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1. Autonomous sound recording is a promising sampling method for birds and other vocalizing terrestrial wildlife. However, while there are clear advantages of passive acoustic monitoring methods over classical point counts conducted by humans, it has been difficult to quantitatively assess how they compare in their sampling performance. Quantitative comparisons of species richness between acoustic recorders and human point counts in bird surveys have previously been hampered by the differing and often unknown detection ranges or sound detection spaces among sampling methods. 2. We performed two meta-analyses based on 28 studies where bird point counts were paired with sound recordings at the same sampling sites. We compared alpha and gamma richness estimated by both survey methods after equalizing their effective detection ranges. We further assessed the influence of technical sound recording specifications (microphone signal-to-noise ratio, height and number) on the bird sampling performance of sound recorders compared to unlimited radius point counts. 3. We show that after standardizing detection ranges, alpha and gamma richness from both methods are statistically indistinguishable, while there might be an avoidance effect in point counts. Furthermore, we show that microphone signalto-noise ratio (a measure of its quality), height and number positively affect performance through increasing the detection range, allowing sound recorders to match the performance of human point counts. 4. Synthesis and applications. We demonstrate that when used properly, high-end sound recording systems can sample terrestrial wildlife just as well as human observers conducting point counts. Correspondingly, we suggest a first standard methodology for sampling birds with autonomous sound recorders to obtain results comparable to point counts and enable practical sampling. We also give recommendations for carrying out effective surveys and making the most out of autonomous sound recorders.

From videos by Frank Ocean to the Guardians of the Galaxy movie, tapes have become increasingly prominent in cinema, advertising, and, of course, the music industry. And as tape sales in the US grew by 74% last year, there is no doubt that it is time to address past, present and future of the 'Compact Cassette' as icon of popular culture. New generations of individuals, record labels and DIY communities are rediscovering its unique characteristics, adapting them to a contemporary context. In the age of soulless MP3s and similar digital formats there is a real appreciation for the physical qualities of an item that can be collected, exchanged and individually designed. This volume covers themes such as recording and design, distro as distribution source, interviews and provides a step-by-step manual to conducting own tape experiments.

Autonomous sound recording techniques have gained considerable traction in the last decade, but the question remains whether they can replace human observation surveys to sample sonant animals. For birds in particular, survey methods have been tested extensively using point counts and sound recording surveys. Here, we review the latest evidence for this taxon within the frame of a systematic map. We compare sampling effectiveness of these two survey methods, the output variables they produce, and their practicality. When assessed against the standard of point counts, autonomous sound recording proves to be a powerful tool that samples at least as many species. This technology can monitor birds in an exhaustive, standardized, and verifiable way. Moreover, sound recorders give access to entire soundscapes from which new data types can be derived (vocal activity, acoustic indices). Variables such as abundance, density, occupancy, or species richness can be obtained to yield data sets that are comparable to and compatible with point counts. Finally, autonomous sound recorders allow investigations at high temporal and spatial resolution and coverage, which are more cost effective and cannot be achieved by human observations alone, even though small-scale studies might be more cost effective when carried out with point counts. Sound recorders can be deployed in many places, they are more scalable and reliable, making them the better choice for bird surveys in an increasingly data-driven time. We provide an overview of currently available recorders and discuss their specifications to guide future study designs.

We used dc magnetization and ac susceptibility to investigate the magnetic relaxation of ferrofluids made of 8 nm average-diameter Fe[sub.3]O[sub.4] nanoparticles dispersed in hexane. Samples of different concentrations (δ) spanning two orders of magnitude ranging from 0.66 to 0.005 mg (Fe[sub.3]O[sub.4])/mL (hexane) were used to vary the interparticle interaction strength. Our data reveal a critical concentration, δ[sub.c] = 0.02 mg/mL, below which the ferrofluid behaves like an ideal nanoparticle ensemble where the superspins relax individually according to a Néel–Brown activation law τ(T)=τ[sub.0]exp(EB/kBT) with a characteristic time τ[sub.o] ~10[sup.−9] s. That is further confirmed by the observed invariance of the relative peak temperature variation per frequency decade ∆=∆T/T·∆log(f), which stays constant at ~0.185 when δ < δ[sub.c]. At higher concentrations, between 0.02 and 0.66 mg/mL, we found that Δ exhibits a monotonic increase with the inverse concentration, 1/δ, and the collective superspin dynamics is described by a Vogel–Fulcher law, τ(T)=τ[sub.0]exp[EB/kBT−T0]. Within this regime, the dipolar interaction strength parameter T[sub.0] increases from T[sub.0] = 0 K at δ[sub.c] = 0.02 mg/mL to T[sub.0] = 14.7 K at δ = 0.66 mg/mL.

Recording cellular events could advance our understanding of cellular history and responses to stimuli. The construction of intracellular memory devices, however, is challenging. Tang and Liu used Cas9 nucleases and base editors to record amplitude, duration, and order of stimuli as stable changes in both genomic and extrachromosomal DNA content (see the Perspective by Ho and Bennett). The recording of multiple stimuli—including exposure to antibiotics, nutrients, viruses, and light, as well as Wnt signaling—was achieved in living bacterial and human cells. Recorded memories could be erased and re-recorded over multiple cycles. Science , this issue p. eaap8992 ; see also p. 150 Base editors and CRISPR nucleases generate “cell data recorders” that enable durable, analog, rewritable recording of multiple stimuli. We present two CRISPR-mediated analog multi-event recording apparatus (CAMERA) systems that use base editors and Cas9 nucleases to record cellular events in bacteria and mammalian cells. The devices record signal amplitude or duration as changes in the ratio of mutually exclusive DNA sequences (CAMERA 1) or as single-base modifications (CAMERA 2). We achieved recording of multiple stimuli in bacteria or mammalian cells, including exposure to antibiotics, nutrients, viruses, light, and changes in Wnt signaling. When recording to multicopy plasmids, reliable readout requires as few as 10 to 100 cells. The order of stimuli can be recorded through an overlapping guide RNA design, and memories can be erased and re-recorded over multiple cycles. CAMERA systems serve as “cell data recorders” that write a history of endogenous or exogenous signaling events into permanent DNA sequence modifications in living cells.

We have investigated the structural, magnetic, and electrical transport properties of Pr[sub.0.7] Sr[sub.0.3] Mn[sub.(1−x)]Co[sub.x] O[sub.3] nanopowders (x = 0, 0.05, 0.10 and 0.15). The Pechini Sol-gel method was used to synthesize these nanopowders. X-ray diffraction at room temperature shows that all the nano powders have an orthorhombic structure of Pnma space group crystallography. The average crystallite size of samples x = 0, 0.05, 0.10, and 0.15 are 33.78 nm, 29 nm, 33.61 nm, and 24.27 nm, respectively. Semi-quantitative chemical analysis by energy dispersive spectroscopy (EDS) confirms the expected stoichiometry of the sample. Magnetic measurements indicate that all samples show a ferromagnetic (FM) to paramagnetic (PM) transition with increasing temperature. The Curie temperature T[sub.C] gradually decreases (300 K, 270 K, 250 K, and 235 K for x = 0, 0.05, 0.10, and 0.15, respectively) with increasing Co concentrations. The M-H curves for all compounds reveal the PM behavior at 300 K, while the FM behavior characterizes the magnetic hysteresis at low temperature (5 K). The electrical resistivity measurements show that all compounds exhibit metallic behavior at low temperature (T < Tρ) well fitted by the relation ρ = ρ[sub.0] + ρ[sub.2]T[sup.2] + ρ[sub.4.5]T[sup.4.5] and semiconductor behavior above Tρ (T > Tρ), for which the electronic transport can be explained by the variable range hopping model and the adiabatic small polaron hopping model. All samples have significant magnetoresistance (MR) values, even at room temperature. This presented research provides an innovative and practical approach to develop materials in several technological areas, such as ultra-high density magnetic recording and magneto resistive sensors.

We provide necessary and sufficient conditions for when an algebraic stack admits a good moduli space and prove a semistable reduction theorem for points of algebraic stacks equipped with a Θ-stratification. These results provide a generalization of the Keel–Mori theorem to moduli problems whose objects have positive dimensional automorphism groups and give criteria on the moduli problem to have a separated or proper good moduli space. To illustrate our method, we apply these results to construct proper moduli spaces parameterizing semistable G-bundles on curves and moduli spaces for objects in abelian categories.

Ground weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas. Plain Language Summary Understanding and predicting rainfall is a complex task, especially in areas where the availability of data from surface stations is limited, a common feature in many developing regions with insufficient rain gauge coverage. Recently, new opportunistic methods of rainfall measurement have emerged. Among them, is the use of the relationship between rainfall intensity and the sound produced by droplets hitting a surface. Sound recorders offer a low‐cost solution and could provide an interesting means to increase spatial coverage of rainfall measurements, but also to fill information gaps under dense forests where conventional devices do not work. Our study developed a new technique and applied it to the Central Amazon region, by training a supervised machine learning model applied to sound recordings obtained in a tropical rainforest. To our knowledge, for the first time, such techniques are validated in locations far from the calibration site. We showed that reasonable results can be obtained for sites with distinct vegetation types and up to 30 km of distance from where the training data was acquired. Our findings demonstrate a strong capability for estimating hourly rainfall rates. Key Points Rainfall intensity estimated from sound measurements in the Amazon rainforest, tipping bucket rain gauge, and machine learning models The best model successfully detects rainfall in 88% of the cases, with R2 > 0.87 for hourly rainfall rates on the training site Model validated over two sites in the Amazon, 97% accuracy identifying rainfall events, R2 of 0.69 and 0.93 for hourly rainfall rate