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Spin qubits are considered to be among the most promising candidates for building a quantum processor. Group IV hole spin qubits are particularly interesting owing to their ease of operation and compatibility with Si technology. In addition, Ge offers the option for monolithic superconductor–semiconductor integration. Here, we demonstrate a hole spin qubit operating at fields below 10 mT, the critical field of Al, by exploiting the large out-of-plane hole g -factors in planar Ge and by encoding the qubit into the singlet-triplet states of a double quantum dot. We observe electrically controlled g -factor difference-driven and exchange-driven rotations with tunable frequencies exceeding 100 MHz and dephasing times of 1 μs, which we extend beyond 150 μs using echo techniques. These results demonstrate that Ge hole singlet-triplet qubits are competing with state-of-the-art GaAs and Si singlet-triplet qubits. In addition, their rotation frequencies and coherence are comparable with those of Ge single spin qubits, but singlet-triplet qubits can be operated at much lower fields, emphasizing their potential for on-chip integration with superconducting technologies. A singlet-triplet spin qubit using holes in a Ge quantum well is demonstrated, and can be operated at low magnetic fields of a few millitesla.

Frequency combs represent exceptionally precise measurement tools due to the coherence of their spectral lines. While optical frequency comb sources constitute a well-established technology, superconducting circuits provide a relatively unexplored on-chip platform for low-dissipation comb emitters able to span from gigahertz to terahertz frequencies. We demonstrate coherent microwave frequency comb generation by leveraging the ac Josephson effect in a superconducting quantum interference device. A time-dependent magnetic drive periodically generates voltage pulses, which in the frequency domain correspond to a comb with dozens of spectral modes here reported up to mode 46. The emitted power at the device level ranges from −170 dBm to −130 dBm per harmonic, corresponding to 40 dB dynamic range in the 4-8 GHz bandwidth. The micrometer-scale footprint and minimal dissipation inherent to superconducting systems foster the integration of our comb generator with advanced cryogenic electronics. Transferring optical techniques to the solid-state domain may enable new applications in quantum technologies. The authors demonstrate the use of a superconducting quantum interference device with time dependent magnetic drive as a source for coherent frequency combs in the microwave C-band (4-8 GHz), operating at 60 mK. The device has μ m-size physical footprint, extremely low dissipation, and large bandwidth.

Gate-tunable transmons (gatemons) employing semiconductor Josephson junctions have recently emerged as building blocks for hybrid quantum circuits. In this study, we present a gatemon fabricated in planar Germanium. We induce superconductivity in a two-dimensional hole gas by evaporating aluminum atop a thin spacer, which separates the superconductor from the Ge quantum well. The Josephson junction is then integrated into an Xmon circuit and capacitively coupled to a transmission line resonator. We showcase the qubit tunability in a broad frequency range with resonator and two-tone spectroscopy. Time-domain characterizations reveal energy relaxation and coherence times up to 75 ns. Our results, combined with the recent advances in the spin qubit field, pave the way towards novel hybrid and protected qubits in a group IV, CMOS-compatible material. A gatemon is a transmon-type superconducting qubit made using a hybrid superconductor-semiconductor approach enabling tunability with electric gates. Here the authors demonstrate a gatemon qubit in planar germanium, which is anticipated to have various advantages.

Smoothness (i.e. non-intermittency) of movement is a clinically important property of the voluntary movement with accuracy and proper speed. Resting head position and head voluntary movements are impaired in cervical dystonia. The current work aims to evaluate if the smoothness of voluntary head rotations is reduced in this disease. Twenty-six cervical dystonia patients and 26 controls completed rightward and leftward head rotations. Patients’ movements were differentiated into “towards-dystonia” (rotation accentuated the torticollis) and “away-dystonia”. Smoothness was quantified by the angular jerk and arc length of the spectrum of angular speed (i.e. SPARC, arbitrary units). Movement amplitude (mean, 95% CI) on the horizontal plane was larger in controls (63.8°, 58.3°–69.2°) than patients when moving towards-dystonia (52.8°, 46.3°–59.4°; P = 0.006). Controls’ movements (49.4°/s, 41.9–56.9°/s) were faster than movements towards-dystonia (31.6°/s, 25.2–37.9°/s; P < 0.001) and away-dystonia (29.2°/s, 22.9–35.5°/s; P < 0.001). After taking into account the different amplitude and speed, SPARC-derived (but not jerk-derived) indices showed reduced smoothness in patients rotating away-dystonia (1.48, 1.35–1.61) compared to controls (1.88, 1.72–2.03; P < 0.001). Poor smoothness is a motor disturbance independent of movement amplitude and speed in cervical dystonia. Therefore, it should be assessed when evaluating this disease, its progression, and treatments.

Current clinical guidelines suggest methylphenidate (MPH) as first-line option for treatment of attention-deficit/hyperactivity disorder (ADHD). However, the Lombardy ADHD Registry initiative has recently raised some concerns about discrepant therapeutic approaches between different clinical centers. In this naturalistic observational study, we report the experience of a third-level referral center in monitoring care services for young ADHD patients. We described the clinical characteristics of the children/adolescents who were diagnosed with ADHD from 2017 and prescribed or not with MPH. We further followed-up ADHD patients who were taking MPH for 12 weeks to assess any clinical amelioration using the Clinical Global Impression—Severity scale, controlling for other outcome predictors. One fourth of patients received a MPH prescription after the ADHD diagnosis. Those children/adolescents showed more complex clinical manifestations, with greater ADHD difficulties and neuropsychiatric comorbidities. ADHD patients displayed significant improvements of functioning after 12 weeks of the initiation of MPH use, but not after 4 weeks. IQ level and the presence of co-occurring autism predicted ADHD severity at baseline. After controlling for other predictors’ effect, the severity at the first visit predicted ADHD severity after 4 weeks of MPH use, which in turn predicted the clinical functioning at the 12-week visit.

Supercurrent diodes are nonreciprocal electronic elements whose switching current depends on their flow direction. Recently, a variety of composite systems combining different materials and engineered asymmetric superconducting devices have been proposed. Yet, ease of fabrication and tunable sign of supercurrent rectification joined to large efficiency have not been assessed in a single platform so far. We demonstrate that all-metallic superconducting Dayem nanobridges naturally exhibit nonreciprocal supercurrents under an external magnetic field, with a rectification efficiency up to ~ 27%. Our niobium nanostructures are tailored so that the diode polarity can be tuned by varying the amplitude of an out-of-plane magnetic field or the temperature in a regime without magnetic screening. We show that sign reversal of the diode effect may arise from the high-harmonic content of the current phase relation in combination with vortex phase windings present in the bridge or an anomalous phase shift compatible with anisotropic spin-orbit interactions. The sign of switching currents in supercurrent diodes depends on their flow direction, however effective strategies to control it in single platforms with large efficiency are missing. The authors realise a supercurrent diode in superconducting weak links that is tunable both in amplitude and sign of switching current by an out of-plane magnetic field in a regime without magnetic screening.

Functional near-infrared spectroscopy (fNIRS) is particularly suitable for measuring brain activity during motor tasks, due to its portability and good motion tolerance. In such cases, the trials’ duration may vary depending on the experimental conditions or the participant’s response, therefore a comparison of hemodynamic responses across repetitions cannot be properly performed. In this work, we present a MATLAB (R2023a) function (TaskNorm.m) developed for time-normalizing fNIRS data recorded during trials with different durations. It is based on a spline interpolation method that rescales the time -axis to the percentage of the trial with a fixed number of samples. This allows us to successively average across repetitions to obtain the mean hemodynamic responses and complete the standard data processing. The algorithm was tested on eight subjects (four with developmental coordination disorder, age: 9.78 ± 0.30 and four typically developing children, age: 9.02 ± 0.30) performing three different tasks. The results show that the TaskNorm function works as expected, allowing both a comparison and averaging of the data across multiple repetitions. The performance of the function is independent of the task or the pre-processing pipeline applied. The proposed function is publicly available and importable into the HomER3 package (v1.72.0), representing a further step in the ongoing standardization process of fNIRS data analysis.

This cross-sectional study aims to identify clusters of internalizing and externalizing traits during adolescence using a bottom-up approach. The second aim is to investigate whether the different clusters differ by environmental risk factors and specific epigenetic profiles. A total of 205 adolescents, who had been referred for psychopathology in childhood, were recruited. Behavioral problems were assessed using the Child Behavior Checklist/6–18 (CBCL). Different clusters of psychopathological profiles were analyzed using a Finite mixture model. Differences in environmental risk factors and epigenetic profiles were tested with χ2-tests and Bonferroni-corrected t-tests. Two clusters were identified: a LOW cluster (51% of the sample), characterized by the presence of subclinical mean scores in both internalizing and externalizing problems, and a HIGH cluster (49% of the sample), characterized by high mean scores in both domains. The HIGH cluster had a significantly greater number of perinatal complications and changes in methylation of specific CpG sites of Brain-derived neurotrophic factor, Insulin-like growth factor-2, and Oxytocin receptor, whereas no difference was found for FK506-binding protein 5. Our results confirm the existence of a strong association between early adverse events, DNA methylation, and the presence of behavioral problems and psychopathological traits in adolescence.

Deep neural networks are successful learning tools for building nonlinear models. However, a robust deep learning-based classification model needs a large dataset. Indeed, these models are often unstable when they use small datasets. To solve this issue, which is particularly critical in light of the possible clinical applications of these predictive models, researchers have developed approaches such as virtual sample generation. Virtual sample generation significantly improves learning and classification performance when working with small samples. The main objective of this study is to evaluate the ability of the proposed virtual sample generation to overcome the small sample size problem, which is a feature of the automated detection of a neurodevelopmental disorder, namely autism spectrum disorder. Results show that our method enhances diagnostic accuracy from 84%–95% using virtual samples generated on the basis of five actual clinical samples. The present findings show the feasibility of using the proposed technique to improve classification performance even in cases of clinical samples of limited size. Accounting for concerns in relation to small sample sizes, our technique represents a meaningful step forward in terms of pattern recognition methodology, particularly when it is applied to diagnostic classifications of neurodevelopmental disorders. Besides, the proposed technique has been tested with other available benchmark datasets. The experimental outcomes showed that the accuracy of the classification that used virtual samples was superior to the one that used original training data without virtual samples.