ‐gal was administered orally at a dose of 500 mg/kg, and Met was administrated orally at doses of 1 and 10 mg/kg for 6 weeks. Anxiety‐like behavior was evaluated by the elevated plus‐maze. Physical power was assessed by vertical grid holding test and forced swimming capacity test. The brains were assessed for the level of superoxide dismutase (SOD) and brain‐derived neurotrophic factor (BDNF). Ovariectomy caused anxiety and declined the physical power as well as BDNF and SOD levels. d ‐gal administration in ovariectomized mice exacerbated these deleterious effects. Met hampered the anxiety‐like behavior and strengthened the physical power of d ‐gal‐treated ovariectomized mice. Met also increased the SOD and BDNF levels in the brains of d ‐gal‐treated ovariectomized animals. Based on the obtained results, we suggest Met administration as a novel therapeutic approach for the treatment of age‐related conditions in the absence of female sex hormones.     

Solid-state dye-sensitized solar cells based on Zn1−xSnxO nanocomposite photoanodes

Solid-state dye-sensitized solar cells (ss-DSSCs) comprising Sn²⁺-substituted ZnO nanopowder were purposefully tailored via a co-precipitation method. The solar cells assembled in this work were sensitized with N719 ruthenium dye and insinuated with spiro-OMeTAD as a solid hole transport layer (HTL). Evidently, significant enhancement in cell efficiency was accomplished with Sn²⁺ ions-substituted ZnO photoelectrodes by maintaining the weight ratio of SnO at 5%. The overall power conversion efficiency was improved from 3.0% for the cell with pure ZnO to 4.3% for the cell with 5% SnO substitution. The improvement in the cell efficiency with Sn²⁺-substituted ZnO photoelectrodes is attributed to the considerably large surface area of the nanopowders for dye adsorption, efficient charge separation and the suppression of charge recombination provided by SnO. Furthermore, the energy distinction between the conduction band edges of SnO and ZnO implied a type II band alignment. Moreover, the durability as well as the stability of 15 assembled cells were studied to show the outstanding long-term stability of the devices made of Sn²⁺ ion substituted ZnO, and the PCE of each cell remained stable and ∼96% of its primary value was retained for up to 100 h. Subsequently, the efficacy was drastically reduced to ∼35% after 250 h of storage.

Solid-state dye-sensitized solar cells (ss-DSSCs) comprising Sn²⁺-substituted ZnO nanopowder were purposefully tailored via a co-precipitation method.     

Central macular thickness in patients with sickle cell disease and no signs of retinopathy: a cross-sectional study of Jordanian patients

ObjectivesTo measure central macular thickness in Jordanian patients with sickle cell disease who did not have retinopathy and compare the findings with age- and sex-matched controls using spectral domain optical coherence tomography (SDOCT).MethodsIn this cross-sectional study, participants underwent visual acuity testing, slit-lamp bio-microscopy, dilated ophthalmoscopy, and SDOCT imaging to measure central macular thickness. Macular quadrant measurements and thickness difference indexes (TDIs) were compared between groups.ResultsTwenty eyes with sickle cell disease and 20 control eyes were enrolled. The median visual acuity in both groups was 20/20. The mean macular thickness was significantly lower in eyes with sickle cell disease than in matched controls (mean difference, 22.15 ± 6.44 µm). Peripheral quadrants were all significantly thinner in eyes with sickle cell disease, especially in superior and temporal quadrants. TDIs were lower in eyes with sickle cell disease than in control eyes.ConclusionsEyes with sickle cell disease that had no clinical evidence of retinopathy exhibited significantly lower central macular thickness in all quadrants, compared with eyes in age- and sex-matched controls. SDOCT is a non-invasive imaging modality that can detect preclinical changes in eyes with sickle cell disease and can be used to screen and monitor the disease process.     

Model-free decision making is prioritized when learning to avoid harming others

Moral behavior requires learning how our actions help or harm others. Theoretical accounts of learning propose a key division between “model-free” algorithms that cache outcome values in actions and “model-based” algorithms that map actions to outcomes. Here, we tested the engagement of these mechanisms and their neural basis as participants learned to avoid painful electric shocks for themselves and a stranger. We found that model-free decision making was prioritized when learning to avoid harming others compared to oneself. Model-free prediction errors for others relative to self were tracked in the thalamus/caudate. At the time of choice, neural activity consistent with model-free moral learning was observed in subgenual anterior cingulate cortex (sgACC), and switching after harming others was associated with stronger connectivity between sgACC and dorsolateral prefrontal cortex. Finally, model-free moral learning varied with individual differences in moral judgment. Our findings suggest moral learning favors efficiency over flexibility and is underpinned by specific neural mechanisms.

A central component of human morality is a prohibition against harming others (1, 2). People readily avoid actions that might harm another person (3–7), and this basic harm aversion is so strong that many people even find it distressing to perform pretend harmful actions, such as shooting someone with a fake gun (8). Harm aversion is disrupted in clinical disorders such as psychopathy that have a strong developmental component (9), and although harm aversion is robust in healthy adults, anyone who has watched young children fighting over a coveted toy knows that such an aversion is not present from birth. Indeed, a large literature documents the emergence of moral conduct over the course of development (7, 10, 11). Cross-cultural differences in morality suggest moral behavior is fine-tuned to local environmental demands (12), and laboratory experiments demonstrate how individuals can quickly adapt moral behavior to changing norms (13, 14). All this evidence highlights a critical role for learning in the development of harm aversion and moral behavior more broadly (6). Once having learned as children that harming others is morally wrong, adults still need to learn which actions to take to avoid harm in novel contexts.Recent work in computational neuroscience has advanced our knowledge of how organisms learn the value of actions and outcomes via reward and punishment (15, 16). An important theoretical distinction has been made between “model-based” and “model-free” learning systems (17, 18). Model-based learning is often described as deliberative learning, whereas model-free learning is thought to be habitual. The model-based system builds a “world model” of the environment and selects actions by prospectively searching the model for the best course of action (19, 20). In contrast, the computationally efficient model-free system assigns values to actions simply through trial and error. The distinction between these systems can be illustrated by giving the example of how we navigate home from work. The model-based system could easily replan if a particular route home was unexpectedly blocked, whereas a purely model-free learner can only plan a route home by directly experiencing each of the different routes (21). These two systems are also somewhat neurally dissociable, with model-based learning preferentially engaging lateral prefrontal cortex (LPFC), posterior parietal cortex, and caudate (20, 22, 23) and model-free learning preferentially engaging putamen (24, 25), although both systems update their representations via prediction errors encoded in overlapping regions of ventral striatum (20). Model-based and model-free systems often make similar recommendations about which actions are more valuable, but when they conflict an arbitration process allocates control between them (12, 13, 23, 26, 27). However, despite extensive theorizing that the model-based/model-free distinction may help to characterize puzzling features of moral learning and decision making (3, 28–30), it remains unknown whether the moral consequences of actions affect the balance between model-based and model-free control, and whether common or distinct neural processes are engaged when learning to avoid harmful outcomes to self and others.Past work on the neural basis of moral decision making provides support for competing hypotheses. On the one hand, the sophistication of human morality seems to demand the kinds of complex representations afforded by model-based learning, suggesting learning to avoid harming others may preferentially engage the model-based system. Supporting this view, people are easily able to learn to avoid harmful actions without directly experiencing their outcomes, in line with a model-based learning strategy when avoiding harm to others (3, 28, 31). Moreover, moral decision making in healthy adults consistently engages brain regions most strongly associated with the model-based system, including LPFC, caudate, and temporoparietal junction (TPJ) (24, 26, 32). Deciding to follow moral norms like fairness and honesty, and enforcing those norms on others via costly punishment, engages LPFC (33–38), and disrupting LPFC function reduces moral norm compliance and enforcement (39, 40). During decisions to avoid harming others, LPFC encodes the blameworthiness of harmful choices and modulates action values in caudate and thalamus (4), two subcortical areas shown to play a critical role in associative learning and pain processing as well as moral decision making (41–46).On the other hand, one principal function of model-free learning is to cache value in actions that are reliably adaptive, sacrificing flexibility for efficiency. Given that harming others is typically prohibited, actions that harm others may represent a special class of actions that are prioritized for model-free learning, similar to how certain classes of stimuli, like snakes and spiders, are “prepared” for aversive classical conditioning (47). In other words, since avoiding harm to others is hugely important for social life, learning processes that fast-track harm-avoidant action selection to a habitual, automatic process may be socially adaptive. Supporting this view, recent work suggests that morality constrains mental representations of what actions are considered possible; harmful actions are removed from choice sets as a default (48), and choices that harm others are slower than helpful choices, suggesting an automatic tendency to avoid harm (5, 49–51). Furthermore, recent studies of model-free learning to gain rewards for oneself and others have highlighted a distinct encoding of prediction errors concerning others’ outcomes in the subgenual anterior cingulate cortex (sgACC) (52, 53), a region that has been implicated in social and moral decision making more broadly (53–57). Model-free processes that distinguish learning about how one’s actions affect others could provide a neural mechanism for prioritizing model-free learning in moral contexts.To test these competing hypotheses, we used computational modeling and functional MRI (fMRI) to probe the relative balance between model-based vs. model-free processes, and their neural bases, when people learn to avoid moderately painful electric shocks for themselves and a stranger. Forty-one participants attended a 3.5-h experimental session. After undergoing an extensive pain thresholding procedure (Methods), they completed a hybrid version of two paradigms previously proposed to reliably dissociate model-free vs. model-based learning (Fig. 1) (20, 23, 32). We optimized the task in a way that allowed us to address the specific hypotheses examined in the present study (see SI Appendix, Supplementary Text for details) and included as many as 272 trials per participant to accurately sample decisions for both self and other. Our final analysis included 36 participants who made a total of 9,792 choices.Open in a separate windowFig. 1.Model-free and model-based aversive learning task. Participants completed a two-stage decision-making task to assess the tendency to engage in model-free and model-based learning. The task was a hybrid of two tasks previously shown to assess model-free and model-based learning processes (20, 27). We used this task to probe learning to avoid aversive (shock) outcomes for either oneself or another person (the “receiver,” referred to as “other” hereafter). At the beginning of each block, an instruction cue signaled the recipient of the outcome (self or other). At the first stage, two images were displayed that probabilistically led to one of two states (common [∼70% of the time] or uncommon [rare] transition [∼30% of the time]), depicted by different colors surrounding the boxes. In this example, to “blue zone” or “yellow zone” for the other participant and “turquoise zone” or “purple zone” for self. Participants then made a second choice between two pictures in the colored zone which was followed by an outcome of shock or no shock. The probability with which the boxes at the second stage delivered a shock or no-shock outcome drifted throughout the experiment (bounded between 0 and 1 with a drift rate of 0.2) and participants were instructed to keep learning throughout. Ten percent of the total electric shocks accumulated in the “self” condition were delivered to the participant themselves at the end of the experiment, while 10% of the electric shocks accumulated in the “other” condition were delivered to the partner participant.     

A Golgi study of neurons in the camel cerebellum (Camelus dromedarius)

Neurons in the cerebellar cortex of camels were studied using modified Golgi impregnation methods. Neurons were classified according to their position, morphology of their soma, density and distribution of dendrites, and the course of their axons. Accordingly, eight types of neurons were identified. Three types were found in the molecular layer: upper and lower stellate cells and basket cells, and four types were found in the granular layer: granule cells, Golgi Type II cells, Lugaro cells, and unipolar brush cells. Only the somata of Purkinje cells were found in the Purkinje cell layer. The molecular layer is characterized by the presence of more dendrites, dendritic spines, and transverse fibers. Golgi cells also show extensive dendritic branching and spines. The results illustrate the neuronal features of the camel cerebellum as a large mammal living in harsh environmental conditions. These findings should contribute to advancing our understanding of species-comparative anatomy in achieving better coordination of motor activity.     

Voiceless Arabic vowels recognition using facial EMG

This work attempts to recognize the Arabic vowels based on facial electromyograph (EMG) signals, to be used for people with speech impairment and for human computer interface. Vowels were selected since they are the most difficult letters to recognize by people in Arabic language. Twenty subjects (7 females and 13 males) were asked to pronounce three Arabic vowels continuously in a random order. Facial EMG signals were recorded over three channels from the three main facial muscles that are responsible for speech. The EMG signals are then pre-processed to eliminate noise and interference signals. Segmentation procedure was implemented to extract the time event that corresponds to each vowel based on a moving standard deviation window. The accuracy of the segmentation procedure was found to be 94%. The recognition of the vowels was carried out by extracting features from the EMG in three domains: the temporal, the spectral, and the time frequency using the wavelet packet transform. Classification of the extracted features was then finally performed using different classification methods implemented in the WEKA software. The random forest classifier with time frequency features showed the best performance with an accuracy of 77% evaluated using a 10-fold cross-validation.