Sorting of multiple molecular species on cell membranes

Eukaryotic cells maintain their inner order by a hectic process of distillation of molecular factors taking place on the surface of their lipid membranes. To understand the properties of this molecular sorting process, a physical model of the process has been recently proposed [arXiv:1811.06760], based on (a) the phase separation of a single, initially dispersed molecular species into spatially localized sorting domains on the lipid membrane, and (b) domain-induced membrane bending leading to the nucleation of submicrometric lipid vesicles, naturally enriched in the molecules of the engulfed sorting domain. The analysis of the model has shown the existence of an optimal region of the parameter space where sorting is most efficient. Here, the model is extended to account for the simultaneous distillation of a pool of distinct molecular species. We find that the mean time spent by sorted molecules on the membrane increases with the heterogeneity of the pool (i.e., the number of distinct molecular species sorted) according to a simple scaling law, and that a large number of distinct molecular species can in principle be sorted in parallel on a typical cell membrane region without significantly interfering with each other. Moreover, sorting is found to be most efficient when the distinct molecular species have comparable homotypic affinities. We also consider how valence (i.e., the average number of interacting neighbors of a molecule in a sorting domain) affects the sorting process, finding that higher-valence molecules can be sorted with greater efficiency than lower-valence molecules

Android source code vulnerability detection: a systematic literature review

The use of mobile devices is rising daily in this technological era. A continuous and increasing number of mobile applications are constantly offered on mobile marketplaces to fulfil the needs of smartphone users. Many Android applications do not address the security aspects appropriately. This is often due to a lack of automated mechanisms to identify, test, and fix source code vulnerabilities at the early stages of design and development. Therefore, the need to fix such issues at the initial stages rather than providing updates and patches to the published applications is widely recognized. Researchers have proposed several methods to improve the security of applications by detecting source code vulnerabilities and malicious codes. This Systematic Literature Review (SLR) focuses on Android application analysis and source code vulnerability detection methods and tools by critically evaluating 118 carefully selected technical studies published between 2016 and 2022. It highlights the advantages, disadvantages, applicability of the proposed techniques and potential improvements of those studies. Both Machine Learning (ML) based methods and conventional methods related to vulnerability detection are discussed while focusing more on ML-based methods since many recent studies conducted experiments with ML. Therefore, this paper aims to enable researchers to acquire in-depth knowledge in secure mobile application development while minimizing the vulnerabilities by applying ML methods. Furthermore, researchers can use the discussions and findings of this SLR to identify potential future research and development directions

Relationship between blood remifentanil concentration and stress hormone levels during pneumoperitoneum in patients undergoing laparoscopic cholecystectomy

The effect of remifentanil on stress response to surgery is unclear. However, there are not clinical studies investigating the relationship between blood remifentanil concentrations and stress hormones. Therefore, the aim of the present study was to assess the association between blood remifentanil concentrations measured after pneumoperitoneum and cortisol (CORT) or prolactin (PRL) ratio (intraoperative/preoperative value), in patients undergoing laparoscopic cholecystectom

The effect of sucralfate-containing ointment on quality of life in people with symptoms associated with haemorrhoidal disease and its complications: The results of the emocare survey

Background and aim: A rectal ointment containing 3% of sucralfate and herbal extracts (calendula, witch hazel leaf (hamamelis), chamomile), became available in Italy in 2019 for the treatment of symptoms associated with haemorrhoidal disease. This survey evaluated the effect of the mentioned sucralfate ointment, on quality of life (QoL) and symptom frequency in participants seeking treatment for haemorrhoidal disease from community pharmacies in Italy. Methods: EMOCARE was a multicentre prospective survey conducted at community pharmacies in Italy. Eligible participants (≥18 years) were those with haemorrhoidal symptoms in the last 7 days and were willing to initiate a treatment with the sucralfate ointment and herbal extracts (calendula, witch hazel leaf (hamamelis), chamomile). A survey was administered by the investigating pharmacists at the beginning and end (~14 days) of treatment. The primary endpoint was the change in HEMO-FISS-QoL scores. Results: Of the 290 (mean age 53.1 years old; 58.3% female) enrolled, 287 attended the follow-up visit. After a mean duration of 13 days, the sucralfate ointment significantly improved total HEMO-FISS-QoL scores (mean change from baseline: -10.41; 95%CI -11.95, -8.86; P<0.001) and mean scores for all domains of the HEMO-FISS-QoL scale (-11.13 [95%CI -12.95, -9.30] for physical disorders, -6.14 [95%CI -7.42, -4.85] for psychology, -18.79 [95% CI -21.67, -15.90] for defaecation, and -6.46 [95%CI -8.40, -4.51] for sexuality; all P<0.001 versus baseline). At the end of treatment, 39.4% of participants reported that they no longer had haemorrhoidal symptoms and the frequency of all assessed symptoms were reduced significantly from baseline (all P<0.05). Conclusions: After a mean 13 days of treatment  the sucralfate ointment with herbal extracts improved HEMO-FISS-QoL scores and reduced symptoms in people with haemorrhoidal disease

Lesion mapping and functional characterization of hemiplegic children with different patterns of hand manipulation

Brain damage in children with unilateral cerebral palsy (UCP) affects motor function, with varying severity, making it difficult the performance of daily actions. Recently, qualitative and semi-quantitative methods have been developed for lesion classification, but studies on mild to moderate hand impairment are lacking. The present study aimed to characterize lesion topography and preserved brain areas in UCP children with specific patterns of hand manipulation. A homogeneous sample of 16 UCP children, aged 9 to 14 years, was enrolled in the study. Motor assessment included the characterization of the specific pattern of hand manipulation, by means of unimanual and bimanual measures (Kinematic Hand Classification, KHC; Manual Ability Classification System, MACS; House Functional Classification System, HFCS; Melbourne Unilateral Upper Limb Assessment, MUUL; Assisting Hand Assessment, AHA). The MRI morphological study included multiple methods: (a) qualitative lesion classification, (b) semi-quantitative classification (sq-MRI), (c) voxel-based morphometry comparing UCP and typically developed children (VBM-DARTEL), and (d) quantitative brain tissue segmentation (q-BTS). In addition, functional MRI was used to assess spared functional activations and cluster lateralization in the ipsilesional and contralesional hemispheres of UCP children during the execution of simple movements and grasping actions with the more affected hand. Lesions most frequently involved the periventricular white matter, corpus callosum, posterior limb of the internal capsule, thalamus, basal ganglia and brainstem. VMB-DARTEL analysis allowed to detect mainly white matter lesions. Both sq-MRI classification and q-BTS identified lesions of thalamus, brainstem, and basal ganglia. In particular, UCP patients with synergic hand pattern showed larger involvement of subcortical structures, as compared to those with semi-functional hand. Furthermore, sparing of gray matter in basal ganglia and thalamus was positively correlated with MUUL and AHA scores. Concerning white matter, q-BTS revealed a larger damage of fronto-striatal connections in patients with synergic hand, as compared to those with semi-functional hand. The volume of these connections was correlated to unimanual function (MUUL score). The fMRI results showed that all patients, but one, including those with cortical lesions, had activation in ipsilesional areas, regardless of lesion timing. Children with synergic hand showed more lateralized activation in the ipsilesional hemisphere both during grasping and simple movements, while children with semi-functional hand exhibited more bilateral activation during grasping. The study demonstrates that lesion localization, rather than lesion type based on the timing of their occurrence, is more associated with the functional level of hand manipulation. Overall, the preservation of subcortical structures and white matter can predict a better functional outcome. Future studies integrating different techniques (structural and functional imaging, TMS) could provide further evidence on the relation between brain reorganization and specific pattern of manipulation in UCP children

Stress evaluation in simulated autonomous and manual driving through the analysis of skin potential response and electrocardiogram signals

The evaluation of car drivers\u2019 stress condition is gaining interest as research on Autonomous Driving Systems (ADS) progresses. The analysis of the stress response can be used to assess the acceptability of ADS and to compare the driving styles of different autonomous drive algorithms. In this contribution, we present a system based on the analysis of the Electrodermal Activity Skin Potential Response (SPR) signal, aimed to reveal the driver\u2019s stress induced by different driving situations. We reduce motion artifacts by processing two SPR signals, recorded from the hands of the subjects, and outputting a single clean SPR signal. Statistical features of signal blocks are sent to a Supervised Learning Algorithm, which classifies between stress and normal driving (non-stress) conditions. We present the results obtained from an experiment using a professional driving simulator, where a group of people is asked to undergo manual and autonomous driving on a highway, facing some unexpected events meant to generate stress. The results of our experiment show that the subjects generally appear more stressed during manual driving, indicating that the autonomous drive can possibly be well received by the public. During autonomous driving, however, significant peaks of the SPR signal are evident during unexpected events. By examining the electrocardiogram signal, the average heart rate is generally higher in the manual case compared to the autonomous case. This further supports our previous findings, even if it may be due, in part, to the physical activity involved in manual driving

Supervised learning techniques for stress detection in car drivers

6noIn this paper we propose the application of supervised learning techniques to recognize stress situations in drivers by analyzing their Skin Potential Response (SPR) and the Electrocardiogram (ECG). A sensing device is used to acquire the SPR from both hands of the drivers, and the ECG from their chest. We also consider a motion artifact removal algorithm that allows the generation of a single cleaned SPR signal, starting from the two SPR signals, which could be characterized by artifacts due to vibrations or movements of the hands on the wheel. From both the cleaned SPR and the ECG signals we compute some statistical features that are used as input to six Machine Learning Algorithms for stress or non-stress episodes classification. The SPR and ECG signals are also used as input to Deep Learning Algorithms, thus allowing us to compare the performance of the different classifiers. The experiments have been carried out in a firm specialized in developing professional car driving simulators. In particular, a dynamic driving simulator has been used, with subjects driving along a straight road affected by some unanticipated stress-evoking events, located at different positions. We obtain an accuracy of 88.13% in stress recognition using a Long Short-Term Memory (LSTM) network.openopenZontone P.; Affanni A.; Bernardini R.; Del Linz L.; Piras A.; Rinaldo R.Zontone, P.; Affanni, A.; Bernardini, R.; Del Linz, L.; Piras, A.; Rinaldo, R

Explainable Machine Learning Exploiting News and Domain-Specific Lexicon for Stock Market Forecasting

In this manuscript, we propose a Machine Learning approach to tackle a binary classification problem whose goal is to predict the magnitude (high or low) of future stock price variations for individual companies of the SP 500 index. Sets of lexicons are generated from globally published articles with the goal of identifying the most impactful words on the market in a specific time interval and within a certain business sector. A feature engineering process is then performed out of the generated lexicons, and the obtained features are fed to a Decision Tree classifier. The predicted label (high or low) represents the underlying company's stock price variation on the next day, being either higher or lower than a certain threshold. The performance evaluation we have carried out through a walk-forward strategy, and against a set of solid baselines, shows that our approach clearly outperforms the competitors. Moreover, the devised Artificial Intelligence (AI) approach is explainable, in the sense that we analyze the white-box behind the classifier and provide a set of explanations on the obtained results

Synthesis of dimethyl carbonate by transesterification of propylene carbonate with methanol on ceo2-la2o3 oxides prepared by the soft template method

In this study, CeO2, La2O3, and CeO2-La2O3 mixed oxide catalysts with different Ce/La molar ratios were prepared by the soft template method and characterized by different techniques, including inductively coupled plasma atomic emission spectrometry, X-ray diffraction, N2 physisorption, thermogravimetric analysis, and Raman and Fourier transform infrared spectroscopies. NH3 and CO2 adsorption microcalorimetry was also used for assessing the acid and base surface properties, respectively. The behavior of the oxides as catalysts for the dimethyl carbonate synthesis by the transesterification of propylene carbonate with methanol, at 160 °C under autogenic pressure, was studied in a stainless-steel batch reactor. The activity of the catalysts was found to increase with an increase in the basic sites density. The formation of dimethyl carbonate was favored on medium-strength and weak basic sites, while it underwent decomposition on the strong ones. Several parasitic reactions occurred during the transformation of propylene carbonate, depending on the basic and acidic features of the catalysts. A reaction pathway has been proposed on the basis of the components identified in the reaction mixture