Detecting correlations among functional-sequence motifs

Sequence motifs are words of nucleotides in DNA with biological functions, e.g., gene regulation. Identification of such words proceeds through rejection of Markov models on the expected motif frequency along the genome. Additional biological information can be extracted from the correlation structure among patterns of motif occurrences. In this paper a log-linear multivariate intensity Poisson model is estimated via expectation maximization on a set of motifs along the genome of E. coli K12. The proposed approach allows for excitatory as well as inhibitory interactions among motifs and between motifs and other genomic features like gene occurrences. Our findings confirm previous stylized facts about such types of interactions and shed new light on genome-maintenance functions of some particular motifs. We expect these methods to be applicable to a wider set of genomic features

Combined Analysis of Cortical (EEG) and Nerve Stump Signals Improves Robotic Hand Control

Background. Interfacing an amputee's upper-extremity stump nerves to control a robotic hand requires training of the individual and algorithms to process interactions between cortical and peripheral signals. Objective. To evaluate for the first time whether EEG-driven analysis of peripheral neural signals as an amputee practices could improve the classification of motor commands. Methods. Four thin-film longitudinal intrafascicular electrodes (tf-LIFEs-4) were implanted in the median and ulnar nerves of the stump in the distal upper arm for 4 weeks. Artificial intelligence classifiers were implemented to analyze LIFE signals recorded while the participant tried to perform 3 different hand and finger movements as pictures representing these tasks were randomly presented on a screen. In the final week, the participant was trained to perform the same movements with a robotic hand prosthesis through modulation of tf-LIFE-4 signals. To improve the classification performance, an event-related desynchronization/synchronization (ERD/ERS) procedure was applied to EEG data to identify the exact timing of each motor command. Results. Real-time control of neural (motor) output was achieved by the participant. By focusing electroneurographic (ENG) signal analysis in an EEG-driven time window, movement classification performance improved. After training, the participant regained normal modulation of background rhythms for movement preparation (?/? band desynchronization) in the sensorimotor area contralateral to the missing limb. Moreover, coherence analysis found a restored ? band synchronization of Rolandic area with frontal and parietal ipsilateral regions, similar to that observed in the opposite hemisphere for movement of the intact hand. Of note, phantom limb pain (PLP) resolved for several months. Conclusions. Combining information from both cortical (EEG) and stump nerve (ENG) signals improved the classification performance compared with tf-LIFE signals processing alone; training led to cortical reorganization and mitigation of PLP

A Fluorescent Dye Method Suitable for Visualization of One or More Rat Whiskers

Financial support of the Human Frontier Science Program (http://www.Hfsp.org; project RG0015/2013), the European Research Council Advanced grants CONCEPT (http://erc.europa.eu; project 294498) and MicroMotility (project 340685), and Italian MIUR grant HANDBOT (http://hubmiur.pubblica.istruzione.it/web/ricerca/home; project GA 280778). GN gratefully acknowledges support by SISSA through the excellence program NOFYSAS 2012. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Decoding of grasping information from neural signals recorded using peripheral intrafascicular interfaces

The restoration of complex hand functions by creating a novel bidirectional link between the nervous system and a dexterous hand prosthesis is currently pursued by several research groups. This connection must be fast, intuitive, with a high success rate and quite natural to allow an effective bidirectional flow of information between the user's nervous system and the smart artificial device. This goal can be achieved with several approaches and among them, the use of implantable interfaces connected with the peripheral nervous system, namely intrafascicular electrodes, is considered particularly interesting

Engagement of the rat hindlimb motor cortex across natural locomotor behaviors

Contrary to cats and primates, cortical contribution to hindlimb locomotor movements is not critical in rats. However, the importance of the motor cortex to regain locomotion after neurological disorders in rats suggests that cortical engagement in hindlimb motor control may depend on the behavioral context. To investigate this possibility, we recorded whole-body kinematics, muscle synergies, and hindlimb motor cortex modulation in freely moving rats performing a range of natural locomotor procedures. We found that the activation of hindlimb motor cortex preceded gait initiation. During overground locomotion, the motor cortex exhibited consistent neuronal population responses that were synchronized with the spatiotemporal activation of hindlimb motoneurons. Behaviors requiring enhanced muscle activity or skilled paw placement correlated with substantial adjustment in neuronal population responses. In contrast, all rats exhibited a reduction of cortical activity during more automated behavior, such as stepping on a treadmill. Despite the facultative role of the motor cortex in the production of locomotion in rats, these results show that the encoding of hindlimb features in motor cortex dynamics is comparable in rats and cats. However, the extent of motor cortex modulations appears linked to the degree of volitional engagement and complexity of the task, reemphasizing the importance of goal-directed behaviors for motor control studies, rehabilitation, and neuroprosthetics. © 2016 the authors

Microneurography as a tool to develop decoding algorithms for peripheral neuro-controlled hand prostheses

The usability of dexterous hand prostheses is still hampered by the lack of natural and effective control strategies. A decoding strategy based on the processing of descending efferent neural signals recorded using peripheral neural interfaces could be a solution to such limitation. Unfortunately, this choice is still restrained by the reduced knowledge of the dynamics of human efferent signals recorded from the nerves and associated to hand movements.Findings: To address this issue, in this work we acquired neural efferent activities from healthy subjects performing hand- related tasks using ultrasound-guided microneurography, a minimally invasive technique, which employs needles, inserted percutaneously, to record from nerve fibers. These signals allowed us to identify neural features correlated with force and velocity of finger movements that were used to decode motor intentions. We developed computational models, which confirmed the potential translatability of these results showing how these neural features hold in absence of feedback and when implantable intrafascicular recording, rather than microneurography, is performed.Conclusions: Our results are a proof of principle that microneurography could be used as a useful tool to assist the development of more effective hand prostheses

Colorectal Cancer Stage at Diagnosis Before vs During the COVID-19 Pandemic in Italy

IMPORTANCE Delays in screening programs and the reluctance of patients to seek medical attention because of the outbreak of SARS-CoV-2 could be associated with the risk of more advanced colorectal cancers at diagnosis. OBJECTIVE To evaluate whether the SARS-CoV-2 pandemic was associated with more advanced oncologic stage and change in clinical presentation for patients with colorectal cancer. DESIGN, SETTING, AND PARTICIPANTS This retrospective, multicenter cohort study included all 17 938 adult patients who underwent surgery for colorectal cancer from March 1, 2020, to December 31, 2021 (pandemic period), and from January 1, 2018, to February 29, 2020 (prepandemic period), in 81 participating centers in Italy, including tertiary centers and community hospitals. Follow-up was 30 days from surgery. EXPOSURES Any type of surgical procedure for colorectal cancer, including explorative surgery, palliative procedures, and atypical or segmental resections. MAIN OUTCOMES AND MEASURES The primary outcome was advanced stage of colorectal cancer at diagnosis. Secondary outcomes were distant metastasis, T4 stage, aggressive biology (defined as cancer with at least 1 of the following characteristics: signet ring cells, mucinous tumor, budding, lymphovascular invasion, perineural invasion, and lymphangitis), stenotic lesion, emergency surgery, and palliative surgery. The independent association between the pandemic period and the outcomes was assessed using multivariate random-effects logistic regression, with hospital as the cluster variable. RESULTS A total of 17 938 patients (10 007 men [55.8%]; mean [SD] age, 70.6 [12.2] years) underwent surgery for colorectal cancer: 7796 (43.5%) during the pandemic period and 10 142 (56.5%) during the prepandemic period. Logistic regression indicated that the pandemic period was significantly associated with an increased rate of advanced-stage colorectal cancer (odds ratio [OR], 1.07; 95%CI, 1.01-1.13; P = .03), aggressive biology (OR, 1.32; 95%CI, 1.15-1.53; P < .001), and stenotic lesions (OR, 1.15; 95%CI, 1.01-1.31; P = .03). CONCLUSIONS AND RELEVANCE This cohort study suggests a significant association between the SARS-CoV-2 pandemic and the risk of a more advanced oncologic stage at diagnosis among patients undergoing surgery for colorectal cancer and might indicate a potential reduction of survival for these patients

Studio e realizzazione di algoritmi per l'analisi di segnali corticali prelevati mediante interfacce invasive

I grandi passi in avanti che le neuroscienze stanno facendo si devono in larga misura alla possibilità di effettuare ed elaborare registrazioni multiple nei siti neurologici: i recenti progressi nel campo dei sistemi di acquisizione commerciali consentono infatti di registrare moltissimi canali (dell'ordine delle centinaia) contemporaneamente. Le registrazioni simultanee di treni di spike di neuroni diversi aprono una finestra che guarda direttamente il meccanismo di funzionamento delle unità neurali in gruppo, paragonabili a tanti strumenti che suonano. Ascoltare e capire la musica è in una qualche maniera possibile, ma per poterla riprodurre è necessario sapere bene come funzionano i singoli strumenti e non solo, serve anche conoscere come vengono usati questi strumenti, in relazione con gli altri, per produrre quella specifica melodia. Questo lavoro di tesi, svolto all'interno del progetto Neurobotics, ha come scopo la realizzazione di un algoritmo di spike sorting. Il progetto Neurobotics si pone come obiettivo la realizzazione di piattaforme cibernetiche interfacciabili direttamente con il sistema nervoso. Le scelte fatte nel corso di questa tesi sono state sempre e comunque motivate da questo obiettivo, ovvero sono state utilizzate tecniche tali da essere implementate efficacemente all'interno di queste piattaforme. Inoltre i dati devono essere elaborati in real-time affinchè la risposta neurale possa essere utilizzabile per controllare sistemi robotici di questo tipo, infatti, i dati devono essere elaborati in "real-time" affinchè la risposta ad uno stimolo neurale sia tempestiva. Il problema del sorting è stato spezzato in due fasi sequenziali: l'individuazione degli eventi spike e la classificazione degli eventi individuati. Le wavelet si sono mostrate un potente strumento di analisi del segnale sia nella prima parte dell'elaborazione che nella seconda, perchè in grado di raccogliere una quantità di informazione notevolmente maggiore rispetto ai metodi utilizzati classicamente in questo specifico settore. Il "Wavelet Detection Method" (WDM) non solo prevede costi computazionali relativamente bassi, ma provvede ad un notevole miglioramento nell'elaborazione dei dati con bassi valori del SNR per segnali neurali con bassi "firing rate" (circostanza, questa, che spesso si verifica). Il WDM è, inoltre, un metodo molto flessibile, che può essere applicato ai dati neurali di qualsiasi tipo, senza la supervisione di un utente; caratteristica questa molto importante, perchè significa che l'algoritmo è robusto, oltre che predisposto ad una applicazione "on-line". Allo stesso modo, anche il metodo di classificazione proposto sfrutta la trasformata wavelet per la "feature extraction", seguito dal "super-paramagnetic clustering" (SPM) per la distinzione degli eventi individuati in gruppi. Analogamente, per la classificazione è stata utilizzata una tecnica non supervisionata con buone performance in termini di risparmio di tempo. L'idea di fondo è quella di usare come oggetti da classificare i coefficienti wavelet. Il sistema di classificazione SPM si basa sul principio che per una divisione in gruppi sia sufficiente la conoscenza delle interazioni dell'oggetto con i K elementi più vicini nello spazio delle caratteristiche, e quindi non richiede alcuna assunzione a priori sulla distribuzione dei dati che si vanno analizzando: anche questo aspetto è importante allo scopo di rendere il sistema di elaborazione robusto e non supervisionato. In conclusione, in questo lavoro di tesi si sono accostati metodi leader nei loro specifici campi di applicazione, ottenendo un sistema ibrido che può essere utilizzato per il sorting senza assunzioni a priori sulla distribuzione dei dati